CV ➤
Biosketch ➤
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I am a faculty member at the Johns Hopkins Bloomberg School of Public Health, the department of Health Policy and Management. I have a joint appointment at the Johns Hopkins School of Medicine, the Section of Biomedial Informatics and Data Science.

I am the co-director of the Johns Hopkins Center for Population Health IT (CPHIT), which focuses on advancing the use of IT in various areas of population health. Our research at CPHIT is often translated into pragmatic IT and analytic solutions that affect real-world population health management programs and risk stratification tools such as the Johns Hopkins ACG, which is widely used in the U.S. and around the world.

Within the context of population health IT, my personal research focuses on the application of informatics solutions to advance the science of population health analytics. Some examples of my research include: evaluating the added-value of new sources of data (e.g., EHR data instead of claims) in population health analytics; assessing challenges of data quality on population health studies; and, utilizing health information exchange infrastructure to develop population health analytic platforms (e.g., linking new data sources/types, and centralizing risk stratification efforts).

I have published multiple peer-reviewed publications, authored several book chapters, and prepared various reports for the federal government. I serve on the editorial boards of JAMIA Open, Medical Care, and Population Health Management journals, and regularly review manuscripts for high-impact journals. The American Medical Informatics Association's symposium and Academy Health's annual research meeting are the typical venues that I present my research. I am an elected fellow of the American College of Medical Informatics (FACMI), and a fellow of the American Medical Informatics Association (FAMIA).

I have developed several courses in health informatics. I was the Co-PI of an ONC award to develop a national curriculum for population health informatics and train more than 9000 healthcare professionals nationally. I am currently the director of the DrPH Informatics track program at the Johns Hopkins School of Public Health, and the director of the PhD program in Health Informatics at the Johns Hopkins School of Medicine.

You can find my CV, research interests, publications, teaching activities, and bibliometrics on this website. You can also find additional information on my JHSPH-HPM's web page as well as JHSoM-BIDS's website. Please email me if you need any additional information.

Hadi Kharrazi (►) (Pic)


Population health is a complex interdisciplinary domain.

My current research focuses on the application and evaluation of informatics solutions within the context of population health. This emerging and rapidly growing domain of research and development is called "Population Health Informatics" (PHI). See this paper and this chapter for additional information about PHI.

A key role of PHI is to improve the population health analytic cycle, which starts with data collection, followed by data preparation, data mining, model development & validation, knowledge sharing, and finally closing the loop by a learning health system that applies these models and feeds new data back in the loop (see diagram below).

I am specifically interested to assess the opportunities and challenges of integrating non-traditional data sources (e.g. EHR data, social determinants of health data) to improve population health analytics (focusing on stage #1 to #4 of the diagram).

diagram depicting various stage of the population health analytics

Here is the list of the population health informatics sub-domains and my research contributions to each of them (selected list of publications – see my CV for the full list):

( 1 ) Identifying New Data Sources
Finding external data sources that can be merged with a given population-level data source is often a burdensome task. Systematically reviewing these data sources and developing "data catalogs" that can make them readily available to population health researchers is an approach to overcome this problem. Funded by AHRQ and NIH, we devised a framework on how to systematically review data sources (not publications), what data specs should be coded, and how the data should be presented. Our work was focused on data sources used in Obesity and Suicide Prevention studies.

( 2 ) Measuring and Comparing Data Quality
Population health analytic often deals with real-world data, thus data quality issues (e.g., completeness, accuracy, timeliness, provenance) can affect the results and their generalizability to various healthcare settings. In addition, data quality will be vital to the integrity of population health analytic in the near future as the field is moving toward using new sources of data (e.g., EHRs, HIEs) that have varied levels of quality. My work has focused on challenges in using EHR data for clinical phenotyping as well as using HIE data to predict hospital readmissions. My work on cleaning BMI data (e.g., weight and height) in large datasets, and assessing the reliability of geo-driven social determinants of health is currently underway.

( 3 ) Extracting Novel Data Types
A major contribution of PHI to population health analytic is providing new methods to extract novel types of data from various data sources. For example, the free-text of EHR includes ample information about individual patients that can be used in risk stratification efforts; however, these extra information are often missing in encoded fields of EHRs. In a prior study we assessed the value of free-text in identifying geriatric syndromes (which are predictable for utilization). We also assessed how the physician's mention of frailty in the free-text is associated with this information. We are currently evaluating the added-value of free-text for social determinants of health.

