Naveed Rabbani is a fellow physician and researcher in clinical informatics at Stanford Medicine. He holds a BS in Electrical Engineering from Stanford University and an MD from Harvard Medical School. As a physician and engineer, he is passionate about using technology to improve quality of and access to healthcare. His areas of expertise are in pediatric medicine, digital health, remote health monitoring, and clinical applications of data science. Dr. Rabbani has served as a consultant for health tech start-ups and conducted research in industry and academia at Philips Research, Boston Children’s Hospital, UCSF, and the University of Washington.

Clinical Focus

  • Clinical Informatics
  • Pediatrics
  • Machine Learning
  • Artificial Intelligence
  • Fellow

Honors & Awards

  • Resident and Fellow Physician Union Research Award, Resident and Fellow Physician Union (2020)
  • Alexandra J. Miliotis Pediatric Oncology Research Fellow, Harvard Medical School (2015)
  • Frederick Emmons Terman Engineering Scholastic Award, Stanford University (2013)

Professional Education

  • BS, Stanford University, Electrical Engineering (2013)
  • MD, Harvard Medical School (2018)
  • Residency, University of Washington, Pediatrics (2021)
  • Board Certification, American Board of Pediatrics, Pediatrics (2021)

All Publications

  • Targeting Repetitive Laboratory Testing with Electronic Health Records-Embedded Predictive Decision Support: A Pre-Implementation Study. Clinical biochemistry Rabbani, N., Ma, S. P., Li, R. C., Winget, M., Weber, S., Boosi, S., Pham, T. D., Svec, D., Shieh, L., Chen, J. H. 2023


    INTRODUCTION: Unnecessary laboratory testing contributes to patient morbidity and healthcare waste. Despite prior attempts at curbing such overutilization, there remains opportunity for improvement using novel data-driven approaches. This study presents the development and early evaluation of a clinical decision support tool that uses a predictive model to help providers reduce low-yield, repetitive laboratory testing in hospitalized patients.METHODS: We developed an EHR-embedded SMART on FHIR application that utilizes a laboratory test result prediction model based on historical laboratory data. A combination of semi-structured physician interviews, usability testing, and quantitative analysis on retrospective laboratory data were used to inform the tool's development and evaluate its acceptability and potential clinical impact.KEY RESULTS: Physicians identified culture and lack of awareness of repeat orders as key drivers for overuse of inpatient blood testing. Users expressed an openness to a lab prediction model and 13/15 physicians believed the tool would alter their ordering practices. The application received a median System Usability Scale score of 75, corresponding to the 75th percentile of software tools. On average, physicians desired a prediction certainty of 85% before discontinuing a routine recurring laboratory order and a higher certainty of 90% before being alerted. Simulation on historical lab data indicates that filtering based on accepted thresholds could have reduced 22% of repeat chemistry panels.CONCLUSIONS: The use of a predictive algorithm as a means to calculate the utility of a diagnostic test is a promising paradigm for curbing laboratory test overutilization. An EHR-embedded clinical decision support tool employing such a model is a novel and acceptable intervention with the potential to reduce low-yield, repetitive laboratory testing.

    View details for DOI 10.1016/j.clinbiochem.2023.01.002

    View details for PubMedID 36623759

  • Investigating real-world consequences of biases in commonly used clinical calculators. The American journal of managed care Yoo, R. M., Dash, D., Lu, J. H., Genkins, J. Z., Rabbani, N., Fries, J. A., Shah, N. H. 2023; 29 (1): e1-e7


