Electronic health records (EHRs) led to improvements in patient safety, care delivery, and efficiency; however, they have also resulted in significant increases in documentation time. EHR documentation burden, defined as “added work (e.g., documentation) or extraneous actions (e.g., clicks) performed in the EHR beyond that which is required for good clinical care”, has been linked to increased medical errors, poorer patient outcomes, reduced care quality, cognitive overload, and ultimately, burnout among clinicians. Relative to other clinical practice settings where patient flows are more predictable and of lower intensity, emergency department (ED) clinicians report markedly higher workload.
Furthermore, EHR implementation research in the ED indicates that incongruities between EHR design and usability and the clinical workflow may intensify clinician workflow fragmentation. In our prior work, we identified workflow fragmentation, which we define as task switching, as one potential approach for evaluating documentation burden in ED practice settings. Yet, no standardized, scalable measures of documentation burden have been developed. Despite shortcomings, there have been increasing efforts to leverage information from EHR event logs as an alternative to direct clinical observation methods in evaluating user-centric behaviors and interactions with health information technology systems.
Using EHR event logs, this dissertation aims to advance the study of evaluating burden by investigating EHR-mediated workflow fragmentation as a measure of EHR documentation burden among physicians and registered nurses (hereinafter interchangeably referred to as “clinicians”) in the ED. First, I review the literature on the existing quantitative approaches employed for measuring clinician documentation burden in clinical practice settings. Next, I explore EHR factors perceived to contribute to clinician documentation burden as well as the perceived role of workflow fragmentation on clinician documentation burden in the ED.
Lastly, I investigate data-driven approaches to abstract clinically relevant concepts from EHR event logs for studying EHR documentation burden—culminating into a computational framework to evaluate ED clinician documentation burden in the context of cognitive burden. Collectively, the work conducted in this dissertation contributes computational methods that are foundational for investigating clinician documentation burden measurement at scale using EHR event logs, informed by current evidence and clinician perspectives, and grounded in theory.
| Identifer | oai:union.ndltd.org:columbia.edu/oai:academiccommons.columbia.edu:10.7916/k2z1-hh05 |
| Date | January 2023 |
| Creators | Moy, Amanda Josephine |
| Source Sets | Columbia University |
| Language | English |
| Detected Language | English |
| Type | Theses |
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