Health affairs
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Big data has the potential to create significant value in health care by improving outcomes while lowering costs. Big data's defining features include the ability to handle massive data volume and variety at high velocity. New, flexible, and easily expandable information technology (IT) infrastructure, including so-called data lakes and cloud data storage and management solutions, make big-data analytics possible. ⋯ Without the right IT infrastructure, analytic tools, visualization approaches, work flows, and interfaces, the insights provided by big data are likely to be limited. Big data's success in creating value in the health care sector may require changes in current polices to balance the potential societal benefits of big-data approaches and the protection of patients' confidentiality. Other policy implications of using big data are that many current practices and policies related to data use, access, sharing, privacy, and stewardship need to be revised.
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High bed occupancy rates have been considered a matter of reduced patient comfort and privacy and an indicator of high productivity for hospitals. Hospitals with bed occupancy rates of above 85 percent are generally considered to have bed shortages. Little attention has been paid to the impact of these shortages on patients' outcomes. ⋯ Being admitted to a hospital outside of normal working hours or on a weekend or holiday was also significantly associated with increased mortality. The health risks of bed shortages, including mortality, could be better documented as a priority health issue. Resources should be allocated to researching the causes and effects of bed shortages, with the aim of creating greater interest in exploring new methods to avoid or reduce bed shortages.
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The US health care system is rapidly adopting electronic health records, which will dramatically increase the quantity of clinical data that are available electronically. Simultaneously, rapid progress has been made in clinical analytics--techniques for analyzing large quantities of data and gleaning new insights from that analysis--which is part of what is known as big data. ⋯ We discuss the types of insights that are likely to emerge from clinical analytics, the types of data needed to obtain such insights, and the infrastructure--analytics, algorithms, registries, assessment scores, monitoring devices, and so forth--that organizations will need to perform the necessary analyses and to implement changes that will improve care while reducing costs. Our findings have policy implications for regulatory oversight, ways to address privacy concerns, and the support of research on analytics.
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Health care has lagged behind other industries in its use of advanced analytics. The Veterans Health Administration (VHA) has three decades of experience collecting data about the veterans it serves nationwide through locally developed information systems that use a common electronic health record. In 2006 the VHA began to build its Corporate Data Warehouse, a repository for patient-level data aggregated from across the VHA's national health system. ⋯ It illustrates how advanced analysis is already supporting the VHA's activities, which range from routine clinical care of individual patients--for example, monitoring medication administration and predicting risk of adverse outcomes--to evaluating a systemwide initiative to bring the principles of the patient-centered medical home to all veterans. The article also shares some of the challenges, concerns, insights, and responses that have emerged along the way, such as the need to smoothly integrate new functions into clinical workflow. While the VHA is unique in many ways, its experience may offer important insights for other health care systems nationwide as they venture into the realm of big data.