Adequate adherence to treatment is indispensable in preventing adverse consequences in heart failure patients. Such adherence can be managed through heart failure clinics and various methods of follow-up. In recent years, the use of telemonitoring has shown promising benefits in supporting clinicians' follow-up, as well as contributing to patients' self-care. This article presents the development and evaluation of a telemonitoring application for heart failure, through a Web-based interface for clinicians and a mobile application for patients. The application was evaluated through a 6-month pilot observational descriptive study in 20 outpatients with reduced ejection fraction and two nurses, in the context of a heart failure clinic. A technological acceptance questionnaire was applied to all patients and nurses at the end of the study period. In use, the application generated 64 real-time alerts for early decision-making to prevent complications, and 91% of patients did not present hospital readmissions. Such results, along with high user acceptance, show potential utility of the application as an effective complementary strategy for follow-up of patients with heart failure.
Objective: Electronic medical records (EMR) typically contain both structured attributes as well as narrative text. The usefulness of EMR for research and administration is hampered by the difficulty in automatically analyzing their narrative portions. Accordingly, this paper proposes SPIRE, a strategy for prioritizing EMR, using natural language processing in combination with analysis of structured data, in order to identify and rank EMR that match specific queries from clinical researchers and health administrators.
Materials and Methods: The resulting software tool was evaluated technically and validated with three cases (heart failure, pulmonary hypertension and diabetes mellitus) compared against expert obtained results.
Results and Discussion: Our preliminary results show high sensitivity (70%, 82% and 87% respectively) and specificity (85%, 73.7% and 87.5%) in the resulting set of records. The AUC was between 0.84 and 0.9.
Conclusions: SPIRE was successfully implemented and used in the context of a university hospital information system, enabling clinical researchers to obtain prioritized EMR to solve their information needs through collaborative search templates with faster and more accurate results than other existing methods.
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