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GVSU College of Computing

Health Informatics and Bioinformatics, M.S

Program Overview

  • Intersection of healthcare, biology, computer science, information science, and statistics
  • Leverages biomedical data, information, and knowledge to support scientific research and healthcare advancements
  • Facilitates scientific inquiry, problem-solving, and decision-making, with the goal of improving human health and biological discoveries
  • Professional Science Master’s Program
    • Combines advanced technical training with professional skills to prepare graduates for leadership roles in healthcare and bioinformatics

Health Informatics and Bioinformatics Master's Program at Grand Valley State University

Coursework

The curriculum includes:

  • specific courses for critical content in Health Informatics and Bioinformatics
  • laboratory experiences essential for the development of requisite skill and knowledge sets
  • a common seminar series

Program Overview

M.S. Health Informatics & Bioinformatics Program Information

Characteristics of this Professional Science Master program include:

  • Interdisciplinary studies
  • No thesis required
  • Involve both the university and its industry partners
  • Emphasis on teamwork, problem-solving, communication
  • Scientific knowledge and technical skills
  • Designed to integrate university coursework with hospital, business, and industrial internships to better prepare students for the variety of career paths associated with the life science and health science industry

Final Internship and Capstone

All HIB students must complete an internship as part of their Professional Science Master's curriculum. More information on finding an internship and getting credit for it is at our PSM website.

Please contact Chris Babbitt, our internship partner coordinator, at [email protected], Professor Holli Reyes, and the Career Center for assistance in resume building, interviewing, and finding an internship.

HIB students will also complete the Health Informatics and Bioinformatics Capstone course (CIS 691).

Other Information

What is the difference between the two concentrations?

Health Informatics

Health Informatics is a field that bridges healthcare and technology, focusing on the efficient management of health data and digital systems to improve patient care and medical decision-making. By integrating data science, information systems, and biomedical research, health informatics enhances healthcare delivery and operational efficiency. Professionals in this field contribute to the development and implementation of electronic health records, clinical decision support systems, and telemedicine platforms, ensuring seamless and secure healthcare processes. As defined by the U.S. National Library of Medicine, “health informatics is the interdisciplinary study of the design, development, adoption, and application of IT-based innovations in healthcare services delivery, management, and planning.” (U.S. National Library of Medicine, n.d.).

Bioinformatics

Bioinformatics combines biology, computer science, and statistical analysis to interpret complex biological data. It plays a crucial role in fields such as genomics, proteomics, and personalized medicine, where vast amounts of biological information require computational tools for organization and analysis. This discipline supports advancements in medical research, drug discovery, and genetic studies by leveraging machine learning, artificial intelligence, and big data analytics. According to the National Center for Biotechnology Information (NCBI), “bioinformatics is an interdisciplinary field that develops methods and software tools for understanding biological data, particularly when the data sets are large and complex.” (NCBI, n.d.).

AMIA Academic Forum

GVSU's Health Informatics and Bioinformatics Program is a member of AMIA's Academic Forum. Learn more about the Academic Forum. 

Academic Forum logo

Student Achievement Data

Reporting period: 2025-26 academic year. Last updated: October 28, 2026.

The M.S. in Health Informatics and Bioinformatics publishes the following measures so that prospective students and the public can make informed decisions about the program. The program reviews and updates these measures every year after the fall census. Unless otherwise stated, figures cover the 2025-26 academic year (Fall 2025, Winter 2026, and Summer 2026).

Admissions

Measure, 2025-2026

Number

Rate

Applications received

98

n/a

Applicants admitted

68

69% of applicants

Admitted students who enrolled

20

29% of admitted applicants

Source: GVSU Office of Institutional Analysis, GVSU Graduate Student Profile, Health and Bioinformatics. https://reports.ia.gvsu.edu/gradprof/gradprof.php

Enrollment

Term measurement 

Fall 2022

Fall 2023

Fall 2024

Fall 2025

Students enrolled

53

69

66

50

Source: GVSU Office of Institutional Analysis, Program Summary for MS in Health and Bioinformatics, Total Enrollment. https://reports.ia.gvsu.edu/pgmsumm/

Retention

Of new students entering in Fall 2022, Fall 2023, and Fall 2024, 93%, 96%, and 100%, respectively, were still enrolled in the program the following fall.

Source: GVSU Office of Institutional Analysis, Program Summary for MS in Health and Bioinformatics, New Student Characteristics. https://reports.ia.gvsu.edu/pgmsumm/

Retention

Entering cohort

Students

One-year retention

Two-year retention

2021-2022

34

58.8%

50%

2022-2023

38

89.5%

78.9%

2023-2024

40

92.5%

87.5%

2024-2025

8

100%

not yet available

Source: GVSU Registrar, Retention and Graduation Rates for Graduate Programs, 2026 edition, Health and Bioinformatics. Retention counts a student as retained if they have graduated or remain enrolled. https://www.gvsu.edu/registrar/graduation-rates-164.htm

Time to degree completion

The M.S. in Health Informatics and Bioinformatics is a 36-credit program with an official length of two years / four semesters of full-time study. Students who graduated between July 1, 2025, and June 30, 2026, completed the degree in an average of 2.23 years (40 graduates). Part-time students take longer by design.

Source: GVSU Office of Institutional Analysis, average time to degree, 2025-26 graduates; GVSU Registrar, program duration record HBIN.

