At its core data science is meant to challenge the common wisdom. Susan Lindquist, a geneticist and biochemist at MIT, states, \u201cIf you\u2019ve 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\u2019s just human nature.\u201d Part of the problem is a turf issue, she noted, but also a gap in understanding because experts in other fields \u201cdon\u2019t get why my ideas work.\u201d\nBelieve 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.\nThe history of science is replete with such theories that only became accepted by the scientific community after a long, uphill battle.\n\nSemmelweis (1846) asked healthcare providers to wash hands; over 130 years later CDC published the first national hand hygiene guidelines.\nJenner (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\u2019s.\nSnow (1849) refused to believe Cholera was contracted by bad air, but rather through the mouth; his \u201cgerm-theory\u201d of disease was not widely accepted until the 1860s.\n\nAll the wonderful evidence-based science in the world cannot cover up some previous ideas that are part of our ugly history, including heroin cough syrup for children sold by Bayer & Co. or making Lysol the best-selling method of contraception during the Great Depression. Even with these unfortunate cases in our recent history, there are still many scientists whose ideas struggle to find acceptance.\nAre you ready for a revolution?\nA quick review of history will demonstrate that every 50 years there is a revolution in healthcare based on the trends of that period.\nIn 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\u2019 work \u2014 and most significantly \u2014 improve patient outcomes.\nI get it: undergraduate studies, medical school, residency and fellowships certainly add up to expertise cultivated over many years; and for that reason, some healthcare providers may have a hard time with receiving outside help. Many of these providers\u2019 view excursions into the medical field by non-medical individuals as intruding. However, Data science does not discriminate by field of study and finds data patterns in the most unexpected places.\nThe 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 \u2014 if at all \u2014 in modern healthcare.\nData science should be leveraged to make progress in areas that concern many patients and hospital CFOs:\nTable 1. Top 20 most expensive conditions treated in U.S. hospitals, all healthcare insurance, 2011\n\n\n\n\nRank\n\n\nCategory\n\n\nHospital Cost, U.S. $, in millions\n\n\nNational Costs, %\n\n\nNumber of Hospital Discharges\n\n\n\n\n1\n\n\nSepsis\n\n\n20.3\n\n\n5.2\n\n\n1,094,000\n\n\n\n\n2\n\n\nDegenerative joint disease\n\n\n14.8\n\n\n3.8\n\n\n964,000\n\n\n\n\n3\n\n\nComplications of device, implant, or graft\n\n\n12.9\n\n\n3.3\n\n\n699,000\n\n\n\n\n4\n\n\nBirths\n\n\n12.4\n\n\n3.2\n\n\n3,818,000\n\n\n\n\n5\n\n\nAcute myocardial infarction\n\n\n11.5\n\n\n3.0\n\n\n612,000\n\n\n\n\n6\n\n\nBack problems\n\n\n11.2\n\n\n2.9\n\n\n667,000\n\n\n\n\n7\n\n\nPneumonia\n\n\n10.6\n\n\n2.7\n\n\n1,114,000\n\n\n\n\n8\n\n\nHeart Failure\n\n\n10.5\n\n\n2.7\n\n\n970,000\n\n\n\n\n9\n\n\nHeart Disease\n\n\n10.4\n\n\n2.7\n\n\n605,000\n\n\n\n\n10\n\n\nRespiratory failure\n\n\n8.7\n\n\n2.3\n\n\n404,000\n\n\n\n\n11\n\n\nCirculation of blood to the brain\n\n\n8.3\n\n\n2.2\n\n\n597,000\n\n\n\n\n12\n\n\nIrregular heartbeat\n\n\n7.6\n\n\n2.0\n\n\n795,000\n\n\n\n\n13\n\n\nComplications of medical care\n\n\n6.9\n\n\n1.8\n\n\n529,000\n\n\n\n\n14\n\n\nCOPD\n\n\n5.7\n\n\n1.5\n\n\n729,000\n\n\n\n\n15\n\n\nRehab services\n\n\n5.5\n\n\n1.4\n\n\n420,000\n\n\n\n\n16\n\n\nDiabetes with complications\n\n\n5.4\n\n\n1.4\n\n\n561,000\n\n\n\n\n17\n\n\nGallstones\n\n\n5.1\n\n\n1.3\n\n\n469,0000\n\n\n\n\n18\n\n\nHip fracture\n\n\n4.9\n\n\n1.3\n\n\n316,000\n\n\n\n\n19\n\n\nMood disorders\n\n\n4.9\n\n\n1.2\n\n\n896,000\n\n\n\n\n20\n\n\nUnspecified kidney failure\n\n\n4.7\n\n\n1.2\n\n\n498,000\n\n\n\n\nTotal for top 20 conditions\n\n\n182.3\n\n\n47.1\n\n\n16,755,000\n\n\n\n\nTotal for all hospitalizations\n\n\n387.3\n\n\n100\n\n\n38,591,000\n\n\n\n\nSource: Agency for Healthcare Research and Quality (AHRQ), Center for Delivery, Organization, and Markets, Healthcare Cost and Utilization Project (HCUP), Nationwide Inpatient Sample (NIS), 2011\n Pixabay \nWhat does healthcare in the 2020s look like?\nCurrently, 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.\nForward-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.\nData science represents an opportunity for many types of innovators \u2014 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.\nWhat are you saying?\nThe 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.