Kristian Stensland, MD, MPH, MS
Assistant Professor of Urology
Assistant Professor of Learning Health Sciences
Medical School
University of Michigan
Department of Urology
NCRC Building 16; 2800 Plymouth Road
Ann Arbor, Michigan 48109
Department of Urology
NCRC Building 16; 2800 Plymouth Road
Ann Arbor, Michigan 48109
Available to mentor
Kristian Stensland, MD, MPH, MS
Assistant Professor
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About
Dr. Kristian Stensland is a urologic oncologist and health services researcher. His research focuses on improving the conduct, efficiency, and equity of clinical trials through informatics, infrastructure, and implementation science.
Qualifications
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MS, Health Infrastructures and Learning SystemsUniversity of Michigan, Ann Arbor, MI, United States
2020 - 2021
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MPH, Quantitative MethodsHarvard TH Chan School of Public Health, 677 Huntington Avenue, Boston, MA, 02115, United States
2016 - 2017
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MD, Distinction in ResearchIcahn School of Medicine at Mount Sinai, 1 Gustave L Levy Place, New York, NY, 10029, United States
2009 - 2014
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BA, Spanish Literature, ChemistryVanderbilt University, Nashville, TN, United States
2005 - 2009
Center Memberships
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Center MemberInstitute for Healthcare Policy and Innovation
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Center MemberAI and Digital Health Innovation
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Center MemberRogel Cancer Center
Research Overview
My research focuses on improving the conduct, efficiency, and equity of clinical trials through informatics, infrastructure, and implementation science.
Recent Publications
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2026 May 10;PresentationClinical Trial Investigation Across Different Practice Settings: Resources, Review Processes, and Patient Recruitment
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Lewicki P, Srivastava A, Jiang R, Johnson A, Ghani K, Ginsburg K, Borza T, Stensland K, Salami SS, Davicioni E, Dunn RL, Daignault-Newton S, Palapattu GS, Spratt DE, Cher M, Schipper M, Dess RT, Morgan TM, Singhal U. JNCI Cancer Spectr, 2026 Apr 24;Journal ArticleUnderstanding randomized controlled trial generalizability through an embedded molecular diagnostics trial.
DOI:10.1093/jncics/pkag040 PMID: 42026952 -
Krischak M, Stensland K. Urol Pract, 2026 Mar; 13 (2): 129Journal ArticleEditorial Commentary.
DOI:10.1097/UPJ.0000000000000927 PMID: 41329825 -
George AK, Miocinovic R, Patel AR, Lomas DJ, Correa AF, Chen DYT, Rastinehad AR, Schwartz MJ, Sidana A, Stensland KD, Helfand BT, Gahan JC, Meng X, Yu A, Brisbane WG, Vourganti S, Barqawi AB, Uchio EM, Wysock JS, Polascik TJ, McClure TD, Fainberg J, Coleman JA. European Urology, 2026 Jan 1; 89 (1): 57 - 68.Journal ArticleIrreversible Electroporation for Prostate Tissue Ablation in Patients with Intermediate-risk Prostate Cancer: Results from the PRESERVE Trial
DOI:10.1016/j.eururo.2025.06.003 PMID: 40685282 -
Miller SR, Chung DH, Gonzalez RT, Jackson WC, Caram MEV, Tsao PA, Stensland K, Gulati R, Shah Y, Wale D, Elliott D, Caverly T, Hofer TP, Saini S, Green MD, Schipper M, Dess RT, Bryant AK. Journal of Nuclear Medicine Official Publication Society of Nuclear Medicine, 2025 Dec 3; 66 (12): 1891 - 1897.Journal ArticleImpact of PSMA PET Staging on Initial Treatment in Newly Diagnosed Prostate Cancer
DOI:10.2967/jnumed.125.270825 PMID: 41101975 -
Lewicki P, Clark S, Shoemaker E, Wang B, Ross J, Daignault-Newton S, Carlozzi N, Martin-Schwarze A, Meurer W, Sales A, Ghani K, Dauw C, Stensland K. Contemporary Clinical Trials Communications, 2025 Dec 1; 48:Journal ArticleRationale and protocol for a prospective clinical trial enrollment improvement hybrid study within a trial
DOI:10.1016/j.conctc.2025.101548 -
Lewicki P, Jiang R, Radhakrishnan A, Bryant A, Schipper M, Morgan T, Stensland K. Annals of Internal Medicine, 2025 Jun 6;Journal ArticlePredicting long-term risk of prostate cancer mortality following a prostate specific antigen screening test: prognostic model development
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Stensland K. 2026 Jan 28;PresentationTesting Trial Improvements
Featured News & Stories
Health Lab
Model predicts long term mortality risk from prostate cancer
Existing risk calculators for prostate cancer have less accurate estimates or predict risk through tests based on biopsy, which requires tissue samples and extra processing times. U-M researchers have developed a new model that can help doctors and patients understand their PSA results and what it means for patient life expectancy.