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Record W2470735918 · doi:10.2196/resprot.5756

Supporting Goal-Oriented Primary Health Care for Seniors with Complex Care Needs Using Mobile Technology: Evaluation and Implementation of the Health System Performance Research Network, Bridgepoint Electronic Patient Reported Outcome Tool

2016· article· en· W2470735918 on OpenAlexafffundvenueabout
Carolyn Steele Gray, Walter P. Wodchis, Ross Upshur, Cheryl Cott, Brian McKinstry, Stewart W Mercer, Ted E. Palen, Kednapa Thavorn

Bibliographic record

VenueJMIR Research Protocols · 2016
Typearticle
Languageen
FieldMedicine
TopicChronic Disease Management Strategies
Canadian institutionsOttawa HospitalUniversity of OttawaInstitute for Clinical Evaluative SciencesToronto Rehabilitation InstituteSinai Health SystemLunenfeld-Tanenbaum Research InstitutePublic Health OntarioUniversity of Toronto
FundersCanadian Institutes of Health ResearchUniversity of TorontoOntario Ministry of Health and Long-Term Care
KeywordseHealthHealth careMedicinemHealthNursingQuality of life (healthcare)Intervention (counseling)Psychological intervention

Abstract

fetched live from OpenAlex

BACKGROUND: Older adults experiencing multiple chronic illnesses are at high risk of hospitalization and health decline if they are unable to manage the significant challenges posed by their health conditions. Goal-oriented care approaches can provide better care for these complex patients, but clinicians find the process of ascertaining goals "too complex and too-time consuming," and goals are often not agreed upon between complex patients and their providers. The electronic patient reported outcomes (ePRO) mobile app and portal offers an innovative approach to creating and monitoring goal-oriented patient-care plans to improve patient self-management and shared decision-making between patients and health care providers. The ePRO tool also supports proactive patient monitoring by the patient, caregiver(s), and health care provider. It was developed with and for older adults with complex care needs as a means to improve their quality of life. OBJECTIVE: Our proposed project will evaluate the use, effectiveness, and value for money of the ePRO tool in a 12-month multicenter, randomized controlled trial in Ontario; targeting individuals 65 or over with two or more chronic conditions that require frequent health care visits to manage their health conditions. METHODS: Intervention groups using the ePRO tool will be compared with control groups on measures of quality of life, patient experience, and cost-effectiveness. We will also evaluate the implementation of the tool. RESULTS: The proposed project presented in this paper will be funded through the Canadian Institute for Health Research (CIHR) eHealth Innovation Partnerships Program (eHIPP) program (CIHR-348362). The expected completion date of the study is November, 2019. CONCLUSIONS: We anticipate our program of work will support improved quality of life and patient self-management, improved patient-centered primary care delivery, and will encourage the adoption of goal-oriented care approaches across primary health care systems. We have partnered with family health teams and quality improvement organizations in Ontario to ensure that our research is practical and that findings are shared widely. We will work with our established international network to develop an implementation framework to support continued adaptation and adoption across Canada and 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)0.030
metaresearch head score (Gemma)0.039
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Protocol · Consensus signal: none
Teacher disagreement score0.030
Threshold uncertainty score0.161

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0300.039
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.003
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.151
GPT teacher head0.549
Teacher spread0.398 · 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 designNot applicable
Domainnot available
GenreProtocol

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

Citations43
Published2016
Admission routes4
Has abstractyes

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