Impact of EMR/EHR and Computer Decision Support Systems on Nursing Homes and Long-Term Care

Long-Term Care (LTCs) facilities and nursing homes have been an essential part of the healthcare industry to meet the needs of older adults. However, Electronic Medical Records (EMRs/ EHR within LTC facilities and nursing homes have lagged more than in other healthcare sectors. This research analyzed the impact of implementing EMR/EHR and Computerized Decision Support Systems within LTC facilities and nursing homes. In nursing homes and LTC facilities where EMR/EHR has been implemented, patient outcomes improved by reduced pressure ulcers and increased identification of patients at risk for malnutrition and falls. Integration of CDSSs with EMR/EHR improved documentation and prescribing of medicines. Partnerships between large hospital networks and nursing homes/LTC facilities may increase the implementation of these technologies in the latter

Falls Prediction in Care Homes Using Mobile App Data Collection

Falls are one of the leading causes of unintentional injury related deaths in older adults. Although, falls among elderly is a well documented phenomena; falls of care homes’ residents was under-researched, mainly due to the lack of documented data. In this study, we use data from over 1,769 care homes and 68,200 residents across the UK, which is based on carers who routinely documented the residents’ activities, using the Mobile Care Monitoring mobile app over three years. This study focuses on predicting the first fall of elderly living in care homes a week ahead. We intend to predict continuously based on a time window of the last weeks. Due to the intrinsic longitudinal nature of the data and its heterogeneity, we employ the use of Temporal Abstraction and Time Intervals Related Patterns discovery, which are used as features for classification. We had designed an experiment that reflects real-life conditions to evaluate the framework. Using four weeks of observation time window performed best

Resident and Facility Factors Associated with Rehospitalization from Skilled Nursing Facilities

ABSTRACT Older adults often require short-term nursing home care after an acute hospital stay to receive skilled nursing or rehabilitation services. Rehospitalization after a skilled nursing facility (SNF) admission is a potential indicator of poor nursing home quality that is associated with substantial risks of complications and increased costs of care. This study examined resident and facility factors associated with 30-day rehospitalizations during a one-year study period from SNFs in New Mexico. The Minimum Data Set 3.0 was used to explore resident factors and Nursing Home Compare data was used for facility factors. Among residents admitted to the SNF from an acute care hospital for 30-days or fewer (n = 2,370), 317 (13.4%) were rehospitalized. In bivariate analyses, several resident characteristics during their SNF stay were associated with significantly increased probability of rehospitalization, including an unhealed pressure ulcer, delirium, shortness of breath, and oxygen use. In multivariable models, the relative odds of rehospitalization were increased in those who identified as American Indian or Alaska Native, residents who rejected care, those with symptoms of delirium, and those who required greater mobility assistance with activities of daily living. The relative odds of rehospitalization were decreased in women and in residents with dementia. However, overall, none of the models improved prediction of rehospitalization. The Nursing Home Compare 5-star rating showed a decline in nurse staff ratings from 2015 to 2016. Policy implications include value-based penalties linked to high SNF rehospitalization rates and policies focused on reducing Medicare costs, while improving nursing home quality

Personalized functional health and fall risk prediction using electronic health records and in-home sensor data

Research has shown the importance of Electronic Health Records (EHR) and in-home sensor data for continuous health tracking and health risk predictions. With the increased computational capabilities and advances in machine learning techniques, we have new opportunities to use multi-modal health big data to develop accurate health tracking models. This dissertation describes the development, evaluation, and testing of systems for predicting functional health and fall risks in community-dwelling older adults using health data and machine learning techniques. In an initial study, we focused on organizing and de-identifying EHR data for analysis using HIPAA regulations. The dataset contained nine years of structured and unstructured EHR data obtained from TigerPlace, a senior living facility at Columbia, MO. The de-identification of this data was done using custom automated algorithms. The de-identified EHR data was used in several studies described in this dissertation. We then developed personalized functional health tracking models using geriatric assessments in the EHR data. Studies show that higher levels of functional health in older adults lead to a higher quality of life and improves the ability to age-in-place. Even though several geriatric assessments capture several aspects of functional health, there is limited research in longitudinally tracking the personalized functional health of older adults using a combination of these assessments. In this study, data from 150 older adult residents were used to develop a composite functional health prediction model using Activities of Daily Living (ADL), Instrumental Activities of Daily Living (IADL), Mini-Mental State Examination (MMSE), Geriatric Depression Scale (GDS), and Short Form 12 (SF12). Tracking functional health objectively could help clinicians to make decisions for interventions in case of functional health deterioration. We next constructed models for fall risk prediction in older adults using geriatric assessments, demographic data, and GAITRite assessment data. A 6-month fall risk prediction model was developed with data from 93 older adult residents. Explainable AI techniques were used to provide explanations to the model predictions, such as which specific features increased the risk of fall in a particular model prediction. Such explanations to model predictions provide valuable insights for targeted interventions. In another study, we developed deep neural network models to predict fall risk from de-identified nursing notes data from 162 older adult residents from TigerPlace. Clinical nursing notes have been shown to contain valuable information related to fall risk factors. This analysis provides the groundwork for future experiments to predict fall risk in older adults using clinical notes. In addition to using EHR data to predict functional health and fall risk in older adults, two studies were conducted to predict fall and functional health from in-home sensor data. Models for in-home fall prediction using depth sensor imagery have been successfully used at TigerPlace. However, the model is prone to false fall alarms in several scenarios, such as pillows thrown on the floor and pets jumping from couches. A secondary fall analysis was performed by analyzing fall alert videos to further identify and remove false alarms. In the final study, we used in-home sensor data streaming from depth sensors and bed sensors to predict functional health and absolute geriatric assessment values. These prediction models can be used to predict the functional health of residents in absence of sparse and infrequent geriatric assessments. This can also provide continuous tracking of functional health in older adults using the streaming in-home sensor data

