Exploring key components and factors that influence the use of clinical decision- support tools for prescribing to older patients with kidney disease: the perspective of healthcare providers
Bibliographic record
Abstract
BACKGROUND: Clinical decision-support (CDS) tools are systems that provide healthcare providers (HCPs) with recommendations based on knowledge and patient-specific factors to facilitate informed decisions. OBJECTIVES: To identify the key components of a CDS tool that are most important to HCPs in caring for older adults with kidney disease, and to understand the facilitators and barriers toward using CDS tools in daily clinical practice. METHODS: Design: A cross-sectional survey of Canadian HCPs was undertaken. DATA COLLECTION: Participants affiliated with a provincial college, nephrology organization, or advocacy body were contacted. The survey was conducted between August and October 2021. INSTRUMENT: A 59-item questionnaire was developed and divided into five main domains/themes. Analysis was done descriptively. RESULTS: Sixty-three participants completed the questionnaire. Physicians (60%) and pharmacists (22%) comprised the majority of the participants. Most of the participants were specialized in nephrology (65%). The most important components in a CDS tool for prescribing to older patients with kidney disease were the safety and efficacy of the medication (89%), the goal of therapy (89%), and patient's quality of life (87%). 90% were willing to use CDS tools and 57% were already using some CDS tools for prescribing. The majority of the participants selected the validation of CDS tools (95%), accompanying the recommendations by the supporting evidence (84%), and the affiliation of the tools with known organizations (84%), as factors that facilitate the use of CDS tools. CONCLUSION: CDS tools are being used and are accepted by HCPs and have value in their assistance in engaging patients in making well-informed decisions.
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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 | 0.009 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".