Modeling Financial Outcomes and Quantifying Risk in Episode-Based Payment Models

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The American Journal of Managed Care, August 2021, Volume 27, Issue 8

This article provides a description of prospective financial simulation methodology and use cases with empirical data for episode-based bundled payments, including implications for contract negotiations and value-based care redesign.


Objectives: Health systems and provider groups currently lack a systematic mechanism to evaluate the financial implications of value-based alternative payments. We sought to develop a method to prospectively quantify the financial implications, including risk and uncertainty of (1) transitioning from a fee-for-service to an episode-based payment model and (2) modifying episode-specific clinical cost drivers. Finally, we highlight practical applications for the model to help facilitate stakeholder engagement in the transition to value-based payment models.

Study Design: We created a financial simulation from empirical data to demonstrate the feasibility and potential use cases within the context of a hypothetical episode-based payment model for prostate cancer surgery (prostatectomy).

Methods: We used Monte Carlo simulation methods to predict financial outcomes under various clinical and payment model scenarios for our pilot prostatectomy episode use case. We input patient-level empirical cost, reimbursement, and clinical data for a cohort of 157 patients at our institution into our model to quantify expected financial outcomes (payments, financial margins) and financial risk for stakeholders (payer, hospital, providers) under an episode-based payment model.

Results: Compared with the status quo, there is a range of expected financial outcomes for various stakeholders depending on the financial parameters (episode price, shared savings, downside risk, stop-loss) in an episode-based payment model. Modifying clinical cost drivers has a profound impact on these outcomes. Uncertainty is high due to the small number of episodes.

Conclusions: The simulation demonstrates that both financial parameters and clinical cost drivers significantly affect the expected financial outcomes for stakeholders in value-based payment models.

Am J Manag Care. 2021;27(8):e278-e286.


Takeaway Points

Value-based payment model adoption is hampered by unknown financial impact, particularly regarding downside risk, and poor provider engagement. We describe our method for prospectively quantifying the expected financial impact and risk (upside and downside) of transitioning from a fee-for-service to an episode-based payment model using empirical financial and clinical data. We provide use cases for the simulation’s output that:

  • quantify how the value-based payment arrangement parameters affect each stakeholder’s financial outcome, including risk exposure; and
  • identify the highest-impact modifiable targets of value-based care redesign for engaging clinicians in the transition to value.


Fee-for-service (FFS) reimbursement incentivizes maximal use of services without accountability for quality, outcomes, or appropriateness, and it contributes to low-value care in the United States.1 Ever-growing concerns about affordability and quality have led to widespread efforts to deploy alternative payment models (APMs), including episode-based payments (EBPs), that reward high-quality, low-cost care.2,3 In an EBP, often referred to as a “bundled” payment, an accountable care entity receives a lump sum for relevant medical services within a defined time period or clinical care cycle. In contrast to other population-based APMs, like accountable care organizations or capitation, EBP models are particularly relevant for specialty care providers and surgeons.4-6

However, there is trepidation around this transition to value-based reimbursement. Health systems and provider groups lack structured, systematic mechanisms to assess the potential impact on institutional and provider finances.7,8 Current strategies estimate health system reimbursement under an alternative model relative to FFS. This method conceptualizes “cost” as payer reimbursement to the health system, rather than true service-delivery costs. Because internal production costs are neither well understood nor systematically tied to reimbursement, such analyses provide an incomplete picture of the true financial implications of transitioning to value-based payments.9 Health care organizations must understand the impact of adopting alternative payments in the context of true costs to deliver care.10 Thus far, uncertain financial implications have stymied stakeholder enthusiasm around EBP models.11

We propose a health system–driven framework to systematically evaluate the impact of adopting EBPs for discrete episodes of care. We illustrate the approach through a case study of a prostate cancer surgery episode at a tertiary institution. The objectives are to develop a mechanism to (1) quantify the impact on stakeholder finances of adopting an EBP and (2) inform value-based care redesign efforts by quantifying the impact of clinical cost drivers. Herein, we discuss our process for collecting and analyzing episode-specific clinical and financial data, describe our simulation model for predicting financial outcomes, and illustrate uses of the methodology to gain prospective insights on the financial implications of a hypothetical EBP for prostate cancer surgery.


