Improving wait times to care for individuals with multimorbidities and complex conditions using value stream mapping
Bibliographic record
Abstract
BACKGROUND: Recognizing the significant impact of wait times for care for individuals with complex chronic conditions, we applied a LEAN methodology, namely - an adaptation of Value Stream Mapping (VSM) to meet the needs of people with multiple chronic conditions and to improve wait times without additional resources or funding. METHODS: Over an 18-month time period, staff applied a patient-centric approach that included LEAN methodology of VSM to improve wait times to care. Our framework of evaluation was grounded in the needs and perspectives of patients and individuals waiting to receive care. Patient centric views were obtained through surveys such as Patient Assessment of Chronic Illness Care (PACIC) and process engineering based questions. In addition, LEAN methodology, VSM was added to identify non-value added processes contributing to wait times. RESULTS: The care team successfully reduced wait times to 2 months in 2014 with no wait times for care anticipated in 2015. Increased patient engagement and satisfaction are also outcomes of this innovative initiative. In addition, successful transformations and implementation have resulted in resource efficiencies without increase in costs. Patients have shown significant improvements in functional health following Integrated Chronic Care Service (ICCS) intervention. The methodology will be applied to other chronic disease management areas in Capital Health and the province. CONCLUSION: Wait times to care in the management of multimoribidities and other complex conditions can add a significant burden not only on the affected individuals but also on the healthcare system. In this study, a novel and modified LEAN methodology has been applied to embed the voice of the patient in care delivery processes and to reduce wait times to care in the management of complex chronic conditions.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".