An Ergonomic Protocol for Patient Transfer That Can Be Successfully Taught Using Simulation Methods

Article Outline

• Abstract
• Introduction
• Method
  • Patient Transfer Protocol Development
  • Development of Online Support Materials
  • Development of Data Collection Tools
  • Simulation Intervention
  • Rater Training
  • Statistical Methods
• Results
  • Patient Transfer Protocol
  • Improvement in Patient Transfer
  • Interrater Reliability
  • Development of Data Collection Tools
• Discussion
  • Limitations
  • Conclusion
• Acknowledgments
• Supplementary Data
• References
• Copyright

Abstract 

Introduction

Nursing personnel injury related to patient transfer is epidemic, and reduction of injury rates is a national priority. Hierarchical task analysis (HTA) was chosen to address this issue.

Method

HTA methods were used to create an optimum task set and protocol which consisted of Internet-based education, simulation practice, and debriefing. Participants (N = 71) were randomly assigned to teams to perform simulated transfers. Pre- to postintervention transfer success was evaluated by ergonomic experts.

Results

Each team improved significantly from pre- to postintervention (N = 19), with every protocol step demonstrating improvement (N = 10). Interrater reliability of the evaluation instrument was calculated (.43–.83).

Conclusion

Simulation was used successfully to improve transfer success. This approach shows promise in reduction of transfer-related nursing injury.

KeyWords: simulation, hierarchical task analysis, back injury, ergonomics

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Introduction 

Bernardo Ramazzini (1633–1714), the father of modern occupational medicine, first identified and studied workplace-related disease in the late 1600s (Ramazzini & Wright, 1940). The striking fact is that in the more than 3 centuries since Ramazzini first identified the problem of workplace-related injury, it persists, with an epidemic now seen in nurses and nurse aides. Nurse aides and nurses have the highest rate of workers' compensation claims within the health care industry and are consistently among the top 10 of nonfatal work-related injury groups among all U.S. workers. When combined, these two groups are second in total injuries among all U.S. occupations, exceeding groups such as laborers and dock workers (de Castro, 2004a, de Castro, 2004b, Edlich et al., 2001, U.S. Department of Labor, 2007, U.S. Department of Labor, 2007). This high incidence of musculoskeletal injury (MSI) in the nursing profession has been widely reported and analyzed (Blakeney, 2003, Charney, 2005, de Castro et al., 2006, Edlich et al., 2005). The National Institute of Occupational Safety and Health (2006) reports an annual prevalence rate of back pain among nurses of 40% to 50%, with up to 52% of nurses reporting having had chronic back pain of 14 or more days within the past 6 months. The National Institute of Occupational Safety and Health also reports a lifetime prevalence rate of back pain or injury among nursing personnel of 35% to 80%. These statistics are highly significant because there is an emerging national and international nursing shortage. Currently there are 2.9 million nurses in the U.S. workforce, with an average age of 48. Up to 40% of practicing nurses are projected to retire in the next 5 years, and experts predict a deficit of at least 1 million nurses by the year 2020 (American Association of Colleges of Nursing, 2006; Murray, 2002, Unruh and Fottler, 2005, Williams et al., 2006). As many as 38% of nurses suffer from back pain severe enough to require time off from work during their careers, with up to 12% leaving the profession annually because of this issue. Prevention of nursing injury would therefore have a significant impact on the emerging nursing shortage (de Castro et al., 2006, Edlich et al., 2005).

To reduce injury rates, a hospital-based training program is necessary to teach proper techniques and to monitor compliance. Development of a comprehensive, ergonomically sound program would require development of (a) a protocol that includes the optimum task set for moving patients; (b) an effective, standardized, and scalable training process; and (c) the ability to monitor program compliance in the clinical setting.

Before an effective simulation training program for patient transfer can be implemented, the ergonomic issues that constitute the transfer task must be clearly defined. The task analysis process used in ergonomic science fits well with this approach. Task analysis entails defining and describing either a job or the particular task or set of tasks within a job (Stanton, 2006). A subtype, hierarchical task analysis, has been used extensively in ergonomics research and fieldwork for more than 30 years (Stanton, 2006). As the name implies, hierarchical task analysis not only lists each step of a particular task but also analyzes and attempts to place each step in the order in which it should or could be performed (Annett et al., 2000, Shepherd, 1998, Stanton, 2006). The power of hierarchical task analysis results from its ability to facilitate description of individual or team behaviors by acknowledging that most tasks are comprised of “a sub-goal hierarchy linked by plans” (Stanton, 2006, 58). Because this approach deconstructs tasks into discrete components, it can be used not only to measure overall task completion but also to improve task performance by identifying problematic steps in the system or process.

