Time for a Paradigm Shift: Managing Smarter by Moving from Data and Information to Knowledge and Wisdom in Healthcare Decision-Making
Bibliographic record
Abstract
Senior decision-makers in the Canadian healthcare system have to continuously make significant, and complex, policy and program decisions.However, it appears that, often, the evidence they have available is fairly simple descriptive information, collected for operational purposes.Trying to solve complex problems with fairly simple data may lead to suboptimal decisions.This article presents a new knowledge development system (KDS) that should allow senior decision-makers and others to manage smarter and take their decision-making to the next level.A KDS represents the integration of information systems, and research and analysis, into one system.It can generate sophisticated, strategic information around complex issues, which should ultimately lead to wiser decisions.This article describes the KDS, provides an example of a current KDS and concludes by presenting a self-diagnostic tool for decision-makers to allow them to determine whether their organization could benefit from a KDS.H ealthcare organizations such as ministries of health, regional health authorities and other organizations collect large amounts of data.However, they appear to struggle with translating these data into strategic knowledge and insights that can be used as inputs into evidence-based decision-making at the clinical, operational, administrative, policy and executive levels.Several reasons seem to account for this difficulty.Information systems are often developed to meet the operational needs of different organizational components.For example, separate systems are developed for finance, human resources and care delivery.Some types of data that exist outside the organization and are critical for certain types of analyses -such as data on the population served (for population health and epidemiological analyses)may not be readily available or, if available, may not be systematically integrated into the data architecture of the organization.In addition, over the past several years, the focus seems to have been on developing information systems and electronic health records rather than on analyzing data to take maximum advantage of the data that are already available.Thus, organizations may have suboptimal knowledge development, not because of a lack of data but because the data that exist are not fully used to generate new knowledge.Finally, because data may not be used to meet the real needs of organizational actors (e.g., front-line care delivery staff, policy developers, planners etc.), people may not recognize the potential of existing information systems to provide insights into key issues.Thus, a separation often exists between collecting data and using the data to develop new knowledge in healthcare organizations.
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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 | 0.048 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.011 | 0.044 |
| Scholarly communication | 0.028 | 0.042 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".