About

Randall P. Ellis is a professor in the Department of Economics at Boston University, with a background in Industrial Organization and Econometrics. He applies his expertise primarily to health economics, covering both US and international economic topics. His recent work focuses on health care payment systems, insurance, innovation, and predictive modeling using big data. He is based in the Arts & Sciences Economics department, located at 270 Bay State Road, Boston, MA. Dr. Ellis holds a PhD from the Massachusetts Institute of Technology and can be contacted via email at ellisrp@bu.edu or by phone at 617-353-2741.

Research topics

• Medicine
• Environmental health
• Pediatrics
• Business
• Surgery
• Pathology
• Family medicine
• Actuarial science
• Psychiatry

Selected publications

• Factors Associated With Semaglutide Initiation Among Adults With Obesity

  JAMA Network Open · 2025-01-21 · 17 citations

  articleOpen access

  Importance: Semaglutide, a novel glucagon-like peptide-1 (GLP-1) receptor agonist medication, was approved for weight management in individuals with obesity in June 2021. There is limited evidence on factors associated with uptake among individuals in this subgroup without diabetes. Objective: To explore factors associated with semaglutide initiation among a population of commercially insured individuals with obesity but no diagnosed diabetes. Design, Setting, and Participants: This retrospective observational cohort study used data from the Merative MarketScan Commercial Claims and Encounters Database and included adults in the US aged 18 years or older with a first diagnosis of obesity in an outpatient or inpatient setting between June 5, 2021, and July 1, 2022. Inclusion criteria were no prior antiobesity medication, GLP-1, bariatric surgery, or diabetes-related claim in the 12 months prior to obesity diagnosis, and continuous enrollment in the 12 months preceding and 6 months following obesity diagnosis. Analysis was conducted from February to November 2024. Exposures: Medication classes prescribed, clinical diagnoses, and sociodemographic factors. Exposures were identified within the 12 months prior to obesity diagnosis. Main Outcomes and Measures: Factors associated with incident semaglutide prescription within 6 months after obesity diagnoses were identified using a 10-fold cross-classified random forest model. The top 20 features of the model feature importance list were ranked in a Shapley Additive Explanations plot and used in a multivariable logistic regression model to quantify associations with semaglutide initiation. Results: In this study of 97 456 individuals, 58 124 (59.6%) were female, 26 582 (27.3%) were aged 45 to 54 years, 50 705 (52.0%) resided in the South region, and 49 390 (50.7%) were covered by preferred provider organization plans. Of all participants, 1963 (2.0%) initiated semaglutide within 6 months of their initial obesity diagnosis. The random forest model had an area under the receiver operating characteristic curve of 0.71 (95% CI, 0.69-0.74). The most important exposures identified via Shapley Additive Explanations were sex, use of antidepressants, and employer industry. The top 20 factors were used in the logistic regression model, and significant associations were found with semaglutide initiation, including being female (adjusted odds ratio [aOR], 2.30; 95% CI, 2.05-2.58), use of certain medication classes including antidepressants (aOR,1.62; 95% CI, 1.46-1.78), and being covered by a point-of-service plan (aOR, 1.78; 95% C, 1.42-2.22). Conclusions and Relevance: This cohort study found that key sociodemographic, health care, and clinical factors are associated with receipt of semaglutide in those without diabetes. These findings suggest that insurance plan type and structure may be a crucial intervention point for improving equity in obesity treatment access.

  PublisherOA PDFDOI

• Supporting Primary Care for Medically and Socially Complex Patients in Medicaid Managed Care

