How Big Data is driving the Shift to Value Based Healthcare
The Internet of Things has provided the healthcare industry with a growing network of both medical and everyday objects that can share information. With an estimated 50 billion connected devices in the world by 2020, the vast amount of available data will only continue to grow as healthcare organizations continue to leverage these devices and capabilities for patient monitoring and telehealth to save money and improve patient outcomes. A recent poll showed that 46% of health IT leaders said that big data is the trend that will have the biggest impact on the healthcare industry in 2017. Driving this trend is the shift from fee-for-service compensation, which rewards physicians for treatment volume, to risk-sharing arrangements that prioritize outcomes (value based healthcare). The move to value based care is related to the reduction in reimbursement/compensation due to medical error. Medical errors are now the 3rd leading cause of death in the US. An error, such as a deviation from a standard intra-venous procedure, can lead to high infection rates which impacts reimbursement and that hits the bottom line. It is no surprise then that Healthcare systems are investing in big data analytic approaches to determine how to improve the quality of care, match the right clinician or clinical team to the patient or personalize clinical training. Industry is incentivized to compile and exchange information to further build available data and improve decision making, ultimately resulting in an improved value of care. Big data can be leveraged multiple ways across the industry and ultimately lead to an improved value of care.
These are the two of the biggest use cases for big data in the healthcare industry:
Predictive Analysis for Plan of Care
80% of executives think that predictive analytics could significantly improve the future of the healthcare industry. Predictive analytics allows physicians to essentially consult hundreds of thousands of cases to evaluate potential actions to take and what risks they pose. Adoption rate currently is slow with just 31% of executives actively utilizing forward-looking data analytics, while19% have not even made plans to do so. However, predictive analytics adoption rate has doubled since 2015 as early adopters have seen financial benefits in value based care systems. Healthcare provider Advocate leveraged predictive analytics and reduced readmission rates by 20% after only one year of use.
Leveraging predictive analysis tools to forecast when medical errors can occur help healthcare organizations maintain quality of care and prevent revenue loss. DI’s Skill-DETECT for example, models skill degradation of clinicians by identifying deviations from National and Agency Standards of Care. The system can then align the skill degradation with continuing education opportunities which can include those offered by the healthcare leadership and development organization.
Strategic Planning and Alignment with Population Health Trends
Healthcare organizations can now use the vast amounts of data available during various decision making phases. Big data can be used in hiring and staffing for hospitals by leveraging data to forecast the needs of the community. The University of Florida used Google Maps and free public health data to create heat maps for municipalities that included factors from population growth to chronic disease rates, and then compared those factors to the availability of medical services in those areas. After merging the maps with internal data, the information used in decision making was both visually compelling and objective, as it was critical for population health management. As a result of the project, the university found three Florida counties that were underserved for breast cancer screening and redirected its mobile care units accordingly. UF integrated big data into its decision making process to ensure proper resource allocation and better patient results.
AARP’s Medigap plan leverages big data to drive their population health program offerings. When customers access any of their health program services, it generates data that is aggregated for reporting and research, which in turn informs program management and refinement. The 4M customers on the Medigap plan complete health risk appraisal (HRA) surveys about their past and current health conditions; the data is then analyzed and those considered to have significant health challenges are contacted about joining one of the population health programs to help them remain well or coordinate care for their chronic health conditions.
With the shift to value-based care and a US emphasis on population health management and health IT adoption, the healthcare big data analytics market will continue to grow. With 50% of Medicare payments being linked to quality and alternative payment models by 2018, rapid adoption of big data will help healthcare companies maintain financial success.
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