Using Human-centred Design to Explore a Day in the Life of a Population Health Management Analyst
Bibliographic record
Abstract
Introduction: Population Health is one of the International Foundation for Integrated Care’s 9 pillars of integrated care and Population Health Management (PHM) is gaining interest as a means of addressing some of the greatest challenges in population health and wellbeing around the world. In our workshop at ICIC 2023, an international audience validated 5 key structures required to operationalize Integrated Care and PHM: Population Registry; Provider Registry; Integrated Shared Care Record; Population Health Management Information System; and Collaborative Governance. Attendees from numerous international jurisdictions noted progress in each area, but widely varying strategies and level of maturity. Since, our team has been working with the Ontario Health Team Coalition for PHM as part of Ontario, Canada’s health system transformation. This group has explored the digital solutions and infrastructure required to support PHM in a Canadian Context using human-centred design principles. Erwin and Krishnan (2016) describe human-centred design (HCD) as “redesigning healthcare to fit with people”. It involves a deep understanding of people and their needs, engaging stakeholders throughout the design process, and using a systems approach to understand the intersection between the different parts of the system and the downstream impacts to service or product design decisions. Objective: Participants will be introduced to the concept of human-centred design and application of the approach in Ontario to explore a Population Health Management Analyst role. Participants will be invited to share/consider whether this role exists within their system and how HCD could enhance design of systems to support these people. We hope to foster discussion, share learnings, and develop relationships between participants. Workshop details (60-minutes) 5 minutes – introduction and context 10 minutes – PHM and human-centred design 10 minutes – a “day-in-the-life” of a Population Health Management Analyst 25 minutes – small group discussion - Does this role exist within your healthcare system? - What information do/should they have access to? - How is/can this information be integrated? - How is/can this information used to carry out PHM? - What are some of the key considerations in establishing this role and supporting infrastructure? 10 minutes – report back/summary Summary: Our 2023 workshop was well-received largely because it afforded attendees the opportunity to discuss their local context and hear from others about their experiences. ICIC attendees generally agree on the principles of Integrated Care and PHM. However, implementation remains challenging largely due to the diversity of regional context between healthcare systems. While participants in our 2023 workshop agreed that a Population Health Management Information System is required, none identified complete satisfaction with their current state. Group discussion revealed insights into how differing health system context was contributing to succeeding/struggling to build and/or harness the benefits of a Population Health Management Information System. Human-centred design allows participants to remove themselves from these contexts and explore the core functions of a role like a PHM Analyst. From there, participants will be supported to unpack how this is (or could be) established within their local system; while learning from the experiences of others.
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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 | 0.054 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.036 |
| Scholarly communication | 0.019 | 0.010 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".