Personal Emergency Response System Research Articles

Factors Associated with Falls in Community-Dwelling Older Adults: A Subgroup Analysis from a Telemergency Service.

According to the number of falls, fallers can be single (only one fall) or recurrent (two or more falls), with different risk profiles for loss of independence and frailty. The presence of risk factors in community-dwelling single- and recurrent fallers using a wearable fall-detection device, such as the Personal Emergency Response System (PERS), as part of a telemergency service, is still unknown. This article evaluates how using a PERS, within a telemergency service, helps identify risk profiles and assessment of any differences between non-fallers and fallers in community-dwelling older adults. A sub-group analysis was performed, dividing users into non-fallers (n = 226) and fallers (≥1 fall; n = 89); single-fallers (n = 66) and recurrent fallers (n = 23). Median age was higher in fallers (87.7 years vs. 86), whereas recurrent fallers were less independent, had fewer comorbidities, and had more low-extremity disabilities. The use of the PERS for medical problems (Adjusted OR = 0.31), excluding falls, support calls (Adjusted OR = 0.26), and service demands (Adjusted OR = 0.30), was significantly associated with a fall risk reduction. The findings suggest that the integration within a telemergency service may impact on fall-risk factors.

Ambulance deployment without transport: a retrospective difference analysis for the description of emergency interventions without patient transport in Bavaria

BackgroundNot all patients who call the ambulance service are subsequently transported to hospital. In 2018, a quarter of deployments of an emergency ambulance in Bavaria were not followed by patient transport. This study describes factors that influence patient transport rates.MethodThis is a retrospective cross-sectional study based on data from all Integrated Dispatch Centres of the Free State of Bavaria in 2018. Included were ambulance deployments without emergency physician involvement, which were subdivided into ambulance deployments without transport and ambulance deployments with transport. The proportion of transported patients were determined for the primary reasons for deployment and for the different community types. On-scene time was compared for calls with and without patient transport. Differences were tested for statistical significance using Chi2 tests and the odds ratio was calculated to determine differences between groups.ResultsOf 510,145 deployments, 147,621 (28.9%) could be classified as ambulance deployments without transport and 362,524 (71.1%) as ambulance deployments with transport.The lowest proportion of patients transported was found for activations where the fire brigade was involved (“fire alarm system” 0.6%, “fire with emergency medical services” 5.4%) and “personal emergency response system active alarm” (18.6%). The highest transport rates were observed for emergencies involving “childbirth/delivery” (96.9%) and “trauma” (83.2%). A lower proportion of patients is transported in large cities as compared to smaller cities or rural communities; in large cities, the odds ratio for emergencies without transport is 2.02 [95% confidence interval 1.98–2.06] referenced to rural communites. The median on-scene time for emergencies without transport was 20.8 min (n = 141,052) as compared to 16.5 min for emergencies with transport (n = 362,524). The shortest on-scene times for emergencies without transport were identified for activations related to “fire alarm system” (9.0 min) and “personal emergency response system active alarm” (10.6 min).ConclusionThis study indicates that the proportion of patients transported depends on the reason for deployment and whether the emergency location is urban or rural. Particularly low transport rates are found if an ambulance was dispatched in connection with a fire department operation or a personal emergency medical alert button was activated. The on-scene-time of the rescue vehicle is increased for deployments without transport. The study could not provide a rationale for this and further research is needed.Trial registration This paper is part of the study “Rettungswageneinsatz ohne Transport” [“Ambulance deployment without transport”] (RoT), which was registered in the German Register of Clinical Studies under the number DRKS00017758.

