Design of a Payment Decision–Support Tool for Coordinated Specialty Care for Early Psychosis

Frank et al. (10) proposed a multipart payment model for CSC that contains a bundled case rate payment (covering case identification, client engagement, and retention), a per-service component (covering CSC services delivered to specific clients after enrollment), and an outcome-based payment that is financed by withholding a portion of the per-service payment (to mitigate perverse incentives associated with the per-service payment) and rewards CSC provider teams for achieving prespecified outcome targets.

To facilitate payment decision making reflecting local preferences, existing payment mechanisms, and CSC service delivery, we adapted Frank and colleagues’ (10) framework in the following ways. First, we made the bundled case rate payment a “must-have” component but allowed decision makers to decide what types of CSC services should be bundled and covered under the case rate payment. This design reflects the general consensus that a bundled payment is aligned with CSC implementation but also affords local stakeholders the flexibility to tap into existing payment mechanisms for selected CSC services (e.g., services delivered by licensed clinicians that are usually most readily reimbursable), if so desired. A common issue with CSC financing is that supported employment, supported education, peer-specialist services, and other services provided by nonclinicians or nonlicensed providers cannot be billed to insurance under the existing payment mechanisms and rules. By allowing decision makers to bundle these services into the case rate, our payment model may maximize the chances that these nonclinical services be covered in CSC (11).

Second, we made outcome-based payment an optional component to allow for stakeholder flexibility in deciding whether and when to start holding provider organizations accountable for client outcomes. In the early stage of CSC implementation, the priorities of many provider teams are team building, community outreach to establish robust referral sources, and team workflow. It might make more sense to institute an outcome-based payment at a later phase. In addition, in our model, the outcome-based payment is financed by withholding a portion of the case rate payment (as explained in the following) and is paid out for each client who achieves a prespecified outcome in a given reporting period. This is in contrast with the common “all-or-nothing” approach in which providers receive the incentive payment only if a measured outcome of the entire patient panel crosses a threshold (12). This all-or-nothing approach provides little incentive for incremental improvement for providers whose performance is either far below the threshold or above the threshold. Moreover, with the small panel size in CSC (typically <50), mean outcome measures of the panel have low reliability and, therefore, subject provider teams to substantial risks.

Third, our design framework lets the local payers and providers decide on sources of financing of services not covered under the bundled payment, thus making a hybrid model possible. Figure 1 illustrates our framework.

As shown in Figure 1, within each component of the payment model, we specify design choices that allow decision makers to tailor their payment models to local preferences and circumstances. The only choice for the case rate payment is the type or types of CSC services covered by this component. We followed a study conducted in New York State (13) and grouped all services into four categories: clinical services directly involving a client (or the client’s family members) in an individual or group setting and typically provided by licensed clinicians; supported employment and supported education services and peer-specialist services directly involving or directed toward a client and provided by nonclinician specialists; care or case management services involving or tied to a specific client, including care coordination with the client’s non-CSC providers; and administrative and team operational tasks, which may or may not be tied to specific clients and include scheduling, documentation, community outreach and education, staff training and supervision, and other ongoing tasks to support the operation of the team. This grouping reflects differential availability of existing payment mechanisms, with “clinical services” associated with the greatest availability, two middle groups associated with rare but some emerging payment mechanisms (e.g., the Medicaid Home and Community Based Services [5] and Health Homes [14] provisions), and the “administrative and operational tasks” associated with almost no systematic, insurance-based payment. By allowing users to select among the four types of CSC services, we afforded decision makers the flexibility of combining existing payment mechanisms with the case rate payment to support CSC.

Under outcome-based payment, we provided two design choices. One is concerned with the outcome measures that CSC teams will report on and be held accountable for. The three outcomes indicated in Figure 1 reflect important recovery-oriented goals of CSC but are not exhaustive of all client outcomes that local decision makers may deem important such as, for example, the achievement of independent living. The second design choice under outcome-based payment concerns the amount of funds available for outcome-based payment, operationalized as a percentage of the total case rate payment. This “withhold” approach arguably provides stronger incentives for achieving a given outcome target, a behavioral economics principle known as “loss aversion” (14). An additional advantage, from the perspective of the payer, is that it sets a cap on the total payment (case rate and outcome-based payment) and thus eases budget planning. However, CSC providers will likely perceive such a design as penalizing, because, as long as their performance is not perfect, they would receive a lower payment than that received if outcome-based payment were not adopted. To better align incentives, we incorporated a larger markup for the case rate payment if an outcome-based payment is selected, as detailed in the following.

