All Publications

  • Clinical Recommender Algorithms to Simulate Digital Specialty Consultations. Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing Noshad, M., Jankovic, I., Chen, J. H. 2022; 27: 290-300


    Advances in medical science simultaneously benefit patients while contributing to an over-whelming complexity of medicine with a decision space of thousands of possible diagnoses, tests, and treatment options. Medical expertise becomes the most important scarce health-care resource, reflected in tens of millions in the US alone with deficient access to specialty care. Combining the growing wealth of electronic medical record data with modern recommender algorithms has the potential to synthesize the clinical community's expertise into an executable format to manage this information overload and improve access to personalized care suggestions. We focus here specifically on outpatient consultations for (Endocrine) specialty expertise, one of the highest demand and most amenable areas for electronic consultation systems. Specifically we develop and evaluate models to predict the clinical orders of these initial specialty referral consultations using an ensemble of feed-forward neural networks as compared to multiple baseline algorithms. As benchmarks closer to the existing standard of care, we used diagnosis-based clinical checklists based on our review of literature and practice guidelines (e.g., Up-to-Date) for each common referral diagnosis as well as existing electronic consult referral guides. Results indicate that such automated algorithms trained on historical data can provide more personalized decision support with greater accuracy than existing benchmarks, with the potential to power fully digital consultation services that could consolidate utilization of scarce medical expertise, improving consistency of quality and access to care for more patients.

    View details for PubMedID 34890157

  • Engaging Housestaff as Informatics Collaborators: Educational and Operational Opportunities. Applied clinical informatics Shenson, J. A., Jankovic, I., Hong, H. J., Weia, B., White, L., Chen, J. H., Eisenberg, M. 2021; 12 (5): 1150-1156


    BACKGROUND: In academic hospitals, housestaff (interns, residents, and fellows) are a core user group of clinical information technology (IT) systems, yet are often relegated to being recipients of change, rather than active partners in system improvement. These information systems are an integral part of health care delivery and formal efforts to involve and educate housestaff are nascent.OBJECTIVE: This article develops a sustainable forum for effective engagement of housestaff in hospital informatics initiatives and creates opportunities for professional development.METHODS: A housestaff-led IT council was created within an academic medical center and integrated with informatics and graduate medical education leadership. The Council was designed to provide a venue for hands-on clinical informatics educational experiences to housestaff across all specialties.RESULTS: In the first year, five housestaff co-chairs and 50 members were recruited. More than 15 projects were completed with substantial improvements made to clinical systems impacting more than 1,300 housestaff and with touchpoints to nearly 3,000 staff members. Council leadership was integrally involved in hospital governance committees and became the go-to source for housestaff input on informatics efforts. Positive experiences informed members' career development toward informatics roles. Key lessons learned in building for success are discussed.CONCLUSION: The council model has effectively engaged housestaff as learners, local champions, and key informatics collaborators, with positive impact for the participating members and the institution. Requiring few resources for implementation, the model should be replicable at other institutions.

    View details for DOI 10.1055/s-0041-1740258

    View details for PubMedID 34879406

  • Machine learning for initial insulin estimation in hospitalized patients. Journal of the American Medical Informatics Association : JAMIA Nguyen, M., Jankovic, I., Kalesinskas, L., Baiocchi, M., Chen, J. H. 2021


