Expanding the Design Space for Fall Prevention in Acute Orthopedic Hospital Care: Human-Centered Design Study
Bibliographic record
Abstract
BACKGROUND: In-hospital fall prevention is a complex phenomenon most efficiently addressed via a wide range of multifactorial interventions. Technology may contribute, but research in this field has so far mainly focused on detecting falls. As a result, new knowledge from a system perspective is needed regarding when and how new technologies may support fall prevention among patients who have been hospitalized. OBJECTIVE: This study aimed to explore and describe clinical practices in an acute orthopedic hospital ward for fall prevention from a system perspective; determine the needs and possibilities related to support for clinical practices for fall prevention; and test whether a framework for studying interactions between people, activities, contexts, and technologies can be used to support observations of complex phenomena such as clinical fall prevention. METHODS: This qualitative study followed the principles of human-centered design while combining focused ethnography with a workshop. Eight health care professionals representing different staff categories in an acute hospital ward of an orthopedic clinic participated in on-site interviews or were observed in their clinical practice. Data from these events were subjected to qualitative content analysis to describe the clinical practices for fall prevention observed in terms of people, activities, context, and tools. In a workshop, a larger group of clinic personnel provided their views on fall prevention, described the activities and tools they observed to prevent falls, and discussed needs for further support. RESULTS: This study determined that health personnel considered fall prevention in all their interactions with patients, which included a wide range of activities for fall prevention wherein staff categories played complementary roles. These staff-patient meetings were goal oriented, responsive, and patient centered. The staff often served as key "tools" in assessment, communication, and coaching, while digital tools (mainly computer-based software programs) were used for information retrieval, documentation, and communication. The personnel worked to prevent patient falls both during hospitalization and after discharge. They believed that the long-term perspective was much more difficult to address in their clinical practice, and they expressed a need for more homelike environments in the hospital. CONCLUSIONS: The view on technology-based in-hospital fall prevention can be broadened not only to mainly include monitoring and alarm systems, information systems in general, or computer-based information in particular systems but also to support activities performed by health personnel that engage patients in fall prevention. For example, tools such as these can be implemented in training involving daily activities and mobility within safe yet more homelike clinical contexts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.058 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| 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".