PhD Program

The Doctor of Philosophy in Health Informatics consists of 70 credits which focus on research and culminate in a dissertation. This degree is the best fit for those who desire to pursue a career in academia or research.

PhD students have the unique opportunity to work with our faculty in a myriad of research endeavors. This research enables students to publish papers at national conferences and in well-established health informatics journals, so that they are qualified to pursue their own research interests after graduation.

Students also have a choice to pursue a minor in a related field in order to capitalize on the interdisciplinary nature of health informatics.

Please note this plan of study was updated in September 2014 to reflect the Fall 2014 requirements. Your requirements may be different depending on which semester you started the program. Please see the student handbook for more information.

PhD Program Information

Clinical Informatics Track

Clinical Informatics Track

The Clinical Informatics track provides instruction and training for students interested in clinical applications methods and applications. The curriculum includes instruction in health data and coding, systems analysis, human-computer interaction, current informatics research, and current applications such as decision support systems, natural language processing, and predictive modeling. Additionally, students learn biostatistical methods, relational database theory and practice, analytics and data science methodologies, consumer health informatics, and interprofessional practice. Electives supplement individual student interests in areas such as computer programming, health data management, health care finance, and public and population health (with scope to include person-empowered participation and inter-professional engagement). Courses use a mixture of theoretical and applied subject matter to provide a solid grounding in current informatics thinking and practice.

 Students who pursue the Clinical Informatics track must complete the Health Informatics MS degree en route to completing the PhD. Students must consult with the program to coordinate completion of coursework and other requirements for the Health Informatics MS, the Health Informatics PhD, and the Clinical Informatics track. Students who have an MS in Health Informatics from a comparable program may be exempt from this requirement in whole or in part, subject to program review and approval.

Course Number Course Name Credits Semesters
HINF 5430 Foundations of Health Informatics I 3 Fall
HINF 5431 Foundations of Health Informatics II 3 Spring
HINF 5436 AHC Informatics Grand Rounds (x2) 2 credits total Fall, Spring
HINF 5440 Foundations of Translational Bioinformatics 3 Spring
HINF 5496 Internship in Health Informatics 3-6 Fall, Spring, Summer
HINF 5510 Applied Health Care Databases 3 Fall
HINF 5520 Informatics Methods for Health Care Quality, Outcomes, and Patient Safety 2 Spring
HINF 5531 Health Care Analytics and Data Science 3 Spring
HINF 8430 Foundations of Health Informatics I Lab 2 Fall
HINF 8431 Foundations of Health Informatics II Lab 2 Spring
HINF 8440 Foundations of Translational Bioinformatics Lab 2 Spring
HINF 8525 Health Informatics Teaching 2 Occasional
HINF 8888 Doctoral Thesis Credits 24 Fall, Spring, Summer
NURS 5116 Consumer Health Informatics 4 Fall
NURS 7108 Population Health Informatics 2 Fall
PUBH 6450 Biostatistics I 4 Fall, Spring
PUBH 6451 Biostatistics II 4 Spring
Electives   2-5  
Total   70  

Data Science and Informatics for Learning Health Systems Track

Data Science and Informatics for Learning Health Systems Track

The Data Science and Informatics for Learning Health Systems track builds on the highly regarded data science program offered jointly by the School of Engineering, School of Public Health, and School of Statistics. It also takes advantage of School of Nursing's breadth of nursing and health informatics courses. It requires students to fulfill the requirements of the Masters in Data Science program and use their elective courses to gain exposure to health sciences and health care in the form of a suite of required foundational courses: Foundations of Health Informatics I and Lab, Foundations of Translational Bioinformatics I and Lab and the US Health Care System offered by the Institute for Health Informatics. The MS capstone project will address a research question related to health sciences or healthcare. Specialization to the health care field intensifies at the PhD level by offering additional courses focusing on advanced analytics and its applications to healthcare. The thesis research will naturally relate to health science or healthcare.

Students who pursue the Data Science and Informatics for Learning Health Systems track are expected to earn the University’s Data Science MS degree en route to completing the PhD. Students must consult with the program to coordinate completion of coursework and other requirements for the Data Science MS, the Health Informatics PhD, and the Data Science and Informatics for Learning Health Systems track. Credits earned in the University’s Data Science MS program may be used to fulfill required courses or elective credits in the Data Science and Informatics for Learning Health Systems track, subject to program approval. Students who have an MS in Data Science from a comparable program may be exempt from this requirement in whole or in part, subject to program review and approval.

See the Data Science Program for current requirements.