( 4 ) Advancing Predictive Modeling Techniques
Perhaps the most vital impact of informatics in population health analytic is to assess the added-value of novel data sources/types in improving model performance. We have studied the value of EHR data (structured diagnostic and medication data) compared to insurance claims. This study became the foundation for EHR-derived risk stratification integration. We then studied the value of comparing EHR prescription data to claims' filling data to measure patient's adherence and using it as a predictor for utilization. Using the geriatric frailty data generated by previous studies (see #3), we assessed the value of such free-text-derived frailty markers in improving utilization prediction. And, we also evaluated the added-value of common laboratory results in improving risk stratification models. Our work on integrating vital signs (e.g., BMI and blood pressure) in our predictive models are in-press. We are planning to integrate social determinants of health and other novel data sources in the near future.

( 5 ) Learning Health System & Policy Implications
PHI is often affected by health policy and organizational changes. Our prior research resulted in defining the PHI domain, and identifying the high priority R&D efforts. We also edited a special issue on PHI (and community health informatics) to expand the idea of PHI. Recently we have predicted the adoption of higher functions of EHRs among hospitals which will be key to deploying population health management solutions.

More about PHI's current challenges and future opportunities can be found here:

What is PHI?

Ongoing (PI or Co-PI)
  • Addressing Suicide Research Gaps: Understanding Mortality Outcomes
    NIMH 1R01MH124724 | ~$3200k | PI
  • Developing and Assessing the Validity of Claims-based Indicators of Frailty & Functional Disabilities and Testing their Use in EHRs and linked EHR-claims
    AHRQ ACTION-IV RFTO#2 | ~$500k | PI
  • Assessing Disparities in Occurrence and Outcomes of Type 2 Diabetes ADEs Using Claims and EHRs
    FDA CERSI | ~$750k | PI (Co-PI: Weiner)
  • Novel Predictive Model for Risk stratification of Congestive Heart Failure Patients at Discharge
    JHSPH TDAF 2019 | $50k | PI
  • Case-identification of persons with ADRD: A methods study to compare diagnoses in structured and unstructured electronic health record data
    NIA Hopkins’ Economics of Alzheimer’s Disease & Services (HEADS) Center | $45k | PI
  • Behavioral, Social and Systems Science: Extracting Social Science Data from Epic Electronic Health Record System [Phase III]
    JHSoM ICTR BSSS Nexus Award | ~$100k | PI
Completed (PI or Co-PI)
  • Addressing Suicide Research Gaps: Understanding Mortality Outcomes in the Mid-Atlantic Region
    NIMH 1R56-MH117560-01 | ~$490k | PI (Co-PI: Wilcox)
  • Integrating the Geo-Social Predictive Model Platform in ACGs
    Johns Hopkins Healthcare Solutions | ~$400k | PI
  • Analytical Framework to Project BMI Trajectory for VHA Veterans at the Population Level [Phase-IV]
    VHA IPA | ~$255k | PI
  • Geo-Social [Risk Prediction and Visualization] Analytic Platform (GSAP)
    DST Health Solutions | ~$325k | PI
  • Helping Older adults improve their Medication Experience [HOME]
    JHSPH TDAF C14627 | $25k | PI
  • Developing and Testing an Approach for the Integration of Population and Patient Level Information in Support of Patient Centered Primary Care at the VHA
    VHA PACT Evaluation | ~$175k | Co-PI (PI: Weiner)
  • Baltimore Falls Reduction Initiative Engaging Neighborhoods and Data [B’FRIEND]
    Multiple Funding Sources | ~$100k | PI
  • The Development and Testing of the Frailty Component of a Novel EHR-Based Geriatric e-risk Measure for Predictive Modeling
    Atrius Health | ~$25k | Co-PI (PI: Weiner)
  • Developing Next Generation EHR-Supported Predictive Modeling: Developing the Johns Hopkins e-ACG System
    eACG Development | ~$2000k | Co-PI (PI: Weiner)
  • Behavioral, Social and Systems Science: Extracting Social Science Data from Epic Electronic Health Record System [Phase II]
    JHSoM ICTR BSSS Nexus Award | ~$65k | PI
  • Behavioral, Social and Systems Science: Extracting Social Science Data from Epic Electronic Health Record System [Phase I]
    JHSoM ICTR BSSS Nexus Award | ~$65k | PI
  • Workforce Development Programs: Population Health IT
    ONC WF-WF-15-300 | ~$975k | Co-PI (PI: Lehmann)
  • Analytical Framework to Project BMI Trajectory for VHA Veterans at the Population Level [Phase-III]
    VHA IPA | ~$250k | PI
  • Community-wide HIE- based Hospital Readmission Risk Prediction and Notification System
    AHRQ R21-HS022578 | ~$300k | PI
  • Evaluation of Stage 3 Meaningful Use Objectives among Eligible Hospitals
    AHRQ ACTION-II RFTO32 | ~$650k | PI
  • Derivation and evaluation of a hospital readmission risk-prediction model based on Maryland’s Health Information Exchange
    JHSPH Faculty Innovation Funding | ~$35k | PI
  • Developing an Obesity Trajectory Population‐based Risk Prediction Model [Phase-II]
    VHA IPA | ~$150k | PI (Co-PI: Weiner)
  • Framework for a VHA Population Health Program [Phase-I]
    VHA IPA | ~$70k | PI (Co-PI: Weiner)
  • Clinical Knowledge Hub - Conceptual Integration of Rules, Data Sets, and Queries
    NLM R01-LM009897 | ~$495k | PI (Co-PI: Schadow)
  • Interdisciplinary Curriculum Development: Fundamentals of Clinical Care for Health Informatics
    IU CET Award | ~$10k | PI
  • Using Health Games to Improve Treatment Adherence in Patients with Type 1 Diabetes
    IU RTR Award | ~$15k | PI
Others (Co-investigator)
  • Please see my CV for other ongoing or completed grants/projects that I was not the primary PI or Co-PI (i.e., served as one of the co-investigators).
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LearnHIT ➤

