Healthcare utilization in older patients using personal emergency response systems: an analysis of electronic health records and medical alert data : Brief Description: A Longitudinal Retrospective Analyses of healthcare utilization rates in older patients using Personal Emergency Response Systems from 2011 to 2015

The Use of Digital Health Services Among Patients and Citizens Living at Home: Scoping Review

BACKGROUND

The development of digital health services reflects not only the technical development of services but also a change in attitude and the way of thinking. It has become a cornerstone for engaging and activating patients and citizens in health management while living at home. Digital health services are also aimed at enhancing the efficiency and quality of services, while simultaneously providing services more cost-effectively. In 2020, the COVID-19 pandemic accelerated worldwide the development and use of digital services in response to requirements for social distancing and other regulations.

OBJECTIVE

The aim of this review is to identify and summarize how digital health services are being used among patients and citizens while living at home.

METHODS

The Joanna Briggs Institute (JBI) methodology for scoping reviews was used as guidance. A search conducted in 3 databases (CINAHL, PubMed, Scopus) resulted in 419 papers. The reporting was conducted by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for scoping review (PRISMA-ScR), and the analysis of the included papers was performed using a framework consisting of 5 clusters describing the use of digital health services. After screening and excluding papers that did not match the inclusion criteria, 88 (21%) papers from 2010 to 2022 were included in the final analysis.

RESULTS

Results indicated that digital health services are used in different situations and among different kinds of populations. In most studies, digital health services were used in the form of video visits or consultations. The telephone was also used regularly for consultations. Other services, such as remote monitoring and transmitting of recorded information and the use the of internet or portals for searching information, were observed as well. Alerts, emergency systems, and reminders were observed to offer possibilities of use, for example, among older people. The digital health services also showed to have potential for use in patient education.

CONCLUSIONS

The development of digital services reflects a shift toward the provision of care regardless of time and place. It also reflects a shift toward emphasis on patient-centered care, meaning activating and engaging patients in their own care as they use digital services for various health-related purposes. Despite the development of digital services, many challenges (eg, adequate infrastructure) still prevail worldwide.

Predictive analytics and tailored interventions improve clinical outcomes in older adults: a randomized controlled trial

This study explored the potential to improve clinical outcomes in patients at risk of moving to the top segment of the cost acuity pyramid. This randomized controlled trial evaluated the impact of a Stepped-Care approach (predictive analytics + tailored nurse-driven interventions) on healthcare utilization among 370 older adult patients enrolled in a homecare management program and using a Personal Emergency Response System. The Control group (CG) received care as usual, while the Intervention group (IG) received Stepped-Care during a 180-day intervention period. The primary outcome, decrease in emergency encounters, was not statistically significant (15%, p = 0.291). However, compared to the CG, the IG had significant reductions in total 90-day readmissions (68%, p = 0.007), patients with 90-day readmissions (76%, p = 0.011), total 180-day readmissions (53%, p = 0.020), and EMS encounters (49%, p = 0.006). Predictive analytics combined with tailored interventions could potentially improve clinical outcomes in older adults, supporting population health management in home or community settings.

Exploring patterns of personal alarm system use and impacts on outcomes

OBJECTIVES

To describe the patterns of personal emergency response systems (PERS) use in a statewide cohort of older Australians.

METHODS

PERS data from clients enrolled in the Personal Alarm Victoria program between January 2014 and June 2017 were analysed. Alarm activation reasons were extracted, and a medical record audit was performed for a sub-cohort of patients admitted to a local hospital following an alarm event. Descriptive statistics were used.

RESULTS

There were 42,180 clients enrolled during the study (mean age 80 years, 80% female, 93% living alone). An ambulance attended 44% of the fall-related events and 81% of events coded as unwell. Activation reasons were distributed equally between a fall and feeling unwell, and a repeating pattern of activation reasons was observed. In our sub-cohort (n = 92), the majority of admissions (86%) followed an alarm activation coded as unwell.

