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J Am Coll Radiol. 2019 Sep;16(9 Pt B):1343-1346. doi: 10.1016/j.jacr.2019.05.044. Epub 2019 Jun 22.

Clinical Documentation and Patient Care Using Artificial Intelligence in Radiation Oncology.

Author information

1
Department of Radiation Oncology, Providence St Joseph Health, Eureka, California; Department of Radiation Medicine, Oregon Health & Science University, Portland, Oregon. Electronic address: join.luh@stjoe.org.
2
Department of Radiation Medicine, Oregon Health & Science University, Portland, Oregon; Department of Biomedical Engineering, Oregon Health & Science University, Portland, Oregon; Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, Oregon; Computational Biology Program, VA Portland Healthcare System, Division of Hospital and Specialty Medicine, Oregon Health & Science University, Portland, Oregon.
3
Stanford University, Primary Care and Population Health, Palo Alto, California.

Abstract

Detailed clinical documentation is required in the patient-facing specialty of radiation oncology. The burden of clinical documentation has increased significantly with the introduction of electronic health records and participation in payer-mandated quality initiatives. Artificial intelligence (AI) has the potential to reduce the burden of data entry associated with clinical documentation, provide clinical decision support, improve quality and value, and integrate patient data from multiple sources. The authors discuss key elements of an AI-enhanced clinic and review some emerging technologies in the industry. Challenges regarding data privacy, regulation, and medicolegal liabilities must be addressed for such AI technologies to be successful.

PMID:
31238022
DOI:
10.1016/j.jacr.2019.05.044

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