what is single case research design

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Chapter 10: Single-Subject Research

Single-Subject Research Designs

Learning Objectives

• Describe the basic elements of a single-subject research design.
• Design simple single-subject studies using reversal and multiple-baseline designs.
• Explain how single-subject research designs address the issue of internal validity.
• Interpret the results of simple single-subject studies based on the visual inspection of graphed data.

General Features of Single-Subject Designs

Before looking at any specific single-subject research designs, it will be helpful to consider some features that are common to most of them. Many of these features are illustrated in Figure 10.2, which shows the results of a generic single-subject study. First, the dependent variable (represented on the  y -axis of the graph) is measured repeatedly over time (represented by the  x -axis) at regular intervals. Second, the study is divided into distinct phases, and the participant is tested under one condition per phase. The conditions are often designated by capital letters: A, B, C, and so on. Thus Figure 10.2 represents a design in which the participant was tested first in one condition (A), then tested in another condition (B), and finally retested in the original condition (A). (This is called a reversal design and will be discussed in more detail shortly.)

Another important aspect of single-subject research is that the change from one condition to the next does not usually occur after a fixed amount of time or number of observations. Instead, it depends on the participant’s behaviour. Specifically, the researcher waits until the participant’s behaviour in one condition becomes fairly consistent from observation to observation before changing conditions. This is sometimes referred to as the steady state strategy  (Sidman, 1960) [1] . The idea is that when the dependent variable has reached a steady state, then any change across conditions will be relatively easy to detect. Recall that we encountered this same principle when discussing experimental research more generally. The effect of an independent variable is easier to detect when the “noise” in the data is minimized.

Reversal Designs

The most basic single-subject research design is the  reversal design , also called the  ABA design . During the first phase, A, a  baseline  is established for the dependent variable. This is the level of responding before any treatment is introduced, and therefore the baseline phase is a kind of control condition. When steady state responding is reached, phase B begins as the researcher introduces the treatment. There may be a period of adjustment to the treatment during which the behaviour of interest becomes more variable and begins to increase or decrease. Again, the researcher waits until that dependent variable reaches a steady state so that it is clear whether and how much it has changed. Finally, the researcher removes the treatment and again waits until the dependent variable reaches a steady state. This basic reversal design can also be extended with the reintroduction of the treatment (ABAB), another return to baseline (ABABA), and so on.

The study by Hall and his colleagues was an ABAB reversal design. Figure 10.3 approximates the data for Robbie. The percentage of time he spent studying (the dependent variable) was low during the first baseline phase, increased during the first treatment phase until it leveled off, decreased during the second baseline phase, and again increased during the second treatment phase.

Why is the reversal—the removal of the treatment—considered to be necessary in this type of design? Why use an ABA design, for example, rather than a simpler AB design? Notice that an AB design is essentially an interrupted time-series design applied to an individual participant. Recall that one problem with that design is that if the dependent variable changes after the treatment is introduced, it is not always clear that the treatment was responsible for the change. It is possible that something else changed at around the same time and that this extraneous variable is responsible for the change in the dependent variable. But if the dependent variable changes with the introduction of the treatment and then changes  back  with the removal of the treatment (assuming that the treatment does not create a permanent effect), it is much clearer that the treatment (and removal of the treatment) is the cause. In other words, the reversal greatly increases the internal validity of the study.

There are close relatives of the basic reversal design that allow for the evaluation of more than one treatment. In a  multiple-treatment reversal design , a baseline phase is followed by separate phases in which different treatments are introduced. For example, a researcher might establish a baseline of studying behaviour for a disruptive student (A), then introduce a treatment involving positive attention from the teacher (B), and then switch to a treatment involving mild punishment for not studying (C). The participant could then be returned to a baseline phase before reintroducing each treatment—perhaps in the reverse order as a way of controlling for carryover effects. This particular multiple-treatment reversal design could also be referred to as an ABCACB design.

In an  alternating treatments design , two or more treatments are alternated relatively quickly on a regular schedule. For example, positive attention for studying could be used one day and mild punishment for not studying the next, and so on. Or one treatment could be implemented in the morning and another in the afternoon. The alternating treatments design can be a quick and effective way of comparing treatments, but only when the treatments are fast acting.

Multiple-Baseline Designs

There are two potential problems with the reversal design—both of which have to do with the removal of the treatment. One is that if a treatment is working, it may be unethical to remove it. For example, if a treatment seemed to reduce the incidence of self-injury in a developmentally disabled child, it would be unethical to remove that treatment just to show that the incidence of self-injury increases. The second problem is that the dependent variable may not return to baseline when the treatment is removed. For example, when positive attention for studying is removed, a student might continue to study at an increased rate. This could mean that the positive attention had a lasting effect on the student’s studying, which of course would be good. But it could also mean that the positive attention was not really the cause of the increased studying in the first place. Perhaps something else happened at about the same time as the treatment—for example, the student’s parents might have started rewarding him for good grades.

One solution to these problems is to use a  multiple-baseline design , which is represented in Figure 10.4. In one version of the design, a baseline is established for each of several participants, and the treatment is then introduced for each one. In essence, each participant is tested in an AB design. The key to this design is that the treatment is introduced at a different  time  for each participant. The idea is that if the dependent variable changes when the treatment is introduced for one participant, it might be a coincidence. But if the dependent variable changes when the treatment is introduced for multiple participants—especially when the treatment is introduced at different times for the different participants—then it is extremely unlikely to be a coincidence.

As an example, consider a study by Scott Ross and Robert Horner (Ross & Horner, 2009) [2] . They were interested in how a school-wide bullying prevention program affected the bullying behaviour of particular problem students. At each of three different schools, the researchers studied two students who had regularly engaged in bullying. During the baseline phase, they observed the students for 10-minute periods each day during lunch recess and counted the number of aggressive behaviours they exhibited toward their peers. (The researchers used handheld computers to help record the data.) After 2 weeks, they implemented the program at one school. After 2 more weeks, they implemented it at the second school. And after 2 more weeks, they implemented it at the third school. They found that the number of aggressive behaviours exhibited by each student dropped shortly after the program was implemented at his or her school. Notice that if the researchers had only studied one school or if they had introduced the treatment at the same time at all three schools, then it would be unclear whether the reduction in aggressive behaviours was due to the bullying program or something else that happened at about the same time it was introduced (e.g., a holiday, a television program, a change in the weather). But with their multiple-baseline design, this kind of coincidence would have to happen three separate times—a very unlikely occurrence—to explain their results.

In another version of the multiple-baseline design, multiple baselines are established for the same participant but for different dependent variables, and the treatment is introduced at a different time for each dependent variable. Imagine, for example, a study on the effect of setting clear goals on the productivity of an office worker who has two primary tasks: making sales calls and writing reports. Baselines for both tasks could be established. For example, the researcher could measure the number of sales calls made and reports written by the worker each week for several weeks. Then the goal-setting treatment could be introduced for one of these tasks, and at a later time the same treatment could be introduced for the other task. The logic is the same as before. If productivity increases on one task after the treatment is introduced, it is unclear whether the treatment caused the increase. But if productivity increases on both tasks after the treatment is introduced—especially when the treatment is introduced at two different times—then it seems much clearer that the treatment was responsible.

In yet a third version of the multiple-baseline design, multiple baselines are established for the same participant but in different settings. For example, a baseline might be established for the amount of time a child spends reading during his free time at school and during his free time at home. Then a treatment such as positive attention might be introduced first at school and later at home. Again, if the dependent variable changes after the treatment is introduced in each setting, then this gives the researcher confidence that the treatment is, in fact, responsible for the change.

Data Analysis in Single-Subject Research

In addition to its focus on individual participants, single-subject research differs from group research in the way the data are typically analyzed. As we have seen throughout the book, group research involves combining data across participants. Group data are described using statistics such as means, standard deviations, Pearson’s  r , and so on to detect general patterns. Finally, inferential statistics are used to help decide whether the result for the sample is likely to generalize to the population. Single-subject research, by contrast, relies heavily on a very different approach called  visual inspection . This means plotting individual participants’ data as shown throughout this chapter, looking carefully at those data, and making judgments about whether and to what extent the independent variable had an effect on the dependent variable. Inferential statistics are typically not used.

In visually inspecting their data, single-subject researchers take several factors into account. One of them is changes in the  level  of the dependent variable from condition to condition. If the dependent variable is much higher or much lower in one condition than another, this suggests that the treatment had an effect. A second factor is  trend , which refers to gradual increases or decreases in the dependent variable across observations. If the dependent variable begins increasing or decreasing with a change in conditions, then again this suggests that the treatment had an effect. It can be especially telling when a trend changes directions—for example, when an unwanted behaviour is increasing during baseline but then begins to decrease with the introduction of the treatment. A third factor is  latency , which is the time it takes for the dependent variable to begin changing after a change in conditions. In general, if a change in the dependent variable begins shortly after a change in conditions, this suggests that the treatment was responsible.

In the top panel of Figure 10.5, there are fairly obvious changes in the level and trend of the dependent variable from condition to condition. Furthermore, the latencies of these changes are short; the change happens immediately. This pattern of results strongly suggests that the treatment was responsible for the changes in the dependent variable. In the bottom panel of Figure 10.5, however, the changes in level are fairly small. And although there appears to be an increasing trend in the treatment condition, it looks as though it might be a continuation of a trend that had already begun during baseline. This pattern of results strongly suggests that the treatment was not responsible for any changes in the dependent variable—at least not to the extent that single-subject researchers typically hope to see.

The results of single-subject research can also be analyzed using statistical procedures—and this is becoming more common. There are many different approaches, and single-subject researchers continue to debate which are the most useful. One approach parallels what is typically done in group research. The mean and standard deviation of each participant’s responses under each condition are computed and compared, and inferential statistical tests such as the  t  test or analysis of variance are applied (Fisch, 2001) [3] . (Note that averaging  across  participants is less common.) Another approach is to compute the  percentage of nonoverlapping data  (PND) for each participant (Scruggs & Mastropieri, 2001) [4] . This is the percentage of responses in the treatment condition that are more extreme than the most extreme response in a relevant control condition. In the study of Hall and his colleagues, for example, all measures of Robbie’s study time in the first treatment condition were greater than the highest measure in the first baseline, for a PND of 100%. The greater the percentage of nonoverlapping data, the stronger the treatment effect. Still, formal statistical approaches to data analysis in single-subject research are generally considered a supplement to visual inspection, not a replacement for it.

Key Takeaways

• Single-subject research designs typically involve measuring the dependent variable repeatedly over time and changing conditions (e.g., from baseline to treatment) when the dependent variable has reached a steady state. This approach allows the researcher to see whether changes in the independent variable are causing changes in the dependent variable.
• In a reversal design, the participant is tested in a baseline condition, then tested in a treatment condition, and then returned to baseline. If the dependent variable changes with the introduction of the treatment and then changes back with the return to baseline, this provides strong evidence of a treatment effect.
• In a multiple-baseline design, baselines are established for different participants, different dependent variables, or different settings—and the treatment is introduced at a different time on each baseline. If the introduction of the treatment is followed by a change in the dependent variable on each baseline, this provides strong evidence of a treatment effect.
• Single-subject researchers typically analyze their data by graphing them and making judgments about whether the independent variable is affecting the dependent variable based on level, trend, and latency.
• Does positive attention from a parent increase a child’s toothbrushing behaviour?
• Does self-testing while studying improve a student’s performance on weekly spelling tests?
• Does regular exercise help relieve depression?
• Practice: Create a graph that displays the hypothetical results for the study you designed in Exercise 1. Write a paragraph in which you describe what the results show. Be sure to comment on level, trend, and latency.

Long Descriptions

Figure 10.3 long description: Line graph showing the results of a study with an ABAB reversal design. The dependent variable was low during first baseline phase; increased during the first treatment; decreased during the second baseline, but was still higher than during the first baseline; and was highest during the second treatment phase. [Return to Figure 10.3]

Figure 10.4 long description: Three line graphs showing the results of a generic multiple-baseline study, in which different baselines are established and treatment is introduced to participants at different times.

For Baseline 1, treatment is introduced one-quarter of the way into the study. The dependent variable ranges between 12 and 16 units during the baseline, but drops down to 10 units with treatment and mostly decreases until the end of the study, ranging between 4 and 10 units.

