Professor of Medical Informatics

A Professor of Medical Informatics occupies one of the more demanding — and intellectually varied — faculty roles in the health sciences. The job requires simultaneous competence in teaching clinical informatics concepts to graduate students, running an externally funded research program, navigating relationships with affiliated health systems, and staying current enough with a field that changes faster than most academic disciplines to remain credible at conferences.

The teaching load at a research-focused institution typically runs two to three courses per year — graduate seminars in topics like EHR data architecture, HL7 FHIR standards, clinical NLP, or health outcomes informatics. But the course work is the visible minority of the workload. The research enterprise — grant writing, student supervision, data access negotiations with health system partners, manuscript revision cycles — consumes the majority of a productive faculty member's time.

Health informatics research almost always requires access to patient data, which means IRB approvals, data use agreements, and often a formal partnership with a hospital or integrated delivery network. Building and maintaining those relationships is as much a part of the job as the analysis itself. Faculty who treat health system partners transactionally rarely get second datasets.

At medical school-affiliated departments, the faculty mix often includes clinician-investigators with joint MD and informatics credentials who hold clinical appointments alongside their faculty role. This creates productive collaboration and sometimes friction — the research timelines of a full-time PhD faculty member and a clinician with a half-time faculty appointment rarely align naturally.

The administrative dimension — curriculum committee service, accreditation self-studies, graduate admissions review, grant review panels at NIH study sections — is real and grows with rank. Associate and Full Professors are expected to carry more of this load, and it competes directly with the research time that produced the promotion in the first place.

For candidates who genuinely want to shape how the next generation of health informaticists thinks about data, evidence, and clinical implementation, the role offers a platform that few other careers provide.

Education:

• PhD in biomedical informatics, health informatics, computer science, or a related field (required at R1 institutions)
• MD, DO, or clinical PhD with formal informatics training (valued at medical school departments)
• Postdoctoral fellowship in clinical or translational informatics (increasingly expected at competitive R1 programs)

Certifications and professional credentials:

• American Board of Preventive Medicine (ABPM) board certification in Clinical Informatics — not universally required but competitive advantage at medically affiliated departments
• AMIA 10x10 program completion for candidates transitioning from clinical backgrounds
• IRB Principal Investigator certification (typically institutional, completed before first independent study)

Research and technical skills:

• EHR data models: OMOP CDM, i2b2, PCORnet; familiarity with extraction and phenotyping workflows
• Health data standards: HL7 FHIR, SNOMED CT, ICD-10-CM, LOINC, CDA/CCDA
• Programming: Python or R for health data analysis; SQL for clinical data warehouse queries; experience with SPARQL or ontology tools is a plus
• Clinical NLP: tools like cTAKES, MetaMap, and transformer-based models (BioBERT, ClinicalBERT) for unstructured clinical text
• Study design: observational research methods, causal inference, or clinical trial design depending on research area
• Grant mechanisms: NIH R01, R21, K awards, AHRQ R18/R01, PCORI pipeline award structures

Teaching and mentorship:

• Prior teaching experience as an instructor of record or graduate teaching assistant
• Evidence of effective doctoral mentorship — graduated students, first-author publications with students
• Curriculum design experience, particularly in updating courses for rapidly evolving technology domains

Soft skills that distinguish strong candidates:

• Patience for long institutional timelines (data access negotiations, IRB amendments, grant resubmissions)
• Comfort communicating across audiences — clinical, technical, and policy stakeholders rarely share vocabulary
• Persistence through the manuscript rejection and grant resubmission cycles that define academic research

The academic job market in medical informatics is tighter than the industry job market by a significant margin, but the field is in better shape than most humanities or social science disciplines and considerably healthier than it was a decade ago.

Federal investment is a major driver. NIH's National Library of Medicine has steadily expanded its informatics funding portfolio, and the 21st Century Cures Act and subsequent interoperability regulations have created a policy environment that makes health data research both more feasible (more standardized data) and more urgent (more systems producing it). PCORI, AHRQ, and NSF supplement NLM funding in ways that give informatics researchers more grant pathways than many biomedical fields.

Demand for informatics training is growing at the graduate level. Health systems are hiring data scientists, informaticists, and EHR analysts at a rate that outpaces the supply of formally trained graduates, which is creating pressure on academic programs to expand and on departments to add faculty. Programs that did not exist ten years ago are now accredited and growing — which creates faculty positions that have no historical baseline.

At the same time, the academic market for any tenure-track position in the health sciences is genuinely competitive. Strong candidates typically hold a postdoc, have at least one first-author paper in a respected informatics journal, and ideally have demonstrated early grant success through a K award, foundation funding, or a co-investigator role on an active R01.

The AI inflection point is creating a specific submarket. Departments and medical schools are actively recruiting faculty with expertise in clinical AI evaluation, algorithmic fairness in health systems, and foundation model applications in clinical settings. Candidates who can credibly bridge machine learning methods and clinical validity are receiving multiple offers. This demand is expected to remain elevated through at least the late 2020s as health systems grapple with deploying and governing AI tools in live clinical environments.

For faculty who establish a funded research program and graduate strong doctoral students, the career is stable and professionally rewarding. The salary ceiling is real — a Full Professor at a major medical school with a funded lab earns well, but not what a comparable technical leader earns in industry — but the autonomy, the intellectual scope, and the ability to shape a field through students make the trade-off worthwhile for those who are suited to it.

Dear Search Committee,

I am applying for the Assistant Professor position in Medical Informatics at [University]. I completed my PhD in Biomedical Informatics at [University] in May and am currently finishing a postdoctoral fellowship at the [Institute], where I have been working on EHR-based phenotyping of patients with early-stage heart failure using OMOP CDM data from a twelve-hospital network.

My dissertation developed a pipeline for identifying undiagnosed atrial fibrillation in clinical notes using a fine-tuned ClinicalBERT model, achieving 0.89 F1 on a held-out validation cohort. That work is under review at JAMIA, and a methods paper describing the annotation framework was published last year in the Journal of Biomedical Informatics. I have presented this research at AMIA Annual Symposium in back-to-back years and received feedback that directly shaped two grant aims I am now developing.

On the teaching side, I served as the primary instructor for a 15-student graduate seminar on clinical data standards during my second postdoc year. I designed the FHIR module from scratch after finding that existing course materials hadn't kept pace with the R4 specification or the CMS interoperability rules. Student evaluations averaged 4.7 out of 5.0, and three students from that seminar have since contacted me about doctoral programs.

I am preparing an NIH K99/R00 application targeting a July submission, with specific aims focused on scalable phenotyping methods for rare cardiovascular conditions using federated data networks. I would welcome the opportunity to discuss how that research agenda aligns with [University]'s existing clinical partnerships and departmental priorities.

Thank you for your consideration.

[Your Name]