Evaluating the ‘Health Links’: A Case Study of the Role of Organizational Factors in Integrating Care in Ontario, Canada
Bibliographic record
Abstract
Introduction: Adults with multiple chronic illnesses account for more than 75% of health care spending. Many are considered complex due to multimorbidity, high service use, and psychosocial vulnerability.Practice Change Implemented: The Health Links (HLs) are networks of multiple health and social service organizations that voluntarily partner to deliver integrated care to complex, high-cost patients in Ontario, Canada.Aim and Theory of Change: We explored how organizational and network factors (e.g., resources, culture) influenced the implementation of the HLs. We conducted case studies of three HLs within one regional health authority. Data was analyzed using The Context and Capabilities for Integrating Care (CCIC) Framework, which stipulates that organizational and network factors (within the Basic Structures, People & Values, and Key Processes domains) influence an organization/network’s readiness and capacity to integrate.Targeted Population and Stakeholders: The HLs initiative is targeted at patients with four or more chronic or “high-cost” conditions, including a focus on individuals living with mental health and addictions, palliative care patients, and the frail elderly. The organizations involved vary by HL and often include hospitals, primary care practices, community support agencies, social services organizations, and emergency response.Timeline: The HLs were implemented in 2012 with 19 early adopters; there are now 82 HLs in various stages of implementation in the province. In the spring/summer of 2016, we conducted semi-structured interviews with leaders and providers working within three HLs. Interviews were supplemented with surveys and document review.Highlights: (Innovation, Impact and Outcomes) Preliminary results show that successful implementation was linked to the key organizational facilitators of leadership, patient-centredness, and team-based delivery of care. Leaders that prioritized the initiative were able to facilitate inter-organizational collaboration. Similarly, partnerships were facilitated by an explicit focus on patient-centredness and patient outcomes, rather than on formal governance and accountability structures.Comments on Sustainability: Partnering organizations will have to address several barriers going forward, including: poor awareness of HLs in the community, inefficient identification of patients, dwindling clinician engagement due to low perceived value of the initiative over and above regular care, and limitations to patient data sharing within the network.Comments on Transferability: Using the CCIC Framework, we identified organizational and network factors that supported integration of care in HLs networks. The findings are limited to three HLs networks in Ontario, however, the framework can be used across cases to support the measure of factors and the transfer of best practices to other integrated care initiatives.Conclusions: (Comprising Key Findings) Preliminary results suggest that there are common factors that most influence the implementation of integrated care initiatives, including leadership, clinician engagement, patient-centeredness, and delivery of care.Discussions: The CCIC Framework enabled a comprehensive analysis of organizational and network context. These results can be used to help prioritize key areas for discussion, measurement, and change management.Lessons Learned: Despite continued interest in partnering, we found a loss of clinician engagement and buy-in over time in HLs that did not meaningfully involve clinicians and did not demonstrate value to the patient.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.032 | 0.008 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".