Data science has the potential to lower costs, improve care and personalize medicine. Just as Google changed advertising with data science, a health care revolution will occur as new tools, techniques, and data sources are available. Modern medicine focuses on the average patient yet does not usually allow for differences between patients. A treatment is deemed effective or ineffective, safe or unsafe, based on double-blind studies that rarely considers the differences between patients. Data science and the proliferation of sensors generating medical data changes this dynamic.
Data science has the potential to help us make better policy and resource decisions at lower cost and make improved medical decisions based on a patients specific biology. We can now work on massive data sets effectively, combining data from clinical trials and direct observation by practicing physicians. When we combine data with the resources needed to work on the data, we can start asking the important questions like what treatments work and for whom.
Data science allows for completely different approach to treatment. Rather than a treatment that works 80% of the time, or even 100% of the time for 80% of the patients, a treatment might be effective for a small group. It might be entirely specific to the individual - the next cancer patient may have a different protein that’s out of control, an entirely different genetic cause for the disease. Treatments that are specific to one patient don’t exist in medicine as it’s currently practiced.
Three (3) innovations - all involving data science - will converge to improve and personalize health care:
2. Body Sensors
3. Electronic Medical Records (EMR)
Our health depends on our genes and environmental factors. Recent advances in genomics allows us to determine our entire DNA sequence and understand how our specific genome sequence can better manage health. It is also possible to measure tens of thousands of components in blood to obtain a clear picture of our molecular picture during healthy and disease states.
Next-generation genomic technologies allow data scientists to drastically increase the amount of genomic data collected on large study populations. When combined with new informatics approaches that integrate many kinds of data with genomic data in disease research - we will better understand the genetic bases of drug response and disease.
Cheap DNA sequencing in the doctor’s office will soon be available. In conjunction with inexpensive compute power, the availability of EMR data to study whether treatments are effective and improved techniques for analyzing data - personalized medicine at lower costs should become a reality with a proper legal regulatory scheme that creates the right incentives.
Wearable body sensors are already a reality - albeit in primitive stage. Heart rate monitors, blood monitors, body water sensors, lactate acid sensors, testosterone and estrogen sensors and other body measuring devices will soon provide a wealth of data to help us better manage health.
The proliferation of sensors providing personal health data and cheap compute power to store and process are creating a meeting of medical science and data science. The result should be better health care for everyone at lower costs.
Electronic Medical Records
Electronic medical records are now required by law in most developed nations. Data becomes infinitely more powerful when you can mix data from different sources. Physician offices, hospitals and the increasing use of body sensors are creating a treasure chest of health data to allow data scientists to slice, dice and re-combine all this data into new forms of health knowledge and understanding.
This new information can help us avoid paying for treatments that are ineffective and help us design a system where the consumer pays only for outcomes.
At this time, when physicians order a treatment, whether it’s surgery or an over-the-counter medication, they are applying a “standard of care” treatment or some variation that is based on their own intuition - effectively hoping for the best. At this time modern medicine does not understand the relationship between treatments and outcomes. The proliferation of health data and data science will change the physicians "standard of care" and "intuition" mind-set to one of personalized care based on both evidence and intuitive experience.
Data science will allow us to predict more accurately which treatments will be effective for which patient, and which treatments won’t - improving health care at lower costs.
Personalized medicine (PM) customizes health care - with treatment tailored to the individual patient. PM may be defined as a comprehensive, prospective approach to preventing, diagnosing, treating and monitoring disease in ways that achieve optimal individual health care decisions.
The United States spends over USD $2.6 trillion on health care every year, an amount that constitutes an unsustainable fiscal burden for society. These costs include over USD $600 billion of unexplained variations in treatments - treatments that cause no differences in outcomes, or even make the patient’s condition worse. This is unacceptable and unsustainable.
We all want a smarter, more cost-effective health care system where treatments are designed to be effective on our individual biologies; where treatments are administered effectively; where physicians and hospitals are used cost-effectively; and where we pay for outcomes, not procedures.
Data science will play a role in creating this new system by creating a better understanding of the relationship between treatments, outcomes, patients and costs.