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Record W4413358173 · doi:10.5334/ijic.nacic24147

Supporting a population health approach in primary care: can electronic health records act as patient registries to support integrated mental health care?

2025· article· en· W4413358173 on OpenAlexaboutno aff
Sarah Jarmain, Matthew Meyer, Eric Wong

Bibliographic record

VenueInternational Journal of Integrated Care · 2025
Typearticle
Languageen
FieldMedicine
TopicChronic Disease Management Strategies
Canadian institutionsnot available
Fundersnot available
KeywordsMental healthHealth recordsIntegrated carePrimary careElectronic health recordNursingHealth carePopulation healthMedicinePopulationPrimary health careClinical decision support systemFamily medicineMedical emergencyPublic healthPsychiatryEnvironmental healthPolitical science

Abstract

fetched live from OpenAlex

Background: The Thames Valley Mental Health and Addiction Collaborative Care Network is a proof-of-concept initiative between psychiatry, primary care, and community mental health to create an integrated care network. Grounded in practice-based population health management, we have been working to leverage primary care electronic health records (EHRs) as point of care registries. Audience: Health system leaders, designers, planners and clinicians, especially in the areas of primary care, mental health and digital health. Approach: The Thames Valley Mental Health and Addiction Collaborative Care Network is a proof-of-concept initiative between psychiatry, primary care, and community mental health and addictions in the London, Ontario region to create an integrated care delivery network, within existing resources. Our unique model of collaborative mental health care incorporates the key collaborative care elements (based on the University of Washington AIMS Centre), with a strong population health focus, delivered within a team-based primary care setting. Practice-based population health management (PB-PHM) has been identified as an important component for the management of chronic disease within primary care. A key enabler of PB-PHM is a patient registry which can support the identification of patients within a target population, provide prompts and reminders about preventative health care, and inform quality improvement practices. However, there is limited information available about how to implement such registries and current primary care EHRs have limited functionality in this regard. This workshop will introduce the audience to population health management, describe the elements of a point-of-care registry and describe our journey of co-designing a point-of-care registry using primary care EHRs. This interactive workshop will introduce participants to the concepts of population health management and the role of patient registries in supporting an equity driven quadruple aim. Through the use of design thinking tools, participants will explore the jobs to be done in practice-based population health management and discuss the ways in which existing electronic health records can support patient segmentation, preventative care, chronic disease management and patient engagement in a team-based primary care setting. The presenters will review limitations of the currently available tools, the primary care practice environment and opportunities for future development. Outline for 60 minute workshop- 5 minutes Introduction- 5 minutes describing the collaborative care model, principles of population health management, and elements of a patient registry- 30 minutes - through the use of design thinking tools, participants will explore the jobs to be opportunities and challenges implementing PB-PHM and discuss the ways in which existing electronic health records can be developed as registries- 0 minutes - summary of lessons learned and take-awaysOutcomes: Following this presentation, participants will be able to: Understand the components of population health management in the context of integrated health systems Describe how practice-based population health management and patient registries can enhance chronic disease management within a primary care setting using the example of collaborative mental health care Identify the ways that existing primary care electronic health records (EHRs) can support practice-based population health management Describe the challenges and opportunities with current primary care EHRs and practice context in designing patient care digital registries

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 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.699
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.012
GPT teacher head0.346
Teacher spread0.335 · 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 teacher head, not a consensus.

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

Citations1
Published2025
Admission routes1
Has abstractyes

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