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To understand this influence, we conducted a qualitative study of documents related to the development of a tool that relied on artificial intelligence (AI) to suggest modifications in opioid prescribing, called NarxCare. The study began with keyword searches of the Opioid Industry Document Archive (OIDA), which contained over 3 million documents at the time of the study, to examine the pharmaceutical industry\u2019s role in shaping the digital transformation of opioid prescribing. Our findings highlight industry-driven investments, educational campaigns, corporate policy activities, and the reliance on proprietary data that facilitated the widespread implementation of NarxCare. The increasing reliance on NarxCare raises concerns about its limited transparency, unknown reliability, and potential bias which may disproportionately affect certain patient groups based on race, socioeconomic status, or health conditions. This paper argues that the promotion of technologies like NarxCare allows the pharmaceutical industry to reinforce the narrative that opioids can be effective when prescribed responsibly, using advanced, data-driven strategies. Marketed as tools that assist clinicians in making more informed prescribing decisions, NarxCare contributes to the portrayal of the industry as a responsible actor in the regulation and distribution of opioids. Shifting attention to individual risk factors rather than systemic challenges enables the pharmaceutical industry to sidestep its role in the opioid crisis and evade scrutiny for its influence over regulation, the sponsorship of education and research, lobbying, supply chain control, and public health narratives. While NarxCare aims to improve prescribing safety, it requires critical evaluation in terms of effectiveness, ethical considerations, and the continued influence of the pharmaceutical industry in its design and implementation.<\/jats:p>","DOI":"10.1007\/s11606-025-09600-9","type":"journal-article","created":{"date-parts":[[2025,5,12]],"date-time":"2025-05-12T18:01:08Z","timestamp":1747072868000},"page":"2423-2432","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["The Rise of Clinical Decision Support Algorithms in Pain Management 2009\u20132024"],"prefix":"10.1007","volume":"40","author":[{"given":"Dan","family":"Kabella","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dorie","family":"Apollonio","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Halle","family":"Young","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5838-8592","authenticated-orcid":false,"given":"Kelly R.","family":"Knight","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,5,12]]},"reference":[{"key":"9600_CR1","doi-asserted-by":"crossref","unstructured":"Herzberg D. 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