Natural Language Processing for Detecting Medication-Related Notes in Heart Failure Telehealth Patients.

Heart Failure is a severe chronic disease of the heart. Telehealth networks implement closed-loop healthcare paradigms for optimal treatment of the patients. For comprehensive documentation of medication treatment, health professionals create free text collaboration notes in addition to structured information. To make this valuable source of information available for adherence analyses, we developed classifiers for automated categorization of notes based on natural language processing, which allows filtering of relevant entries to spare data analysts from tedious manual screening. Furthermore, we identified potential improvements of the queries for structured treatment documentation. For 3,952 notes, the majority of the manually annotated category tags was medication-related. The highest F1-measure of our developed classifiers was 0.90. We conclude that our approach is a valuable tool to support adherence research based on datasets containing free-text entries.

Concept for Visualisation of Guideline Adherence of Medication Prescriptions in a Heart Failure Telehealth System.

BACKGROUND: Heart failure is a chronic disease that affects around 26 million people worldwide. Projections assume a substantial increase in prevalence over the next years. To improve the survival rate and quality of life in patients suffering from heart failure, the European Society of Cardiology published guidelines for diagnosis and treatment. Adherence of healthcare professionals' medication prescriptions with regard to these guidelines is critical for optimal outcomes. METHODS: Data from the conceptional phase of the existing disease management network 'HerzMobil Tirol' were analysed. Prescriptions and patient- reported intake data of the four major substances of recommended heart failure medication were used to calculate the relative prescribed doses as a percentage of the recommended target doses. A concept for visualisation of the prescription status was developed in cooperation with health professionals. RESULTS: The documented prescriptions were analysed and used to develop a mock-up in order to visualise the prescription status for the individual patient. CONCLUSION: Analysis and visualisation can be managed by displaying the calculated daily relative dose per substance group in a traffic light system.