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Record W3091153132 · doi:10.2196/23851

The State of Evidence in Patient Portals: Umbrella Review

2020· review· en· W3091153132 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of Medical Internet Research · 2020
Typereview
Languageen
FieldHealth Professions
TopicElectronic Health Records Systems
Canadian institutionsUniversity of AlbertaUniversity of Victoria
FundersFaculty of Nursing, University of AlbertaUniversity of Alberta
KeywordsPatient portalGrading (engineering)Systematic reviewKnowledge translationQualitative researchMedicineEvidence-based practiceMEDLINEHealth carePsychologyKnowledge managementAlternative medicineComputer sciencePathology

Abstract

fetched live from OpenAlex

BACKGROUND: Patient portals have emerged as a recognized digital health strategy. To date, research on patient portals has grown rapidly. However, there has been limited evaluation of the growing body of evidence on portal availability, use, clinical or health behavior and outcomes, and portal adoption over time. OBJECTIVE: This paper aims to comprehensively consolidate the current state of evidence on patient portals using the umbrella review methodology, introduce our approach for evaluating evidence for quantitative and qualitative findings presented in included systematic reviews, and present a knowledge translation tool that can be used to inform all stages of patient portal adoption. METHODS: For this study, a modified version of the Joanna Briggs Institute umbrella review method was used. Multiple databases were searched for systematic reviews focused on patient portals, and the final sample included 14 reviews. We conducted a meta-level synthesis of findings from quantitative, qualitative, and mixed methods primary studies reported in systematic reviews. We organized the umbrella review findings according to the Clinical Adoption Meta-Model (CAMM). Vote-counting, GRADE (Grading of Recommendations, Assessment, Development, and Evaluations), and CERQual (Confidence in the Evidence from Review of Qualitative Research) were used to assess the umbrella review evidence. RESULTS: Our knowledge translation tool summarizes the findings in the form of an evidence map. Organized by the CAMM categories, the map describes the following factors that influence portal adoption and effects over time: patient contexts, patient's interest and satisfaction, portal design, facilitators and barriers, providers' attitudes, service utilization, behavioral effects, clinical outcomes, and patient-reported outcomes. The map lists the theories and mechanisms recognized in the included portal research while identifying the need for business models and organizational theories that can inform all stages of portal adoption. Our GRADE and CERQual umbrella review evaluation resulted in the majority of evidence being rated as moderate to low, which reflects methodological issues in portal research, insufficient number of studies, or mixed results in specific focus areas. The 2 findings with a high rating of evidence were patients' interest in using portals for communication and the importance of a simple display of information in the portals. Over 40 portal features were identified in the umbrella review, with communication through secure messaging and appointment booking mentioned in all systematic reviews. CONCLUSIONS: Our umbrella review provides a meta-level synthesis to make sense of the evidence on patient portals from published systematic reviews. Unsystematic and variable reporting of portal features undermines the ability to evaluate and compare portal effects and overlooks the specific context of portal use. Research designs sensitive to the social, organizational, policy, and temporal dimensions are needed to better understand the underlying mechanisms and context that leverage the identified factors to improve portal use and effects.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.081
metaresearch head score (Gemma)0.055
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.695
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0810.055
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0030.001
Research integrity0.0010.017
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.658
GPT teacher head0.681
Teacher spread0.023 · 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