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Jejo Koola

Title(s)Assistant Clinical Professor, Medicine
SchoolHealth Sciences
Phone858-246-2563
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    Collapse Biography 
    Collapse Education and Training
    Vanderbilt University, Nashville, TNMS12/2018Biomedical Informatics
    Department of Veterans Affairs, Nashville, TN09/2016Fellowship in Biomedical Informatics
    Medical College of Virginia, Richmond, VAMedicine06/2011Internal Medicine Residency
    Medical Univ. of South Carolina, Charleston, SCMD05/2008Medicine
    Purdue University, West Lafayette, INBiology, Computer Science

    Collapse Overview 
    Collapse Overview
    Dr. Jejo Koola joined UCSD faculty in 2016. He received his MD degree from the Medical University of South Carolina and subsequently completed his residency in Internal Medicine at the Medical College of Virginia in Richmond, Virginia in 2011. Following residency, Dr. Koola completed a post-doctoral fellowship in Biomedical Informatics through the Department of Veterans Affairs in conjunction with Vanderbilt University in 2016.

    Dr. Koola has a joint appointment in Hospital Medicine and Biomedical Informatics. Clinically, he sees a wide variety of hospitalized patients at UCSD on the hospitalist service. His research interests are in using machine learning to build predictive models using big data. He applies models to improving the care of multi-morbid patients, particularly patients with advanced liver disease. He is currently involved in a multi-center study implementing predictive analytics for cirrhosis care at the Department of Veterans Affairs. Additionally, he leads a study diagnosing hepatic encephalopathy via digital phenotyping.

    Dr. Koola also serves as a medical director within UC San Diego Health Information Systems and the medical director for the UCSD Clinical Informatics Consult Service.

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    Surveillance and prediction of post-acute kidney injury outcomes using digital phenotyping
    UCSD Academy of Clinician Scholars Oct 1, 2019 - Sep 30, 2020
    Role: PI
    Role Description: Acute Kidney Injury (AKI) occurs in 2 – 5% of hospitalized patients and nearly 70% of critically ill patients. AKI is associated with increased inpatient mortality, hospital length of stay, and post-discharge morbidity. Moreover, its effects may be long-lasting and are associated with cognitive dysfunction, frailty, and reduced quality of life. More real-time information is necessary from patients in the home setting post hospital discharge. This study leverages predictive modeling and digital phenotyping to track post-AKI cognitive and functional changes and their relationship to patient-centered outcomes. Digital phenotyping allows for unobtrusive, continuous monitoring thereby delivering actionable risk predictions conducive to tailored interventions.
    Diagnosing “Chemo Brain” Using Digital Phenotyping
    University of California San Diego Health Sciences Academic Senate GrantDec 1, 2018 - Dec 1, 2019
    Role: PI
    Role Description: The overall objective of this project is to develop methods of identifying chemotherapy related cognitive impairment. Up to 70% of patients have reported significant reduction in cognitive function, colloquially termed “chemo brain,” from chemotherapy. Studying these changes can be difficult within the clinical setting due to the resource requirements. We will evaluate the feasibility of using “digital phenotyping,” assessing the interaction between a patient’s health and their technology use, to diagnose chemotherapy related cognitive impairment. We will use multiple data streams from passive/wearable sensors to assess their daily cognitive status.
    Assessing Cognitive Status of Patients with Cirrhosis Using Digital Phenotyping
    University of California San Diego Health Sciences Academic Senate GrantMay 1, 2018 - May 1, 2019
    Role: PI
    Role Description: The overall objective of this project is to develop methods of identifying early hepatic encephalopathy before the patient requires hospitalization. More specifically we will evaluate the feasibility of using “digital phenotyping,” assessing the interaction between a patient’s health and their technology use, to diagnose hepatic encephalopathy. We will use multiple data streams from a FitBit and the patient’s routine interactions with their regular smartphone to assess their daily cognitive status.
    Assessing efficacy of passive and active forms of expressive art therapy in inpatient services
    University of California San Diego Health Sciences Academic Senate GrantJun 1, 2017 - May 31, 2018
    Role: Co-Investigator
    Automated Surveillance and Intervention among Patients with Liver Cirrhosis
    Department of Veterans Affairs HSR&D IIR 13-052Jun 1, 2014 - Jun 1, 2018
    Role: Co-Investigator
    San Diego Biomedical Informatics Education & Research (SABER)
    NIH/NLM T15LM011271Jul 1, 2012 - Jun 30, 2022
    Role: Co-Investigator
    Role Description: Role Description: Director, Summer Internship Program

