AI for Improved Patient Outcomes

1-Day Course: Offered Online and In-Person

Build and evaluate AI and predictive modeling tools in medicine.

Johns Hopkins University Certificate of Completion
Maximum of 6.25 AMA PRA Category 1 Credits
Course designed and presented by Johns Hopkins faculty member Daniel Byrne
Real-world relevant content and use cases

JHU Staff and Faculty May Qualify for Tuition Remission

Who Should Attend?

Physician-scientists eager to build and test AI tools
Biomedical informatics professionals
Biostatisticians interested in AI
Nursing leaders and researchers.
Chief Medical Informatics Officers
Healthcare executives and administrators evaluating AI vendors
Entrepreneurs in the AI healthcare space

Upcoming Dates and Offerings

August 20, 2025: In Person, Baltimore – $1,470

September 24, 2025: Online Live – $1,365

January 14, 2026: In Person, Baltimore – $1,470

April 17, 2026: Online Live – $1,365

You will be redirected to Hopkins Medicine

Artificial intelligence has the potential to revolutionize patient care by improving outcomes, reducing clinician workload, and delivering tangible, evidenced-based improvements.

It also risks financial losses, wasted resources, and even harm to patient safety through misapplication or poor validation.

Providers MUST critically evaluate AI tools and cut through marketing hype to ensure AI investments genuinely advance healthcare effectiveness.

In this full-day course, offered online and in-person, Professor Daniel Byrne gives you actionable strategies for development and validation of AI tools, emphasizing statistical rigor and addressing common challenges such as regression to the mean, label leakage, and misinterpreted accuracy metrics.

Through detailed case studies—including successful applications like predicting blood clots in pediatric patients—you’ll gain immediate, practical insights into integrating AI into clinical workflows. Grasp the principles required for conducting effective, pragmatic randomized controlled trials and identify clinically valuable AI applications aimed at enhancing patient safety and reducing clinician burnout.

Providers, executives, administrators, and researchers will leave with a clear path to implementing effective, evidence-based AI strategies that elevate patient care and optimize clinical efficiency.

Acquire Essential Knowledge in AI in Healthcare

Develop practical skills to apply AI effectively in clinical and administrative settings, enhancing both patient outcomes and professional advancement.
Critically evaluate emerging AI technologies in healthcare by distinguishing evidence-based innovations from overstated claims.
Identify key barriers to AI implementation in medicine and apply structured, realistic strategies for successful integration within healthcare systems.
Design and execute pragmatic, randomized controlled trials to rigorously assess the clinical effectiveness and impact of AI tools.
Draw actionable insights from real-world case studies to avoid common pitfalls and accelerate effective, evidence-based AI adoption in healthcare.

Meet Your Instructor

Daniel Byrne, with more than 40 years of AI development and testing experience across various medical domains, brings unparalleled expertise to this course. As the former director of artificial intelligence research at Vanderbilt University, he has mentored hundreds of physician-scientists and earned numerous teaching awards.

His extensive background in pragmatic randomized controlled trials and biostatistics ensures a rigorous and practical learning experience. For the past 25 years, he was a faculty member in the Department of Biostatistics at Vanderbilt. Byrne holds a bachelor’s degree in biology and computer science from the State University of New York at Albany and a master’s degree in biostatistics from New York Medical College. He is the author of more than 160 scientific papers and two books: Publishing Your Medical Research and Artificial Intelligence for Improved Patient Outcomes – Principles for Moving Forward with Rigorous Science.

Payment Options and JHU Tuition Remission

Full-time, benefits-eligible Johns Hopkins University faculty or staff members are eligible to receive tuition remission if:

you are a full-time, benefits-eligible faculty or staff member who has been employed by JHU for at least 120 days
you continue in a full-time position while enrolled in courses

You receive 100% remission if:

your department certifies that the course benefits your professional development
your department is willing to assume the cost if you do not attend the course or if you cancel with less than one week’s notice. The one-week cancellation notice is waived only for emergencies with written documentation.

ELIGIBILITY: Full-time Johns Hopkins University faculty and staff qualify for tuition remission after the employee completes 120 days of full-time employment at the university. Please visit the benefits website for information and requirements.

Visiting faculty and staff, residents, interns, postdoctoral fellows, retirees, and dependents are not eligible for tuition remission.

There is an annual limit of 2 classes per calendar year for noncredit professional development courses taken at JHU. For more information on this policy, please refer to the HR website.

You will receive a link to the tuition remission form via email to start the process of gaining tuition remission.

Accreditation Statement / Credit Designation Statement

The Johns Hopkins University School of Medicine is accredited by the Accreditation Council for Continuing Medical Education (ACCME) to provide continuing medical education for physicians.

CREDIT DESIGNATION STATEMENT
The Johns Hopkins University School of Medicine designates this live activity for a maximum of 6.25 AMA PRA Category 1 CreditsTM. Physicians should claim only the credit commensurate with the extent of their participation in the activity.

Policy on PRESENTER and PROVIDER Disclosure
It is the policy of the Johns Hopkins School of Medicine that the presenter and provider globally disclose conflicts of interest. The Johns Hopkins School of Medicine OCME has established policies in place to identify and mitigate relevant conflicts of interest prior to this educational activity. Detailed disclosure will be made prior to presentation of the education.

OTHER CREDITS
The Johns Hopkins University has approved this activity for 6.25 contact hours for non-physicians.

How to Obtain Credit / Internet CME Policy

Post activity, an online evaluation will be available to attendees to evaluate the activity and individual presentations and to identify future educational needs. Upon completion of the evaluation, the learner must attest to the number of hours in attendance. Credits earned will be added to the learner’s transcript and immediately available for print. The last day to access the evaluation and attest to your credits is 45 days after you have attended .
An outcome survey will be sent to all learners within two months post activity to assist us in determining what impact this activity had on the learner’s practice.

The Office of Continuing Medical Education (OCME) at the Johns Hopkins School of Medicine is committed to protecting the privacy of its members and customers. Johns Hopkins School of Medicine OCME maintains its internet site as an information resource and service for physicians, other health professionals and the public. OCME at the Johns Hopkins School of Medicine will keep your personal and credit information confidential when you participate in a CME Internet-based program. Your information will never be given to anyone outside of the Johns Hopkins School of Medicine CME program. CME collects only the information necessary to provide you with the services that you request.

AI for Improved Patient Outcomes

Upcoming Dates and Offerings

August 20, 2025: In Person, Baltimore – $1,470

September 24, 2025: Online Live – $1,365

January 14, 2026: In Person, Baltimore – $1,470

April 17, 2026: Online Live – $1,365

You will be redirected to Hopkins Medicine