Hiding in plain sight: insights about health-care trends gained through open health data
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Abstract (arXiv)
The open data movement constitutes an approach to achieving accountability for government organizations, and is aligned with one of the sustainable development goals outlined by the United Nations. In the area of health care, government agencies at the Federal and State levels have released open health data consisting of de-identified patient outcomes, costs and ratings. We have applied big data analytics to understand patterns and trends in open health data. We envision the use of this data by concerned citizens to understand both national and local trends in health expenditures. We have built an open-source tool, BOAT (Big Data Open Source Analytics Tool, https://github.com/fdudatamining) to facilitate analytical exploration of open health data sets. We used BOAT to analyze data from the New York Statewide Planning and Research Cooperative System and determined that there has been a significant increase (40 percent) in the incidences of mental health issues amongst adolescents from 2009-2014. Using BOAT we analyzed costs for hip replacement surgery for 168,676 patients is in New York State, and showed that 88% of these patients had surgery costs of less than 30,000. This figure provides a basis to understand the decision by The California Public Employees' Retirement System to cap hip replacement reimbursements at 30,000, resulting in significant savings. Our tool could enable researchers, hospitals, insurers and citizens to obtain an unbiased view on health-care expenditures, costs and emerging trends. Our tool is especially valuable in the current economic environment, where a significant amount of reporting is controlled by special interests groups and lobbies.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). First posted on arXiv 30 Oct 2017.
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Citing papers per year: 2023: 3.
3 papers.
- 2023A system for exploring big data: an iterative k-means searchlight for outlier detection on open health datacited by 1
- 2023PIKS: A Technique to Identify Actionable Trends for Policy-Makers Through Open Healthcare Datacited by 1
- 2023Building predictive models of healthcare costs with open healthcare datacited by 0
- 2023PIKS: A Technique to Identify Actionable Trends for Policy-Makers Through Open Healthcare Data5 Apr 2023
- 2023Building predictive models of healthcare costs with open healthcare data5 Apr 2023
- 2023A system for exploring big data: an iterative k-means searchlight for outlier detection on open health data5 Apr 2023