For Baseline 2, treatment is introduced halfway through the study. The dependent variable ranges between 10 and 15 units during the baseline, then has a sharp decrease to 7 units when treatment is introduced. However, the dependent variable increases to 12 units soon after the drop and ranges between 8 and 10 units until the end of the study.

For Baseline 3, treatment is introduced three-quarters of the way into the study. The dependent variable ranges between 12 and 16 units for the most part during the baseline, with one drop down to 10 units. When treatment is introduced, the dependent variable drops down to 10 units and then ranges between 8 and 9 units until the end of the study. [Return to Figure 10.4]

Figure 10.5 long description: Two graphs showing the results of a generic single-subject study with an ABA design. In the first graph, under condition A, level is high and the trend is increasing. Under condition B, level is much lower than under condition A and the trend is decreasing. Under condition A again, level is about as high as the first time and the trend is increasing. For each change, latency is short, suggesting that the treatment is the reason for the change.

In the second graph, under condition A, level is relatively low and the trend is increasing. Under condition B, level is a little higher than during condition A and the trend is increasing slightly. Under condition A again, level is a little lower than during condition B and the trend is decreasing slightly. It is difficult to determine the latency of these changes, since each change is rather minute, which suggests that the treatment is ineffective. [Return to Figure 10.5]

• Sidman, M. (1960). Tactics of scientific research: Evaluating experimental data in psychology . Boston, MA: Authors Cooperative. ↵
• Ross, S. W., & Horner, R. H. (2009). Bully prevention in positive behaviour support. Journal of Applied Behaviour Analysis, 42 , 747–759. ↵
• Fisch, G. S. (2001). Evaluating data from behavioural analysis: Visual inspection or statistical models.  Behavioural Processes, 54 , 137–154. ↵
• Scruggs, T. E., & Mastropieri, M. A. (2001). How to summarize single-participant research: Ideas and applications.  Exceptionality, 9 , 227–244. ↵

The researcher waits until the participant’s behaviour in one condition becomes fairly consistent from observation to observation before changing conditions. This way, any change across conditions will be easy to detect.

A study method in which the researcher gathers data on a baseline state, introduces the treatment and continues observation until a steady state is reached, and finally removes the treatment and observes the participant until they return to a steady state.

The level of responding before any treatment is introduced and therefore acts as a kind of control condition.

A baseline phase is followed by separate phases in which different treatments are introduced.

Two or more treatments are alternated relatively quickly on a regular schedule.

A baseline is established for several participants and the treatment is then introduced to each participant at a different time.

The plotting of individual participants’ data, examining the data, and making judgements about whether and to what extent the independent variable had an effect on the dependent variable.

Whether the data is higher or lower based on a visual inspection of the data; a change in the level implies the treatment introduced had an effect.

The gradual increases or decreases in the dependent variable across observations.

The time it takes for the dependent variable to begin changing after a change in conditions.

The percentage of responses in the treatment condition that are more extreme than the most extreme response in a relevant control condition.

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Educational Research Basics by Del Siegle

Single subject research.

“ Single subject research (also known as single case experiments) is popular in the fields of special education and counseling. This research design is useful when the researcher is attempting to change the behavior of an individual or a small group of individuals and wishes to document that change. Unlike true experiments where the researcher randomly assigns participants to a control and treatment group, in single subject research the participant serves as both the control and treatment group. The researcher uses line graphs to show the effects of a particular intervention or treatment.  An important factor of single subject research is that only one variable is changed at a time. Single subject research designs are “weak when it comes to external validity….Studies involving single-subject designs that show a particular treatment to be effective in changing behavior must rely on replication–across individuals rather than groups–if such results are be found worthy of generalization” (Fraenkel & Wallen, 2006, p. 318).

Suppose a researcher wished to investigate the effect of praise on reducing disruptive behavior over many days. First she would need to establish a baseline of how frequently the disruptions occurred. She would measure how many disruptions occurred each day for several days. In the example below, the target student was disruptive seven times on the first day, six times on the second day, and seven times on the third day. Note how the sequence of time is depicted on the x-axis (horizontal axis) and the dependent variable (outcome variable) is depicted on the y-axis (vertical axis).

Once a baseline of behavior has been established (when a consistent pattern emerges with at least three data points), the intervention begins. The researcher continues to plot the frequency of behavior while implementing the intervention of praise.

In this example, we can see that the frequency of disruptions decreased once praise began. The design in this example is known as an A-B design. The baseline period is referred to as A and the intervention period is identified as B.

Another design is the A-B-A design. An A-B-A design (also known as a reversal design) involves discontinuing the intervention and returning to a nontreatment condition.

Sometimes an individual’s behavior is so severe that the researcher cannot wait to establish a baseline and must begin with an intervention. In this case, a B-A-B design is used. The intervention is implemented immediately (before establishing a baseline). This is followed by a measurement without the intervention and then a repeat of the intervention.

Multiple-Baseline Design

Sometimes, a researcher may be interested in addressing several issues for one student or a single issue for several students. In this case, a multiple-baseline design is used.

“In a multiple baseline across subjects design, the researcher introduces the intervention to different persons at different times. The significance of this is that if a behavior changes only after the intervention is presented, and this behavior change is seen successively in each subject’s data, the effects can more likely be credited to the intervention itself as opposed to other variables. Multiple-baseline designs do not require the intervention to be withdrawn. Instead, each subject’s own data are compared between intervention and nonintervention behaviors, resulting in each subject acting as his or her own control (Kazdin, 1982). An added benefit of this design, and all single-case designs, is the immediacy of the data. Instead of waiting until postintervention to take measures on the behavior, single-case research prescribes continuous data collection and visual monitoring of that data displayed graphically, allowing for immediate instructional decision-making. Students, therefore, do not linger in an intervention that is not working for them, making the graphic display of single-case research combined with differentiated instruction responsive to the needs of students.” (Geisler, Hessler, Gardner, & Lovelace, 2009)

Regardless of the research design, the line graphs used to illustrate the data contain a set of common elements.

Generally, in single subject research we count the number of times something occurs in a given time period and see if it occurs more or less often in that time period after implementing an intervention. For example, we might measure how many baskets someone makes while shooting for 2 minutes. We would repeat that at least three times to get our baseline. Next, we would test some intervention. We might play music while shooting, give encouragement while shooting, or video the person while shooting to see if our intervention influenced the number of shots made. After the 3 baseline measurements (3 sets of 2 minute shooting), we would measure several more times (sets of 2 minute shooting) after the intervention and plot the time points (number of baskets made in 2 minutes for each of the measured time points). This works well for behaviors that are distinct and can be counted.

Sometimes behaviors come and go over time (such as being off task in a classroom or not listening during a coaching session). The way we can record these is to select a period of time (say 5 minutes) and mark down every 10 seconds whether our participant is on task. We make a minimum of three sets of 5 minute observations for a baseline, implement an intervention, and then make more sets of 5 minute observations with the intervention in place. We use this method rather than counting how many times someone is off task because one could continually be off task and that would only be a count of 1 since the person was continually off task. Someone who might be off task twice for 15 second would be off task twice for a score of 2. However, the second person is certainly not off task twice as much as the first person. Therefore, recording whether the person is off task at 10-second intervals gives a more accurate picture. The person continually off task would have a score of 30 (off task at every second interval for 5 minutes) and the person off task twice for a short time would have a score of 2 (off task only during 2 of the 10 second interval measures.

I also have additional information about how to record single-subject research data .

I hope this helps you better understand single subject research.

I have created a PowerPoint on Single Subject Research , which also available below as a video.

I have also created instructions for creating single-subject research design graphs with Excel .

Fraenkel, J. R., & Wallen, N. E. (2006). How to design and evaluate research in education (6th ed.). Boston, MA: McGraw Hill.

Geisler, J. L., Hessler, T., Gardner, R., III, & Lovelace, T. S. (2009). Differentiated writing interventions for high-achieving urban African American elementary students. Journal of Advanced Academics, 20, 214–247.

Del Siegle, Ph.D. University of Connecticut [email protected] www.delsiegle.info

Revised 02/02/2024

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• Perspective
• Published: 22 November 2022

Single case studies are a powerful tool for developing, testing and extending theories

• Lyndsey Nickels   ORCID: orcid.org/0000-0002-0311-3524 1 , 2 ,
• Simon Fischer-Baum   ORCID: orcid.org/0000-0002-6067-0538 3 &
• Wendy Best   ORCID: orcid.org/0000-0001-8375-5916 4  

Nature Reviews Psychology volume  1 ,  pages 733–747 ( 2022 ) Cite this article

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• Neurological disorders

Psychology embraces a diverse range of methodologies. However, most rely on averaging group data to draw conclusions. In this Perspective, we argue that single case methodology is a valuable tool for developing and extending psychological theories. We stress the importance of single case and case series research, drawing on classic and contemporary cases in which cognitive and perceptual deficits provide insights into typical cognitive processes in domains such as memory, delusions, reading and face perception. We unpack the key features of single case methodology, describe its strengths, its value in adjudicating between theories, and outline its benefits for a better understanding of deficits and hence more appropriate interventions. The unique insights that single case studies have provided illustrate the value of in-depth investigation within an individual. Single case methodology has an important place in the psychologist’s toolkit and it should be valued as a primary research tool.

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The authors thank all of those pioneers of and advocates for single case study research who have mentored, inspired and encouraged us over the years, and the many other colleagues with whom we have discussed these issues.

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Nickels, L., Fischer-Baum, S. & Best, W. Single case studies are a powerful tool for developing, testing and extending theories. Nat Rev Psychol 1 , 733–747 (2022). https://doi.org/10.1038/s44159-022-00127-y

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The Family of Single-Case Experimental Designs

Leonard h. epstein.

1 Jacobs School of Medicine and Biomedical Sciences, Division of Behavioral Medicine, Department of Pediatrics, University at Buffalo, Buffalo, New York, United States of America,

Jesse Dallery

2 Department of Psychology, University of Florida, Gainesville, Florida, United States of America

Single-case experimental designs (SCEDs) represent a family of research designs that use experimental methods to study the effects of treatments on outcomes. The fundamental unit of analysis is the single case—which can be an individual, clinic, or community—ideally with replications of effects within and/or between cases. These designs are flexible and cost-effective and can be used for treatment development, translational research, personalized interventions, and the study of rare diseases and disorders. This article provides a broad overview of the family of single-case experimental designs with corresponding examples, including reversal designs, multiple baseline designs, combined multiple baseline/reversal designs, and integration of single-case designs to identify optimal treatments for individuals into larger randomized controlled trials (RCTs). Personalized N-of-1 trials can be considered a subcategory of SCEDs that overlaps with reversal designs. Relevant issues for each type of design—including comparisons of treatments, design issues such as randomization and blinding, standards for designs, and statistical approaches to complement visual inspection of single-case experimental designs—are also discussed.

1. Introduction

Single-case experimental designs (SCEDs) represent a family of experimental designs to examine the relationship between one or more treatments or levels of treatment and changes in biological or behavioral outcomes. These designs originated in early experimental psychology research ( Boring, 1929 ; Ebbinghaus, 1913 ; Pavlov, 1927 ), and were later expanded and formalized in the fields of basic and applied behavior analysis ( Morgan & Morgan, 2001 ; Sidman, 1960 ). SCEDs have been extended to a number of fields, including medicine ( Lillie et al., 2011 ; Schork, 2015 ), public health ( Biglan et al., 2000 ; Duan et al., 2013 ), education ( Horner et al., 2005 ), counseling psychology ( Lundervold & Belwood, 2000 ), clinical psychology ( Vlaeyen et al., 2020 ), health behavior ( McDonald et al., 2017 ), and neuroscience ( Soto, 2020 ).

