Answering questions posed by health system stakeholders using linked administrative health data at the Institute for Clinical Evaluative Sciences (ICES)
Bibliographic record
Abstract
ABSTRACTObjectivesThere is a growing need to broaden access to administrative health data in order to support decision making and planning by health system stakeholders. An initiative funded by the Ontario Ministry of Health and Long-Term Care, the Applied Health Research Question (AHRQ) portfolio leverages the linked administrative health data holdings and the scientific and clinical expertise at ICES to answer questions generated by stakeholders that will have a direct impact on health care policy, planning or practice. ApproachEligible requesters include government ministries, health care providers and planners. Requests detail the purpose of the research question, the related scientific literature, and the planned use and intended impact of the research findings. An internal review team meets monthly to adjudicate; requests demonstrably needing research findings rapidly are adjudicated on an ad hoc basis. Eligible requests are those that aim to inform evidence-based decision making, do not advocate for a particular answer and are feasible in terms of data availability. All projects are reviewed by the internal privacy office to ensure that use of the administrative health data is in accordance with both data sharing agreements and legislation governing use of personal health information. At no cost to the requesting organization, ICES scientists and research staff formulate the analysis plan, conduct the analysis and prepare the research product (data tables, a slide deck and/or a written report); and, may opt to publish noteworthy findings. All research products must be cleared for risk of re-identification prior to being shared externally. ResultsRequests have steadily increased from 43 submissions in fiscal year 2012/13, to 59 in 2014/15 and 74 to date in 2015/16. In fiscal year 2014/15, provincial government and government agencies were the most frequent requesters (39%), followed by hospitals and other health care providers (19%), disease advocacy groups (12%) and professional associations (10%). Requests include assessment of health care utilization; health system performance and evaluation; and chronic disease prevalence and treatment. Time to complete reports varies from 5 days to 24 months, depending on project complexity and requirements. Requesters report that AHRQ research findings have influenced decision-making, policy development and health care practice; and have inspired future research. ConclusionThis initiative demonstrates the value and feasibility of using the linked administrative health data to answer questions to meet the unique needs of health planners and policymakers, and presents an opportunity for collaboration beyond the academic research community.
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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 | 0.470 | 0.570 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.021 | 0.008 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.039 | 0.011 |
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".