Examining the Use of Text Messages Among Multidisciplinary Care Teams to Reduce Avoidable Hospitalization of Nursing Home Residents with Dementia: Protocol for a Secondary Analysis.

BACKGROUND: Reducing avoidable nursing home (NH)-to-hospital transfers of residents with Alzheimer disease or a related dementia (ADRD) has become a national priority due to the physical and emotional toll it places on residents and the high costs to Medicare and Medicaid. Technologies supporting the use of clinical text messages (TMs) could improve communication among health care team members and have considerable impact on reducing avoidable NH-to-hospital transfers. Although text messaging is a widely accepted mechanism of communication, clinical models of care using TMs are sparsely reported in the literature, especially in NHs. Protocols for assessing technologies that integrate TMs into care delivery models would be beneficial for end users of these systems. Without evidence to support clinical models of care using TMs, users are left to design their own methods and protocols for their use, which can create wide variability and potentially increase disparities in resident outcomes. OBJECTIVE: Our aim is to describe the protocol of a study designed to understand how members of the multidisciplinary team communicate using TMs and how salient and timely communication can be used to avert poor outcomes for NH residents with ADRD, including hospitalization. METHODS: This project is a secondary analysis of data collected from a Centers for Medicare & Medicaid Services (CMS)-funded demonstration project designed to reduce avoidable hospitalizations for long-stay NH residents. We will use two data sources: (1) TMs exchanged among the multidisciplinary team across the 7-year CMS study period (August 2013-September 2020) and (2) an adapted acute care transfer tool completed by advanced practice registered nurses to document retrospective details about NH-to-hospital transfers. The study is guided by an age-friendly model of care called the 4Ms (What Matters, Medications, Mentation, and Mobility) framework. We will use natural language processing, statistical methods, and social network analysis to generate a new ontology and to compare communication patterns found in TMs occurring around the time NH-to-hospital transfer decisions were made about residents with and without ADRD. RESULTS: After accounting for inclusion and exclusion criteria, we will analyze over 30,000 TMs pertaining to over 3600 NH-to-hospital transfers. Development of the 4M ontology is in progress, and the 3-year project is expected to run until mid-2025. CONCLUSIONS: To our knowledge, this project will be the first to explore the content of TMs exchanged among a multidisciplinary team of care providers as they make decisions about NH-to-hospital resident transfers. Understanding how the presence of evidence-based elements of high-quality care relate to avoidable hospitalizations among NH residents with ADRD will generate knowledge regarding the future scalability of behavioral interventions. Without this knowledge, NHs will continue to rely on ineffective and outdated communication methods that fail to account for evidence-based elements of age-friendly care. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/50231.

Disparities in Text Messaging Interventions to Improve Diabetes Management in the United States.

Substantial progress has been made in the development of evidence-based interventions to facilitate the management of type 2 diabetes. The increase in ownership of mobile phones has made short messaging services (SMS, or text messaging) a feasible way to enhance information delivery. The goals of this study were to 1) summarize characteristics of diabetes SMS interventions implemented in the United States and 2) identify the extent to which disadvantaged populations are represented in SMS-based diabetes management intervention studies. We conducted a literature search to identify published studies of type 2 diabetes self-management SMS interventions conducted with adults in the United States. Of the 792 articles retrieved, only 9 met inclusion criteria. We systematically extracted data on the theoretical basis, recruitment, incentives, inclusion/exclusion criteria, strategies toward ensuring a racially/ethnically or income-diverse sample, text message delivery, and study duration. Sixty-three percent of the participants across the nine studies were non-white. Only two studies reported participants' education level, and four captured non-English-speaking status. Interventions varied in offering one-way, two-way, or a combination of messaging strategies. Five studies did not describe cultural adaptations or report results separately for different cultural groups. None of the studies provided cell phones, and not having texting capability was an exclusion criterion for six studies. There is a dearth of published research on type 2 diabetes management interventions using text messaging among racially/ethnically or income-diverse populations. Future interventions should be better tailored to these target populations and include the collection of complete sociodemographic data and cell phone/smartphone availability, thereby ensuring cultural appropriateness.

Combining Physiology-Based Modeling and Evolutionary Algorithms for Personalized, Noninvasive Cardiovascular Assessment Based on Electrocardiography and Ballistocardiography.