CONCLUSION

We demonstrated recurring patterns associated with the reasons for alarm use.

Deciding about the use of a Personal Safety Alerting Device-The need for a legitimation process: A qualitative study

Aims

To explore reasons, thoughts, motives, and influencing factors regarding the use or non‐use of Personal Safety Alerting Devices (PSADs) in the daily lives of community‐dwelling older persons.

Design

A qualitative descriptive study design was used.

Methods

Six focus groups were conducted with a total of 32 older persons between February–August 2016. Data analysis followed the Qualitative Analysis Guide of Leuven.

Results

The participants described the use or non‐use of PSADs as a decision resulting from a “legitimation process”. This process implies that a person needs to perceive the necessity for a PSAD and then determine the right moment at which to start using it. During this process, each person weighs her or his “ageing self” and “perception of technology” then decides whether to start using a device or to delay its use. “Critical events” initiate this process, compelling the person to consider their own safety and their possible need for assistance.

Conclusion

The legitimation process suggests that the initiation of PSAD use represents a turning point in life. Using a PSAD is not simply a matter of obtaining one. It is a complex decision‐making process establishing legitimation for its use, which is interwoven with one's individual ageing, self‐perception, and the meaning attributed to the device.

Impact

Older persons need to be supported; in particular, they require time to go through the legitimation process for PSAD use. Nurses can empower them in this process, such that they perceive using a PSAD as a means to restore their frailty balance and feel enabled to (re)gain control over their own life and thus to preserve their independence.

Impact of Critical Illness on Resource Utilization: A Comparison of Use in the Year Before and After ICU Admission

Supplemental Digital Content is available in the text.

Increasingly, patients admitted to an ICU survive to hospital discharge; many with ongoing medical needs. The full impact of an ICU admission on an individual’s resource utilization and survivorship trajectory in the United States is not clear. We sought to compare healthcare utilization among ICU survivors in each year surrounding an ICU admission.

Healthy lifestyle discussions between healthcare providers and older cancer survivors: Data from 12 cancer centers in the Southeastern United States

Background

Little is known about the prevalence of healthy lifestyle (HLS) discussions between providers and older cancer survivors.

Methods

We utilized cross‐sectional data from older cancer survivors (≥65 years) seen at 12 southeastern cancer centers during 2013‐2015. Data on demographics, time since diagnosis, weight, height, and healthy behaviors were collected. Respondents were asked if providers (oncologists, other physicians, and/or nurses) discussed exercise, healthy diet, weight management, and/or smoking cessation during clinical encounters. Descriptive statistics and bivariate associations between HLS topics and survivor characteristics were calculated.

Results

Among 1460 cancer survivors, mean age was 74 years (SD 6), most were white (81%), and >1 year postdiagnosis (84%). The majority (71%) reported discussing at least one of three HLS topics (exercise 49%, healthy diet 53%, vegetable consumption 28%); 17% received counseling on all three. Weight loss was recommended to 33% of overweight/obese survivors and smoking cessation to 85% of current smokers. Oncologists and nurses discussed HLS less frequently compared to other physicians. Younger survivors (65‐74 years) received recommendations for exercise, weight loss, and tobacco cessation more often than older survivors (≥75 years). Compared to white respondents, minorities reported discussions on all topics more often except for tobacco cessation. Excluding tobacco cessation, survivors with recent cancer diagnoses (<1 year) reported HLS discussions more often than survivors >1 year postdiagnosis.

Conclusion

Despite the American Cancer Society's recommendations, older survivors reported a low prevalence of HLS discussions with their providers, with some variation by demographic groups. Strategies are needed to promote these important discussions in this population.

