A smart hospital-driven approach to precision pharmacovigilance.

Researchers, regulatory agencies, and the pharmaceutical industry are moving towards precision pharmacovigilance as a comprehensive framework for drug safety assessment, at the service of the individual patient, by clustering specific risk groups in different databases. This article explores its implementation by focusing on: (i) designing a new data collection infrastructure, (ii) exploring new computational methods suitable for drug safety data, and (iii) providing a computer-aided framework for distributed clinical decisions with the aim of compiling a personalized information leaflet with specific reference to a drug's risks and adverse drug reactions. These goals can be achieved by using 'smart hospitals' as the principal data sources and by employing methods of precision medicine and medical statistics to supplement current public health decisions.

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).

Discovery and use of efficient biomarkers for objective disease state assessment in Alzheimer's disease.

Objective and early detection of Alzheimer's disease (AD) is a demanding problem requiring consideration of manymodal observations. Potentially, many features could be used to discern between people without AD and those at different stages of the disease. Such features include results from cognitive and memory tests, imaging (MRI, PET) results, cerebral spine fluid data, blood markers etc. However, in order to define an efficient and limited set of features that can be employed in classifiers requires mining of data from many patient cases. In this study we used two databases, ADNI and Kuopio LMCI, to investigate the relative importance of features and their combinations. Optimal feature combinations are to be used in a Clinical Decision Support System that is to be used in clinical AD diagnosis practice.

Relationship of psychological and physiological variables in long-term self-monitored data during work ability rehabilitation program.

Individual wellness comprises both psychological and physiological wellbeing, which are interrelated. In long-term monitoring of wellness, both components should be included. Work-related stress and burnout are persistent problems in industrial countries. Early identification of work-related stress symptoms and early intervention could reduce individual suffering and improve the working productivity and creativity. The goal of this study was to explore the relationship between physiological and psychological variables measured at home by the users themselves or automatically. In all, 17 (3 males and 14 females, age 40-62) people participating in a work ability rehabilitation program (due to work overload) were monitored for three months. Physiological and behavioral variables (activity, bed occupancy, heart rate (HR) and respiration during night, HR during day, blood pressure, steps, weight, room illumination, and temperature) were measured with different unobtrusive wireless sensors. Daily self-assessment of stress, mood, and behaviors (exercise, sleep) were collected using a mobile phone diary. The daily self-assessment of stress and the Derogatis stress profile questionnaire were used as reference for stress status. Results show modest, but significant pooled overall correlations between self-assessed stress level, and physiological and behavioral variables (e.g., sleep length measured with wrist-worn activity monitor: rho = -0.22, p < 0.001, and variance of nightly bedroom illumination: rho = 0.13, p < 0.001). Strong, but sometimes conflicting correlations can be found at individual level, suggesting individual reactions to stress in daily life.