About

Dr. David Veenstra is a Professor in the Comparative Health Outcomes, Policy & Economics (CHOICE) Institute within the Department of Pharmacy at the University of Washington. His primary research interests focus on the clinical, economic, and policy implications of precision medicine. His major projects include evaluating the cost effectiveness of population-level genomic screening and examining evidence thresholds and preferences for precision medicine. Dr. Veenstra's work has been funded by organizations such as the National Human Genome Research Institute, Centers for Disease Control, and the National Cancer Institute. He has collaborated extensively with organizations like the Academy of Managed Care Pharmacy (AMCP) and the Institute for Clinical and Economic Review (ICER) to advance the practical application of cost-effectiveness analysis in managed care decision making. He teaches courses in health economics and managed care, and has authored multiple book chapters and peer-reviewed publications.

Research topics

• Computer Science
• Medicine
• Machine Learning
• Biology
• Internal medicine
• Risk analysis (engineering)
• Intensive care medicine
• Genetics
• Environmental health
• Oncology
• Obstetrics
• Demography
• Gynecology
• Business

Selected publications

• Real-world untreated risk of hospitalization and death in a nirmatrelvir/ritonavir treatment-eligible population with mild-to-moderate COVID-19 in the United States: a systematic literature review

  BMC Infectious Diseases · 2026-02-03

  articleOpen access

  No study has systematically reviewed published estimates of real-world risk of hospitalization and death among untreated COVID-19 patients. We aimed to characterize the risk of hospitalization and death in real-world US clinical practice for untreated patients at high-risk for progression to severe COVID-19 and critically assess differences in patient populations. We conducted a systematic literature review to identify US real-world evidence studies (December 21, 2021 – January 30, 2024) of patients aged 12 years and older diagnosed with mild-to-moderate COVID-19, at high risk for progression to severe COVID-19, and treated with nirmatrelvir/ritonavir (NMV/r) or untreated/best supportive care. Primary outcomes were reported risks of all-cause hospitalization, death, and hospitalization or death at 1 month. To account for heterogeneity across studies and confounding within studies, outcomes were estimated as: 1) observed risk, untreated risk as reported, 2) within-study adjusted risk, applying an adjusted treatment effect within studies to calculate risk in the untreated population, and 3) adjusted and standardized estimate, using the relative risk reduction for all-cause hospitalization or death from the study with highest validity. Of 1023 studies screened, we retained 23 for data extraction after applying inclusion and exclusion criteria (384,793 NMV/r patients from 22 studies; 1,062,757 no treatment from 14 studies). Studies were heterogenous, primarily retrospective, utilizing claims (e.g., TriNetX) and integrated health system data (e.g., Veterans Affairs). Most (n = 21, 91%) were US only; 20 (87%) were cohorts from December 2021 onward. Risk of all-cause hospitalization ranged from 0.9–7.7% (observed), 0.8–2.0% (within-study adjusted), and 2.3–6.9% (adjusted and standardized). Hospitalization or death ranged from 0.6–10.2% (observed), 0.4–5.6% (within-study adjusted), and 1.0–15.6% (adjusted and standardized). Death risk ranged from 0.1–3.1% (observed) and 0.0–0.9% (within-study adjusted). Estimating the risk of hospitalization and death for untreated high-risk COVID-19 patients from the literature is limited by inherent differences in study designs, patient populations, and reporting. Observed risk of hospitalization ranges from 1 to 8%, and the risk of death from 0 to 3%. Understanding the hospitalization risk among untreated patients provides context for the clinical and economic value of current antiviral treatments. This study was sponsored by Pfizer, Inc.

  PublisherDOI

• Using user-centered design to better understand challenges faced during genetic analyses by novice genomic researchers

  Frontiers in Bioinformatics · 2026-05-15

  articleOpen access

  The lack of user-centered design principles in the current landscape of commonly-used bioinformatics software tools poses challenges for novice genomics researchers (NGRs) entering the modern genomics ecosystem. Comparing the usability of one analysis software to that of another is a non-trivial task and requires evaluation criteria that incorporates perspectives from both existing literature and a diverse, underrepresented user base of NGRs. To better characterize these barriers, we utilized a two-pronged approach consisting of a retrospective literature review of existing bioinformatics tools and prospective semi-structured interviews of the needs of NGRs. From both knowledge sources, the key findings that resulted in poor adoption and sustained use of most bioinformatics tools included: poor documentation, lack of readily-accessible informational content, challenges with installation and dependency coordination, and inconsistent error messages/progress indicators. Combining the results from the literature review and the insights gained by interviewing the NGRs, an evaluation rubric was created that can be utilized to grade existing and future bioinformatics tools. This rubric acts as a summary of key components needed for software tools to cater to the diverse needs of both NGRs and experienced users. Due to the rapidly evolving nature of genomics research, it becomes increasingly important to critically evaluate existing tools and develop new ones that will help build a strong foundation for future exploration.