Unterschiede zwischen Rettungsdiensteinsätzen mit und ohne Patiententransport

ZusammenfassungHintergrundDie Inanspruchnahme des Rettungsdienstes in Bayern steigt seit Jahren an. Wir haben die Hypothese aufgestellt, dass Notfalleinsätze ohne Patiententransport (RoT) häufig Ausdruck einer unzureichenden Alarmierungsplanung sind. Das Ziel der Studie war es, für solche Einsätze die Unterschiede zwischen den Integrierten Leitstellen (ILS) in Bezug auf die Merkmale Transportquoten und Spannweiten nach Einsatzgrund sowie Uhrzeiten und Wochentage zu beschreiben.MethodeRetrospektive Querschnittstudie der Daten aller 26 ILS des Freistaats Bayern im Jahr 2018. Transportquoten für wesentliche Einsatzgründe bei Notfalleinsätzen ohne Notarztbeteiligung wurden in Abhängigkeit von Leitstellenbereich, Tageszeit und Wochentag vergleichend analysiert. Einsätze wurden kategorisiert als RoT oder TP (Rettungswageneinsatz mit Transport).ErgebnisseVon 510.145 Einsätzen waren 147.621 (28,9 %) RoT und 362.524 (71,1 %) TP. Für alle untersuchten Einsatzgründe zeigten sich deutliche regionale Unterschiede in der Transportquote. Die höchste Spannweite unter den ILS ergab sich für die Einsatzgründe „Brandmeldeanlage“ (16,8 Prozentpunkte), „Hausnotruf aktiver Alarm“ (16,1) sowie „Herz/Kreislauf“ (14,6). In den Morgenstunden sinkt das Einsatzaufkommen bei steigenden TP. Die wenigsten RoT fanden zwischen 8 und 10 Uhr statt. Die Analyse der Wochentage ergab kleine Unterschiede in der Häufigkeit von RoT an Montagen sowie an Wochenenden ohne planerische Relevanz.SchlussfolgerungWir haben deutliche Unterschiede in den Spannweiten festgestellt. Dies könnte auf örtlich unterschiedliche Alarmierungsplanungsvorgaben oder Dispositionsentscheidungen der ILS hindeuten. Die Leitstellen weisen hier wahrscheinlich ein erhebliches Potenzial zur Steuerung und Verbesserung der Ressourcenallokation auf.

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.

A randomized trial examining the effect of predictive analytics and tailored interventions on the cost of care

This two-arm randomized controlled trial evaluated the impact of a Stepped-Care intervention (predictive analytics combined with tailored interventions) on the healthcare costs of older adults using a Personal Emergency Response System (PERS). A total of 370 patients aged 65 and over with healthcare costs in the middle segment of the cost pyramid for the fiscal year prior to their enrollment were enrolled for the study. During a 180-day intervention period, control group (CG) received standard care, while intervention group (IG) received the Stepped-Care intervention. The IG had 31% lower annualized inpatient cost per patient compared with the CG (3.7 K, $8.1 K vs. $11.8 K, p = 0.02). Both groups had similar annualized outpatient costs per patient ($6.1 K vs. $5.8 K, p = 0.10). The annualized total cost reduction per patient in the IG vs. CG was 20% (3.5 K, $17.7 K vs. $14.2 K, p = 0.04). Predictive analytics coupled with tailored interventions has great potential to reduce healthcare costs in older adults, thereby supporting population health management in home or community settings.

Predictive Modeling of 30-Day Emergency Hospital Transport of German Patients Using a Personal Emergency Response: Retrospective Study and Comparison with the United States.

BackgroundPredictive analytics based on data from remote monitoring of elderly via a personal emergency response system (PERS) in the United States can identify subscribers at high risk for emergency hospital transport. These risk predictions can subsequently be used to proactively target interventions and prevent avoidable, costly health care use. It is, however, unknown if PERS-based risk prediction with targeted interventions could also be applied in the German health care setting.ObjectiveThe objectives were to develop and validate a predictive model of 30-day emergency hospital transport based on data from a German PERS provider and compare the model with our previously published predictive model developed on data from a US PERS provider.MethodsRetrospective data of 5805 subscribers to a German PERS service were used to develop and validate an extreme gradient boosting predictive model of 30-day hospital transport, including predictors derived from subscriber demographics, self-reported medical conditions, and a 2-year history of case data. Models were trained on 80% (4644/5805) of the data, and performance was evaluated on an independent test set of 20% (1161/5805). Results were compared with our previously published prediction model developed on a data set of PERS users in the United States.ResultsGerman PERS subscribers were on average aged 83.6 years, with 64.0% (743/1161) females, with 65.4% (759/1161) reported 3 or more chronic conditions. A total of 1.4% (350/24,847) of subscribers had one or more emergency transports in 30 days in the test set, which was significantly lower compared with the US data set (2455/109,966, 2.2%). Performance of the predictive model of emergency hospital transport, as evaluated by area under the receiver operator characteristic curve (AUC), was 0.749 (95% CI 0.721-0.777), which was similar to the US prediction model (AUC=0.778 [95% CI 0.769-0.788]). The top 1% (12/1161) of predicted high-risk patients were 10.7 times more likely to experience an emergency hospital transport in 30 days than the overall German PERS population. This lift was comparable to a model lift of 11.9 obtained by the US predictive model.ConclusionsDespite differences in emergency care use, PERS-based collected subscriber data can be used to predict use outcomes in different international settings. These predictive analytic tools can be used by health care organizations to extend population health management into the home by identifying and delivering timelier targeted interventions to high-risk patients. This could lead to overall improved patient experience, higher quality of care, and more efficient resource use.

