Restructuring Oncology Payments: Estimates of Resource Use by Phases of Oncology Care in Breast Cancer Patients
payment reform, episodes of care
Background/Aims: Under the fee-for-service system, oncologists do not get paid for many high-value and potentially cost-saving services, but may generate revenue from markups on certain chemotherapy drugs. The inappropriate incentives were at the core of an alternative payment model proposed by American Society of Clinical Oncology (ASCO). ASCO proposed new payment groups to support vital diagnostic, treatment and care management services not compensated under the current system. The aims are to: a) estimate the cost of oncology care by types of services for each payment group proposed by ASCO; b) estimate time and resource use for each payment group; and c) assess whether cost, resource use and clinician time varies by clinical measures.
Methods: We will use electronic health records (EHR) data to identify breast cancer patients diagnosed or treated from 2012 to 2014 at a large health care system in Northern California. Billings will be assigned to payment groups (e.g. new patient, treatment month, transition month, monitoring month) following ASCO’s proposed algorithm. Costs will be stratified by service types (e.g. infusion, drug expenses, lab tests) and payment groups. We will use EHR access log data to estimate clinician time by service or activity type (e.g. treatment planning, patient education, etc.) within each payment group. Comorbidities and treatment regimens will be ascertained from patient encounter data for relevant diagnosis codes. Natural language processing algorithms will be used to extract patient performance status (Eastern Cooperative Oncology Group or Karnofsky functional status). Drug toxicity and drug administration factors will be calculated according to the Moffitt scale and the Michiana Hematology Oncology patient acuity scale. Average cost, time and resource use from aims (a) and (b) will be adjusted by a composite factor comprising of comorbidity score, performance status, drug toxicity factor and drug administration factor.
Results: We have obtained IRB approval and are extracting EHR data for the study. Based on past annual averages, we expect to have data on approximately 2,500 breast cancer patients diagnosed or treated at the health care system.
Conclusion: This study will provide preliminary but critical evidence needed for consideration and implementation of ASCO’s payment model.
Rai A, Liang SY, Thompson CA, Luft HS. Restructuring oncology payments: estimates of resource use by phases of oncology care in breast cancer patients. J Patient Cent Res Rev. 2016;3:175.