An innovative multimorbidity patient-centered care model in Chile: implementation evaluation results
Bibliographic record
Abstract
BACKGROUND: The impact of non-communicable diseases and multimorbidity challenges health systems worldwide. Latin America faces an urgent need to develop practical innovations in that regard. The Centro de Innovación en Salud ANCORA UC implemented a new Multimorbidity Patient-Centered Care Model (MPCM) pilot in Chile between 2017 and 2020. MPCM aimed to reorganize health services from a fragmented diagnosis-based perspective towards a new approach based on patient's needs and offer intervention strategies according to their multimorbidity risk. This article aims to report the evaluation of the implementation process of MPCM in the Southeast Metropolitan Health District in Chile. METHODS: The study design corresponds to an implementation collaborative evaluation of MPCM innovation using qualitative methodology. Two main questions guided the research: (1) How has MPCM been implemented in its pilot phase? Moreover, (2) What are the main learnings from the MPCM pilot phase and their contribution to its scalability at the national level? In addition, the Consolidated Framework for Implementation Research and the Outcomes for Implementation Research were considered in the theoretical approach. RESULTS: Thirty-five (35) interviews were conducted with 69 professionals and key stakeholders involved in the implementation process of MPCM, including health practitioners, transition nurses who coordinate the intervention with the affiliated hospitals, managers, and the implementation team. Overall, the results were positive, suggesting that a complex innovation of this kind may be implemented successfully. Key lessons learned should be considered for scaling up MPCM to the national level. Some critical barriers to implementation were high staff turnover and the COVID-19 pandemic, while leadership and team commitment were relevant facilitators. CONCLUSIONS: This study represents a new step in evaluating an innovative model for addressing multimorbidity in Chile. The scaling up phase requires careful consideration of all lessons learned, as well as a robust evaluation and monitoring plan. This research represents the first evaluative analysis of MPCM in the context of a complex innovation adapted to enhance public health policies using implementation evaluation approaches. Implementation Science is a fundamental approach to fostering quality improvement strategies for health care in Latin America.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".