Quants in Value-Based Care Organizations
3 core functions of quants in value-based care organizations
This blog1 post has been simmering in the back of my mind for a while. After listening to Giuseppe Paleologo (aka Gappy)'s appearance on Flirting with Models and hearing his nuanced and detailed description of the quantitative research and risk roles in buy-side firms, I decided to put pen to paper. Thanks again, Gappy!
The healthcare landscape has undergone a significant transformation in recent years, moving away from traditional fee-for-service models towards more innovative approaches that prioritize patient outcomes and cost-effectiveness. This evolution has given rise to two interconnected concepts: value-based care and risk-bearing entities. Value-based care emerged as a model focused on quality outcomes and cost reduction, while risk-bearing entities developed as organizations willing to assume financial responsibility for healthcare costs. More specifically, these approaches have reshaped how healthcare is delivered and financed, encouraging a more coordinated, efficient, and quality-driven system by shifting financial responsibility from health insurance companies and government programs (Medicaid and Medicare) to provider groups and care delivery organizations.
In this post, we explore the growing importance of quantitative teams for risk-bearing entities in the value-based care space, where data analysis and insights are required to operate complex care delivery models and appropriate risk pricing and management. Note that the impact of quantitative specialists is not limited to risk-bearing entities or the value-based care space; however, given the importance of risk and value creation through outcomes, healthcare quants should play a paramount role in these organizations.
While job titles may vary across organizations - from data scientists and health economists to actuarial scientists and business intelligence analysts - there are core functions that remain consistent. Ultimately, all of these core functions aim to understand and optimize the value creation of the organization given risk budget and financial budget (staffing) constraints. I focus on risk management and value generation by reducing the cost of care, and I avoid the quality pillar of value-based care.
3 Pillars of Quantitative Analysis in Value-Based Care
1. Growth and Contract Design
In this initial phase, quantitative experts play a crucial role by ensuring that:
the contract is designed and priced appropriately based on the underlying population and distribution of outcomes,
the contract design will yield appropriate (and fair) measurement of performance, and
the contract's downside risk is understood and reflected in the risk budget.
Let's unpack these points!
Appropriate opportunity sizing: The first stage of the growth and contracting process is quantifying the potential impact of your care model on the underlying contract population's healthcare utilization patterns. First, you need to understand the risk segmentation and clinical characteristics of your population. This will allow you to understand the distribution of cost of care and the prevalence of the high impact sub-cohorts that will vary the financial impact of your interventions on the population. Secondly, for each intervention and sub-cohort, you need to model the impact of your care model on the cost of care. Remember that the cost of care is a function of utilization and unit prices, and your modeling should reflect the potential impact of your care model on utilization patterns and unit prices. Most often, it is a substitution effect where your care model will reduce the utilization of high unit cost services while increasing the utilization of low unit cost services. The substitution may be direct, such as using generics or biosimilars. Or it may be more involved, like a dementia program that deploys home care management services to substitute high cost acute care utilization by shifting where care is delivered. Note that most companies approach this exercise as a point estimation and scenario (tends to be all positive) analysis exercise. However, it is better to take an approach where you can estimate the distribution of your impact at the intervention level and at the aggregate care model level, which will help you with risk pricing and budgeting.
Appropriate risk pricing: Value-based care contracts may have different designs where the savings (losses) from reduction (increase) in cost of care are shared between two parties. Note that one party keeping all the savings (or losses) is still a shared arrangement. In the design of the contract, quant teams can aid in ensuring that the additional unit of downside risk is compensated appropriately by the additional unit of upside revenue potential. Once the potential distribution of intervention outcomes is modeled (opportunity sizing), then the potential gross savings and financial outcomes in different contract structures can also be modeled and compared using risk-adjusted expected revenue metrics. Note that estimation of your potential gross savings is only as good as your modeling assumptions, and they will improve as you gather more evidence over time. But even in the early days, using a consistent framework to compare contract structures will be useful.
Appropriate measurement of performance: In value-based care contracts, performance is measured against a benchmark that is either constructed using historical data on a control population or constructed using a contemporaneous control population. In either case, the role of quantitative teams is to ensure that the over/under performance (reduced/increased cost of care) versus the benchmark will be due to the interventions delivered by your organization as purely as possible by making sure the trending, matching, risk-adjustment, etc. are done appropriately. In addition, contracts may include carve-outs and winsorization to reduce the variance in the measurement. The quantitative teams need to ensure that these approaches do not hinder the measurement of performance by censoring key intervention cohorts. For example, winsorization and carve-outs may work well to reduce the impact of outliers for a primary care model managing a heterogeneous population, but they may require additional considerations for a specialty care model that is targeting the "outlier" population.
Appropriate risk budgeting: During contracting, there are two important considerations: 1) Making sure the significant negative scenarios are tolerable for the company, and its balance sheet. 2) Making sure the risk exposure is used for more favorable contracts where your organization has higher expected revenue per unit of downside risk.
In the early days of a value-based care start-up, risk budgeting will be a secondary concern. You need to sign contracts and have revenue to grow as a business. However, as the organization matures, making sure the downside is measured and accounted for becomes a more significant and more strictly (even legally) enforced risk capital constraint. Creating a risk-adjusted expected revenue framework in the early days allows organizations to manage their risk budget thoughtfully before they are locked into multi-year suboptimal contracts and hit their risk constraint.
2. Intervention Design, Evaluation, and Optimization
Once contracts are in place, the focus shifts to designing and refining interventions to generate value in a structured, repeatable, and scalable care model. Again, quantitative experts play a crucial role to:
Identification and prioritization of intervention cohorts: Can you identify patients who would benefit from receiving an intervention?
