2023
DOI: 10.1145/3569892
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Batman and Robin in Healthcare Knowledge Work: Human-AI Collaboration by Clinical Documentation Integrity Specialists

Abstract: This paper describes the successful collaboration ‘in the wild’ between Clinical Documentation Integrity Specialists (CDIS) and an Artificial Intelligence (AI)-embedded software to conduct knowledge work. CDIS review patient charts in near real time to improve clinicians’ documentation, with the goal to make medical documentation more accurate, consistent and complete. CDIS collaborate with an AI-embedded “Computer Assisted Coding” (CAC) system that scans records from the Electronic Healthcare Record and auto-… Show more

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Cited by 19 publications
(8 citation statements)
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References 36 publications
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“…[60] However, the collaboration between HCP and AI is key to success in improving the accuracy, consistency, and completeness of medical documentation while minimizing documentation errors. [51,61] It is also important to develop operationalization and implementation plans with accountable, fair, and inclusive AI approaches to ensure the trustworthiness of the digital scribes. [62,63]…”
Section: Discussionmentioning
confidence: 99%
“…[60] However, the collaboration between HCP and AI is key to success in improving the accuracy, consistency, and completeness of medical documentation while minimizing documentation errors. [51,61] It is also important to develop operationalization and implementation plans with accountable, fair, and inclusive AI approaches to ensure the trustworthiness of the digital scribes. [62,63]…”
Section: Discussionmentioning
confidence: 99%
“…For example, summarizing prior patient reports requires relatively lower expertise -a medical student level task-where having a 'good enough' summary could be still more useful than no summary. As argued elsewhere [19,62,119], this suggests a focus in AI development on simpler, more standardized use cases as a potentially lower risk and more responsible approach when starting to introduce AI innovations into clinical practice.…”
Section: Implications For Designing Ai In Healthcarementioning
confidence: 91%
“…To summarize, we used: Starting our project, one of our goals was to identify low hanging fruit -situations where simple AI interventions could improve clinical work. Based on prior research highlighting the value 'imperfect AI' can bring [12,73] as well as our own work, we focused on AI model performance to sensitize our team to situations where moderate model performance can still bring enough value. Additionally, we repeatedly probed team members to think of simpler versions of concepts.…”
Section: Moving From Ideation To Prototypingmentioning
confidence: 99%