Using real-world data and artificial intelligence to advance health services research
- Collection:
- Health Policy and Services Research
- Contributor(s):
- AcademyHealth, issuing body.
- Publication:
- [Washington, DC] : AcademyHealth, [2021]
- Language(s):
- English
- Format:
- Text
- Subject(s):
- Data Analysis Health Services Research United States
- Genre(s):
- Technical Report
- Abstract:
- The field of health services research (HSR) can capitalize on burgeoning sources of real-world data to parse new and perennial questions about health care costs, quality, and access, as well as potentially increase the timeliness and relevance of research findings. But first, the field must prepare the infrastructure, including academia, research funding, and peer-reviewed publications, to deliver on the promise and avoid the pitfalls of greater and more sophisticated use of real-world data. While researchers have long used structured real-world data like claims to answer questions in policy and practice, myriad new unstructured data sources, including free text in electronic health records (EHRs) and images from X-rays and other technologies, are emerging for exploration. Similarly, new methodologies, such as machine learning and natural language processing, are being applied to real-world data to gain deeper insights about which care is right for which patients. This brief summarizes key points from a February 2021 meeting convened by AcademyHealth to examine greater use of real-world data in HSR and related issues, including safeguarding against the introduction of racial and other biases; addressing privacy concerns; establishing data standards; developing data resources as public goods; and helping researchers gain needed skills to design and conduct studies and interpret and disseminate findings.
- Copyright:
- Reproduced with permission of the copyright holder. Further use of the material is subject to CC BY license. (More information)
- Extent:
- 1 online resource (1 PDF file (8 pages))
- NLM Unique ID:
- 9918384882106676 (See catalog record)
- Permanent Link:
- http://resource.nlm.nih.gov/9918384882106676