In today’s digital world, data is king. And health care providers without comprehensive data analytics solutions to leverage information are at a disadvantage when it comes to negotiating the best contractual terms with payers in the burgeoning value-based care marketplace.
Every hospital or health system leader benefits from value-based care if they can illustrate the organization’s quality performance and define risk across the patient population, and engage with payers equipped with that information.
Powerful population health management (PHM) platforms can do what was unimaginable only a few years ago—aggregate, normalize and analyze patient data, then stratify risk and determine cost-of-care averages among specific patient populations. These solutions provide a flexible, adaptable platform that levels the playing field between payers and providers.
By analyzing data provided by payers, identifying comparable populations within the network to benchmark against, and using the right metrics to gauge performance, providers can thrive under value-based models—using data to recommend changing contract terms on an annual basis.
Three Case Scenarios of Data-Optimized Payer Contracting
Case in point, we helped negotiate terms for a client’s value-based contract that was based on a total PMPM and measured against the network’s performance.
At the end of the contract term, payer-provided data was used to show that comparisons shouldn’t be made to the statewide network, but rather a like population within the same geographic area. We worked alongside the payer to determine the radius around the client (a clinically integrated network with a 40,000+ patient population) which would serve as the comparable population.
By orchestrating more cost-effective care, the client achieved remarkable results including the following:
• Generated $4.69 million in 2016 shared savings payments across 16,000 patients
• Outperformed their geographic comparison group by $27.97 PMPM, equal to annual savings of $5.37 million
• Decreased spending by high-ED-utilization patients
• Decreased inpatient utilization rates by 15 percent
• Increased primary care visits by 3 percent
• Maintained a 90th percentile ranking nationwide in patient satisfaction measures
The following year, it was demonstrated to the payer that utilization metrics didn’t truly reflect performance since the comparison to the like geographic population was not apples to apples. That is, patients who could afford a PPO network tended to have different utilization metrics than those who selected HMO products, where there is restricted network performance. This information was used to renegotiate 2018, using total cost of total medical expense, regardless of utilization metrics.
For another client, a physician’s organization with 600+ independent physicians serving 18,000 Medicare and commercial patients, more favorable terms were negotiated after highlighting payer discrepancies with regard to risk-stratification methodologies.
When comparing methodologies, one used by the payer and one by the client, we identified 16 percent of the patient population who, perhaps due to recent conversion to this payer, did not have the 18-month history required by the payer to risk stratify patients and determine a risk score—thereby impacting the population’s overall risk score.
We showed that comparing the client’s data to incomplete historical payer data—based on a risk-stratification methodology incorporating patients’ current conditions—was not a fair comparison.
Since the payer was unwilling to change its methodology, a new arrangement – an absolute medical expense ratio-based program – was negotiated.
For a third client, an independent practice association comprised of 23 FQHCs negotiating a value-based contract for Medicaid enrollees only, we used data to establish alternative quality metrics that would reflect the performance of the organization and eliminate measures that were totally reliant on the compliance of patients within the program. The metrics first proposed by the payer, where the client was 40 percent below the benchmark, would have excluded the client from receiving MIPS/shared savings bonuses.
These are just a few examples of how providers can use data analytics to negotiate contractual terms with payers. As value-based care and population health management continue to grow, healthcare leaders are recognizing the power of data, especially when it comes to contract negotiations.
Author:
Dr. Sanjay Seth brings over 30 years of clinical, administrative and consulting experience to the HealthEC leadership team, where he develops strategies to support providers and organizations participating in care delivery programs borne out of Health Reform and the Accountable Care Act. Dr. Seth also provides consulting services to provider groups and organizations to help them transform their services and optimize the health and wellness of their population, including physician engagement strategies, care coordination programs, population risk management, ACO strategies and payer/provider contract negotiations.
Dr. Seth has supported two physician groups in the formation of Accountable Care Organizations under the MSSP initiative, creating collaborative care coordination agreements and introducing technology and processes to manage ACO operations. He also established a payer supported Virtual Patient Centered Medical Home program for over 15,000 lives with care coordination and technology to comply with quality measures and utilization metrics.
Prior to joining HealthEC, he was a part of the turnaround team for Interfaith Medical Center, Newark Beth Israel Hospital Center at Orange and East Orange General Hospital, leading the implementation of complex hospital and physician clinical, financial, contractual and compensation relationships. Dr. Seth has also led numerous physician groups in their formation or re-structuring efforts including modification of billing systems, implementation of EMR’s and development of partnership agreements.
Dr. Seth studied medicine in Bangalore, India and holds a Masters in Health Administration from Cornell University, Ithaca, NY.