Co-designing a dashboard of predictive analytics and decision support to drive care quality and client outcomes in aged care: a mixed-method study protocol

A Longitudinal Study of the Use and Effects of Fall-Risk-Increasing Drugs in Residential Aged Care

OBJECTIVES

Fall-risk-increasing drugs (FRIDs)-psychotropics and cardiovascular disease (CVD) drugs-may elevate the risk of falling, with strong evidence observed in psychotropic FRIDs, whereas findings from cardiovascular disease (CVD) FRIDs remain inconclusive. Existing studies on FRIDs and falls are often hampered by methodologic limitations. Leveraging longitudinal observational data, we aimed to determine the long-term patterns of FRID use and their association with falls in residential aged care (RAC) homes.

DESIGN

A retrospective longitudinal cohort study.

SETTING AND PARTICIPANTS

A total of 4207 permanent residents newly admitted to 27 RAC homes in Sydney, Australia.

METHOD

The outcomes were incidence of all and injurious falls. We measured exposure to each FRID over 60 months using the Proportion of Days Covered (PDC) metric. We used group-based multitrajectory modeling to determine concurrent usage patterns of psychotropics and CVD FRIDs and applied negative binomial regression to assess their associations with the outcomes.

RESULTS

A total of 83.6% (n = 3516) and 77.3% (n = 3254) residents used psychotropic and CVD FRIDs, respectively. The PDC values ranged from 67.3% (opioids) to 86.9% (antidepressants) for specific psychotropics and 79.0% (α-adrenoceptor antagonists) to 89.6% (β blockers) for CVD FRIDs. We identified 4 groups: group 1, low psychotropics-low CVDs use (16.7%, n = 701); group 2, low psychotropics-high CVDs (25.0%, n = 1054); group 3, high psychotropics-high CVDs (41.0%, n = 1723); and group 4, high psychotropics-low CVDs (17.3%, n = 729). Group 4 had a significantly higher rate of falls than the other groups for both outcomes, including relative to group 3, in which exposure to both FRID classes was high.

CONCLUSIONS AND IMPLICATIONS

Our findings reveal concerningly high FRID use in RAC homes and highlight a critical difference in the impact of the 2 major FRID classes on falls. Psychotropics were strongly associated with falls, whereas the studied CVD FRIDs did not elevate risk of falling.

Big data research in nursing: A bibliometric exploration of themes and publications

AIMS

To comprehend the current research hotspots and emerging trends in big data research within the global nursing domain.

DESIGN

Bibliometric analysis.

METHODS

The quality articles for analysis indexed by the science core collection were obtained from the Web of Science database as of February 10, 2023.The descriptive, visual analysis and text mining were realized by CiteSpace and VOSviewer.

RESULTS

The research on big data in the nursing field has experienced steady growth over the past decade. A total of 45 core authors and 17 core journals around the world have contributed to this field. The author's keyword analysis has revealed five distinct clusters of research focus. These encompass machine/deep learning and artificial intelligence, natural language processing, big data analytics and data science, IoT and cloud computing, and the development of prediction models through data mining. Furthermore, a comparative examination was conducted with data spanning from 1980 to 2016, and an extended analysis was performed covering the years from 1980 to 2019. This bibliometric mapping comparison allowed for the identification of prevailing research trends and the pinpointing of potential future research hotspots within the field.

CONCLUSIONS

The fusion of data mining and nursing research has steadily advanced and become more refined over time. Technologically, it has expanded from initial natural language processing to encompass machine learning, deep learning, artificial intelligence, and data mining approach that amalgamates multiple technologies. Professionally, it has progressed from addressing patient safety and pressure ulcers to encompassing chronic diseases, critical care, emergency response, community and nursing home settings, and specific diseases (Cardiovascular diseases, diabetes, stroke, etc.). The convergence of IoT, cloud computing, fog computing, and big data processing has opened new avenues for research in geriatric nursing management and community care. However, a global imbalance exists in utilizing big data in nursing research, emphasizing the need to enhance data science literacy among clinical staff worldwide to advance this field.

