Analytics and the future of healthcare
January 30, 2012 in Medical Technology
Healthcare will be a hot topic during the 2012 U.S. presidential campaign as the Patient Protection and Affordable Care Act signed into law by President Barack Obama nearly two years ago is attacked or defended by the respective candidates and their surrogates. However, no matter who wins the White House this year, the U.S. healthcare system will be reformed, and more likely transformed, in the near future, and analytics is certain to play a leading role in that transformation. In fact, reform is already well underway, driven by increased competition within the healthcare industry, the trend toward “accountable care” and the realization that spiraling costs make the current system unsustainable.
According to the Centers for Medicare Medicaid Services, U.S. national health expenditure totaled $2.5 trillion in 2009, or $8,086 per person, and accounted for 17.6 percent of gross domestic product. The United States spends more money per person per year on healthcare than any other nation in the world, yet the World Health Organization ranked the U.S. healthcare system 37th in overall performance (just behind Costa Rica and just ahead of Slovenia) in 2000, the last year the rankings were compiled.
Why is healthcare so expensive in the United States and why doesn’t all that money produce better outcomes across the populace? Some of the more notorious contributors to the problem include misaligned incentives among the various stakeholders, bloated administration costs (someone has to shuffle all that paperwork), fraud and abuse, overtreatment and defensive treatment (from fear of malpractice suits), system failures and a lack of coordinated care, almost all of which are target-rich environments for analytical intervention.
To be sure, the United States offers arguably the best healthcare in the world, but at what price? According to the American Journal of Medicine, medical bills capsized 62 percent of the people who went bankrupt in 2007. Clearly, healthcare in the United States can benefit from a strong dose of analytics to help improve the performance of a massive, complex, fragmented, hugely expensive system struggling to sustain itself.
Volume vs. Value
The U.S. healthcare system has historically operated on a fee-for-service model. The more patients a doctor sees, the more operations a surgeon performs, the more beds a hospital fills, the more money the care provider in question makes. While patient outcomes and experiences are obviously a concern for all involved, they don’t impact the fee schedule. In short, the fee-for-service model emphasizes volume over value. That is about to change.
The mandate requiring individuals to purchase health insurance has turned into a popular talking point for politicians, but the provision of the Accountable Care Act that has caught the attention of the healthcare industry is the one that imposes financial penalties for providers who don’t meet certain standards of care for Medicare and Medicaid patients. The most prominent yardstick is hospital readmissions – patients who come in with certain ailments and then have to be readmitted to the hospital within 30 days after they are discharged. If a hospital’s number of such readmits exceeds a national standard, the hospital will suffer financially in terms of Medicare and Medicaid reimbursements. That means patient outcomes are now part of the healthcare fee structure, which makes it a whole new ball game.
“If I, as a healthcare provider, am now financially at risk if you as a patient have to be readmitted to my hospital within 30 days, it changes the relationship I have with you,” explains Steve Conti, senior director of clinical innovation and population management at Seton Healthcare Family and head of the analytics committee at the Integrated Care Collaboration (ICC), a nonprofit alliance of healthcare providers in Central Texas. “In a fee-for-service environment, the system is not financially affected by how many times you get admitted. It may call into question the quality of the care you receive, but from a purely financial perspective, it is advantageous to have you readmitted. In a value-based system, it’s just the opposite.”
Conti predicts that within five years, the U.S. healthcare industry will move from a largely fee-for-service, volume-based system to a value-based system. “And the way you get to that new type of structure is through analytics,” he says.
ICC, one of the seven highest-rated Health Information Exchanges (HIEs) in the nation, has embraced and employed analytics since the alliance was founded in 1997. In support of its mission to provide high-quality healthcare in a cost-effective manner, particularly for patients who can least afford it, the ICC operates a regional health information exchange called ICare that contains data on more than a million patients and more than 8 million encounters (provider visits) at 70 locations throughout the Central Texas region.
According to Conti, analytics coupled with the wealth of patient data available in ICare enables ICC provider organizations to identify and reduce duplications in services, thus cutting costs and driving value. ICC also uses analytics and a team of epidemiologists and database analysts to measure and assess everything from readmission rates to clinical ventures. The team uses statistical models to compare how its member providers are managing their diabetic care clinics, for example, to see which ones are doing well and where there’s opportunity for improvement.
“Healthcare is too expensive,” Conti concludes. “When we look at the national expenditure for healthcare it becomes pretty evident that it’s unsustainable. As you back out from that, it causes large health organizations to begin to ask the tough questions. How are we contributing to that cost, and what can we do to become a change leader in the process of making healthcare more affordable, more effective, more efficient and more accessible? And the only way we can understand and improve the process is by having strong analytic capabilities.”
Until fairly recently, the provider side of the healthcare industry had been reluctant to embrace analytics. Humans are naturally resistant to change, and doctors are notoriously wary of ceding the control they’ve historically wielded regarding their patients’ diagnoses and treatment to others, let alone a “mathematical model.” After all, who could possibly know a patient’s medical history and issues better than the patient’s personal doctor?
For-profit hospital organizations had reason to resist employing analytics because “optimizing” their systems could theoretically hurt profits. Imagine a major hospital group that used analytics and electronic health records pre-Affordable Care Act to eliminate overtreatment and unnecessary lab tests and imaging, while simultaneously cutting patient queue times, improving patient outcomes and reducing readmissions. At the end of the fiscal year, everyone would be happy except the company CEO, whose bonus is tied to profits and who has to explain a multi-million dollar drop in revenue to shareholders.