Stephanie Garcia | September 19, 2022
Real world health data are critical for Patient-Centered Outcomes Research (PCOR). However, it’s often difficult, expensive, and time consuming for researchers to access real-world clinical health data because of privacy concerns, security restrictions, and usage issues. Although PCOR researchers, health information technology developers, and informaticists often depend on anonymized or de-identified clinical health data for testing theories, data models, algorithms, and prototype innovations, re-identification of anonymized data remains a possible security risk. Synthetic health data can provide a no-risk data source to complement research and support testing needs until real clinical health data are available.
Read Full Post.
Kathryn Marchesini | September 7, 2022
When talking about artificial intelligence (AI) today, people are usually referring to predictive models—often driven by machine learning (ML) techniques—that “learn” from historic data and make predictions, recommendations, or classifications (outputs) which inform or drive decision making. The power of ML is in its enormous flexibility. You can build a model to predict or recommend just about anything, and we have seen it transform many sectors.
Read Full Post.
Mera Choi | August 3, 2022
The Cures Act Final Rule includes regulatory requirements to implement secure, standards-based application programming interfaces (APIs). To support the acceleration of API adoption in health care, ONC has published a series of perspective reports that focus on key stakeholders and their views on APIs.
Read Full Post.
Kathryn Marchesini | June 15, 2022
The same basic technology that can predict what movie you might want to watch, what song you might want to listen to, or what item you might want to buy online, can also predict the onset of diseases, forecast costs of care, and recommend treatment options for your doctors, nurses, and pharmacists.
Read Full Post.
Mera Choi | June 13, 2022
