Developing the Patient Falls Risk Report: A mixed-methods study on sharing falls-related clinical information from home care with primary care providers
Bibliographic record
Abstract
Background: Only 24% of Canadian primary care providers communicate with home care providers about the needs and services of patients. This service gap puts vulnerable people at risk of adverse events. One tool that may enhance communication between home care and primary care is the interRAI home care (interRAI-HC), a mandated comprehensive geriatric assessment in home care. The focus of this study was on development of a one-page document for sharing falls-related clinical information from the interRAI-HC with primary care providers (i.e., the Patient Falls Risk Report). Target audience: Primary care providers who feel siloed from the rest of the health care system. About the intervention: The Patient Falls Risk Report is a structured, one-page, faxable form that contains falls-related patient information derived from interRAI-HC assessments. The report is evidence-based and includes personalized information on future falls risk, balance, cognition, pain, foot problems, medications, and physical activity levels, as well as recommendations for falls prevention in older persons. Stakeholder engagement and other research methods: This mixed-methods intervention development study began with one-on-one stakeholder engagement via semi-structured interviews with primary care providers. We first explored their views on falls-related information sharing. Then, we tested a prototype of the Patient Falls Risk Report for usability and utility, thematically analyzing the findings to iteratively to develop the tool. The report was then evaluated again with voluntary self-report surveys based on the System Usability Scale. Results: A sample of 9 interview participants co-developed the Patient Falls Risk Report to improve its clarity and level of detail. All participants stated that they would use the report in their practices and most believed that it could support care provision due to its inclusion of relevant, actionable information. In the end, a survey sample of 27 participants determined that the report was highly usable, with an overall usability score of 83.4 (95% CI = 78.7, 88.2). However, the need for improved shared care planning and community responsibility was also emphasized. Impact: This study demonstrates that information collected from existing clinical assessments can be shared with primary care providers in a useful manner. It also highlights criteria to inform the design of future information-sharing interventions, especially those harnessing interRAI assessments. Key Learning: Primary care providers need tailored and consistent streams of communication with other health care providers so that collaboration and integration can become a reality. Next Steps: We are at a turning point in health care, where the functionality of health information systems is improving, and fax is becoming increasingly obsolete. Based on the recently published results of this study [1], we are currently collaborating with government and eHealth organizations on an implementation plan for interRAI information sharing with patients and caregivers in Ontario, Canada. 1. Nova AA, Heckman G, Giangregorio LM, Alarakhia M. Developing the Patient Falls Risk Report: A Mixed-Methods Study on Sharing Falls-Related Clinical Information from Home Care with Primary Care Providers. Canadian Journal on Aging / La Revue canadienne du vieillissement. 2022;1–14.
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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 | 0.062 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".