SCEDs provide a framework to determine whether changes in a target behavior(s) or symptom are in fact a function of the intervention. The fundamentals of an SCED involve repeated measurement, replication of conditions (e.g., baseline and intervention conditions), and the analysis of effects with respect to each individual serving as his or her own control. This process can be useful for identifying the optimal treatment for an individual ( Dallery & Raiff, 2014 ; Davidson et al., 2021 ), treating rare diseases ( Abrahamyan et al., 2016 ), and implementing early phase translational research ( Czajkowski et al., 2015 ). SCEDs can be referred to as ‘personalized (N-of-1) trials’ when used this way, but they also have broad applicability to a range of scientific questions. Results from SCEDs can be aggregated using meta-analytic techniques to establish generalizable methods and treatment guidelines ( Shadish, 2014 ; Vannest et al., 2018 ). Figure 1 presents the main family of SCEDs, and shows how personalized (N-of-1) trials fit into these designs ( Vohra et al., 2016 ). The figure also distinguishes between experimental and nonexperimental single-case designs. In the current article, we provide an overview of SCEDs and thus a context for the articles in this special issue focused on personalized (N-of-1) trials. Our focus is to provide the fundamentals of these designs, and more detailed treatments of data analysis ( Moeyaert & Fingerhut, 2022 ; Schork, 2022 ) conduct and reporting standards ( Kravitz & Duan, 2022 ; Porcino & Vohra, 2022 ), and other methodological considerations are provided in this special issue. Our hope is that this article will inspire a diverse array of students, engineers, scientists, and practitioners to further explore the utility, rigor, and flexibility of these designs.

A = Baseline, B and C refer to different treatments.

The most common approach to evaluating the effectiveness of interventions on outcomes is using randomized controlled trials (RCTs). RCTs provide an idea of the average effect of an intervention on outcomes. People do not all change at the same rate or in the same way, however; variability in both how people change and the effect of the intervention is inevitable ( Fisher et al., 2018 ; Normand, 2016 ; Roustit et al., 2018 ). These sources of variability are conflated in a typical RCT, leading to heterogeneity of treatment effects (HTE). Research on HTE has shown variability in outcomes in RCTs, and in some studies very few people actually exhibit the benefits of that treatment ( Williams, 2010 ). One approach in RCTs is to assess moderators of treatment response to identify individual differences that may predict response to a treatment. This approach may not limit variability in response, and substantial reduction in variability of treatment for subgroups in comparison to the group as a whole is far from assured. Even if variability is reduced, the average effect for that subgroup may not be representative of individual members of the subgroup.

SCEDs can identify the optimal treatment for an individual person rather than the average person in a group ( Dallery & Raiff, 2014 ; Davidson et al., 2021 ; Hekler et al., 2020 ). SCEDs are multiphase experimental designs in which a great deal of data is collected on a single person, said person serves as his or her own control ( Kazdin, 2011 , 2021 ), and the order of presentation of conditions can be randomized to enhance experimental control. That is, a person’s outcomes in one phase are compared to outcomes in another phase. In a typical study, replications are achieved within and/or across several individuals; this allows for strong inferences about causation between behavior and the treatment (or levels thereof). Achieving replications is synonymous with achieving experimental control.

We provide an overview of three experimental designs that can be adapted for personalized medicine: reversal, multiple baseline, and combined reversal and multiple baseline designs, and we discuss how SCEDs can be integrated into RCTs. These designs focus on demonstrating experimental control of the relationship between treatment and outcome. Several general principles common to all of the designs are noteworthy ( Lobo et al., 2017 ). First, in many studies, treatment effects are compared with control conditions with a no- intervention baseline as the initial condition. To reduce threats to internal validity of the study, the order of assignment of interventions can be randomized ( Kratochwill & Levin, 2010 ) and, when possible, the intervention and data collection can be blinded. The demonstration of experimental control across conditions or people needs to be replicated several times (three replications is the minimum) to ensure confidence of the relationship between treatment and outcome ( Kratochwill et al., 2010 ; Kratochwill & Levin, 2015 ). Demonstrating stability of data within a phase or, otherwise, no trend in the direction of treatment effects prior to starting treatment is particularly important. Stability refers to the degree of variability in the data path over time (e.g., data points must fall within a 15% range of the median for a condition). Thus, phase length needs to be flexible for the sake of determining stability and trend within a phase, but a minimum of 5 data points per phase has been recommended ( Kratochwill et al., 2013 ). The focus of the intervention’s effects is on clinically rather than statistically significant effects with the target effect prespecified and considered in interpretation of the relevance of the effect for clinical practice ( Epstein et al., 2021 ). In addition, multiple dependent outcomes can be simultaneously measured ( Epstein et al., 2021 ). SCEDs can be used to test whether a variable mediates the effect of a treatment on symptoms or behavior ( Miočević et al., 2020 ; Riley & Gaynor, 2014 ). Visual inspection of graphical data is typically used to determine treatment effects, and statistical methods are commonly used to assist in interpretation of graphical data ( Epstein et al., 2021 ). Furthermore, a growing number of statistical approaches can summarize treatment effects and provide effect sizes ( Kazdin, 2021 ; Moeyaert & Fingerhut, this issue; Pustejovsky, 2019 ; Shadish et al., 2014 ). Data across many SCED trials can be aggregated to assess the generality of the treatment effects to help address for whom and under what conditions an intervention is effective ( Branch & Pennypacker, 2013 ; Shadish, 2014 ; Van den Noortgate & Onghena, 2003 ).

2. Reversal Designs

A reversal design collects behavioral or biological outcome data in at least two phases: a baseline or no treatment phase (labeled as ‘A’) and the experimental or treatment phase (labeled as ‘B’). The design is called a reversal design because there must be reversals or replications of phases for each individual; for example, in an ABA design, the baseline phase is replicated ( Kazdin, 2011 ). Ideally, three replications of treatment effects are used to demonstrate experimental control ( Kratochwill et al., 2010 ; Kratochwill & Levin, 1992 ). Figure 2 shows hypothetical results from an A1B1A2B2 design. The graph shows three replications of treatment effects (A1 versus B1, B1 versus A2, A2 versus B2) across four participants. Each phase was carried out until stability was evident from visual inspection of the data as well as absence of trends in the direction of the desired effect. The replication across participants increases the confidence in the effectiveness of the intervention. Extension of this design is possible by comparing multiple interventions, as well. The order of the treatments should be randomized, especially when the goal is to combine SCEDs across participants.

A1 = First Baseline, B1 First Treatment, A2 = Return to Baseline, B2 = Return to Treatment. P1–P4 represent different hypothetical participants.

Reversal designs can be more dynamic and compare several treatments. A common approach in personalized medicine would be to compare two or more doses of or different components of the same treatment ( Ward-Horner & Sturmey, 2010 ). For example, two drug doses could be compared using an A1B1C1B2C2 design, where A represents placebo and B and C represent the different drug doses ( Guyatt et al., 1990 ). In the case of drug studies, the drug/placebo administration can be double blinded. A more complex design could be A1B1A2C1A3C2A4B2, which would yield multiple replications of the comparison between drug and placebo. Based on the kinetics of the drug and the need for a washout period, the design could also be A1B1C1B2C2. This would provide three demonstration of treatment effects: B1 to C1, C1 to B2, and B2 to C2. Other permutations could be planned strategically to identify the optimal dose for each individual.

Advantages of SCED reversal designs are their ability to experimentally show that a particular treatment was functionally related to a particular change in an outcome variable for that person . This is the core principle of personalized medicine: an optimal treatment for an individual can be identified ( Dallery & Raiff, 2014 ; Davidson et al., 2021 ; Guyatt et al., 1990 ; Hekler et al., 2020 ; Lillie et al., 2011 ). These designs can work well for studying the effect of interventions on rare diseases in which collecting enough participants with similar characteristics for an RCT would be unlikely. An additional strength is the opportunity for the clinical researcher who also delivers clinical care to translate basic science findings or new findings from RCTs to their patients, who can potentially benefit ( Dallery & Raiff, 2014 ; Hayes, 1981 ). Research suggests that the trickledown of new developments and hypotheses to their support in RCTs can take more than 15 years; many important advancements in the medical and behavior sciences are likely not to be implemented rapidly enough ( Riley et al., 2013 ). The ability to test new intervention developments using scientific principles could speed up their translation into practice.

Limitations to SCED designs, however, are worth noting. Firstly, in line with the expectation that the outcome returns to baseline levels, reversals may require removal of the treatment. If the effect is not quickly reversible, then the designs are not relevant. A washout period may be placed in-between phases if the effect is not immediately reversible; for example, a drug washout period could be planned based on the half-life of drug. Secondly, the intervention should have a relatively immediate effect on the outcome. If many weeks to months are needed for some interventions to show effects, a reversal design may not be optimal unless the investigator is willing to plan a lengthy study. Thirdly, the design depends on comparing stable data over conditions. If achieving stability due to uncontrolled sources of biological or environmental variation is not possible, a reversal design may not be appropriate to evaluate a treatment, though it may be useful to identify the sources of variability ( Sidman, 1960 ). Finally, for a reversal to a baseline, a no-treatment phase may be inappropriate in investigating treatment effects for a very ill patient.

3. Multiple Baseline Designs

An alternative to a reversal design is the multiple baseline design, which does not require reversal of conditions to establish experimental control. There are three types of multiple baseline designs: multiple baseline across people, behaviors, and settings. The most popular is the multiple baseline across people, in which baselines are established for three or more people for the same outcome ( Cushing et al., 2011 ; Meredith et al., 2011 ). Treatment is implemented after different durations of baseline across individuals. The order of treatment implementation across people can be randomized ( Wen et al., 2019 ). Figure 3 shows an example across three individuals. In this hypothetical example, baseline data for each person are relatively stable and not decreasing, and reductions in the dependent variable are only observed after introduction of the intervention. Inclusion of one control person, who remains in baseline throughout the study and provides a control for extended monitoring, is also possible. Another variation is to collect baseline data intermittently in a ‘probe’ design, which can minimize burden associated with simultaneous and repeated measurement of outcomes ( Byiers et al., 2012 ; Horner & Baer, 1978 ). If the outcomes do not change during baseline conditions and the changes only occur across participants after the treatment has been implemented—and this sequence is replicated across several people—change in the outcome may be safely attributed to the treatment. The length of the baselines still must be long enough to show stability and no trend toward improvement until the treatment is implemented.

P1–P3 represent different hypothetical participants.

The two other multiple baseline designs focus on individual people: the multiple baseline across settings and the multiple baseline across behaviors ( Boles et al., 2008 ; Lane-Brown & Tate, 2010 ). An example of a multiple baseline across settings would be a dietary intervention implemented across meals. An intervention that targets a reduction in consumption of high–glycemic index foods, or foods with added sugar across meals, could be developed with the order of meals randomized. For example, someone may be randomized to reduce sugar-added or high–glycemic index foods for breakfast without any implementation at lunch or dinner. Implementation of the diet at lunch and then dinner would occur after different durations of baselines in these settings. An example of multiple baseline across behaviors might be to use feedback to develop a comprehensive exercise program that involves stretching, aerobic exercise, and resistance training. Feedback could target improvement in one of these randomly selected behaviors, implemented in a staggered manner.

The main limitation to a multiple baseline design is that some people (or behaviors) may be kept in baseline or control conditions for extended periods before treatment is implemented. Of course, failure to receive an effective treatment is common in RCTs for people who are randomized to control conditions, but unlike control groups in RCTs, all participants eventually receive treatment.

Finally, while the emphasis in personalized medicine is the identification of an optimal treatment plan for an individual person, situations in which multiple baselines across people prove relevant for precision medicine may arise. For example, identification of a small group of people with common characteristics—perhaps with a rare disease and for which a multiple-baseline-across-people design could be used to test an intervention more effectively than a series of personalized designs—is possible. In a similar vein, differential response to a common treatment in a multiple-baseline-across-people design can help to identify individual differences that can compromise the response to a treatment.

4. Integrating Multiple Baseline and Reversal Designs

While reversal designs can be used to compare effects of interventions, multiple baseline designs provide experimental control for testing one intervention but do not compare different interventions. One way to take advantage of the strengths of both designs is to combine them. For example, the effects of a first treatment could be studied using a multiple-baseline format and, after experimental control has been established, return to baseline prior to the commencement of a different treatment, which may be introduced in a different order. These comparisons can be made for several different interventions with the combination of both designs to demonstrate experimental control and compare effects of the interventions.

Figure 4 shows a hypothetical example of a combined approach to identify the best drug to decrease blood pressure. Baseline blood pressures are established for three people under placebo conditions before new drug X is introduced across participants in a staggered fashion to establish relative changes in blood pressure. All return to placebo after blood pressures reach stability, drug Y is introduced in a staggered sequence, participants are returned to placebo, and the most effective intervention for each individual (drug X or Y) is reintroduced to replicate the most important result: the most effective medication. This across-subjects design establishes experimental control for two different new drug interventions across three people while also establishing experimental control for five comparisons within subjects (placebo—drug X, drug Y—placebo, placebo—drug Y, drug Y—placebo, placebo—more effective drug). Though this combined design strengthens confidence beyond either reversal or multiple baseline designs, in many situations, experimental control demonstrated using a reversal design is sufficient.