Episode Design and Cohort Identification

Select the episode. An episode of care may span an inpatient hospitalization, surgical procedure, or medical condition. Broader episode definitions present greater opportunity to address variation and low-value care, but they introduce complexity in payer reimbursement.12

Radical prostatectomy is the surgical standard of care for localized prostate cancer. This episode satisfies the Health Care Payment Learning & Action Network’s criteria for prioritization for value-based APMs and is a likely focus for future EBPs (eAppendix A [eAppendices available at]).13,14

Define episode period. The closest urology visit prior to surgery initiates the episode, which continues 90 days postoperatively (eAppendix B). Although many medical and surgical EBP models begin at the index hospitalization,12 we capture variation in preoperative care to identify opportunities for value improvement during this phase. In the preoperative period, we include only services rendered for prostate cancer or preoperative care (International Classification of Diseases, Ninth Revision [ICD-9] code 185.0 and International Classification of Diseases, Tenth Revision [ICD-10] code C61, and ICD-9 codes V72.81-V72.84 and ICD-10 codes Z01.810-Z01.818, respectively). We include all medical services during the index admission and 90-day postoperative period (eAppendix C).15

Identify patient cohort. After obtaining institutional review board approval, we identified patients undergoing robot-assisted laparoscopic radical prostatectomy in 2016 at our primary teaching facility. We utilized the hospital’s hybrid analytics and information technology group to extract cases using ICD-9 or ICD-10 codes for prostate cancer (185.0 or C61, respectively) and Common Procedural Terminology (CPT) code for laparoscopic radical prostatectomy (55866). This group provides reporting, analysis, monitoring, and actionable business intelligence by combining data across different hospital systems. We cross-referenced the cohort with operative schedules to assure the veracity of our search. We identified 157 cases performed by 5 urologic oncology faculty members.

Obtain Model Input Data

Demographic and clinical. Our bioinformatics team extracted demographic and clinical data, including patient age, body mass index, comorbidities, education, income, tobacco use, tumor grade, and cancer stage. We abstracted clinical data from the inpatient admission, including length of stay, operating room time, escalation of care, and postdischarge events, including discharge disposition, readmissions, and emergency department visits (Table 1 [part A and part B]).

Financial. We then obtained granular cost and reimbursement data for each patient. Table 216 reports aggregate financial input data and detail on cost-accounting methods and reimbursement data sources. Despite a mix of payers, we calculated reimbursement according to Medicare fee schedules to simulate a Medicare-specific EBP model. The confidential nature of commercial contracts preclude inclusion in this report, but we previously verified the assumption that internal costs are independent of payer.17

Costs. We separated costs by phase (preoperative, inpatient, postoperative). We further divided inpatient costs into 2 phases: surgery/perioperative and inpatient ward (personnel and hospital). We utilize previously reported institution-specific time-driven activity-based costing estimates for outpatient (all costs) and inpatient (personnel costs only) for robotic radical prostatectomy.18 For all other operating room/perioperative and inpatient ward costs, we used our hospital’s activity-based costing data.

Reimbursement. We also separated reimbursement by phase (preoperative, inpatient, postoperative) and type (professional, nonprofessional). Nonprofessional reimbursement included the Inpatient Prospective Payment System’s reimbursement for laparoscopic radical prostatectomy (CPT 55866), with or without pelvic lymph node dissection (CPT 38571), and technical component reimbursement for outpatient services such as radiology and laboratory testing. We searched the Medicare Physician Fee Schedule for patient-level professional reimbursement for all physician services, excluding anesthesia, which we estimated using data from the department of anesthesiology’s finance office.16

Episode Payment Model Design

The 5 urologic oncologists collaborated to define a consensus high-value clinical care pathway to inform the inclusion of clinical services in the payment model (eAppendix D). We developed an EBP model by defining modifiable financial and clinical parameters according to the specifics of the clinical episode and model participants.7,12,19-22 Table 3 reports the key model components.

