I posted comments on the HealthCIO.com site in response to article suggesting ‘suggestive’ rather than ‘predictive’ analytics (“PA”) provides real, demonstrable benefits and that, rather than PA, should be today’s primary focus. My point is with the proper vision and commitments here, PA tools offer powerful, exciting new tools to improve health care, both from patient care and financial perspective, reducing claims fraud and improving processes.And these same tools are spawning exciting new ‘analytics-centric’ ventures which I see as a high potential new venture sector and is one of my focus areas.
You can visit the HealthCIO.com site to see the original article “A Suggestion About Predictive Analytics” at http://tinyurl.com/7nw6twd and also see a reply to my comments. Copy of my comments follows:
‘SUGGESTIVE’ AND ‘PREDICTIVE’ ANALYTICS WILL BOTH HELP IMPROVE HEALTH CARE
Thanks for sharing your insights. As a former CEO of a predictive analytics company, and currently leading a new ‘analytics-centric’ leading edge, personal health and wellness company, among other activities, I am pleased to also contribute my perspectives here.
I like your idea of contrasting Suggestive vs. Predictive Analytics- there is obvious proven benefit in using analytics to improve quality at the point of patient care.
With regard to predictive analytics, I am pleased to offer comments:
–Predictive analytics is often muddled in with other statistical tools as you say, it is often difficult to appreciate and understand just how powerful these tools are and what is their specific contribution
–Rather than saying there are two flavors of PA, “easy and hard”, I suggest a better approach is to say there are two PA target opportunity areas in health care (and also in other sectors):
— Using PA to analyze the “known unknowns” – all of the patient treatment enhancements you described fall into this category- addressing known issues and processes, using analytics to improve processes, quality of care, and doing this more efficiently and at lower cost.
—Using PA to analyze the “unknown unknowns” – this is the real and power of predictive analytics and I believe really offers high upside for all health care players, and patients as well
–Look at the magnitude of today’s health care issues. As one example, increasing complexity of medication regimens used by patients, coupled with a fragmented health care system involving multiple prescribers, has made the occurrence of serious drug-drug interactions more likely today than ever before. For example, one study suggests Preventable Adverse Drug Events injure 1.5 million people a year, costs the U.S. healthcare system $3.5 billion and resulting in an estimated 44,000 to 98,000 deaths every year. Some studies show even higher numbers.
–Our aging population exacerbates the above issues. Studies show 41 percent of seniors take
5 or more prescription medications, and more than half has 2 or more prescribing physicians. And 24 percent- about 1 out of 4 – seniors having 3 or more chronic conditions have not shared information with their health care providers during the last 12 months. No wonder medication errors among seniors on Medicare are estimated at almost $900 million.
–We can use the real power of PA to better understand the “unknown-unknown” drivers here that are impacting our health care system, and create powerful new tools, improved processes and do this more efficiently while improving patient care.
–The “unknown-unknown” data I would like to see addresses questions such as why do we have adverse drug events; what are the rules we should be looking at and changing to reduce these events save lives, and reduce health costs; what are the underlying drivers and patterns for adverse drug events- do these vary by geography, treatment modalities, user demographics, specific types of medical facilities, maybe how and where medical practitioners are trained. “Unknown-unknowns” may, for example, identify certain treatment modalities and drug regimens used by select groups of medical professionals which drive adverse drug events. Predictive analytics, an inductive rather than deductive process, offers a powerful tool to help us identify these and many other critical underlying health care drivers.
I agree there are many PA projects that today may seem academic, but I do see great possibilities to improve our health care system, using powerful new predictive analytics computing tools and platforms coupled with more traditional analytics (both suggestive and deductive ‘rule based’ analytics), to dramatically improve the quality of our health care system. These new analytics and tools will address clinical issues such as the growing problem of adverse drug events, as well as addressing Medicare and other health care claims fraud and errors.
We are making progress, but I still believe we can be doing much more to achieve significant improvement in our nation’s health care system and very clear to me predictive analytics and other tools, with the proper vision and commitments, will play a substantive role.
Paul B. Silverman writes about entrepreneurship, healthcare, analytics, and strategy management and serves as Advisor, Speaker, Educator, and Managing Partner of the Gemini Business Group, LLC, a new venture development firm, and author of “8 Building Blocks To Launch, Manage, And Grow A Successful Business.” He also serves as Adjunct Professor in the School of Business at George Mason University. See more at Paul B. Silverman Blog and sign up for Entrepreneurship Today! email updates to track latest new venture developments.
v. is what we need to focus on to improve health care