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College of
Health Sciences

Thomas Morland
Assistant Professor, Department of Population Health Sciences 
Associate, Department of General Internal Medicine

Thomas Morland, MD

Research interests

Thomas Morland, MD, is an outpatient general internist with 10 years of primary care experience. In his clinical role, he strives for accurate early diagnosis, application of the most effective evidence-based interventions, and generally maximizing efforts to stabilize, or even reverse, chronic disease. His clinical work has demonstrated the impact consistent application of the best interventions can have for patients, and also provided insight into the limitations of the current standard of care and the potential for iatrogenic harm in healthcare. 

In his research he designs, implements and evaluates next-generation approaches in detection and management of chronic conditions and in medication safety using variety of techniques including machine learning-enabled predictive modeling, prospective trials, retrospective statistical studies and quasi-experimental paradigms. He also employs pharmacoeconomic techniques to assess the effects of population interventions on healthcare utilization and total cost of care. Specific projects have included development of a machine learning-enabled drug-induced long QTc model, validation of the Perdue/IU passive digital markers of dementia model, a clinical and economic evaluation of Geisinger’s pharmacy medication therapy disease management (MTDM) program, and validation of the Complete Health Improvement Program (CHIP) for Geisinger Health Plan members with type 2 diabetes.


  • Raghunath S, Pfeifer JM, Kelsey CR, Nemani A, Ruhl JA, Hartzel DN, Ulloa Cerna AE, Jing L, vanMaanen DP, Leader JB, Schneider G, Morland TB, Chen R, Zimmerman N, Fornwalt BK, Haggerty CM. An ECG-based machine learning model for predicting new-onset atrial fibrillation is superior to age and clinical features in identifying patients at high stroke risk. J Electrocardiol. 2022 Nov 8;76:61-65
  • El-Manzalawy Y, Abbas M, Hoaglund I, Cerna AU, Morland TB, Haggerty CM, Hall ES, Fornwalt BK. OASIS +: leveraging machine learning to improve the prognostic accuracy of OASIS severity score for predicting in-hospital mortality. BMC Med Inform Decis Mak. 2021 May 13;21(1):156.
  • Abbas M, Morland TB, Hall ES, El-Manzalawy Y. Associations between Google Search Trends for Symptoms and COVID-19 Confirmed and Death Cases in the United States. Int J Environ Res Public Health. 2021 Apr 25;18(9):4560.
  • Morland TB, Synnestvedt M, Honeywell S Jr, Yang F, Armstrong K, Guerra C. Effect of a Financial Incentive for Colorectal Cancer Screening Adherence on the Appropriateness of Colonoscopy Orders. Am J Med Qual. 2017 May/Jun;32(3):292-298.
  • Pessoa L, Padmala S, Morland T. Fate of unattended fearful faces in the amygdala is determined by both attentional resources and cognitive modulation. Neuroimage. 2005 Oct 15;28(1):249-55.


ScB, cognitive neuroscience, Brown University (2004)
M.D., Yale University (2010)
Internship in primary care-internal medicine, Hospital of the University of Pennsylvania (2011)
Residency in primary care-internal medicine, Hospital of the University of Pennsylvania (2013


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