  PublisherOA PDFDOI

• Do Targeted NGS Panels Include NSCLC Guideline-Recommended Biomarkers?

  The American Journal of Managed Care · 2026-02-12

  article

  OBJECTIVES: National Comprehensive Cancer Network (NCCN) Clinical Practice Guidelines in Oncology recommend the assessment of 12 molecular biomarkers to determine eligibility for targeted drug therapies in metastatic non-small cell lung cancer (mNSCLC). However, variation exists in the composition of next-generation sequencing (NGS) panels. This study evaluates the inclusion of recommended mNSCLC biomarkers in commercially available targeted NGS panels. STUDY DESIGN: A descriptive database review was conducted to characterize molecular testing panels. METHODS: Targeted panels were classified as 2 to 50 genes. The DEX Diagnostics Exchange Registry and the Concert Genetic Testing Unit Test Identifier database were searched for NGS-based lung and multicancer indications. Tests were excluded if their indication precluded usage in lung cancer, NGS technology was not used, or the panel was retired. Each panel was assessed for the inclusion of biomarkers from V.11.2024 NCCN guidelines: ALK, BRAF, EGFR, ERBB, KRAS, NTRK1/2/3, RET, ROS1, MET exon 14 skipping, and MET amplification. RESULTS: Seventy-seven targeted NGS panels were included. The mean number of biomarkers captured in lung-specific vs multicancer panels was similar (6.6 vs 6.4). Most biomarkers were single-nucleotide variants (82%), and the most common were EGFR (91%), KRAS (87%), and BRAF (86%). Fusions and rearrangements were represented in less than half (41%) of panels, and NTRK fusions were often absent, with NTRK1, NTRK2, and NTRK3 appearing in only 23%, 14%, and 19% of the included panels. The inclusion of copy number variant, specifically MET amplification, was rare (12%). Only 5 (6%) panels captured all 12 recommended biomarkers. CONCLUSIONS: Of 77 unique panels evaluated, only 5 captured all recommended biomarkers for mNSCLC. Ensuring targeted panels assess all relevant biomarkers is crucial for optimal patient treatment.

  PublisherDOI

• Analytic Choices Shape Genomic Risk Estimates from Electronic Health Records: Coronary Heart Disease in eMERGE IV

  medRxiv · 2026-04-30

  articleOpen access

  Background: Electronic health records (EHR) are an important data source for genomic studies, but challenges exist in ascertaining cases and observation start time. We used data derived from the Electronic Medical Records and Genomics (eMERGE) IV study to examine how analytic assumptions regarding case ascertainment and EHR entry time influence estimation of monogenic and polygenic risks for coronary heart disease (CHD). Methods: We assessed agreement between CHD cases ascertained from EHR phenotyping and survey. Associations of monogenic variants and high (top 5%) PRS of CHD were evaluated using multivariate relative risk (RR) regression under three alternative case definitions: EHR-algorithm-defined, self-reported, and combined. Time-to-event analyses (Kaplan-Meier method and Cox proportional hazards models) were conducted under three entry time specifications: (1) entry at the first EHR record, (2) entry at the start of the latest consecutive observation period, and (3) no left truncation. Results: The agreement between CHD cases ascertained by the EHR-based algorithm versus self-report was 37.2% among individuals identified as cases by at least one source, with the EHR algorithm demonstrating higher accuracy. The adjusted RR [95% confidence interval (CI)] associated with high PRS was 2.05 [1.50, 2.81] for EHR-defined, 1.49 [1.04, 2.13] for self-reported, and 1.66 [1.27, 2.18] for combined CHD. Estimated cumulative incidence by age 75 was 0.188 using the first EHR code as left truncation and 0.225 using the most recent observation period. Hazard ratio (HR) estimates were similar across the three left truncation scenarios. Conclusion: The choice of case definition meaningfully influenced RR estimates, whereas alternative specifications of EHR entry time affected absolute cumulative incidence estimates but has minimal impact on HR. These findings highlight the impact of analytical choices in EHR and survey-data-based studies that are applicable beyond the context of CHD.

