Wade Schulz, MD, PhD
Assistant ProfessorCards
About
Titles
Assistant Professor
Director of Informatics, Laboratory Medicine; Director, CORE Center for Computational Health, Center for Outcomes Research & Evaluation (CORE)
Biography
Dr. Schulz is an Assistant Professor of Laboratory Medicine and computational health care researcher at Yale School of Medicine. He received his PhD in Microbiology, Immunology, and Cancer Biology and MD from the University of Minnesota. He is the Director of Informatics for the Department of Laboratory Medicine and Medical Director of Data Science for Yale New Haven Health System. Dr. Schulz has over 20 years’ experience in software development with a focus on enterprise system architecture and has a research interests in the management of large, biomedical data sets and the use of real-world data for predictive modeling. At Yale, he has led the implementation of a distributed data analysis and predictive modeling platform, for which he received the Data Summit IBM Cognitive Honors award. Other projects within his research group include computational phenotyping and the development of clinical prescriptive models for precision medicine initiatives. His clinical areas of expertise include molecular diagnostics and transfusion medicine, where he has ongoing work assessing the use, safety, and efficacy of pathogen-reduced blood products.
Appointments
Laboratory Medicine
Assistant ProfessorPrimaryBiomedical Informatics & Data Science
Assistant ProfessorSecondary
Other Departments & Organizations
- Apheresis/Transfusion Service
- Biomedical Informatics & Data Science
- Center for Biomedical Data Science
- Center for Infection and Immunity
- Clinical Laboratory Informatics
- Computational Biology and Biomedical Informatics
- Laboratory Medicine
- Yale Center for Genomic Health
- Yale Combined Program in the Biological and Biomedical Sciences (BBS)
- Yale Medicine
- YSM Biobank
Education & Training
- Fellow, Transfusion Medicine
- Yale School of Medicine (2018)
- Resident, Clinical Pathology
- Yale School of Medicine (2017)
- MD
- University of Minnesota, Medical School (2014)
- PhD
- University of Minnesota, Microbiology, Immunology, and Cancer Biology (2012)
- BS
- University of Minnesota, Genetics, Microbiology (2006)
Research
Overview
Medical Research Interests
Artificial Intelligence; Blood Transfusion; Clinical Laboratory Information Systems; Data Mining; Data Warehousing; Information Management; Medical Informatics Computing
Publications
Featured Publications
From Data to Wisdom: Biomedical Knowledge Graphs for Real-World Data Insights
Hänsel K, Dudgeon S, Cheung K, Durant T, Schulz W. From Data to Wisdom: Biomedical Knowledge Graphs for Real-World Data Insights. Journal Of Medical Systems 2023, 47: 65. PMID: 37195430, PMCID: PMC10191934, DOI: 10.1007/s10916-023-01951-2.Commentaries, Editorials and LettersConceptsKnowledge graphReal-world dataGraph modelBiomedical data integrationGraph data modelIntegrated Knowledge GraphBiomedical knowledge graphsElectronic health recordsData integrationData insightsData modelInsight generationBiomedical informationHealth recordsArt researchGraphNovel approachCombination of dataDisease phenotypingInformationPrecision medicine researchHealthcareIntegrationEHRIntriguing opportunityThe Current Landscape and Emerging Applications for Real‐World Data in Diagnostics and Clinical Decision Support and its Impact on Regulatory Decision Making
Andre E, Carrington N, Siami FS, Hiatt JC, McWilliams C, Hiller C, Surinach A, Zamorano A, Pashos CL, Schulz WL. The Current Landscape and Emerging Applications for Real‐World Data in Diagnostics and Clinical Decision Support and its Impact on Regulatory Decision Making. Clinical Pharmacology & Therapeutics 2022, 112: 1172-1182. PMID: 35213741, PMCID: PMC9790425, DOI: 10.1002/cpt.2565.Peer-Reviewed Original ResearchA multicenter evaluation of computable phenotyping approaches for SARS-CoV-2 infection and COVID-19 hospitalizations