Monitoring and measurement of operational effectiveness: Can you deliver the intervention to the identified members effectively and consistently?
Measurement of efficacy and outcomes: Do you observe the desired outcomes among the members who receive the intervention?
Again let’s double click into each point!
Identification and prioritization of intervention cohorts: As we mentioned above in the opportunity sizing section, different interventions in the care model may target different high impact sub-cohorts, or the intensity/dose of the intervention may change across different sub-cohorts depending on the needs and the expected change in outcomes across these cohorts. Data analysis and insights may help deliver interventions to the appropriate individuals at the appropriate time. This is necessary to optimize outcomes given the resource and staffing constraints. Approaches like using a generic risk score (like the Medicare Risk Adjustment model) will not work at the intervention level. Especially for specialty care models, a generic risk score that is trained and constructed to work well across a heterogeneous population may not be discerning enough. For example, all metastatic solid tumor cancer patients may be considered "high" risk in a PCP model, but a cancer specific care delivery model may need to distinguish between cancer types and spread of disease.
Measurement of operational effectiveness and identification of fail points: In an ideal world, the entire identified intervention cohort should receive the intervention. But delivering healthcare interventions is messy and operationally complex. Hence, there will be fail points, and it is important to understand and fix them. Does your team deliver the interventions? Does your team deliver the intervention timely? Can you reach and engage patients to receive your intervention? If patients do not want to receive your interventions, can you understand why? Do you observe variation across demographic groups, geographies, and potentially different teams in your organization? It is important to capture the data to be able to answer these questions and review them periodically with the leaders of your care delivery organization. For example, if you observe large variations across the effectiveness of different teams and individuals, you may incorporate more training. Even better, you may invest in technology solutions to increase overall effectiveness and reduce the variation among team members by codifying your interventions and processes into workflows. Note that these questions and potential technology investments will also improve the scalability of your care model besides efficacy. More on workflows, technology investments, and scalability in future posts!
Measurement of efficacy and outcomes: Most importantly, the value-based care model is predicated on improving outcomes. Hence, you want to understand the efficacy of your interventions among the people who receive them. This is where you will see the benefit of conducting a formal and deep opportunity analysis by comparing the efficacy of your interventions to the a priori outcome assumptions that were incorporated into your opportunity analysis. If you are not observing the desired and intended outcomes, then you need to ask:
Is the intervention designed correctly?
Do we need to revisit our hypothesis? More importantly, you may need to update your efficacy and savings assumptions in your opportunity analysis to avoid taking financial risk that you cannot successfully manage in prospective contracts.
3. Interim Adjudication and Savings Estimation
In the previous section, we talked about examining the performance of each intervention in real-time. The interim adjudication process allows your organization to examine the financial implications of the outcomes you are driving within the parameters of each contract. Note that this is a retrospective exercise as you are looking at the cost of care in the past during a performance period. However, you can still ask important questions such as:
Savings decomposition and explainability: Did all your interventions in aggregate lead to positive financial results? Ironically, you can answer this question with a higher level of certainty as you observe more data during a performance period while having less time to improve the results. This is why intervention level and forward looking measurements from the previous section are important to have a faster reaction time. Can you validate the potentially leading outcome measures from your interventions in the actual claims and utilization metrics? In other words, can you explain the financial outcomes during the interim adjudication with the clinical interventions you have implemented? (This is similar to P&L decomposition in finance). Ideally, the financial outcomes correlate to your interventions, which indicates that the results are driven by your care model. Hence, they are systematic and repeatable, as opposed to idiosyncratic savings (losses). Note that understanding the impact of idiosyncratic factors will allow your team to improve the performance measurement methodology.
Risk and financial budget: Do you need to update your risk budget and financial projections and revenue expectations based on the revenue (losses) you are expected to incur? These are important questions for the leadership organization to manage the balance sheet and cash flows of your organization.
Postlude
I hope this blog post has helped introduce career opportunities to those with a background in quantitative fields and encouraged them to apply their analytical skills to the meaningful challenge of improving patient outcomes and bending the cost curve in healthcare.
For leaders in value-based care organizations, I have also attempted to provide a framework to structure their quantitative teams by outlining the three key functional areas. This intentional role definition can guide hiring processes, ensuring that organizations build well-rounded teams with complementary skills to address the complex challenges in value-based care and ultimately build financially successful businesses to be able to broaden their impact and reach.
While we cover different functions in a fairly linear fashion across the life cycle of a contract, there is a feedback loop across all these functions. As we mentioned above, the opportunity analysis starts with assumptions on the outcomes of your interventions and translates them into a financial model and gross savings assumptions for contracting. The intervention evaluation and interim adjudication help your organization understand if your care model is driving the assumed outcomes and achieving the gross savings targeted in different contracts.
Beyond the monitoring of individual contracts, as you gain (lose) confidence in the efficacy of your interventions and have a sense of your potential revenue (loss) through interim adjudication, you may take on more (less) risk as an organization. As you target different segments and contract structures, you may need to change your growth strategy and contract structures. By embracing this perspective, risk-bearing entities can optimize their contracting and risk budgeting, resource allocation across cohorts in their population and interventions, and drive continuous improvement through data-driven feedback loops.
Hope you enjoyed this article. Please send your comments and questions to practicalvbc@gmail.com.
Disclaimer: These are my opinions and do not reflect the opinions of my former and current employers.