Machine Megaphone, Human Voice: How Implementation of Artificial Intelligence and Machine Learning Can Positively Influence Clinical Decision Making by Analyzing Patient Reported Outcomes

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Publication Date

7-24-2024

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Presentation: 25:13

Abstract

Viewing the history of healthcare as an aging toddler, patient-centric care has had a slow crawl to relevance. Beginning in the 1950s and continuing throughout the last decade, there are numerous examples of incorporating the patient voice to positively impact care decisions in the real-world setting. However, while patient reported outcomes (PROs) and similar subjective measures show immense promise in keeping the patient center of care in clinical decisions, there remains issues to widespread adoption of such tools in routine clinical practice. It has been proposed that one way to correct these inadequacies is by incorporation of artificial intelligence (AI) or machine learning (ML) systems. This capstone aimed to answer the question of whether there is evidence that AI/ML implementation has a positive impact on clinical outcomes by incorporation of the patient voice by influencing routine care decisions. A targeted literature review of PubMed was performed on May 2024 and used search terminology including the AI/ML, PRO, clinical decision support (CDS), and real-world evidence (RWE) spaces. 91 publications were surveyed, with a total of 28 included in analysis after exclusions. Widespread, international interest was evident from the analyzed literature, with 11 of the 28 papers including patient population from different countries, including North America, Europe, and Asia. Disease states heavily relied on PROs for either: markers of diagnosis/prognosis (i.e., mental health, neurological disease, and pain) or measures of disease treatment success (i.e., surgical outcome, oncology, immunology). While benefit was evident in majority of articles, there remains major limitations to be considered for real-world adoption including proper selection of measures, optimization of technology into electronic health records, and unbiased interpretation by the system. Our toddler has grown to adolescence, and it is imperative we realize the ethical and moral obligations needed to ensure we raise up a fully mature adult.

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English

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