Flexible Pressure Sensors and Machine Learning Algorithms for Human Walking Phase Monitoring.

Soft sensors are attracting much attention from researchers worldwide due to their versatility in practical projects. There are already many applications of soft sensors in aspects of life, consisting of human-robot interfaces, flexible electronics, medical monitoring, and healthcare. However, most of these studies have focused on a specific area, such as fabrication, data analysis, or experimentation. This approach can lead to challenges regarding the reliability, accuracy, or connectivity of the components. Therefore, there is a pressing need to consider the sensor's placement in an overall system and find ways to maximize the efficiency of such flexible sensors. This paper proposes a fabrication method for soft capacitive pressure sensors with spacer fabric, conductive inks, and encapsulation glue. The sensor exhibits a good sensitivity of 0.04 kPa-1, a fast recovery time of 7 milliseconds, and stability of 10,000 cycles. We also evaluate how to connect the sensor to other traditional sensors or hardware components. Some machine learning models are applied to these built-in soft sensors. As expected, the embedded wearables achieve a high accuracy of 96% when recognizing human walking phases.

Leveraging Real-World Evidence in Disease-Management Decision-Making with a Total Cost of Care Estimator.

BACKGROUND: Health management is becoming increasingly complex, given a range of care options and the need to balance costs and quality. The ability to measure and understand drivers of costs is critical for healthcare organizations to effectively manage their patient populations. Healthcare decision makers can leverage real-world evidence to explore the value of disease-management interventions in shifting total cost trends. OBJECTIVE: To develop a real-world, evidence-based estimator that examines the impact of disease-management interventions on the total cost of care (TCoC) for a patient population with nonvalvular atrial fibrillation (NVAF). METHODS: Data were collected from a patient-level real-world evidence data set that uses the IMS PharMetrics Health Plan Claims Database. Pharmacy and medical claims for patients meeting the inclusion or exclusion criteria were combined in longitudinal cohorts with a 180-day preindex and 360-day follow-up period. Descriptive statistics, such as mean and median patient costs and event rates, were derived from a real-world evidence analysis and were used to populate the base-case estimates within the TCoC estimator, an exploratory economic model that was designed to estimate the potential impact of several disease-management activities on the TCoC for a patient population with NVAF. Using Microsoft Excel, the estimator is designed to compare current direct costs of medical care to projected costs by varying assumptions on the impact of disease-management activities and applying the associated changes in cost trends to the affected populations. Disease-management levers are derived from literature-based concepts affecting costs along the NVAF disease continuum. The use of the estimator supports analyses across 4 US geographic regions, age, cost types, and care settings during 1 year. RESULTS: All patients included in the study were continuously enrolled in their health plan (within the IMS PharMetrics Health Plan Claims Database) between July 1, 2010, and June 30, 2012. Patients were included in the final analytic file and were indexed based on (1) the service date of the first claim within the selection window (December 28, 2010-July 11, 2011) with a diagnosis of NVAF, or (2) the service date of the second claim for an NVAF medication of interest during the same selection window. The model estimates the current trends in national benchmark data for a hypothetical health plan with 1 million covered lives. The annual total direct healthcare costs (allowable and patient out-of-pocket costs) of managing patients with NVAF in this hypothetical plan are estimated at $184,981,245 ($25,754 per patient, for 7183 patients). A potential 25% improvement from the base-case disease burden and disease management could translate into TCoC savings from reducing the excess costs related to hypertension (-5.3%) and supporting the use of an appropriate antithrombotic treatment that prevents ischemic stroke (-0.7%) and reduces bleeding events (-0.1%). CONCLUSIONS: The use of the TCoC estimator supports population health management by providing real-world evidence benchmark data on NVAF disease burden and by quantifying the potential value of disease-management activities in shifting cost trends.

Disease management in the frail and elderly population: integration of physicians in the intervention.

As financial, social, and quality-of-life challenges associated with chronic disease in the United States continue to proliferate, disease management (DM) has been identified as a viable and positive approach that serves all areas of impact. Using an "in-house" model, Physician Health Partners, LLC, designed, developed, and implemented a DM program for the frail and elderly population. Given the special needs of this population the typical DM intervention was modified to include elements of physician involvement. The Frail and Elderly Program, as the DM program is called, produced statistically significant improvements in functional, behavioral, and clinical status and health-related quality of life. This model can help result in program success with potential benefits for individuals, practices, communities, and all whose lives are touched, directly or indirectly, by chronic disease.