Although nurses experience many different types of musculoskeletal loads in their work, the task of transferring patients is particularly implicated in causing injury (National Institute of Occupational Safety and Health, 2006). Currently, no standardized, universally accepted method accomplishes the overall outcome of “a safe patient transfer” while adhering to all ergonomic principles in prevention of caregiver injury. Partly, this deficiency exists because patient transfers require complex coordination of personnel and equipment and must be tailored to meet individual patient and facility needs. Additionally, moving a patient with heavy nonfixed limbs and a shifting center of gravity is not comparable to moving a static, gravity-centered load, such as a box or other solid object. Regulatory standards from the National Institute for Occupational Safety and Health tend to emphasize the “static” situation and are not designed specifically to address patient transfers. This makes the problem of preventing health care worker injury more difficult to remedy (National Institute of Occupational Safety and Health, 2006).

Didactic educational programs and single-focus interventions such as back belts have been demonstrated to be ineffective in reducing long-term injury rates (Edlich et al., 2001, Gatty et al., 2003, Karas and Conrad, 1996, Melton, 1983, Mitchell et al., 1994, Nelson et al., 2003, Taylor, 1987, Trinkoff et al., 2003, Venning, 1988, Wassell et al., 2000, Yassi et al., 2001). An emerging educational approach that shows promise in increasing retention of skills is hands-on, scenario-based simulation training using a manikin (Ackermann, 2007, Crofts et al., 2007, Morgan et al., 2002, Wayne et al., 2006). Educational approaches using simulation have been reported for training a wide range of simple to complex health care tasks (Alinier et al., 2006, Bradley, 2006, DeVita et al., 2005, Heaven et al., 2006, Hoffmann et al., 2007, Holcomb et al., 2002, Johnsson et al., 2006, Magee, 2003, Mayo et al., 2004; D. Murray, 2006; D. Murray et al., 2002, O'Donnell et al., 2005, Sarker et al., 2006, Sarker et al., 2006, Sorenson, 2002, Steadman et al., 2006, Yee et al., 2005). Notably, the Institute of Medicine has recommended the use of educational methods incorporating simulation to improve safety within the health care system (Kohn, Corrigan, & Donaldson, 1999). The advantages of using a manikin for patient transfer training are cost, standardization, and the lack of risk of injury to “live” patients during transfer training by novices.

The purpose of this report is to (a) describe development of a valid, measurable, and standardized patient transfer protocol based on an optimum task set; (b) describe development of a related simulation intervention; (c) evaluate patient transfer performance in a simulation lab; and (d) describe development of methods to score patient transfer in real time, including a mobile data collection system. Furthermore, this approach has the potential to benefit public health in that patient transfers are common, nursing personnel are frequently injured, loss of the nursing workforce is a national concern, and evidence suggests that inadequate nurse staffing is linked to adverse patient outcome (Aiken et al., 2003, Clarke and Aiken, 2006, Rogers et al., 2004).

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Method 

Patient Transfer Protocol Development 

To develop a broadly applicable patient transfer protocol, the optimum task set for performing a patient transfer was needed. The method for deriving this task set was as follows: A panel of health care experts was recruited, including physical therapists, occupational therapists, professional nurses, hospital administrators, ergonomic experts, and an occupational medicine physician. The expert panel followed a nine-step hierarchical task analysis process (Table 1; Annett et al., 2000, Shepherd, 1998, Stanton, 2006). Through this process, a comprehensive list of all activities during a patient transfer was compiled by the panel. The equipment used and the policies and procedures referring to patient transfer were considered. Although the expert panel was clear with respect to the overall goal of an optimum patient transfer, the order and operational definitions for the overall task set required multiple revisions. In hierarchical task analysis methods, the overall goal is defined as the superordinate goal. The comprehensive task list was refined through review of the literature and clinical observation of actual patient transfers, with ongoing feedback to the panel. The ergonomic experts “field-tested” the protocol during development and were in consensus relative to the steps and order of the process. A process map was developed, and transfer activities were grouped and condensed in order to develop the optimum task set. Each main task was broken down into its component subtasks to ensure that each main task could be operationally defined.

