Using Patient Portals to Improve Patient Outcomes: Systematic Review
Bibliographic record
Abstract
BACKGROUND: With the advent of electronic health record (EHR) systems, there is increasing attention on the EHR system with regard to its use in facilitating patients to play active roles in their care via secure patient portals. However, there is no systematic review to comprehensively address patient portal interventions and patient outcomes. OBJECTIVE: This study aimed to synthesize evidence with regard to the characteristics and psychobehavioral and clinical outcomes of patient portal interventions. METHODS: In November 2018, we conducted searches in 3 electronic databases, including PubMed, EMBASE, and Cumulative Index to Nursing and Allied Health Literature, and a total of 24 articles met the eligibility criteria. RESULTS: All but 3 studies were conducted in the United States. The types of study designs varied, and samples predominantly involved non-Hispanic white and highly educated patients with sizes ranging from 50 to 22,703. Most of the portal interventions used tailored alerts or educational resources tailored to the patient's condition. Patient portal interventions lead to improvements in a wide range of psychobehavioral outcomes, such as health knowledge, self-efficacy, decision making, medication adherence, and preventive service use. Effects of patient portal interventions on clinical outcomes including blood pressure, glucose, cholesterol, and weight loss were mixed. CONCLUSIONS: Patient portal interventions were overall effective in improving a few psychological outcomes, medication adherence, and preventive service use. There was insufficient evidence to support the use of patient portals to improve clinical outcomes. Understanding the role of patient portals as an effective intervention strategy is an essential step to encourage patients to be actively engaged in their health care.
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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 | 0.010 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".