Closing the Data Divide in Healthcare

Authors

  • Isani Singh Harvard Medical School Author

DOI:

https://doi.org/10.65539/74rfap42

Keywords:

electronic medical records, health information technology, data interoperability, artificial intelligence, health equity, patient safety

Abstract

The lack of a universal electronic medical record system creates inefficiencies, safety risks, and health inequities. Drawing from personal experiences navigating multiple EMR systems during clinical rotations, this article argues for a federally mandated universal healthcare data system. Such a system could enable AI-powered real-time learning to improve care coordination, reduce waste, enhance patient safety, and promote health equity while addressing concerns about data privacy and provider autonomy.

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References

1. Turbow S, Hollberg JR, Ali MK. Electronic Health Record Interoperability: How Did We Get Here and How Do We Move Forward? JAMA Health Forum. 2021 Mar 17;2(3):e210253. DOI: https://doi.org/10.1001/jamahealthforum.2021.0253

2. Kaissis GA, Makowski MR, Rückert D, Braren RF. Secure, privacy-preserving and federated machine learning in medical imaging. Nat Mach Intell. 2020 Jun;2(6):305–11. DOI: https://doi.org/10.1038/s42256-020-0186-1

3. Rieke N, Hancox J, Li W, Milletarì F, Roth HR, Albarqouni S, et al. The future of digital health with federated learning. npj Digit Med. 2020 Sep 14;3(1):119. DOI: https://doi.org/10.1038/s41746-020-00323-1

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Published

2025-12-25

How to Cite

Closing the Data Divide in Healthcare. (2025). Harvard Medical Student Review, 102(1), 34-36. https://doi.org/10.65539/74rfap42