Table 1. Nine-Step Hierarchical Task Analysis Process for Developing the Patient Transfer Protocol

Development of Online Support Materials 

The patient transfer protocol was then used as a process map in development of an Internet-based training curriculum that supported the scenario-based simulation training program. The program was designed to be appropriate for all categories of health care professionals, including registered nurses, licensed practical nurses, nurse assistants, and any other direct care personnel (Figure 1). All course materials were designed to reinforce the patient transfer protocol steps and were posted to a proprietary learning management system Web site at the University of Pittsburgh's Winter Institute for Simulation, Education and Research. The project was approved by the University of Pittsburgh Biomedical institutional review board, and participants required a password for access to this Web site. Participants received the password after providing consent.

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• Figure 1 

  Screenshot of online course materials based on the patient transfer protocol.

Development of Data Collection Tools 

The investigators developed and evaluated two data collection tools that were programmed with the patient transfer protocol. The first tool was the protocol in the form of a checklist programmed into the Laerdal SimMan^® software system, Version 2.3 (Figure 2). Ergonomic experts rated transfers in the simulation lab using this system. In addition, the evidence-based rationale for each main task step was programmed into the SimMan^® Debriefing Viewer Program, which facilitated a structured and best-evidence–supported posttransfer debriefing.

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• Figure 2 

  Laerdal SimMan universal patient simulator interface (Version 3.2) programmed with the patient transfer protocol.

(With permission from Laerdal Inc., Stavanger, Norway.)

The second data collection tool was the Hewlett-Packard (HP) iPAQ handheld computer (view online extra Figure 3 at www.nursingsimulation.org) running the Windows Mobile 5.0 operating system. The handheld computers were programmed with the patient transfer protocol as a checklist. The programming used embedded visual BASIC to create a graphic user interface. This handheld tool was used by nonexpert raters during the study.

Simulation Intervention 

Nurses (n = 48) and nursing assistants (n = 23) were recruited to participate in a 4-hour simulation intervention at the Winter Institute for Simulation, Education and Research, Pittsburgh. After consent was obtained, baseline transfer skill was determined by having teams (3–4 participants per team) perform two simulated transfers using the Tuff Kelly Transfer Manikin (Laerdal Inc., Stavanger, Norway). This manikin is specifically designed to simulate patient transfers across a variety of settings, is inexpensive, and can easily be adapted for training of this task in almost any setting. After the second transfer was performed, a structured video debriefing was conducted for each transfer event. The SimMan Debriefing Viewer Program was used to provide in-room debriefing. The SimMan log file display included evidence-based statements providing the rationale for each transfer step, as well as the rating of the expert. An embedded video link facilitated review of each transfer event and allowed expert commentary as part of the structured debriefing method. Online patient transfer protocol materials were reviewed with each team. The debriefings and online material review constituted the intervention, which lasted for 1.5 hr. Following this intervention, transfer skill was reassessed by having the same participant teams perform two different simulated patient transfers. The entire protocol took place over 3 hours.

Rater Training 

Our eventual goal was to implement the simulation intervention and then conduct observational studies of actual patient transfers. Recognizing that the cost of ergonomic experts might be prohibitive, we hypothesized that nonexperts could be rapidly trained to accurately score simulated transfer events and thus eventually be prepared to score actual patient transfers. Establishing the interrater reliability between experts and nonexperts was critical to attaining this goal. Nonexpert raters (N = 7) used the handheld tools to rate pre- and postintervention transfer events concurrently with the expert raters in the simulation lab. These nonexperts had varied backgrounds, including nurse anesthesia, critical care nursing, health care finance, and health care administration. None had a background in ergonomics or body mechanics. The nonexpert raters were trained by ergonomic experts before the simulation intervention in how to use the transfer protocol, which had been programmed into the HP iPAQ handheld computers. The training included a 1-hour review of the patient transfer protocol and operational definitions for each protocol step (Table 2). Nonexperts then went to clinical units and were supervised for 4 hours of observing and rating actual patient transfers, again under the supervision of the ergonomic experts. The operational definitions were laminated on 4-by-6-inch (10-by-15-cm) cards and given to each rater for reference during patient transfer observation and rating.

Table 2. Patient Transfer Protocol Steps With Operational Definitions

Statistical Methods 

The transfer team was the unit of analysis. Each group performed four scenario-based simulated transfers, two preintervention and two postintervention. Ergonomic expert raters scored each transfer according to the patient transfer protocol. Each task step of the protocol was rated as either completed or not completed for each group transfer. The operational definitions were used to help raters determine the completion status of each protocol step (Table 2). The two preintervention transfer ratings and the two postintervention transfer ratings were averaged for analysis. This average score for each team was compared using a paired samples t test (SPSS 15.0). The level of statistical significance was set a priori at α ≤ .05.

