Design of a Predictive Scheduling System to Improve Assisted Living Services for Elders

International audienceAs the number of older adults increases, and with it the demand for dedicated care, geriatric residences face a shortage of caregivers, who themselves experience work overload, stress and burden. We conducted a long-term field study in three geriatric residences to understand the work conditions of caregivers with the aim of developing technologies to assist them in their work and help them deal with their burden. From this study we obtained relevant requirements and insights of design that were used to design, implement and evaluate two prototypes for supporting caregivers' tasks (e.g. electronic recording and automatic notifications), in order to validate the feasibility of their implementation in-situ and the technical requirements. The evaluation in-situ of the prototypes was conducted for a period of four weeks. The results of the evaluation, together with the data collected from six months of use, motivated the design of a predictive schedule. Such design was iteratively improved and evaluated in participative sessions with caregivers. PRESENCE, the predictive schedule we propose, triggers real-time alerts of risky situations (e.g. falls, entering off-limits areas such as the infirmary or the kitchen) and, informs caregivers of routine tasks that need to be performed (e.g. medication administration, diaper change, etc.). Moreover, PRESENCE helps caregivers to record caring tasks (such as diaper changes or medication) and wellbeing assessments (such as the mood), which are difficult to automatize. This facilitates caregiver's shift handover, and can help to train new caregivers by suggesting routine tasks and by sending reminders and timely information about the residents. It can be seen as a tool to reduce the workload of caregivers and medical staff. Instead of trying to substitute the caregiver with an automatic caring system, as proposed by others, we propose the design of our predictive schedule system that blends caregiver's assessments and measurements from sensors. We show the feasibility of predicting caregiver's tasks and a formative evaluation with caregivers that provides preliminary evidence of its utility

Medical data processing and analysis for remote health and activities monitoring

Recent developments in sensor technology, wearable computing, Internet of Things (IoT), and wireless communication have given rise to research in ubiquitous healthcare and remote monitoring of human\u2019s health and activities. Health monitoring systems involve processing and analysis of data retrieved from smartphones, smart watches, smart bracelets, as well as various sensors and wearable devices. Such systems enable continuous monitoring of patients psychological and health conditions by sensing and transmitting measurements such as heart rate, electrocardiogram, body temperature, respiratory rate, chest sounds, or blood pressure. Pervasive healthcare, as a relevant application domain in this context, aims at revolutionizing the delivery of medical services through a medical assistive environment and facilitates the independent living of patients. In this chapter, we discuss (1) data collection, fusion, ownership and privacy issues; (2) models, technologies and solutions for medical data processing and analysis; (3) big medical data analytics for remote health monitoring; (4) research challenges and opportunities in medical data analytics; (5) examples of case studies and practical solutions

Perceived Readiness for Hospital Discharge in Adult Medical-Surgical Patients

Purpose: The purpose of the study was to identify predictors and outcomes of adult medical-surgical patients\u27 perceptions of their readiness for hospital discharge. Design: A correlational, prospective, longitudinal design with path analyses was used to explore relationships among transition theory-related variables. Setting: Midwestern tertiary medical center. Sample: 147 adult medical-surgical patients. Methods: Predictor variables included patient characteristics, hospitalization factors, and nursing practices that were measured prior to hospital discharge using a study enrollment form, the Quality of Discharge Teaching Scale, and the Care Coordination Scale. Discharge readiness was measured using the Readiness for Hospital Discharge Scale administered within 4 hours prior to discharge. Outcomes were measured 3 weeks postdischarge with the Post-Discharge Coping Difficulty Scale and self-reported utilization of health services. Findings: Living alone, discharge teaching (amount of content received and nurses\u27 skill in teaching delivery), and care coordination explained 51% of readiness for discharge score variance. Patient age and discharge readiness explained 16% of variance in postdischarge coping difficulty. Greater readiness for discharge was predictive of fewer readmissions. Conclusions: Quality of the delivery of discharge teaching was the strongest predictor of discharge readiness. Study results provided support for Meleis\u27 transitions theory as a useful model for conceptualizing and investigating the discharge transition. Implications for Practice: The study results have implications for the CNS role in patient and staff education, system building for the postdischarge transition, and measurement of clinical care outcomes