Evaluating the Impact of a Risk Assessment System With Tailored Interventions in Germany: Protocol for a Prospective Study With Matched Controls.

BackgroundWith a worldwide increase in the elderly population, and an associated increase in health care utilization and costs, preventing avoidable emergency department visits and hospitalizations is becoming a global priority. A personal emergency response system (PERS), consisting of an alarm button and a means to establish a live connection to a response center, can help the elderly live at home longer independently. Individual risk assessment through predictive modeling can help indicate what PERS subscribers are at elevated risk of hospital transport so that early intervention becomes possible.ObjectiveThe aim is to evaluate whether the combination of risk scores determined through predictive modeling and targeted interventions offered by a case manager can result in a reduction of hospital admissions and health care costs for a population of German PERS subscribers. The primary outcome of the study is the difference between the number of hospitalizations in the intervention and matched control groups.MethodsAs part of the Sicher Zuhause program, an intervention group of 500 PERS subscribers will be tracked for 8 months. During this period, risk scores will be determined daily by a predictive model of hospital transport, and at-risk participants may receive phone calls from a case manager who assesses the health status of the participant and recommends interventions. The health care utilization of the intervention group will be compared to a group of matched controls, retrospectively drawn from a population of PERS subscribers who receive no interventions.ResultsDifferences in health care utilization and costs between the intervention group and the matched controls will be determined based on reimbursement records. In addition, qualitative data will be collected on the participants’ satisfaction with the Sicher Zuhause program and utilization of the interventions offered as part of the program.ConclusionsThe study evaluation will offer insight into whether a combination of predictive analytics and case manager-driven interventions can help in avoiding hospital admissions and health care costs for PERS subscribers in Germany living at home independently. In the future, this may lead to improved quality of life and reduced medical costs for the population of the study.Trial RegistrationDeutsches Register Klinischer Studien (DRKS), DRKS00017328; https://www.drks.de/drks_web/navigate.do?navigationId=trial.HTML&TRIAL_ID=DRKS00017328International Registered Report Identifier (IRRID)DERR1-10.2196/17584

Quantifying the economic benefit of the personal alarm and emergency response system in Australia: a cost analysis of the reduction in ambulance attendances.

Objectives MePACS is a triage and support-based personal alarm emergency response system designed to assist older and/or disabled people to live safely in their homes. The aim of this study was to estimate avoidable ambulance attendances and transports to emergency departments and quantify the cost savings attributed to MePACS compared with a comparison cohort without a personal alarm system. Methods Alarm activation and demographic data for clients registered in the program from June 2016 to May 2017 and funded through the Personal Alert Victoria program were extracted from routinely collected MePACS electronic data. Information on alarm use, event outcomes and ambulance attendances was extracted. Using published Ambulance Victoria data, a comparison cohort was simulated to model the experience of a similar cohort without access to a personal alarm system who experienced a health emergency and called Australia's emergency call service number. The incremental management cost, incorporating the operation cost of MePACS and ambulance fees, was calculated to compare the potential cost savings of MePACS with the comparison cohort. Results Among 18421 eligible clients, there were 7856 emergency alarm activations due to falls or medical events from 4275 clients (79.5% female; 81.1% aged >75 years; 91.8% living alone). MePACS resulted in approximately one-third of ambulance attendances being avoided. Potential annual cost savings of A$1414732.8 (A$76.8 per person per year; 95% confidence interval A$60.0-93.6 per person per year; P<0.001) were attributed to avoidable ambulance utilisations for 18421 MePACS clients. Conclusion Triage-based personal alarm systems, such as MePACS, provide a cost-saving strategy because of fewer ambulance attendances and transports to emergency departments for older and/or disabled people living in the community. What is known about the topic? Personal alarm systems, either a safety alarm, a panic alarm or a medical alarm, have been widely used to support older people and at-risk groups to live independently. The health effects of personal alarm systems are well documented. There is limited information published on the potential economic benefits of these interventions. What does this paper add? We identified the potential economic benefits associated with the MePACS triage-based personal alarm system operating within Victoria, Australia. Personal alarm systems that are triage based and use the support of family members and carers may provide financial benefits not provided by alarm systems that do not provide this type of service. What are the implications for practitioners? The management of older people and at-risk groups living alone is a health care priority. Alternative models to calling emergency services, such as triage-based personal emergency response systems, may provide a low-cost, effective approach.

