Development of a Novel Computational Model for Evaluating Fall Risk in Patient Room Design

The Impact of Design Factors on User Behavior in a Virtual Hospital Room to Explore Fall Prevention Strategies

OBJECTIVES

Falls in hospitals pose a significant safety risk, leading to injuries, prolonged hospitalization, and lasting complications. This study explores the potential of augmented reality (AR) technology in healthcare facility design to mitigate fall risk.

BACKGROUND

Few studies have investigated the impact of hospital room layouts on falls due to the high cost of building physical prototypes. This study introduces an innovative approach using AR technology to advance methods for healthcare facility design efficiently.

METHODS

Ten healthy participants enrolled in this study to examine different hospital room designs in AR. Factors of interest included room configuration, door type, exit side of the bed, toilet placement, and the presence of IV equipment. AR trackers captured trajectories of the body as participants navigated through these AR hospital layouts, providing insights into user behavior and preferences.

RESULTS

Door type influenced the degree of backward and sideways movement, with the presence of an IV pole intensifying the interaction between door and room type, leading to increased sideways and backward motion. Participants displayed varying patterns of backward and sideways travel depending on the specific room configurations they encountered.

CONCLUSIONS

AR can be an efficient and cost-effective method to modify room configurations to identify important design factors before conducting physical testing. The results of this study provide valuable insights into the effect of environmental factors on movement patterns in simulated hospital rooms. These results highlight the importance of considering environmental factors, such as the type of door and bathroom location, when designing healthcare facilities.

The Added Value of Combined Timed Up and Go Test, Walking Speed, and Grip Strength on Predicting Recurrent Falls in Chinese Community-dwelling Elderly

Purpose

To determine whether combined performance-based models could exert better predictive values toward discriminating community-dwelling elderly with high risk of any-falls or recurrent-falls.

Participants and Methods

This prospective cohort study included a total of 875 elderly participants (mean age: 67.10±5.94 years) with 513 females and 362 males, recruited from Hangu suburb area of Tianjin, China. All participants completed comprehensive assessments.

Methods

We documented information about sociodemographic information, behavioral characteristics and medical conditions. Three functional tests—timed up and go test (TUGT), walking speed (WS), and grip strength (GS) were used to create combined models. New onsets of any-falls and recurrent-falls were ascertained at one-year follow-up appointment.

Results

In total 200 individuals experienced falls over a one-year period, in which 66 individuals belonged to the recurrent-falls group (33%). According to the receiver operating characteristic curve (ROC), the cutoff points of TUGT, WS, and GS toward recurrent-falls were 10.31 s, 0.9467 m/s and 0.3742 kg/kg respectively. We evaluated good performance as “+” while poor performance as “–”. After multivariate adjustment, we found “TUGT >10.31 s” showed a strong correlation with both any-falls (adjusted odds ratio (OR)=2.025; 95% confidence interval (CI)=1.425–2.877) and recurrent-falls (adjusted OR=2.150; 95%CI=1.169–3.954). Among combined functional models, “TUGT >10.31 s, GS <0.3742 kg/kg, WS >0.9467 m/s” showed strongest correlation with both any-falls (adjusted OR=5.499; 95%CI=2.982–10.140) and recurrent-falls (adjusted OR=8.260; 95%CI=3.880–17.585). And this combined functional model significantly increased discriminating abilities on screening recurrent-fallers than a single test (C-statistics=0.815, 95%CI=0.782–0.884, P<0.001), while not better than a single test in predicting any-fallers (P=0.083).

Conclusion

Elderly people with poor TUGT performance, weaker GS but quicker WS need to be given high priority toward fall prevention strategies for higher risks and frequencies. Meanwhile, the combined “TUGT–, GS–, WS+” model presents increased discriminating ability and could be used as a conventional tool to discriminate recurrent-fallers in clinical practice.

Designing a Patient Room as a Fall Protection Strategy: The Perspectives of Healthcare Design Experts

Despite decades of research into patient falls, there is a dearth of evidence about how the design of patient rooms influences falls. Our multi-year study aims to better understand how patient room design can increase stability during ambulation, serving as a fall protection strategy for frail and/or elderly patients. The aim of this portion of the study was to ascertain the architect’s perspective on designing a room to mitigate the risk of falls, as well as to evaluate the face validity of a predictive algorithm to assess risk in room design using the input of a design advisory council (AC). The purpose of this paper is to provide insight into the design process and decision-making for patient rooms; summarize the impressions of industry experts about the configurations and layout of the patient rooms tested in a preliminary augmented reality model; establish the face validity of modeled heat maps depicting risk; and report the results of a pre-meeting and post-meeting survey of expert opinions. Feedback was coded using human factors/ergonomic (HF/E) design principles, and the findings will be used to guide further development of an “optimal” prototype room for human subject testing. The results confirm the challenges that architects face as they balance competing priorities and reveal how a participatory process focusing on preventing falls can shift assumptions about design strategies, especially subtle changes (e.g., toilet orientation).