BL = Baseline. Drug X and Drug Y represent hypothetical drugs to lower blood pressure, and Best Drug represents a reversal to the most effective drug as identified for each hypothetical participant, labeled P1–P3.

5. Other Varieties of Single-Case Experimental Designs

Other less commonly used designs within the family of SCEDs may be useful for personalized medicine. One of the most relevant may be the alternating treatment design ( Barlow & Hayes, 1979 ; Manolov et al., 2021 ), in which people are exposed to baseline and one or more treatments for very brief periods without the concern about stability before changing conditions. While the treatment period may be short, many more replications of treatments—and ineffective treatments—can be identified quickly. This type of design may be relevant for drugs that have rapid effects with a short half-life and behavioral interventions that have rapid effects ( Coyle & Robertson, 1998 )—for example, the effects of biofeedback on heart rate ( Weems, 1998 ). Another design is the changing criterion design, in which experimental control is demonstrated when the outcome meets certain preselected criteria that can be systematically increased or decreased over time ( Hartmann & Hall, 1976 ). The design is especially useful when learning a new skill or when outcomes change slowly over time ( Singh & Leung, 1988 )—for example, gradually increasing the range of foods chosen in a previously highly selective eater ( Russo et al., 2019 ).

6. Integrating Single-Case Experimental Designs Into Randomized Controlled Trials

SCEDs can be integrated into RCTs to compare the efficacy of treatments chosen for someone based on SCEDs versus a standardized or usual care treatment ( Epstein et al., 2021 ; Schork & Goetz, 2017 ). Such innovative designs may capture the best in SCEDs and randomized controlled designs. Kravitz et al. (2018) used an RCT in which one group ( n = 108) experienced a series of reversal AB conditions, or a personalized (N-of-1) trial. The specific conditions were chosen for each patient from among eight categories of treatments to reduce chronic musculoskeletal pain (e.g., acetaminophen, any nonsteroidal anti-inflammatory drug, acetaminophen/oxycodone, tramadol). The other group ( n = 107) received usual care. The study also incorporated mobile technology to record pain-related data daily (see Dallery et al., 2013 , for a discussion of technology and SCEDs). The results suggested that the N-of-1 approach was feasible and acceptable, but it did not yield statistically significant superior results in pain measures compared to the usual care group. However, as noted by Vohra and Punja (2019) , the results do not indicate a flaw in the methodological approach: finding that two treatments do not differ in superiority is a finding worth knowing.

Another example of a situation where an integrated approach may be useful is selecting a diet for weight control. Many diets for weight control that vary in their macronutrient intake—such as low carb, higher fat versus low fat, and higher carb—have their proponents and favorable biological mechanisms. However, direct comparisons of these diets basically show that they achieve similar weight control with large variability in outcome. Thus, while the average person on a low-fat diet does about the same as the average person on a lowcarb diet, some people on the low-carb diet do very well, while some fail. Some of the people who fail on the low-fat diet would undoubtedly do well on the low-carb diet, and some who fail on the low-fat diet would do well on the low-carb diet. Further, some would fail on both diets due to general problems in adherence.

Personalized medicine suggests that diets should be individualized to achieve the best results. SCEDs would be one way to show ‘proof of concept’ that a particular diet is better than a standard healthy diet. First, people would be randomized to experimental (including SCEDs) or control (not basing diet on SCEDs). Subject selection criteria would proceed as in any RCT. For the first 3 months, people in the experimental group would engage in individual reversal designs in which 2-week intervals of low-carb and low-fat diets would be interspersed with their usual eating, and weight loss, diet adherence, food preferences, and the reinforcing value of foods in the diet would be measured to assess biological, behavioral, and subjective changes.

Participants in the control group would experience a similar exposure to the different types of diets, but the diet to which they are assigned would be randomly chosen rather than chosen using SCED methods. In this way, they would have similar exposure to diets during the first 3 months of the study, but this experience would not impact group assignment. As with any RCT, the study would proceed with regular measures (e.g., 6, 12, 24 months) and the hypothesis that those assigned to a diet that results in better initial weight loss, and that they like and are motivated to continue, would do better than those receiving a randomly selected diet. The study could also be designed with three groups: a single-case design experimental group similar to the approach in the hypothetical study above and two control groups, one low-fat and one low-carb.

An alternative design would be to have everyone experience SCEDs for the first 3 months and then be randomized to either the optimal treatment identified during the first 3 months or an intervention randomly chosen among the interventions to be studied. This design has the advantage of randomization being after 3 months of study so that dropouts and non-adherers within the first 3 months would not be randomized in an intent-to-treat format.

The goal of either hypothesized study, or any study that attempts to incorporate SCEDs into RCTs, is that matching participants to treatments will provide superior results in comparison to providing the same treatment to everyone in a group. Two hypotheses can be generated in these types of designs: first, that the mean changes will differ between groups, and second, that the variability will differ between groups with less variability in outcome for people who have treatment selected after a single-case trial than people who have a treatment randomly selected. A reduction in variability plus mean differences in outcome should increase the effect size for people treated using individualized designs, increase power, and allow for a smaller sample size to ensure confidence about the differences observed between groups.

7. Limitations of Single-Case Experimental Designs

Single-case experimental designs have their common limitations. If a measure changes with repeated testing without intervention, it may not be useful for an SCED unless steps can be taken to mitigate such reactivity, such as more unobtrusive monitoring ( Kazdin, 2021 ). Given that the effects of interventions are evaluated over time, systematic environmental changes or maturation could influence the relationship between a treatment and outcome and thereby obscure the effect of a treatment. However, the design logic of reversal and multiple baseline designs largely control for such influences. Since SCEDs rely on repeated measures and a detailed study of the relationship between treatment and outcome, studies that use dependent measures that cannot be sampled frequently are not candidates for SCEDs. Likewise, the failure to identify a temporal relationship between the introduction of treatment and initiation of change in the outcome can make attribution of changes to the intervention challenging. A confounding variable’s association with introduction or removal of the intervention, which may cause inappropriate decisions about the effects of the intervention, is always possible. Dropout or uncontrolled events that occur to individuals can introduce confounding variables to the SCED. These problems are not unique to SCEDs and also occur with RCTs.

8. Single-Case Experimental Designs in Early Stage Translational Research

The emphasis of a research program may be on translating basic science findings to clinical interventions. The goal may be to collect early phase translational research as a step toward a fully powered RCT—( Epstein et al., 2021 ). The fact that a large amount of basic science does not get translated into clinical interventions is well known ( Butler, 2008 ; Seyhan, 2019 ); this served in part as the stimulus for the National Institutes of Health (NIH) to develop a network of clinical and translational science institutes in medical schools and universities throughout the United States. A common approach to early phase translational research is to implement a small, underpowered RCT to secure a ‘signal’ of a treatment effect and an effect size. This is a problematic approach to pilot research, and it is not advocated by the NIH as an approach to early phase translational research ( National Center for Complementary and Integrative Health, 2020 ). The number of participants needed for a fully powered RCT may be substantially different from the number projected from a small-sample RCT. These small, underpowered, early phase translational studies may provide too large an estimate of an effect size, leading to an underpowered RCT. Likewise, a small-sample RCT can lead to a small effect size that can, in turn, lead to a failure to implement a potentially effective intervention ( Kraemer et al., 2006 ). Therefore, SCEDs—especially reversal and multiple baseline designs—are evidently ideally suited to early phase translational research. This use complements the utility of SCEDs for identifying the optimal treatment for an individual or small group of individuals.

9. Conclusion

Single-case experimental designs provide flexible, rigorous, and cost-effective approaches that can be used in personalized medicine to identify the optimal treatment for an individual patient. SCEDs represent a broad array of designs, and personalized (N-of-1) designs are a prominent example, particularly in medicine. These designs can be incorporated into RCTs, and they can be integrated using meta-analysis techniques. SCEDs should become a standard part of the toolbox for clinical researchers to improve clinical care for their patients, and they can lead to the next generation of interventions that show maximal effects for individual cases as well as for early phase translational research to clinical practice.

Acknowledgments

We thank Lesleigh Stinson and Andrea Villegas for preparing the figures.

Disclosure Statement

Preparation of this special issue was supported by grants R01LM012836 from the National Library of Medicine of the National Institutes of Health and P30AG063786 from the National Institute on Aging of the National Institutes of Health. Funding to authors of this article was supported by grants U01 HL131552 from the National Heart, Lung, and Blood Institute, UH3 DK109543 from the National Institute of Diabetes, Digestive and Kidney Diseases, and RO1HD080292 and RO1HD088131 from the Eunice Kennedy Shriver National Institute of Child Health and Human Development. The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; or decision to submit the manuscript for publication. The views expressed in this paper are those of the authors and do not represent the views of the National Institutes of Health, the U.S. Department of Health and Human Services, or any other government entity.

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In This Article Expand or collapse the "in this article" section Single-Case Experimental Designs

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Single-Case Experimental Designs by S. Andrew Garbacz , Thomas R. Kratochwill LAST REVIEWED: 29 July 2020 LAST MODIFIED: 29 July 2020 DOI: 10.1093/obo/9780199828340-0265

Single-case experimental designs are a family of experimental designs that are characterized by researcher manipulation of an independent variable and repeated measurement of a dependent variable before (i.e., baseline) and after (i.e., intervention phase) introducing the independent variable. In single-case experimental designs a case is the unit of intervention and analysis (e.g., a child, a school). Because measurement within each case is conducted before and after manipulation of the independent variable, the case typically serves as its own control. Experimental variants of single-case designs provide a basis for determining a causal relation by replication of the intervention through (a) introducing and withdrawing the independent variable, (b) manipulating the independent variable across different phases, and (c) introducing the independent variable in a staggered fashion across different points in time. Due to their economy of resources, single-case designs may be useful during development activities and allow for rapid replication across studies.

Several sources provide overviews of single-case experimental designs. Barlow, et al. 2009 includes an overview for the development of single-case experimental designs, describes key considerations for designing and conducting single-case experimental design research, and reviews procedural elements, assessment strategies, and replication considerations. Kazdin 2011 provides detailed coverage of single-case experimental design variants as well as approaches for evaluating data in single-case experimental designs. Kratochwill and Levin 2014 describes key methodological features that underlie single-case experimental designs, including philosophical and statistical foundations and data evaluation. Ledford and Gast 2018 covers research conceptualization and writing, design variants within single-case experimental design, definitions of variables and associated measurement, and approaches to organize and evaluate data. Riley-Tillman and Burns 2009 provides a practical orientation to single-case experimental designs to facilitate uptake and use in applied settings.

Barlow, D. H., M. K. Nock, and M. Hersen, eds. 2009. Single case experimental designs: Strategies for studying behavior change . 3d ed. New York: Pearson.

A comprehensive reference about the process of designing and conducting single-case experimental design studies. Chapters are integrative but can stand alone.

Kazdin, A. E. 2011. Single-case research designs: Methods for clinical and applied settings . 2d ed. New York: Oxford Univ. Press.

A complete overview and description of single-case experimental design variants as well as information about data evaluation.

Kratochwill, T. R., and J. R. Levin, eds. 2014. Single-case intervention research: Methodological and statistical advances . New York: Routledge.

The authors describe in depth the methodological and analytic considerations necessary for designing and conducting research that uses a single-case experimental design. In addition, the text includes chapters from leaders in psychology and education who provide critical perspectives about the use of single-case experimental designs.

Ledford, J. R., and D. L. Gast, eds. 2018. Single case research methodology: Applications in special education and behavioral sciences . New York: Routledge.

Covers the research process from writing literature reviews, to designing, conducting, and evaluating single-case experimental design studies.

Riley-Tillman, T. C., and M. K. Burns. 2009. Evaluating education interventions: Single-case design for measuring response to intervention . New York: Guilford Press.

Focuses on accelerating uptake and use of single-case experimental designs in applied settings. This book provides a practical, “nuts and bolts” orientation to conducting single-case experimental design research.

