Big Money, Uncertain Return
Hospitals are spending billions collecting and analyzing medical data. The one data point no one is tracking: the payoff.
Ten years ago, Kaiser Permanente began building a $4 billion electronic-health-record system that includes a comprehensive collection of health-care data ranging from patients’ treatment records to research-based clinical advice. Now Kaiser has added advanced analytics tools and data from more sources, including a pilot program that integrates information from patients’ medical devices.
Faced with new government regulations and insurer pressure to control costs, other health-care organizations are following Kaiser’s example and increasing their use of analytics. The belief: that mining their vast quantities of patient data will yield insights into the best treatments at the lowest cost.
But just how big will the financial payoff be? Terhilda Garrido, vice president of health IT transformation and analytics at Kaiser, admits she doesn’t know. Nor do other health-care leaders. The return on investment for health-care analytics programs remains elusive and nearly impossible for most to calculate.
“We can all accept the fact that analytics contribute to the outcomes, so that justifies more investments into analytics. But I don’t think anybody can definitely put together a proven case,” says Stephen M. Stewart, CIO of the Iowa-based Henry County Health Center, which is spending $100,000 to $150,000 annually on analytics.
Although the health-care industry has long used data to determine medical protocols, the use of computerized records and analytics software is still in its infancy. Most organizations only started to install electronic health records in recent years, encouraged by nearly $24 billion in federal grants. Those digital records are crucial for analytics programs because they pull in and computerize reams of data on each individual patient, allowing health-care organizations to access exponentially greater amounts of information than what was contained in old paper records or disparate computerized files. They also allow clinicians to look across populations of patients for evidence of which treatments work best.
Opportunities to identify the most effective treatments could slip away if CIOs and their teams aren’t able to quantify the return on their analytics investments. Health-care providers are under increasing pressure to cut costs in an era of capped billing, and executives at medical organizations won’t okay spending their increasingly limited dollars on data warehouses, analytics software, and data scientists if they can’t be sure they’ll see real benefit.
A new initiative at Cleveland Clinic shows the opportunities and challenges. By analyzing patients’ records on their overall health and medical conditions, the medical center determines which patients coming in for hip and knee replacements can get postoperative services in their own homes (the most cost-effective option), which ones will need a short stay in a skilled nursing facility, and which ones will have longer stints in a skilled nursing facility (the most costly option). The classifications control costs while still ensuring the best possible medical outcomes, says CIO C. Martin Harris.
That does translate into real—and significant—financial benefits, but Harris wonders how to calculate the payoff from his data investment. Should the costs of every system from which patient data is pulled be part of the equation in addition to the costs of the data warehouse and analytics tools? Calculating how much money is saved by implementing better protocols is not straightforward either. Harris hesitates to attribute better, more cost-effective patient outcomes solely to analytics when many other factors are also likely contributors.