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Record W3185737158 · doi:10.2196/30431

Implementation of Patient Engagement Tools in Electronic Health Records to Enhance Patient-Centered Communication: Protocol for Feasibility Evaluation and Preliminary Results

2021· article· en· W3185737158 on OpenAlexvenueno aff
Ming Tai‐Seale, Rebecca Rosen, Bernice Ruo, Michael Hogarth, Chris Longhurst, Lina Lander, Amanda Walker, Cheryl D. Stults, Albert Chan, Kathleen M. Mazor, Lawrence Garber, Marlene Millen

Bibliographic record

VenueJMIR Research Protocols · 2021
Typearticle
Languageen
FieldHealth Professions
TopicPatient-Provider Communication in Healthcare
Canadian institutionsnot available
FundersPatient-Centered Outcomes Research Institute
KeywordsDocumentationWorkflowPatient portalMedicineHealth carePatient safetyProtocol (science)Medical recordPatient experienceMedical emergencyNursingComputer scienceAlternative medicine

Abstract

fetched live from OpenAlex

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.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.146
metaresearch head score (Gemma)0.119
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.146
Threshold uncertainty score0.772

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1460.119
Meta-epidemiology (narrow)0.0040.003
Meta-epidemiology (broad)0.0030.005
Bibliometrics0.0060.005
Science and technology studies0.0060.004
Scholarly communication0.0040.003
Open science0.0030.005
Research integrity0.0050.006
Insufficient payload (model declined to judge)0.0330.010

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.

Opus teacher head0.685
GPT teacher head0.698
Teacher spread0.013 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreProtocol

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".

Quick stats

Citations24
Published2021
Admission routes1
Has abstractyes

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