Bio

Bio


Suzanne Tamang is based at the Center for Population Health Sciences She received her Ph.D. in Computer Science from the City University of New York and completed her postdoctoral training at the Stanford's Center for Biomedical Bioinformatics.

At Stanford, Suzanne's collaborations span the Alcoa Research Consortium, the Clinical Excellence Research Center and the Stanford Cancer Institute. She is also affiliated with the Department of Rheumatology at UCSF.

Academic Appointments


Boards, Advisory Committees, Professional Organizations


  • Research Core, Stanford Center for Population Health Sciences (2017 - Present)
  • Research Associate, University of California, San Francisco (2016 - Present)

Professional Education


  • Postdoctoral Training, Stanford School of Medicine, Biomedical Informatics (2015)
  • Doctor of Philosophy, Graduate Center, City University of New York (CUNY), Computer Science (2013)
  • Master of Science, Brooklyn College, CUNY, Computer Science and Health Science (2006)
  • Bachelor of Science, Brooklyn College, CUNY, Biology

Publications

All Publications


  • Predicting patient 'cost blooms' in Denmark: a longitudinal population-based study. BMJ open Tamang, S., Milstein, A., Sørensen, H. T., Pedersen, L., Mackey, L., Betterton, J., Janson, L., Shah, N. 2017; 7 (1)

    Abstract

    To compare the ability of standard versus enhanced models to predict future high-cost patients, especially those who move from a lower to the upper decile of per capita healthcare expenditures within 1 year-that is, 'cost bloomers'.We developed alternative models to predict being in the upper decile of healthcare expenditures in year 2 of a sample, based on data from year 1. Our 6 alternative models ranged from a standard cost-prediction model with 4 variables (ie, traditional model features), to our largest enhanced model with 1053 non-traditional model features. To quantify any increases in predictive power that enhanced models achieved over standard tools, we compared the prospective predictive performance of each model.We used the population of Western Denmark between 2004 and 2011 (2 146 801 individuals) to predict future high-cost patients and characterise high-cost patient subgroups. Using the most recent 2-year period (2010-2011) for model evaluation, our whole-population model used a cohort of 1 557 950 individuals with a full year of active residency in year 1 (2010). Our cost-bloom model excluded the 155 795 individuals who were already high cost at the population level in year 1, resulting in 1 402 155 individuals for prediction of cost bloomers in year 2 (2011).Using unseen data from a future year, we evaluated each model's prospective predictive performance by calculating the ratio of predicted high-cost patient expenditures to the actual high-cost patient expenditures in Year 2-that is, cost capture.Our best enhanced model achieved a 21% and 30% improvement in cost capture over a standard diagnosis-based model for predicting population-level high-cost patients and cost bloomers, respectively.In combination with modern statistical learning methods for analysing large data sets, models enhanced with a large and diverse set of features led to better performance-especially for predicting future cost bloomers.

    View details for DOI 10.1136/bmjopen-2016-011580

    View details for PubMedID 28077408

    View details for PubMedCentralID PMC5253526

  • Enhanced Quality Measurment Event Detection EGEMS (Wash DC) Tamang, S. R., Hernandez-Boussard, T., Ross, E., Gaskin, G., Shah, N. H. 2017; 5 (1)

    View details for DOI 10.13063/2327-9214.1270

  • New Paradigms for Patient-Centered Outcomes Research in Electronic Medical Records: An Example of Detecting Urinary Incontinence Following Prostatectomy. EGEMS (Washington, DC) Hernandez-Boussard, T., Tamang, S., Blayney, D., Brooks, J., Shah, N. 2016; 4 (3): 1231-?

    Abstract

    National initiatives to develop quality metrics emphasize the need to include patient-centered outcomes. Patient-centered outcomes are complex, require documentation of patient communications, and have not been routinely collected by healthcare providers. The widespread implementation of electronic medical records (EHR) offers opportunities to assess patient-centered outcomes within the routine healthcare delivery system. The objective of this study was to test the feasibility and accuracy of identifying patient centered outcomes within the EHR.Data from patients with localized prostate cancer undergoing prostatectomy were used to develop and test algorithms to accurately identify patient-centered outcomes in post-operative EHRs - we used urinary incontinence as the use case. Standard data mining techniques were used to extract and annotate free text and structured data to assess urinary incontinence recorded within the EHRs.A total 5,349 prostate cancer patients were identified in our EHR-system between 1998-2013. Among these EHRs, 30.3% had a text mention of urinary incontinence within 90 days post-operative compared to less than 1.0% with a structured data field for urinary incontinence (i.e. ICD-9 code). Our workflow had good precision and recall for urinary incontinence (positive predictive value: 0.73 and sensitivity: 0.84).Our data indicate that important patient-centered outcomes, such as urinary incontinence, are being captured in EHRs as free text and highlight the long-standing importance of accurate clinician documentation. Standard data mining algorithms can accurately and efficiently identify these outcomes in existing EHRs; the complete assessment of these outcomes is essential to move practice into the patient-centered realm of healthcare.

    View details for DOI 10.13063/2327-9214.1231

    View details for PubMedID 27347492

    View details for PubMedCentralID PMC4899050

  • Detecting unplanned care from clinician notes in electronic health records. Journal of oncology practice / American Society of Clinical Oncology Tamang, S., Patel, M. I., Blayney, D. W., Kuznetsov, J., Finlayson, S. G., Vetteth, Y., Shah, N. 2015; 11 (3): e313-9

    Abstract

    Reduction in unplanned episodes of care, such as emergency department visits and unplanned hospitalizations, are important quality outcome measures. However, many events are only documented in free-text clinician notes and are labor intensive to detect by manual medical record review.We studied 308,096 free-text machine-readable documents linked to individual entries in our electronic health records, representing care for patients with breast, GI, or thoracic cancer, whose treatment was initiated at one academic medical center, Stanford Health Care (SHC). Using a clinical text-mining tool, we detected unplanned episodes documented in clinician notes (for non-SHC visits) or in coded encounter data for SHC-delivered care and the most frequent symptoms documented in emergency department (ED) notes.Combined reporting increased the identification of patients with one or more unplanned care visits by 32% (15% using coded data; 20% using all the data) among patients with 3 months of follow-up and by 21% (23% using coded data; 28% using all the data) among those with 1 year of follow-up. Based on the textual analysis of SHC ED notes, pain (75%), followed by nausea (54%), vomiting (47%), infection (36%), fever (28%), and anemia (27%), were the most frequent symptoms mentioned. Pain, nausea, and vomiting co-occur in 35% of all ED encounter notes.The text-mining methods we describe can be applied to automatically review free-text clinician notes to detect unplanned episodes of care mentioned in these notes. These methods have broad application for quality improvement efforts in which events of interest occur outside of a network that allows for patient data sharing.

    View details for DOI 10.1200/JOP.2014.002741

    View details for PubMedID 25980019

    View details for PubMedCentralID PMC4438112

  • Text Mining for Adverse Drug Events: the Promise, Challenges, and State of the Art DRUG SAFETY Harpaz, R., Callahan, A., Tamang, S., Low, Y., Odgers, D., Finlayson, S., Jung, K., LePendu, P., Shah, N. H. 2014; 37 (10): 777-790