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    Collapse Bibliographic 
    Collapse Publications
    Publications listed below are automatically derived from MEDLINE/PubMed and other sources, which might result in incorrect or missing publications. Researchers can login to make corrections and additions, or contact us for help.
    List All   |   Timeline
    1. Koola JD, Ho SB, Cao A, Chen G, Perkins AM, Davis SE, Matheny ME. Predicting 30-Day Hospital Readmission Risk in a National Cohort of Patients with Cirrhosis. Dig Dis Sci. 2019 Sep 17. PMID: 31531817.
      View in: PubMed
    2. Nguyen NH, Koola J, Dulai PS, Prokop LJ, Sandborn WJ, Singh S. Rate of Risk Factors for and Interventions to Reduce Hospital Readmission in Patients With Inflammatory Bowel Diseases. Clin Gastroenterol Hepatol. 2019 Aug 27. PMID: 31470176.
      View in: PubMed
    3. Koola JD, Chen G, Malin BA, Fabbri D, Siew ED, Ho SB, Patterson OV, Matheny ME. A clinical risk prediction model to identify patients with hepatorenal syndrome at hospital admission. Int J Clin Pract. 2019 Jul 26; e13393. PMID: 31347754.
      View in: PubMed
    4. Miller A, Koola JD, Matheny ME, Ducom JH, Slagle JM, Groessl EJ, Minter FF, Garvin JH, Weinger MB, Ho SB. Application of contextual design methods to inform targeted clinical decision support interventions in sub-specialty care environments. Int J Med Inform. 2018 09; 117:55-65. PMID: 30032965.
      View in: PubMed
    5. Koola JD, Davis SE, Al-Nimri O, Parr SK, Fabbri D, Malin BA, Ho SB, Matheny ME. Development of an automated phenotyping algorithm for hepatorenal syndrome. J Biomed Inform. 2018 04; 80:87-95. PMID: 29530803.
      View in: PubMed
    6. Koola J, Cao A, Perkins A, Chen G, Ho SB, Matheny ME.Hepatology. Post-Discharge Mortality in a National Cohort of Veterans Affairs Patients with Cirrhosis. 2016; S1(64):862A.
    7. Herbsman T, Forster L, Molnar C, Dougherty R, Christie D, Koola J, Ramsey D, Morgan PS, Bohning DE, George MS, Nahas Z. Motor threshold in transcranial magnetic stimulation: the impact of white matter fiber orientation and skull-to-cortex distance. Hum Brain Mapp. 2009 Jul; 30(7):2044-55. PMID: 18973261.
      View in: PubMed
    8. Nahas Z, Teneback C, Chae JH, Mu Q, Molnar C, Kozel FA, Walker J, Anderson B, Koola J, Kose S, Lomarev M, Bohning DE, George MS. Serial vagus nerve stimulation functional MRI in treatment-resistant depression. Neuropsychopharmacology. 2007 Aug; 32(8):1649-60. PMID: 17203016.
      View in: PubMed
    9. Hajcak G, Molnar C, George MS, Bolger K, Koola J, Nahas Z. Emotion facilitates action: a transcranial magnetic stimulation study of motor cortex excitability during picture viewing. Psychophysiology. 2007 Jan; 44(1):91-7. PMID: 17241144.
      View in: PubMed
    10. Borckardt JJ, Nahas Z, Koola J, George MS. Estimating resting motor thresholds in transcranial magnetic stimulation research and practice: a computer simulation evaluation of best methods. J ECT. 2006 Sep; 22(3):169-75. PMID: 16957531.
      View in: PubMed
    11. Mu Q, Nahas Z, Johnson KA, Yamanaka K, Mishory A, Koola J, Hill S, Horner MD, Bohning DE, George MS. Decreased cortical response to verbal working memory following sleep deprivation. Sleep. 2005 Jan; 28(1):55-67. PMID: 15700721.
      View in: PubMed
    12. Mishory A, Molnar C, Koola J, Li X, Kozel FA, Myrick H, Stroud Z, Nahas Z, George MS. The maximum-likelihood strategy for determining transcranial magnetic stimulation motor threshold, using parameter estimation by sequential testing is faster than conventional methods with similar precision. J ECT. 2004 Sep; 20(3):160-5. PMID: 15343000.
      View in: PubMed
    13. Koola J, Ho SB, Matheny ME.An Electronic Health Record Phenotyping Algorithm for Identifying Patients with Hepatorenal Syndrome.