Episode analytics is a method of using patient-centric data to define episodes of care. These episodes of care can be used to define standards of care – from both a cost and quality perspective – and then project these standards forward to establish bundled payment budgets and quality targets. This can be considered a global method of controlling costs. But what if episode analytics can be used in a predictive sense to determine the next top spenders?
Health care spending is not equal. For the civilian* population, 20 percent of spend is on behalf of one percent of the population, and five percent of the population is responsible for nearly 50 percent of all spend. These members are easily identifiable through claims analytics, and are often the focus of case management efforts to help control their costs. While these care management efforts are effective, they can’t reverse historical spending – nor can they ameliorate the episodes of care that drove the spending. The question is, can episode analytics be used to identify the episode characteristics that can predict the next one percent in order to practice preventive care?
Because SAS® Episode Analytics is patient-centric, it provides a full view of the episodes of care the patient has experienced. This view, however, is rather unique. Not only is all care included, but it is categorized in several manners. First, the care is associated with all episodes that are appropriate. If a follow-up visit after surgery includes diagnosis codes indicating chronic care, the chronic care episode(s) are associated with the visit, in addition to the surgical episode. This identification is hierarchical in nature as well. If the care initiates an episode of care, it is fully allocated to that episode, but can also be associated – not allocated – to another episode. Additionally, care can equally be split in the allocation. This hierarchical categorization of care is unique and allows insight into connections – or lack thereof – in care.
Another feature of comprehensive episode analytics is categorizing care as typical care or a potentially avoidable complication (or PAC). This is not only a method to quantify quality – but also identify future, undesirable, member health implications. With SAS, these PACs are categorized based on clinical criteria, such as adverse effect of drug or peripheral embolism. There are over 200 categories PACs identifiable today. These complication categories have the full claim history – including not only the procedures but also the diagnoses – behind them.
The combination of hierarchical associations as well as complication categorizations provides a valuable tool to analyzing historical claims. This new insight into member claims history provides new tools for analytics, and predictive engines. These engines can, in turn, be used to predict the members – and providers – that can benefit from future actions.
*Civilian excludes residents of institutions – such as long-term care facilities and penitentiaries, as well as military and other non-civilian members of the population. “Care” reflects personal health care and does not include medical research, public health spending, school health or wellness programs. From “The Concentration of Health Care Spending,” National Institute for Health Care Management (NIHCM) Foundation.