Addressing the Health Needs of an Aging America: New Opportunities for Evidence-Based Policy Solutions

This report systematically maps research findings to policy proposals intended to improve the health of the elderly. The study identified promising evidence-based policies, like those supporting prevention and care coordination, as well as areas where the research evidence is strong but policy activity is low, such as patient self-management and palliative care. Future work of the Stern Center will focus on these topics as well as long-term care financing, the health care workforce, and the role of family caregivers

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

IntroductionThere is a clear need for improved care quality and quality monitoring in aged care. Aged care providers collect an abundance of data, yet rarely are these data integrated and transformed in real-time into actionable information to support evidence-based care, nor are they shared with older people and informal caregivers. This protocol describes the co-design and testing of a dashboard in residential aged care facilities (nursing or care homes) and community-based aged care settings (formal care provided at home or in the community). The dashboard will comprise integrated data to provide an 'at-a-glance' overview of aged care clients, indicators to identify clients at risk of fall-related hospitalisations and poor quality of life, and evidence-based decision support to minimise these risks. Longer term plans for dashboard implementation and evaluation are also outlined.MethodsThis mixed-method study will involve (1) co-designing dashboard features with aged care staff, clients, informal caregivers and general practitioners (GPs), (2) integrating aged care data silos and developing risk models, and (3) testing dashboard prototypes with users. The dashboard features will be informed by direct observations of routine work, interviews, focus groups and co-design groups with users, and a community forum. Multivariable discrete time survival models will be used to develop risk indicators, using predictors from linked historical aged care and hospital data. Dashboard prototype testing will comprise interviews, focus groups and walk-through scenarios using a think-aloud approach with staff members, clients and informal caregivers, and a GP workshop.Ethics and disseminationThis study has received ethical approval from the New South Wales (NSW) Population & Health Services Research Ethics Committee and Macquarie University's Human Research Ethics Committee. The research findings will be presented to the aged care provider who will share results with staff members, clients, residents and informal caregivers. Findings will be disseminated as peer-reviewed journal articles, policy briefs and conference presentations

The use of predictive fall models for older adults receiving aged care, using routinely collected electronic health record data : a systematic review

Background: Falls in older adults remain a pressing health concern. With advancements in data analytics and increasing uptake of electronic health records, developing comprehensive predictive models for fall risk is now possible. We aimed to systematically identify studies involving the development and implementation of predictive falls models which used routinely collected electronic health record data in home-based, community and residential aged care settings. Methods: A systematic search of entries in Cochrane Library, CINAHL, MEDLINE, Scopus, and Web of Science was conducted in July 2020 using search terms relevant to aged care, prediction, and falls. Selection criteria included English-language studies, published in peer-reviewed journals, had an outcome of falls, and involved fall risk modelling using routinely collected electronic health record data. Screening, data extraction and quality appraisal using the Critical Appraisal Skills Program for Clinical Prediction Rule Studies were conducted. Study content was synthesised and reported narratively. Results: From 7,329 unique entries, four relevant studies were identified. All predictive models were built using different statistical techniques. Predictors across seven categories were used: demographics, assessments of care, fall history, medication use, health conditions, physical abilities, and environmental factors. Only one of the four studies had been validated externally. Three studies reported on the performance of the models. Conclusions: Adopting predictive modelling in aged care services for adverse events, such as falls, is in its infancy. The increased availability of electronic health record data and the potential of predictive modelling to document fall risk and inform appropriate interventions is making use of such models achievable. Having a dynamic prediction model that reflects the changing status of an aged care client is key to this moving forward for fall prevention interventions

Reducing Falls and Fall-Related Hospital Transfers in Geriatric Assisted Living Residents

Background: Falls among older adults are frequent, rendering significant costs to both the individual and the healthcare system, and are preventable. In 2021, fall-related deaths in adults over the age of 65 were calculated at a rate of 78.0 per 100,000 people, and nonfatal falls were reported at 28% of all older adults (Centers for Disease Control and Prevention [CDC], 2023). In Pennsylvania, approximately 27.9% of older adults fell in 2020 (CDC, 2023). A multifactorial approach to reducing falls can be achieved through the implementation of fall risk screening tools and intervention bundles (Beato et al., 2019; Burland et al., 2013; Francis-Coad et al., 2018; Hewitt et al., 2017; McGibbon et al., 2019; Montero-Odasso et al., 2021; Moyer et al., 2017; Norman & Hirdes, 2020; Nunan et al., 2018; Sherington et al., 2017). Problem: Assisted living facilities do not have the tools to decrease the incidence of falls. Falls and fall-related hospital transfers can be decreased by implementing fall risk assessment tools and exercise interventions. Methods: The use of fall risk assessment tools and exercise interventions to reduce falls were supported by a thorough review of the literature. This project applied a convenience sample of residents from an assisted living facility and applied the plan, do, study, act (PDSA) translation model. Intervention: Participants voluntarily engaged in an 8-week exercise intervention focusing on strength, balance, and ambulation training. Results: Results of the project revealed a reduction in the risk of falls through descriptive statistics and the Wilcoxon ranked-sign test of pre- and post-project implementation data analysis. Conclusion: Implementation of fall risk assessment tools and exercise interventions reduces the fall risk gait, balance, and total fall risk scores