Health Sciences Informatics, PhD

Health Sciences Informatics, PhD

The PhD in Health Sciences Informatics offers the opportunity to participate in groundbreaking research projects in clinical informatics and data science at one of the world’s finest biomedical research institutions. In keeping with the traditions of the Johns Hopkins University and the Johns Hopkins Hospital, the PhD in Health Sciences Informatics program seeks excellence and commitment in its students to further the prevention and management of disease through the continued exploration and development of health informatics, health IT, and data science. Resources include a highly collaborative clinical faculty committed to research at the patient, provider, and system levels. The admissions process is highly selective and finely calibrated to complement the expertise of faculty mentors.

Areas of research:

• Standard Terminologies
• Precision Medicine Analytics
• Population Health Analytics
• Clinical Decision Support
• Translational Bioinformatics
• Health Information Exchange (HIE)
• Multi-Center Real-World Data
• Telemedicine

Individuals wishing to prepare themselves for careers as independent researchers in health sciences informatics, with applied experience in informatics across the entire health/healthcare life cycle, should apply for admission to the PhD in Health Sciences Informatics program.

Admission Criteria

Applicants to the PhD in Health Sciences Informatics program will be considered with the following types of degrees and qualifications:

• MA, MS, MPH, MLIS, MD, PhD, or other terminal degree, with relevant technical and quantitative competencies and evidence of scholarly accomplishment; or
• In exceptional circumstances, BA or BS, with relevant technical and quantitative competencies, with some combination of scholarly accomplishment and/or professional experience in a relevant field (e.g., biomedical research, data science, public health, etc.)

Relevant fields include: medicine, dentistry, veterinary science, nursing, ancillary clinical sciences, public health, librarianship, biomedical science, bioengineering and pharmaceutical sciences, and computer and information science. An undergraduate minor or major in information or computer science is highly desirable. Professional work experience in one of these fields is also very advantageous. 

The PhD in Health Sciences Informatics application is made available online through the Johns Hopkins School of Medicine Graduate School's Admissions website. Applicants should track submission of supporting documentation through the admissions portal. By December 15, the following documentation must be submitted to the admissions online portal:

• Curriculum Vitae (including list of peer-reviewed publications and scientific presentations)
• Three letters of recommendation
• Statement of purpose
• Official transcripts from undergraduate and any graduate studies
• Certification of terminal degree
• A portfolio of published research, writing samples, and/or samples of website or system development

If you have questions about your qualifications for the PhD in Health Sciences Informatics program, please contact JHInformatics@jhu.edu.

Program Requirements

The PhD in Health Sciences Informatics curriculum is highly customized based on the student's background and needs. Students are able to select from a variety of courses and milestones will be developed in partnership with the student's advisor and the PhD in Health Sciences Informatics Program Director.

The PhD in Health Sciences Informatics curriculum is founded on four high-level principles:

• Achieving a balance between theory and research, and between breadth and depth of knowledge
• Creating a curriculum around student needs, background, and goals
• Teaching and research excellence
• Modeling professional behavior locally and nationally.

Individualized curriculum plans are developed to build proficiencies in the following areas:

• Foundations of biomedical informatics: e.g., lifecycle of information systems, decision support
• Information and computer science: e.g., software engineering, programming languages, design and analysis of algorithms, data structures.
• Research methodology: research design, epidemiology, and systems evaluation; mathematics for computer science (discrete mathematics, probability theory), mathematical statistics, applied statistics, mathematics for statistics (linear algebra, sampling theory, statistical inference theory, probability); ethnographic methods.
• Implementation sciences: methods from the social sciences (e.g., organizational behavior and management, evaluation, ethics, health policy, communication, cognitive learning sciences, psychology, and sociological knowledge and methods), health economics, evidence-based practice, safety, quality.
• Specific informatics domains: clinical informatics, public health informatics, analytics
• Practical experience: experience in informatics research, experience with health information technology.