Purpose: This study proposes a novel approach to obtain personalized estimates of cardiovascular parameters by combining (i) electrocardiography and ballistocardiography for noninvasive cardiovascular monitoring, (ii) a physiology-based mathematical model for predicting personalized cardiovascular variables, and (iii) an evolutionary algorithm (EA) for searching optimal model parameters. Methods: Electrocardiogram (ECG), ballistocardiogram (BCG), and a total of six blood pressure measurements are recorded on three healthy subjects. The R peaks in the ECG are used to segment the BCG signal into single BCG curves for each heart beat. The time distance between R peaks is used as an input for a validated physiology-based mathematical model that predicts distributions of pressures and volumes in the cardiovascular system, along with the associated BCG curve. An EA is designed to search the generation of parameter values of the cardiovascular model that optimizes the match between model-predicted and experimentally-measured BCG curves. The physiological relevance of the optimal EA solution is evaluated a posteriori by comparing the model-predicted blood pressure with a cuff placed on the arm of the subjects to measure the blood pressure. Results: The proposed approach successfully captures amplitudes and timings of the most prominent peak and valley in the BCG curve, also known as the J peak and K valley. The values of cardiovascular parameters pertaining to ventricular function can be estimated by the EA in a consistent manner when the search is performed over five different BCG curves corresponding to five different heart-beats of the same subject. Notably, the blood pressure predicted by the physiology-based model with the personalized parameter values provided by the EA search exhibits a very good agreement with the cuff-based blood pressure measurement. Conclusion: The combination of EA with physiology-based modeling proved capable of providing personalized estimates of cardiovascular parameters and physiological variables of great interest, such as blood pressure. This novel approach opens the possibility for developing quantitative devices for noninvasive cardiovascular monitoring based on BCG sensing.

Tracking personalized functional health in older adults using geriatric assessments.

BACKGROUND: Higher levels of functional health in older adults leads to higher quality of life and improves the ability to age-in-place. Tracking functional health objectively could help clinicians to make decisions for interventions in case of health deterioration. Even though several geriatric assessments capture several aspects of functional health, there is limited research in longitudinally tracking personalized functional health of older adults using a combination of these assessments. METHODS: We used geriatric assessment data collected from 150 older adults to develop and validate a functional health prediction model based on risks associated with falls, hospitalizations, emergency visits, and death. We used mixed effects logistic regression to construct the model. The geriatric assessments included were 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). Construct validators such as fall risks associated with model predictions, and case studies with functional health trajectories were used to validate the model. RESULTS: The model is shown to separate samples with and without adverse health event outcomes with an area under the receiver operating characteristic curve (AUC) of > 0.85. The model could predict emergency visit or hospitalization with an AUC of 0.72 (95% CI 0.65-0.79), fall with an AUC of 0.86 (95% CI 0.83-0.89), fall with hospitalization with an AUC of 0.89 (95% CI 0.85-0.92), and mortality with an AUC of 0.93 (95% CI 0.88-0.97). Multiple comparisons of means using Turkey HSD test show that model prediction means for samples with no adverse health events versus samples with fall, hospitalization, and death were statistically significant (p < 0.001). Case studies for individual residents using predicted functional health trajectories show that changes in model predictions over time correspond to critical health changes in older adults. CONCLUSIONS: The personalized functional health tracking may provide clinicians with a longitudinal view of overall functional health in older adults to help address the early detection of deterioration trends and decide appropriate interventions. It can also help older adults and family members take proactive steps to improve functional health.

Brownian dynamics assessment of enhanced diffusion exhibited by 'fluctuating-dumbbell enzymes'.

Recent experiments have reported that diffusion of enzymes can be enhanced in the presence of their substrates. Using a fluctuating dumbbell model of enzymes, it has been argued that such an enhancement can be rationalized by the reduction of the enzyme size and by the suppression of the hydrodynamically coupled conformational fluctuations, induced by binding a substrate or an inhibitor to the enzyme [Nano Lett. 2017, 17, 4415]. Herein, we critically examine these expectations via extensive Brownian dynamics simulations of a similar model. The numerical results show that neither of the two mechanisms can cause an enhancement comparable to that reported experimentally, unless very large, physically counter-intuitive, enzyme deformations are invoked.

Longitudinal Changes in Risk Stratification for a Managed Population.

