The Development Model for Integrated Care: a validated tool for evaluation and development
Bibliographic record
Abstract
Purpose – Integrating health, social and informal care and seeking for new effective collaborations is a major topic in many countries, and requires innovation and improvement in current practices. Conceptual quality management models can facilitate practice improvement. However, a generic quality management model for integrated care was lacking. The purpose of this paper is to describe the results of multiple studies that resulted in a validated generic quality management model for integrated care. The Development Model for Integrated Care (DMIC) is the basis for a digital tool for self-evaluation and is being used in multiple ways in a large number of integrated care settings. Design/methodology/approach – A literature review, a Delphi study and concept mapping study were executed to identify the essential ingredients of integrated care. A next step was an expert study on the development process of integrated care over time. Lastly, a survey study in 84 integrated care networks was performed to empirically validate the model. Based on the model, a digital self-assessment tool was created to apply the model in practice. Findings – The studies showed that integrated care is a complex and multi-component concept but generic elements can be assessed. The literature and expert study resulted in a set of 89 elements of integrated care. The elements were grouped in nine clusters; “quality care”, “performance management”, “inter-professional teamwork”, “delivery system”, “roles and tasks”, “patient-centredness”, “commitment”, “transparent entrepreneurship” and “result-focused learning”. Four developmental phases named “the initiative and design phase”, “the experimental and execution phase”, “the expansion and monitoring phase” and “the consolidation and transformation phase” were found. The findings showed that the model is applicable for multiple integrated care settings. Research limitations/implications – The DMIC has the potential to serve as a research framework for integrated care, and the use as an evaluation tool on multiple levels. Further research is suggested about more explicitly involving the perspectives of clients, research on the involvement of multiple stakeholders and their professional backgrounds and the use of the model in other countries. Practical implications – The DMIC is the basis of a digital web-based assessment tool, which is being used in the Netherlands in multiple integrated care settings. Applying the tool helps in assessing the current state of integrated care practice and defining suggestions for further improvement and development. It is also being used to benchmark multiple settings and is adopted in guidelines or care standards for integrated care. Originality/value – A generic conceptual and validated model that can be supportive for integrated care practices, policy and research was lacking. The results of the summarized studies in this paper present such a conceptual model for integrated care and gives suggestions for further use in an international audience. Results in a Canadian study showed that the model can also be used in other settings and countries. This contributes to the opportunities for use of the model in integrated care practice, policy and research also in other countries.
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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 | 0.095 | 0.153 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".