Discrepancies between Expected and Actual Implementation: The Process Evaluation of PERS Integration in Nursing Homes.

Recent studies prove that when implementing new technology technology-driven and one-size-fits-all approaches are problematic. This study focuses on the process of implementing personal emergency response system (PERS) at nursing homes. The aim is to understand why the implementation of PERS has not met initial expectations. Multiple methods were used in two Swedish nursing homes, including document analysis, questionnaires (n = 42), participant observation (67 h), and individual interviews (n = 12). A logic model was used to ascertain the discrepancies that emerged between expected and actual implementation, and the domestication theory was used to discuss the underlying meanings of the discrepancies. The discrepancies primarily focused on staff competence, system readiness, work routines, and implementation duration. Corresponding reasons were largely relevant to management issues regarding training, the procurement systems, individual and collective responsibilities as well as invisible work. The uptake of technology in daily practice is far more nuanced than a technology implementation plan might imply. We point out the importance of preparing for implementation, adjusting to new practices, and leaving space and time for facilitating implementation. The findings will be of use to implementers, service providers, and organizational managers to evaluate various measures in the implementation process, enabling them to perform technology implementation faster and more efficiently.

ATA2020 Session Summaries (Abstracts)

ATA2020 Session Summaries (Abstracts)

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

BackgroundTelehealth 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.ObjectiveThe 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).MethodsWe 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.ResultsIn 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.ConclusionsPatient 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.

An effective cloud based personal emergency response system by providing privacy protection for the medical data

Timely access to the emergency medical services is challenging tasks due to the increasing percentage of population. Especially prehospital emergency situations are neglected for quite a long time. The agglomeration of medical gadgets and other system applications that bridges the gap to healthcare IT systems through Internet or computer networks is placed under the domain (IoMT) Internet Of Medical Things . In this project an efficient medical data monitoring and an emergency response system has been developed. IoT in healthcare is made to bridge the gap by providing the connectivity through internet making sure that the information is secured and available on the timely access. The wireless monitoring device collects data from various sensors, which is shared into the database using ZigBee cc2530 Transmitter Receiver through UART communication. The wireless device is embedded with sensors like Pulse oximeter (senses the heart beat rate and blood oxygen concentration)and temperature sensor. This data is monitored regularly and if there is any abnormality in the values then an alert is sent to the doctor to intimate the condition of the patient. This paper provides an interaction of the patient with the doctors. The interaction is enabled using public and private chat application where any number of patients can interact with a doctor. It is challenging task to personalize specific healthcare data i.e. medical records for various application users in an appropriate and secured fashion. Thus, the patient’s health records i.e. prescription, scan reports, etc. are uploaded by the patient which can be viewed by the doctor. These files are encrypted using an encryption algorithm before uploading it into the cloud. Similarly it is decrypted while downloaded by the doctor for reference. The data collected is used for the prediction of diseases like heart attack and the report is sent to the doctor. Hence the project’s aim is, when the recorded value exceeds the threshold range, an automatic intimation with the patient’s collected data is sent to the concerned doctor.

