Health Data

Portrait of Kathryn Marchesini

Increasing the Transparency and Trustworthiness of AI in Health Care

Kathryn Marchesini | April 13, 2023

This is part five of a blog series on predictive models, artificial intelligence (AI) & machine learning (ML) in health. We encourage readers to (re)visit the four previous blog posts for important context to what follows.
Through a series of blog posts over the last year, we’ve described our understanding of the current and potential uses of predictive models and machine learning algorithms in health care, and the role that ONC can play in shaping their development and use.

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Portrait of Yuriy Pylypchuk

Use of Telemedicine among Physicians and Development of Telemedicine Apps

Yuriy Pylypchuk | March 9, 2023

In 2020, as the U.S. health care system faced the unprecedented challenge of combating the COVID-19 pandemic, telemedicine emerged as an invaluable care delivery tool; both providers and patients heavily relied on it for providing and receiving care. Through an analysis of National Electronic Health Record Survey (NEHRS) 2021 data and from in-house data collected from electronic health record (EHR) developers’ app galleries, we have an opportunity to inform the public about physicians’ experiences with telemedicine during the pandemic.

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Portrait of Chelsea Richwine

Flipping the Script: Leveraging EHR Data to Inform Clinical Burden Reduction Approaches

Chelsea Richwine | February 6, 2023

In 2020, the U.S. Department of Health and Human Services (HHS) released a strategy to reduce regulatory and administrative burden relating to the use of health IT, including electronic health record (EHR) systems. Key burden reduction goals addressed in the report include reducing the amount of time and effort required to record information in EHRs to meet regulatory requirements and improving the usability of EHRs.

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Portrait of Chelsea Richwine

Evidence on the Growing Use of Health IT to Address the Opioid Epidemic

Chelsea Richwine | January 19, 2023

In the midst of a growing drug overdose crisis exacerbated by the COVID-19 pandemic, clinicians’ use of prescription drug monitoring programs (PDMPs) and electronic prescribing of controlled substances (EPCS) technology is critical to improving opioid prescribing practices, informing treatment decisions, and supporting safe and effective patient care. Recent efforts, such as mandating use of EPCS technology and integrating PDMPs into electronic health record (EHR) systems, aim to improve prescribing practices by increasing the utility of information contained in PDMPs and decreasing prescription diversion and doctor shopping.

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Portrait of Kathryn Marchesini

Back to the Future: What Predictive Decision Support Can Learn from DeLoreans and The Big Short

Kathryn Marchesini | December 13, 2022

In the third blog in our series on artificial intelligence (AI) and machine learning (ML)-driven predictive models (data analytics tool or software) in health care, we discussed some potential risks (sometimes referred to as model harms) related to these emerging technologies and how these risks could lead to adverse impacts or negative outcomes. Given these potential risks, some have questioned whether they can trust the use of these technologies in health care.

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