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Record W4409337707 · doi:10.5334/ijic.icic24281

Case management for individuals with complex care needs: Factors assisting and hindering implementation

2025· article· en· W4409337707 on OpenAlexaboutno aff
Charlotte Schwarz, Catherine Hudon, Maud‐Christine Chouinard, Mireille Lambert, Dana Howse, Mathieu Bisson, Alison Luke, Kris Aubrey‐Bassler, Joanna Zed, Vivian R. Ramsden, Marilyn Macdonald, Judy L. Porter, Jennifer Taylor, Donna Rubenstein, Linda Wilhelm, Mike Warren, Shelley Doucet

Bibliographic record

VenueInternational Journal of Integrated Care · 2025
Typearticle
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsnot available
Fundersnot available
KeywordsProcess managementIntegrated careCase managementNursingBusinessHealth careKnowledge managementMedicineComputer sciencePolitical science

Abstract

fetched live from OpenAlex

Introduction: Case management (CM) is an effective integrated model of care for patients with chronic conditions and complex care needs, given the focus on improving patient engagement in healthcare and improving self-management skills. While CM often leads to positive outcomes, little is known about the factors which promote or hinder implementation. Objective: This study seeks to examine factors which facilitated and hindered the implementation of a 12-month CM intervention in primary care clinics for individuals with chronic conditions and complex care needs, and report outcomes from the perspective of patients, nurse case managers, clinic managers, and providers. Intervention: A CM intervention was delivered by a nurse case manager (NCM), which included four activities: patient needs assessment, care planning, coordination of services, and self-management support. Design: We employed a qualitative descriptive design, using a participatory approach. The implementation was co-designed and co-led by researchers, clinicians, and patient partners, in collaboration with primary care clinics in five Canadian provinces. Participatory approach: This study involved various stakeholders, including patient partners, clinicians, researchers, and decision-makers. Patient partners were active members of the research team and played a major role in the development and governance of the larger study. Data collection: Semi-structured interviews or focus groups were conducted with patients (n= 44) and care providers (n=23), including case managers, clinic managers and primary care providers. Data analysis: Analysis of interview data was conducted using inductive thematic analysis to identify factors which facilitated or hindered implementation and outcomes. Results: Facilitators of the implementation included a holistic collaborative team-based clinic, an engaged and supportive clinic manager, the active involvement of care providers, dedicated and protected time for NCMs to complete CM tasks, and patient readiness. The implementation was hindered at clinics where staff were not engaged, leading to low recruitment numbers and difficulties carrying out the intervention. NCMs who did not have set time in their schedules or did not integrate the CM duties into their regular role struggled to carry out the program. Difficulty coordinating with specialists and external services also acted as a barrier. A final barrier to implementation was lack of access to appropriate services for patients with complex mental health needs. Outcomes included improved patient health and wellbeing, enhanced professional collaboration, expanded professional practice, more appropriate and efficient use of health services, and increased patient satisfaction. Learnings: CM is increasingly being used internationally as a way to better integrate people centered care for patients with complex care needs. The findings from this study provide insight into what worked well in implementing a 12-month CM intervention, as well as areas for improvement. Next steps: The findings from this study will be used to spread and scale CM in primary care within various provinces and First Nations, Métis, and Inuit communities in Canada over the next five years

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)0.021
metaresearch head score (Gemma)0.131
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: Empirical
Teacher disagreement score0.031
Threshold uncertainty score0.109

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0210.131
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.003
Scholarly communication0.0040.002
Open science0.0020.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.044
GPT teacher head0.459
Teacher spread0.416 · 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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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