  JAMA Network Open · 2025-02-03 · 2 citations

  articleOpen access

  Importance: In 2023, the Massachusetts Medicaid and Children's Health Insurance Program (MassHealth) required accountable care organizations (ACOs) to increase payments to primary care practices and shift to monthly payments, currently calibrated to historical revenues and enhanced practice capabilities, such as being staffed to address behavioral health needs. To prevent rewarding practices for avoiding difficult patients, future payments to primary care practices should reflect their patients' apparent need. Objective: To describe MassHealth's initiative and a complexity-adjusted payment model. Design, Setting, and Participants: This cross-sectional study of payment model development and performance was conducted between February 2022 and November 2024. Participants included all 2019 Massachusetts Medicaid managed-care eligible members who were enrolled for 183 days or longer. Exposures: Medical and social complexity. Main Outcomes and Measures: For each member, the primary care activity level (PCAL) outcome proxies the resources that primary care clinicians need to provide comprehensive, coordinated care. Models were evaluated via R2 and through ratios of observed-to-expected (ie, estimated by the model) outcomes for selected subgroups, which will be approximately 1.0 when payments and expected costs are well matched. The implications of paying practices using PCAL (vs a model based only on age and sex) were explored by examining financial and practice-level characteristics in high and low deciles of practice-level estimated mean. Results: Among 1 092 742 MassHealth members enrolled in 3602 primary care practices (1 014 252 person-years; mean [SD] age, 25.9 [18.4] years; 538 065 [53.1%] female), the PCAL model achieved R2 = 69.6% and estimates within 10% of observed PCAL spending for high-risk populations (mental health disorders, substance use disorders, complex chronic conditions, and disabilities) and across racial and ethnic groups. Age-adjusted and sex-adjusted payments would overpay practices in the lowest-need decile by 10% and underpay those in the highest-need decile by 34%, while the PCAL model would match payment to estimated need almost exactly in the lowest decile and underpay by just 6% in the highest decile. Conclusions and Relevance: MassHealth's 2023 reform invests in primary care. This cross-sectional study developed a risk model that can adjust primary care payments to patient needs. Neither age and sex adjustments nor inflated historical payments would provide adequate resources to primary care practices caring for the most complex patients.

  PublisherDOI

• Scope and Incentives for Risk Selection in Health Insurance Markets With Regulated Competition: A Conceptual Framework and International Comparison

  Medical Care Research and Review · 2024-01-29 · 4 citations

  articleOpen access

  In health insurance markets with regulated competition, regulators face the challenge of preventing risk selection. This paper provides a framework for analyzing the scope (i.e., potential actions by insurers and consumers) and incentives for risk selection in such markets. Our approach consists of three steps. First, we describe four types of risk selection: (a) selection by consumers in and out of the market, (b) selection by consumers between high- and low-value plans, (c) selection by insurers via plan design, and (d) selection by insurers via other channels such as marketing, customer service, and supplementary insurance. In a second step, we develop a conceptual framework of how regulation and features of health insurance markets affect the scope and incentives for risk selection along these four dimensions. In a third step, we use this framework to compare nine health insurance markets with regulated competition in Australia, Europe, Israel, and the United States.

  PublisherOA PDFDOI

• A Novel Machine Learning Algorithm for Creating Risk-Adjusted Payment Formulas

  JAMA Health Forum · 2024-04-19 · 9 citations

  articleOpen accessCorresponding

  Importance: Models predicting health care spending and other outcomes from administrative records are widely used to manage and pay for health care, despite well-documented deficiencies. New methods are needed that can incorporate more than 70 000 diagnoses without creating undesirable coding incentives. Objective: To develop a machine learning (ML) algorithm, building on Diagnostic Item (DXI) categories and Diagnostic Cost Group (DCG) methods, that automates development of clinically credible and transparent predictive models for policymakers and clinicians. Design, Setting, and Participants: DXIs were organized into disease hierarchies and assigned an Appropriateness to Include (ATI) score to reflect vagueness and gameability concerns. A novel automated DCG algorithm iteratively assigned DXIs in 1 or more disease hierarchies to DCGs, identifying sets of DXIs with the largest regression coefficient as dominant; presence of a previously identified dominating DXI removed lower-ranked ones before the next iteration. The Merative MarketScan Commercial Claims and Encounters Database for commercial health insurance enrollees 64 years and younger was used. Data from January 2016 through December 2018 were randomly split 90% to 10% for model development and validation, respectively. Deidentified claims and enrollment data were delivered by Merative the following November in each calendar year and analyzed from November 2020 to January 2024. Main Outcome and Measures: Concurrent top-coded total health care cost. Model performance was assessed using validation sample weighted least-squares regression, mean absolute errors, and mean errors for rare and common diagnoses. Results: This study included 35 245 586 commercial health insurance enrollees 64 years and younger (65 901 460 person-years) and relied on 19 clinicians who provided reviews in the base model. The algorithm implemented 218 clinician-specified hierarchies compared with the US Department of Health and Human Services (HHS) hierarchical condition category (HCC) model's 64 hierarchies. The base model that dropped vague and gameable DXIs reduced the number of parameters by 80% (1624 of 3150), achieved an R2 of 0.535, and kept mean predicted spending within 12% ($3843 of $31 313) of actual spending for the 3% of people with rare diseases. In contrast, the HHS HCC model had an R2 of 0.428 and underpaid this group by 33% ($10 354 of $31 313). Conclusions and Relevance: In this study, by automating DXI clustering within clinically specified hierarchies, this algorithm built clinically interpretable risk models in large datasets while addressing diagnostic vagueness and gameability concerns.