Predictive Modeling of 30-Day Emergency Hospital Transport of Patients Using a Personal Emergency Response System: Prognostic Retrospective Study

BACKGROUND

Telehealth programs have been successful in reducing 30-day readmissions and emergency department visits. However, such programs often focus on the costliest patients with multiple morbidities and last for only 30 to 60 days postdischarge. Inexpensive monitoring of elderly patients via a personal emergency response system (PERS) to identify those at high risk for emergency hospital transport could be used to target interventions and prevent avoidable use of costly readmissions and emergency department visits after 30 to 60 days of telehealth use.

OBJECTIVE

The objectives of this study were to (1) develop and validate a predictive model of 30-day emergency hospital transport based on PERS data; and (2) compare the model's predictions with clinical outcomes derived from the electronic health record (EHR).

METHODS

We used deidentified medical alert pattern data from 290,434 subscribers to a PERS service to build a gradient tree boosting-based predictive model of 30-day hospital transport, which included predictors derived from subscriber demographics, self-reported medical conditions, caregiver network information, and up to 2 years of retrospective PERS medical alert data. We evaluated the model's performance on an independent validation cohort (n=289,426). We linked EHR and PERS records for 1815 patients from a home health care program to compare PERS-based risk scores with rates of emergency encounters as recorded in the EHR.

RESULTS

In the validation cohort, 2.22% (6411/289,426) of patients had 1 or more emergency transports in 30 days. The performance of the predictive model of emergency hospital transport, as evaluated by the area under the receiver operating characteristic curve, was 0.779 (95% CI 0.774-0.785). Among the top 1% of predicted high-risk patients, 25.5% had 1 or more emergency hospital transports in the next 30 days. Comparison with clinical outcomes from the EHR showed 3.9 times more emergency encounters among predicted high-risk patients than low-risk patients in the year following the prediction date.

CONCLUSIONS

Patient data collected remotely via PERS can be used to reliably predict 30-day emergency hospital transport. Clinical observations from the EHR showed that predicted high-risk patients had nearly four times higher rates of emergency encounters than did low-risk patients. Health care providers could benefit from our validated predictive model by targeting timely preventive interventions to high-risk patients. This could lead to overall improved patient experience, higher quality of care, and more efficient resource utilization.

Personal health data: A systematic mapping study

BACKGROUND

Personal health data (PHD) research has been intensified over the last years, attracting the attention of scientists from different fields, such as software engineers, computer scientists and medical professionals. The increasing interest of researchers can be attributed to the exponential growth of the available PHD due to the widespread adoption of ubiquitous technology in everyday life, as well as to the potential of the ongoing digital transformation in healthcare. This increasing interest requires that academia has an overview of the published scientific literature to plan future endeavors.

OBJECTIVE

The main objective of this study is to identify and address research gaps in literature regarding PHD.

METHOD

This paper conducts a systematic mapping study to summarize the existing PHD approaches in literature and to organize the selected studies according to six classification criteria: publication source, publication year, research types, empirical types, contribution types and research topic.

RESULTS

In total 79 papers have been included after fulfilling the inclusion criteria and have been classified accordingly. There is an increasing amount of attention that has been paid to PHD since 2014. The majority of papers is published in journals. The two main research types found were solution proposals and evaluation research. The majority of the selected papers were empirically evaluated. The main contribution types were methods and frameworks. Data privacy is the most frequently addressed topic in PHD literature, followed by data sharing.

CONCLUSIONS

The findings of this systematic mapping study have implications for both researchers who are planning new studies in PHD and for practitioners who are working in connected health and would like to have an overview on the existent studies on PHD research area.

Health Care Cost Analyses for Exploring Cost Savings Opportunities in Older Patients: Longitudinal Retrospective Study

BACKGROUND

Half of Medicare reimbursement goes toward caring for the top 5% of the most expensive patients. However, little is known about these patients prior to reaching the top or how their costs change annually. To address these gaps, we analyzed patient flow and associated health care cost trends over 5 years.

OBJECTIVE

To evaluate the cost of health care utilization in older patients by analyzing changes in their long-term expenditures.

