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Primary care provider perspectives on the value of opportunistic CT screening.
Eltorai, Adam E M; McKinney, Suzannah E; Rockenbach, Marcio A B C; Karuppiah, Saby; Bizzo, Bernardo C; Andriole, Katherine P.
Afiliación
  • Eltorai AEM; Department of Radiology, Brigham & Women's Hospital, Harvard Medical School, Boston, MA, United States of America.
  • McKinney SE; Data Science Office, Mass General Brigham, Boston, MA, United States of America.
  • Rockenbach MABC; Data Science Office, Mass General Brigham, Boston, MA, United States of America.
  • Karuppiah S; Department of Family Medicine, HCA Healthcare, Kansas City, MO, United States of America.
  • Bizzo BC; Data Science Office, Mass General Brigham, Boston, MA, United States of America.
  • Andriole KP; Department of Radiology, Brigham & Women's Hospital, Harvard Medical School, Boston, MA, United States of America; Data Science Office, Mass General Brigham, Boston, MA, United States of America. Electronic address: kandriole@bwh.harvard.edu.
Clin Imaging ; 112: 110210, 2024 Aug.
Article en En | MEDLINE | ID: mdl-38850710
ABSTRACT

BACKGROUND:

Clinical adoption of AI applications requires stakeholders see value in their use. AI-enabled opportunistic-CT-screening (OS) capitalizes on incidentally-detected findings within CTs for potential health benefit. This study evaluates primary care providers' (PCP) perspectives on OS.

METHODS:

A survey was distributed to US Internal and Family Medicine residencies. Assessed were familiarity with AI and OS, perspectives on potential value/costs, communication of results, and technology implementation.

RESULTS:

62 % of respondents (n = 71) were in Family Medicine, 64.8 % practiced in community hospitals. Although 74.6 % of respondents had heard of AI/machine learning, 95.8 % had little-to-no familiarity with OS. The majority reported little-to-no trust in AI. Reported concerns included AI accuracy (74.6 %) and unknown liability (73.2 %). 78.9 % of respondents reported that OS applications would require radiologist oversight. 53.5 % preferred OS results be included in a separate "screening" section within the Radiology report, accompanied by condition risks and management recommendations. The majority of respondents reported results would likely affect clinical management for all queried applications, and that atherosclerotic cardiovascular disease risk, abdominal aortic aneurysm, and liver fibrosis should be included within every CT report regardless of reason for examination. 70.5 % felt that PCP practices are unlikely to pay for OS. Added costs to the patient (91.5 %), the healthcare provider (77.5 %), and unknown liability (74.6 %) were the most frequently reported concerns.

CONCLUSION:

PCP preferences and concerns around AI-enabled OS offer insights into clinical value and costs. As AI applications grow, feedback from end-users should be considered in the development of such technology to optimize implementation and adoption. Increasing stakeholder familiarity with AI may be a critical prerequisite first step before stakeholders consider implementation.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Tomografía Computarizada por Rayos X Límite: Female / Humans / Male País/Región como asunto: America do norte Idioma: En Revista: Clin Imaging Asunto de la revista: DIAGNOSTICO POR IMAGEM Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Estados Unidos

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Tomografía Computarizada por Rayos X Límite: Female / Humans / Male País/Región como asunto: America do norte Idioma: En Revista: Clin Imaging Asunto de la revista: DIAGNOSTICO POR IMAGEM Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Estados Unidos