Integrating Clinical Decision Support into Workflow

Purpose: The aims were to (1) identify barriers and facilitators related to integration of clinical decision support (CDS) into workflow and (2) develop and test CDS design alternatives. Scope: To better understand CDS integration, we studied its use in practice, focusing on CDS for colorectal cancer (CRC) screening and followup. Phase 1 involved outpatient clinics of four different systems—120 clinic staff and providers and 118 patients were observed. In Phase 2, prototyped design enhancements to the Veterans Administration’s CRC screening reminder were compared against its current reminder in a simulation experiment. Twelve providers participated. Methods: Phase 1 was a qualitative project, using key informant interviews, direct observation, opportunistic interviews, and focus groups. All data were analyzed using a coding template, based on the sociotechnical systems theory, which was modified as coding proceeded and themes emerged. Phase 2 consisted of rapid prototyping of CDS design alternatives based on Phase 1 findings and a simulation experiment to test these design changes in a within-subject comparison. Results: Very different CDS types existed across sites, yet there are common barriers: (1) lack of coordination of “outside” results and between primary and specialty care; (2) suboptimal data organization and presentation; (3) needed provider and patient education; (4) needed interface flexibility; (5) needed technological enhancements; (6) unclear role assignments; (7) organizational issues; and (8) disconnect with quality reporting. Design enhancements positively impacted usability and workflow integration but not workload. Conclusions: Effective CDS design and integration requires: (1) organizational and workflow integration; (2) integrating outside results; (3) improving data organization and presentation in a flexible interface; and (4) providing just-in time education, cognitive support, and quality reporting

Redesign of a computerized clinical reminder for colorectal cancer screening: a human-computer interaction evaluation

<p>Abstract</p> <p>Background</p> <p>Based on barriers to the use of computerized clinical decision support (CDS) learned in an earlier field study, we prototyped design enhancements to the Veterans Health Administration's (VHA's) colorectal cancer (CRC) screening clinical reminder to compare against the VHA's current CRC reminder.</p> <p>Methods</p> <p>In a controlled simulation experiment, 12 primary care providers (PCPs) used prototypes of the current and redesigned CRC screening reminder in a within-subject comparison. Quantitative measurements were based on a usability survey, workload assessment instrument, and workflow integration survey. We also collected qualitative data on both designs.</p> <p>Results</p> <p>Design enhancements to the VHA's existing CRC screening clinical reminder positively impacted aspects of usability and workflow integration but not workload. The qualitative analysis revealed broad support across participants for the design enhancements with specific suggestions for improving the reminder further.</p> <p>Conclusions</p> <p>This study demonstrates the value of a human-computer interaction evaluation in informing the redesign of information tools to foster uptake, integration into workflow, and use in clinical practice.</p

Applying human factors principles to alert design increases efficiency and reduces prescribing errors in a scenario-based simulation

OBJECTIVE: To apply human factors engineering principles to improve alert interface design. We hypothesized that incorporating human factors principles into alerts would improve usability, reduce workload for prescribers, and reduce prescribing errors. MATERIALS AND METHODS: We performed a scenario-based simulation study using a counterbalanced, crossover design with 20 Veterans Affairs prescribers to compare original versus redesigned alerts. We redesigned drug-allergy, drug-drug interaction, and drug-disease alerts based upon human factors principles. We assessed usability (learnability of redesign, efficiency, satisfaction, and usability errors), perceived workload, and prescribing errors. RESULTS: Although prescribers received no training on the design changes, prescribers were able to resolve redesigned alerts more efficiently (median (IQR): 56 (47) s) compared to the original alerts (85 (71) s; p=0.015). In addition, prescribers rated redesigned alerts significantly higher than original alerts across several dimensions of satisfaction. Redesigned alerts led to a modest but significant reduction in workload (p=0.042) and significantly reduced the number of prescribing errors per prescriber (median (range): 2 (1-5) compared to original alerts: 4 (1-7); p=0.024). DISCUSSION: Aspects of the redesigned alerts that likely contributed to better prescribing include design modifications that reduced usability-related errors, providing clinical data closer to the point of decision, and displaying alert text in a tabular format. Displaying alert text in a tabular format may help prescribers extract information quickly and thereby increase responsiveness to alerts. CONCLUSIONS: This simulation study provides evidence that applying human factors design principles to medication alerts can improve usability and prescribing outcomes