Current Courses
JHU Course Catalog ➤

Retired or Ad-hoc Courses
  • Advances in HIT and Preventative Medicine Applications (JHU Graduate)
  • Web Analytics for Public Health (JHU Graduate)
  • Concepts, Theories and Current Trends in Health Informatics (JHU Graduate)
  • Clinical Decision Support Systems (IUPUI Graduate)
  • Fundamentals of Clinical Care in Health Informatics (IUPUI Graduate)
  • Health Information Exchange (IUPUI Graduate)
  • Foundation of Health Informatics (IUPUI Graduate)
  • Web-Database Concepts in Health Informatics (IUPUI Graduate)
  • Clinical Information Systems (IUPUI Graduate)
  • Electronic Information Management (Dalhousie Undergrad)
  • Clinical Decision Support Systems (Dalhousie Graduate)

Invited Sessions
  • Research and Evaluation Methods for Health Policy (JHU Graduate)
  • Fundamentals of Health Policy and Management (JHU Undergrad)
  • Digital Health for Medical Students (JHU Graduate)
  • Health Information Systems: Design to Development (JHU Graduate)

ONC Curriculum
  • Led the design and development of Component #21 (Population Health Informatics) of ONC's national health IT curriculum.
  • Developed the website to host the entire ONC curriculum in a usable format (300+ hrs of lecture).
ONC Curriculum ➤

Johns Hopkins: Advisor/Mentor (Current)
  • Victor Bagwell (DrPH)
  • Keane Tzong (DrPH)
  • Paulina Sosa (DrPH)
  • Prince Baawuah (DrPH)
  • Haoying Echo Wang (DrPH)
  • Jennifer Chase (DrPH)
  • Priyanka Dua Sood (DrPH)
  • John Harry Munroe (MPH)
  • Anjali Srivastava (MPH)
  • Zachary Goldberg (MPH)
  • Shidin Balakrishnan (MPH)
  • Yuko Takiyoshi (MPH)
  • Masatoshi Ishikawa (MPH)
  • Amyna Husain (MPH)