Interrater reliability was calculated as follows: Expert ratings were compared with nonexpert ratings during simulated transfer events. The expert data were entered into the SimMan platform, and the nonexpert data were entered into the HP iPAQ handheld units. Each move was named according to a specific naming protocol in order to eliminate misidentification. Expert and nonexpert ratings were compared for agreement. All patient transfer protocol steps were rated as either correct or incorrect according to the operational guidelines. The Cohen's kappa statistic, which assesses agreement of categorical data, was calculated for each patient transfer protocol step.

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Results 

Patient Transfer Protocol 

As described, an optimum task set or protocol for doing patient transfers was developed using the hierarchical task analysis method (Table 2). This protocol was then used for programming the Laerdal SimMan software platform as well as the handheld HP iPAQ data collection units. The iterative process of expert panel analysis, live clinical observation, and ongoing redescription of transfer steps facilitated final description of a detailed patient transfer protocol map with operational definitions for each step.

Improvement in Patient Transfer 

The 71 nurses and nurse aides were divided into 19 teams of 3 to 4 people each. The members of each transfer team remained consistent for all four moves. Ergonomic experts performed the rating of the transfer events. The overall rating, or score, for each team transfer event was calculated as the number of steps rated completed, divided by the total number of steps. Patient transfer protocol success increased significantly from pre- to postintervention for every team and for every protocol step (n = 10). Mean pre- to postintervention improvement by team was 52% ± 15 (range 10%–75%), with mean improvement by protocol step increasing an average of 51% ± 18 (range 11%–76%). The greatest pre- to postintervention positive change occurred in Step 1, Identifying the Patient and Move Requirement (76% positive change). The smallest pre- to postintervention positive change occurred in Step 4, Gather Equipment (11% positive change; Figure 4).

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• Figure 4 

  Pre- to postintervention change in success by patient transfer protocol step.

Each of the 19 teams completed two moves preintervention (Pre1, Pre2, n = 38) and two moves postintervention (Post1, Post2, n = 36). Two postintervention expert patient transfer observations were lost because of computer data storage error. As stated, preintervention and postintervention ratings were averaged for each team (Table 3). The average for the ratings of the two preintervention transfers across all 19 teams was 34% ± 12. The average for the ratings of the two postintervention transfers for the 19 teams was 86% ± 10. The improvement from pre- to postintervention was highly significant (t₁₈ = 14.76, p < .0004; Table 3, view online extra Figure 5 at www.nursingsimulation.org).

Table 3. Pre- Versus Postintervention Success: Patient Transfer Protocol Ratings by Ergonomic Experts

Note. Pre1, Pre2 = two moves performed preintervention. Post1, Post2 = two moves performed postintervention.

Interrater Reliability 

The rating of simulated patient transfers was conducted concurrently by experts and nonexperts. Each transfer step within the protocol was rated as completed or not completed according to the operational definitions. For 24 of the 74 recorded transfers, two nonexperts were rating the event at the same time as the expert rater. This resulted in 98 matched transfer event ratings (expert vs. nonexpert). The measure of agreement used was the Cohen's kappa statistic. Kappa values were calculated for each step of the protocol and ranged from 0.43 to 0.83 (Table 4; Landis & Koch, 1977). The lowest kappa scores were for Step 5, Prepare the Environment, and Step 6, Communicate to the Patient, indicating a moderate level of agreement between experts and nonexperts for these steps. The highest kappa scores were seen with Step 3, Enlist Appropriate Number of Personnel, and Step 1, Identify the Patient and Move Requirement, indicating substantial to near perfect agreement between experts and nonexperts for these steps.

Table 4. Interrater Reliability for Patient Transfer Protocol by Step

Note. All kappa values statistically significant, p < .0004.

Development of Data Collection Tools 

The patient transfer protocol programmed into the Laerdal SimMan software system facilitated scoring of each team's performance. This system also supported ergonomic experts in conducting a structured and evidence supported debriefing through use of the SimMan Debriefing Viewer Program. This program generated an accurate record of events, or log file. The SimMan log file display included the ergonomic expert's rating (correct or incorrect) of each transfer step and displayed the evidence-based rationale for each transfer step. Concurrently, the HP iPAQ handheld computers programmed with the patient transfer protocol were used by nonexperts to rate team performance during the transfer events. These programmed handheld devices provided a consistent, mobile assessment tool designed for “live,” unobtrusive scoring of transfer events.

