By Valerie Alley, Elizabeth Martin, Geoff Rohde, and Angie Wolthuis

Tales from the Field, a monthly column, consists of reports of evidence-based performance improvement practice and advice, presented by graduate students, alumni, and faculty of Boise State University’s Organizational Performance and Workplace Learning Department.

As a part of Dr. Don Winiecki’s Needs Assessment class at Boise State University, our student team conducted a needs assessment for Holy Medical Center (pseudonym).  This case study describes the process we used to conduct the assessment.

The Setting
Established in 1917, Holy Medical Center is a not-for-profit acute care hospital serving a rural community. A recent acquisition of Holy Medical Center by HC Regional Medical Center (pseudonym) provided Holy Medical with the resources necessary to deliver cutting-edge medical care in their rural setting. To maintain consistency and access to medical records across the newly formed healthcare system, in May 2013, Holy Medical Center implemented the same electronic health record (EHR) that HC Regional Medical Center implemented in 2009. The implementation of the EHR drastically changed both patient care processes and the workflow of all clinical providers in the hospital.

The Performance Issue
A patient transfer occurs when one unit within the hospital transfers a patient to another unit. The hospital’s clinical educator, responsible for the performance improvement of nurses, noted some inconsistencies in the patient transfer process after EHR implementation. She engaged us to determine if there was a gap in performance and to provide solutions to fill the gap, if identified.

We used several data sources in an effort to triangulate our findings and verify the existence of a performance gap. Table 1 outlines the methodologies and data sources we used to assess the gap and verify the need for a complete needs assessment.

Data Collection Methods  Data Sources
Five Individual Interviews
  • Physician Liaison, Clinical Informatics Department (CID)
  • Order Set Coordinator, CID
  • Clinical Liaison, CID
  • Clinical Educator, Clinical Education Department
  • Charge Nurse, Emergency Room
Audit of 14 electronic patient charts
  • Due to concerns of violating patient confidentiality, hospital personnel audited the charts.

 Table 1: Data Collection Methods

When one unit transfers a patient to another unit within the hospital, the expectation is that any orders that remain active in the system are discontinued (deleted from the active chart indicating the order is no longer valid) or completed (this action indicates the order is fulfilled). The hospital refers to this process as orders management. To minimize confusion for the receiving unit nurses and to maintain patient safety, the expectation is that the nurses from the sending unit manage the orders 100% of the time before a patient transfers.

Interviews with those who work closely with the nurses and audits of electronic patient records allowed us to identify a performance gap in the orders management process. On average, the nurses were completing the orders management process approximately 30% of the time.

A Systematic Approach to Cause Analysis
As Schensul and LeCompte (2013) suggest, we took a systematic approach to collecting data during our cause analysis. We started with open-ended interviews with nursing unit directors. Asking broad questions helped us identify common themes. We followed up on the common themes by reviewing relevant materials, and asking trainers, nurses, and clinical informatics liaisons follow-up questions with which to triangulate the data. Figure 1 illustrates both our data collection methodology and the value of triangulating data when completing the cause analysis on one identified performance issue.


Figure 1. Illustration of Systematic Data Collection.

As Figure 1 illustrates, interviews with the nursing unit directors alerted us that nurses did not necessarily see the “need” to complete the orders management process. To explore this further, we conducted a review of the training materials, which led to our discovery that they did not include the topic of orders management during the pre-implementation training.

The training team confirmed in their interviews that they only briefly covered the topic of orders management during training. Furthermore, they indicated that it is not the responsibility of the nurses, but rather the physician’s responsibility to complete the orders management process. The nurses implied, in their interviews, that they are confused as to what role they play in this process.

Finally, the clinical informatics liaisons, experts in both the system and system workflow processes, indicated that the expectation, from a process standpoint, is that the nurses should be doing the orders management. However, they also suggested that the nurses do not always do it, which was confirmed through audits of patient charts.

We used this systematic approach with each significant piece of data we identified. This data collection process allowed us to both follow data trends and verify possible causes of the performance gap.

The In-Depth Cause Analysis
Once we gathered and analyzed data, it was essential to synthesize it. As illustrated in Table 2, we used Gilbert’s (2007) behavior engineering model (BEM) as the primary framework for guiding this part of our analysis. We also consulted Marker’s (2007) synchronized analysis model (SAM) to analyze data and potential issues that were outside the scope of the BEM. We synthesized and triangulated the data gathered from observations, interviews, and a review of training materials and other supporting documents, using the BEM as a framework for categorization. As suggested by LeCompte and Schensul (1999), we utilized deductive analysis to identify possible causes of the performance gap. This method allowed us to sort the data systematically. It also permitted us to see visually which BEM factors may be contributing to the performance gap.


