Bio

Bio


Clinical Neonatologist, Clinical Informaticist, > 5 years of Industry experience with primary research focus on health tech innovation, medical devices (wearable sensors, non-invasive sensing), global health (affordable, low-cost medical device development)

Clinical Focus


  • Neonatology
  • Clinical Informatics
  • Health Tech Innovation
  • Medical Device Validation

Administrative Appointments


  • Reviewer, Stanford MD, Pediatrics Residency and Neonatology fellowship admissions (2019 - Present)
  • Advisor, Stanford Health Innovations & Future Technologies (2018 - Present)

Honors & Awards


  • Moskowitz Scholar, Mayo Clinic
  • Artificial Intelligence in Medicine and Equity Grant, Robert Wood Johnson Foundation & Stanford, Department of Medicine
  • Marshall Klaus Perinatal Award, American Academy of Pediatrics
  • Innovators in General Pediatrics, Packard Foundation
  • mHealth platform for Maternal/Child Health Grant, .
  • CATCH Grant, American Academy of Pediatrics
  • MIT $50/100K Entrepreneurship Competition, Winner, MIT, Cambridge

Professional Education


  • Fellowship, Stanford University, Healthcare Design
  • Fellowship, Stanford University School of Medicine, Palo Alto, CA, Neonatology
  • Board Certification, American Board of Preventive Medicine, Clinical Informatics
  • Board Certification, American Board of Pediatrics, Pediatrics
  • Residency, Columbia University Medical Center, New York, NY, Pediatrics
  • Fellowship, Harvard Medical School - Massachusetts General Hospital, Boston, MA, Biomedical Informatics
  • MS, Massachusetts Institute of Technology, Biological Engg/Toxicology
  • MBBS, Kasturba Medical College, India, Medicine, Surgery

Community and International Work


  • India NeoDesign Network

    Topic

    Maternal and Child Health

    Location

    International

    Ongoing Project

    Yes

    Opportunities for Student Involvement

    Yes

Patents


  • "United States Patent 8999242 Method and apparatus for monitoring alteration of flow characteristics in a liquid sample", Apr 7, 2015
  • "United States Patent US20100000862A1 Integrated Blood Glucose Measurement Device", Apr 30, 2014
  • "United States Patent US20100249965A1 Integrated Blood Glucose Measurement Device", May 1, 2009
  • "United States Patent US20100000862A1 Integrated Blood Glucose Measurement Device", Jul 7, 2008

Research & Scholarship

Current Research and Scholarly Interests


Wearable senors, unobtrusive vital sign monitoring, natural language processing/text mining

Projects


  • Non-invasive continuous BP monitoring using wearable sensors, Lucile Packard Children's Hospital

    Location

    Stanford, CA

    Collaborators

    • Xina Quan, CT, PyrAmes Health
  • NICU of the Future (unobtrusive neonatal sensing)

    Location

    Stanford, CA

Publications

All Publications


  • Liver Failure and Rash in a 6-week-old Girl PEDIATRICS IN REVIEW Mediratta, R., Schwenk, H., Rao, A., Chitkara, R. 2018; 39 (6): 315–U22

    View details for PubMedID 29858298

  • Comparing two anesthesia information management system user interfaces: a usability evaluation CANADIAN JOURNAL OF ANESTHESIA-JOURNAL CANADIEN D ANESTHESIE Wanderer, J. P., Rao, A. V., Rothwell, S. H., Ehrenfeld, J. M. 2012; 59 (11): 1023-1031