Course List

Code	Title	Credits
Foundation
ME.250.854	Health Sciences Informatics Mentored Research 1	1 - 18
ME.800.811	Introduction to Responsible Conduct of Research	1
ME.250.860	Student Seminar and Grand Rounds 2	1

Course List

Code	Title	Credits
Core Courses
Core Informatics Research
Complete these 6 courses in Year 1
ME.250.861	Health Science Informatics Research Methods I	3
ME.250.862	Health Sciences Informatics Research Methods II	3
ME.250.863	Health Sciences Informatics Research Methods III	3
ME.250.864	Health Sciences Informatics Research Methods IV	3
PH.700.604	Methods in Bioethics	3
PH.340.606	Methods for Conducting Systematic Reviews and Meta-Analyses	4
Core Applied Informatics
Complete 5 in Year 1
ME.250.953	Introduction to Biomedical Informatics	3
ME.250.955	Applied Clinical Informatics	3
ME.250.771	Introduction to Precision Medicine Data Analysis	3
ME.250.782	Observational Health Research Methods on Medical Records	3
ME.250.952	Leading Change Through Health IT	3
ME.250.777	Clinical Decision Analysis	3
Core Data Science Methods
Complete 4 in Year 1 and 4 in Year 2
ME.250.957	Database Querying in Health	3
ME.250.770	Clinical Data Analysis with Python	3
PH.140.651	Methods in Biostatistics I	4
PH.140.652	Methods in Biostatistics II	4
PH.140.653	Methods in Biostatistics III	4
PH.140.654	Methods in Biostatistics IV	4
PH.140.611	Statistical Reasoning in Public Health I	3
PH.140.612	Statistical Reasoning in Public Health II	3
PH.140.621	Statistical Methods in Public Health I	4
PH.140.622	Statistical Methods in Public Health II	4
PH.140.623	Statistical Methods in Public Health III	4
PH.140.624	Statistical Methods in Public Health IV	4
PH.140.646	Essentials of Probability and Statistical Inference I: Probability	4
PH.140.647	Essentials of Probability and Statistical Inference II: Statistical Inference	4
PH.140.648	Essentials of Probability and Statistical Inference III: Theory of Modern Statistical Methods	4
PH.140.649	Essentials of Probability and Statistical Inference IV	4
PH.140.620	Advanced Data Analysis Workshop	2
PH.140.751	Advanced Methods in Biostatistics I	3
PH.140.752	Advanced Methods in Biostatistics II	4
PH.140.753	Advanced Methods in Biostatistics III	4
PH.140.754	Advanced Methods in Biostatistics IV	4
PH.140.664	Causal Inference in Medicine and Public Health I	4
PH.140.665	Causal Inference in Medicine and Public Health II	3
PH.340.751	Epidemiologic Methods 1	5
PH.340.752	Epidemiologic Methods 2	5
PH.340.753	Epidemiologic Methods 3	5
PH.340.754	MHS Culminating Experience in Analytic Epidemiology	3
PH.340.600	Stata Programming I (Basic)	2
PH.340.776	Study Design and Analysis for Causal Inference With Time-Varying Exposures	3
PH.340.725	Methods for Clinical and Translational Research	1
PH.340.728	Advanced Methods for Design and Analysis of Cohort Studies	5
PH.410.615	Research Design in the Social and Behavioral Sciences	3
EN.553.636	Introduction to Data Science	3
EN.601.675	Machine Learning	3
EN.601.682	Machine Learning: Deep Learning	4

Course List

Code	Title	Credits
Selective Group 1 (Other BIDS domains)
Complete 4 in Year 2
ME.250.901	HSI: Knowledge Engineering and Decision Support	3
ME.250.750	Design Discovery for Healthcare	3
ME.250.783	Imaging Informatics	3
ME.250.778	Implementing Fast Healthcare Interoperability Resources	3
ME.250.782	Observational Health Research Methods on Medical Records	3
ME.250.788	Observational Research Methods in R	3
AS.410.736	Genomic and Personalized Medicine	4
EN.580.428	Genomic Data Visualization	3
PH.140.636	Scalable Computational Bioinformatics	4
ME.250.784	Clinical Decision Support (CDS) Application Interoperability	3
ME.250.755	Natural Language Processing in the Health Sciences	3
PH.390.750	Introduction to Clinical Research	2