Courses for the MS in Data Science

Statistics- 6 credits

  • Theory of Statistics I & II STAT 5101/5102 (4/4)
  • Applied Regression STAT 5302 (4)
  • Time Series Analysis STAT 5511 (3)
  • Applied Multivariate Methods STAT 5401 (3)
  • Computing and Generalized Linear Models STAT 8051 (3)
  • Introduction to Bayesian Analysis PUBH 7440 (3)

Algorithm- 6 credits

  • Introduction to Machine Learning CSCI 5521 (3)
  • Introduction to Data Mining CSCI 5523 (3)
  • Machine Learning CSCI 5525 (3)
  • Statistical Learning and Data Mining PUBH 7475 (3)

Infrastructure- 6 credits

  • Introduction to Distributed Systems CSCI 5105 (3)
  • Introduction to Parallel Computing CSCI 5451 (3)
  • Principles of Database Systems CSCI 5707 (3)
  • Cloud Computing / Big Data (under development)

HINF required courses to be taken as electives- 9 credits
(at least 3 credits must be 8000-level)

  • Foundations of Health Informatics I HINF 5430 (3)
  • Foundations of Health Informatics I Lab HINF 8430 (2)
  • AHC Informatics Grand Rounds HINF 5436 x2 (2 total)
  • Foundations of Translational Bioinformatics HINF 5440 (3)
  • Foundations of Translational Bioinformatics Lab HINF 8440 (2)

Other

  • Data Science Capstone Credits (3)
 

Courses to take after completing the MS in Data Science

  • Health Informatics Teaching
  • Clinical Data Mining
  • Computational Causal Analytics
  • Applied Health Care Databases: Database Prinicples and Data Evaluation
  • Advanced Readings or Research in Health Informatics
  • Intership in Health Informatics
  • Thesis credits

Electives

  • Health Informatics II HINF 5431 (3)
  • Foundations of Health Informatics II Lab HINF 8431 (2)
  • Foundations of Biomedical Natural Language Processing HINF 5610 (3)
  • Data Visualization for the Health Sciences HINF 5620 (3)
  • Introduction to the Mathematics of Image and Data Analysis MATH 5467 (4)
  • Clinical Decision Support: Theory NURS 7113 (2)
  • Issues in Environmental Health PUBH 6102 (2)
  • Operations Research and Quality in Health Care PUBH 6560 (3)
  • Decision Analysis for Health Care PUBH 6717 (2)
  • Principles of Management in Health Services Organizations PUBH 6751 (2)
  • Continuous Quality Improvement: Methods and Techniques PUBH 6765 (3)
  • Advanced Methods in Health Decision Science PUBH 6809 (3)
  • Data and Information for Population Health Management PUBH 6814 (2)
  • Cost-Effectiveness Analysis in Health Care PUBH 6862 (3)
  • Public Health Systems Analysis and Design PUBH 6876 (2)
  • Epidemiologic Methods I PUBH 6341 (3)
  • Advanced Longitudinal Data Analysis PUBH 8452 (3)
  • Advanced Survival Analysis PUBH 8462 (3)
  • Spatial Biostatistics PUBH 8472 (3)

Translational Bioinformatics Track

Translational Bioinformatics Track

The Translational Bioinformatics track bridges genomics and bioinformatics to precision medicine through its methods and techniques development and innovation that directly relate to the study of basic biological science and diseases. The computational methods related to genomics, epigenomics, transcriptomics, proteomics, metabolomics and pharmacogenomcis are included, which build the connection of molecular findings and phenotypes to characterize disease susceptibility or determine disease markers, and predict response to treatment and prognosis. The program offers three specialized areas: structural and functional genomics, microbiomics and metagenomics, and cancer genomics.

Students pursuing the Translational Bioinformatics track are expected to earn the Health Informatics MS degree en route to completing the PhD. Students must consult with the program to coordinate completion of coursework and other requirements for the Health Informatics MS, the Health Informatics PhD, and the Translational Bioinformatics track. Students who have an MS in Health Informatics from a comparable program may be exempt from this requirement in whole or in part, subject to program review and approval.

