Impact of a problem-oriented view on clinical data retrieval

Abstract

Objective The electronic health record (EHR) data deluge makes data retrieval more difficult, escalating cognitive load and exacerbating clinician burnout. New auto-summarization techniques are needed. The study goal was to determine if problem-oriented view (POV) auto-summaries improve data retrieval workflows. We hypothesized that POV users would perform tasks faster, make fewer errors, be more satisfied with EHR use, and experience less cognitive load as compared with users of the standard view (SV). Methods Simple data retrieval tasks were performed in an EHR simulation environment. A randomized block design was used. In the control group (SV), subjects retrieved lab results and medications by navigating to corresponding sections of the electronic record. In the intervention group (POV), subjects clicked on the name of the problem and immediately saw lab results and medications relevant to that problem. Results With POV, mean completion time was faster (173 seconds for POV vs 205 seconds for SV; P < .0001), the error rate was lower (3.4% for POV vs 7.7% for SV; P = .0010), user satisfaction was greater (System Usability Scale score 58.5 for POV vs 41.3 for SV; P < .0001), and cognitive task load was less (NASA Task Load Index score 0.72 for POV vs 0.99 for SV; P < .0001). Discussion The study demonstrates that using a problem-based auto-summary has a positive impact on 4 aspects of EHR data retrieval, including cognitive load. Conclusion EHRs have brought on a data deluge, with increased cognitive load and physician burnout. To mitigate these increases, further development and implementation of auto-summarization functionality and the requisite knowledge base are needed.

Does directly integrating health information exchange (HIE) data with the electronic health record increase HIE use by clinicians in the emergency department?

“…26 In addition, the display of data in a structured, problem-oriented format, such as Health Dart's chief complaint view, improves efficiency of data retrieval and user satisfaction. 28,45,46 Contrary to our prediction, the effect of the app's implementation on INPC use was not as strong in encounters that occurred after UI enhancements. The UI enhancements appeared to The results of the COVID-19 modeling should be interpreted with caution as the lower volume of patients and higher rate of infectious diseases and respiratory conditions in encounters during the pandemic increase the potential for confounding.…”

“…26 In addition, the display of data in a structured, problem-oriented format, such as Health Dart ’s chief complaint view, improves efficiency of data retrieval and user satisfaction. 28,45,46…”

“…27 Problem-oriented views have been shown to improve data retrieval workflows, allowing providers to complete EHR tasks more efficiently, with fewer errors and cognitive task load, and greater user satisfaction. [28][29][30][31] These limitations have two major consequences. First, the lack of widescale interoperability and barriers to information access imposes an exhausting litany of clerical tasks on clinicians which contributes to burnout and waste.…”

“…27 Problem-oriented views have been shown to improve data retrieval workflows, allowing providers to complete EHR tasks more efficiently, with fewer errors and cognitive task load, and greater user satisfaction. 28–31…”

Measuring and Maximizing Undivided Attention in the Context of Electronic Health Records

“…Cognitive load can be mitigated by improving EHR usability, so that, for example, the test results one needs to review to safely choose a new anti-hypertensive medication are presented on the same screen as the medication order choices. Other approaches that decrease the cognitive load include creating clear, concise displays of all of the patient's information relevant to a particular condition 18 , reducing the number of alerts and decreasing the clutter from low value information.…”