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The insights of allied health professionals transitioning from a matrix structure to a centralized profession-based structure within a public hospital setting

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• Published: 30 August 2024

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• Gemma Turato   ORCID: orcid.org/0000-0002-9589-7425 1 ,
• John Whiteoak 2 &
• Florin Oprescu 2  

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To manage the challenges associated with increasing costs and demand for healthcare, administrators often propose a re-structure of the workforce to gain more efficiencies. However, this can have detrimental impacts on professions such as allied health if the uniqueness of this workforce is not taken into consideration before, during and after re-structuring. In the dynamic setting of public hospital bed-based services, allied health is highly complex, consisting of diverse professionals (e.g., audiology, physiotherapy, occupational therapy, podiatry, pharmacy, dietetics, social work, and speech pathology), each requiring different technical expertise, training pathways, professional governance, and accountability. This case study evaluates the outcomes of a re-structure of allied health professionals working in bed-based services who transitioned from a matrix to a centralized structure of service delivery. Qualitative data were collected in a survey across three years to gain the perceptions from allied health staff about the impacts of the new structure. The results demonstrated that a centralized profession-based structure with single points of accountability was superior to a matrix structure in this context. The benefits identified included improved governance, administration efficiencies and cost-savings gained by having the budget and professional management aligned. This resulted in improved workforce planning and flexibility that delivered care to patients based on clinical priority. Further benefits included professional skills training pathways and succession planning across clinical specialties which enhanced career opportunities, all of which improved wellbeing and morale. These findings add to the sparse research pertaining to the components (structural, human and systems) to consider when incorporating allied health professionals in a proposed organizational design and the contingencies they require to operate successfully within certain contexts.

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Introduction

To manage challenges associated with increasing costs and demand for healthcare, administrators have been looking for more efficient approaches in delivering quality care and enhancing performance. A common approach aimed at improving overall performance in many large organizations such as public hospitals has been to re-structure. However, past evidence has suggested that implementing structural change without due consideration of the unique requirements of health professionals such as allied health employees can have negative implications (Braithwaite et al. 2006 ; Law and Boyce 2003 ; Mickan et al. 2019 ). A review of allied health professionals (AHPs) working in Victoria, Australia concluded that there is no one best structural model for AHPs as they are a support workforce that connects and contributes to local priority requirements and systems (Buchan and Law 2016 ). Consequently, avoiding the ‘one size fits all’ approach is critical when an organization proposes a new structure that involves AHPs (Braithwaite et al. 2006 ; Law and Boyce 2003 ). This is the basic premise of organizational design theory which recognizes that there is not just one most suitable solution for organizing and that different organizations are not equally effective or efficient (Galbraith 1973 ; Burton and Obel 2018 ).

Organizational theory is defined by Jones ( 2013 ) as the “study of how organizations function and how they affect and are affected by the environment in which they operate” (pp. 30). The focus of organizational theory is to understand how to organize people and resources in order to achieve the organizations objectives (Greenwood and Miller 2020 ). Hence, in order to achieve its goals, the organization needs to focus on both structure and culture. Additionally, organizational design is about how and why various functions are chosen and puts pressure on individuals and work groups to behave in certain ways. Therefore, if the proper balance is not achieved, this can have significant impact on the organization’s performance (Jones 2013 ).

The multi-contingency theory of organizational design developed by Burton and Obel ( 2004 ) explains this further, by relating variations in organizational design to variations in the situation of the organization (i.e., its contingencies), which they conclude should be chosen based on the particular context. Furthermore, the description of the context should be multi-dimensional, including structural components (i.e., goals, strategy, structure, and tasks), human components (i.e., leadership, work processes, and people) and coordination (i.e., control systems, decision systems, information systems, and incentive mechanisms).

In this view, organizational design is more a process than a structure that is constantly adapting and evolving and planning for contingencies that may arise (Burton and Obel 2004 ). Further, the design of an organization shapes the flow of information, resources, and support, which effectively determines the powerholders (Myers 1996 ). Allied health employees are a good example of when the power imbalance in large healthcare organizations can create perceptions of inequitable treatment and lead to workers feeling disenfranchised. Even though AHPs constitute the second largest workforce within the health industry in Australia and make a significant contribution to health improvements, this contribution remains under recognized due to much of the health policy and funding focused predominantly on medicine and nursing (Buchan and Law 2016 ). Subsequently, AHPs are often not visible enough on the health policy agenda and there is little evidence available regarding their cost benefit and value. Consequently, there has been a relative lack of examination of the allied health workforce and how they contribute and influence in healthcare organizations. This has resulted in organizational designs that have minimal consideration given to AHPs and the contingencies that may impact their performance and wellbeing, which has ultimately resulted in unfavorable outcomes (Boyce 2006 ; Robinson and Compton 1996 ; Turato et al. 2023 ).

While there is some debate on the correct definition of this workforce (Turnbull et al. 2009 ), according to the Allied Health Professions Australia (AHPA, 2022), they are considered as health professionals that are separate to medicine, dental and nursing and provide specialized support across a variety of health services. Typically, these include audiology, dietetics, exercise physiology, occupational therapy, pharmacy, physiotherapy, podiatry, psychology, radiography, social work, and speech pathology. They usually operate autonomously and practice in an evidence-based paradigm, using an internationally recognized body of knowledge to provide optimal patient outcomes (AHPA, 2022).

The focus of AHPs in public hospital bed-based services is to work within a multidisciplinary team that delivers a coordinated approach to patient care (AHPA, 2022). This fast-paced context requires AHPs to make rapid clinical decisions frequently and be adaptable and flexible across clinical areas when under pressure to meet clinical demands (Philip 2015 ). They require highly technical knowledge and skills to deliver care in this setting (e.g., social worker organizing crisis accommodation, physiotherapist managing a severe respiratory condition to avoid an admission to the intensive care unit, occupational therapist fabricating a complex hand splint following trauma surgery, pharmacist assisting the medical team with medication management and dosage for acutely unwell patients). These types of pressures distinguish a public hospital bed-based setting from a community or primary care setting, in which the client group is not in the acute stages of treatment or requiring highly technical expertise from AHPs (Philip 2015 ). The relatively limited evidence suggests that for AHPs to function optimally in public hospital bed-based services requires an efficient and effective organizational design that takes into consideration both the multi-faceted nature of the allied health workforce and the layers of professional and clinical governance required to manage them effectively.

Given the specific needs of AHPs working in this sector of the hospital, this case study provides distinctive insights from AHPs, to better understand their operating requirements. This is important for hospital systems to understand if they are proposing to re-structure with the aim of delivering more efficient, sustainable, and effective services. Not taking into consideration the unique requirements of AHPs to function optimally and treating them the same as nursing and medicine is likely to result in poorer outcomes and impact performance (Turato et al. 2023 ). One of the key differences of AHPs when compared to nursing and medicine is the need for each individual allied health professional to navigate multiple identities with respect to their profession, the overarching allied health structure and alignment, and their inter-professional teams (Porter and Wilton 2020 ). The diversity of AHPs, each with different technical expertise, training pathways, sectors of practice and professional governance, makes AHPs working in hospital bed-based services highly complex. Therefore, as healthcare becomes increasingly complex, requiring seamless interdisciplinary teamwork and maximal return on investments in the health workforce, it is critical that the organizations in which AHPs work have considered the structural, human and system components of their design so that the widespread potential AHPs represent is fully realized (Australian Health Workforce Advisory Committee 2006 ).

In this study, the insights and experiences of hospital bed-based AHPs who were involved in a transition from a matrix to a centralized allied health structure were explored. The focus of the research was a large multi-site Australian public hospital and health service with five clinical orientated groupings (i.e., medical, surgical, women’s and children’s, community, and mental health). In 2017, the organization expanded to tertiary level services with the addition of a new hospital. In preparation for this, AHPs were dispersed into each of the five clinical groups under the management of a medical and nursing director. However, the matrix structure did not deliver upon the anticipated outcomes for AHPs working in the medical and surgical clinical groups with a range of unfavorable concerns reported (e.g., ambiguity, reduced workforce flexibility, increased cost, and low morale). Following consultation with key stakeholders and AHPs, a centralized allied health structure was implemented for AHPs working in the medical and surgical clinical units. To measure the outcomes, qualitative data were collected through an online survey in June 2020, 2021 and 2022 through open-ended questions and confirmatory meetings to verify generated themes.

This paper presents the findings of this iterative process and highlights the importance of implementing the ‘right structure’ which has the appropriate governance and support systems for AHPs working within hospital bed-based services. Further, it demonstrates the importance of healthcare administrators needing to be well informed about the complexities of AHPs before they consider embarking on structural change that incorporates AHPs in this context. This research contributes to relevant theory and practice by providing a deeper understanding of the type of structure and functions that may enhance AHPs experience of working in hospital bed-based services. Furthermore, the paper emphasizes that the unique contextual nuances of the work of AHPs are often overlooked during a re-structure, and this can have detrimental outcomes (Turato et al. 2023 ). Given there is limited empirical research about AHPs re-structuring in public hospital bed base services, understanding the experiences and insights of AHPs going through structural change, adds to the evidence that may enhance future structural re-organizations pertaining to this workforce and further maximize their potential and productivity in public hospital settings.

Background/theory

Organizational theory.

Organizational theory has developed over three eras’, with early organizational theorists classifying organizational structures as either mechanistic or organic (Anand and Daft 2007 ). The first era predominantly adopted mechanistic structures during the mid-1800s to the late 1970s and were designed for stable and simple organizational environments with low to moderate uncertainty. They were described as self-contained, top-down pyramids containing internal organizational processes that took in raw materials, transformed them into products which were then distributed to customers (Anand and Daft 2007 ). The second era included organic organizational structures and systems which were designed for unstable, complex, and changing environments, which mechanistic structures could not manage. This era gained momentum in the 1980s and extended through the mid-1990s and incorporated horizontal organic organizational designs with a flattened hierarchical, hybrid structure and cross-functional teams (Daft 2016 ).

A third era formed in the mid-1990s and extends to the present day, being driven by factors, such as the internet, global competition with low-cost labor; automation of supply chains and outsourcing of expertise to speed up production and delivery of products and services. During this period, structures evolved, including the functional, divisional, matrix, global geographic, modular, team-based, and virtual (Daft 2016 ). Given this case study focuses on the centralized divisional and matrix structures, a brief outline of each will be covered next.

The divisional structure incorporates several functional departments grouped under a division head. Each functional group in a division has its own marketing, sales, accounting, manufacturing, and production team. The advantages include, each specialty area can be more focused on the business and budget; employees understand their responsibilities; improved efficiencies of services; and easier coordination due to all the functions being accessible. The disadvantages of this structure include divisions becoming isolated and insular from one another and different systems, such as accounting, finance, and sales, may suffer from poor and infrequent communication and coordination of the organizations mission, direction, and values (Daft 2016 ).

The matrix structure is an organic structure aimed at responding to environmental uncertainty, complexity, and instability (Burton et al. 2015 ; Daft 2016 ). The matrix structure originated at a time in the 1960s when the United States aerospace firms contracted with the government. Since that time, this structure has been imitated and used by other industries and companies since it provides flexibility and helps integrate decision-making in functionally organized companies. The matrix design has formal authority along two dimensions: employees report to a functional, departmental boss and simultaneously to a product or project team boss. This dual reporting has been described as one of the significant weaknesses of matrix structures due to the confusion and conflicts employees experience in reporting to two bosses. Hence, a detailed design of the decision-making process at each junction point is required for a successful matrix organization (Burton et al. 2015 ; Daft 2016 ). Further, to be successful a matrix structure requires important contingencies, such as climate, leadership, knowledge sharing, information technology and incentives that are correctly designed and aligned with one another (Burton et al. 2015 ). The next section will briefly outline specific allied health structural approaches and summarize the implications for the provision of care by AHPs reported in literature.

Allied health models

During the mid-1990s, AHPs were commonly incorporated into the emerging organizational structures in healthcare, with a growing body of research being published about the impacts on AHPs (Law and Boyce 2003 ). It is generally recognized that allied health structures can be classified into four types: (1) the traditional medical model, (2) division of allied health, (3) clinical matrix and (4) integrated decentralization model (Boyce 1991 ). The traditional medical model is where individual profession-based departments report to a medical director (Boyce 1991 ; Law and Boyce 2003 ). This model is common practice in many smaller hospitals in which there are small numbers of AHPs. However, the model is rare in larger hospital settings in Australia where there are high employee numbers within each profession requiring professional governance and oversight (Boyce 2006 ).