CLINICAL RELEVANCE

This study focused on the thematic trends and evolution of research on the big data in nursing research. Moreover, this study may contribute to the understanding of researchers, journals, and countries around the world and generate the possible collaborations of them to promote the development of big data in nursing science.

Development and internal validation of a dynamic fall risk prediction and monitoring tool in aged care using routinely collected electronic health data: a landmarking approach

OBJECTIVES

Falls pose a significant challenge in residential aged care facilities (RACFs). Existing falls prediction tools perform poorly and fail to capture evolving risk factors. We aimed to develop and internally validate dynamic fall risk prediction models and create point-based scoring systems for residents with and without dementia.

MATERIALS AND METHODS

A longitudinal cohort study using electronic data from 27 RACFs in Sydney, Australia. The study included 5492 permanent residents, with a 70%-30% split for training and validation. The outcome measure was the incidence of falls. We tracked residents for 60 months, using monthly landmarks with 1-month prediction windows. We employed landmarking dynamic prediction for model development, a time-dependent area under receiver operating characteristics curve (AUROCC) for model evaluations, and a regression coefficient approach to create point-based scoring systems.

RESULTS

The model identified 15 independent predictors of falls in dementia and 12 in nondementia cohorts. Falls history was the key predictor of subsequent falls in both dementia (HR 4.75, 95% CI, 4.45-5.06) and nondementia cohorts (HR 4.20, 95% CI, 3.87-4.57). The AUROCC across landmarks ranged from 0.67 to 0.87 for dementia and from 0.66 to 0.86 for nondementia cohorts but generally remained between 0.75 and 0.85 in both cohorts. The total point risk score ranged from -2 to 57 for dementia and 0 to 52 for nondementia cohorts.

DISCUSSION

Our novel risk prediction models and scoring systems provide timely person-centered information for continuous monitoring of fall risk in RACFs.

CONCLUSION

Embedding these tools within electronic health records could facilitate the implementation of targeted proactive interventions to prevent falls.

Usability and Acceptability of Clinical Dashboards in Aged Care: Systematic Review

BACKGROUND

The use of clinical dashboards in aged care systems to support performance review and improve outcomes for older adults receiving care is increasing.

OBJECTIVE

Our aim was to explore evidence from studies of the acceptability and usability of clinical dashboards including their visual features and functionalities in aged care settings.

METHODS

A systematic review was conducted using 5 databases (MEDLINE, Embase, PsycINFO, Cochrane Library, and CINAHL) from inception to April 2022. Studies were included in the review if they were conducted in aged care environments (home-based community care, retirement villages, and long-term care) and reported a usability or acceptability evaluation of a clinical dashboard for use in aged care environments, including specific dashboard visual features (eg, a qualitative summary of individual user experience or metrics from a usability scale). Two researchers independently reviewed the articles and extracted the data. Data synthesis was performed via narrative review, and the risk of bias was measured using the Mixed Methods Appraisal Tool.

RESULTS

In total, 14 articles reporting on 12 dashboards were included. The quality of the articles varied. There was considerable heterogeneity in implementation setting (home care 8/14, 57%), dashboard user groups (health professionals 9/14, 64%), and sample size (range 3-292). Dashboard features included a visual representation of information (eg, medical condition prevalence), analytic capability (eg, predictive), and others (eg, stakeholder communication). Dashboard usability was mixed (4 dashboards rated as high), and dashboard acceptability was high for 9 dashboards. Most users considered dashboards to be informative, relevant, and functional, highlighting the use and intention of using this resource in the future. Dashboards that had the presence of one or more of these features (bar charts, radio buttons, checkboxes or other symbols, interactive displays, and reporting capabilities) were found to be highly acceptable.

CONCLUSIONS

A comprehensive summary of clinical dashboards used in aged care is provided to inform future dashboard development, testing, and implementation. Further research is required to optimize visualization features, usability, and acceptability of dashboards in aged care.

Epidemiology of Falls in 25 Australian Residential Aged Care Facilities: A Retrospective Longitudinal Cohort Study Using Routinely Collected Data

BACKGROUND

Falls are frequent among older adults and have significant health and economic consequences. There have been few studies on the epidemiology of falls in residential aged care facilities (RACFs). This study aimed to determine the incidence of falls in RACFs using longitudinal routinely collected incident data over five years (Jul 2014-Dec 2019).

