The electronic health record audit file: the patient is waiting

Hirsch, Annemarie G.; Lerch, Virginia; Tang, Xiaoqin; Berger, Andrea; Clark, Deserae N; Stewart, Walter F.

doi:10.1093/jamia/ocw088

Cited by 19 publications

(10 citation statements)

References 15 publications

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“…[12][13][14] One promising approach for studying workflow is to use the vast amount of timing data currently available in EHR systems, through timestamps recorded in audit logs during routine use. [15][16][17] A major benefit of EHR systems is that clinical data may be applied for secondary use beyond direct provision of clinical care; current efforts have focused on areas such as clinical research, public health, adverse event reporting, and quality assurance. We are studying the use of timestamps of EHR data entries to reconstruct the timing of workflow events, which has multiple applications for analysis.…”

Section: Introductionmentioning

confidence: 99%

Secondary use of electronic health record data for clinical workflow analysis

Hribar

Read-Brown

Goldstein

et al. 2017

Journal of the American Medical Informatics Association

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Objective Outpatient clinics lack guidance for tackling modern efficiency and productivity demands. Workflow studies require large amounts of timing data that are prohibitively expensive to collect through observation or tracking devices. Electronic health records (EHRs) contain a vast amount of timing data – timestamps collected during regular use – that can be mapped to workflow steps. This study validates using EHR timestamp data to predict outpatient ophthalmology clinic workflow timings at Oregon Health and Science University and demonstrates their usefulness in 3 different studies. Materials and Methods Four outpatient ophthalmology clinics were observed to determine their workflows and to time each workflow step. EHR timestamps were mapped to the workflow steps and validated against the observed timings. Results The EHR timestamp analysis produced times that were within 3 min of the observed times for >80% of the appointments. EHR use patterns affected the accuracy of using EHR timestamps to predict workflow times. Discussion EHR timestamps provided a reasonable approximation of workflow and can be used for workflow studies. They can be used to create simulation models, analyze EHR use, and quantify the impact of trainees on workflow. Conclusion The secondary use of EHR timestamp data is a valuable resource for clinical workflow studies. Sample timestamp data files and algorithms for processing them are provided and can be used as a template for more studies in other clinical specialties and settings.

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Section: Introductionmentioning

confidence: 99%

Secondary use of electronic health record data for clinical workflow analysis

Hribar

Read-Brown

Goldstein

et al. 2017

Journal of the American Medical Informatics Association

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“…Data from access and audit logs provide a rare “behind-the-scenes” glimpse into how health care team members spend their time and where they direct their attention. These log data have been used successfully to understand questions of the health care team’s communication [26], trainee skill [27], and clinical workflow [6,7]. As we demonstrate in this paper, EHR logs can also provide valuable implementation data that can be used to improve future CDS interventions.…”

Section: Discussionmentioning

confidence: 96%

Use of Electronic Health Record Access and Audit Logs to Identify Physician Actions Following Noninterruptive Alert Opening: Descriptive Study

