How Big Data Can Help Healthcare Providers Manage Risk

Healthcare providers are faced with the daunting challenge of implementing new revenue models, controlling costs and improving quality in order to meet the demands of the Patient Protection and Affordable Care Act, also known as "Obamacare." The prevailing theme is patient-centered, preventative care. To combine all these aspects successfully, hospitals will need to make better use of data and analytics; not only providing greater insight into a patient's physical health, but also being mindful of the financial health of all parties.

It has been well publicized that Obamacare is an attempt to break the vicious cycle of rising healthcare costs, which amounted to $2.7 trillion in 2011, twice as much as in 2000. But for healthcare providers who fail to re-think their entire approach, it will add three new financial risks. First, the reforms will reduce revenues from historical employer-sponsored health plans, as people opt for cheaper individual plans with higher deductibles. Compounding this problem is the likelihood that employer organizations will consider whether offering health coverage still makes sense. A survey by Deloitte in 2012 suggests that between 5-25 percent of large companies, and 10-50 percent of smaller ones, could drop their plans over the next decade increasing participation in health insurance exchanges.  The shift to a more complex, consumer-led insurance system means hospitals will need to identify how much of the bill patients are responsible for and how able and willing they are to pay. That will exacerbate the challenge of uncompensated care from bad debt, which has already doubled to over $40 billion for hospitals in the past decade, according to the American Hospital Association, as employers balked at bearing the full costs of fast-rising insurance premiums.

Secondly, the reform measures will likely squeeze providers' margins as they begin to see utilization of services rise with approximately 30 million uninsured gaining coverage through Medicaid and the exchange system. An additional seven million people are expected to enroll in Medicaid next year, rising to 11 million within four years. True, the volume of business will shift historically uncompensated care to reimbursable volume, but the portion of Medicaid reimbursement has a low reimbursement rate for services.

The third change, accelerated by the reforms, is the push to bundled payments for comprehensive healthcare, rather than defined fee for service. A three-year pilot scheme will introduce bundled payments for Medicare patients, but private payors like UnitedHealth Group, Humana and Aetna are already making bundled payments to groups of providers in an attempt to manage their own risk as competition increases.

These growing pressures explain why hundreds of hospitals are now starting to collaborate with physicians, clinics, insurers and employers to set up accountable care organizations, which give them greater size and the opportunity to develop more integrated and cost-effective approaches to managing the health of defined populations. ACOs have the potential to transform healthcare radically from a pay-per-service industry to one that succeeds by maintaining health in the most cost-effective way, based on a fixed fee per person. But how can today's hospitals best make the shift from a focus on getting patients in and out of beds with decent outcomes, to become proactive holistic health organizations — and how can they manage that process profitably?

The model many ACOs aspire to is Kaiser Permanente, which has revenues of around $50 billion from its own fixed-fee health plan, serving over nine million people, mostly in California, through 37 hospitals, over 600 outpatient clinics and 17,000 salaried physicians. Over the past decade, Kaiser has succeeded by building a sufficiently large and diverse network to provide comprehensive care for its members, thus keeping costs down and patients happy to stay within the system. It has also invested heavily in electronic health records which help it to coordinate patient care across its many parts, improving outcomes and preventing duplication. Now, CEO George Halvorson told the New York Times in March, he is experimenting with ways to re-engineer Kaiser's network to bend the cost curve further, using hospitals and physician far less for managing chronic conditions, while incentivizing patients to take greater responsibility for their own preventative care.

Can other organizations take this step now considering it has taken Kaiser several decades, many ups and downs and billions of dollars in technology investment to get there? Clearly the transition over the next few years will be tumultuous, and success will depend on getting the financial arrangements right. But the big data revolution offers one short cut, giving providers tools to think about patients in a holistic manner, taking into account both physical and financial health, and therefore getting the right care models in place to drive efficiency and keep risks low.

The big data revolution

Big Data is about collecting massive amounts of unstructured information from many different sources and using advanced analytics to identify patterns that support decision-making. It is used widely in high-volume consumer businesses to segment people more efficiently by identifying common behaviors. Credit card companies, for example, segment very different products at people depending on their spending patterns and credit histories. Hospitals already have a very deep but narrow view of their patients’ health, while payors have a broader horizontal view. Add to this picture financial, social and consumer data, and it becomes possible for healthcare organizations to get a holistic picture of the patient population they are serving, while also focusing on specific high-risk individuals and groups.  

Healthcare entities should look for lessons learned from other highly consumer and risk-driven business models. Technology has advanced to a point where digitized information can be more easily consolidated to build predictive models based on big data. For illustrative purposes, consider a costly chronic condition such as diabetes. By combining historical claim information with demographic and social data, healthcare organizations can build a model that will segment a covered population based on risk. In a zip code where individuals have a pre-disposition to diabetes, it is possible to alter the prevention model. By providing educational services, free testing and easy-to-access preventative care, diabetes can be better managed. That will lessen the likelihood of an acute episode such as kidney failure, which is very costly for both the patient and the healthcare provider. Alternatively, areas that do not meet that criterion may demonstrate patterns of seeking preventative care proactively and will not require such an investment. The success of a predictive model will require big data and analytical capabilities not yet prevalent in the healthcare industry.

As hospitals face the growing pressures of cutting costs, bundled payments and quality care reimbursement, they will need to develop and analyse their patient populations in a holistic manner. It will be an uncertain few years for all providers, but with better analytics, the risks will at least be clearer and the decisions will be more informed and easier to make.

Jim Bohnsack serves as vice president for TransUnion's Healthcare division tasked with developing and executing growth strategies. With direct responsibilities for product development, go-to-market strategy and business development, Mr. Bohnsack has a demonstrated track record of rapidly growing and scaling the business. Over the past fourteen years, Jim has built market eminence in the areas of healthcare technology and revenue cycle management.

More Articles on Big Data:

Target Physicians, Win Market Share Using Big Data
3 Questions Hospital Leaders Should Ask When Using Big Data

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