Course Number Course Name Credits Semesters
HINF 5430 Foundations of Health Informatics I 3 Fall
HINF 5436 AHC Informatics Grand Rounds (x2) 2 total Fall, Spring
HINF 5440 Foundations of Translational Bioinformatics 3 Spring
HINF 5496 Internship in Health Informatics 3 Fall, Spring, Summer
HINF 5650 Integrative Genomics and Computational Methods 3 Spring
HINF 8220 Computational Causal Analytics 3 Spring
HINF 8430 Foundations of Health Informatics I Lab 2 Fall
HINF 8440 Foundations of Translational Bioinformatics Lab 2 Spring
HINF 8492 Advanced Readings and Research 3 Fall, Spring, Summer
HINF 8525 Health Informatics Teaching 2 Fall, Spring
HINF 8888 Doctoral Thesis Credits 24 Fall, Spring, Summer
BIOC 8007 Molecular Biology of DNA 2 Fall
BIOC 8008 Molecular Biology of RNA 2 Fall
CSCI 5421 Advanced Algorithms and Data Structures 3 Fall, Spring
CSCI 5525 Machine Learning 3 Fall, even years
STAT 8051 Advanced Regression Techniques: Linear, Nonlinear and Nonparametric Methods 3 Fall
STAT 8052 Applied Statistical Methods 2: Design of Experiments and Mixed-Effects Modeling 3 Spring
Electives   4  
Total   70  

 

Electives:

  • HINF 5431 - Foundations of Health Informatics II
  • HINF 8431 - Foundations of Health Informatics II Lab
  • HINF 5450 - Foundations of Precision Medicine Informatics
  • HINF 5610 - Foundations of Biomedical Natural Language Processing
  • MEDC 5245 - Introduction to Drug Design
  • PHAR 6224 - Pharmacogenomics: Genetic Basis for Variability in Drug Response
  • PUBH 7415 - Introduction to Clinical Trials
  • PUBH 7420 - Clinical Trials: Design, Implementation, and Analysis
  • PUBH 8445 - Statistics for Human Genetics and Molecular Biology
  • STAT 8053 - Applied Statistical Methods 3: Multivariate Analysis and Advanced Regression

Precision and Personalized Medicine Informatics Track

Precision and Personalized Medicine Informatics Track

The Precision and Personalized Medicine Informatics track provides a didactic program for students training in informatics who will develop specialized knowledge in precision informatics methods applied to personal and population health-focused problems. The scope of this track includes social determinants of health and inter-professional research and expertise. Students will develop skills in quantitative methods and biomedical sciences for their application to precision medicine. In addition, students will gain an understanding of medical and biological science to provide needed context on which to apply informatics methods.

Students who pursue the Precision and Personalized Medicine Informatics track are expected to earn the Health Informatics MS degree en route to completing the PhD. Students must consult with the program to coordinate completion of coursework and other requirements for the Health Informatics MS, the Health Informatics PhD, and the Precision and Personalized Medicine Informatics track. Students who have an MS in Health Informatics from a comparable program may be exempt from this requirement in whole or in part, subject to program review and approval.

Course Number Course Name Credits Semesters
HINF 5430 Foundations of Health Informatics I 3 Fall
HINF 5436 AHC Informatics Grand Rounds (x2) 2 total Fall, Spring
HINF 5440 Foundations of Translational Bioinformatics 3 Spring
HINF 5450 Foundations of Precision Medicine Informatics 3 TBD
HINF 5496 Internship in Health Informatics 3 Fall, Spring, Summer
HINF 5510 Applied Health Care Databases: Database Principles and Data Evaluation 3 Fall
HINF 5520 Informatics Methods for Health Care Quality. Outcomes, and Patient Safety 2 Spring
HINF 5531
HINF 5630
Health Data Analytics and Data Science OR
Clinical Data Mining
3 Spring
Fall
HINF 8430 Foundations of Health Informatics I Lab 2 Fall
HINF 8440 Foundations of Translational Bioinformatics Lab 2 Spring
HINF 8492 Advanced Readings and Research 3 Fall, Spring, Summer
HINF 8525 Health Informatics Teaching 2 Fall, Spring
HINF 8888 Thesis Credits 24 Fall, Spring, Summer
PHAR 6224 Pharmacogenomics: Genetic Basis for Variability in Drug Response 2 Spring
PHAR 7401 Fundamentals of Biostastical Inference 4 Fall
PHAR 7402 Biostatistics Modeling and Methods 4 Spring
Electives   5  
TOTAL   70  


Electives

  • HINF 5431 - Foundations of Health Informatics II
  • MATH 5652 - Introduction to Stochastic Processes
  • MATH 5445 - Mathematical Analysis of Biological Networks
  • PUBH 7430 - Statistical Methods for Correlated Data
  • PUBH 7440 - Introduction to Bayesian Analysis
  • PUBH 7445 - Statistics for Human Genetics and Molecular Biology
  • PUBH 8432 - Probability Models for Biostatistics
  • PUBH 8442 - Bayesian Decision Theory and Data Analysis
  • PUBH 8445 - Statistics for Human Genetics and Molecular Biology
  • PUBH 8446 - Advanced Statistical Genetics and Genomics
  • STAT 5511 - Time Series Analysis
  • STAT 5401 - Applied Multivariate Methods