eHealth
Revolutionizing Healthcare through Technology-Driven Medical Advancement

Operating at the intersection of artificial intelligence (AI) and health, eHealth will transform the way healthcare is delivered

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eHealth is healthcare supported by technology. Operating at the intersection of artificial intelligence (AI) and health, eHealth will transform the way healthcare is delivered, the manner and speed at which treatments are developed, and help to personalize medicine to individual patients.

Michigan Medicine researchers are on the forefront of eHealth adoption and are actively using technological tools to make healthcare more efficient and effective, from predicting disease before it happens to more quickly diagnosing conditions to screening potential drugs via the analysis of massive data sets.

Recent Podcast Episodes in eHealth
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Health Lab Podcast
How AI is helping to predict death and complications after heart procedures
It showed high levels of accuracy at predicting death, major bleeding events and the need for blood transfusion. Visit Health Lab to read the full story. The BMC2 Probability of Events Following PCI application can be found here.
Health Lab Podcast in brackets with a background with a dark blue translucent layers over cells
Health Lab Podcast
A smart watch that could help your heart
The device would be the first clinical-grade, diagnostic wrist-worn device for long term Afib monitoring. Visit Health Lab to read the full story.
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The Fundamentals
Good collaborators are why I stayed
An interview with Dr. Steven Kunkel on research at U-M Medical School.
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featured news & stories
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Health Lab
New tools that leverage NIH’s ‘All of Us’ dataset could improve anesthesia and surgical care
In a report in JAMA Surgery, researchers propose two novel tools that leverage the All of Us dataset to look at acute health events such as surgery.
surgeon close up operating in bright lighted room
Health Lab
In 10 seconds, AI model detects cancerous brain tumor often missed during surgery
Researchers have developed an AI powered model that — in 10 seconds — can determine during surgery if any part of a cancerous brain tumor that could be removed remains, a study published in Nature suggests.
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Health Lab
Racial differences in medical testing could introduce bias to AI models
Black patients are less likely than white patients to receive certain medical tests that doctors use to diagnose severe disease, impacting artificial intelligence data. But researchers have found a way to correct the bias in these data sets.
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eHealth Researchers
Meet the Experts

Michigan Medicine is leading the way in eHealth research, transforming how healthcare is delivered and experienced.

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Latest Publications
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Operational Ontology for Oncology (O3): A Professional Society-Based, Multistakeholder, Consensus-Driven Informatics Standard Supporting Clinical and Research Use of Real-World Data From Patients Treated for Cancer
Mayo, CS, Feng, MU, Brock, KK, Kudner, R, Balter, P, Buchsbaum, JC, Caissie, A, Covington, E, Daugherty, EC, Dekker, AL, Fuller, CD, Hallstrom, AL, Hong, DS, Hong, JC, Kamran, SC, Katsoulakis, E, Kildea, J, Krauze, AV, Kruse, JJ, McNutt, T, Int J Radiat Oncol Biol Phys, 2023 Nov; DOI:10.1016/j.ijrobp.2023.05.033
PMID: 37244628
Reducing Unnecessary Oophorectomies for Benign Ovarian Neoplasms in Pediatric Patients
Minneci, PC, Bergus, KC, Lutz, C, Aldrink, J, Bence, C, Breech, L, Dillon, PA, Downard, C, Ehrlich, PF, Fallat, M, Fraser, JD, Grabowski, J, Helmrath, M, Hertweck, P, Hewitt, G, Hirschl, RB, Kabre, R, Lal, DR, Landman, M, Leys, C, JAMA, 2023 Oct; DOI:10.1001/jama.2023.17183
PMID: 37787794
An Update on Reported Adoption of 2021 CKD-EPI Estimated Glomerular Filtration Rate Equations
Genzen, JR, Souers, RJ, Pearson, LN, Manthei, DM, Chambliss, AB, Shajani-Yi, Z, Miller, WG, Clin Chem, 2023 Oct; DOI:10.1093/clinchem/hvad116
PMID: 37559439
Improving alcohol treatment engagement using integrated behavioral interventions in alcohol-associated liver disease: A randomized pilot trial.
Mellinger, JL, Medley, S, Kidwell, KM, Asefah, H, Winder, GS, Fernandez, AC, Lok, ASF, Blow, F, Hepatol Commun, 2023 Oct; DOI:10.1097/HC9.0000000000000181
PMID: 37708435
Physics-Guided Deep Scatter Estimation by Weak Supervision for Quantitative SPECT
Kim, H, Li, Z, Son, J, Fessler, JA, Dewaraja, YK, Chun, SY, IEEE Trans Med Imaging, 2023 Oct; DOI:10.1109/TMI.2023.3270868
PMID: 37104110
Real-World Data: Applications and Relevance to Cancer Clinical Trials
Gross, AJ, Pisano, CE, Khunsriraksakul, C, Spratt, DE, Park, HS, Sun, Y, Wang, M, Zaorsky, NG, Semin Radiat Oncol, 2023 Oct; DOI:10.1016/j.semradonc.2023.06.003
PMID: 37684067
Optimizing Informed Consent in Cancer Clinical Trials
Perni, S, Jimenez, R, Jagsi, R, Semin Radiat Oncol, 2023 Oct; DOI:10.1016/j.semradonc.2023.06.001
PMID: 37684064
Incorporating Value-Based Decisions in Breast Cancer Treatment Algorithms
Wang, T, Dossett, LA, Surg Oncol Clin N Am, 2023 Oct; DOI:10.1016/j.soc.2023.05.008
PMID: 37714643