Course List

Code	Title	Credits
Selective Group 2 (Clinical informatics methods)
Complete 4 in Year 2
ME.250.755	Natural Language Processing in the Health Sciences	3
ME.250.957	Database Querying in Health	3
ME.250.770	Clinical Data Analysis with Python	3
ME.250.783	Imaging Informatics	3
PH.140.751	Advanced Methods in Biostatistics I	3
PH.140.752	Advanced Methods in Biostatistics II	4
PH.140.753	Advanced Methods in Biostatistics III	4
PH.140.754	Advanced Methods in Biostatistics IV	4
PH.140.664	Causal Inference in Medicine and Public Health I	4
PH.140.665	Causal Inference in Medicine and Public Health II	3
ME.250.784	Clinical Decision Support (CDS) Application Interoperability	3
PH.410.615	Research Design in the Social and Behavioral Sciences	3
EN.601.475	Machine Learning	3
EN.601.482	Machine Learning: Deep Learning	4
PH.340.728	Advanced Methods for Design and Analysis of Cohort Studies	5
PH.340.776	Study Design and Analysis for Causal Inference With Time-Varying Exposures	3
PH.340.725	Methods for Clinical and Translational Research	1

Course List

Code	Title	Credits
Selective Group 3 (Public health informatics)
Complete 4 in Year 2
ME.250.782	Observational Health Research Methods on Medical Records	3
PH.309.631	Population Health Informatics	3
PH.309.620	Managed Care and Health insurance	3
PH.309.635	Population Health: Analytic Methods and Visualization Techniques	3
PH.309.716	Advanced Methods in Health Services Research: Analysis	3
PH.309.712	Assessing Health Status and Patient Outcomes	3
PH.309.616	Introduction to Methods for Health Services Research and Evaluation I	2
PH.311.615	Quality of Medical Care	3
PH.301.615	Seminar in Health Disparities	3
PH.306.655	Ethical Issues in Public Health	3
PH.306.670	Issues in LGBTQ Health Policy	3
PH.313.643	Health Economics	3
PH.318.623	Social Policy for Marginalized and Disenfranchised Populations in the U.S.	3
PH.300.650	Crisis and Response in Public Health Policy and Practice	3
PH.300.715	Advanced Research and Evaluation Methods in Health Policy	4
PH.312.633	Health Management Information Systems	3
PH.305.684	Health Impact Assessment	3
PH.317.605	Methods in Quantitative Risk Assessment	4
PH.340.770	Public Health Surveillance	3
PH.340.728	Advanced Methods for Design and Analysis of Cohort Studies	5
PH.340.701	Epidemiologic Applications of Gis	2
PH.221.645	Large-scale Effectiveness Evaluations of Health Programs	4
PH.601.731	Spatial Analysis for Public Health	4

¹ Repeat at least 10 times (i.e., 8 quarters and 2 summers).

² Repeat at least 8 times (i.e., 8 quarters).

Notes

Courses are offered during Quarters 1, 2, 3, 4, and Summer. Course credits are listed as quarter credits; the conversion rate to semester credits is .5 semester credits. For example, a 3 quarter credit Q1 course is 1.5 semester credits.

PhD in Health Sciences Informatics students may be permitted to take other courses in the Selective Groups, as seen fit by their mentor and approved by the PhD in Health Sciences Informatics Program Director.

Other Requirements

Other requirements include: Qualifying Exam (in Year 2); Proposal Defense (in Year 2 or 3); Dissertation Research (Years 2-5); and Final Dissertation Defense (Year 4-5). Additionally, students must fulfill an annual ethics requirement by participating in a departmental Responsible Conduct of Research training session each year starting year 2, and an annual Individual Development Plan (IDP) requirement.

Learning Outcomes

Graduates in the PhD in Health Sciences Informatics program will be able to:

• Select, compare, and evaluate appropriate quantitative and qualitative research designs for biomedical informatics projects 
• Appraise and critique the advantages and disadvantages of different data science and statistical methods in analyzing clinical and social data sources 
• Critically assess biomedical informatics policies and guidelines (e.g., ethical, privacy, security) intended to improve outcomes among all patients 
• Demonstrate the ability to engage in a productive research career, including conference presentations, peer-reviewed publications, and grant writing 
• Demonstrate the ability to support teaching and provide valuable educational experience to biomedical informatics students