Aleksandra Tesanovic

eHealth personalization in the next generation RPM systems

Remote patient management (RPM) systems enable (i) monitoring of vital signs of patients at their home, and (ii) providing patients in their homes instructional, educational, or motivational feedback. As such, RPM systems collect a lot of (different types of) data about patients. Although richness of data provides an opportunity for tailoring and personalizing information services, there is a limited understanding of the necessary architecture, methodology, and tailoring criteria to facilitate personalization. In this paper we present (i) a possible next generation RPM system that enables personalization of educational content and its delivery to patients, (ii) introduce a generic methodology for personalization and emphasize the role of knowledge discovery (KDD) and (iii) outline the KDD process with a case study showing an example patient model and adaptation rules.

Depression as an independent prognostic factor for all-cause mortality after a hospital admission for worsening heart failure

BACKGROUND Depression is associated with increased mortality amongst patients with chronic heart failure (HF). Whether depression is an independent predictor of outcome in patients admitted for worsening of HF is unclear. METHODS OPERA-HF is an observational study enrolling patients hospitalized with worsening HF. Depression was assessed by the Hospital Anxiety and Depression Scale (HADS-D) questionnaire. Comorbidity was assessed by the Charlson Comorbidity Index (CCI). Kaplan-Meier and Cox regression analyses were used to estimate the association between depression and all-cause mortality. RESULTS Of 242 patients who completed the HADS-D questionnaire, 153, 54 and 35 patients had no (score 0-7), mild (score 8-10) or moderate-to-severe (score 11-21) depression, respectively. During follow-up, 35 patients died, with a median time follow-up of 360days amongst survivors (interquartile range, IQR 217-574days). In univariable analysis, moderate-to-severe depression was associated with an increased risk of death (HR: 4.9; 95% CI: 2.3 to 10.2; P<0.001) compared to no depression. Moderate-to-severe depression also predicted all-cause mortality after controlling for age, CCI score, NYHA class IV, NT-proBNP and treatment with mineralocorticoid receptor antagonist, beta-blocker and diuretics (HR: 3.0; 95% CI: 1.3 to 7.0; P<0.05). CONCLUSIONS Depression is strongly associated with an adverse outcome in the year following discharge after an admission to hospital for worsening HF. The association is only partly explained by the severity of HF or comorbidity. Further research is required to demonstrate whether recognition and treatment of depression improves patient outcomes.

Prognostic value of psychosocial factors for first and recurrent hospitalizations and mortality in heart failure patients: insights from the OPERA-HF study.

Psychosocial factors are rarely collected in studies investigating the prognosis of patients with heart failure (HF), and only time to first event is commonly reported. We investigated the prognostic value of psychosocial factors for predicting first or recurrent events after discharge following hospitalization for HF.

Heart failure hospitalization prediction in remote patient management systems

Healthcare systems are shifting from patient care in hospitals to monitored care at home. It is expected to improve the quality of care without exploding the costs. Remote patient management (RPM) systems offer a great potential in monitoring patients with chronic diseases, like heart failure or diabetes. Patient modeling in RPM systems opens opportunities in two broad directions: personalizing information services, and alerting medical personnel about the changing conditions of a patient. In this study we focus on heart failure hospitalization (HFH) prediction, which is a particular problem of patient modeling for alerting. We formulate a short term HFH prediction problem and show how to address it with a data mining approach. We emphasize challenges related to the heterogeneity, different types and periodicity of the data available in RPM systems. We present an experimental study on HFH prediction using, which results lay a foundation for further studies and implementation of alerting and personalization services in RPM systems.

HeartCycle: From insights to clinically evaluated ICT solutions for Telehealth

HeartCycle is a large European Integrated Project (IP) and develops technologies and services for Telehealth, which is to remotely monitor and manage patients at home and motivate them to be compliant to treatment regimens and to a beneficial lifestyle. Telehealth allows healthcare professionals to better control the progress of the therapy, detect upcoming adverse events early and react in time with personalized care plan adjustments, leading to prevent relapses, stabilizing the patient and avoid costly hospitalizations.

Heart Failure Risk Models and Their Readiness for Clinical Practice

The aging population is putting an ever increasing burden on healthcare costs, of which care for Heart Failure patients constitutes a major portion. High readmission rates are observed for this large and increasing patient population, which contribute to a large extent to the costs involved in care for Heart Failure. Risk models, when applied in a Clinical Decision Support system, have the potential to help to optimize care based upon expected mortality or readmission. By tailoring care and optimizing care transitions, healthcare costs can be reduced and quality of life of patients may be improved. Although numerous risk models for hospitalized Heart Failure patients have been coined, the uptake of such models in clinical practice is currently very limited. In a quest to identify risk models with high potential and the conditions for successful adaptation, a literature review was performed, identifying 55 Heart Failure risk models, and opportunities explored to apply such models in clinical practice.

Evolving embedded product lines: opportunities for aspects

The traditional constraints on software development and architectures in the consumer electronics domain, including the low cost of manufacturing of a product, support for families of products, etc., have been a key driver for the development of component-based product lines (e.g., in consumer electronics at Philips). In this paper we show that adding new features to a product line over time results in crosscutting changes to a system and its constituting components. Given the nature of problems experienced when evolving consumer products with new features, we outline opportunities for using aspect-oriented technologies to address some of these problems.

Engineering active behavior of embedded software to improve performance and evolution: an aspect-oriented approach

In this paper we propose a novel aspect-oriented scheme for implementing active behavior in embedded software with requirements on data freshness. The scheme improves system performance by combining active behavior in terms of event-condition-action (ECA) rules and on-demand updating. We design and implement the scheme in terms of aspects, thereby exploiting aspect-oriented programming technology to efficiently handle crosscutting nature of active behavior. The benefits of our approach are demonstrated using a case study of an embedded database system called COMET. Namely, simulations on the COMET database indicate that its performance increases by incorporating our scheme. Furthermore, using the COMET example we show that aspect-oriented implementation of active behavior has benefits when it comes to easier evolution of the system.

A holistic framework for understanding acceptance of Remote Patient Management (RPM) systems by non-professional users

The successful integration of Information and Communication Technologies (ICT) in healthcare facilitates the use of the sophisticated medical equipment and computer applications by medical practitioners. If earlier medical systems were mainly used by the health professionals (e.g. medical staff or nurses), nowadays with the appearance of Internet health systems are becoming available to the broader user groups, particularly patients and their families. eHealth has become an active research and development area within healthcare industry. Another important tendency in the development of ICT for health is a shift from “hospital-centered” to “person-centered” health systems which can enable maintaining and improving the quality of care without exploding costs. While technological side has been intensively developed within several research areas, the adoption of eHealth from a users perspective has gained too less research attention. Our current understanding of factors that affect acceptance of ICT-based eHealth systems by prospective non-professional users (patients) is still in its infancy. In information systems research the Unified Theory of Acceptance and Usage of Technology (UTAUT) has been applied by many researchers. There are already some uses of it in the eHealth area. In this paper we consider the UTAUT-model and its eHealth applications and suggest a holistic framework for further studies of user acceptance in Remote Patient Management (RPM).