A policy brief from the Harvey L. Neiman Health Policy Institute outlines two "innovative" healthcare payment models it says can "achieve the dual goals of health system sustainability and providing patients affordable, cost-effective access to medical imaging and other specialty services."
The brief, "Beyond Fee-For-Service: Emerging Payment Models in Radiology," argues that these new payment models can align the provision and payment of specialty care with efforts to sustain a high quality healthcare system. This can happen despite concerns that shared savings programs will incentivize providers to cut costs by having them assume a greater financial risk for a patient's care, which could result in reduced access to specialty care.
"One of the potential harms of making providers responsible financially for the totality of a patient's care is that it can create incentives to withhold services in order to maximize profits," Neiman Institute CEO Richard Duszak (pictured), also a FierceHealthIT Editorial Advisory Board member, said in an announcement. "This undermines the primary goal of health reform, to provide better care and improve outcomes in order to contain costs through enhanced long-term health and wellness."
According to Duszak, the brief suggests providers can use big data to achieve these goals, particularly in the area of inpatient and episodic care, such as screening mammography.
Inpatient services can be identified and defined in a straightforward manner, and are sufficiently standardized so that analytics can support the development of alternative delivery and payment systems. A first step in doing this is to understand how specialists are involved in inpatient care.
For example, in the case of ischemic stroke, an analysis of MS-DRG (Medicare Severity Diagnosis Related Groups) systems files "showed the tremendous variability in the share of imaging costs in these episodes." The data indicated that the expected cost of a typical episode was closer to the 75th percentile than to the median, which means "that the average cost of care is heavily influenced by a relatively small number of very expensive encounters rather than the most common episodes."
Drilling down deeper into the data could allow providers to directly benchmark their own services, and identify other providers who are able to balance lower costs with outcomes that are similar or better than peers (and allow for the development and sharing of best-practice care delivery methods).
Episodic imaging care, such as screening mammography, is almost always directed by a radiologist, the brief points out, and the variability in the services from the beginning of the imaging episode to the endpoint (diagnosis) also is driven by radiologists.
"Analysis of that variability, with regard to utilization, costs, and outcomes, would similarly help identify excessive, unjustifiable, or low utility care [as in our inpatient model] to preferentially drive value," the brief states. "More importantly, targeted analysis using national benchmarks would, as with our inpatient episodic model, create rich opportunities to share best-practice care delivery methods with radiologists, facilities, and other stakeholders."
Specialist-focused inpatient MS-DRG and claims-based screening imaging episode benchmarking are just two alternative possibilities for redesigning specialist care payments, the brief concludes. "As new tools are developed to leverage the potential of big data analytics, additional models, particularly those that better incorporate comprehensive patient clinical data and real-time analysis, are likely to emerge as a result of such pilot model innovation."