Financial Simulation Model

We constructed a simulation to gain confidence in the computation of stakeholders’ payments under the EBP. This entails sampling patient cohorts consistent with individual patient-level empirical cost and clinical data from our cohort to compute payment distributions under an EBP and the extant FFS arrangement. We built the simulation model using the following steps:

  • Step 1: Collect empirical patient-level clinical and granular financial data inputs (Table 2 [A]) for the original patient cohort.
  • Step 2: Generate simulated patient data by independently fitting distributions for each empirical input, considering correlations among input variables. We assume patients are independent of each other and use the Cholesky matrix decomposition technique to add desired correlation to independently drawn data. For simplicity, we account for primary correlations and ignore cascading effects (eAppendix E).
  • Step 3: Validate simulated data by graphically comparing empirical and simulated distributions for independent input variables (eAppendix F).
  • Step 4: Calculate relevant financial outcome for each stakeholder using the simulated patient cohort data according to the defined EBP model. Although the 2 required stakeholders are the payer and the accountable entity (usually a health system or provider group), we additionally split the accountable entity into the hospital and participating physicians for this exercise. The relevant financial outcome differs by stakeholder (payer and physician: per-episode payment; accountable entity [hospital plus physician] and hospital: per-episode financial margin [payment minus internal costs]). We also compute corresponding hypothetical outcomes under FFS using the sampled reimbursement data for each patient. By simulating many patient cohorts (200), we generate the distribution of outcomes for each stakeholder under both payment models. Based on these distributions, we report dollar change and risk metrics in the EBP with respect to the current state of FFS.

Risk Evaluation

We additionally consider financial outcomes in terms of the level of risk assumed by each stakeholder. Standard deviation is an aggregate volatility measure and is inadequate to quantify risk in digestible terms for clinicians. Therefore, we consider 2 additional financial risk metrics, inspired by value at risk and conditional value at risk, to communicate pertinent financial risk of the EBP in terms of best- and worst-case scenarios compared with the status quo. We compute the probability that a stakeholder is better off under an EBP (value at risk) and the mean gains (or losses) given that the stakeholder performs better (or worse) in the EBP (conditional value at risk). The latter essentially illustrates the episode’s “risk corridor” (eAppendix G).


We created an interactive, web-based interface that allows stakeholders to dynamically evaluate financial outcomes under a range of payment model parameters and clinical scenarios. The following case studies illustrate 2 potential applications.

Case Study 1: Financial Implications of Transitioning From FFS to EBP (Figure 1)

For this application, we maintain clinical parameters at their baseline levels (Table 216). We report the financial outcomes under EBP for a simulated cohort as the median per-episode change in dollar amount with respect to the FFS payment or financial margin for each stakeholder. Positive (or negative) change compared with FFS reflects a more (or less) favorable outcome for that stakeholder.

For our baseline EBP scenario (Table 3), we set the episode price at the historical mean payment. Federal bundled payment programs frequently mandate a downward adjustment (often around 3%) to ensure payer savings, but we omitted this discount.12 We set a symmetrical, 50/50 shared savings (losses) rate between the payer and the accountable entity with the upper limit of aggregate shared savings (losses) for the accountable entity at 20% (8%) of mean episode cost. The per-episode stop-loss threshold was set to 3 SDs above the mean.21 The episode physician payment reflects publicly available Medicare reimbursement rates and includes the 5% Medicare bonus for qualifying as an advanced APM. We assumed that within the accountable entity, the physicians receive the base reimbursement and then evenly split any remaining savings or losses with the hospital. We set the likelihood that the accountable entity meets a minimum quality threshold to become eligible for shared savings at 100%. We simulated 200 cohorts of 160 simulated patients (estimated annual case volume). Figure 1 shows the dollar change in per-patient financial outcome (payment or margin) under the baseline EBP model relative to status quo FFS. The corresponding table additionally reports the probability that the stakeholder fares better in the EBP (row 2) and the mean dollar change in financial outcome compared with FFS given that the stakeholder fares better (row 3) or worse (row 4) in the EBP. The final 2 rows estimate the likely best- and worst-case risk corridor.

