An electronic health record (EHR) log analysis shows limited clinician engagement with unsolicited genetic test results

Nephrologists' Views on a Workflow for Returning Genetic Results to Research Participants

Introduction

Returning research-based genetic results (gRoR) to participants in nephrology research can improve care; however, the practice raises implementational questions and no established guidelines for this process currently exist. Nephrologists' views on this issue can inform the process but are understudied.

Methods

We developed a conceptual workflow for gRoR from literature and experience, covering aspects such as which results to return, how, and by whom. We surveyed US nephrologists to gauge their views on the workflow and anticipated barriers and collected participants' demographics, including professional backgrounds.

Results

A total of 201 adult and pediatric nephrologists completed the survey. Most of them agreed that all diagnostic kidney-related results (93%), secondary findings (80%), and kidney-related risk variants (83%) should be returned. No significant differences were found between adult and pediatric nephrologists' responses, except that 48% of adult nephrologists versus 26% of pediatric nephrologists supported returning polygenic risk scores (PRS) (P < 0.01). Seventy-nine percent wanted to know about research results before clinical confirmation. Most of them (63%) believed a genetic counselor should return clinically confirmed results. Key barriers included the cost of clinical validation (77%) and the unavailability of genetic counseling services (63%). Facilitators included educational resources on genetic kidney diseases (91%), a referral list of experts (89%), and clear clinical care guidelines (89%). We discuss findings' implications and provide "points to consider."

Conclusion

There is significant interest in gRoR among nephrologists; however, logistical and economic concerns need addressing. Identified facilitators can help large nephrology studies planning to return genetic results to participants.

Bridging the Gap in Genomic Implementation: Identifying User Needs for Precision Nephrology

Introduction

Genomic medicine holds transformative potential for personalized nephrology care; however, its clinical integration poses challenges. Automated clinical decision support (CDS) systems in the electronic health record (EHR) offer a promising solution but have shown limited impact. This study aims to glean practical insights into nephrologists' challenges using genomic resources, informing precision nephrology decision support tools.

Methods

We conducted an anonymous electronic survey among US nephrologists from January 19, 2021 to May 19, 2021, guided by the Consolidated Framework for Implementation Research. It assessed practice characteristics, genomic resource utilization, attitudes, perceived knowledge, self-efficacy, and factors influencing genetic testing decisions. Survey links were primarily shared with National Kidney Foundation members.

Results

We analyzed 319 surveys, with most respondents specializing in adult nephrology. Although respondents generally acknowledged the clinical use of genomic resources, varying levels of perceived knowledge and self-efficacy were evident regarding precision nephrology workflows. Barriers to genetic testing included cost/insurance coverage and limited genomics experience.

Conclusion

The study illuminates specific hurdles nephrologists face using genomic resources. The findings are a valuable contribution to genomic implementation research, highlighting the significance of developing tailored interventions to support clinicians in using genomic resources effectively. These findings can guide the future development of CDS systems in the EHR. Addressing unmet informational and workflow support needs can enhance the integration of genomics into clinical practice, advancing personalized nephrology care and improving kidney disease outcomes. Further research should focus on interventions promoting seamless precision nephrology care integration.

Clinical decision support with a comprehensive in-EHR patient tracking system improves genetic testing follow up

OBJECTIVE

We sought to develop and evaluate an electronic health record (EHR) genetic testing tracking system to address the barriers and limitations of existing spreadsheet-based workarounds.

MATERIALS AND METHODS

We evaluated the spreadsheet-based system using mixed effects logistic regression to identify factors associated with delayed follow up. These factors informed the design of an EHR-integrated genetic testing tracking system. After deployment, we assessed the system in 2 ways. We analyzed EHR access logs and note data to assess patient outcomes and performed semistructured interviews with users to identify impact of the system on work.

RESULTS

We found that patient-reported race was a significant predictor of documented genetic testing follow up, indicating a possible inequity in care. We implemented a CDS system including a patient data capture form and management dashboard to facilitate important care tasks. The system significantly sped review of results and significantly increased documentation of follow-up recommendations. Interviews with key system users identified a range of sociotechnical factors (ie, tools, tasks, collaboration) that contribute to safer and more efficient care.

DISCUSSION

Our new tracking system ended decades of workarounds for identifying and communicating test results and improved clinical workflows. Interview participants related that the system decreased cognitive and time burden which allowed them to focus on direct patient interaction.

CONCLUSION

By assembling a multidisciplinary team, we designed a novel patient tracking system that improves genetic testing follow up. Similar approaches may be effective in other clinical settings.