Graduation rates

Entering cohort

Students

Graduated within two years

Graduated within three years

2021-2022

34

32.4%

52.9%

2022-2023

38

31.6%

81.6%

2023-2024

40

40%

not yet available

Source: GVSU Registrar, Retention and Graduation Rates for Graduate Programs, 2026 edition, Health and Bioinformatics. Rates are cumulative. https://www.gvsu.edu/registrar/graduation-rates-164.htm

Employment after completion

Measure

Number

Rate

Graduates, 2025-2026 academic year

41

n/a

Graduates whose status the program confirmed

25

61% of graduates

Employed in the field

24

96% of confirmed

Continuing to doctoral study

1

4% of confirmed

Status not yet confirmed

16

39% of graduates

The program contacts every graduate after completion through the graduate exit survey, direct follow-up, and professional networking profiles. The employment rate is calculated as graduates employed divided by graduates whose status the program was able to confirm. Graduates who did not respond are reported separately and not counted as unemployed. Positions held by recent graduates include Clinical Information Systems Analyst, Clinical Data Analyst, Data Analyst, Clinical Research Coordinator, and Research Data Technician, at employers including Bronson Healthcare, Corewell Health, HealthWest, Van Andel Institute, the Michigan Health Information Network, and university research centers.

Source: HIB program graduate outcomes records, 2025-26 graduates, de-identified.

The university's First Destination Survey, administered by the GVSU Career Center, provides an independent measure for the prior year. Of 2024-2025 graduates who responded (17 respondents), 76.5% reported being employed, 70.6% full-time and 5.9% part-time.

Source: GVSU Career Center, First Destination Survey, 2024-2025 graduates, via the Office of Institutional Analysis.

Source: GVSU Catalog, Health Informatics and Bioinformatics, M.S. https://www.gvsu.edu/catalog/program/master-of-science-in-health-informatics-and-bioinformatics.htm

Recent Publications

  • Bousbih, G., & Sawesi, S. (2026). From smear to screen: Harnessing machine learning and deep learning to transform automated malaria diagnosis: A systematic review. In Proceedings of the Fifth International Conference on Innovations in Computing Research (ICR’26) (Vol. 2074, No. 26, pp. 348–358). Springer Nature. https://doi.org/10.1007/978-3-032-32636-2_30
  • Irungu, M., Sawesi, S., Rashrash, M., Presingu, P., Kane, K., Schommer, J., & Brown, L. (2026). Predicting pharmacy choice for managed care network design. Journal of Managed Care & Specialty Pharmacy, 32(6), 679–690. https://doi.org/10.18553/jmcp.2026.32.6.679
  • Jadhav, A., Sawesi, S., & Rashrash, B. (2024). Bias and generalizability challenges in machine learning models for leptospirosis. In 2024 IEEE International Conference on Big Data (BigData) (pp. 4989–4995). IEEE. https://doi.org/10.1109/BigData62323.2024.10825588
  • Khan, M. O. F., Rashrash, M., Sawesi, S., & Anam, F. (2025). Key findings and recommendations from the American Association of Colleges of Pharmacy Curriculum Quality Survey of U.S. pharmacy school faculty. American Journal of Pharmaceutical Education, 89(4), Article 101391. https://doi.org/10.1016/j.ajpe.2025.101391
  • Liu, Y.-T., Triche, T. J., Jr., & DeBruine, Z. J. (2025). Scalable non-negative matrix factorization of the Human Cell Census reveals interpretable transcriptional programs [Preprint]. bioRxiv. https://doi.org/10.64898/2025.12.30.697085
  • Opiyo, D., & Sawesi, S. (2026). Machine learning-based prediction of heart disease using a merged UCI dataset and web application. In Proceedings of the Fifth International Conference on Innovations in Computing Research (ICR’26) (Vol. 2074, No. 26, pp. 369–385). Springer Nature. https://doi.org/10.1007/978-3-032-32636-2_32
  • Presingu, P., Sawesi, S., Irungu, M., Kane, K., Rashrash, M., Schommer, J., & Brown, L. (2025). Consumer data insights on pharmacy utilization: Comparative study of 2015 and 2021 surveys. Journal of the American Pharmacists Association, Article 102964. https://doi.org/10.1016/j.japh.2025.102964
  • Sawesi, S., Dolezel, D. M., Presingu, P., & Irungu, M. (2026). Value, structure, and curriculum in US graduate health informatics programs: Cross-sectional study. JMIR Medical Education, 12, e87479. https://doi.org/10.2196/87479
  • Sawesi, S., Jadhav, A., & Rashrash, B. (2025). Machine learning and deep learning techniques for prediction and diagnosis of leptospirosis: Systematic literature review. JMIR Medical Informatics, 13, e67859. https://doi.org/10.2196/67859
  • Sawesi, S., Mansour, S., & Olajide, F. (2025). Enhancing health informatics education through virtual reality-based training: A crossover study. In EDULEARN25 proceedings: 17th International Conference on Education and New Learning Technologies (pp. 2725–2734). IATED. https://doi.org/10.21125/edulearn.2025.0763
  • Sawesi, S., Sabbineni, H., Shagamreddy, R. R., & Rashrash, B. (2026). Cybersecurity and privacy risks of generative AI mental-health chatbots: A systematic review and regulatory framework. Journal of Multidisciplinary Healthcare. https://doi.org/10.2147/JMDH.S581251
Page last modified November 10, 2026