Predictive Modeling of Emergency Hospital Transport Based on a Personal Emergency Response System (PERS): Comparison to Clinical Outcomes

Background: With the worldwide increase in the elderly population, chronic diseases and associated healthcare utilization such as costly emergency department visits and subsequent hospitalizations are also on the rise. Predictive analytics can be used to identify patients at high risk for emergency utilization, using Electronic Health Record (EHR) data collected before or at hospital discharge. In addition, non-hospital data may be useful for prediction of changes in risk outside of hospital settings. Inexpensive monitoring of elderly via a Personal Emergency Response System (PERS) to identify patients at high risk for emergency hospital transport could be used to target interventions and prevent avoidable, costly long-term healthcare utilization.

Exploratory analysis of real personal emergency response call conversations: considerations for personal emergency response spoken dialogue systems.

BackgroundThe purpose of this study was to derive data from real, recorded, personal emergency response call conversations to help improve the artificial intelligence and decision making capability of a spoken dialogue system in a smart personal emergency response system. The main study objectives were to: develop a model of personal emergency response; determine categories for the model’s features; identify and calculate measures from call conversations (verbal ability, conversational structure, timing); and examine conversational patterns and relationships between measures and model features applicable for improving the system’s ability to automatically identify call model categories and predict a target response.MethodsThis study was exploratory and used mixed methods. Personal emergency response calls were pre-classified according to call model categories identified qualitatively from response call transcripts. The relationships between six verbal ability measures, three conversational structure measures, two timing measures and three independent factors: caller type, risk level, and speaker type, were examined statistically.ResultsEmergency medical response services were the preferred response for the majority of medium and high risk calls for both caller types. Older adult callers mainly requested non-emergency medical service responders during medium risk situations. By measuring the number of spoken words-per-minute and turn-length-in-words for the first spoken utterance of a call, older adult and care provider callers could be identified with moderate accuracy. Average call taker response time was calculated using the number-of-speaker-turns and time-in-seconds measures. Care providers and older adults used different conversational strategies when responding to call takers. The words ‘ambulance’ and ‘paramedic’ may hold different latent connotations for different callers.ConclusionsThe data derived from the real personal emergency response recordings may help a spoken dialogue system classify incoming calls by caller type with moderate probability shortly after the initial caller utterance. Knowing the caller type, the target response for the call may be predicted with some degree of probability and the output dialogue could be tailored to this caller type. The average call taker response time measured from real calls may be used to limit the conversation length in a spoken dialogue system before defaulting to a live call taker.

Propensity for Japanese-American older adults' use of medical alert services in Hawaii.

Hawaii is a fast-aging, multi-ethnic state. At present, ethnic Japanese represent the largest segment of the elderly population (aged ≥65 years). Studies in Western countries have identified an association between ethnicity and the use of homecare services. The present study sought to substantiate this association in relation to the use of a personal emergency response system service by older ethnic Japanese adults in Hawaii. Questionnaires were mailed to 585 elderly subscribers of a company providing personal emergency response system services in Hawaii in 2014. The questionnaire inquired about the subscriber's ethnicity, functional disability, number of medications taken and falls experienced. Additional information on the sex, age and service payer was obtained for each of the responding subscribers from the corporate database. The χ2 -test and t-test were carried out to assess the associations between factors. A total of 244 questionnaires were analyzed (41.7% response rate; mean age of respondents 86.1 + 7.8 years). Compared with non-Japanese older adults, ethnic Japanese older adults had a higher propensity to use the personal emergency response system services, were older, used fewer prescribed medications and were more likely to have these services paid for by family members or to pay themselves. Japanese culture might influence the use of personal emergency response system services. Ethnic Japanese older adults might want to receive homecare services focusing on preventing minor accidents from becoming medical crises because of their high level of concern for health, their independence and the availability of family support. Healthcare planners should consider such factors in the provision of care in Hawaii. Geriatr Gerontol Int 2017; 17: 1392-1398.

