Data Quality and Completeness in a Web Stroke Registry as the Basis for Data and Process Mining

Electronic health records often show missing values and errors jeopardizing their effective exploitation. We illustrate the re-engineering process needed to improve the data quality of a web-based, multicentric stroke registry by proposing a knowledge-based data entry support able to help users to homogeneously interpret data items, and to prevent and detect treacherous errors. The re-engineering also improves stroke units coordination and networking, through ancillary tools for monitoring patient enrollments, calculating stroke care indicators, analyzing compliance with clinical practice guidelines, and entering stroke units profiles. Finally we report on some statistics, such as calculation of indicators for assessing the quality of stroke care, data mining for knowledge discovery, and process mining for comparing different processes of care delivery. The most important results of the re-engineering are an improved user experience with data entry, and a definitely better data quality that guarantees the reliability of data analyses

Antepartum Fetal Monitoring through a Wearable System and a Mobile Application

Prenatal monitoring of Fetal Heart Rate (FHR) is crucial for the prevention of fetal pathologies and unfavorable deliveries. However, the most commonly used Cardiotocographic exam can be performed only in hospital-like structures and requires the supervision of expert personnel. For this reason, a wearable system able to continuously monitor FHR would be a noticeable step towards a personalized and remote pregnancy care. Thanks to textile electrodes, miniaturized electronics, and smart devices like smartphones and tablets, we developed a wearable integrated system for everyday fetal monitoring during the last weeks of pregnancy. Pregnant women at home can use it without the need for any external support by clinicians. The transmission of FHR to a specialized medical center allows its remote analysis, exploiting advanced algorithms running on high-performance hardware able to obtain the best classification of the fetal condition. The system has been tested on a limited set of pregnant women whose fetal electrocardiogram recordings were acquired and classified, yielding an overall score for both accuracy and sensitivity over 90%. This novel approach can open a new perspective on the continuous monitoring of fetus development by enhancing the performance of regular examinations, making treatments really personalized, and reducing hospitalization or ambulatory visits. Keywords: tele-monitoring; wearable devices; fetal heart rate; telemedicin

Development of a FHIR Layer on Top of the OMOP Common Data Model for the CAPABLE Project.

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The Case Manager: Driving Medical Reasoning in a Distributed Environment for Home Patient Monitoring.

The CAPABLE project has been funded by the EU Horizon 2020 Programme over the years 2020-24 to support home care. A system is being designed and implemented supporting remote monitoring and virtual coaching for cancer patients. The system is based on a distributed modular architecture involving many components encapsulating various knowledge. The Case Manager has been designed as a separate component with the aim of coordinating the problem solving strategies. A first version of the Case Manager has been released and used by the components in a prototypical scenario shown at the first project review

First Use of Model Predictive Control in Outpatient Wearable Artificial Pancreas

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Day and night closed-loop control in adults with type 1 diabetes: a comparison of two closed-loop algorithms driving continuous subcutaneous insulin infusion versus patient self-management.

OBJECTIVE: To compare two validated closed-loop (CL) algorithms versus patient self-control with CSII in terms of glycemic control. RESEARCH DESIGN AND METHODS: This study was a multicenter, randomized, three-way crossover, open-label trial in 48 patients with type 1 diabetes mellitus for at least 6 months, treated with continuous subcutaneous insulin infusion. Blood glucose was controlled for 23 h by the algorithm of the Universities of Pavia and Padova with a Safety Supervision Module developed at the Universities of Virginia and California at Santa Barbara (international artificial pancreas [iAP]), by the algorithm of University of Cambridge (CAM), or by patients themselves in open loop (OL) during three hospital admissions including meals and exercise. The main analysis was on an intention-to-treat basis. Main outcome measures included time spent in target (glucose levels between 3.9 and 8.0 mmol/L or between 3.9 and 10.0 mmol/L after meals). RESULTS: Time spent in the target range was similar in CL and OL: 62.6% for OL, 59.2% for iAP, and 58.3% for CAM. While mean glucose level was significantly lower in OL (7.19, 8.15, and 8.26 mmol/L, respectively) (overall P = 0.001), percentage of time spent in hypoglycemia (<3.9 mmol/L) was almost threefold reduced during CL (6.4%, 2.1%, and 2.0%) (overall P = 0.001) with less time ≤2.8 mmol/L (overall P = 0.038). There were no significant differences in outcomes between algorithms. CONCLUSIONS: Both CAM and iAP algorithms provide safe glycemic control

An Advanced Platform for Managing Complications of Chronic Diseases

The paper describes a generic platform for telemedicine services aimed at supporting chronic outpatients

An Agent-Based Architecture for Home Care Monitoring and Education of Chronic Patients

The paper proposes an architecture encompassing remote agents located by patients homes and a central agent located at the treating site. Remote agents are mostly implemented on mobile devices in order to maximize their portability and be able to follow patients during their daily activities. The central agent besides keeping the clinical staff up to date with the patient state, may also autonomously send out notifications, alarms and reminders to remote agents