Introduction to the ACM TIST Special Issue on Intelligent Healthcare Informatics

Healthcare Informatics is a research area dealing with the study and application of computer science and information and communication technology to face both theoretical/methodological and practical issues in healthcare, public health, and everyday wellness. Intelligent Healthcare Informatics may be defined as the specific area focusing on the use of artificial intelligence (AI) theories and techniques to offer important services (such as a component of complex systems) to allow integrated systems to perceive, reason, learn, and act intelligently in the healthcare arena. One of the many peculiarities of healthcare is that decision support systems need to be integrated with several heterogeneous systems supporting both collaborative work and process coordination and the management and analysis of a huge amount of clinical and health data, to compose intelligent, process-aware health information systems. After some pioneering work focusing explicitly on specific medical aspects and providing some efficient, even ad hoc, solutions, in recent years, AI in healthcare has been faced by researchers with different backgrounds and interests, taking into consideration the main results obtained in the more general and theoretical/methodological area of intelligent systems. Moreover, from a focus on reasoning strategies and deep knowledge representation, research in healthcare intelligent systems moved to data-intensive clinical tasks, where there is the need for supporting healthcare decision making in the presence of overwhelming amounts of clinical data. Significant solutions have been provided through a multidisciplinary combination of the results from the different research areas and their associated cultures, ranging from algorithms, to information systems and databases, to human-computer interaction, to medical informatics. To this regard, it is interesting to observe that, from one side, medical informaticians benefited by the general solutions coming from the generic computer science area, tailoring them to specific medical domains, while from the other side, computer scientists found several (still open) challenges in the medical and, more generally, health domains. This ACM Transactions on Intelligent Systems and Technology (ACM TIST) special issue contains articles discussing fundamental principles, algorithms, or applications for process-aware health information systems. Such articles are a sound answer to the research challenges for novel techniques, combinations of tools, and so forth to build effective ways to manage and deal in an integrated way with healthcare processes and data

Clustering Arabic Tweets for Sentiment Analysis

The focus of this study is to evaluate the impact of linguistic preprocessing and similarity functions for clustering Arabic Twitter tweets. The experiments apply an optimized version of the standard K-Means algorithm to assign tweets into positive and negative categories. The results show that root-based stemming has a significant advantage over light stemming in all settings. The Averaged Kullback-Leibler Divergence similarity function clearly outperforms the Cosine, Pearson Correlation, Jaccard Coefficient and Euclidean functions. The combination of the Averaged Kullback-Leibler Divergence and root-based stemming achieved the highest purity of 0.764 while the second-best purity was 0.719. These results are of importance as it is contrary to normal-sized documents where, in many information retrieval applications, light stemming performs better than root-based stemming and the Cosine function is commonly used

Clustering Arabic Tweets for Sentiment Analysis

The focus of this study is to evaluate the impact of linguistic preprocessing and similarity functions for clustering Arabic Twitter tweets. The experiments apply an optimized version of the standard K-Means algorithm to assign tweets into positive and negative categories. The results show that root-based stemming has a significant advantage over light stemming in all settings. The Averaged Kullback-Leibler Divergence similarity function clearly outperforms the Cosine, Pearson Correlation, Jaccard Coefficient and Euclidean functions. The combination of the Averaged Kullback-Leibler Divergence and root-based stemming achieved the highest purity of 0.764 while the second-best purity was 0.719. These results are of importance as it is contrary to normal-sized documents where, in many information retrieval applications, light stemming performs better than root-based stemming and the Cosine function is commonly used

Implementation and Application of Artificial Intelligence in Selected Public Services

Data-intensive technologies, such as artificial intelligence, imply huge opportunities for transforming the delivery of healthcare and social services, improving people’s quality of life and working in the health and welfare system. The aim of this paper is to present examples of the implementation of artificial intelligence techniques in healthcare and social services and to sketch the trends and challenges in the adoption of artificial intelligence techniques, with an emphasis on the public sector and selected public services. Analysis is based on a realistic assessment of current artificial intelligence technologies and their anticipated development. Besides the benefits and potential opportunities for healthcare and social services, there are also challenges for governments. Understanding the huge potential of artificial intelligence as well as its limitations will be a key step forward, but it is essential to avoid the trap of an overestimation of artificial intelligence potential

