Lipika Samal, MD, MPH completed a three-year NIH-funded fellowship at Johns Hopkins University School of Medicine, a Masters of Public Health at Johns Hopkins Bloomberg School of Public Health, and additional coursework in a National Library of Medicine-funded training program in Biomedical Informatics. During her fellowship she assisted colleagues with numerous articles about the use of health information technology (HIT) by patients including: 1) a survey of women visiting the Baltimore city sexually transmitted infections clinic, 2) a survey of HIV patients, 3) a survey of diabetes patients, and 4) a federally funded systematic review of the literature about consumer health informatics. Each of these articles contributed to the literature on HIT interventions to improve the health of vulnerable populations.
After fellowship, she joined the Brigham and Women’s Hospital (BWH) as a 75% research effort clinician investigator in the Division of General Internal Medicine. In keeping with the reason that she came to BWH, she sought to study the impact of HIT on quality of care. Her first manuscript in this area was published in the American Journal of Managed Care. A separate analysis of the relationship between HIT use and racial/ethnic disparities was published in the Archives of Internal Medicine (now JAMA Internal Medicine). In her fourth year as a faculty member, she published another analysis in JAMA Internal Medicine examining the lack of association between Meaningful Use of electronic health records (EHRs) and clinical quality measures.
As her research interests have become more focused, she has begun to study the impact of HIT on chronic kidney disease (CKD). She has been awarded a K23 award by the NIDDK to develop and test an HIT tool which calculates risk of progression to end stage renal disease and prompts referral from primary care to specialist care. As PI or co-Investigator on several NIH-funded grants, she has laid the groundwork for the proposed research by 1) developing a software application that can calculate the 5-year risk of developing kidney failure using clinical data, 2) validating the clinical data extracted from electronic health records, 3) becoming an expert in automated data extraction from EHRs, and 4) performing qualitative research to improve the acceptance of the tool in routine primary care.