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

Abstract

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.

Keywords: clinical decision support; genetic testing; transition of care.

Figure 1.

Swimlane diagram of the clinical genetics inpatient consultation workflow. Patients admitted to the hospital may be suspected by their primary inpatient care team of having an underlying genetic etiology for their medical problems. A consultation may be requested. The genetics team (including an attending physician) evaluates the patients and leaves recommendations for the primary care team. These recommendations often include genetic testing, which is then sent to the hospital genomics lab or an outside reference lab. Because genetic testing may take weeks or months, the patient may be discharged prior to testing resulting. Once testing is complete, the test report is entered into the patient’s EHR record. Ideally, the medical geneticist becomes aware of the result and interprets them in the context of the patient. The geneticist informs the patient or their family members and documents their recommendations in the EHR for other care team members.

Figure 2.

Project overview. (A) We became aware of anecdotal reports of results going unnoticed because of multiple overlapping failures (ie, the Swiss cheese model of adverse events) in our practice. (B) To better understand the problem, we performed an analysis of patient characteristics that were associated with the presence or absence of documented follow up recommendations. (C) Guided by this analysis, we gathered initial requirements for a successful system to address these issues. We adopted an agile project development lifecycle and (D) designed the system, (E) built and deployed the system, and (F) initiated a limited clinical beta test. (G) Based on our testing, we identified issues and iterated on the system. (H) We then provided user education and deployed the system to the entire inpatient service. Following implementation, we (I) analyzed patient outcomes and (J) conducted semistructured interviews of users.

Figure 3.

Data capture instrument for the in-EHR tracking system. We created a data capture form based on our EHR vendor’s standard functionality (a SmartForm™). The system automatically enters data by pulling it from elsewhere in the system. For example, “Inpatient Consult” is automatically selected if the patient is currently admitted. However, clinicians may override results. Clinicians are then asked to fill in information which cannot be readily ascertained from other sources. The data is stored in discrete fields for later use in reporting and documentation.

Figure 4.

Management dashboard for the in-EHR tracking system. The dashboard is implemented using the vendor’s standard functionality (a Reporting Workbench™ Report). The top half of the management dashboard is a patient list which provides a high-level overview of the patient’s status within their genetic diagnostic odyssey. The standard functionality provides powerful filtering options to allow the clinician to, for example, only view their own patients by default. Icons provide key information about patient status. The bottom half of the dashboard provides additional details about the selected patient, including their genetic test results and recent communications with the patient’s family. There is also access to the data entry form (Figure 3) to document changes in patient status.

Figure 5.

Changes in documentation of follow-up recommendations following system implementation. The left panel shows the percentage of patients who underwent genetic testing who had subsequent documentation of recommendations by genetics in the 60 days immediately following the results becoming available. The bar graph compares patients where the tracking system was not used (orange) to those where it was used (blue) in the 12 months preceding and 6 months following the implementation of the CDS tracking system. The numbers of patients were compared with a Fisher’s exact test. The right panel shows the same data but for each of the 8 consulting attending physicians who evaluated patients during both periods. Consulting attending number 4 (green dots and line) who decreased documented follow up percentage consulted on only 8 patients total, indicating possible stochasticity rather than an effect of the system itself.

Figure 6.

Survival analysis of time until genetic testing result review. The top panel shows the percentage of patients whose EHR lab results sections were reviewed by a genetics team member (genetic counselor, genetics resident, or genetics attending physician) over time for patients who were not tracked by the system (orange) compared to those who were tracked by the system (blue). Crosses indicate patients that were censored because their genetic testing had only been resulted for that long at the time of analysis. The bottom panel indicates the number of patients at risk for not having their results reviewed.