Under these baseline EBP parameters, the payer and physicians are likely (99% and 58%, respectively) to fare better compared with FFS (payer pays $286.13 less per episode and physicians receive $44.24 higher payment, respectively). Conversely, the model predicts an unfavorable outcome for the accountable entity, driven by a decrease in the hospital’s margin. The hospital has only a 3% chance of faring better in the EBP, with an expected decrease in per-episode financial margin of $341.15 compared with status quo FFS.

Acknowledging there is no prescriptive “correct” level for any of the financial payment model parameters selected here, our model allows stakeholders to explore a spectrum of options, potentially in the context of EBP contract negotiations. To illustrate this, we adjust the EBP parameters to shift more downside risk onto the payer by reducing the accountable entity’s maximum aggregate loss from 8% to 5% and lowering individual patient outlier threshold from 3 to 2 SDs above the mean. The financial outcomes become somewhat less unfavorable for the accountable entity as compared with the baseline EBP scenario (Figure 1: third box plot vs second box plot; second stakeholder column in the corresponding table).

Case Study 2: Impact of Modifying Individual Clinical Cost Drivers on Financial Outcomes (Figure 2)

Here we assume the baseline EBP financial parameters are locked in. We can now explore how changes in clinical cost drivers affect financial outcomes for each stakeholder. This application helps inform value-based care redesign efforts by prospectively evaluating the financial impact of changes in resource utilization, efficiency, and outcomes that drive EBP performance. This process also illustrates the business case for aligning stakeholders around value.

Figure 2 quantifies the financial outcomes of achieving value-based care redesign goals and the opportunity for all parties to benefit within a value-based APM. For this case study, we evaluated the impact of reducing preoperative MRI utilization from 35.7% (current state) to 20%, reducing operating room time by 5%, reducing preoperative cost variation by 50%, and reducing high-cost outliers from 3.2% (current state) to 1% within the EBP. For the accountable entity, achieving these clinical care delivery goals changes the financial outcome of shifting from FFS to this EBP from an unfavorable situation ($294.73 reduction) to a favorable ($624.06 increase) in per-episode margin compared with FFS. Meanwhile, physician payments rise drastically, from $44.24 higher than FFS in the current clinical state to $503.64 higher. Demonstrating the financial alignment among the stakeholders in this type of arrangement, the payer’s outcome simultaneously becomes more favorable with these care delivery changes: Median per-episode payment goes from $286.13 lower to $369.40 lower than what the payer would expect to pay in FFS. Full outcomes for all stakeholders under all clinical scenarios are shown in eAppendix H.


We describe our method for systematically analyzing financial outcomes of a discrete episode of care under different payment arrangements. Our financial simulation is based on patient-level empirical cost, reimbursement, and clinical data that capture real-world variability in patient care. Through our prostatectomy case study, we reveal insights into (1) the financial impact of transitioning from FFS to EBP models and (2) the financial impact of value-based care redesign targets. Health systems currently lack such systematic, prospective modeling to anticipate the financial implications of value-based payment reform, make decisions on adopting alternative payments, and maximize the value of care delivery.

Our framework offers a solution to several knowledge gaps and could address strategic hurdles that are impeding adoption of EBP models. First, we present a novel mechanism to quantify and provide transparency around the financial risk of transitioning from FFS to EBP, the uncertainty around which hinders adoption of value-based payment models.11 Our model provides the flexibility to evaluate an array of clinical circumstances and payment model structures. The output is both a method of analysis and a tool for communicating uncertainty and opportunity.

Second, our granular, internal cost accounting methods disentangle the arbitrary relationship between reimbursement and actual costs of delivering a service.10 Due to inherent cost accounting challenges, episode “costs” are traditionally viewed from the payer perspective in the form of price-standardized Medicare reimbursement. By comparing historical Medicare spending against prospective financial targets, health systems estimate short-term reimbursement in an APM relative to the status quo. However, this strategy lacks the specificity to inform care redesign that maximizes value. We demonstrate how analyzing internal service-line costs relative to reimbursement provides a more nuanced understanding of the financial implications of transitioning away from FFS. This model may also help health systems succeed in EBP models by identifying the most efficient targets for value-based care redesign.10

Finally, our user interface facilitates stakeholder engagement. Clinical stakeholders can tailor relevant clinical cost drivers for specific episodes. Stakeholders may more effectively participate in payment model design and contract negotiation by better understanding the importance of specific financial model parameters. Extrapolated over a time for an expected case volume, payers can project the global change in service-line spending, risk-bearing entities can project the global change in financial margin, and providers can prospectively estimate reimbursement. Delivery systems can also use this model to calculate expected return on investment of value improvement initiatives.


