For medical malpractice insurers, market pressures continued in 2018 despite overall profitability, according to a May report by AM Best. One way to combat potential headwinds is by lowering defense costs using advanced analytic techniques. In 2017, NORCAL Group began using Milliman’s Datalytics-Defense®, which uses proprietary data-mining techniques to analyze companies’ defense cost invoices and produce actionable insights. The results shown in the case study infographic below demonstrate the extent NORCAL was able to reduce their defense costs, all the while maintaining their overall claims-with-payment ratio.
Opioid use disorder (OUD) affects all age groups across the United States. There are about 1.5 million commercially insured Americans with OUD, and there are also a significant number of people on the verge of being recognized as having the disorder. Opioid prescriptions seem to be stabilizing or dropping, but there is growth in synthetic opioids, which are starting to take off in many communities around the country. In this episode of Critical Point, Milliman’s Stoddard Davenport and Joseph Boschert discuss their latest research on opioid use in the United States, including how advanced analytics can help predict whether a patient may develop OUD.
To listen to this episode of Critical Point, click here.
A new blog post at Milliman’s Healthcare Analytics Blog assesses the analytics associated with bundled payments as a means of reducing healthcare costs while improving the quality of care.
Planning and data analysis is important so that healthcare providers that agree to accept bundled payments know how to split and share that single payment. Here is an excerpt describing bundled payment claims analytics:
Accurate data is critical for a sound analysis. To begin the process, data must be grouped into bundled events, or episodes. Using inpatient admissions as the starting point, group all claims, for the same patient throughout the course of admission and for at least 90 days post discharge (a pre-admission time period may also be considered). Organize the claims into time periods (inpatient, 30 day post discharge, etc) and maintain detailed claim information, such as procedure codes, diagnosis codes and provider IDs to enable a thorough analysis of the drivers of cost throughout the course of treatment.
Once the data are grouped, review the results by admission type to identify the higher volume and higher average cost events, as displayed in the report below. Higher frequency and higher cost events may represent the greatest opportunity for savings, but this is just the beginning of the analysis. Beyond, the average cost per event, it is important to drill into admission types of interest and assess the variance in cost across similar bundled events. Care for event types with greater variance in cost may be more difficult to standardize and pose more financial risk for providers under a bundled payment contract.
Read the entire assessment here.
Also, read this healthcare reform briefing paper entitled “Evaluating bundled payment contracting” for additional considerations for bundled payments.