Applying the lessons of design thinking: a unique programme of care for acutely unwell, community-dwelling COVID-19 patients
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic limited access to primary care and in-person assessments requiring healthcare providers to re-envision care delivery for acutely unwell outpatients. Design thinking methodology has the potential to support the robust evolution of a new clinical model. AIM: To demonstrate how design thinking methodology can rapidly and rigorously create and evolve a safe, timely, equitable and patient-centred programme of care, and to share valuable lessons for effective implementation of design thinking solutions to address complex problems. METHOD: We describe how design thinking methodology was employed to create a new clinical model of care. Using the example of a novel telemedicine programme to support acutely unwell, community-dwelling COVID-19-positive patients called the London Urgent COVID-19 Care Clinic (LUC3), we show how continuous quality outcomes (safety, timeliness, equity and patient-centredness), as well as patient experience survey responses, can drive iterative changes in programme delivery. RESULTS: The inspiration phase identified four key needs for this patient population: monitoring COVID-19 signs and symptoms; self-managing COVID-19 symptoms; managing other comorbidities in the setting of COVID-19; and escalating care as needed. Guided by these needs, a cross-disciplinary stakeholder group was engaged in the ideation and implementation phases to create a unique and comprehensive telemedicine programme (LUC3). During the implementation phase, LUC3 assessed 2202 community-based patients diagnosed with acute COVID-19; the collected quality outcomes and end-user feedback led to evolution of programme delivery. CONCLUSION: Design thinking methodology provided an essential framework and valuable lessons for the development of a safe, equitable, timely and patient-centred telemedicine care programme. The lessons learnt here-the importance of inclusive collaboration, using empathy to guide equity-focused interventions, leveraging continuous metrics to drive iteration and aiming for good-if-not-perfect plans-can serve as a road map for using design thinking for targeted healthcare problems.
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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 | 0.008 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".