    OBJECTIVES: To evaluate whether one summary metric of calculator performance sufficiently conveys equity across different demographic subgroups, as well as to evaluate how calculator predictive performance affects downstream health outcomes.STUDY DESIGN: We evaluate 3 commonly used clinical calculators-Model for End-Stage Liver Disease (MELD), CHA2DS2-VASc, and simplified Pulmonary Embolism Severity Index (sPESI)-on the cohort extracted from the Stanford Medicine Research Data Repository, following the cohort selection process as described in respective calculator derivation papers.METHODS: We quantified the predictive performance of the 3 clinical calculators across sex and race. Then, using the clinical guidelines that guide care based on these calculators' output, we quantified potential disparities in subsequent health outcomes.RESULTS: Across the examined subgroups, the MELD calculator exhibited worse performance for female and White populations, CHA2DS2-VASc calculator for the male population, and sPESI for the Black population. The extent to which such performance differences translated into differential health outcomes depended on the distribution of the calculators' scores around the thresholds used to trigger a care action via the corresponding guidelines. In particular, under the old guideline for CHA2DS2-VASc, among those who would not have been offered anticoagulant therapy, the Hispanic subgroup exhibited the highest rate of stroke.CONCLUSIONS: Clinical calculators, even when they do not include variables such as sex and race as inputs, can have very different care consequences across those subgroups. These differences in health care outcomes across subgroups can be explained by examining the distribution of scores and their calibration around the thresholds encoded in the accompanying care guidelines.

    View details for DOI 10.37765/ajmc.2023.89306

    View details for PubMedID 36716157

  • National Trends in Pediatric Ambulatory Telehealth Utilization and Follow-Up Care. Telemedicine journal and e-health : the official journal of the American Telemedicine Association Rabbani, N., Chen, J. H. 2022


    Introduction: As telemedicine becomes standard in pediatrics, further research is required to ensure optimal adoption. This study seeks to characterize visits best suited for telemedicine by analyzing usage trends and encounter attributes associated with immediate in-person follow-up. Methods: Analysis of ambulatory pediatric encounters from the first quarter of 2021 in a nationwide insurance claims database. Results: Telemedicine comprised 9.5% (138,346) of ambulatory encounters. Among telemedicine visits, 7.5% (10,304) yielded in-person follow-up within 3 days. Encounters involving infants and diagnoses of the perinatal period were most frequently followed by in-person visits (11% and 20%, respectively). Mental health visits were least likely to have in-person follow-up. Conclusions: In 2021, telemedicine remained a common modality of care in pediatrics. Varying medical needs still require in-person evaluation, whereas other diagnoses may be conducive to even greater expansion. Insights from this study inform further research into optimization of pediatric telemedicine utilization and development of guidelines.

    View details for DOI 10.1089/tmj.2022.0137

    View details for PubMedID 35544068

  • Association Between Cytomegalovirus Serostatus, Antiviral Therapy, and Allograft Survival in Pediatric Heart Transplantation. Transplant international : official journal of the European Society for Organ Transplantation Rabbani, N., Kronmal, R. A., Wagner, T., Kemna, M., Albers, E. L., Hong, B., Friedland-Little, J., Spencer, K., Law, Y. M. 2022; 35: 10121


    Background: Cytomegalovirus (CMV) is an important complication of heart transplantation and has been associated with graft loss in adults. The data in pediatric transplantation, however, is limited and conflicting. We conducted a large-scale cohort study to better characterize the relationship between CMV serostatus, CMV antiviral use, and graft survival in pediatric heart transplantation. Methods: 4,968 pediatric recipients of solitary heart transplants from the Scientific Registry of Transplant Recipients were stratified into three groups based on donor or recipient seropositivity and antiviral use: CMV seronegative (CMV-) transplants, CMV seropositive (CMV+) transplants without antiviral therapy, and CMV+ transplants with antiviral therapy. The primary endpoint was retransplantation or death. Results: CMV+ transplants without antiviral therapy experienced worse graft survival than CMV+ transplants with antiviral therapy (10-year: 57 vs 65%). CMV+ transplants with antiviral therapy experienced similar survival as CMV- transplants. Compared to CMV seronegativity, CMV seropositivity without antiviral therapy had a hazard ratio of 1.21 (1.07-1.37 95% CI, p-value = .003). Amongst CMV+ transplants, antiviral therapy had a hazard ratio of .82 (0.74-.92 95% CI, p-value < .001). During the first year after transplantation, these hazard ratios were 1.32 (1.06-1.64 95% CI, p-value .014) and .59 (.48-.73 95% CI, p-value < .001), respectively. Conclusions: CMV seropositivity is associated with an increased risk of graft loss in pediatric heart transplant recipients, which occurs early after transplantation and may be mitigated by antiviral therapy.