We note several limitations. First, although quality measures are an essential component of value, this model focuses primarily on financial outcomes. It remains challenging to define feasible, specific quality metrics that occur within the episode time frame, are adequately risk-adjusted, and are under the control of those at risk.23,24 However, to acknowledge the importance of quality, we created a modifiable parameter representing the likelihood that the accountable entity meets a generic minimum quality standard, making them eligible for gain-sharing.19 Specific quality metrics, methods of data collection, and minimum quality thresholds may vary across service lines and institutions, and they must be clearly delineated for each episode.

Second, the insights generated by our prototype are valid only internally, due to the institution-specific nature of the internal production costs and reimbursement structure.25 Replication of these methods for independent provider groups, in which the accountable entity is not split between a facility and providers, may yield variable results but would be simpler to model. This represents a learning use case through which we developed a replicable, scalable framework to help facilitate the transformation from volume to value.

Third, we modified only a limited number of financial model parameters (maximum aggregate loss and stop-loss threshold) to illustrate a potential use of the model. However, we could have explored countless modifications to the financial parameters to evaluate their impact on overall financial outcomes.

Fourth, this financial model addresses only variable or marginal costs. Many experts view cost savings attributed to reduced utilization at the margin as a “savings illusion” due to massive fixed costs.26 However, others argue that within the appropriate time frame and with adequate managerial attention, up to 95% of health care costs become variable.10 This emphasizes the need for systemwide scaling of this methodology in conjunction with an active, institutional commitment to systematically redesign care delivery.

Finally, this tool is not a stand-alone solution to value transformation. Rather, it provides insights and actionable information to support value-based care initiatives, clinician engagement, and EBP design that align payers, hospitals, and physicians around high-value care.27


The transformation to value-based care in the United States faces profound challenges. Although societal, political, economic, and psychological barriers continue to impede the transition, health systems and payers are forging ahead with APM design and implementation. We present a systematic framework for prospectively generating institution-specific financial insights into the value of care delivery for defined episodes. Importantly, this model allows stakeholders to better understand the financial risk of adopting APMs. Health systems must replicate, validate, operationalize, and scale this process to effectively drive systematic delivery system redesign.

Author Affiliations: Veterans Affairs/University of California, Los Angeles (UCLA) National Clinician Scholars Program (DCJ), Los Angeles, CA; Department of Urology, UCLA David Geffen School of Medicine (DCJ, AP, MSL, CSS), Los Angeles, CA; Department of Decisions, Operations and Technology Management, UCLA Anderson School of Management (FB), Los Angeles, CA; ValU Care Redesign, UCLA Health System (EK, CA), Los Angeles, CA.

Source of Funding: Funded in part by the Veterans Affairs National Clinician Scholars Program and the American Urological Association Urology Care Foundation Research Scholars Grant.

Author Disclosures: The authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

Authorship Information: Concept and design (DCJ, FB, EK, CA, MSL, CSS); acquisition of data (DCJ, EK, CA, MSL, CSS); analysis and interpretation of data (DCJ, FB, EK, AP, MSL, CSS); drafting of the manuscript (DCJ, FB, EK, CA, CSS); critical revision of the manuscript for important intellectual content (DCJ, FB, EK, MSL, CSS); statistical analysis (DCJ, FB, AP); provision of patients or study materials (DCJ); obtaining funding (DCJ, MSL, CSS); administrative, technical, or logistic support (DCJ, AP, MSL); and supervision (DCJ, MSL).

Address Correspondence to: David C. Johnson, MD, MPH, Department of Urology, University of North Carolina, 2105 Physician’s Office Building, 170 Manning Dr, CB 7235, Chapel Hill, NC 27599-7235. Email:


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