ArticlePLOS digital health2025
A case study on barriers to the research implementation of a novel technology in an academic medical center.
Article in PLOS digital health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
0 citing papers in PubMed.
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Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Natural Language Processing allows extracting unstructured text data from electronic health records (EHR), but historically required extensive coding and expertise. Amazon Comprehend Medical (ACM) offers a scalable solution for mining EHR data without extensive natural language processing expertise. This case study examined barriers and facilitators to implementing ACM in an academic medical center. We reviewed correspondence regarding ACM implementation between study investigators and respective experts within the medical center. We qualitatively coded the correspondence for barriers and facilitators using the Consolidated Framework for Implementation Research (CFIR) framework as a guide. Key findings included the involvement of non-traditional stakeholders in the approval process and unexpected limitations of anticipated facilitators. The study revealed that implementing novel technologies like ACM in academic medical settings requires careful consideration of safety protocols, which may slow adoption. Our findings can guide research teams in navigating the implementation of similar technologies, balancing innovation with necessary safeguards.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.