Remember the economic heydays of the Reagan years? Then, you may recall a footnote in history about the former First Lady Nancy Reagan trip to her psychic. Back then people laughed at the notion of seeing a soothsayer to know your future, but then again it was an economic boom time (well, most of it was anyway).
Of course, these days, most CFOs would probably love to have someone look at their collective palms and tell them where to trim the budget next or where to grow their facilities. Not to worry, I'm not suggesting you find a local swammy to guide your budget process, but you may want to invest in predictive modeling.
This technology has come a long way from data trending and is now being used to actually help not only predict areas of business ripe for growth, or rife with loss, it can also help with patient care. Just ask Kathy Jardone, COO, RN, CRRN, at Community Health Partners in Naples, FL. She and the team of nurses at this physician hospital organization, which serves patients in Collier and Southern Lee counties, are using predictive modeling that they say can help save lives and money.
In early 2009, Jardone says Community Health Partners began working with First Service Administrators, Inc. (FSAI) and a technology called D2Explorer, which is a Web-native, Application Service Provider product that leverages data to identify, manage, and minimize financial and clinical risk related to a population. The technology also offered them identification, financial trending and reporting, utilization and clinical care gaps, and a comparison against normative benchmarks. These are just a few of the capabilities of some predictive modeling programs.
"With this, we've already been able to detect two patients with breast cancer early on and get them into the system for treatment—we estimate that's about a half a million in savings right there," explains Jardone. "We want to stay solvent and save money, but outcomes are the most important thing to us."
Prior to adding the new technology to their system, nurses were often cross referencing data in multiple systems, Jardone says, and they were unable to see key indicators that might help them catch patient problems early on, such as if a patient had failed to renew their diabetes prescription or if they hadn't come in for their annual physical but they have a history of high blood pressure.
By adding this technology, the staff was able to pull all the available data sources into one view screen, Jardone explains, and then nurses could readily identify specific areas where interventions would have the biggest clinical and financial impact.
Now, when a patient presents, Jardone says, the nurses are "proactive rather than reactive." While it's still early for Jardone to present return on investment in dollars, she says they are already seeing it in the quality of care the nurses can provide. However, they anticipate that the money they save will likely end up on unnecessary emergency room visits. In the meantime, Jardone says they are tracking both the soft and hard return on investments for this predictive modeling technology.
How it works
Each predictive modeling product approaches the process differently however; this is how the vendor that Community Health Partners chose tackles it. FSAI, which is based in Lakeland, Fl, is a risk management company. They work with a Waltham, MA-based company, Verisk Health, Inc., to leverage the data so facilities can understand their medical and financial risks and thereby manage costs and clinical outcomes more effectively.
It works like this: Community Health Partners provides FSAI with the hospital's insurance claims data every 30 days. They then forward it to Verisk for the data to be scrubbed and statistics to be pulled from these materials. It is then sent back to FSAI where they analyze and interpret the data.
Doug Berman, senior vice president of strategic partnerships for Verisk Health, explains the data is aggregated in one place and sorted, and they work with FSAI to understand where the current spend of the facility is in terms of cost and network metrics by provider and disease.