Adoption Space and the Idea-to-Market Process of Health Technologies.

Although Europe 'produces' excellent science, it has not been equally successful in translating scientific results into commercially successful companies in spite of European and national efforts invested in supporting the translation process. The Idea-to-Market process is highly complex due to the large number of actors and stakeholders. ITECH was launched to propose recommendations which would accelerate the Idea-to-Market process of health technologies leading to improvements in the competitiveness of the European health technology industry in the global markets. The project went through the following steps: defining the Idea-to-Market process model; collection and analysis of funding opportunities; identification of 12 gaps and barriers in the Idea-to-Market process; a detailed analysis of these supported by interviews; a prioritization process to select the most important issues; construction of roadmaps for the prioritized issues; and finally generating recommendations and associated action plans. Seven issues were classified as in need of actions. Three of these are part of the ongoing Medical Device Directive Reform (MDR), namely health technology assessment, post-market surveillance and regulatory process, and therefore not within the scope of ITECH. Recommendations were made for eHealth taxonomy; Education and training; Clinical trials and Adoption space and Human Factors Engineering (HFE).

Behavioral Informatics and Computational Modeling in Support of Proactive Health Management and Care.

Health-related behaviors are among the most significant determinants of health and quality of life. Improving health behavior is an effective way to enhance health outcomes and mitigate the escalating challenges arising from an increasingly aging population and the proliferation of chronic diseases. Although it has been difficult to obtain lasting improvements in health behaviors on a wide scale, advances at the intersection of technology and behavioral science may provide the tools to address this challenge. In this paper, we describe a vision and an approach to improve health behavior interventions using the tools of behavioral informatics, an emerging transdisciplinary research domain based on system-theoretic principles in combination with behavioral science and information technology. The field of behavioral informatics has the potential to optimize interventions through monitoring, assessing, and modeling behavior in support of providing tailored and timely interventions. We describe the components of a closed-loop system for health interventions. These components range from fine grain sensor characterizations to individual-based models of behavior change. We provide an example of a research health coaching platform that incorporates a closed-loop intervention based on these multiscale models. Using this early prototype, we illustrate how the optimized and personalized methodology and technology can support self-management and remote care. We note that despite the existing examples of research projects and our platform, significant future research is required to convert this vision to full-scale implementations.

Building new computational models to support health behavior change and maintenance: new opportunities in behavioral research.

Adverse and suboptimal health behaviors and habits are responsible for approximately 40 % of preventable deaths, in addition to their unfavorable effects on quality of life and economics. Our current understanding of human behavior is largely based on static "snapshots" of human behavior, rather than ongoing, dynamic feedback loops of behavior in response to ever-changing biological, social, personal, and environmental states. This paper first discusses how new technologies (i.e., mobile sensors, smartphones, ubiquitous computing, and cloud-enabled processing/computing) and emerging systems modeling techniques enable the development of new, dynamic, and empirical models of human behavior that could facilitate just-in-time adaptive, scalable interventions. The paper then describes concrete steps to the creation of robust dynamic mathematical models of behavior including: (1) establishing "gold standard" measures, (2) the creation of a behavioral ontology for shared language and understanding tools that both enable dynamic theorizing across disciplines, (3) the development of data sharing resources, and (4) facilitating improved sharing of mathematical models and tools to support rapid aggregation of the models. We conclude with the discussion of what might be incorporated into a "knowledge commons," which could help to bring together these disparate activities into a unified system and structure for organizing knowledge about behavior.

Telemonitoring and Mobile Phone-Based Health Coaching Among Finnish Diabetic and Heart Disease Patients: Randomized Controlled Trial.

