Too much or too little? Investigating the usability of high and low data displays of the same electronic medical record

Investigation of eye movement measures of mental workload in healthcare: Can pupil dilations reflect fatigue or overload when it comes to health information system use?

The use of health information systems (HIS) can result in high workloads and, consequently, poor performance characterized by e.g. increased occurrence of errors among clinicians. Pupillometry offers a good possibility to measure mental workload in a dynamic work setting. Currently, there is a lack of empirical research in the context of healthcare settings. Therefore, the aim of the present study was to examine whether specific eye movement measures are suitable for measuring mental workload in the healthcare setting, especially when working with HIS. 49 persons participated in our simulation-lab study. They had to complete a system-related task as well as an increasing n-back task. Both tasks were modified regarding task difficulty. Results show significant differences for objective and subjective workload measures between increasing task levels. There are also hints for an overload/fatigue indicator in pupil data. Our results are limited in terms of external validity, causality and effects. Future work should focus on high-fidelity simulations and less time-consuming analysis approaches.

Estimating Cognitive Load in a Mobile Personal Health Record Application: A Cognitive Task Analysis Approach

OBJECTIVES

Mobile health applications that are designed without considering usability criteria can lead to cognitive overload, resulting in the rejection of these apps. To avoid this problem, the user interface of mobile health applications should be evaluated for cognitive load. This evaluation can contribute to the improvement of the user interface and help prevent cognitive overload for the user.

METHODS

In this study, we evaluated a mobile personal health records application using the cognitive task analysis method, specifically the goals, operators, methods, and selection rules (GOMS) approach, along with the related updated GOMS model and gesture-level model techniques. The GOMS method allowed us to determine the steps of the tasks and categorize them as physical or cognitive tasks. We then estimated the completion times of these tasks using the updated GOMS model and gesture-level model.

RESULTS

All 10 identified tasks were split into 398 steps consisting of mental and physical operators. The time to complete all the tasks was 5.70 minutes and 5.45 minutes according to the updated GOMS model and gesture-level model, respectively. Mental operators covered 73% of the total fulfillment time of the tasks according to the updated GOMS model and 76% according to the gesture-level model. The inter-rater reliability analysis yielded an average of 0.80, indicating good reliability for the evaluation method.

CONCLUSIONS

The majority of the task execution times comprised mental operators, suggesting that the cognitive load on users is high. To enhance the application's implementation, the number of mental operators should be reduced.

Patient Safety Issues From Information Overload in Electronic Medical Records

BACKGROUND AND OBJECTIVE

Electronic health records (EHRs) have become ubiquitous in medicine and continue to grow in informational content. Little has been documented regarding patient safety from the resultant information overload. The objective of this literature review is to better understand how information overload in EHR affects patient safety.

METHODS

A literature search was performed using the Transparent Reporting of Systematic Reviews and Meta-Analyses standards for literature review. PubMed and Web of Science were searched and articles selected that were relevant to EHR information overload based on keywords.

RESULTS

The literature search yielded 28 articles meeting the criteria for the study. Information overload was found to increase physician cognitive load and error rates in clinical simulations. Overabundance of clinically irrelevant information, poor data display, and excessive alerting were consistently identified as issues that may lead to information overload.

CONCLUSIONS

Information overload in EHRs may result in higher error rates and negatively impact patient safety. Further studies are necessary to define the role of EHR in adverse patient safety events and to determine methods to mitigate these errors. Changes focused on the usability of EHR should be considered with the end user (physician) in mind. Federal agencies have a role to play in encouraging faster adoption of improved EHR interfaces.

Systematic review of applied usability metrics within usability evaluation methods for hospital electronic healthcare record systems: Metrics and Evaluation Methods for eHealth Systems

BACKGROUND AND OBJECTIVES

Electronic healthcare records have become central to patient care. Evaluation of new systems include a variety of usability evaluation methods or usability metrics (often referred to interchangeably as usability components or usability attributes). This study reviews the breadth of usability evaluation methods, metrics, and associated measurement techniques that have been reported to assess systems designed for hospital staff to assess inpatient clinical condition.

METHODS

Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology, we searched Medline, EMBASE, CINAHL, Cochrane Database of Systematic Reviews, and Open Grey from 1986 to 2019. For included studies, we recorded usability evaluation methods or usability metrics as appropriate, and any measurement techniques applied to illustrate these. We classified and described all usability evaluation methods, usability metrics, and measurement techniques. Study quality was evaluated using a modified Downs and Black checklist.

RESULTS

The search identified 1336 studies. After abstract screening, 130 full texts were reviewed. In the 51 included studies 11 distinct usability evaluation methods were identified. Within these usability evaluation methods, seven usability metrics were reported. The most common metrics were ISO9241-11 and Nielsen's components. An additional "usefulness" metric was reported in almost 40% of included studies. We identified 70 measurement techniques used to evaluate systems. Overall study quality was reflected in a mean modified Downs and Black checklist score of 6.8/10 (range 1-9) 33% studies classified as "high-quality" (scoring eight or higher), 51% studies "moderate-quality" (scoring 6-7), and the remaining 16% (scoring below five) were "low-quality."

CONCLUSION

There is little consistency within the field of electronic health record systems evaluation. This review highlights the variability within usability methods, metrics, and reporting. Standardized processes may improve evaluation and comparison electronic health record systems and improve their development and implementation.