predictive analytics

Finding the right payment model with the right analytics tools

By: Siva Nandiwada, Associate Vice President and Global Delivery Head– Healthcare & Life Sciences, Infosys

Key challenges in the US Healthcare system are the increasing cost of healthcare without corresponding increase in the quality of care.  Some of the root causes for this model are – lack of collaboration and data sharing between payers and providers, lack of patient education / engagement, lack of governance on quality outcomes and payment models.

One of the key initiatives in this regard is the creation of Accountable Care Organizations (ACO). ACOs could be either Payer sponsored or Provider Sponsored and will have a stronger collaboration between payers and providers focusing on key goals such as – improving the quality of care delivered, offering better healthcare outcomes to patients and decreasing the cost of care. In this construct, ACOs are incentivized to deliver outcomes i.e.  Provider payment models shift from Fee for service to Outcomes. Some of the widely adopted models are global payments, 2-sided shared savings, partial capitation, 1-sided shared savings and episode payments.

Other payment models too are being recommended, like the Pioneer Accountable Care Organization (PACO) Model. According to a report by the US Department of Human Health Services (HHS), with this payment model, healthcare organizations have realized savings close to $384 million in nearly 2 years. The Centers for Medicare & Medicaid Services (CMS) have stated in a report that PACO is the first patient care model that has met Medicare beneficiaries’ requirements, especially for expansion criteria. Another payment model that enables healthcare organizations to offer better incentives to physicians is the Advance Payment ACO Model. It provides advance payments to organizations and retrieves it later from their shared savings.

Two more ACO models are seeing increased adoption among healthcare organizations – the Investment Model and Next Generation ACO Model. The first model is an extension of the Advance Payment Model. Through it, organizations can get advance continuous payments to send their healthcare providers to under-served areas. The second model is designed for healthcare organizations that have previously coordinated and managed healthcare for various patient populations. When healthcare organizations align with these payment models, they will be looking into, as well as generating large amounts of data. This can be quite overwhelming, especially for healthcare organizations that are just starting in the digital adoption journey.

With analytics tools, healthcare industry can mine this big data and enjoy big benefits. How can this be done? CMS and other healthcare agencies can leverage big data to suggest the payment models likely to offer the best returns to organizations. With the right analytics tools, they can track, monitor, gather, analyse and interpret critical information about the amount of money organization are spending and saving. They can compare this data with the benchmarks the ACO has set, to understand which models are more profitable than others. Based on this, healthcare agencies can suggest which models can be adopted, retained or discarded.

By leveraging big data, healthcare organizations can improvise existing payment models to develop new ones that reward physicians for providing patient care more closely aligned with the value- based services goal. Companies can also benefit from this. Tools and programs with the capability to analyse companies’ data against healthcare provider outcome information, will match them based on the effectiveness of the later. Companies assigned to the more effective care providers will have to pay lesser amounts on employee group insurance programs as compared to others.

This benefit will even be extended to companies with employee groups that have a higher risk, as the physicians they are paired with have quite a high chance of offering better outcomes even for these groups.

Physicians will get incentives based on the care quality they deliver employees. Another area in which big data has great application value in payment models is in patient readmission reduction, a goal that the Affordable Care Act (ACA) has set. In fact, to prevent the rise in readmission rates, a penalty of 3% is being levied on healthcare organizations that see high patient readmissions in 30 days. Healthcare organizations can use analytics and business intelligence tools to analyse data that can give insights to can help reduce the number of times a patient visits for a healthcare need or a related one. Analytics and business intelligence tools can help healthcare organizations to get insights that are relevant. This will allow organizations to understand payment models in retrospect, discover more about new models in real-time and get predictive insights that will help them take precautionary measures for the future.

One blue cross blue shield payer in the US recently reported over 100 M USD savings through implementation of PCMH (Patient Centered Medical Home) model – a payer sponsored ACO variant by focusing on chronically ill patients, setting right quality measures and aligning incentives to PCPs(Primary Care Physicians) to deliver outcomes. Use of digital technologies such as Provider portals, implementing integrated care management solutions to address gaps in the care and implementation of data and analytics solutions is critical to achieve these benefits.

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