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Record W4387790611 · doi:10.2196/preprints.43765

Patient and Health Care Provider Perspectives on Patient Access to Test Results via Web Portals: Scoping Review (Preprint)

2022· preprint· en· W4387790611 on OpenAlexaff
Olga Petrovskaya, Albina Karpman, Joanna Schilling, Simran Singh, Larissa Wegren, Vera Caine, Elizabeth Kusi-Appiah, Willow Geen

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldHealth Professions
TopicElectronic Health Records Systems
Canadian institutionsSouth Health CampusMacEwan UniversityUniversity of AlbertaUniversity of Victoria
Fundersnot available
KeywordsCINAHLPatient portalInclusion (mineral)Test (biology)MEDLINEHealth carePreprintData extractionMedicineWorld Wide WebPsychologyMedical educationPsychological interventionNursingComputer scienceSocial psychologyPolitical science

Abstract

fetched live from OpenAlex

BACKGROUND A frequently used feature of electronic patient portals is the viewing of test results. Research on patient portals is abundant and offers evidence to help portal implementers make policy and practice decisions. In contrast, no comparable comprehensive summary of research addresses the direct release of and patient access to test results. OBJECTIVE This scoping review aims to analyze and synthesize published research focused on patient and health care provider perspectives on the direct release of laboratory, imaging, and radiology results to patients via web portals. METHODS PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines were followed. Searches were conducted in CINAHL, MEDLINE, and other databases. Citations were screened in Covidence using the inclusion and exclusion criteria. Primary studies that focused on patient and health care provider perspectives on patient access to laboratory and imaging results via web portals were included. An updated search was conducted up to August 2023. Our review included 27 articles—20 examining patient views, 3 examining provider views, and 4 examining both patient and provider views. Data extraction and inductive data analysis were informed by sensitizing concepts from sociomaterial perspectives, and 15 themes were generated. RESULTS Patient perspectives (24 papers) were synthesized using nine themes: (1) patterns of use and patient characteristics; (2) emotional response when viewing the results and uncertainty about their implications; (3) understanding test results; (4) preferences for mode and timing of result release; (5). information seeking and patients’ actions motivated by viewing results via a portal; (6) contemplating changes in behavior and managing own health; (7) benefits of accessing test results via a portal; (8) limitations of accessing test results via a portal; and (9) suggestions for portal improvement. Health care provider perspectives (7 papers) were synthetized into six themes: (1) providers’ view of benefits of patient access to results via the portal; (2) effects on health care provider workload; (3) concerns about patient anxiety; (4) timing of result release into the patient portal; (5) the method of result release into the patient portal: manual versus automatic release; and (6) the effects of hospital health information technology system on patient quality outcomes. CONCLUSIONS The timing of the release of test results emerged as a particularly important topic. In some countries, the policy context may motivate immediate release of most tests directly into patient portals. However, our findings aim to make policy makers, health administrators, and other stakeholders aware of factors to consider when making decisions about the timing of result release. This review is sensitive to the characteristics of patient populations and portal technology and can inform result release framework policies. The findings are timely, as patient portals have become more common internationally.

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)0.033
metaresearch head score (Gemma)0.147
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.033
Threshold uncertainty score0.173

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0330.147
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.005
Bibliometrics0.0180.024
Science and technology studies0.0010.001
Scholarly communication0.0060.006
Open science0.0020.003
Research integrity0.0030.001
Insufficient payload (model declined to judge)0.0120.001

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 head0.106
GPT teacher head0.496
Teacher spread0.390 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations0
Published2022
Admission routes1
Has abstractyes

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