BACKGROUND: There is a strong will and need to find alternative models of health care delivery driven by the ever-increasing burden of chronic diseases. OBJECTIVE: The purpose of this 1-year trial was to study whether a structured mobile phone-based health coaching program, which was supported by a remote monitoring system, could be used to improve the health-related quality of life (HRQL) and/or the clinical measures of type 2 diabetes and heart disease patients. METHODS: A randomized controlled trial was conducted among type 2 diabetes patients and heart disease patients of the South Karelia Social and Health Care District. Patients were recruited by sending invitations to randomly selected patients using the electronic health records system. Health coaches called patients every 4 to 6 weeks and patients were encouraged to self-monitor their weight, blood pressure, blood glucose (diabetics), and steps (heart disease patients) once per week. The primary outcome was HRQL measured by the Short Form (36) Health Survey (SF-36) and glycosylated hemoglobin (HbA1c) among diabetic patients. The clinical measures assessed were blood pressure, weight, waist circumference, and lipid levels. RESULTS: A total of 267 heart patients and 250 diabetes patients started in the trial, of which 246 and 225 patients concluded the end-point assessments, respectively. Withdrawal from the study was associated with the patients' unfamiliarity with mobile phones­of the 41 dropouts, 85% (11/13) of the heart disease patients and 88% (14/16) of the diabetes patients were familiar with mobile phones, whereas the corresponding percentages were 97.1% (231/238) and 98.6% (208/211), respectively, among the rest of the patients (P=.02 and P=.004). Withdrawal was also associated with heart disease patients' comorbidities­40% (8/20) of the dropouts had at least one comorbidity, whereas the corresponding percentage was 18.9% (47/249) among the rest of the patients (P=.02). The intervention showed no statistically significant benefits over the current practice with regard to health-related quality of life­heart disease patients: beta=0.730 (P=.36) for the physical component score and beta=-0.608 (P=.62) for the mental component score; diabetes patients: beta=0.875 (P=.85) for the physical component score and beta=-0.770 (P=.52) for the mental component score. There was a significant difference in waist circumference in the type 2 diabetes group (beta=-1.711, P=.01). There were no differences in any other outcome variables. CONCLUSIONS: A health coaching program supported with telemonitoring did not improve heart disease patients' or diabetes patients' quality of life or their clinical condition. There were indications that the intervention had a differential effect on heart patients and diabetes patients. Diabetes patients may be more prone to benefit from this kind of intervention. This should not be neglected when developing new ways for self-management of chronic diseases. TRIAL REGISTRATION: ClinicalTrials.gov NCT01310491; http://clinicaltrials.gov/ct2/show/NCT01310491 (Archived by WebCite at http://www.webcitation.org/6Z8l5FwAM).

Moving the science of behavioral change into the 21st century: part 2.

What follows is the second part of a two-part special series of articles that illustrate through examples the breadth and depth of the field of behavioral-change science and highlight the challenges in moving it in to the 21st century. The first part appeared in the September/October issue of IEEE Pulse (see [1]-[3]).

Rethinking health: ICT-enabled services to empower people to manage their health.

Lifestyle is a key determinant in the prevention and management of chronic diseases. If we would exercise regularly, eat healthy, control our weight, sleep enough, manage stress, not smoke and use alcohol only moderately, 90% of type II diabetes, 80% of coronary heart disease, and 70% of stroke could be prevented. Health statistics show that lifestyle related diseases are increasing at an alarming rate. Public health promotion campaigns and healthcare together are not effective enough to stop this "tsunami". The solution that is offered is to empower people to manage their health with the assistance of ICT-enabled services. A lot of R&D and engineering effort is being invested in Personal Health Systems. Although some progress has been made, the market for such systems has not yet emerged. The aim of this critical review is to identify the barriers which are holding back the growth of the market. It looks into the theoretical foundations of behavior change support, the maturity of the technologies for behavior change support, and the business context in which behavior change support systems are used.

Personalized behavior change support for disease prevention.

Healthy lifestyle is essential in prevention of chronic diseases. However, people need motivation and support to achieve and to maintain behavior changes. Moreover, effective behavior change support should be personalized to individual's unique characteristics, needs and context. This paper presents a blueprint of an ICT system, which is able to provide holistic, dynamic support for healthy behaviors, engaging also various co-producers in the health journey of a person. Three main concepts in the system are Virtual Individual, which maintains the user's personal profile, PGS-Mall, which collects various health and well-being services into one place, and HealthGuides, which support healthy choices in everyday life and coordinate the interactions between the user, the system, and other co-producers of health.

Rethinking health.