Using event logs to observe interactions with electronic health records: an updated scoping review shows increasing use of vendor-derived measures

OBJECTIVE

The aim of this article is to compare the aims, measures, methods, limitations, and scope of studies that employ vendor-derived and investigator-derived measures of electronic health record (EHR) use, and to assess measure consistency across studies.

MATERIALS AND METHODS

We searched PubMed for articles published between July 2019 and December 2021 that employed measures of EHR use derived from EHR event logs. We coded the aims, measures, methods, limitations, and scope of each article and compared articles employing vendor-derived and investigator-derived measures.

RESULTS

One hundred and two articles met inclusion criteria; 40 employed vendor-derived measures, 61 employed investigator-derived measures, and 1 employed both. Studies employing vendor-derived measures were more likely than those employing investigator-derived measures to observe EHR use only in ambulatory settings (83% vs 48%, P = .002) and only by physicians or advanced practice providers (100% vs 54% of studies, P < .001). Studies employing vendor-derived measures were also more likely to measure durations of EHR use (P < .001 for 6 different activities), but definitions of measures such as time outside scheduled hours varied widely. Eight articles reported measure validation. The reported limitations of vendor-derived measures included measure transparency and availability for certain clinical settings and roles.

DISCUSSION

Vendor-derived measures are increasingly used to study EHR use, but only by certain clinical roles. Although poorly validated and variously defined, both vendor- and investigator-derived measures of EHR time are widely reported.

CONCLUSION

The number of studies using event logs to observe EHR use continues to grow, but with inconsistent measure definitions and significant differences between studies that employ vendor-derived and investigator-derived measures.

Incomplete Penetrance of Population-Based Genetic Screening Results in Electronic Health Record

The clinical value of population-based genetic screening projects depends on the actions taken on the findings. The Healthy Nevada Project (HNP) is an all-comer genetic screening and research project based in northern Nevada. HNP participants with CDC Tier 1 findings of hereditary breast and ovarian cancer syndrome (HBOC), Lynch syndrome (LS), or familial hypercholesterolemia (FH) are notified and provided with genetic counseling. However, the HNP subsequently takes a “hands-off” approach: it is the responsibility of notified participants to share their findings with their healthcare providers, and providers are expected to implement the recommended action plans. Thus, the HNP presents an opportunity to evaluate the efficiency of participant and provider responses to notification of important genetic findings, using electronic health records (EHRs) at Renown Health (a large regional hospital in northern Nevada). Out of 520 HNP participants with findings, we identified 250 participants who were notified of their findings and who had an EHR. 107 of these participants responded to a survey, with 76 (71%) indicating that they had shared their findings with their healthcare providers. However, a sufficiently specific genetic diagnosis appeared in the EHRs and problem lists of only 22 and 10%, respectively, of participants without prior knowledge. Furthermore, review of participant EHRs provided evidence of possible relevant changes in clinical care for only a handful of participants. Up to 19% of participants would have benefited from earlier screening due to prior presentation of their condition. These results suggest that continuous support for both participants and their providers is necessary to maximize the benefit of population-based genetic screening. We recommend that genetic screening projects require participants’ consent to directly document their genetic findings in their EHRs. Additionally, we recommend that they provide healthcare providers with ongoing training regarding documentation of findings and with clinical decision support regarding subsequent care.

Predicting next-day discharge via electronic health record access logs

OBJECTIVE

Hospital capacity management depends on accurate real-time estimates of hospital-wide discharges. Estimation by a clinician requires an excessively large amount of effort and, even when attempted, accuracy in forecasting next-day patient-level discharge is poor. This study aims to support next-day discharge predictions with machine learning by incorporating electronic health record (EHR) audit log data, a resource that captures EHR users' granular interactions with patients' records by communicating various semantics and has been neglected in outcome predictions.

MATERIALS AND METHODS

This study focused on the EHR data for all adults admitted to Vanderbilt University Medical Center in 2019. We learned multiple advanced models to assess the value that EHR audit log data adds to the daily prediction of discharge likelihood within 24 h and to compare different representation strategies. We applied Shapley additive explanations to identify the most influential types of user-EHR interactions for discharge prediction.

RESULTS

The data include 26 283 inpatient stays, 133 398 patient-day observations, and 819 types of user-EHR interactions. The model using the count of each type of interaction in the recent 24 h and other commonly used features, including demographics and admission diagnoses, achieved the highest area under the receiver operating characteristics (AUROC) curve of 0.921 (95% CI: 0.919-0.923). By contrast, the model lacking user-EHR interactions achieved a worse AUROC of 0.862 (0.860-0.865). In addition, 10 of the 20 (50%) most influential factors were user-EHR interaction features.

CONCLUSION

EHR audit log data contain rich information such that it can improve hospital-wide discharge predictions.