Khera R, Mortazavi BJ, Sangha V, Warner F, Patrick Young H, Ross JS, Shah ND, Theel ES, Jenkinson WG, Knepper C, Wang K, Peaper D, Martinello RA, Brandt CA, Lin Z, Ko AI, Krumholz HM, Pollock BD, Schulz WL. A multicenter evaluation of computable phenotyping approaches for SARS-CoV-2 infection and COVID-19 hospitalizations. Npj Digital Medicine 2022, 5: 27. PMID: 35260762, PMCID: PMC8904579, DOI: 10.1038/s41746-022-00570-4.Peer-Reviewed Original ResearchCOVID-19 hospitalizationMayo ClinicDiagnosis codesCOVID-19 diagnosisPositive SARS-CoV-2 PCRYale New Haven Health SystemPositive SARS-CoV-2 testSARS-CoV-2 infectionSARS-CoV-2 PCRSARS-CoV-2 testCOVID-19Higher inhospital mortalitySARS-CoV2 infectionElectronic health record dataICD-10 diagnosisPositive laboratory testsHealth record dataInhospital mortalityAdditional patientsAntigen testSecondary diagnosisPrincipal diagnosisMulticenter evaluationPositive testComputable phenotype definitionsClinical characteristics and outcomes for 7,995 patients with SARS-CoV-2 infection
McPadden J, Warner F, Young HP, Hurley NC, Pulk RA, Singh A, Durant TJS, Gong G, Desai N, Haimovich A, Taylor RA, Gunel M, Dela Cruz CS, Farhadian SF, Siner J, Villanueva M, Churchwell K, Hsiao A, Torre CJ, Velazquez EJ, Herbst RS, Iwasaki A, Ko AI, Mortazavi BJ, Krumholz HM, Schulz WL. Clinical characteristics and outcomes for 7,995 patients with SARS-CoV-2 infection. PLOS ONE 2021, 16: e0243291. PMID: 33788846, PMCID: PMC8011821, DOI: 10.1371/journal.pone.0243291.Peer-Reviewed Original ResearchConceptsSARS-CoV-2 infectionYale New Haven HealthSARS-CoV-2Hospital mortalityRisk of admissionMale sexRisk factorsSARS-CoV-2 testingInvasive mechanical ventilationSevere acute respiratory syndrome virusBurden of diseaseRT-PCR testingAcademic health systemDiverse patient populationsRespiratory syndrome virusEthnic groupsAdult patientsClinical characteristicsDischarge dispositionRespiratory supportPrimary outcomeTreatment guidelinesMechanical ventilationRetrospective studyPatient populationTemporal relationship of computed and structured diagnoses in electronic health record data
Schulz WL, Young HP, Coppi A, Mortazavi BJ, Lin Z, Jean RA, Krumholz HM. Temporal relationship of computed and structured diagnoses in electronic health record data. BMC Medical Informatics And Decision Making 2021, 21: 61. PMID: 33596898, PMCID: PMC7890604, DOI: 10.1186/s12911-021-01416-x.Peer-Reviewed Original ResearchConceptsElectronic health recordsStructured diagnosisOutpatient blood pressureElectronic health record dataAcademic health systemLow-density lipoproteinHealth record dataBlood pressureStructured data elementsAdministrative claimsHypertensionClinical informationHyperlipidemiaClinical phenotypeEquivalent diagnosisVital signsHealth systemDiagnosisProblem listAdditional studiesHealth recordsRecord dataTimely accessEHR dataPatientsAssociation between primary or booster COVID-19 mRNA vaccination and Omicron lineage BA.1 SARS-CoV-2 infection in people with a prior SARS-CoV-2 infection: A test-negative case–control analysis
Lind M, Robertson A, Silva J, Warner F, Coppi A, Price N, Duckwall C, Sosensky P, Di Giuseppe E, Borg R, Fofana M, Ranzani O, Dean N, Andrews J, Croda J, Iwasaki A, Cummings D, Ko A, Hitchings M, Schulz W. Association between primary or booster COVID-19 mRNA vaccination and Omicron lineage BA.1 SARS-CoV-2 infection in people with a prior SARS-CoV-2 infection: A test-negative case–control analysis. PLOS Medicine 2022, 19: e1004136. PMID: 36454733, PMCID: PMC9714718, DOI: 10.1371/journal.pmed.1004136.Peer-Reviewed Original ResearchConceptsSARS-CoV-2 infectionBooster vaccinationPrior infectionOmicron infectionPrimary vaccinationMRNA vaccinationOdds ratioAcute respiratory syndrome coronavirus 2 infectionSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infectionPrior SARS-CoV-2 infectionTest-negative case-control analysisYale New Haven Health SystemTest-negative case-control studyCOVID-19 mRNA vaccinationSyndrome coronavirus 2 infectionOmicron variant infectionPrior infection statusCoronavirus 2 infectionCase-control studyCase-control analysisOdds of infectionRisk of infectionRace/ethnicityBooster dosesDate of testA primer for quantum computing and its applications to healthcare and biomedical research
Durant T, Knight E, Nelson B, Dudgeon S, Lee S, Walliman D, Young H, Ohno-Machado L, Schulz W. A primer for quantum computing and its applications to healthcare and biomedical research. Journal Of The American Medical Informatics Association 2024, 31: 1774-1784. PMID: 38934288, PMCID: PMC11258415, DOI: 10.1093/jamia/ocae149.Peer-Reviewed Original ResearchKamino: A Scalable Architecture to Support Medical AI Research Using Large Real World Data