In the allied health division model (i.e., centralized profession-based structure), a director of allied health is a member of the executive leadership group and AHPs are centralized into one division (Boyce 2001 ; Law and Boyce 2003 ). The main advantages of this model are argued to be improved communication flow between senior management and departmental managers, and it positions allied health as having more status and a collective identity (Boyce 2001 ). Disadvantages purported are the concentration of power in management, competition between the professions and less identification with a whole of organization view (Boyce 2001 ).

In healthcare organizations implementing the matrix structure involved giving financial control to clinical units and services being organized around patients rather than providers (Braithewaite et al. 2006 ; Law & Boyce 2003 ). This resulted in dispersing individual AHPs into clinical units with a dual authority relationship between professional and operational reporting lines (Boyce 2006 ). Often, an allied health advisor position at the executive level is appointed to address allied health issues occurring within the clinical sub-units (Boyce 2001 ). In the public hospital setting, literature suggests that a matrix structure delivers multiple benefits, such as reduced hospitalization time and costs, better accessibility for patients, and improved coordination of care (Braithwaite et al. 2006 ; Burton et al. 2015 ; Callan et al. 2007 ; Mueller and Neads 2005 ). The aim of including AHPs was to encourage better collaboration and cooperation across the multidisciplinary team (Porter and Wilton 2020 ). However, the growing evidence available reports many negative outcomes, including operational inefficiencies, loss of professional identity, ambiguity over dual reporting lines, low morale, poor job satisfaction and negative impacts to service delivery (Braithwaite et al. 2006 ; Callan et al. 2007 ; Porter and Wilton 2020 ; Robinson and Compton 1996 ; Turato et al. 2023 ).

The hybrid model classified as the integrated decentralization model is a combination of the allied health division and matrix structure (Boyce 1991 ). In this structure, allied health budgets remain under the control of allied health; however, clear documentation of how AHPs will provide care to each of the clinic units is often required. In this model, it is suggested that collaboration brings benefits of transparent operational and strategic planning, including the ability to implement research, clinical education, individual staff development and professional specific quality clinical services (Mueller and Neads 2005 ). However, this model requires good relationships between key stakeholders to ensure its viability.

The allied health models described above provide a brief overview of each with some demonstrating more potential advantages for AHPs. While the insights available on the impacts of structural change on AHPs is growing there is still limited research about the impacts for AHPs going through such change. In this study, the perceptions and experiences of AHPs who have transitioned from a matrix to a centralized allied health structure within public hospital bed-based services are explored. The aims of the study being to first add to the current gap in knowledge about factors that may mitigate negative experiences of AHPs when hospital administrators are considering a re-structure in this setting. Second, what structures and/or supporting strategies are required to meet the complex needs of AHPs working in this dynamic context. Hence, this case study addresses the following research question:

RQ1: What are the insights and experiences of AHPs transitioning from a matrix to a centralized profession-based structure within public hospital bed-based services?

Research context and case background

The region in Australia in which the study took place is described as peri-urban with an estimated population of 400,000 people in 2022. It is the fifth most populated area in Queensland and has grown steadily at an average annual rate of 2.4% year-on-year since 2018. It is a center for tourism, attracting more than 3.2 million visitors each year. The economy has outpaced most other regional economies in Australia in terms of growth over the last 15 years across several key sectors including healthcare, education, finance, and professional business services (Connection Australia 2023 ). The case study research occurred at a multi-campus hospital and health service, with a new tertiary facility opening in 2017. This facility provides tertiary level services to the community and the clinical capability to care for highly complex inpatient and ambulatory care services. The health service is an independent statutory body governed by a Board under the Hospital and Health Boards Act 2011. The health service operates according to a service agreement which identifies the services to be provided, funding arrangements, performance indicators and targets to ensure the expected health outcomes for the community are achieved.

To prepare for this expansion, the allied health workforce (approximately 600 staff) was integrated into the broader organization’s matrix structure in 2014. The organization believed this would support a multidisciplinary culture that was collaborative, reduce service gaps and improve consistency of allied health services. This in turn would involve AHPs in clinical directorate operational planning and improve the reporting of AHPs performance. The individual AHPs were assigned to one of five clinical orientated service groupings. These five groups were medical, surgical, women’s and families, mental health, and community. Medical imaging and pharmacy remained as standalone groups that reported operationally and professionally to a director of those professions. Each director subsequently reported to the service director in the medical group. The new tertiary facility provided the hospital and health service with a total bed count of approximately 884 beds in 2018, which increased to 1032 beds by 2022. The staffing grew from approximately 4500 full time (FTE) equivalent employees to 6500 in 2022 with an operating budget in the 2021–22 annual report of 1.45 billion dollars.

A consequence of the matrix structure was that the allied health executive lead and professional director roles were abolished. Figure  1 illustrates that these roles were replaced by allied health operational manager roles for each service group that were part of the multidisciplinary service group leadership team and a clinical director of allied health role which provided overall professional leadership for allied health.

Allied Health organizational chart following alignment to the matrix-oriented clinical directorate structure

The AHPs in the matrix structure reported operationally to an allied health manager and professionally to a professional leader role (i.e., horizontal gray line in Fig.  1 ) that did not have operational or budgetary responsibility. This resulted in many AHPs having dual reporting responsibilities to either an allied health manager or lead for operational requirements and a professional lead for professional governance (Turato et al. 2023 ).

The structural change to a matrix alignment was met with a range of negative consequences particularly within the medical and surgical groups (Turato et al. 2023 ). Some of these included confusion over reporting lines with multiple conversations needed to resolve workforce matters. Another included increased costs and inefficiencies due to more administration (e.g., several AHPs were aggregate employees with more than one position number for each clinical unit they were working for, with some staff having up to four position numbers). The increase in position numbers multiplied the paperwork involved to manage the employee, hence increasing the cost, time, potential errors, and re-work required. This led to limited opportunities for staff rotations and career opportunities due to the administration needed to manage this. Others included limited growth in staffing levels due to the budget being owned by each clinical unit and often allied health staffing was not advocated for or understood by the clinical unit (e.g., decisions about increasing allied health FTE and in which profession often had no robust planning or reasoning). A further concern raised by AHPs was the overall voice of allied health in the organization was minimized due to the matrix structure, which resulted in AHPs reporting a perceived lower status within the organization. All these factors ultimately led to lowered morale and wellbeing being reported (i.e., public sector employee opinion survey results from 2017 to 2019).

The negative impacts reported led to a strategic decision to implement a centralized allied health structure by amalgamating AHPs in the medical and surgical groups. The posited aims of the shift back to a centralized structure included:

Reduce patient risk through an enhanced discipline lens.

Decrease confusion over reporting lines and improved communication.

Decrease duplication of tasks for AHPs within each service group.

Improve flexibility to mobilize AHPs based on clinical priority.

Improve governance and accountability for AHPs.

Decrease administration time and structural inefficiencies.

Improve support to the facilities outside of the tertiary facility.

Improve the ability to implement new models of care, innovation, and research.

The centralized allied health structure commenced in January 2020 and re-introduced what had been dismantled in 2014. The structure abolished the professional lead and allied health manager roles and created professional director roles that were responsible for both operational and professional requirements. The clinical director allied health role was re-aligned to an executive director allied health role which reported to the chief executive. Figure  2 illustrates the organizational chart for the centralized allied health structure.

Allied Health Centralized Structure

Study design

This research presents qualitative data that were collected through an open-ended questionnaire using an online survey. The questions focused on why and/or how AHPs perceived the new centralized structure and was repeated annually for 3 years (i.e., 2020, 2021 and 2022). Follow-up confirmatory meetings with each profession were also conducted to confirm the themes derived from the survey feedback.

The survey asked participants to consent for their data to be used for research. Participants who did not provide consent were removed from the final research analysis. The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Ethics Committee of The Prince Charles Hospital, Queensland Health Human Research Committee (HREC 18/QPCH/238 on 30/08/2021).

Participants/data collection

Table 1 provides the types of professions and number of staff who consented to participate in the study. The new allied health structure incorporated hospital bed-based services within the Medical and Surgical groups with a total FTE of approximately 400 by 2022. During the planning phase of the study, staff expressed concern over being identifiable if they participated in a survey. For example, the professions with small numbers of staff (i.e., audiology, podiatry, psychology) perceived they could be identifiable if they were asked to disclose gender, age range, years in the profession / organization, level of education etc. Therefore, to encourage increased participation, demographic data were kept to a minimum, with the focus of the survey being on gaining qualitative feedback on the structural change.

This sector of the workforce is typically made up of a range of staffing levels from assistants, new graduates, base grade, senior, advanced and management levels. The staff who took part within each profession are typically representative of the professional roles that work in this sector. Not surprisingly, the professions of physiotherapy, occupational therapy and social work have higher numbers of clinicians working within bed-based services and hence have higher representation in the survey results. Additionally, the total number of employees who participated in the survey by profession is indicated in brackets in Table  1 to demonstrate how representative the sample is of the total population for that profession.

The profession of pharmacy was initially not included in the new centralized structure. This profession was incorporated into the new structure in 2021, but after the 2021 survey was administered. This would explain the no response rate from this profession in 2020 and 2021, but a higher completion rate in 2022. Medical imaging was not incorporated within the new allied health centralized structure and did not participate in the survey.

The response rate ranged from 25% (2020), 23% (2021) and 24% (2022) across the three years. This is a comparable response rate to a similar study published by Porter & Wilton ( 2020 ) on professional identity, in which they collected data following an organizational re-structuring in which AHPs were integrated into a matrix structure, within a large multi-site health network in Victoria, Australia. The survey response rate for each survey conducted pre and post was 23.4% and 20.8%.

The survey asked two open-ended questions about what AHPs perceived to be the barriers or issues regarding the centralized structure and what they believed were the enablers or suggestions to improve the implementation. In addition, participants were asked to provide feedback on the structural change against the aims and reasons for implementing the change (see Appendix 1 for a copy of the survey).

Data analysis

To analyze the narrative data, a manual thematic analysis was completed using a six-phase thematic analysis methodology developed by Braun and Clarke, ( 2006 ). The data analysis method contains a rigorous coding and categorizing methodology that is driven by the data collected during the evaluation process, rather than any analytic preconceptions (Nowell et al. 2017 ). The analysis involved an inductive approach that first identifies themes, which are analyzed initially in a descriptive form before progressing to an interpretive form. The interpretive form attempts to look beyond the surface of the data where the broader meaning and ultimate implications of the themes/patterns are deduced via engagement with the literature (Braun and Clarke 2006 ).

To extrapolate the underlying themes, the first phase involved migrating the raw narrative data from the surveys to column A in a Microsoft Excel worksheet (one sheet per survey question 3, 4 and 5). The primary researcher spent time reading and re-reading the raw data for each of these questions, noting down initial ideas, thoughts, and potential codes/themes (in column B). The primary researcher used this part of the process as a key phase of data analysis—in other words, as an interpretive act (Lapadat and Lindsay 1999 ) in which the primary researcher looked for meaning in the data.

The second phase included generating a single column of all comments per question 3, 4 and 5. The single column of data per question was sorted and reviewed to remove any duplicate entries. The next step was the coding process to determine the potential themes emerging from the data. There were no pre-determined thematic areas in place before the coding process was initiated. For the coding process, each individual cell (participant comment) from the single column of data per question 3, 4, and 5 was reviewed and assigned a potential thematic area, to which a cell color code was applied (yellow, blue, orange, gray etc.).

This data-driven, inductive approach ultimately led to the identification of initial thematic areas, each labeled with a different color. The types of themes that came out of the data were based on similarities of words to create the theme. For example, for the barriers/issues question 4, many participants talked about the problems related to arduous administration tasks. These types of comments were then coded to capture the essence of what the participants were expressing e.g., inefficient, and arduous administration. The codes were then assigned to potential themes for question 4 of which one included “inefficient administration.” In contrast, for question 3 pertaining to whether the aims were being met, many perceived significant improvement and efficiencies with administration, with the code phrased as, “improved administration.” The codes from question 3 were then placed against potential themes with those related to better administration coming under the theme of “ improved systems / processes ” for further analysis and discussion. If some points fell under two themes, the worksheet cell with the raw data was duplicated and each cell color-coded appropriately to ensure everything was recorded. Using the Excel sorting tool, the data was sorted by the color assigned to each cell, and therefore by thematic area. This sorting and collation approach brought together all the key points on each theme which determined the frequency of a theme raised by participants, which in turn assisted the researchers in determining the prominence of a theme (e.g., for the aims question, the frequency of respondents perceiving whether they believed all the aims were being met, versus whether they thought one or more were not being met was carefully considered in the analysis). After sorting and combining similar statements, the initial color-coded data analysis resulted in a list of comments sorted against potential codes/themes by each of the selected questions.

