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Development of a person-centered patient portal in oncology using stakeholder co-design.

2018· article· en· W2892955820 on OpenAlexaff
J. Kildea, Tarek Hijal, Laurie Hendren

Bibliographic record

VenueJournal of Clinical Oncology · 2018
Typearticle
Languageen
FieldMedicine
TopicTelemedicine and Telehealth Implementation
Canadian institutionsMcGill UniversityMcGill University Health Centre
Fundersnot available
KeywordsPatient portalMedicineStakeholderHealth careWorkloadMedical emergencyNursingComputer sciencePublic relations

Abstract

fetched live from OpenAlex

221 Background: Patient portals are software products that provide care recipients (patients) with access to some or all of their personal health information (PHI) within a healthcare institution’s electronic medical record (EMR). Most patient portals are just that–windows into an EMR. However, in today’s connected world, a patient portal can offer much more than just access to PHI. By exploiting modern technology and by recognizing that care recipients are people with complex needs that extend beyond just the delivery of care, patient portals can be person-centered. Such person-centered needs include: the ability to plan ahead and know one’s position in a waiting list, to feel in control of one’s own care, to understand one’s treatment options, and to share in all decision-making about one’s care. Methods: We used stakeholder co-design, involving care recipients, care providers (clinicians) and technical experts at all levels, to design and develop a person-centered patient portal from within the healthcare system. Results: Our mobile-friendly patient portal, known as Opal (opalmedapps.com), contains the following: One-stop multi-speciality and multi-institutional operation; Appointment schedule with personalized appointment preparation guidelines; Appointment check-in and waiting room management tools; Monitoring of step-by-step radiotherapy treatment planning; Access to PHI contextualized with explanatory information; Access to personalized educational materials; Patient-reported outcomes questionnaires with response visualizations; An automated rules-based system to minimize clinician workload; Triggered notifications to alert patients and clinicians to actionable events. Conclusions: Opal is currently in beta release involving a small number of oncology patients at our centre who are providing feedback about its use and usefulness. Their initial feedback is very positive and enthusiastic. A full pilot study is starting by July 2018.

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)0.026
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.033
Threshold uncertainty score0.174

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0330.026
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0030.002
Open science0.0020.006
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0070.001

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 head0.616
GPT teacher head0.559
Teacher spread0.056 · 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 designQualitative
Domainnot available
GenreEmpirical

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
Published2018
Admission routes1
Has abstractyes

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