A major premise underlying the payment design was that the case rate payment needed to reflect the costs of delivering evidence-based CSC. Thus, to estimate the case rate payment, the first step was to estimate the costs of CSC service delivery and team operation. Because the makeup of the CSC team (in terms of roles and professional credentials of its personnel) and the costs of staffing the team may vary significantly, the payment design tool needs to collect data on these specific details to support a tailored payment rate. A screenshot from our tool that solicits such information from the user is available in part 1 of an online supplement to this article. In addition, the tool collects data on the (average) fringe benefit rate and indirect cost rate that will be applied to the direct costs of staffing the team to derive total costs. Our tool then calculates the costs of delivering the CSC services to be covered by the case rate.

For each client engaged in a payment period (e.g., a month in a PCPM case rate), a fixed or flat case rate is calculated by first estimating the cost of operating and staffing the CSC team to deliver CSC services covered by the case rate and then dividing by the number of clients receiving services (i.e., CSC team caseload):where j indexes the types of professionals (by credential) who make up the team, and w_j is the wage rate, and FTE_j is the full time equivalent(s) pertaining to credential j. S stands for the share of the total operating costs of the CSC team accounted for by the types of services to be covered under the case rate payment; S needs to be empirically estimated. Our data source and approaches to estimate the shares of total costs accounted for by different types of CSC services are described in part 2 of the online supplement. m stands for a markup factor (e.g., 1.10) that we apply to the cost estimate to provide a small margin to account for CSC-related costs not captured by the estimated costs of team staffing.

As described earlier, if the decision makers choose to include an outcome-based payment, they also decide on a proportion of total case rate payment that is “withheld” and made available for outcome-based payment. For each client who achieves an outcome target (e.g., no psychiatric hospitalization or emergency department visit during a reporting period), the provider team is expected to receive the following outcome-based payment (R_O):where n_o is the number of outcome targets selected, and N_o is the number of clients eligible for outcome-based payment over the reporting period. N_o is usually smaller than the team caseload because stakeholders may decide to exclude clients who are newly enrolled in the program (e.g., enrolled within the first 3 months) in determining outcome-based payment, because the provider team has had limited time to influence the outcomes for these clients. Our tool then estimates the total outcome-based payment that a provider team is expected to receive over a reporting period by generating a panel of simulated clients and their outcomes. Our data source and approaches to estimate the total outcome-based payment received by a team over 12 months are explained in part 3 of the online supplement.

For illustrative purposes, we present two scenarios of payment design and estimation based on the analytical algorithms outlined in part 4 of the online supplement. The two scenarios differ in the types of CSC services covered under the case rate payment but are identical otherwise. As shown, the PCPM payment rate was estimated to be $1,619 per month if all four types of CSC services were covered under the case rate payment and $802 per month if the case rate payment covered all but “clinical services.”

For our tool to support real-world payment decisions, it must be suited to the needs of the target user groups and perceived as intuitive and easy to use (15, 16). Therefore, as part of the development process, we conducted user-centered design sessions with key informants from the payer and provider communities, by using a prototype of the tool. In several cases, these interviews have also informed important decisions about the algorithm. For example, interviews with CSC provider teams indicated that resource intensity in CSC service delivery often does not present the type of regularity as that seen in collaborative care (17): once enrolled, it might take months to fully engage clients with CSC. Moreover, as is typical in psychosis, clients may experience ups and downs throughout their tenure in the program and may therefore require a changing intensity of services. This qualitative input helped us decide against a variable case rate reflecting a dichotomy between the acute and the maintenance phases of services. In the next phase of tool development, we plan to conduct user testing by engaging payer and provider partners in the same testing session to specifically assess the utility and feasibility of the tool to facilitate stakeholder collaboration.