METHODS

A retrospective cohort study using fall incident data from 25 RACFs in Sydney, NSW, Australia. Incidents relating to a population of 6,163 aged care residents aged ≥65 years were included. Outcome measures were incidents of all falls; injurious falls, and requiring hospitalisation. Risk-adjusted incidence rate (IR) for each outcome indicator for each of the 25 facilities was calculated.

RESULTS

A total of 27,878 falls were reported over 3,906,772 resident days (a crude rate of 7.14/1000 resident days; 95% confidence interval (CI) 6.81-7.48). Of these, 10,365 (37.2%) were injurious and 2,733 (9.8%) required hospitalisation. The crude IR for injurious falls was 2.65/1000 resident days (95% CI 2.53-2.78) and 0.70 (95% CI 0.66-0.74) for falls requiring hospitalisation. The incidence of falls was significantly higher in respite compared to permanent residents for all falls (adjusted incident rate ratio (aIRR) 1.33; 95% CI 1.18-1.51) and injurious falls (aIRR 1.30; 95% CI 1.14-1.48) and for men compared to women for all outcomes (all falls aIRR 1.69; 95% CI 1.54-1.86; injurious falls aIRR 1.87; 95% CI 1.71-2.04 and falls requiring hospitalisation aIRR 1.29; 95% CI 1.12-1.48). The risk-adjusted IRs per 1000 resident days between facilities varied substantially (all falls 0.57-12.93 falls; injurious falls 0.25-4.47 and falls requiring hospitalisation 0.10-1.70).

CONCLUSION

Falls are frequent in RACFs, often resulting in injury and hospitalisation. The study provides robust and comprehensive information that may help inform future initiatives to minimise the incidence of falls in RACFs.

The use and predictive performance of the Peninsula Health Falls Risk Assessment Tool (PH-FRAT) in 25 residential aged care facilities: a retrospective cohort study using routinely collected data

BACKGROUND

The Peninsula Health Falls Risk Assessment Tool (PH-FRAT) is a validated and widely applied tool in residential aged care facilities (RACFs) in Australia. However, research regarding its use and predictive performance is limited. This study aimed to determine the use and performance of PH-FRAT in predicting falls in RACF residents.

METHODS

A retrospective cohort study using routinely-collected data from 25 RACFs in metropolitan Sydney, Australia from Jul 2014-Dec 2019. A total of 5888 residents aged ≥65 years who were assessed at least once using the PH-FRAT were included in the study. The PH-FRAT risk score ranges from 5 to 20 with a score > 14 indicating fallers and ≤ 14 non-fallers. The predictive performance of PH-FRAT was determined using metrics including area under receiver operating characteristics curve (AUROC), sensitivity, specificity, sensitivity_{Event Rate(ER)} and specificity_ER.

RESULTS

A total of 27,696 falls were reported over 3,689,561 resident days (a crude incident rate of 7.5 falls /1000 resident days). A total of 38,931 PH-FRAT assessments were conducted with a median of 4 assessments per resident, a median of 43.8 days between assessments, and an overall median fall risk score of 14. Residents with multiple assessments had increased risk scores over time. The baseline PH-FRAT demonstrated a low AUROC of 0.57, sensitivity of 26.0% (sensitivity_ER 33.6%) and specificity of 88.8% (specificity_ER 82.0%). The follow-up PH-FRAT assessments increased sensitivity_ER values although the specificity_ER decreased. The performance of PH-FRAT improved using a lower risk score cut-off of 10 with AUROC of 0.61, sensitivity of 67.5% (sensitivity_ER 74.4%) and specificity of 55.2% (specificity_ER 45.6%).

CONCLUSIONS

Although PH-FRAT is frequently used in RACFs, it demonstrated poor predictive performance raising concerns about its value. Introducing a lower PH-FRAT cut-off score of 10 marginally enhanced its predictive performance. Future research should focus on understanding the feasibility and accuracy of dynamic fall risk predictive tools, which may serve to better identify residents at risk of falls.