    Abstract

    Anesthesia information management systems (AIMS) have been developed by multiple vendors and are deployed in thousands of operating rooms around the world, yet not much is known about measuring and improving AIMS usability. We developed a methodology for evaluating AIMS usability in a low-fidelity simulated clinical environment and used it to compare an existing user interface with a revised version. We hypothesized that the revised user interface would be more useable.In a low-fidelity simulated clinical environment, twenty anesthesia providers documented essential anesthetic information for the start of the case using both an existing and a revised user interface. Participants had not used the revised user interface previously and completed a brief training exercise prior to the study task. All participants completed a workload assessment and a satisfaction survey. All sessions were recorded. Multiple usability metrics were measured. The primary outcome was documentation accuracy. Secondary outcomes were perceived workload, number of documentation steps, number of user interactions, and documentation time. The interfaces were compared and design problems were identified by analyzing recorded sessions and survey results.Use of the revised user interface was shown to improve documentation accuracy from 85.1% to 92.4%, a difference of 7.3% (95% confidence interval [CI] for the difference 1.8 to 12.7). The revised user interface decreased the number of user interactions by 6.5 for intravenous documentation (95% CI 2.9 to 10.1) and by 16.1 for airway documentation (95% CI 11.1 to 21.1). The revised user interface required 3.8 fewer documentation steps (95% CI 2.3 to 5.4). Airway documentation time was reduced by 30.5 seconds with the revised workflow (95% CI 8.5 to 52.4). There were no significant time differences noted in intravenous documentation or in total task time. No difference in perceived workload was found between the user interfaces. Two user interface design problems were identified in the revised user interface.The usability of anesthesia information management systems can be evaluated using a low-fidelity simulated clinical environment. User testing of the revised user interface showed improvement in some usability metrics and highlighted areas for further revision. Vendors of AIMS and those who use them should consider adopting methods to evaluate and improve AIMS usability.

    View details for DOI 10.1007/s12630-012-9771-z

    View details for Web of Science ID 000310340200003

    View details for PubMedID 23055030

  • Evolution of data management tools for managing self-monitoring of blood glucose results: a survey of iPhone applications. Journal of diabetes science and technology Rao, A., Hou, P., Golnik, T., Flaherty, J., Vu, S. 2010; 4 (4): 949-957

    Abstract

    Studies have indicated that sharing of self-monitoring of blood glucose (SMBG) data and subsequent feedback from the health care provider (HCP) can help achieve glycemic goals such as a reduction in glycated hemoglobin. Electronic SMBG data management and sharing tools for the PC and smartphones may help in reducing the effort to manage SMBG data.We reviewed software and top-ranking applications (Apps) for the iPhone platform to document the variety of useful features. Additionally, in an attempt to assess metrics such as task analysis and user friendliness of diabetes Apps, we observed and surveyed patients with diabetes as they recorded and relayed sample SMBG results to their hypothetical HCP using three Apps.Observation and survey demonstrated that the WaveSense Diabetes Manager allowed the participants to complete preselected SMBG data entry and relay tasks faster than other Apps. The survey revealed patient behavior patterns that would be useful in future App development.Being able to record, analyze, seamlessly share, and obtain feedback on the SMBG data using an iPhone/iTouch App might potentially benefit patients. Trends in SMBG data management and the possibility of having interoperability of blood glucose monitors and smartphones may open up new avenues of diabetes management for the technologically savvy patient.

    View details for PubMedID 20663461

  • Individuals achieve more accurate results with meters that are codeless and employ dynamic electrochemistry. Journal of diabetes science and technology Rao, A., Wiley, M., Iyengar, S., Nadeau, D., Carnevale, J. 2010; 4 (1): 145-150

    Abstract

    Studies have shown that controlling blood glucose can reduce the onset and progression of the long-term microvascular and neuropathic complications associated with the chronic course of diabetes mellitus. Improved glycemic control can be achieved by frequent testing combined with changes in medication, exercise, and diet. Technological advancements have enabled improvements in analytical accuracy of meters, and this paper explores two such parameters to which that accuracy can be attributed.Four blood glucose monitoring systems (with or without dynamic electrochemistry algorithms, codeless or requiring coding prior to testing) were evaluated and compared with respect to their accuracy.Altogether, 108 blood glucose values were obtained for each system from 54 study participants and compared with the reference values. The analysis depicted in the International Organization for Standardization table format indicates that the devices with dynamic electrochemistry and the codeless feature had the highest proportion of acceptable results overall (System A, 101/103). Results were significant when compared at the 10% bias level with meters that were codeless and utilized static electrochemistry (p = .017) or systems that had static electrochemistry but needed coding (p = .008).Analytical performance of these blood glucose meters differed significantly depending on their technologic features. Meters that utilized dynamic electrochemistry and did not require coding were more accurate than meters that used static electrochemistry or required coding.

    View details for PubMedID 20167178

  • A simple approach for the computation of multiple periodicities in biological time series BIOLOGICAL RHYTHM RESEARCH Rao, A. V., Sharma, V. K. 2002; 33 (5): 487-502