RADON: Rational decomposition and orchestration for serverless computing

Emerging serverless computing technologies, such as function as a service (FaaS), enable developers to virtualize the internal logic of an application, simplifying the management of cloud-native services and allowing cost savings through billing and scaling at the level of individual functions. Serverless computing is therefore rapidly shifting the attention of software vendors to the challenge of developing cloud applications deployable on FaaS platforms. In this vision paper, we present the research agenda of the RADON project (http://radon-h2020.eu), which aims to develop a model-driven DevOps framework for creating and managing applications based on serverless computing. RADON applications will consist of fine-grained and independent microservices that can efficiently and optimally exploit FaaS and container technologies. Our methodology strives to tackle complexity in designing such applications, including the solution of optimal decomposition, the reuse of serverless functions as well as the abstraction and actuation of event processing chains, while avoiding cloud vendor lock-in through models

Implementating a Transitional Care Program to Reduce Hospital Readmissions in Medicare Recipients: A Research Translation Pilot Project

Patients discharged from hospital to home, especially the chronically ill and older adults, are too frequently readmitted within 30 days. The Centers for Medicare and Medicaid Services (n.d.; 2017) along with other interdisciplinary researchers have proposed, studied, and implemented strategies to decrease this excessive and expensive phenomenon. After the implementation of the Hospital Readmission Reduction Program in 2009, preventable readmissions have decreased but remain at unacceptable levels. Care transitions from hospital to home have been implicated as perilous and fraught with communication breakdown and lack of patient support and follow up. Strategies aimed at both the hospitalization phase and the 30-day transitional phase when the patient returns home have been developed and implemented. This research translation project implemented a program of transitional care management in a community clinic in Las Vegas, Nevada in accordance of the guidelines of the transitional care model (TCM). Five patients were referred to the clinic by two home health agencies. The project coordinator provided transitional care for these patients for the duration of their home health certification. All of the patients were high risk for rehospitalization according to evidence-based screening tools. At the end of 30 days, none of the five patients had been rehospitalized. Additionally, two patients were referred from another medical practice and the project coordinator evaluated them through chart review and saw them once. The sample size and non-randomized sampling method precluded generalization of the findings. However, the project revealed important qualitative data relative to risks and interventions impacting rehospitalization risk as well as issues, barriers, and facilitators related to the practice of transitional care in the community setting. Several of these findings were not specifically identified within the TCM. Themes were derived from findings and a causal network was developed. Patients received excellent and effective transitional care and the project added to the body of knowledge of transitional care implementation

Secondary prevention and cognitive function after stroke: a study protocol for a 5-year follow-up of the ASPIRE-S cohort

Introduction Cognitive impairment is common following stroke and can increase disability and levels of dependency of patients, potentially leading to greater burden on carers and the healthcare system. Effective cardiovascular risk factor control through secondary preventive medications may reduce the risk of cognitive decline. However, adherence to medications is often poor and can be adversely affected by cognitive deficits. Suboptimal medication adherence negatively impacts secondary prevention targets, increasing the risk of recurrent stroke and further cognitive decline. The aim of this study is to profile cognitive function and secondary prevention, including adherence to secondary preventive medications and healthcare usage, 5 years post-stroke. The prospective associations between cognition, cardiovascular risk factors, adherence to secondary preventive medications, and rates of recurrent stroke or other cardiovascular events will also be explored. Methods and analysis This is a 5-year follow-up of a prospective study of the Action on Secondary Prevention Interventions and Rehabilitation in Stroke (ASPIRE-S) cohort of patients with stroke. This cohort will have a detailed assessment of cognitive function, adherence to secondary preventive medications and cardiovascular risk factor control. Ethics and dissemination Ethical approval for this study was granted by the Research Ethics Committees at Beaumont Hospital, Dublin and Connolly Hospital, Dublin, Mater Misericordiae University Hospital, Dublin, and the Royal College of Surgeons in Ireland. Findings will be disseminated through presentations and peer-reviewed publications