Practical Guide to Clinical Big Data Sources.

Details

• Alternative Title:
  JAMA Surgery, 2025, v. 160, no. 3: Practical Guide to Clinical Big Data Sources.
• Journal Article:
  JAMA Surgery
• Personal Author:
  Fayanju, Oluwadamilola M. ; Haut, Elliott R. ; Itani, Kamal
• Description:
  Clinical data sources are composed of patient-level data that are generated from interaction with and assessment of patients and are typically abstracted from institutional electronic health records (EHRs). They can be collated at the institution-level by health systems, by government agencies (e.g., Surveillance, Epidemiology, and End Results SEER by the National Cancer Institute NCI), by professional societies through participation that may or may not be tied to accreditation or data access (e.g., the American College of Surgeons ACS and the Society of Thoracic Surgeons STS), and by vendors that source data from multiple institutions sharing a common EHR platform (e.g., Epic Cosmos).1 In addition, EHR-based networks such as All of Us by the National Institute of Health (NIH) and PCORNet by the Patient-Center Outcomes Research Institute (PCORI) work across different EHR platforms using a Common Data Model for reporting.2 All of these sources are distinct from claims-based data sets, which are generated from billing and insurance-related transactions rather than from interactions with people, and are addressed in a different guide in this series. Depending on the variables available, clinical datasets can be used to conduct a wide variety of health services research (HSR) including observational cohort studies, comparative effectiveness research (CER), and economic evaluations. In the case of clinical data sources for which prospective data collection can be centrally coordinated, they can also be used to conduct pragmatic clinical trials (e.g., ACS National Surgical Quality Improvement Program NSQIP,3 Abdominal Core Health Quality Collaborative ACHQC).4 In this manuscript, we describe the strengths and weaknesses of clinical sources of "big data." Specifically, we focus on datasets not previously included in the 2018 JAMA Surgery Practical Guide to Surgical Data Sets Series.3 A more expansive list is available on the website of the Surgical Outcomes Club.
• Content Notes:
  Author manuscript
• Subjects:
  • Big Data
  • Comparative Effectiveness Research
  • Cost-Benefit Analysis
  • Electronic Health Records
  • Genetics
  • Population Health
  • Quality Improvement
  • Treatment Outcome
  • Abdominal Core
  • Accreditation
  More +
• Source:
  JAMA Surgery, 2025, v. 160, no. 3
• DOI:
  https://doi.org/10.1001/jamasurg.2024.6006
• ISSN:
  2168-6254 ; 2168-6262
• Pubmed ID:
  39775674
• Pubmed Central ID:
  PMC12230764
• Document Type:
  Text
• Funding:
  K08 CA241390/CA/NCI NIH HHSUnited States/ ; P30 CA016520/CA/NCI NIH HHSUnited States/ ; P50 CA244690/CA/NCI NIH HHSUnited States/ ; U48DP006801/ACL/ACL HHSUnited States/
• Corporate as Subject:
  National Cancer Institute (U.S.)
• Genre:
  Journal Article
• Pages in Document:
  344-346 (6 pdf pages)
• Volume:
  160
• Issue:
  3
• Download URL:
  https://stacks.cdc.gov/view/cdc/261795/cdc_261795_DS1.pdf
• File Type:
  [PDF - 57.16 KB]
• Collection(s):
  CDC Public Access
• Main Document Checksum:
  urn:sha-512:7a5d6f3d6b16de29736b7da86373bb00655c29856f9dad76bd24b02b20d5875f1d945871c85de59ed3e23c67ee1f78020f4a6aeb6c7136272bd95902edd465f1