New horizons in systems engineering and thinking to improve health and social care for older people
Bibliographic record
Abstract
Existing models for the safe, timely and effective delivery of health and social care are challenged by an ageing population. Services and care pathways are often optimised for single-disease management, while many older people are presenting with multiple long-term conditions and frailty. Systems engineering describes a holistic, interdisciplinary approach to change that is focused on people, system understanding, design and risk management. These principles are the basis of many established quality improvement (QI) tools in health and social care, but implementation has often been limited to single services or condition areas. Newer engineering techniques may help reshape more complex systems. Systems thinking is an essential component of this mindset to understand the underlying relationships and characteristics of a working system. It promotes the use of tools that map, measure and interrogate the dynamics of complex systems. In this New Horizons piece, we describe the evolution of systems approaches while noting the challenges of small-scale QI efforts that fail to address whole-system problems. The opportunities for novel soft-systems approaches are described, along with a recent update to the Systems Engineering Initiative for Patient Safety model, which includes human-centred design. Systems modelling and simulation techniques harness routine data to understand the functioning of complex health and social care systems. These tools could support better-informed system change by allowing comparison of simulated approaches before implementation, but better effectiveness evidence is required. Modern systems engineering and systems thinking techniques have potential to inform the redesign of services appropriate for the complex needs of older people.
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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 | 0.016 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.007 | 0.014 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.012 |
| 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".