Neil Smiley is CEO and Founder of Loopback Analytics, which supplies tools to providers to manage bundled payments. I asked him to comment on the growth of bundled payments and the role of analytics.
Why are hospitals hesitant to adopt bundled payment options? What strategies can long-term care providers take to encourage bundled payment adoption on a large scale?
In most cases, a hospital’s financial responsibility ends upon patient discharge. Bundled payments significantly lengthen the care episode, making hospitals responsible for the costs of downstream care partners. The shift to value-based reimbursement from our current environment of fee-for-service represents a sea-change for the industry. Decades of business practices, clinical relationships, payment structures and core competencies are being reevaluated in light of this new payment model. Such evaluation and reflection takes time, and we are starting to see leading organizations embrace this challenge as an opportunity to develop differentiating capabilities.
Long-term care providers can help accelerate this process through proactive engagement with their hospital colleagues. Hospitals will need better data from their post-acute care partners to have confidence that they can consistently deliver efficient care and strong clinical outcomes. The long-term care providers that can demonstrate these capabilities pave the way for hospitals to better understand, trust and rely on the broader care delivery pathway they must manage with bundled payments.
What are the main benefits patients will reap from bundled payments?
Bundled payments have the potential to significantly improve patient outcomes through better care coordination across providers and care settings.
Under a fee-for-service payment model, there is little financial incentive to help ensure a patient makes a successful transition to skilled nursing after an acute care stay, or ensuring that patient successfully transitions home after leaving a skilled nursing facility.
A Deep-dive Into Specialty Pharma
A specialty drug is a class of prescription medications used to treat complex, chronic or rare medical conditions. Although this classification was originally intended to define the treatment of rare, also termed “orphan” diseases, affecting fewer than 200,000 people in the US, more recently, specialty drugs have emerged as the cornerstone of treatment for chronic and complex diseases such as cancer, autoimmune conditions, diabetes, hepatitis C, and HIV/AIDS.
Under bundled payments, there is now an aligned incentive with providers across care settings to ensure transitions are clinically successful and financially efficient. Patients are likely to get more help in navigating multiple silos of care, when participating providers are financially responsible for the cost of poor outcomes.
What factors are holding back scalability of bundled payments options?
Broader scalability of bundled payment options will be paced by the availability of accurate and timely data from network participants and workable frameworks for sharing risks and rewards among bundled payment partners.
At the national level, CMS has relayed its intention to aggressively advance pay-for-value reimbursement models, with bundles being a prominent example. Ready or not, the expectation is that bundles will quickly expand to include additional conditions and broader mandated participation.
At a health system level, first movers that have invested in the tools, competencies and partnerships needed to succeed in bundle configurations will be courting more bundle opportunities to take advantage of their lead in the market.
What is the role of analytics in bundled payment? Can you provide an example?
Advanced analytics are absolutely essential to success in bundled payments.
Bundled payments require that different providers across the care continuum come together to consistently deliver a clinically successful and financially efficient episode of care. Without deep knowledge of potential partners’ strengths and weaknesses, a bundled payment manager stands a poor chance in creating a winning network.
CMS provides data to healthcare systems, ranging from high-level quality ratings to detailed, individual claims records. With effective application of data analytics, these sources of information, combined with data from network partners can be used to create the most effective care delivery network possible.
How will analytics advance over the next 5 years? 10 years? How do you measure progress?
Looking towards the future, we expect a pronounced shift from retrospective, historical analytics to prospective, predictive analytics. The former allows healthcare systems to accurately assess their current and historical states, and is an essential component for improving operations. The latter allows healthcare systems to avoid costs and adverse clinical events before they even occur. The availability of real-time health data will continue to grow with the proliferation of digital monitoring devices. Machine learning and predictive algorithms are already establishing themselves in matching patients with appropriate resources based on a diverse set of data markers. We foresee significant expansion on this front in the coming years.
Photo: Flickr user Wes Peck
