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 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 | 0.052 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.014 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".