Healthcare Data Analytics Post Reform: What’s Next for Organizations?
Guest post by Paddy Padmanabhan is senior vice president of healthcare analytics for Symphony Analytics.
As healthcare continue to become more “democratized” and patients start taking control of their own medical and healthcare information, the emergence of new healthcare entities like ACOs and HIEs are making huge amounts of data available. As new products and care delivery models start pulling previously underinsured and uninsured members of the population into the healthcare system. Given this new healthcare landscape, recent reports have estimated the market size of healthcare analytics to be $10 billion by 2018. At the same time, a significant shortage of healthcare technology professionals is being forecast, with clinical informatics being one of the most sought-after skills in the coming few years.
In this democratized healthcare environment, previously ignored data sources, such as demographic data and individual credit histories, are now important aspects of analyzing patient profiles. And as we go deeper into internal environments, healthcare companies will start looking at machine data to understand patient and provider behavior
As the complexity of data sources multiplies, health insurance companies are faced with new challenges to manage member engagement, making it difficult for primary care physicians to provide care based on the limited patient information and insights they have from their internal systems alone.
In my conversations with senior healthcare executives, I get a sense that they recognize the situation but are understaffed for even their most basic reporting and analytical needs. Take, for example, the ACO marketplace. Meeting the needs of compliance reporting on thirty-three core quality measures alone requires these entities to invest in and establish a reporting infrastructure, in addition to all the other management information and dashboards they need to manage their businesses successfully and qualify for the shared savings.
Yet, traditionally, healthcare has focused on the “volume” end of analytics, namely data management and governance, and some degree of descriptive analytics. Unlike other markets, such as retail or banking, very little is happening in the area of advanced analytics and predictive modeling.
But there is a new way of approaching the relationships between the “3 P’s” (patient, provider, payer). Traditional parts of healthcare, namely the payers and the providers, have been used to doing business on a fee-for-service model for many years, and all of their information systems are set up to operate in this paradigm. The nature of the relationship was largely adversarial, with the focus being claims and payments, and a constant analysis of care delivery utilization for the purpose of contract negotiations between provider and payer. The new thinking now focuses on the patient as well — a collaborative relationship for improved outcomes and lower costs, and a solid analytical foundation becomes essential to track and manage clinical and financial outcomes.
Take the case of penalties on preventable readmissions. Many hospitals across the nation have been penalized up to 1 percent of their Medicare reimbursements for failing to comply with readmissions thresholds. Hospitals are scrambling to understand the root causes of readmissions, and prevent or minimize these from occurring. Hospital executives are concerned not just with the bottom line impact but the reputational damage that accompanies being on a list of offenders. Payers, on the other hand, are looking at their member populations and their provider networks at a macro level to identify patterns that will help them address the readmissions problem at a cohort level that goes beyond clinical analysis at an individual patient level. New tools and risk-scoring models are required to tackle this problem effectively.
The healthcare system has developed fairly mature analytical capabilities in traditional areas, such as claims and actuarial analysis in the traditional employer-based health insurance model. However they are in the very early stages of understanding how to work in a marketplace that is shifting toward individual members. Internal data alone will no longer cut it, and the risk-management models of employer-based insurance will no longer suffice.
Providers have spent huge sums of money implementing EHR systems and demonstrating meaningful use, to qualify for incentives. Yet the million-dollar questions remains: what to do with the data? Clinical analytics and informatics has never been a focus in the fee-for-service model, so a major change of mindset is required.
There’s no question that there is a huge need for analytics, and the capacity and capabilities required to meet those needs do not exist today within the healthcare system. There also are just not enough data scientists out there to go around, and it cannot be addressed by throwing the next new piece of technology that comes along at the problem.
The solution lies in prioritizing the areas of focus, developing a multi-year roadmap, and determining which areas are core to the business and which ones can be delivered using a combination of technology and consulting support. It’s also worthwhile considering global talent, especially from places like India where there is strong talent with backgrounds in science and applied math to take on at least some of the “heavy-lifting” aspects of an analytics program so that scarce and valuable internal resources can be focused on the domain-intensive aspects of analytical work.
It’s time for the healthcare sector to make bold, disruptive moves and embrace analytics whole-heartedly as a strategic tool for growth and profitability.
Paddy Padmanabhan is senior vice president of healthcare analytics for Symphony Analytics ( www.symphony-analytics.com), a division of Symphony Teleca. He can be reached at firstname.lastname@example.org.