In phase three, the researchers analyzed and interpreted the data to make overall sense of it, rather than just paraphrasing or describing the data. Following the initial coding exercise, the data was copied for each question to a second Excel worksheet. This step involved a “first pass” over the data and involved grouping similar comments to consolidate the data. Every time the pass was performed for each thematic area per question, the data was moved to a new excel worksheet. The reason for the multiple worksheets was that the researchers could go back a step to the previous unconsolidated data set if needed.

To limit researcher bias and ensure the data was credible and accurate, phase four involved a two-researcher confirmation approach, in which each stage of the data analysis was reviewed. The first level involved reviewing at the level of the coded data extracts to determine if they formed a coherent pattern. If this was the case, the researcher then moved onto the second level of this phase to determine the validity of individual themes in relation to the data set. The primary researcher completed a re-read of the entire data set to firstly ascertain whether the themes worked in relation to the data set and secondly to code any additional data within the themes that had been missed in earlier coding stages. The data pass steps were repeated five times (constant comparative analysis) to finally generate the most prominent themes. This process generated a thematic map of the analysis to ensure the analysis and data matched each other. The primary researcher stopped this process once the refinements of the data did not add anything substantial and used two ways to arrange and analyze the data. The first being most prevalent theme to the least prevalent related to the frequency of the information being raised by participants. The second included the Rashomon effect whereby the same event is described from the perspective of more than one participant (Sandelowski 1998 ).

Phase five defined and named the themes, which started once both researchers were satisfied with the thematic map of the data. This involved the researchers defining and further refining the themes to identify the essence of what each theme was about and determining what aspect of the data each theme captured. This phase included reporting of the themes and presenting these to AHPs who had the opportunity to complete the anonymous survey and who volunteered to attend one of a series of confirmation meetings. These meetings clarified and corroborated the generated themes, which confirmed the final set of emergent themes. It is important to note that the aims, barriers, and solutions will be discussed next under four key themes in a combined approach due to many of the solutions being similar to the aims of the new structure and a reverse of the barriers. This avoided duplicating information throughout the results/discussion section.

Results and discussion

Theme 1: systems and processes.

The most prominent theme across the three years was that the centralized structure had greatly improved the systems and processes necessary for AHPs to operate their essential functions within hospital bed-based services. The findings about improvement in systems and processes are similar to the studies outlined in the literature that describe comparable benefits of a centralized allied health profession-based structure in public hospital settings (Law and Boyce 2003 ; Mickan et al 2019 ; Robinson and Compton 1996 ). The following comment sums up the general sentiment of the participants across the three years, “I think overall things are going really well for allied health and the new structure is delivering on the aims it set out to achieve, there is a real sense of hope for the future” (occupational therapist). The survey data indicated a high proportion of AHPs perceived the posited aims of the new structure were being achieved and that a centralized structure in bed-based services for AHPs worked well. This was also confirmed at the confirmatory meetings; however, it was highlighted at these meetings that each profession needed a governance structure that could accommodate their diverse professional requirements.

Thus, a prominent and positive structural feature highlighted by participants was the single point of accountability for each profession, which they believed improved processes by decreasing ambiguity and improving communication (Mickan et al 2019 ). Comments highlighting this included, “ the clear reporting lines and channels of communication improves the speed of response to service needs” (physiotherapist) and “ the new structure is a positive change with less confusion around reporting lines” (speech pathologist). Furthermore, one reporting line decreased the duplication of tasks and subsequently streamlined payroll and administration duties. This resulted in significantly less employees with multiple position numbers, reducing the time required to perform many related administrative tasks, which resulted in improved efficiencies. The following comments reinforced this view point, “the reporting lines are easier to navigate for operational and clinical needs” (social worker) and “ direct reporting lines via each profession means that administrative tasks are more streamlined” (psychologist) and “communication and the ability to implement new models of care appear to be more streamlined and better supported” (physiotherapist) and “the new structure is much more efficient from an admin and payroll perspective, I don’t need to spend hours correcting payroll errors, thank you” (administration staff member). These benefits had substantial implications for not only the AHPs but also the organization due to more streamlined and efficient processes. For example, the grouping of each profession enabled the director of each professional area to complete and approve actions more promptly, reducing duplication of resources and costly administration errors.

Another prominent benefit of single lines of accountability included each director’s ability to flexibly mobilize their staff more seamlessly. This created better delivery of prioritized clinical services due to less arduous negotiations with medical and nursing administrators. It also significantly improved the governance and accountability of staff within each profession. Moreover, the feedback from the participants suggested they believed this reduced patient risk by having an enhanced professional lens through high standards of professional supervision, skills training and support. The following comments sums up the general sentiment, “there is a sense of team, improved accountability and enhanced professional support and career opportunities with the new structure, as well as improved staff mobilization to cover emergent leave” (speech pathologist). Participants reported satisfaction with being able to rotate between clinical specialties which enhanced their career opportunities and succession planning.

One of the few related examples in the literature included research by Robinson and Compton ( 1996 ) which provided practical learnings from their re-structuring from a matrix to a centralized model for physiotherapy staff. Similar to the findings in this case study, they demonstrated for the physiotherapy profession multiple benefits of a centralized model in hospital bed-based services when compared to a matrix model in a very short period. One prominent similarity found not only for physiotherapy, but for all the professions included the operating improvements such as maintenance of staffing levels due to being able to have control of the budget. This resulted in savings being made very quickly due to streamlining processes, such as recruitment, rostering, backfill, ordering non-labor stock, etc. The following comment highlights this, “ we can take a whole of profession approach to movement of staffing into areas of higher need which is appreciated and effective especially throughout times of significant unplanned leave due to COVID and needing to isolate” (occupational therapist).

Many of the system and process efficiencies gained from each profession having control of budget was due to the in-depth understanding and knowledge the directors had about their profession and how best to govern, roster and manage the workforce seamlessly when compared to the previous structure in which the operational manager was not of the same profession, which often resulted in arduous communications between multiple stakeholders to manage clinical demand across the clinical units.

Theme 2: professional identity

The second prominent theme demonstrated that the identity of each profession developed very quickly within the centralized structure and the participants generally expressed they felt more supported and comfortable within their own profession. The data analyzed from the survey results (and confirmed at profession meetings) reported that many respondents perceived that returning to a profession-based structure was positive. A high proportion of participants indicated that operating as professional groups and being in an allied health centralized structure was a better person-environment fit in comparison to the change associated with working in a matrix structure. For many of the AHPs, they perceived a strong sense of familiarity and belonging to their profession and to allied health when compared to their sense of belonging to their clinical unit and the organization. The following comment supports the general sentiment, “ profession specific led teams is proven to work, and this is how other tertiary facilities in Queensland run. Having a different profession govern a discipline they know very little about is a recipe for disaster which we have proven in our previous structure” (dietitian).

This aligns to findings in research which has previously identified the importance of professional identity among AHPs (Braithwaite et al. 2006 ; Porter andWilton 2020 ). This is consistent with the multitude of comments from participants that the new structure was providing strong governance and accountability for each profession. The following comment highlights the general perception from participants, “ the centralized structure provides stronger accountability across the organization and uniform governance and consistency for allied health staff” (psychologist). This is particularly important in hospital bed-based services given the highly specialized skills required to work competently in this setting. In contrast, there was a small number of respondents that identified more positively with the matrix structure, describing a strong allegiance with their multidisciplinary team and/or clinical unit. Therefore, the findings reinforce the importance of managing professional identity of AHPs during structural change, given their experiences of the structural alignment can be perceived differently (Beasley et al. 2020 ; Porter andWilton 2020 ).

This can be explained through Social Identity Theory in which an individual identifies with social groups partly to enhance self-esteem, which is probably why, in this case some of the AHPs did not adjust well to the new structure, even though transitioning to being a member of their own professional group would have been familiar to them (Ashforth and Mael 1989 ). Some AHPs felt a sense of loss for their multidisciplinary leader who provided them with positive reinforcement. The following comment highlights this, “I am concerned that the profession specific model loses the importance of multidisciplinary care and fails to acknowledge how this profession-based change impacts service delivery. I think we need to have the multidisciplinary allied health lead in each clinical unit like before we re-structured to ensure we don’t silo into professions when delivering care to patients” (Physiotherapist).

This case study highlights that any type of structural change in healthcare is likely to impact professional identity in allied health due to the diversity of professions and that this needs to be managed accordingly. Beasley et al. ( 2020 ) recommended organizations recognize that AHPs are autonomous clinicians, who are members of several groups (i.e., own profession, healthcare teams and the organization), with all of these diverse roles influencing both their response and adaption to change. They stated that clear communication and affording opportunities to make decisions and provide feedback can improve employees’ perceptions of change and positively impact their wellbeing (Beasley et al. 2020 ; Byers 2017 ). Furthermore, Braithewaite et al. ( 2005 ) recommended organizations concurrently consider both the previous and the new identities throughout a re-structure by explaining the change initiative and supporting employees to transition from the old to the new identity. This approach can help to ensure security for employees, whose group status is threatened by the change process, as well as broaden perceptions of the ingroup, thereby assisting their acceptance of the new, post-change structure. This point flows into the third prominent theme pertaining to staff morale and wellbeing.

Theme 3: morale and wellbeing

Although the written responses analyzed from the survey questions indicated that a high proportion of participants believed the centralized allied health structure was a positive change, the findings also demonstrated that staff perceived morale and wellbeing to be an issue and was negatively impacted across the time the matrix structure was in place. This case study found low morale and wellbeing before re-structuring AHPs working in hospital bed-based services into a centralized allied health structure. This was suggested to be more inherent in broader change that was occurring and associated with several years of ongoing budget cuts and organizational change in adjusting to a new tertiary facility. While there were many supporting statements the following comment expresses the general perception, “there has been many years of incessant change and a lack of support and resourcing for clinical practitioners, with an expectation to just keep doing more with less due to the ongoing issues with the organizations budget” (social worker).

Another influencing factor that occurred a few months following the re-structure was the COVID-19 pandemic, which participants perceived impacted morale and wellbeing. Literature describes the impact of the COVID-19 pandemic, which overwhelmed and stretched healthcare systems past their limits in terms of capacity and resources, while striving to continue to deliver quality care (Søvold et al. 2021 ). This resulted in significant impacts on the mental health and psychosocial wellbeing of frontline healthcare workers (including AHPs) and increased risk of depression and burnout (Søvold et al. 2021 ; Willis et al. 2021 ). The following comment supports this view, “the workloads are unrelenting and leading to both overt and silent burnout. Increasing patient complexity and volume is beyond that which allied health staff can meet discharge planning and patient care demands” (occupational therapist).

Within the narrative feedback, many comments were made that staff were thankful of being in a profession-based structure at the time the pandemic started. Participants believed the one line of accountability and professional expertise allowed immediate decision-making such as being able to mobilize staff quickly to the areas of most need. The following comment highlights this point, “the new structure brought each profession together as a cohesive team which was invaluable during the pandemic where we needed the support from colleagues in our profession to cope and meet the demands” (physiotherapist). During the pandemic, it became very clear that having a flexible and adaptable workforce and an overall professional director that understood the complexities and skill sets of their professional group was extremely important due to managing higher numbers of emergent sick leave.

The negative impact of the pandemic on staff morale and wellbeing was a prominent theme in both the 2021 and 2022 survey results due to workforce shortages and staff feeling significant pressure to do more with less with comments like, “ although overall I agree the new allied health structure has improved a number of parameters, the projected benefits have been clouded by other variables notably budget constraints and ongoing emergent leave due to the pandemic impacting resources” (social worker). Even though the structure was considered positive, the pandemic added another layer of complexity that impacted staff morale negatively within the new structure. The pandemic placed added strain on staffing levels across allied health, nursing, medicine, operational and administration. The staff shortages impacted the organization’s budget as shifts needed to be replaced or staff paid overtime to ensure there were adequate levels of staffing on the inpatient units. Many staff commented, “staff morale could have been much worse in allied health if the matrix structure had still been in place as we would not have been able to be so adaptable and flexible within in our professions” (speech pathologist). Therefore, even with the pressures described, the perception from AHPs was that morale had improved because of the new structure due to increased collaboration and support within each profession. Even though improvement in morale was evident within the allied health workforce, many of the participants perceived low morale was still an issue that required a targeted approach by the allied health leaders, which is discussed next.