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Discussion 

The simulation intervention resulted in improvement in patient transfer skill for each team of providers and for each step of the patient transfer protocol (Figures 4 and view online extra Figure 5 at www.nursingsimulation.org). Core components of the intervention included preintervention transfer practice, transfer event debriefing, Internet-supported didactic content and postintervention evaluations of transfer success. During debriefing, correct as well as incorrect performance of patient transfer protocol steps was reviewed with participants. The debriefing materials incorporated evidence-based rationale for each patient transfer protocol step. Ergonomic experts then encouraged correct performance through facilitated discussion and demonstration.

The project required development and parallel testing of the patient transfer protocol instrument programmed as a checklist into two unique data collection tools. The first tool was the Laerdal SimMan software, which was used by the experts in rating transfers. This tool was used to both gather data and provide immediate participant reinforcement of completed versus not completed patient transfer skills during debriefing. This approach allowed simultaneous use of input from three sources: expert rating, the event video, and the preprogrammed evidence-based rationale for each patient transfer protocol step. The second data collection tool was the HP iPAQ handheld computer used by nonexperts. Because of its mobility, it can be used to rate participant performance in a variety of settings, including both simulation laboratory and clinical arena.

When the ratings from the two tools were matched, Cohen's kappa statistics were calculated for each protocol step. The kappa values ranged from 0.43 to 0.83, indicating moderate to near perfect agreement between expert raters (ergonomists) and nonexpert raters (health care workers with a wide variety of clinical experiences). The terms moderate, substantial and near-perfect agreement in interpretation of the kappa statistics are based on the classic interpretation by Landis and Koch (1977).

A unique aspect of this project was the use of hierarchical task analysis. Hierarchical task analysis was chosen because it is highly flexible and can be used to analyze anything from an isolated procedure to team performance to the overall function of an entire system (Stanton, 2006). In finalizing the protocol, we adhered to guidelines outlined by Annett et al., 1971, Annett et al., 2000, Shepherd (1998), and Stanton (2006). They described important principles that govern use of the hierarchical task analysis process, which can be summarized as follows (Annett et al., 2000, Shepherd, 1998, Stanton, 2006):

The important relationship between tasks and subtasks is that subtasks are included within the overall task (a hierarchical relationship). Subtasks of main tasks are typically included in the task definition but do not have to be accomplished in a specific sequence in order to be rated as completed or performed correctly (Annett et al., 2000, Stanton, 2006). When hierarchical task analysis is used, guidelines should also be developed that describe temporal and order relationship between levels of description (e.g., x followed by y followed by z or x and y in any order, followed by z two minutes later; Stanton, 2006). Because hierarchical task analysis involves description and then redescription of a system or process in terms of its goals and subgoals, the procedure can go on for any number of iterations. One of the more difficult aspects is in establishing the detail level at which to stop the analysis, as there are no specific formulas or guidelines to determine a definitive endpoint (Annett et al., 1971, Stanton, 2006).

Hierarchical task analysis methods have a number of advantages when applied in this manner: They allow a flexible, robust approach for description of a complex task and provide a clear template for evaluation of each step required to complete the task. In the case of patient transfer, deconstructing a seemingly continuous event into discrete, measurable parts allowed analysis and evaluation of a common, yet complex, health care task. It is then possible for teams to rate performance of the optimal task set in the simulation environment. Another advantage of hierarchical task analysis is the emphasis that this rigorous method places on incorporating evidence-based practice into the development of each protocol step, thereby providing a clear theoretical or practice-based foundation for evaluation of performance and process. Measuring events in a clinical setting can be both difficult and labor intensive. Evaluation of measurement reliability by comparing scores by inexperienced scorers with those by experts is important in determining whether personnel with little experience in ergonomics can be trained to accurately code transfer events in real time. Because the hierarchical task analysis process and resultant operational guidelines identified and clearly defined steps for all raters in evaluation, inexperienced scorers were able to accurately score transfer events. The development and programming of the handheld HP iPAQ data collection tool with a user-friendly software interface resulted in a data collection platform that was flexible, portable, and unobtrusive.