METHODS

This was a retrospective, longitudinal, multicenter study to evaluate health care costs of 2643 older patients from 2011 to 2015. All patients had at least one episode of home health care during the study period and used a personal emergency response service (PERS) at home for any length of time during the observation period. We segmented all patients into top (5%), middle (6%-50%), and bottom (51%-100%) segments by their annual expenditures and built cost pyramids based thereon. The longitudinal health care expenditure trends of the complete study population and each segment were assessed by linear regression models. Patient flows throughout the segments of the cost acuity pyramids from year to year were modeled by Markov chains.

RESULTS

Total health care costs of the study population nearly doubled from US $17.7M in 2011 to US $33.0M in 2015 with an expected annual cost increase of US $3.6M (P=.003). This growth was primarily driven by a significantly higher cost increases in the middle segment (US $2.3M, P=.003). The expected annual cost increases in the top and bottom segments were US $1.2M (P=.008) and US $0.1M (P=.004), respectively. Patient and cost flow analyses showed that 18% of patients moved up the cost acuity pyramid yearly, and their costs increased by 672%. This was in contrast to 22% of patients that moved down with a cost decrease of 86%. The remaining 60% of patients stayed in the same segment from year to year, though their costs also increased by 18%.

CONCLUSIONS

Although many health care organizations target intensive and costly interventions to their most expensive patients, this analysis unveiled potential cost savings opportunities by managing the patients in the lower cost segments that are at risk of moving up the cost acuity pyramid. To achieve this, data analytics integrating longitudinal data from electronic health records and home monitoring devices may help health care organizations optimize resources by enabling clinicians to proactively manage patients in their home or community environments beyond institutional settings and 30- and 60-day telehealth services.

Evaluating the Impact of a Web-Based Risk Assessment System (CareSage) and Tailored Interventions on Health Care Utilization: Protocol for a Randomized Controlled Trial

Background

Soaring health care costs and a rapidly aging population, with multiple comorbidities, necessitates the development of innovative strategies to deliver high-quality, value-based care.

Objective

The goal of this study is to evaluate the impact of a risk assessment system (CareSage) and targeted interventions on health care utilization.

Methods

This is a two-arm randomized controlled trial recruiting 370 participants from a pool of high-risk patients receiving care at a home health agency. CareSage is a risk assessment system that utilizes both real-time data collected via a Personal Emergency Response Service and historical patient data collected from the electronic medical records. All patients will first be observed for 3 months (observation period) to allow the CareSage algorithm to calibrate based on patient data. During the next 6 months (intervention period), CareSage will use a predictive algorithm to classify patients in the intervention group as “high” or “low” risk for emergency transport every 30 days. All patients flagged as “high risk” by CareSage will receive nurse triage calls to assess their needs and personalized interventions including patient education, home visits, and tele-monitoring. The primary outcome is the number of 180-day emergency department visits. Secondary outcomes include the number of 90-day emergency department visits, total medical expenses, 180-day mortality rates, time to first readmission, total number of readmissions and avoidable readmissions, 30-, 90-, and 180-day readmission rates, as well as cost of intervention per patient. The two study groups will be compared using the Student t test (two-tailed) for normally distributed and Mann Whitney U test for skewed continuous variables, respectively. The chi-square test will be used for categorical variables. Time to event (readmission) and 180-day mortality between the two study groups will be compared by using the Kaplan-Meier survival plots and the log-rank test. Cox proportional hazard regression will be used to compute hazard ratio and compare outcomes between the two groups.

Results

We are actively enrolling participants and the study is expected to be completed by end of 2018; results are expected to be published in early 2019.

Conclusions

Innovative solutions for identifying high-risk patients and personalizing interventions based on individual risk and needs may help facilitate the delivery of value-based care, improve long-term patient health outcomes and decrease health care costs.

Trial Registration

ClinicalTrials.gov NCT03126565; https://clinicaltrials.gov/ct2/show/NCT03126565 (Archived by WebCite at http://www.webcitation.org/6ymDuAwQA).