Many recent articles have suggested different reforms for healthcare to reduce costs. These spanned the integration of digital health with clinical care, the widespread use of genetic and genomic tests to differentiate high-risk from low-risk patients and to inform drug development and prescribing decisions, and the use of artificial intelligence (AI) and cognitive computing like IBM’s Watson or Google’s DeepMind. Others have suggested tackling health insurance by repealing the ACA or moving to a single-payer system with the hope of substantial cost savings. But to significantly tackle out-of-control health spending, we need to motivate organizations by making the business case for change and implement data-driven methods that can make a substantial impact on the healthcare crisis—what we call MoneyBall Medicine.
Data analytics and technology has changed the way consumers buy merchandise, handle banking needs, and even how sports teams scout for their player rosters. Although the healthcare industry has long claimed exceptionalism due to regulatory challenges and the special nature of the field, paying more for healthcare hasn’t led to better patient outcomes in the U.S., as numerous healthcare analysts have found. But we’ve come a long way in the past several decades. The Human Genome Project resulted in an explosion of genetic testing companies and insight into human diseases while technological advancements now generate terabytes of data daily at a substantially reduced cost from even a decade ago. Pharmaceutical companies are designing new treatments based on computational modeling of molecules. Patients can email questions or concerns to their doctors after office hours and see lab results via portals to the medical record.
But for all of the progress, patients’ medical records can still be difficult to share in their entirety with medical providers at different hospitals and tests are often duplicated by different doctors who have no way of knowing what the results were previously. Getting a price estimate for office visits, procedures, and treatments is nearly impossible—preventing patients from shopping around for medical care. Combing through existing research to find what treatments are the most effective can be like finding a needle in the haystack, and doctors rarely have instant access to that kind of analysis when they are in front of an individual patient. In short, although there is a lot of data out there, it’s largely fragmented, remains private, and is stored in non-interoperable systems.
Fixing these data problems isn’t just going to be helpful for consumers and doctors, it’s also one of the best business practices, as other industries, such as banking, have shown. The days of providers collecting data for privately-held databases are over. Not only are they expensive to maintain, but data-sharing can lead to novel insights and collaborations across the industry, allowing companies to gain a foothold in an area previously not considered, for example, repurposing a pharmaceutical drug for a completely unrelated indication than it was primarily developed for. Where having the data may have been an advantage a few years ago, in today’s landscape, it’s all about what you do with that data. This is why interoperability and data-sharing will be an essential component of fixing our healthcare system.
Price and quality data are perfect examples of places where data analytics can have a tremendous impact on healthcare costs. The Leapfrog Group is just one company that has been collecting safety data and passing it along to employers who can then use that information to make decisions about healthcare plans for their employees. The federal government collects a variety of data and releases it on a publicly-available website. Knowing the cost-effectiveness of a procedure or device is beneficial, but to drive healthcare costs down, we need to move beyond that to determine whether something is actually necessary in the first place and companies like the AI startup Hindsait are doing just that. If this approach were more widely used, care that is expensive and offers little return in terms of patient outcomes could be reduced or eliminated.
Data analytics will also enable optimization of clinical practice and operational business practices. Mayo Clinic has been the subject of a concerted effort to improve efficiency and reduce costs, demonstrating that even highly-performing health systems can find success in streamlining processes while delivering positive outcomes for patients. Optimizing their processes and standardizing equipment led to substantial cost reductions and changes to clinical workflows. This example illustrates the point that health centers have to be willing to let the data drive the process change, even when it leads in unexpected or new directions. The end result? Hospitals that have embraced data analytics for both patient care and business will thrive, while those that remain on the sidelines will find themselves increasingly irrelevant.
A Deep-dive Into Specialty Pharma
A specialty drug is a class of prescription medications used to treat complex, chronic or rare medical conditions. Although this classification was originally intended to define the treatment of rare, also termed “orphan” diseases, affecting fewer than 200,000 people in the US, more recently, specialty drugs have emerged as the cornerstone of treatment for chronic and complex diseases such as cancer, autoimmune conditions, diabetes, hepatitis C, and HIV/AIDS.
While a variety of pundits and analysts see digital health, genomics, and AI saving healthcare and lowering costs, each of those areas relies on vast amounts of data and its analysis. Precision medicine can work for a single patient because of studies in hundreds and thousands of patients that identified the key genomic alteration that a drug could target. AI and cognitive computing solutions like IBM’s Watson and DeepMind depend on vast amounts of data to train the system so that it can ‘learn.’
Data-driven healthcare is here and innovative companies are fueling new markets, discovering new cures, identifying new diseases, and enabling patients to become smarter healthcare consumers. In virtually every other industry, the use of data analytics has been revolutionary, allowing companies to optimize their product and procedures. We can see the same benefits in healthcare if we embrace new ways of looking at data and technology.
Harry is the author of two related books: Commercializing Novel IVD’s; A Comprehensive Manual for Success and MoneyBall Medicine: Thriving in the New Data-Driven Healthcare Market.
Photo: MilosJokic, Getty Images