  PublisherOA PDFDOI

• Provider Payment Systems and Incentives

  Elsevier eBooks · 2024-05-09

  book-chapterCorresponding
  PublisherDOI

• Managed competition in the United States: How well is it promoting equity and efficiency?

  Health Economics Policy and Law · 2024-01-08 · 2 citations

  articleOpen access1st authorCorresponding

  Managed competition frameworks aim to control healthcare costs and promote access to high-quality health insurance and services through a combination of public policies and market forces. In the United States, managed competition delivery systems are varied and diffused across a patchwork of divided markets and populations. This, coupled with extremely high national health spending per capita, makes a more unified managed competition strategy an appealing alternative to a currently struggling healthcare system. We examine the relative effectiveness of three existing programmes in the U.S. that each rely upon some principles of managed competition: health insurance exchanges instituted by the Affordable Care Act, Medicaid managed care organisations, and Medicare Advantage plans. Although each programme leverages some competitive features, each faces significant hurdles as a candidate for expansion. We highlight these challenges with a survey of academic health economists, and find that provider and insurer consolidation, highly segmented markets, and failing to incentivise competitive efficiencies all dampen the success of existing programmes. Although managed competition for all is a potentially desirable framework for future health reform in the U.S., successful expansion relies on addressing fundamental issues revealed by imperfect existing programmes.

  PublisherOA PDFDOI

• Development and Assessment of a New Framework for Disease Surveillance, Prediction, and Risk Adjustment

  JAMA Health Forum · 2022 · 21 citations

  1st authorCorresponding
  • Medicine
  • Actuarial science
  • Business

  Importance: . Objective: -based classification framework for predicting diverse health care payment, quality, and performance outcomes. Design Setting and Participants: , mean absolute error, the Cumming prediction measure, and comparisons of actual to predicted outcomes by spending percentiles and by diagnostic frequency. The IBM MarketScan Commercial Claims and Encounters Database, 2016 to 2018, was used, which included privately insured, full- or partial-year eligible enrollees aged 0 to 64 years in plans with medical, drug, and mental health/substance use coverage. Main Outcomes and Measures: Fourteen concurrent outcomes were predicted: overall and plan-paid health care spending (top-coded and not top-coded); enrollee out-of-pocket spending; hospital days and admissions; emergency department visits; and spending for 6 types of services. The primary outcome was annual health care spending top-coded at $250 000. Results: was 0.589 in the DXI model, 0.539 for CCSR, and 0.428 for HHS-HCC. Use of DXIs reduced underpayment for enrollees with rare (1-in-a-million) diagnoses by 83% relative to HHS-HCCs. Conclusions: In this diagnostic modeling study, the new DXI classification system showed improved predictions over existing diagnostic classification systems for all spending and utilization outcomes considered.

  PublisherOA PDFDOI

• Primary healthcare effects of a well-designed anti-corruption program

  World Development Perspectives · 2022-02-05 · 6 citations

  article
  PublisherDOI

• Prevalence and Associated Expenditures for Treatment of Chronic Limb-Threatening Ischemia in the Commercially Insured Younger Population

  Journal of Vascular Surgery · 2022-09-20

  articleOpen access
  PublisherOA PDFDOI

• Treatment of Chronic Limb-Threatening Ischemia in the Commercially Insured Younger Population

  Journal of Vascular Surgery · 2022-05-19

  article
  PublisherDOI

Recent grants

• Advanced Risk Adjusters and Predictive Formulas for ICD-10 Based Risk Adjustment

  NIH · $1.2M · 2018–2022

Frequent coauthors

• Arlene S. Ash

  University of Massachusetts Chan Medical School

  32 shared

• Thomas G. McGuire

  27 shared

• Jeffrey J. Siracuse

  Boston Medical Center

  11 shared

• Sonal Vats

  7 shared

• Timothy Layton

  7 shared

• Tzu‐Chun Kuo

  Health Net

  7 shared

• Jayakanth Srinivasan

  Mitre (United States)

  7 shared

• John Z. Ayanian

  6 shared

Education

• Ph.D.

  Massachusetts Institute of Technology