Use and Effects of Patient Access to Medical Records in General Practice Through a Personal Health Record in the Netherlands: Protocol for a Mixed-Methods Study
Bibliographic record
Abstract
BACKGROUND: In the Dutch health care system, general practitioners hold a central position. They store information from all health care providers who are involved with their patients in their electronic health records. Web-based access to the summary record in general practice through a personal health record (PHR) may increase patients' insight into their medical conditions and help them to be involved in their care. OBJECTIVE: We describe the protocol that we will use to investigate the utilization of patients' digital access to the summary of their medical records in general practice through a PHR and its effects on the involvement of patients in their care. METHODS: We will conduct a multilevel mixed-methods study in which the PHR and Web-based access to the summary record will be offered for 6 months to a random sample of 500 polypharmacy patients, 500 parents of children aged <4 years, and 500 adults who do not belong to the former two groups. At the patient level, a controlled before-after study will be conducted using surveys, and concurrently, qualitative data will be collected from focus group discussions, think-aloud observations, and semistructured interviews. At the general practice staff (GP staff) level, focus group discussions will be conducted at baseline and Q-methodology inquiries at the end of the study period. The primary outcomes at the patient level are barriers and facilitators for using the PHR and summary records and changes in taking an active role in decision making and care management and medication adherence. Outcomes at the GP staff level are attitudes before and opinions after the implementation of the intervention. Patient characteristics and changes in outcomes related to patient involvement during the study period will be compared between the users and nonusers of the intervention using chi-square tests and t tests. A thematic content analysis of the qualitative data will be performed, and the results will be used to interpret quantitative findings. RESULTS: Enrollment was completed in May 2017 and the possibility to view GP records through the PHR was implemented in December 2017. Data analysis is currently underway and the first results are expected to be submitted for publication in autumn 2019. CONCLUSIONS: We expect that the findings of this study will be useful to health care providers and health care organizations that consider introducing the use of PHR and Web-based access to records and to those who have recently started using these. TRIAL REGISTRATION: Netherlands Trial Registry NTR6395; http://www.trialregister.nl/trialreg/admin/rctview.asp?TC=6395 (Archived by WebCite at http://www.webcitation.org/71nc8jzwM). REGISTERED REPORT IDENTIFIER: RR1-10.2196/10193.
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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 | 0.093 | 0.069 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.033 | 0.006 |
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".