Economic evaluations of big data analytics for clinical decision-making: a scoping review.

OBJECTIVE: Much has been invested in big data analytics to improve health and reduce costs. However, it is unknown whether these investments have achieved the desired goals. We performed a scoping review to determine the health and economic impact of big data analytics for clinical decision-making. MATERIALS AND METHODS: We searched Medline, Embase, Web of Science and the National Health Services Economic Evaluations Database for relevant articles. We included peer-reviewed papers that report the health economic impact of analytics that assist clinical decision-making. We extracted the economic methods and estimated impact and also assessed the quality of the methods used. In addition, we estimated how many studies assessed "big data analytics" based on a broad definition of this term. RESULTS: The search yielded 12 133 papers but only 71 studies fulfilled all eligibility criteria. Only a few papers were full economic evaluations; many were performed during development. Papers frequently reported savings for healthcare payers but only 20% also included costs of analytics. Twenty studies examined "big data analytics" and only 7 reported both cost-savings and better outcomes. DISCUSSION: The promised potential of big data is not yet reflected in the literature, partly since only a few full and properly performed economic evaluations have been published. This and the lack of a clear definition of "big data" limit policy makers and healthcare professionals from determining which big data initiatives are worth implementing.

Portfolio Analysis of Research Grants in Data Science Funded by the National Heart, Lung, and Blood Institute.

Leveraging emerging opportunities in data science to open new frontiers in heart, lung, blood, and sleep research is one of the major strategic objectives of the National Heart, Lung, and Blood Institute (NHLBI), one of the 27 Institutes/Centers within the National Institutes of Health (NIH). To assess NHLBI's recent funding of research grants in data science and to identify its relative areas of focus within data science, a portfolio analysis from fiscal year 2008 to fiscal year 2017 was performed. In this portfolio analysis, an efficient and reliable methodology was used to identify data science research grants by utilizing several NIH databases and search technologies (iSearch, Query View Reporting system, and IN-SPIRE [Pacific Northwest National Laboratory, Richland, WA]). Six hundred thirty data science-focused extramural research grants supported by NHLBI were identified using keyword searches based primarily on NIH's working definitions of bioinformatics and computational biology. Further analysis characterized the distribution of these grants among the heart, lung, blood, and sleep disease areas as well as the subtypes of data science projects funded by NHLBI. Information was also collected for data science research grants funded by other NIH institutes/centers using the same search and analysis methodology. The funding comparison among different NIH institutes/centers highlighted relative data science areas of emphasis and further identified opportunities for potential data science areas in which NHLBI could foster research advances.

Beyond the EHR money pit: After investing big in health records, systems still face growing IT needs for upgrades, analytics and patient engagement.

Demands for constant upgrades to already-installed electronic health record systems are slowing investment in other important digital technologies like telehealth, remote patient monitoring and online billing.