Having long ago witnessed the power of analytics to improve performance, efficiency, cost and quality of online banking and investment services, I have been an advocate and evangelist of its power to do the same in health care. That’s one reason why I’m excited about the recent tidal wave of news, articles, blogs, announcements and public dialogue about the value of analytics in health care.
The recent Health Affairs article captured my attention partly because it was authored exclusively by industry clinicians and academicians, not by technology vendors. The article, Big Data In Health Care: Using Analytics To Identify And Manage High-Risk And High-Cost Patients^ acknowledges “unprecedented opportunities” to use big data to reduce the costs of health care. Perhaps most importantly, the authors identify six specific opportunities where analytics could and should be used to reduce cost:
- Identify early – and proactively manage – high-cost patients.
- Tailor interventions to reduce readmissions.
- Estimate risk to improve triage.
- Filter out noise to detect valid signals of decompensation.
- Predict severity of illness to prevent adverse events.
- Optimize treatment for diseases affecting multiple organ systems.
It's encouraging to see health care executives acknowledging the need for analytical competency in their organizations; to see the US Congress acknowledging the need for data transparency and interoperability; to hear clinicians asking for analytically-derived decision support tools; to watch prestigious academic organizations expanding advanced degree programs in health informatics and biostatistics; and to hear health IT organizations demanding interoperability of data between EMR (electronic medical records) and their other systems.
I'm delighted by the article above, and by the early wins and impressive results generated in these six areas by friends and colleagues who are using advanced analytics to surface insights in their organizations across the globe. For example, our friends at the UNC School of Medicine are exploring the utility of big data for predicting exacerbation in diabetic patients, an innovation with the potential to simultaneously tackle the items in the list above: 1 (high-cost patients), 2 (tailor interventions) and 5 (predict to prevent).
Another example is the work being done at the Department of Orthopedic Surgery at Denmark's Lillebælt Hospital to use text analytics in automated clinical audits to detect and correct errors. The Lillebælt innovation demonstrates the efficiency gains made possible only through automation and the power to prevent patient injury at a scale which would otherwise be cost-prohibitive.
Perhaps the most exciting news of late is the announcement that Dignity Health is partnering with SAS to build a cloud-based big data analytics platform to enable value-based healthcare. In my opinion, this announcement represents a systemwide commitment to adopting health analytics as a core competency and puts Dignity Health on the road to realizing value in all six of the areas mentioned by Bates, and many more too numerous to list.
These are leading indicators that health care is modernizing and, I’m confident, will ultimately showcase the power of analytics to improve health care. The bottom line: Advanced health analytics is gaining ground in the industry and is picking up speed as more and more providers realize The Power to Know®.
^ Bates, D.W., Saria, S., Ohno-Machado, L., Shah, A., and Escobar, G. (July 2014). "Big Data In Health Care: Using Analytics To Identify And Manage High-Risk And High-Cost Patients," Health Affairs, 33, no.7.