Lifestyle is a key determinant in the prevention and management of chronic diseases. If we would exercise regularly, eat healthy, control our weight, sleep enough, manage stress, not smoke and use alcohol only moderately, 90 % of type II diabetes, 80 % of coronary heart disease, and 70 % of stroke could be prevented. Unfortunately, global health statistics show that health promotion campaigns and healthcare have failed to persuade people to change and manage their lifestyles. A disruptive solution to this "tsunami" of chronic conditions is needed to radically improve people's abilities to manage their health.

In the spotlight: health information systems.

The realization of an health information systems for disease prevention is reported. Lifestyle management aspects are also dealt with along with business model framework for disease prevention.

In the spotlight: Health Information Systems--PHR and value based healthcare.

The author discusses recent progress in personal health records (PHR). The column is structured as follows: first, a look at what is driving health reform concluding with the need to make "value" the first priority. The role of citizens and patients in the value drive is then discussed. They must be empowered to act as co-producers in cooperation with healthcare professionals. Lastly, the role of the PHR and services based on the PHR will be elaborated.

In the Spotlight: Health Information Systems.

This article reviews the integration of health data in health information systems. The importance of electronic health records (EHRs) and their integration as well as current developments in this field are presented.

PICNIC Architecture.

The PICNIC architecture aims at supporting inter-enterprise integration and the facilitation of collaboration between healthcare organisations. The concept of a Regional Health Economy (RHE) is introduced to illustrate the varying nature of inter-enterprise collaboration between healthcare organisations collaborating in providing health services to citizens and patients in a regional setting. The PICNIC architecture comprises a number of PICNIC IT Services, the interfaces between them and presents a way to assemble these into a functioning Regional Health Care Network meeting the needs and concerns of its stakeholders. The PICNIC architecture is presented through a number of views relevant to different stakeholder groups. The stakeholders of the first view are national and regional health authorities and policy makers. The view describes how the architecture enables the implementation of national and regional health policies, strategies and organisational structures. The stakeholders of the second view, the service viewpoint, are the care providers, health professionals, patients and citizens. The view describes how the architecture supports and enables regional care delivery and process management including continuity of care (shared care) and citizen-centred health services. The stakeholders of the third view, the engineering view, are those that design, build and implement the RHCN. The view comprises four sub views: software engineering, IT services engineering, security and data. The proposed architecture is founded into the main stream of how distributed computing environments are evolving. The architecture is realised using the web services approach. A number of well established technology platforms and generic standards exist that can be used to implement the software components. The software components that are specified in PICNIC are implemented in Open Source.

PICNIC Technology.

A key objective of the Professionals and Citizen Network for Integrated Care (PICNIC) project was to provide products for a European and potentially worldwide software market. The approach followed was through the delivery of a number of Open Source (OS) components, to be integrated into applications that deliver similar services across the participating regions, aiming at their exploitation by other regions and the industry. This chapter describes the technology developed during the lifecycle of the PICNIC project, focusing on the three core services of Clinical Messaging, Access to Patient Data, and Collaboration. For each service, the entire process of how to turn its functional specifications into reusable components and common data sets in order to support Information Technology (IT) services for the next generation of secure, user-friendly healthcare networks is presented by means of common documentation tools. Security and privacy issues are also addressed.

Lessons Learned from PICNIC.

This chapter reviews the PICNIC experience from conception to its realisation and draws conclusions on several fronts. It puts PICNIC within the current framework of needs and requirements, summarises shortly its main contributions, and discusses its main contributions. Special emphasis is given to understanding the needs and requirements resulting from a fragmented ICT market and the implications and possibilities that PICNIC has created for a consolidation of the market.

A critical need for biosignal interpretation.

Biosignal interpretation (BSI) methods can be used to integrate information from different physiological signals and patient-state variables both to enable decision making regarding patient status and therapeutic actions and to improve the monitoring of patients and their organ systems. Although BSI research has yielded promising results over the last decade, the number of BSI algorithms implemented in commercially available systems and used routinely in clinical practice remains limited. This is probably due to limits in our understanding of the related clinical problems and our knowledge about the strengths and weaknesses of various BSI methods and tools. We have to continually repeat the cycle of analysis, planning, execution, and evaluation to develop an understanding of the right BSI algorithm for the right clinical problem that can be embedded in a commercial monitoring system. Advancing from the present, more descriptive stage to such formal knowledge and understanding requires time and learning.