Identifying Home Care Clinicians’ Information Needs for Managing Fall Risks
Bibliographic record
Abstract
OBJECTIVES: To help manage the risk of falls in home care, this study aimed to (i) identify home care clinicians' information needs and how they manage missing or inaccurate data, (ii) identify problems that impact effectiveness and efficiency associated with retaining, exchanging, or processing information about fall risks in existing workflows and currently adopted health information technology (IT) solutions, and (iii) offer informatics-based recommendations to improve fall risk management interventions. METHODS: A case study was carried out in a single not-for-profit suburban Medicare-certified home health agency with three branches. Qualitative data were collected over a six month period through observations, semi-structured interviews, and focus groups. The Framework method was used for analysis. Maximum variation sampling was adopted to recruit a diverse sample of clinicians. RESULTS: Overall, the information needs for fall risk management were categorized into physiological, care delivery, educational, social, environmental, and administrative domains. Examples include a brief fall-related patient history, weight-bearing status, medications that affect balance, availability of caregivers at home, and the influence of patients' cultures on fall management interventions. The unavailability and inaccuracy of critical information related to fall risks can delay necessary therapeutic services aimed at reducing patients' risk for falling and thereby jeopardizing their safety. Currently adopted IT solutions did not adequately accommodate data related to fall risk management. CONCLUSION: The results highlight the essential information for fall risk management in home care. Home care workflows and health IT solutions must effectively and efficiently retain, exchange, and process information necessary for fall risk management. Interoperability and integration of the various health IT solutions to make data sharing accessible to all clinicians is critical for fall risk management. Findings from this study can help home health agencies better understand their information needs to manage fall risks.
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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 | 0.011 | 0.071 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".