Information Instrumentation Motivation
Data Resources Incentives
TrendsThe team found:

  • Conflicting information concerning task responsibility in 17 pieces of evidence extracted from 10 interviews
  • Lack of adequate feedback in 10 pieces of evidence extracted from nine interviews
  • Lack of information about support in seven pieces of evidence extracted from seven interviews
TrendsThe team found:

  • Lack of time for nurses to complete the EHR in seven pieces of evidence extracted from seven interviews
  • Confusing job aids in six pieces of evidence extracted from six interviews
No significant data found 


Knowledge Capacity Motives










TrendsThe team found:

  • A lack of confidence among nurses to manage the orders due to inadequate training in five pieces of evidence extracted from four interviews


No significant data found TrendsThe team found: 

  • Inadequate motivation by nurses to manage the orders in eight pieces of evidence extracted from seven interviews
  • Inadequate motivation among nurses to work with CID support team in 10 pieces of evidence from three sources


* These trends are a diffusion effect stemming from a lack of information.


Table 2: Data Synthesis

A synthesis of the data collected helped us determine that the performance issues were primarily environmental in nature, which our recommendations reflect. However, utilizing the SAM allowed us to identify some issues at the organizational level, of which we noted and reported to the client.

Through our data collection, and as shown in both Table 2 and Figure 1, we deduced that there was confusion regarding who is supposed to complete the orders management process on patient transfer.  As Gilbert (2007) points out, for a worker to be successful in his or her role, the expectations of that role must be clear. Gilbert further suggests that the factors of the BEM are interrelated. If the nurses are not clear about their role in the process, this will affect the other factors. We saw the diffusion effect as we synthesized our data; the nurses were not adequately supported in the process (resources) because there was confusion over their role in the process (data). This confusion regarding their roles led to a lack of confidence and motivation; a clear illustration of the diffusion effect, which produced evidence that nurses were “at fault” when, in fact, the problem existed in the system.

Summary of Findings and Recommendations
Through the needs assessment process, we found the following factors contributing to the performance gap related to the orders management process during transfer:

  • Nurses receive incomplete or conflicting information regarding their role in the orders management process.
  • Nurses lack the information regarding the tools available to support them in this process.
  • Nurses do not receive adequate feedback regarding their performance.
  • Nurses experience a generalized lack of motivation to complete the orders management process because of the lack of information provided to them regarding their role in the process.

After identifying the causes for the performance gap, we compiled a list of recommended performance interventions. In collaboration with the client, we narrowed down the list by assessing cost, feasibility, advantages, and disadvantages.

Table 3 outlines our recommendations that the client also agreed would be both feasible for the hospital and effective at filling the performance gap.

BEM Category Recommended Intervention  
  • Provide clear and accurate information to the nurses regarding their responsibilities related to the orders management process during a patient transfer


  • Inform nurses of the support available from the Clinical Informatics Department


  • Provide frequent feedback that is accurate, timely, and specific
  • Redesign and relocate support aids, and provide support in their use


Table 3: Recommended Interventions

We did not make a specific recommendation regarding the identified motivational issue, as we believe that when the nurses are clear about their role in the process and are receiving the proper support and feedback, motivation will no longer be a problem. Evaluation of the effect of these performance interventions will provide evidence that this was either warranted or should be addressed subsequently.

Using sound frameworks and a systematic approach led us to interventions that will fill the performance gap and allow the hospital to fulfill its mission of providing excellent healthcare.

Gilbert, T. (2007). The behavior engineering model. In Human competence: Engineering worthy performance (tribute edition; pp. 73-107). San Francisco, CA: Pfeiffer.

LeCompte, M., & Schensul, J. (1999). Analysis from the top down. In Analyzing & interpreting ethnographic data (Vol. 5, pp. 45-66). Walnut Creek, CA: AltaMira Press.

Marker, A. (2007). Synchronized analysis model (SAM): Linking Gilbert’s behavior engineering model   with environmental analysis models. Performance Improvement, 46(1), 26-32.

Schensul, J., & LeCompte, M. (2013). Essential ethnographic methods: A mixed methods approach (2nd edition). Lanham, MD: AltaMira Press.

About the Authors

Alley_ValerieValerie Alley is an instructional design assistant at the eCampus Department at Boise State University, where she assists professors in moving their face-to-face courses to an online environment. She will complete her master’s degree in organizational performance and workplace learning in the summer of 2014. Valerie may be reached at



Elizabeth_MartinElizabeth Martin is a full-time graduate student and part-time member of the senior sales staff at Beauty First. She plans to complete her master’s degree in organizational performance and workplace learning in the summer of 2014. Elizabeth may be reached at




Rohde_GeoffGeoff Rohde is a reporting analyst at a healthcare firm in Northern California where he works to make managed care performance metrics user-friendly and meaningful. He plans to complete his master’s degree in organizational performance and workplace learning in late 2014. Geoff may be reached at




Wolthuis_AngieAngie Wolthuis is the manager of Learning & Development at Scentsy Inc., where her team focuses on employee performance improvement. She plans to complete her master’s degree in organizational performance and workplace learning in the summer of 2014. Angie may be reached at Patient Safety after the Implementation of an Electronic Health Record.