Lin F, Young P, He H, Huang J, Gagne R, Rice D, Price N, Byron W, Hu Y, Felker D, Button W, Meeker D, Hsiao A, Xu H, Torre C, Schulz W. Kamino: A Scalable Architecture to Support Medical AI Research Using Large Real World Data. 2024, 00: 500-504. DOI: 10.1109/ichi61247.2024.00072.Peer-Reviewed Original ResearchElectronic health recordsAI researchNatural language processing tasksElectronic health record dataLanguage processing tasksComputing resource managementLarge-scale data retrievalMedical AI researchLeveraging electronic health recordsStandard data modelKubernetes orchestratorScalable architectureProcessing tasksResource allocation systemsSecurity considerationsAccess managementData retrievalData modelArchitectural solutionsOMOP CDMReal World DataWorld DataHealth recordsOMOPData
2025
Capacity assessment for EHR-based medical device post-market surveillance for synthetic mid-urethral slings among women with stress urinary incontinence: a NEST consortium study
Matheny M, Perkins A, Rieger-Christ K, Ross J, Mao J, Sedrakyan A, Shah N, Winter R, Reynolds W, Mourtzinos A, Schulz W, Bartlett V, Solotke M, Pandya S, Aryal S, Cho A, Frankenberger E, Park D, Bostrom D, Robbins S, Yustein A, Chughtai B, Trabuco E. Capacity assessment for EHR-based medical device post-market surveillance for synthetic mid-urethral slings among women with stress urinary incontinence: a NEST consortium study. BMJ Surgery Interventions & Health Technologies 2025, 7: e000193. PMID: 39949784, PMCID: PMC11822424, DOI: 10.1136/bmjsit-2023-000193.Peer-Reviewed Original ResearchElectronic health record dataElectronic health recordsHealth systemStress urinary incontinenceSynthetic mesh implantsTertiary care academic medical centerSurgical approachManual chart reviewChart reviewAcademic medical centerMedical device surveillanceHealth recordsAdverse event surveillanceMesh implantationUrinary incontinenceDuration of surveillanceAscertainment rateMid-urethral sling (MUSOutcome measuresSynthetic mid-urethral slingsTreat stress urinary incontinenceDetermination of surgical approachEvent surveillanceRetrospective observational studyMedical Center
2024
Use of electronic health records to characterize patients with uncontrolled hypertension in two large health system networks
Lu Y, Keeley E, Barrette E, Cooper-DeHoff R, Dhruva S, Gaffney J, Gamble G, Handke B, Huang C, Krumholz H, McDonough C, Schulz W, Shaw K, Smith M, Woodard J, Young P, Ervin K, Ross J. Use of electronic health records to characterize patients with uncontrolled hypertension in two large health system networks. BMC Cardiovascular Disorders 2024, 24: 497. PMID: 39289597, PMCID: PMC11409735, DOI: 10.1186/s12872-024-04161-x.Peer-Reviewed Original ResearchConceptsElectronic health recordsHealth recordsHealth systemUncontrolled hypertensionUse of electronic health recordsHypertension managementElectronic health record systemsOneFlorida Clinical Research ConsortiumElectronic health record dataYale New Haven Health SystemBP measurementsICD-10-CM codesHealth system networkPublic health priorityICD-10-CMIncidence rate of deathElevated BP measurementsElevated blood pressure measurementsHealthcare visitsAmbulatory careHealth priorityRetrospective cohort studyEHR dataOneFloridaBlood pressure measurements
Academic Achievements & Community Involvement
Clinical Care
Overview
Wade Schulz, MD, PhD, is a laboratory medicine specialist who focuses on molecular diagnostics and transfusion medicine. He works to identify illnesses by examining the genetic and cellular components of patient samples, and he helps ensure safe blood transfusions for those in need.
As an assistant professor of laboratory medicine at Yale School of Medicine, Dr. Schulz explores advanced methods for applying large collections of health information with computational tools and artificial intelligence to improve disease detection, treatment, and prevention. His work aims to improve patient care in practical, everyday settings.
Dr. Schulz completed his medical and doctoral training at the University of Minnesota. He pursued a residency in clinical pathology and a fellowship in transfusion medicine at Yale School of Medicine. He has been recognized with the Data Summit IBM Cognitive Honors award for his work in data analysis and predictive modeling.
Clinical Specialties
Laboratory Medicine
Yale Medicine News
News
News
- July 02, 2024
Lab Medicine faculty publish primer for quantum computing in healthcare
- July 01, 2024Source: Channel 8 news
Why did so few children get sick from COVID-19?
- June 11, 2024
How Can Artificial Intelligence Advance Medical Education and Research to Transform Patient Care?
- May 28, 2024
Next Generation Research Uses Real-World Data to Identify Most Effective Hypertension Drugs for Patients