    OBJECTIVE: The study sought to determine whether machine learning can predict initial inpatient total daily dose (TDD) of insulin from electronic health records more accurately than existing guideline-based dosing recommendations.MATERIALS AND METHODS: Using electronic health records from a tertiary academic center between 2008 and 2020 of 16 848 inpatients receiving subcutaneous insulin who achieved target blood glucose control of 100-180 mg/dL on a calendar day, we trained an ensemble machine learning algorithm consisting of regularized regression, random forest, and gradient boosted tree models for 2-stage TDD prediction. We evaluated the ability to predict patients requiring more than 6 units TDD and their point-value TDDs to achieve target glucose control.RESULTS: The method achieves an area under the receiver-operating characteristic curve of 0.85 (95% confidence interval [CI], 0.84-0.87) and area under the precision-recall curve of 0.65 (95% CI, 0.64-0.67) for classifying patients who require more than 6 units TDD. For patients requiring more than 6 units TDD, the mean absolute percent error in dose prediction based on standard clinical calculators using patient weight is in the range of 136%-329%, while the regression model based on weight improves to 60% (95% CI, 57%-63%), and the full ensemble model further improves to 51% (95% CI, 48%-54%).DISCUSSION: Owing to the narrow therapeutic window and wide individual variability, insulin dosing requires adaptive and predictive approaches that can be supported through data-driven analytic tools.CONCLUSIONS: Machine learning approaches based on readily available electronic medical records can discriminate which inpatients will require more than 6 units TDD and estimate individual doses more accurately than standard guidelines and practices.

    View details for DOI 10.1093/jamia/ocab099

    View details for PubMedID 34279615

  • New-Onset of Inflammatory Bowel Disease in a Patient Treated With Teprotumumab for Thyroid Associated Ophthalmopathy. Ophthalmic plastic and reconstructive surgery Ashraf, D. C., Jankovic, I., El-Nachef, N., Winn, B. J., Kim, G. E., Kersten, R. C. 2021


    A patient with thyroid-associated ophthalmopathy was treated with teprotumumab and developed symptoms concerning for inflammatory bowel disease after her sixth infusion. Colonoscopy was performed, and mucosal biopsies identified evidence of active colitis consistent with a diagnosis of ulcerative colitis. Despite treatment with budesonide and mesalamine, the patient continued to be symptomatic one and a half months after cessation of teprotumumab and required infliximab to achieve good control of her inflammatory bowel disease. This case represents the first report of new-onset inflammatory bowel disease arising during treatment with teprotumumab.

    View details for DOI 10.1097/IOP.0000000000001943

    View details for PubMedID 33710035

  • Proposed Use of Continuous Glucose Monitoring for Care of Critically Ill COVID-19 Patients. Journal of diabetes science and technology Jankovic, I., Basina, M. 2020: 1932296820965203


    Coronavirus disease 2019 (COVID-19) has disproportionately affected patients with diabetes. Mounting evidence has shown that adequate inpatient glycemic control may decrease the risk of mortality. In critically ill patients, insulin drips are the most effective means of controlling blood glucose. However, resource limitations such as the availability of protective equipment and nursing time have discouraged the use of insulin drips during COVID-19. In this commentary, we review existing evidence on the importance of glycemic control in COVID-19 patients with diabetes and propose a protocol for utilizing continuous glucose monitors (CGMs) to improve glycemic control by decreasing the need for bedside management in critically ill COVID-19 patients.

    View details for DOI 10.1177/1932296820965203

    View details for PubMedID 33084380

  • Clinical Decision Support and Implications for the Clinician Burnout Crisis. Yearbook of medical informatics Jankovic, I., Chen, J. H. 2020; 29 (1): 145–54


    OBJECTIVES: This survey aimed to review aspects of clinical decision support (CDS) that contribute to burnout and identify key themes for improving the acceptability of CDS to clinicians, with the goal of decreasing said burnout.METHODS: We performed a survey of relevant articles from 2018-2019 addressing CDS and aspects of clinician burnout from PubMed and Web of Science. Themes were manually extracted from publications that met inclusion criteria.RESULTS: Eighty-nine articles met inclusion criteria, including 12 review articles. Review articles were either prescriptive, describing how CDS should work, or analytic, describing how current CDS tools are deployed. The non-review articles largely demonstrated poor relevance and acceptability of current tools, and few studies showed benefits in terms of efficiency or patient outcomes from implemented CDS. Encouragingly, multiple studies highlighted steps that succeeded in improving both acceptability and relevance of CDS.CONCLUSIONS: CDS can contribute to clinician frustration and burnout. Using the techniques of improving relevance, soliciting feedback, customization, measurement of outcomes and metrics, and iteration, the effects of CDS on burnout can be ameliorated.

    View details for DOI 10.1055/s-0040-1701986

    View details for PubMedID 32823308