Implementation of Patient Engagement Tools in Electronic Health Records to Enhance Patient-Centered Communication: Protocol for Feasibility Evaluation and Preliminary Results
Bibliographic record
Abstract
BACKGROUND: Patient-physician communication during clinical encounters is essential to ensure quality of care. Many studies have attempted to improve patient-physician communication. Incorporating patient priorities into agenda setting and medical decision-making are fundamental to patient-centered communication. Efficient and scalable approaches are needed to empower patients to speak up and prepare physicians to respond. Leveraging electronic health records (EHRs) in engaging patients and health care teams has the potential to enhance the integration of patient priorities in clinical encounters. A systematic approach to eliciting and documenting patient priorities before encounters could facilitate effective communication in such encounters. OBJECTIVE: In this paper, we report the design and implementation of a set of EHR tools built into clinical workflows for facilitating patient-physician joint agenda setting and the documentation of patient concerns in the EHRs for ambulatory encounters. METHODS: We engaged health information technology leaders and users in three health care systems for developing and implementing a set of EHR tools. The goal of these tools is to standardize the elicitation of patient priorities by using a previsit "patient important issue" questionnaire distributed through the patient portal to the EHR. We built additional EHR documentation tools to facilitate patient-staff communication when the staff records the vital signs and the reason for the visit in the EHR while in the examination room, with a simple transmission method for physicians to incorporate patient concerns in EHR notes. RESULTS: The study is ongoing. The anticipated completion date for survey data collection is November 2021. A total of 34,037 primary care patients from three health systems (n=26,441; n=5136; and n=2460 separately recruited from each system) used the previsit patient important issue questionnaire in 2020. The adoption of the digital previsit questionnaire during the COVID-19 pandemic was much higher in one health care system because it expanded the use of the questionnaire from physicians participating in trials to all primary care providers midway through the year. It also required the use of this previsit questionnaire for eCheck-ins, which are required for telehealth encounters. Physicians and staff suggested anecdotally that this questionnaire helped patient-clinician communication, particularly during the COVID-19 pandemic. CONCLUSIONS: EHR tools have the potential to facilitate the integration of patient priorities into agenda setting and documentation in real-world primary care practices. Early results suggest the feasibility and acceptability of such digital tools in three health systems. EHR tools can support patient engagement and clinicians' work during in-person and telehealth visits. They could potentially exert a sustained influence on patient and clinician communication behaviors in contrast to prior ad hoc educational efforts targeting patients or clinicians. TRIAL REGISTRATION: ClinicalTrials.gov NCT03385512; https://clinicaltrials.gov/ct2/show/NCT03385512. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/30431.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.146 | 0.119 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.033 | 0.010 |
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 source (direct Gemma or distilled Codex), 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".