Modeling financial outcomes and quantifying risk in episode-based payment models.

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.

Financial Margins for Prostate Cancer Surgery: Quantifying the Impact of Modifiable Cost Inputs in an Episode Based Reimbursement Model.

PURPOSE: The United States health care system is rapidly moving away from fee for service reimbursement in an effort to improve quality and contain costs. Episode based reimbursement is an increasingly relevant value based payment model of surgical care. We sought to quantify the impact of modifiable cost inputs on institutional financial margins in an episode based payment model for prostate cancer surgery. MATERIALS AND METHODS: A total of 157 consecutive patients underwent robotic radical prostatectomy in 2016 at a tertiary academic medical center. We compiled comprehensive episode costs and reimbursements from the most recent urology consultation for prostate cancer through 90 days postoperatively and benchmarked the episode price as a fixed reimbursement to the median reimbursement of the cohort. We identified 2 sources of modifiable costs with undefined empirical value, including preoperative prostate magnetic resonance imaging and perioperative functional recovery counseling visits, and then calculated the impact on financial margins (reimbursement minus cost) under an episode based payment. RESULTS: Although they comprised a small proportion of the total episode costs, varying the use of preoperative magnetic resonance imaging (33% vs 100% of cases) and functional recovery counseling visits (1 visit in 66% and 2 in 100%) reduced average expected episode financial margins up to 22.6% relative to the margin maximizing scenario in which no patient received these services. CONCLUSIONS: Modifiable cost inputs have a substantial impact on potential operating margins for prostate cancer surgery under an episode based payment model. High cost health systems must develop the capability to analyze individual cost inputs and quantify the contribution to quality to inform value improvement efforts for multiple service lines.

User-Guided Segmentation of Multi-modality Medical Imaging Datasets with ITK-SNAP.

ITK-SNAP is an interactive software tool for manual and semi-automatic segmentation of 3D medical images. This paper summarizes major new features added to ITK-SNAP over the last decade. The main focus of the paper is on new features that support semi-automatic segmentation of multi-modality imaging datasets, such as MRI scans acquired using different contrast mechanisms (e.g., T1, T2, FLAIR). The new functionality uses decision forest classifiers trained interactively by the user to transform multiple input image volumes into a foreground/background probability map; this map is then input as the data term to the active contour evolution algorithm, which yields regularized surface representations of the segmented objects of interest. The new functionality is evaluated in the context of high-grade and low-grade glioma segmentation by three expert neuroradiogists and a non-expert on a reference dataset from the MICCAI 2013 Multi-Modal Brain Tumor Segmentation Challenge (BRATS). The accuracy of semi-automatic segmentation is competitive with the top specialized brain tumor segmentation methods evaluated in the BRATS challenge, with most results obtained in ITK-SNAP being more accurate, relative to the BRATS reference manual segmentation, than the second-best performer in the BRATS challenge; and all results being more accurate than the fourth-best performer. Segmentation time is reduced over manual segmentation by 2.5 and 5 times, depending on the rater. Additional experiments in interactive placenta segmentation in 3D fetal ultrasound illustrate the generalizability of the new functionality to a different problem domain.