The LIGHT2 project managed the care of approximately 10,000 Medicare (primarily elderly) and Medicaid (low income) patients between 2013 and 2015. Risk tiers based on chronic disease diagnoses and recent healthcare utilization were strongly predictive of future healthcare utilization, and the authors expected that the members of an aging and well-insured population would gradually rise in risk of healthcare utilization over the course of three years. Various analytic techniques were used to characterize the members of higher risk tiers. However, retrospective cohort analysis and simple data visualization discovered the tendency of patients in lower initial risk tiers to remain healthy, and the tendency of patients in higher initial risk tiers to improve. In a time frame of three years, this return to stability was a more important influence on healthcare utilization than risk or aging.

Automated In-Home Fall Risk Assessment and Detection Sensor System for Elders.

PURPOSE OF THE STUDY: Falls are a major problem for the elderly people leading to injury, disability, and even death. An unobtrusive, in-home sensor system that continuously monitors older adults for fall risk and detects falls could revolutionize fall prevention and care. DESIGN AND METHODS: A fall risk and detection system was developed and installed in the apartments of 19 older adults at a senior living facility. The system includes pulse-Doppler radar, a Microsoft Kinect, and 2 web cameras. To collect data for comparison with sensor data and for algorithm development, stunt actors performed falls in participants' apartments each month for 2 years and participants completed fall risk assessments (FRAs) using clinically valid, standardized instruments. The FRAs were scored by clinicians and recorded by the sensing modalities. Participants' gait parameters were measured as they walked on a GAITRite mat. These data were used as ground truth, objective data to use in algorithm development and to compare with radar and Kinect generated variables. RESULTS: All FRAs are highly correlated (p < .01) with the Kinect gait velocity and Kinect stride length. Radar velocity is correlated (p < .05) to all the FRAs and highly correlated (p < .01) to most. Real-time alerts of actual falls are being sent to clinicians providing faster responses to urgent situations. IMPLICATIONS: The in-home FRA and detection system has the potential to help older adults remain independent, maintain functional ability, and live at home longer.

Comparing Aging in Place to Home Health Care: Impact of Nurse Care Coordination On Utilization and Costs.

The goal of this study was to compare utilization and cost outcomes of patients who received long-term care coordination in an Aging in Place program to patients who received care coordination as a routine service in home health care. This research offered the unique opportunity to compare two groups of patients who received services from a single home health care agency, using the same electronic health record, to identify the impact of long-term and routine care coordination on utilization and costs to Medicare and Medicaid programs. This study supports that long-term care coordination supplied by nurses outside of a primary medical home can positively influence functional, cognitive, and health care utilization for frail older people. The care coordinators in this study practiced nursing by routinely assessing and educating patients and families, assuring adequate service delivery, and communicating with the multidisciplinary health care team. Care coordination managed by registered nurses can influence utilization and cost outcomes, and impact health and functional abilities.

A framework for harmonizing sensor data to support embedded health assessment.

The use of in-home and mobile sensing is likely to be a key component of future care and has recently been studied by many research groups world-wide. Researchers have shown that embedded sensors can be used for health assessment such as early illness detection and the management of chronic health conditions. However, research collaboration and data sharing have been hampered by disparate sets of sensors and data collection methods. To date, there have been no studies to investigate common measures that can be used across multiple sites with different types of sensors, which would facilitate large scale studies and reuse of existing datasets. In this paper, we propose a framework for harmonizing heterogeneous sensor data through an intermediate layer, the Conceptual Sensor, which maps physical measures to clinical space. Examples are included for sleep quality and ambulatory physical function.

Developing a comprehensive electronic health record to enhance nursing care coordination, use of technology, and research.

As in acute care, use of health information technology in long-term care holds promise for increased efficiency, better accuracy, reduced costs, and improved outcomes. A comprehensive electronic health record (EHR), which encompasses all health care measures that clinicians want to use-both standard health care assessments and those acquired through emerging technology-is the key to improved, efficient clinical decision making. New technologies using sensors to passively monitor older adults at home are being developed and are commercially available. However, integrating the clinical information systems with passive monitoring data so that clinical decision making is enhanced and patient records are complete is challenging. Researchers at the University of Missouri (MU) are developing a comprehensive EHR to: (a) enhance nursing care coordination at TigerPlace, independent senior housing that helps residents age in place; (b) integrate clinical data and data from new technology; and (c) advance technology and clinical research.