The Personal Emergency Response System as a Technology Innovation in Primary Health Care Services: An Integrative Review

BackgroundMost western countries are experiencing greater pressure on community care services due to increased life expectancy and changes in policy toward prioritizing independent living. This has led to a demand for change and innovation in caring practices with an expected increased use of technology. Despite numerous attempts, it has proven surprisingly difficult to implement and adopt technological innovations. The main established technological innovation in home care services for older people is the personal emergency response system (PERS), which is widely adopted and used throughout most western countries aiming to support “aging safely in place.”ObjectiveThis integrative review examines how research literature describes use of the PERS focusing on the users’ perspective, thus exploring how different actors experience the technology in use and how it affects the complex interactions between multiple actors in caring practices.MethodsThe review presents an overview of the body of research on this well-established telecare solution, indicating what is important for different actors in regard to accepting and using this technology in community care services. An integrative review, recognized by a systematic search in major databases followed by a review process, was conducted.ResultsThe search resulted in 33 included studies describing different actors’ experiences with the PERS in use. The overall focus was on the end users’ experiences and the consequences of having and using the alarm, and how the technology changes caring practices and interactions between the actors.ConclusionsThe PERS contributes to safety and independent living for users of the alarm, but there are also unforeseen consequences and possible improvements in the device and the integrated service. This rather simple and well-established telecare technology in use interacts with the actors involved, creating changes in daily living and even affecting their identities. This review argues for an approach to telecare in which the complexity of practice is accounted for and shows how the plug-and-play expectations producers tend to generate is a simplification of the reality. This calls for a recognition that place and actors matter, as does a sensitivity to technology as an integrated part of complex caring practices.

Predictive Modeling of Emergency Hospital Transport Using Medical Alert Pattern Data: Retrospective Cohort Study

Background: In the transition from a fee-for-service to a fee-for-value system, health care organizations (HCOs) are under pressure to keep patients healthy through preventive services and population health management. Predictive analytics based on the past health behavior of the patient population can be used to predict future risk of decline. Objective: The objective of this study was to develop robust predictive models of impending emergency transports to the hospital based on enrollment and medical alert pattern data from subscribers of a Personal Emergency Response System (PERS) service. This enables targeting of clinical programs to members that need it the most. Methods: De-identified medical alert pattern data of 551,127 subscribers to a PERS service were used. Multivariate logistic regression was performed on subscriber demographics, self-reported medical conditions, variables related to the care giver network and variables derived from up to one year of retrospective medical alert data. A 10-fold cross-validation scheme was used to predict transport to the hospital by emergency medical services in the next 30 days. Furthermore, the model performance was evaluated after retraining using up to 90 days of medical alert data, and using enrollment data only. Results: Emergency hospital transport in the 30-day window was experienced by 2.4% of all subscribers. The area under the receiver operator characteristic curve (auROC) was 0.75 ± 0.01 in the validation cohorts. The model using up to 90 days of data resulted in auROC = 0.71 ± 0.01 and the model using enrollment data only resulted in auROC = 0.62 ± 0.01. Conclusions: Our model for emergency hospital transport in subscribers of a medical alert service showed good discriminatory accuracy on retrospective validation data. While the model yields good discriminatory accuracy with up to 90 days of data, best performance is achieved using up to one year of medical alert data. The model using enrollment data only, without medical alert pattern data, does not perform as well. We are planning a prospective validation of the algorithm to determine the value of the predictive model in assisting HCOs with planning early interventions to avoid emergency department visits and hospitalizations. [iproc 2015;1(1):e19]

Stakeholder Attitudes Toward and Values Embedded in a Sensor-Enhanced Personal Emergency Response System

Stakeholder Attitudes Toward and Values Embedded in a Sensor-Enhanced Personal Emergency Response System

Towards the development of a speech-based and intelligent personal emergency response system: Identification of key conversational features in personal emergency response calls

Towards the development of a speech-based and intelligent personal emergency response system: Identification of key conversational features in personal emergency response calls

Incidents When Older Homebound Women Tried to Reach Help Quickly

During a longitudinal study of the experience of reaching help quickly, 34 homebound women (ages 85 to 97) who lived alone reported 106 reach-help-quickly incidents (RHQIs). The purpose of this study was to expand knowledge about RHQIs and intentions relative to them and to compare those facets of experience for subscribers to a personal emergency response system (PERS) and non-subscribers. We used a descriptive phenomenological method to analyze interview data, discerning six types of RHQIs, including finding myself down right here, realizing that I might not be alright after falling and getting up on my own, and realizing that something I cannot explain is or could be wrong with me. Intentions were focused on self-help before help seeking. The overall phenomenon was Handling a Situation When I Am Alone at Home and Probably Need Help Quickly. Practitioners should explore intentions about handling specific types of RHQIs and offer appropriate anticipatory guidance.