A Qualitative Evaluation of IoT-driven eHealth: Knowledge Management, Business Models and Opportunities, Deployment and Evolution

eHealth has a major potential, and its adoption may be considered necessary to achieve increased ambulant and remote medical care, increased quality, reduced personnel needs, and reduced costs potential in healthcare. In this paper the authors try to give a reasonable, qualitative evaluation of IoT-driven eHealth from theoretical and practical viewpoints. They look at associated knowledge management issues and contributions of IoT to eHealth, along with requirements, benefits, limitations and entry barriers. Important attention is given to security and privacy issues. Finally, the conditions for business plans and accompanying value chains are realistically analyzed. The resulting implementation issues and required commitments are also discussed based on a case study analysis. The authors confirm that IoT-driven eHealth can happen and will happen; however, much more needs to be addressed to bring it back in sync with medical and general technological developments in an industrial state-of-the-art perspective and to get recognized and get timely the benefits

Predictors of metabolic monitoring among schizophrenia patients with a new episode of second-generation antipsychotic use in the Veterans Health Administration

<p>Abstract</p> <p>Background</p> <p>To examine the baseline metabolic monitoring (MetMon) for second generation antipsychotics (SGA) among patients with schizophrenia in the Veterans Integrated Service Network (VISN) 16 of the Veterans Health Administration (VHA).</p> <p>Methods</p> <p>VISN16 electronic medical records for 10/2002-08/2005 were used to identify patients with schizophrenia who received a new episode of SGA treatment after 10/2003, in which the VISN 16 baseline MetMon program was implemented. Patients who underwent MetMon (MetMon+: either blood glucose or lipid testing records) were compared with patients who did not (MetMon-), on patient characteristics and resource utilization in the year prior to index treatment episode. A parsimonious logistic regression was used to identify predictors for MetMon+ with adjusted odds ratios (OR) and 95% confidence intervals (CI).</p> <p>Results</p> <p>Out of 4,709 patients, 3,568 (75.8%) underwent the baseline MetMon. Compared with the MetMon- group, the MetMon+ patients were found more likely to have baseline diagnoses or mediations for diabetes (OR [CI]: 2.336 [1.846-2.955]), dyslipidemia (2.439 [2.029-2.932]), and hypertension (1.497 [1.287-1.743]), substance use disorders (1.460 [1.257-1.696]), or to be recorded as obesity (2.052 [1.724-2.443]). Increased likelihood for monitoring were positively associated with number of antipsychotics during the previous year (FGA: 1.434 [1.129-1.821]; SGA: 1.503 [1.290-1.751]). Other significant predictors for monitoring were more augmentation episodes (1.580 [1.145-2.179]), more outpatient visits (1.007 [1.002-1.013])), hospitalization days (1.011 [1.007-1.015]), and longer duration of antipsychotic use (1.001 [1.001-1.001]). Among the MetMon+ group, approximately 38.9% patient had metabolic syndrome.</p> <p>Discussion</p> <p>This wide time window of 180 days, although congruent with the VHA guidelines for the baseline MetMon process, needs to be re-evaluated and narrowed down, so that optimally the monitoring event occurs at the time of receiving a new episode of SGA treatment. Future research will examine whether or not patients prescribed an SGA are assessed for metabolic syndrome following the index episode of antipsychotic therapy, and whether or not such baseline and follow-up monitoring programs in routine care are cost-effective.</p> <p>Conclusion</p> <p>The baseline MetMon has been performed for a majority of the VISN 16 patients with schizophrenia prior to index SGA over the study period. Compared with MetMon- group, MetMon+ patients were more likely to be obese and manifest a more severe illness profile.</p

Predicting implementation from organizational readiness for change: a study protocol

<p>Abstract</p> <p>Background</p> <p>There is widespread interest in measuring organizational readiness to implement evidence-based practices in clinical care. However, there are a number of challenges to validating organizational measures, including inferential bias arising from the halo effect and method bias - two threats to validity that, while well-documented by organizational scholars, are often ignored in health services research. We describe a protocol to comprehensively assess the psychometric properties of a previously developed survey, the Organizational Readiness to Change Assessment.</p> <p>Objectives</p> <p>Our objective is to conduct a comprehensive assessment of the psychometric properties of the Organizational Readiness to Change Assessment incorporating methods specifically to address threats from halo effect and method bias.</p> <p>Methods and Design</p> <p>We will conduct three sets of analyses using longitudinal, secondary data from four partner projects, each testing interventions to improve the implementation of an evidence-based clinical practice. Partner projects field the Organizational Readiness to Change Assessment at baseline (n = 208 respondents; 53 facilities), and prospectively assesses the degree to which the evidence-based practice is implemented. We will conduct predictive and concurrent validities using hierarchical linear modeling and multivariate regression, respectively. For predictive validity, the outcome is the change from baseline to follow-up in the use of the evidence-based practice. We will use intra-class correlations derived from hierarchical linear models to assess inter-rater reliability. Two partner projects will also field measures of job satisfaction for convergent and discriminant validity analyses, and will field Organizational Readiness to Change Assessment measures at follow-up for concurrent validity (n = 158 respondents; 33 facilities). Convergent and discriminant validities will test associations between organizational readiness and different aspects of job satisfaction: satisfaction with leadership, which should be highly correlated with readiness, versus satisfaction with salary, which should be less correlated with readiness. Content validity will be assessed using an expert panel and modified Delphi technique.</p> <p>Discussion</p> <p>We propose a comprehensive protocol for validating a survey instrument for assessing organizational readiness to change that specifically addresses key threats of bias related to halo effect, method bias and questions of construct validity that often go unexplored in research using measures of organizational constructs.</p

Sensor networks and personal health data management: software engineering challenges

The advances of 5G, sensors, and information technologies enabled proliferation of smart pervasive sensor networks. 5G mobile networks provide low-power, high-availability, high density, and high-throughput data capturing by sensor networks and continuous streaming of multiple measured variables. Rapid progress in sensors that can measure vital signs, advances in the management of medical knowledge, and improvement of algorithms for decision support, are fueling a technological disruption to health monitoring. The increase in size and complexity of wireless sensor networks and expansion into multiple areas of health monitoring creates challenges for system design and software engineering practices. In this paper, we highlight some of the key software engineering and data-processing issues, along with addressing emerging ethical issues of data management. The challenges associated with ensuring high dependability of sensor network systems can be addressed by metamorphic testing. The proposed conceptual solution combines data streaming, filtering, cross-calibration, use of medical knowledge for system operation and data interpretation, and IoT-based calibration using certified linked diagnostic devices. Integration of blockchain technologies and artificial intelligence offers a solution to the increasing needs for higher accuracy of measurements of vital signs, high-quality decision-making, and dependability, including key medical and ethical requirements of safety and security of the data