Processing of Electronic Health Records using Deep Learning: A review

Availability of large amount of clinical data is opening up new research avenues in a number of fields. An exciting field in this respect is healthcare, where secondary use of healthcare data is beginning to revolutionize healthcare. Except for availability of Big Data, both medical data from healthcare institutions (such as EMR data) and data generated from health and wellbeing devices (such as personal trackers), a significant contribution to this trend is also being made by recent advances on machine learning, specifically deep learning algorithms

Clinical outcomes of medication therapy management services in primary health care

This study evaluates whether the integration of pharmacists into health-care teams through the delivery of pharmaceutical care-based medication therapy management (MTM) services can improve the clinical outcomes of patients with chronic health conditions in the primary health-care setting. A retrospective descriptive study of 92 outpatients assisted by MTM pharmacists in primary health-care units was carried out over 28 months (median follow-up: 05 months). Patients were followed up by MTM pharmacists, with a total of 359 encounters and a ratio of 3.9 encounters per patient. The prevalence of hypertension, diabetes mellitus and dyslipidaemia was 29.5%, 22.0% and 19.4%, respectively. There was a high prevalence of drug-related problems with a ratio of 3.4 per patient. Pharmacists performed a total of 307 interventions to prevent or resolve drug-related problems. With regard to control of the most prevalent chronic medical conditions, a high percentage of patients reached their therapy goals by the last encounter with the pharmacist: 90.0% for hypertension, 72.3% for diabetes mellitus and 90.3% for dyslipidaemia. MTM services provided by pharmacists resolved drug therapy problems and improved patients' clinical outcomes. This study provides evidence for health-care managers of the need to expand the clinical role of pharmacists within the Brazilian public health-care system

Understanding and Reducing Clinical Data Biases

The vast amount of clinical data made available by pervasive electronic health records presents a great opportunity for reusing these data to improve the efficiency and lower the costs of clinical and translational research. A risk to reuse is potential hidden biases in clinical data. While specific studies have demonstrated benefits in reusing clinical data for research, there are significant concerns about potential clinical data biases. This dissertation research contributes original understanding of clinical data biases. Using research data carefully collected from a patient community served by our institution as the reference standard, we examined the measurement and sampling biases in the clinical data for selected clinical variables. Our results showed that the clinical data and research data had similar summary statistical profiles, but that there were detectable differences in definitions and measurements for variables such as height, diastolic blood pressure, and diabetes status. One implication of these results is that research data can complement clinical data for clinical phenotyping. We further supported this hypothesis using diabetes as an example clinical phenotype, showing that integrated clinical and research data improved the sensitivity and positive predictive value

The Future of Nursing: Leading Change, Advancing Health

Details recommendations to ensure that nurses can practice to the full extent of their training, improve nursing education, offer leadership opportunities in healthcare redesign and improvement efforts, and boost data collection. Includes case studies