Learn more about Carolyn Wong Simpkins, MD, PhD

Author Bio

Carolyn Wong Simpkins, MD, PhD

Carolyn Wong Simpkins is a physician, health technology executive, and health system transformation leader. She joined the leadership team at Health Catalyst as Chief Medical Informatics Officer to lead the development of medical content and accelerate the infusion of clinical insight its next-generation suite of products, and is also helping to shape machine learning algorithms so they can best be used to influence important care decisions. She brings to this role insights from her experiences practicing medicine in diverse settings, from academic medical centers to critical access hospitals and community health centers, combined with a keen understanding of federal health policy and its systemic implications, gleaned from her time on the staff of the U.S. House of Representatives Ways and Means Health subcommittee and her observations from working globally on health system transformation programs and solutions for the UK based British Medical Journal. She is passionate about data, technology, design and disrupting healthcare paradigms to improve health outcomes for all. Carolyn is a Fellow of the second class of the Liberty Fellowship and a member of the Aspen Global Leadership Network.

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Daniel Orenstein, JD
Carolyn Wong Simpkins, MD, PhD

The Impact of FDA Digital Health Guidance on CDS, Medical Software, and Machine Learning

The FDA recently released guidance documents on the use of clinical decision support (CDS) and medical software that may be of concern to forward-thinking healthcare innovators who rely on these technologies to deliver exceptional care and improve outcomes. What will be the impact of this guidance on machine learning and predictive analytics efforts? How will the guidance affect timelines, costs, and effectiveness of ongoing machine learning implementation?
As healthcare delivery increasingly relies on digital innovation and support, more questions emerge about the governance of the accompanying tools and technology.
This article provides a summary of the FDA guidance on CDS, how CDS is defined, whether or not CDS is exempt from regulation, and how the FDA intends to enforce compliance. It also summarizes the FDA guidance on medical software, what software is exempt from regulation, and helps to answer some of the questions surrounding the digital health space.

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Carolyn Wong Simpkins, MD, PhD
Daniel Orenstein, JD

Machine Learning in Healthcare: How it Supports Clinician Decisions—and Why Clinicians are Still in Charge

Machine learning in healthcare is transforming healthcare with its ability to tackle data variability and complexity. Everyone in healthcare should embrace this new technology and its ability to deliver more precise, faster, data-driven insight to clinical teams.
But just as machine learning has benefits, it also has limitations; for example, it loses its impact when implemented without realistic expectations or without thorough integration with existing clinical processes.
As the FDA works to publish guidance on digital health services, including governance regarding the use of algorithms to support clinical decisions, it’s important for everyone in the industry to hold themselves accountable for the quality of the data and the processes that put this data in front of clinicians.
Machine learning is transforming the way health systems deliver care to patients by surfacing insights to clinicians at the point of care; but, ultimately, the clinician considers the entire clinical picture to determine the most appropriate plan for patients.

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