Spring 2027

Foundations of Data-driven Healthcare

Introduces informatics principles and techniques to support clinical research and quality improvement using data-driven approaches, covering EHR data, study design, and regulatory compliance.

no offering record for this termCredits unavailable
Recorded instructors · Spring 2027 No instructors listed

Summary

1 / 3

Recent recorded grades — Spring 2024: 3.94 GPA, 100.0% A/AB (n=27 letter grades); Spring 2025: 3.76 GPA, 80.0% A/AB (n=25 letter grades); Spring 2026: 3.73 GPA, 93.5% A/AB (n=31 letter grades).

What you’ll be able to do

  • Utilize informatics-based tools in translational research including locate relevant informatics tools; select appropriate informatics tools; and use those tools in research for managing and analyzing biomedical and health information.

  • Describe the essential functions of the major clinical systems ( e.g., the EHR-- (electronic health record and its feeder systems, such as radiology and lab) that are relevant to healthcare analytics and quality improvement and the challenges to using these data for research.

  • Explain the role of health information technology standards in the interoperability of research, clinical, and administrative information systems and on secondary use of data for Clinical Translational Research or CTS.

  • Describe the essential information generation, management, analysis, transformation, summarization, and visualization methods that apply to healthcare analytics and quality improvement, such as clinical data; imaging data; consumer and patient reported data; and population-level and environmental exposure data.

  • Identify and describe available methods for handling large-scale data for machine learning and medical language processing.

  • Illustrate the nature of the contributions in consulting and/or collaborating with biomedical and health informaticians throughout the life cycle of clinical and translational research projects: use the terminology and principles of biomedical informatics; discriminate among the different subdomains of biomedical informatics; and enumerate the roles of biomedical informatics specialists.

  • Identify how structure and organization of information in a domain can affect quality research foci through fundamental data categories and elements, terminologies and taxonomies, and ontologies.

  • Identify, retrieve, and manage biomedical and health science knowledge through literature searches using advanced search techniques (MeSH, PubMed, Google Scholar, etc.).

  • Discuss the fundamental principles and practices that address the ethical, legal, social, privacy, and security implications of bio- medical and health informatics.

Source: UW Guide

Grade history

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average GPA
letter grades
A
AB
B
BC
C
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F

All recorded terms · compare terms & instructors

Prerequisites

Course map

Graduate/professional standing

  • Graduate/professional standing
BMI 573

This is a best-effort interpretation; check the catalog requirements above.

Prerequisite text tree
  • Graduate/professional standing

Professors

Spring 2027

No instructors recorded for this selection.

Historical instructors & teaching patterns

Recorded history may be incomplete and does not establish a future schedule.

Calendar & sections

Spring 2027

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SectionModeEnrolled / capacityWaitlist

Times are Central. Select a meeting for details; export includes recorded dates for the selected sections.

Meeting source records

No records available.

Student experience

the class

Recent recorded grades — Spring 2024: 3.94 GPA, 100.0% A/AB (n=27 letter grades); Spring 2025: 3.76 GPA, 80.0% A/AB (n=25 letter grades); Spring 2026: 3.73 GPA, 93.5% A/AB (n=31 letter grades).

difficulty & workload

No workload feedback recorded.

Topics

  • Information systems for study design, regulatory compliance, and EHR data management.
  • Functions and challenges of major clinical systems like EHRs, radiology, and lab systems.
  • Health IT standards, system interoperability, and secondary data use for Clinical Translational Research.
  • Ethical, legal, social, privacy, and security implications of biomedical and health informatics.

Skills

  • Locate, select, and use informatics-based tools to manage and analyze biomedical and health information.
  • Collect, acquire, secure, store, transfer, process, and analyze electronic health records data for research.
  • Generate, manage, analyze, transform, summarize, and visualize clinical, imaging, patient-reported, and population-level data.
  • Identify and describe methods for handling large-scale data for machine learning and medical language processing.

Grades

Latest available · Spring 2026— not enough history to project Spring 2027.

average GPA
A / AB grades
letter grades
Instructor

Grade distribution · % of letter grades

A
AB
B
BC
C
D
F

Grades over time

Through Spring 2026

More grade details Grade mix, volume & source data

Where this course fits relative to

Latest available grades · Spring 2026 · all course levels

GPA

Higher than % of other courses in this group.

Course GPAs · red marks this course’s range

letter grades

More recorded grades than % of other courses in this group.

Typical course in this group: letter grades.

About this comparison

1280 courses over the same term, each with at least 30 recorded letter grades. Cross-listed courses count once. GPA is not a measure of difficulty or teaching quality. The typical course is the median by recorded grade count; tied values are not counted as lower. Grade counts describe course scale, not unique students or typical section size.

Descriptions compare GPA with this group’s average: at least 0.20 higher or lower; otherwise close to average. Section size uses median recorded enrollment: small up to 30, mid-sized 31–99, large 100+. Lectures and discussion/lab sections are described separately.

Sources & history

UW–Madison

Catalog & offerings

Descriptions, prerequisites, and recorded course offerings.

Catalog observation history

Observations at scan time; dates do not imply when a catalog change took effect.

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Selected offering source records

No offering records for the selected term.

Raw records 
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Student reviews

Original comments behind the course and instructor summaries.

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Madgrades

Grade history

Recorded grade distributions by term, section, and instructor.

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Dataset & model

Recorded 2026-10-14

View the dataset on Hugging Face
Model outputs & technical records
Qwen/Qwen3.8-27B
LLM outputs across runs
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Full model traces

Recorded model configuration, reasoning, and tool conversations.

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Model & dataset provenance 
{
  "model": "Qwen/Qwen3.8-27B",
  "model_revision": "1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0",
  "task_version": "17",
  "output_id": "f42f9c4f0c662b25c432b1b300d86dc4daf71c640308c4b2cd708714dbea7046",
  "requirements_status": "valid",
  "dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
  "observed_at": "2026-10-14 17:50:09.728220+00:00"
}