  PublisherDOI

• Genomic sequencing in diverse and underserved pediatric populations: Parent perspectives on understanding, uncertainty, psychosocial impact, and personal utility of results

  Genetics in Medicine · 2025-01-19 · 2 citations

  articleOpen access
  PublisherOA PDFDOI

• HTA81 Development of a Novel Value Framework for Evaluating Population Genomic Programs Using A Mixed Methods Approach

  Value in Health · 2025-07-01

  articleSenior author
  PublisherDOI

• Diagnosis, Treatment, and Unmet Needs of Dedifferentiated Liposarcoma in the United States: A Multidisciplinary Delphi Study

  Cancers · 2025-08-28

  articleOpen access

  Background: Evidence of the real-world management of dedifferentiated liposarcoma (DDLPS) is limited by the patient size and coding. The objective of this study is to generate consensus expert opinion on locally advanced or metastatic DDLPS diagnosis, treatment, and unmet needs. Methods: A three-round Delphi consensus panel was conducted with 9 DDLPS clinical experts from November to December 2023. Expert panelists were recruited across academic specialty and traditional settings and US regions. The Delphi panel included two rounds of surveys followed by a consensus building workshop. Surveys contained multiple-choice and free response questions, and statements for level of agreement rating. Panelists rated each statement for level of agreement on a 9-point Likert scale. Statements with ≥75% of scores ≥ 7 achieved consensus, and those that did not achieve consensus agreement were modified or removed from subsequent testing. A virtual workshop was held to discuss areas which did not achieve consensus and refine previously agreed upon statements. Results: In total 25 consensus statements were developed by the Delphi panel. Survey 1 achieved 7 consensus statements across the areas of burden, treatment, and unmet needs of DDLPS. Survey 2 generated an additional 10 consensus statements. During the workshop, eight more statements achieved consensus, and four statements were refined for enhanced clarity and precision. The study findings are limited by the number of Delphi panel participants and consensus statements may not be fully representative of clinician perspectives across the US. Conclusions: Consensus areas identified by the Delphi panel help better understand the decision factors for surgical and non-surgical treatments and anticipated utilization. These results could be used to inform both drug development programs as well as care delivery challenges for liposarcoma patients.

  PublisherOA PDFDOI

• A Value Framework for Evaluating Population Genomic Programs: A Mixed Methods Approach

  Journal of Personalized Medicine · 2025-07-12

  articleOpen accessSenior author

  Background/Objectives: Value frameworks are useful tools to explicitly define the dimensions and criteria important for decision-making, but no existing frameworks capture the broad value domains of population genomic programs. Using a mixed methods approach, we aimed to develop a novel value framework for evaluating population genomic programs (PGPs). Methods: We first conducted a targeted literature review of published evidence on the value of PGPs and existing frameworks to evaluate and quantify their impact. Value domains and elements were extracted and summarized to develop a preliminary framework. Semi-structured stakeholder interviews on the preliminary framework were conducted from March 2024 to October 2024 with 11 experts representing 9 countries. A thematic analysis of interview transcripts was conducted to map value elements to domains of the final framework. Results: We identified 348 potentially relevant articles from MEDLINE-indexed and the gray literature sources. After title and abstract screening, 23 articles met the inclusion criteria and underwent full-text review, and 8 reported value elements were extracted and mapped to a preliminary framework for testing in interviews. Stakeholder themes were summarized into the value domains and elements of the final framework, which included health as a primary domain, education and research, enterprise and finance, and labor as the core domains, and agriculture and security as extended domains. Domains and elements may be excluded based on stakeholder objectives and program characteristics. Conclusions: This novel framework for assessing the comprehensive value of PGPs provides a foundational step to assess the value of these programs and may promote more efficient and informed allocation of resources.