Theme 4: Leadership training and resources to support the change

The final prominent theme from the data analysis was extrapolated from the open-ended question pertaining to the enablers that could improve the re-structure. The most prominent were resources and leadership, with many respondents reporting a perceived lack of project support in implementing the new structure. Even though many of the respondents believed the new structure had improved the operating systems, they perceived there was not enough resourcing to support the leaders to implement the change effectively with comments like, “the structure is much better from an operating perspective, however more project resources are needed to help the leaders embed the new structure, particularly business, administration and human resource support for team building and helping staff to accept the change” (physiotherapist). Across the three years, the feedback continued to have a strong theme around lack of resourcing and the need to provide a dedicated project or workforce development officer role(s) so the professional directors could meet all the requirements to implement the change. There was the perception from participants that the under resourcing may have impacted the potential benefits of the re-structure.

Furthermore, the participants described concerns over the professional directors being reactive and that there was a lack of consistency between professions that was reinforcing the perception by participants that the professions were siloing and doing their own thing. The following comment sums up the general sentiment of the survey feedback, “ the professional directors need to establish consistency across the professions in relation to portfolios, expectations, accountability and workloads” (psychologist).

A related theme raised by some participants was the lack of perceived capability and competence pertaining to management and leadership. The director roles were new positions created as part of the re-structure, with some being new incumbents to the organization. It was suggested the directors were not provided with the training they needed to lead and manage complex change. Many participants reinforced this point by providing feedback that for the allied health culture to improve more training was required for some of the professional directors to gain the necessary skills to do this effectively. Comments that reinforce this include, “strong directorship is required to ensure a positive culture, and to support staff to provide safe clinical care, managers need to be empathetic, visible and connect with their staff” (physiotherapist) and “leadership and management training for some professional directors on how to effectively conduct strategic planning, communicate change, lead teams etc. is needed” (social worker).

Mickan et al. ( 2019 ) supported these findings, concluding from their study of allied health managers and employees that for a structure to be successful credible, skilled, and respected allied health leaders were required to enact the systems and processes between AHPs and clinical service managers to ensure the necessary integration within clinical teams. Turato et al. ( 2022 ) findings emphasized the importance of allied health leaders developing the necessary skills in human resource management in hospitals to effectively manage relationships among people. They concluded this would improve morale and wellbeing as allied health managers and leaders would be better equipped to manage incivility through complex change. The following comments emphasize this theme, “ the professional directors should be trained in advanced communication and management skills and they must have a sense of empathy which is almost always overlooked when appointing someone into a management position however, I believe it is a key attribute in managing a large team” (dietitian) and “the allied health leaders need to better understand the needs of clinical practitioners and make an effort to plan collaboratively, in a way that supports direct clinical care” (occupational therapist) .

Conclusions

This study reports on the perceptions, thoughts, and insights of AHPs working in hospital bed-based services that have re-structured from a matrix to a centralized allied health profession-based structure and the consequential impacts on the workforce. The results from the experiences and insights of participants in this case study demonstrated that within public hospital bed-based services, a centralized allied health structure was considered superior to a matrix structure (Boyce 2001 , 2006 ; Mickan et al. 2019 ; Robinson and Compton 1996 ).

The reasons why the centralized structure was superior for AHPs working in bed-based services can be explained through the theory of organizational design, which not only highlights the importance of context, but also in taking a systematic approach to aligning structures, processes, leadership, culture, people, practices, and metrics to enable optimal performance (Burton and Obel 2018 ). Ultimately, the centralized allied health structure was a better person-context fit than the matrix structure. The main reasons being that the centralized structure supported AHPs to operate both administration and clinical practices efficiently through single lines of accountability that could effectively govern and support each profession, all of which improved the culture and morale of AHPs in bed-based services.

In contrast, the previous matrix structure was described by AHPs in this context as complex and confusing, which is reinforced by past research regarding the tell-tale signs of when a matrix structure is failing. The signs included the operational managers not having the necessary knowledge to effectively solve problems being raised by the professional managers. The operations were not coordinated, resource utilization was inefficient and costly, the clinical units were spending excessive time trying to coordinate and negotiate with each other, all of which resulted in staff feeling unhappy and confused (Burton and Obel 2018 ). Further issues described in literature and found in this case study were loss of professional identity, ambiguity, inconsistency, and frequent disagreements which further contributed to lower staff morale among AHPs (Robinson and Compton 1996 ; Turato et al 2023 ).

Hence, this case study demonstrates that an acute and sub-acute bed-based setting is different to other environments in which AHPs work, such as community and primary care settings, outside of the hospital context. In community and primary care settings, staff work autonomously as case managers with a caseload of predominantly medically stable patients that are managed by their local doctor. The pace is slower with minimal pressure to discharge patients quickly from doctors, nurses and administrators that need access to inpatient beds. Therefore, the systems, processes and people can be managed more effectively to cope with sudden changes such as emergent leave, etc. Furthermore, staff who work in these contexts are often more senior AHPs who require less supervision, training, and governance due to having years of experience and expertise. Hence, the structural issues experienced in a fast-paced dynamic hospital setting do not appear to have the same impacts in settings where AHPs work as case managers.

In this case study, the matrix structure did not deliver on the anticipated outcomes for AHPs because the systems, processes and lines of authority required for AHPs to work effectively in this context were not appropriately executed and maintained. Moreover, the climate, leadership, knowledge sharing and decision-making processes at each junction point were not clearly defined or performing (Burton and Obel 2018 ). Further, the findings demonstrate for a matrix structure to work effectively, highly competent allied health managers who have good interpersonal communication, conflict management, negotiation, and political skills to manage up and down the organization is essential (Burton and Obel 2018 ).

This is also true for the centralized structure, even with single lines of accountability, the findings highlighted that leadership/management preparation and training for each professional manager is crucial before, during and after implementation to embed the new structure, manage complex change and ensure efficient performance. The findings demonstrated that this could have done better, including the provision of skilled project resources to support the professional managers to embed the new structure. Finally, the change in professional identity for AHPs is important to consider and manage to ensure consistency within and across professions, particularly in relation to the multidisciplinary team (i.e., individual professional identity versus the overall allied health identities at the clinical unit and management level). Even though the AHPs were returning to a familiar professional identity, it became clear that this needed to be more clearly defined, particularly for those clinicians who had an overall allied health leadership role within the multidisciplinary team.

This case study provides learnings that would be worth further investigation. The first being the importance of considering the evidence and theory of organizational design during the planning phase of a new structure so that all contingencies are considered in relation to AHPs working in contexts such as bed-based services. A number of factors may have contributed to this not being done in this case study, one of which included AHPs not having an allied health role on executive that could inform and provide counsel to administrators about the potential negative outcomes of the matrix structure for AHPs working in bed-based services. Another included the transitioning from regional to tertiary level services and the commissioning team not having a good understanding of what AHPs required to function, but rather taking a global organizational design perspective (i.e., one size fits all approach), which did not consider the specific needs of each professional group and what would be required for a matrix structure to be successful.

The findings in this case study add to the literature and emphasize that the context of public hospital bed-based services is not the same as other contexts and that the way AHPs are structured does have significant impact on their functioning. AHPs in this fast-paced setting need highly specialized skills and clearly defined operational and professional governance structures, systems, and processes in place to function optimally. Furthermore, competent profession-based management and leadership is required to ensure the unique and diverse requirements of each profession is being governed appropriately so AHPs can deliver high quality and prioritized clinical care. Additionally, both executive and professional director organizational representation and advocacy for AHPs in this context is vital so AHPs can contribute positively to the organization’s objectives and performance. In conclusion, there is a need for further research that investigates and reports upon AHPs unique and dynamic professional contexts in which they operate, their position in the healthcare system and the ways in which they respond and adapt to change during organizational re-structures, including the external impacts imposed upon them (Boyce 2001 ; Callan et al. 2007 ; Porter and Wilton 2020 ; Turato et al. 2023 ). Specifically, further research regarding hospital system organizational design components pertaining to AHPs is encouraged, such as resource availability, training, staff turnover, morale, creation of a shared identity, representation, and system efficiencies, such as cost reduction.

Practical implications

This case study warns against organizations re-structuring AHPs in hospital bed-based services without considering the diverse requirements for AHPs to operate successfully in this context. Lack of due diligence in the planning phases of a new or modified organizational design can have substantial detrimental impacts on professional identity, morale and wellbeing and productivity, all of which can lead to poor or delayed outcomes for professional groups such as allied health. Furthermore, Braithewaite (2005) suggests allied health service restructurings would benefit from a combination of process and outcome evaluation measures (e.g., professional identity, retention, staff satisfaction and clinical outcomes). Hence, the findings highlight the importance of considering an evidence-based approach when proposing a new structure in healthcare organizations so critical discussions about how organizational designs can be utilized to enhance service provision by AHPs within particular contexts are prominent. This approach would provide comprehensive evidence for healthcare administrators and commissioning teams to consider before they embark on widespread organizational change (Braithwaite et al. 2005 ; Turato et al. 2023 ).

Data availability

All data have been de-identified and is stored in a workplace drive that is protected by username and password, which can be made available upon request.

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Acknowledgements

The first author would like to acknowledge the support given by the Sunshine Coast Hospital and Health Service with administrative and in-kind provisions.

Institutional review board statement

The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Ethics Committee of The Prince Charles Hospital, Queensland Health Human Research (HREC 18/QPCH/238 on 5 July 2018 with an additional approval letter received by TPCH HREC to complete a follow-up survey on 30/08/2021).

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G.T. designed the study and drafted the initial manuscript, which was reviewed and edited by J.W. & R.O. All the authors were equally involved in the analysis of the results and the discussions that led to G.T. finalising the manuscript, which J.W. and R.O reviewed / edited before G.T. submitted to the journal.

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Allied health survey

The new centralized allied health structure for hospital bed-based services went live on 28 January 2020. It was decided the best way to gain feedback about how the new structure is progressing was by an annual confidential survey and follow-up meetings with each profession. We are now asking for your feedback as your opinion is highly valued and very important. The survey will take about 15–20 min to complete; thank you very much for taking the time to answer the following questions:

Do you consent for the confidential data you input into this survey be utilized for research?

Yes, I give consent.

No, I would rather not give consent.

What is your discipline?

Administration.

Allied Health Assistant.

Nutrition & Dietetics.

Occupational Therapy.

Physiotherapy.

Psychology.

Social Work.

Speech Pathology.

Below are the aims and reasons for implementing the change. Please provide your feedback on which of the aims you believe the new structure is achieving and which require more work. Please be specific and provide as much detail as you can about why an aim is being achieved or not achieved.

Reduced patient risk through an enhanced discipline lens

Decreased confusion over reporting lines and improved communication to allied health staff

Decreased duplication of tasks for allied health staff within each service group, e.g., quality, education and training programs, supervision, rostering, mandatory training, and workforce planning tasks such as leave management

Improved flexibility to mobilize the allied health workforce based on clinical priority

Improved governance and accountability of allied health staff

Decreased administration time required to maintain the centralized allied health structure when comparted to the previous matrix (dispersed) structure: i.e., payroll tasks, maintaining rosters, workforce planning

Improved support to the facilities outside of the tertiary facility

Improved ability to implement new models of care, innovation, and research

Please provide as much detail as to whether you believe the reasons/aims for implementation are being achieved (or not achieved) and why.

Please list any barriers or issues you perceive regarding the new structure, providing as much detail as you can about the barrier and/or issue.

Please add any enablers or suggestions that would improve the new structure, providing as much detail as you can about the enabler or suggestion.

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Turato, G., Whiteoak, J. & Oprescu, F. The insights of allied health professionals transitioning from a matrix structure to a centralized profession-based structure within a public hospital setting. J Org Design (2024). https://doi.org/10.1007/s41469-024-00178-w

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Received : 29 March 2023

Accepted : 20 August 2024

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DOI : https://doi.org/10.1007/s41469-024-00178-w

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