Amroze¹,

Field

Fouayzi

et al. 2019

JMIR Med Inform

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Background Electronic health record (EHR) access and audit logs record behaviors of providers as they navigate the EHR. These data can be used to better understand provider responses to EHR–based clinical decision support (CDS), shedding light on whether and why CDS is effective. Objective This study aimed to determine the feasibility of using EHR access and audit logs to track primary care physicians’ (PCPs’) opening of and response to noninterruptive alerts delivered to EHR InBaskets. Methods We conducted a descriptive study to assess the use of EHR log data to track provider behavior. We analyzed data recorded following opening of 799 noninterruptive alerts sent to 75 PCPs’ InBaskets through a prior randomized controlled trial. Three types of alerts highlighted new medication concerns for older patients’ posthospital discharge: information only (n=593), medication recommendations (n=37), and test recommendations (n=169). We sought log data to identify the person opening the alert and the timing and type of PCPs’ follow-up EHR actions (immediate vs by the end of the following day). We performed multivariate analyses examining associations between alert type, patient characteristics, provider characteristics, and contextual factors and likelihood of immediate or subsequent PCP action (general, medication-specific, or laboratory-specific actions). We describe challenges and strategies for log data use. Results We successfully identified the required data in EHR access and audit logs. More than three-quarters of alerts (78.5%, 627/799) were opened by the PCP to whom they were directed, allowing us to assess immediate PCP action; of these, 208 alerts were followed by immediate action. Expanding on our analyses to include alerts opened by staff or covering physicians, we found that an additional 330 of the 799 alerts demonstrated PCP action by the end of the following day. The remaining 261 alerts showed no PCP action. Compared to information-only alerts, the odds ratio (OR) of immediate action was 4.03 (95% CI 1.67-9.72) for medication-recommendation and 2.14 (95% CI 1.38-3.32) for test-recommendation alerts. Compared to information-only alerts, ORs of medication-specific action by end of the following day were significantly greater for medication recommendations (5.59; 95% CI 2.42-12.94) and test recommendations (1.71; 95% CI 1.09-2.68). We found a similar pattern for OR of laboratory-specific action. We encountered 2 main challenges: (1) Capturing a historical snapshot of EHR status (number of InBasket messages at time of alert delivery) required incorporation of data generated many months prior with longitudinal follow-up. (2) Accurately interpreting data elements required iterative work by a physician/data manager team taking action within the EHR and then examining audit logs to identify corresponding documentation. Conclusions EHR log data could inform future efforts and provide...

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“…Others have applied methods from computational ethnography to EHR audit logs to describe and optimize clinical workflows in the outpatient setting. 21,22 To date, only one single-center study has leveraged EHR features, such as note authorship, ordering provider, and time-in-chart, to develop a model predicting specific resident PPIs (rPPIs). 23 A critical need exists in the development of an approach to accurately attribute patients to providers using common EHR data elements, including audit logs.…”

Section: Background and Significancementioning

confidence: 99%

Attributing Patients to Pediatric Residents Using Electronic Health Record Features Augmented with Audit Logs

et al. 2020

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Objective Patient attribution, or the process of attributing patient-level metrics to specific providers, attempts to capture real-life provider–patient interactions (PPI). Attribution holds wide-ranging importance, particularly for outcomes in graduate medical education, but remains a challenge. We developed and validated an algorithm using EHR data to identify pediatric resident PPIs (rPPIs). Methods We prospectively surveyed residents in three care settings to collect self-reported rPPIs. Participants were surveyed at the end of primary care clinic, emergency department (ED), and inpatient shifts, shown a patient census list, asked to mark the patients with whom they interacted, and encouraged to provide a short rationale behind the marked interaction. We extracted routine EHR data elements, including audit logs, note contribution, order placement, care team assignment, and chart closure, and applied a logistic regression classifier to the data to predict rPPIs in each care setting. We also performed a comment analysis of the resident-reported rationales in the inpatient care setting to explore perceived patient interactions in a complicated workflow. Results We surveyed 81 residents over 111 shifts and identified 579 patient interactions. Among EHR extracted data, time-in-chart was the best predictor in all three care settings (primary care clinic: odds ratio [OR] = 19.36, 95% confidence interval [CI]: 4.19–278.56; ED: OR = 19.06, 95% CI: 9.53–41.65' inpatient: OR = 2.95, 95% CI: 2.23–3.97). Primary care clinic and ED specific models had c-statistic values > 0.98, while the inpatient-specific model had greater variability (c-statistic = 0.89). Of 366 inpatient rPPIs, residents provided rationales for 90.1%, which were focused on direct involvement in a patient's admission or transfer, or care as the front-line ordering clinician (55.6%). Conclusion Classification models based on routinely collected EHR data predict resident-defined rPPIs across care settings. While specific to pediatric residents in this study, the approach may be generalizable to other provider populations and scenarios in which accurate patient attribution is desirable.

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The electronic health record audit file: the patient is waiting

Abstract: Scalable ways to quantify clinical encounter workflow elements may provide the means to develop more efficient approaches to care and improve the patient experience.

Cited by 19 publications

References 15 publications

Secondary use of electronic health record data for clinical workflow analysis

Secondary use of electronic health record data for clinical workflow analysis

Use of Electronic Health Record Access and Audit Logs to Identify Physician Actions Following Noninterruptive Alert Opening: Descriptive Study

Attributing Patients to Pediatric Residents Using Electronic Health Record Features Augmented with Audit Logs

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