“Effectiveness” is at the forefront of the health policy debate. Effectiveness is the assessment of whether any particular medical intervention actually advantages patients when prescribed in practice settings. To be considered effective, the intervention must result in a clinically meaningful improvement for an adequate percentage of patients. Furthermore, it must not result in a clinically important adverse outcome in too many. Clearly, “effectiveness” is a value-laden construct. How is “meaningful improvement” defined and by whom? How is “important adverse outcome” defined and by whom? How are “adequate percentage” and “too many” defined and by whom?
“Cost-effectiveness” is even more value-laden. It is legislated to be off-the-table in the machinations of the Affordable Care Act for reasons that vary from fear of rationing to fear of compromising profit margins. But no one can exclude cost-effectiveness from the patient-doctor dialogue. Considerations of co-pays and deductibles often weigh heavily in the valuation of interventions.
The greatest advance in clinical medicine in my time in the practice, fast approaching 50 years, is that today patients and their doctors can assess effectiveness as collaborators. No longer does an imperious pronouncement by a physician suffice. Rather, the patient should occupy the driver’s seat with the physician as navigator. For each option in intervention, the patient asks, “Based on the available science, what is the best I can expect?”
For nearly 50 years, no prescription drug could be marketed unless the FDA was convinced that it had a tolerable benefit-to-risk ratio based on scientific studies. The bar for devices (like hip replacements) and procedures (like liposuction) is not as high, but there is usually some informative clinical science of this nature. The science is generally designed in the hope of demonstrating a favorable benefit-to-risk ration. Hence, patients and interventions are chosen to measure outcomes in the best case. However, make no mistake; neither the fact of FDA approval nor common practice is an adequate response to “What is the best I can expect?” If the best case falls short in your mind, why would you acquiesce to the intervention?
Recently the Centers for Medicare and Medicaid (CMS) made troves of data publically available. CMS released data on hospital charges, physician utilization, in addition to other data sets. Journalists and academics were excited to potentially confirm their theories on healthcare spending.
We at The Engelberg Center hosted an event, Hacking America’s Health where experts from the Brookings Institution and the government spoke to participants regarding the impacts of data transparency on the nation’s healthcare system. The purpose of the festival is to focus on “innovators from around the world and their transformative solutions to global challenges.”
Out of this discussion emerged a consensus that data transparency could spur disruptive innovation in the health sector but overcoming several key barriers was essential to maximizing the benefits to the public.
Benefits of Data Transparency
1. Help Consumers Make Informed Decisions
Open data offers numerous benefits to consumers. The CMS data unveils the enormous variation in the cost of different treatments. Enabling consumers to find high value care providers improves the efficiency of the market. Price transparency can also uncover providers that charge unusually high prices and puts pressure on them to lower those charges. Finally utilization can reveal if a doctor uses a rare treatment with regularity. All of these data empower health care consumers to choose wisely.
2. Identify Vulnerable Patients
CMS has used open data for numerous projects to help patients. One project involves collaboration with local and state governments. Using Medicare claims information they identified specific patients who could be in special danger in the aftermath of a natural disaster. Without electricity it’s impossible to operate a lifesaving device like a ventilator or nebulizer. The claims data allows emergency officials to notify such individuals about the locations of shelters.
3. Data Mashups
Combining together data sets could help identify bad actors in the health system. For example merging data from the Sunshine Act which describe payments and items given to physicians combined together with utilization data from CMS. This could identify doctors who were using a drug or procedure due to a financial relationship rather than best practice. Other data mashups could also uncover unexpected patterns.