    View details for DOI 10.3389/ti.2022.10121

    View details for PubMedID 35368645

    View details for PubMedCentralID PMC8964945

  • Applications of Machine Learning in Routine Laboratory Medicine: Current State and Future Directions. Clinical biochemistry Rabbani, N., Kim, G. Y., Suarez, C. J., Chen, J. H. 2022


    Machine learning is able to leverage large amounts of data to infer complex patterns that are otherwise beyond the capabilities of rule-based systems and human experts. Its application to laboratory medicine is particularly exciting, as laboratory testing provides much of the foundation for clinical decision making. In this article, we provide a brief introduction to machine learning for the medical professional in addition to a comprehensive literature review outlining the current state of machine learning as it has been applied to routine laboratory medicine. Although still in its early stages, machine learning has been used to automate laboratory tasks, optimize utilization, and provide personalized reference ranges and test interpretation. The published literature leads us to believe that machine learning will be an area of increasing importance for the laboratory practitioner. We envision the laboratory of the future will utilize these methods to make significant improvements in efficiency and diagnostic precision.

    View details for DOI 10.1016/j.clinbiochem.2022.02.011

    View details for PubMedID 35227670

  • Positional Hypoxemia from Persistent Left Superior Vena Cava Draining to the Left Atrium CONGENITAL HEART DISEASE Rabbani, N., DeYoung, S., Gibson, R. L., Conwell, J., Deen, J. F. 2020; 15 (4): 197-216
  • Tracking Clinical Status for Heart Failure Patients using Ballistocardiography and Electrocardiography Signal Features Etemadi, M., Hersek, S., Tseng, J. M., Rabbani, N., Heller, J., Roy, S., Klein, L., Inan, O. T., IEEE IEEE. 2014: 5188-5191


    Heart failure (HF) is an escalating public health problem, with few effective methods for home monitoring. In HF management, the important clinical factors to monitor include symptoms, fluid status, cardiac output, and blood pressure--based on these factors, inotrope and diuretic dosages are adjusted day-by-day to control the disorder and improve the patient's status towards a successful discharge. Previously, the ballistocardiogram (BCG) measured on a weighing scale has been shown to be capable of detecting changes in cardiac output and contractility for healthy subjects. In this study, we investigated whether the BCG and electrocardiogram (ECG) signals measured on a wireless modified scale could accurately track the clinical status of HF patients during their hospital stay. Using logistic regression, we found that the root-mean-square (RMS) power of the BCG provided a good fit for clinical status, as determined based on clinical measurements and symptoms, for the 85 patient days studied from 10 patients (p < 0.01). These results provide a promising foundation for future studies aimed at using the BCG/ECG scale at home to track HF patient status remotely.

    View details for Web of Science ID 000350044705046

    View details for PubMedID 25571162

    View details for PubMedCentralID PMC4600348

  • Physical constraints on the establishment of intracellular spatial gradients in bacteria BMC BIOPHYSICS Tropini, C., Rabbani, N., Huang, K. C. 2012; 5


    Bacteria dynamically regulate their intricate intracellular organization involving proteins that facilitate cell division, motility, and numerous other processes. Consistent with this sophisticated organization, bacteria are able to create asymmetries and spatial gradients of proteins by localizing signaling pathway components. We use mathematical modeling to investigate the biochemical and physical constraints on the generation of intracellular gradients by the asymmetric localization of a source and a sink.We present a systematic computational analysis of the effects of other regulatory mechanisms, such as synthesis, degradation, saturation, and cell growth. We also demonstrate that gradients can be established in a variety of bacterial morphologies such as rods, crescents, spheres, branched and constricted cells.Taken together, these results suggest that gradients are a robust and potentially common mechanism for providing intracellular spatial cues.

    View details for DOI 10.1186/2046-1682-5-17

    View details for Web of Science ID 000311052500001

    View details for PubMedID 22931750

    View details for PubMedCentralID PMC3496868