AHRQ Study Shows Link Between Electronic Medical Record Tasks and Physician Performance

Among physicians using electronic medical records (EMRs), performance is impacted by “task demands” such as computer pointer movements, click behavior and task duration, according to a new AHRQ-funded study. Using a simulated environment, researchers explored the relationship between task demands and workload, task demands and performance and workload and performance. Researchers performed two experiments on two different EMR systems. Each physician completed a set of pre-specified tasks on three routine clinical EMR-based scenarios: urinary tract infection, pneumonia and heart failure. Both experiments showed a significant relationship only between task demands and performance. This result suggests that task demands (such as more clicks or more time) are related to physician performance (such as more omission errors). The authors called for more research to learn whether human-computer behavioral data, as well as time spent to complete EMR-based tasks, could be used as a quality metric to represent performance and perhaps patient outcomes. The study, “Towards a Better Understanding of Task Demands, Workload, and Performance During Physician-Computer Interactions,” appeared in the March 28 issue of the Journal of the American Medical Informatics Association. Access the abstract.

2016 Mar 28. pii: ocw016. doi: 10.1093/jamia/ocw016. [Epub ahead of print]

Toward a better understanding of task demands, workload, and performance during physician-computer interactions.

Mazur LM1, Mosaly PR2, Moore C3, Comitz E2, Yu F4, Falchook AD2, Eblan MJ2, Hoyle LM2, Tracton G2, Chera BS2, Marks LB2.

Author information

Abstract

OBJECTIVE:

To assess the relationship between (1) task demands and workload, (2) task demands and performance, and (3) workload and performance, all during physician-computer interactions in a simulated environment.

METHODS:

Two experiments were performed in 2 different electronic medical record (EMR) environments: WebCIS (n = 12) and Epic (n = 17). Each participant was instructed to complete a set of prespecified tasks on 3 routine clinical EMR-based scenarios: urinary tract infection (UTI), pneumonia (PN), and heart failure (HF). Task demands were quantified using behavioral responses (click and time analysis). At the end of each scenario, subjective workload was measured using the NASA-Task-Load Index (NASA-TLX). Physiological workload was measured using pupillary dilation and electroencephalography (EEG) data collected throughout the scenarios. Performance was quantified based on the maximum severity of omission errors.

RESULTS:

Data analysis indicated that the PN and HF scenarios were significantly more demanding than the UTI scenario for participants using WebCIS (P < .01), and that the PN scenario was significantly more demanding than the UTI and HF scenarios for participants using Epic (P < .01). In both experiments, the regression analysis indicated a significant relationship only between task demands and performance (P < .01).

DISCUSSION:

Results suggest that task demands as experienced by participants are related to participants' performance. Future work may support the notion that task demands could be used as a quality metric that is likely representative of performance, and perhaps patient outcomes.

CONCLUSION:

The present study is a reasonable next step in a systematic assessment of how task demands and workload are related to performance in EMR-evolving environments.