Use of a Checklist to Facilitate the Recognition of a Transfusion Associated Adverse Event

Transfusions are the most common procedure that hospitalized patients undergo. One of the risks associated with a transfusion is a transfusion related adverse event (TAAE). Transfusion associated adverse events can occur in any patient receiving a transfusion. Some patients are at risk of certain TAAEs (e.g., heart failure patients at risk of transfusion associated circulatory overload) while other events (e.g., allergic reactions) cannot always be anticipated. The severity of a TAAE can range from mildly uncomfortable to life threatening. Nurses need to be able to identify the signs and symptoms of a possible TAAE and intervene immediately by stopping the infusion of the blood product, taking immediate action to stabilize the patient and contacting the provider and transfusion medicine services/blood bank. This experimental study describes how the use of a transfusion checklist could facilitate the recognition and management of TAAEs for all clinicians and in particular, student nurses

Evaluation Of Information Visualization For Decision Making Support In An Emergency Department Information System.

The purpose of this dissertation is to propose an evaluation framework to assess various IV techniques in EDIS and provide recommendations for developers

USE OF STAY S.A.F.E. STRATEGY DURING MEDICATION ADMINISTRATION IN REDUCING ERRORS

Healthcare related medical errors are the third leading cause of death in the United States. Interruptions and distractions can lead to an increased risk of nurses making errors in healthcare, particularly during medication administration. Student nurses should receive education during their prelicensure period on the management of interruptions especially before being given the responsibility of performing high risk tasks such as medication administration. Using a novel interruption management strategy called Stay S.A.F.E., nursing students were interrupted during a simulated medication administration. Students were evaluated on the time spent on the task and if errors were committed. Lastly, perceived workload was measured using the NASA Task Load Index (TLX) tool

Doctor of Philosophy

dissertationPreventable adverse events are one of the leading causes of hospitalized patient deaths. Many of these adverse events occur in Intensive Care Units (ICUs) where nurses often work under cognitive, perceptual, and physical overloads. Contributing to these overloads are spatially separated devices which display treatment relevant information such as orders, monitoring information, and equipment status on numerous displays. If essential information of these separate devices was integrated into a single display at the bedside, nurses could potentially reduce their workload and improve their awareness of the patients' treatment plans and physiological status. We conducted a set of three studies for the purpose of designing an efficient and effective ICU display. We observed ICU nurses during their shifts and found that task-relevant information was often presented in the wrong format, unavailable at the point of care or laborious to obtain. Additionally, nurses were sometimes unaware of significant changes in their patient's status and equipment operation. Based on nurses' feedback, we designed an integrated information display that presents all of the information that nurses need at the patient bedside. Nurses selected a display based on the information organization of existing patient monitors, with added medication management and team communication features. The evaluation of paper-based prototypes of both the integrated display and existing ICU displays showed that nurses could answer questions about the patient's status and treatment faster (p<<0.05) and more accurately (p<<0.05) using the integrated display. The number of adverse events in the ICU could potentially be reduced by integrated displays, but to implement them into clinical practice will require significant engineering efforts

A Comparison of Supervised Machine Learning Classification Techniques and Theory-Driven Approaches for the Prediction of Subjective Mental Workload

In the modern world of technological progress, systems and interfaces are becoming more and more complex. As a consequence, it is a crucial to design the human-computer interaction in the most optimal way to improve the user experience. The construct of Mental Workload is a valid metric that can be used for such a goal. Among the different ways of measuring Mental Workload, self-reporting procedures are the most adopted for their ease of use and application. This research is focused on the application of Machine Learning as an alternative to theory-driven approaches for Mental Workload measurement. In particular, the study is aimed at comparing the classification accuracy of a set of induced models, from an existing dataset, to the mental workload indexes generated by well-known subjective mental workload assessment techniques - namely the Nasa Task Load Index and the Workload profile instruments