At its core data science is meant to challenge the common wisdom. Susan Lindquist, a geneticist and biochemist at MIT, states, “If you’ve been studying in a field all your life, having someone from a completely different field come and tell you something important could be rather irritating. It’s just human nature.” Part of the problem is a turf issue, she noted, but also a gap in understanding because experts in other fields “don’t get why my ideas work.”
Believe it or not there was a time in medicine when physicians were not physician-scientists. They thought of illnesses as an imbalance caused by bad air or evil spirits instead of looking at anatomy and empirical data.
Jenner (1796), because he was a simple country doctor, experienced the prejudice of the medical world after discovering the vaccination for small pox; 50 years later the government of the day banned every treatment for smallpox except Jenner’s.
Snow (1849) refused to believe Cholera was contracted by bad air, but rather through the mouth; his “germ-theory” of disease was not widely accepted until the 1860s.
A quick review of history will demonstrate that every 50 years there is a revolution in healthcare based on the trends of that period.
In the 1870s we had germ theory of disease and promotion of public health efforts. In the 1920s we discovered penicillin and propelled forward the use of medication as treatment for disease. In the 1970s we began randomized, controlled trials which ushered in a period of evidence-based medicine. Now approaching the 2020s, we are set for another revolution: using data science to empower physicians’ work — and most significantly — improve patient outcomes.
The sooner and more broadly professionals within healthcare accept data science as beneficial to their cause, the sooner healthcare could reduce rising mortality rates and out-of-control medical costs. Data science, machine learning and Big Data are not a panacea, but significant approaches still being underutilized — if at all — in modern healthcare.
Data science should be leveraged to make progress in areas that concern many patients and hospital CFOs:
Table 1. Top 20 most expensive conditions treated in U.S. hospitals, all healthcare insurance, 2011
Hospital Cost, U.S. $, in millions
National Costs, %
Number of Hospital Discharges
Degenerative joint disease
Complications of device, implant, or graft
Acute myocardial infarction
Circulation of blood to the brain
Complications of medical care
Diabetes with complications
Unspecified kidney failure
Total for top 20 conditions
Total for all hospitalizations
Source: Agency for Healthcare Research and Quality (AHRQ), Center for Delivery, Organization, and Markets, Healthcare Cost and Utilization Project (HCUP), Nationwide Inpatient Sample (NIS), 2011
What does healthcare in the 2020s look like?
Currently, modern medicine treats the 84-year-old diabetic patient with hypertension similarly to the 43-year-old athlete with hypertension, based on both being grouped together in the same clinical trial. Let data science help personalize care by learning what worked best previously for millions of similar patients. This level of customized care offers the promise of better and more applicable treatment and outcomes.
Forward-thinking healthcare providers can take advantage of data science, robust tools, and clear processes for intervention today. There is no need to wait for 2020.
Data science represents an opportunity for many types of innovators — MD, RN, MBA, etc. Data science and data-driven healthcare represent the potential for better outcomes and lower mortality rates for patients. Brace for a revolution in healthcare where we all have the opportunity to help and everyone has a stake.
What are you saying?
The future of healthcare is now. We have what we need for the next revolution in healthcare. Embrace new technology, new methods and large amounts of data. Realize data science is built on science and should be a large part of patient care.
Damian Mingle is Chief Data Scientist for WPC Healthcare, a premier provider of cloud-based operational, financial, and clinical analytic solutions. In this role, Mingle manages a team of experts transforming data into meaningful strategic insights and offering hospital systems, payers, and the HIT vendors descriptive-through-prescriptive analytics. Prior to WPC Mingle held positions with companies like Hospital Corporation of America (HCA), Coventry Healthcare, and Morgan Stanley. He is ranked in the top 1 percent globally as a data scientist through regular competitions.
The opinions expressed in this blog are those of Damian Mingle and do not necessarily represent those of IDG Communications, Inc., its parent, subsidiary or affiliated companies.