The Impact of an Electronic Portal on Patient Encounters in Primary Care: Interrupted Time-Series Analysis
Bibliographic record
Abstract
BACKGROUND: Electronic patient portals are online applications that allow patients access to their own health information, a form of asynchronous virtual care. The long-term impact of portals on the use of traditional primary care services is unclear, but it is an important question at this juncture, when portals are being incorporated into many primary care practices. OBJECTIVE: We sought to investigate how an electronic patient portal affected the use of traditional, synchronous primary care services over a much longer time period than any existing studies and to assess the impact of portal messaging on clinicians' workload. METHODS: We conducted a propensity-score-matched, open-cohort, interrupted time-series evaluation of a primary care portal from its implementation in 2010. We extracted information from the electronic medical record regarding age, sex, education, income, family health team enrollment, diagnoses at index date, and number of medications prescribed in the previous year. We also extracted the annual number of encounters for up to 8 years before and after the index date and provider time spent on secure messaging through the portal. RESULTS: A total of 7247 eligible portal patients and 7647 eligible potential controls were identified, with 3696 patients matched one to one. We found that portal registration was associated with an increase in the number of certain traditional encounters over the time period surrounding portal registration. Following the index year, there was a significant jump in annual number of visits to physicians in the portal arm (0.42 more visits/year vs control, P<.001) but not for visits to nurse practitioners and physician assistants. The annual number of calls to the practice triage nurses also showed a greater increase in the portal arm compared to the control arm after the index year (an additional 0.10 calls, P=.006). The average provider time spent on portal-related work was 5.7 minutes per patient per year. CONCLUSIONS: We found that portal registration was associated with a subsequent increase in the number of some traditional encounters and an increase in clerical workload for providers. Portals have enormous potential to truly engage patients as partners in their own health care, but their impact on use of traditional health care services and clerical burden must also be considered when they are incorporated into primary care.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".