Data science in laboratory medicine certificate program - NEW

Program Description

Every year, laboratorians produce billions of test results — valuable data that can unlock smarter workflows, more efficient lab operations, and better patient outcomes.

This new certificate program provides essential data science training tailored for laboratory professionals, with a focus on solving real-world challenges in lab medicine. It's designed to set you apart in a field that is increasingly driven by data and analytics.

Stay ahead of the curve. Transform data into insight.

Target audience

Target audiences include physicians, laboratory supervisors, laboratory directors, laboratory assistant directors, laboratory managers, laboratory technologists, point-of-care coordinators, pathologists, toxicologists, fellows, and trainees.

Learning objectives

• Explain core principles of data science, statistics, machine learning, data standards, and data governance as they apply to clinical laboratory and pathology practice.
• Develop and prepare laboratory data for analysis by formulating appropriate data requests, organizing and cleaning datasets, and addressing common real-world data quality challenges.
• Apply and interpret analytical, statistical, and visualization methods to analyze relationships, compare groups, and communicate findings using laboratory data.
• Evaluate and implement data-driven and machine learning solutions in laboratory medicine by applying best practices for validation, reporting, data security, and ethical data use.

Modules & faculty

1. How data science can support laboratorians
   Patrick Mathias, MD, PhD, UW Medicine Pathology Content Lead
   • What is data science?, Categories of data science, Applying computational thinking to laboratory medicine problems, and Tackling diverse laboratory problems.
2. Best practices for laboratory data validity
   Michelle Stoffel, MD, PhD, University of Minnesota
   • Key definitions, Clinical data standards and models, and Data governance.
3. Data analysis planning and data requests
   Michelle Stoffel, MD, PhD, University of Minnesota
   • Data analysis in the laboratory, Defining roles in data requests, Developing a data request, Extracting and validating the data, and Delivering and using the data.
4. Mitigating laboratory data problems
   Robert Benirschke, PhD, MS, DABCC, FADLM, Northshore University Health System
   • Data structures and transformations, Data integrity and accuracy, and Data completeness.
5. Laboratory data visualization
   Shannon Haymond, PhD, MSPA, Lurie Children's Hospital of Chicago
   • Data visualization fundamentals, Common types of plots, and Design considerations for effective visualization.
6. Statistics for laboratory medicine
   Anthony Killeen, MD, MSc, PhD, University of Minnesota
   • Data distributions, Comparing groups, Hypothesis testing, Correlation, and Linear regression.
7. Fundamentals of machine learning applications
   Christopher McCudden, PhD, DABCC, FCACB, The Ottawa Hospital
   • Categories of machine learning, Developing machine learning models, and Machine learning applications in laboratory medicine.
8. Evaluating machine learning-based applications
   Shannon Haymond, PhD, MSPA, Lurie Children's Hospital of Chicago
   • Formulating a problem, Collecting and preparing data, Validating and selecting models, Interpreting and explaining models, and Reproducibility.
9. Best practices for healthcare data security
   Patrick Mathias, MD, PhD, UW Medicine Pathology
   • Privacy and security considerations, Why do we protect data?, How do we protect data?, and Best practices for securing lab data. 

Disclosures

The Association for Diagnostics & Laboratory Medicine (formerly AACC) is dedicated to ensuring balance, independence, objectivity, and scientific rigor in all educational activities. All participating planning committee members and faculty are required to disclose to the program audience any financial relationships related to the subject matter of this program. Disclosure information is reviewed in advance in order to manage and resolve any possible conflicts of interest. The intent of this disclosure is to provide participants with information on which they can make their own judgments.

The following faculty reported financial relationships:

• Shannon Haymond
  • Board: Roche Diagnostics
• Anthony Killeen
  • Honorarium: Univants
• Christopher McCudden
  • Honorarium: Roche

The following planners and faculty reported no relevant financial relationships:

• Robert Benirschke
• Patrick Mathias
• Michelle Stoffel

Content validity

All recommendations involving clinical medicine are based on evidence accepted within the profession of medicine as adequate justification for their indications and contraindications in the care of patients; AND/OR all scientific research referred to or reported in support or justification of a patient care recommendation conforms to generally accepted standards of experimental design, data collection, and analysis.

Accreditation statement

This activity is approved for 9.0 ACCENT^® continuing education credits. Activity ID #4490. This activity was planned in accordance with ACCENT Standards and Policies.

Successful completion statement

Verification of Participation certificates are provided to registered participants based on completion of the activity, in its entirety, and the activity evaluation. The evaluation link will be emailed to the participants after all work within ADLM’s learning platform is complete. For questions regarding continuing education, please email [email protected].

Methods of support

This educational activity is sponsored by indigo bioAutomation.

Program Launch Year:2026