A valid hierarchical task analysis process is dependent on accurate descriptions of goals and subgoals. Inadequate description can lead to measurement errors and false interpretation of results. As might be expected, there was not always complete concurrence within the development team in defining transfer steps. Ultimately, the patient transfer protocol steps were selected and operationally defined on the basis of published evidence, clinical observations, and expert opinion. Analysis of the success rate for each patient transfer protocol step was valuable in two respects. First, an increase in overall success from pre- to postintervention was an indicator of the overall effectiveness of the simulation training protocol derived from the hierarchical task analysis. Second, a consistent increase in success rate across every step of the patient transfer protocol served to validate that the overall hierarchical task analysis descriptive process was done correctly; otherwise, failure rates would be likely to remain high at one or more individual steps posttraining. The step that showed only an 11% improvement (Gather Equipment) demonstrated the imperfection of a simulation setting in duplicating clinical care because the patient transfer equipment was too readily accessible in the simulation lab. This is not the case in the clinical setting, and this step will be carefully analyzed during collection of patient transfer data in the clinical environment.

Our findings are the first to report systematic use of hierarchical task analysis methods, combined with simulation, to train providers in patient transfer, a task that is commonly perceived as requiring little skill. Simulation programs in health care education have traditionally been used in training of complex tasks required in the care of critically ill patients, such as airway management skills and, cardiopulmonary resuscitation. Its value in these settings has led to its widespread application by nurses in other settings (Alinier et al., 2006, Bremner et al., 2006, Hoffmann et al., 2007, Larew et al., 2006, Parr and Sweeney, 2006, Robertson, 2006), respiratory therapists (DeVita et al., 2005, Rodehorst et al., 2005, Tuttle et al., 2007), nurse anesthetists (Coopmans et al., 2008, Hotchkiss et al., 2002, O'Donnell et al., 1998, Oswaks, 2002, Sell et al., 2007), and students training in these fields.

Viewed through the lens of hierarchical task analysis, the task of transferring a patient is quite complex and, if not performed properly, has been directly implicated in both patient and provider injury (Edlich et al., 2005). By weaving hierarchical task analysis principles into the development of this training program, we were able to build an evidence-based patient transfer protocol and demonstrate an immediate postintervention improvement in simulated transfer performance. As such, this methodology would appear to have great value in teaching and evaluating other skills used in patient care.

Limitations 

Several limitations were identified within the study. A convenience sample was used, with participants recruited from patient care units specializing in care of head and spinal cord injuries. Participants were randomized to date of training and to group assignment. Because participants were not familiar with the simulation setting, an orientation to the environment and to the manikin was conducted to minimize the impact of an unfamiliar environment on performance. Despite this orientation, participants appeared initially to be reluctant to interact with the manikin as they would with a human. This may have contributed to the low preintervention ratings on the steps of Patient Identification and Communicate to the Patient. Alternatively, this could reflect actual practice patterns as correct identification of patients is a consistent focus of the Joint Commission National Patient Safety Goals (Joint Commission, 2010). Finally, not all conditions in the clinical setting could be duplicated in the simulation lab (e.g., storage area for patient transfer equipment) although participants reported that the simulation scenarios were realistic and similar to their daily practice.

Conclusion 

The use of hierarchical task analysis methodology supported achievement of study aims. Hierarchical task analysis can be used as a means of analyzing a specific health care intervention (patient transfer) with deconstruction of the process into distinct components. Through a multistep process based on methods described by Annett et al., 1971, Annett et al., 2000, Stanton, 2006, and Shepherd (1998), a patient transfer protocol was derived and validated. The validation process included achieving expert consensus, referencing steps to their evidence-based rationale, and performing structured clinical observations with ongoing feedback to the expert development panel for final refinement of the protocol. The patient transfer protocol was then used to formularte a simulation intervention incorporating online curricular support materials, simulated manikin transfers, and structured debriefing. Every participant team demonstrated pre-to-postintervention improvement in transfer skill. In addition, improvement occurred in every patient transfer protocol step.

The patient transfer protocol rating checklist was programmed into two data collection tools: the Laerdal SimMan software and the HP iPAQ PC. The use of two separate data collection tools proved valuable and efficient in generating interrater reliability statistics. Interrater reliability indicated substantial agreement between ratings of experts and nonexperts. It was important to evaluate the mobility and utility of the HP iPAQ PC tool during the simulation intervention. A mobile and effective tool is needed for clinical observations that will help to establish whether patient transfer skills learned in the simulation lab can be applied and measured in clinical practice.

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Acknowledgments 

This project was sponsored by funding from the U.S. Air Force, administered by the U.S. Army Medical Research Acquisition Activity, Ft. Detrick, Maryland (Award # DAMD 17-03-2-0017). Work was performed at the University of Pittsburgh Winter Institute for Simulation, Education and Research (WISER) and the University of Pittsburgh Medical Center Southside Hospital Institute for Research and Rehabilitation.

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Supplementary Data 

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