Integration of electronic patient-reported outcomes into clinical workflows within the Epic electronic medical record.
Bibliographic record
Abstract
102 Background: Electronic patient-reported outcome measures (ePROs) offer a new strategy for symptom assessment that can improve quality of life and prolong survival in routine cancer care. However, ePRO systems are often separate from existing electronic medical records (EMRs) and not well integrated into oncology clinics. In this pilot project, we assessed the feasibility and utility of integrating ePROs into our existing EMR and clinical workflows. Methods: The 10-question Edmonton Symptom Assessment Scale (ESAS) was integrated into the Epic EMR at three outpatient clinics in the Duke Cancer Institute. Patients with active MyChart accounts were offered the ESAS survey prior to their visit, via the patient portal. ePRO data were routed to clinicians in tabular and graphical formats. A “SmartPhrase” facilitated easy data integration into clinical notes. We subsequently interviewed clinicians and optimized workflows. Several patient engagement strategies were used, including automated messages, phone call reminders, and electronic tablets, to increase response rate. Results: It was feasible to quickly customize and activate an ePRO in Epic. Over 10 months, 161 patients completed 208 ePRO surveys. Initially, 10-20% of eligible patients completed the MyChart questionnaire. Patient engagement strategies, including phone calls and personalized MyChart messages, had little effect. Ultimately, tablets were introduced in the clinic check-in process, increasing response rates to >90%. Clinicians reported positive regard for the system, and an impact on patient symptom management. Clinician workflow optimization resulted in minimal “clicks” in the EMR, and the SmartPhrase was used in 128 clinical notes. Conclusions: Integration of ePROs into the clinical setting poses three challenges: technical implementation, workflow optimization, and patient engagement. While technical implementation is important, it was the easiest to solve, with patient engagement as the greatest barrier. Clinicians value an integrated ePRO system that automatically routes data to the clinical note. The key to successful ePRO integration is in ease of use for both patients and clinicians.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".