  PublisherOA PDFDOI

• First-line cyclin-dependent kinase 4 and 6 inhibitors in combination with an aromatase inhibitor for HR+/HER2- metastatic breast cancer: A real-world cost-effectiveness assessment in a US Medicare-eligible population

  Journal of Managed Care & Specialty Pharmacy · 2025-07-30 · 3 citations

  articleOpen access

  BACKGROUND: In hormone receptor-positive, human epidermal growth factor 2-negative (HR+/HER2-) metastatic breast cancer (mBC), cyclin-dependent kinase 4 and 6 inhibitors (CDK4/6is) in combination with an aromatase inhibitor (AI) are the preferred first-line (1L) treatment. Although prior cost-effectiveness models comparing CDK4/6is palbociclib, ribociclib, and abemaciclib have used data from placebo-controlled clinical trials, no analyses in the United States have been conducted using real-world evidence (RWE) for a US Medicare-eligible population. OBJECTIVE: To estimate the long-term clinical outcomes and health care costs of 1L CDK4/6i treatment in patients aged 65 years and older using RWE. METHODS: We developed a partitioned survival model to project patient time in progression-free and progressed disease health states. Progression-free survival (PFS) and overall survival (OS) curves for palbociclib + AI were obtained from an analysis of patients aged 65 years and older treated 1L for HR+/HER2- mBC using the Flatiron Health Analytic Database (Flatiron). Adjusted comparative effectiveness estimates vs palbociclib + AI for both ribociclib + AI (PFS hazard ratio = 0.98 [95% CI = 0.86-1.13]; OS hazard ratio = 1.01 [95% CI: 0.87-1.18]) and abemaciclib + AI (PFS hazard ratio = 0.99 [95% CI = 0.86-1.15]; OS hazard ratio = 1.00 [95% CI = 0.84-1.19]) were obtained from the same analysis. All-cause medical costs and CDK4/6i drug costs were based on an analysis of patients aged 65 years and older in Optum Clinformatics DataMart. We used a Medicare perspective over a lifetime horizon for a cohort of patients with mean age of 73.7 years. Sensitivity analyses were performed to assess the robustness of results to plausible variation in input values. RESULTS: Projected life-years (LYs) with palbociclib + AI, ribociclib + AI, and abemaciclib + AI were similar: 5.16 (95% credible range [CR] = 4.94-5.35), 5.12 (95% CR = 4.53-5.82), and 5.16 (95% CR = 4.49-5.90), respectively. Total lifetime health care costs were also similar ($865,000 [95% CR = $807,400-$925,000], $866,800 [95% CR = $786,000-$965,000], and $901,000 [95% CR = $809,000-$1,004,600], respectively). Sensitivity analyses further supported no differences in LYs or total costs between CDK4/6is. CONCLUSIONS: Based on effectiveness and cost estimates from real-world data, our analyses suggest that palbociclib, ribociclib, and abemaciclib produce similar life expectancy and health care costs in US patients aged 65 years and older with HR+/HER2- mBC.

  PublisherOA PDFDOI

• Influence of Initial Imaging Modality on Peri-Diagnostic Outcomes Among Pancreatic, Liver, Ovarian, and Bladder Cancers Without Routine Screening Programs: A Surveillance, Epidemiology, and End Results-Medicare Study

  Value in Health · 2025-12-06

  article
  PublisherDOI

Recent grants

• Personalized Medicine Economics Research PriMER

  NIH · $1.8M · 2013–2018

• Rational Integration of clinical SEquencing (RISE)

  NIH · $3.4M · 2017–2022

• Evolving Our Partnership: The CSER2 Centralized Support Coordinating Center

  NIH · $5.8M · 2021–2023

• PUFAs and gene variation affecting aspirin antiplatelet response

  NIH · $14.8M · 2016–2023

• Personalized Medicine Economics Research PriMER

  NIH · $413k · 2013–2018

Frequent coauthors

• Scott D. Ramsey

  University of Washington

  118 shared

• Josh J. Carlson

  University of Washington

  102 shared

• Gail P. Jarvik

  Seattle University

  78 shared

• Gregory F. Guzauskas

  University of Washington

  52 shared

• Sean D. Sullivan

  London School of Economics and Political Science

  49 shared

• Rahber Thariani

  Lynn University

  44 shared

• Anirban Basu

  University of Washington

  42 shared

• Louis P. Garrison

  University of Washington

  41 shared

Education

• Other

  University of California, San Francisco

• Ph.D., Pharmaceutical Chemistry

  University of California, San Francisco