“If you can't measure it, you can't manage it” – essential truth, or costly myth?
Bibliographic record
Abstract
If you can't measure it, you can't manage it" -essential truth, or costly myth?In this issue of the journal, Kilbourne et al 1 make a reasoned and cogent argument for a more structured approach to the delivery of mental health care, with the aim of driving up the quality of care and its outcomes.They see mental health services as innovators in models of care delivery (i.e., communitybased, multidisciplinary, and person-centred) but as laggards in learning from and adopting advances in monitoring and improving the quality of continuing care derived from other chronic disease disciplines.The paper advocates enhancements oriented to the structure proposed in Donabedian's framework, namely the organization of care, the clinical care processes, and the health care outcomes achieved.The authors are right to highlight the importance of measurement, on the principle that "if you can't measure it, you can't manage it".Health management information systems are a core "building block" for well-functioning health systems.The purpose of those information systems is to routinely generate quality health information, and they are used directly for management decisions to improve health care delivery -supporting resource management, service monitoring, supervision and quality improvement.On the other hand, W. Edwards Deming 2 warned that the above principle could be "a costly myth".He could be right, and for several reasons.First, intuitively obvious qualitative enhancements can be made without data either to diagnose the problem or confirm the benefits.Data can provide a mechanism to support incremental improvement, but the fundamental transformation towards a "learning health system" is cultural.Well-functioning health management information systems are just one essential component of an interactive suite of health system strengthening measures that may prove necessary developing: a peer-driven quality improvement culture; non-technical workforce skills (leadership, teamwork and communication); and person-centred care practices guided by values and preferences, with agreed care plans, and patients empowered for self-management.Beyond evidence-based guidelines, consideration may also be given to the introduction of care pathways 3 .Every patient goes through a care process, and this varies among patients with particular conditions.Care pathways are about planning and managing those processes, in advance, for defined groups of individuals.Critically, this establishes explicit standards for care processes and outcomes, against which performance can then be judged.Not all health care activities lend themselves to this approach, since not all care is provided for a "welldefined patient group" and a "well-defined period of time".For continuing care of mental health conditions, pathways may need to be drawn up and delivered flexibly, contingent upon differing needs, clinical trajectories and treatment responses.A "stepped care" approach is often used, whereby a patient first receives the most effective, least invasive, least
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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 | 0.046 | 0.115 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.029 |
| Scholarly communication | 0.019 | 0.029 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.023 | 0.057 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".