Johns Hopkins: Advisor/Mentor (Graduated)
  • Carin Hitchens (MPH)
  • Hana Schwartz (MPH)
  • Wei Chen (Epi MSc)
  • Joseph Mercado (DHSI MSc)
  • Gahyun Bahn (MPH)
  • Cristina Mannie (MPH)
  • Xiaomeng Ma (DHSI MSc)
  • Ariel Caldwell (MPH Epi/Biostat)
  • Kristen Harvey (MPH)
  • Takako Kanakubo (MPH-MBA)
  • Jimmy Small (MPH)
  • Afifah Handayani (MPH)
  • Kathryn Esper (MPH)
  • Virna Sales (MPH)
  • Janani Veluchamy (MPH)
  • Sarah Youn (MPH)
  • Brett Cropp (DHSI MSc)
  • Jeffrey Balsewicz (MPH)
  • Matthew Swain (MPH)
  • Stella Ukaoma (MPH)
  • Oscan Minosco y de Cal (MPH)
  • Shmuel Goldberg (MPH)
  • Senyo Norgbey (DHSI MSc)
  • Stella Liang (DrPH - transferred)
  • Rebecca Hyde (HPM PhD)

Johns Hopkins: Other Roles (Current/Graduated)
  • Elizabeth Bess Nieto (DrPH)
  • Ting Helen He (DHSI PhD)
  • Eunkyung Eileen Han (DrPH)
  • Ahmed Elsayed (DrPH)
  • Shanshan Song (DHSI PhD)
  • Ebele Okoli (DrPH)
  • Ben Hamlin (DrPH)
  • Matt Castner (DrPH)
  • Elyse Lasser (DrPH)
  • Bowen Li (DHSI PhD)
  • Supharerk Pai Thawillarp (DrPH)
  • Harlan Pittell (HPM PhD)
  • Sarah Green Gensheimer (HPM PhD)
  • Lindsey Ferris (DrPH)
  • Paul Messino (DrPH)
  • Ashimiyu Durojaiye (DHSI PhD)
  • Roza Vazin (HPM PhD)
  • Joy Lee (HPM PhD)
  • Olatunde Animashaun (DrPH)
  • Remle Newton-Dame (DrPH)
  • Ritu Doijad (DHSI MSc)
  • Gan Shi (DHSI MSc)
  • Colleen Line (MPH)
  • Susan Whitfield (DHSI MSc)
  • Allen Cassandra (DHSI MSc)
  • Angela Chen (DHSI MSc)
  • James Graziano (MPH)
  • Mayank Patel (DHSI MSc)
  • Adler Archer (DHSI MSc)
  • Allyson Helmer (MPH)
  • Damilola Onasanya (MPH)
  • Yash Deo (MPH)
  • Kevon Jackman (PostDoc)
  • Kelly Searle (PostDoc)
  • Claudia Nau (PostDoc)
  • Kirill Dyagilev (PostDoc)
  • Cindy Cai (MD Fellowship)
  • Aly Straus (MD Fellowship)
  • Majid Shafiq (MD Fellowship)
  • Christopher Britt (MD Fellowship)
  • Daniel Shenker (Math BSc)
  • Ashley Li (BME BSc)
  • Anand Bery (MD)
  • Laura Anzaldi (MD)
  • Morgan Opie (CDC Fellowship)
  • Kathryn Foti (Epi PhD)
  • Sonia Sarkar (DrPH)

Not Johns Hopkins: Various Roles (Graduated)
  • Suheila Sawesi (PhD @ IU)
  • Maryam Zolnoori (PhD @ UWM)
  • Amir Karami (PhD @ UMBC)
  • Tamara Locke Blue (MSc @ IU)
  • Sung Park (MSc @ IU)
  • Stu Morton (PhD @ IU)
  • Wilfred Portillo (MSc @ IU)
  • Lynn Vincz (MSc @ IU)
  • Uche Unogu (MSc @ IU)
  • Nick Adam Stepp (MSc @ IU)
  • Fardad Gharghabi (MSc @ IU)
  • Wei Kong (MSc @ IU)
  • Prathik Gadde (MSc @ IU)
  • Heather Coates (MSc @ IU)
  • Christine BenMessaoud (MSc @ IU)