Witte, Nathaniel

The application of artificial intelligence (AI), large language models and generative AI (GenAI) is accelerating rapidly across many industries worldwide. Innovative models and tools are being piloted to understand their business and societal value. Models such as ChatGPT (OpenAI), Gemini (Google) and Copilot (Microsoft) have become increasingly sophisticated and widely adopted. Understanding the advantages and drawbacks of GenAI models is crucial for organisations seeking to leverage GenAI to maximise their efficiency and expand their capabilities. This paper describes the strategic approach, implementation and findings of using GenAI in a health care business context. A GenAI model was used to analyse and summarise strategic and operational information submitted by external organisations seeking to collaborate with Mayo Clinic. The goal was to reduce process cycle time and manual resource intensity. In parallel, a comparative human review was conducted by functional experts. Both the AI-generated and human-reviewed results were evaluated using sentiment analysis (SA) and then categorised as positive, neutral or negative. Attention was paid to possible inaccuracies and biases associated with the use of the GenAI model. GenAI reduced the manual workload by approximately 20−25 per cent and presented information in a well-structured format. However, the SA showed the GenAI results as positively biased and the human-generated results as neutral. These results were most likely influenced by a positive bias in the input documents, the lack of contextual comparators/ standards in the GenAI model and an inherent bias within the model’s algorithm(s). Additionally, limitations in input size and language translation hindered the GenAI model’s effectiveness. The possibility of AI hallucinations/inaccuracies emphasised the importance of validation by a human expert. Furthermore, carefully formatting the input data enhanced the value and relevance of the GenAI model’s outputs. GenAI has the potential to increase efficiency and alleviate manual burden, but it must be thoughtfully implemented to address inaccuracies, biases and privacy concerns. This article is also included in The Business & Management Collection which can be accessed at https:// hstalks.com/business/.

ABSTRACT: Background: When adolescents present with symptoms of unexplained weight loss, underweight, or poor appetite, eating disorders (EDs) are commonly on the list of differential diagnoses. However, the relationship of these symptoms to other psychiatric disorders is often less clear. Methods: Using the Rochester Epidemiology Project database, a retrospective cohort study of adolescents (13–18 years) with billing diagnoses of weight loss, underweight, or loss of appetite was conducted between January 2005 and December 2017. Patients who presented with conditions commonly associated with weight loss, underweight, or poor appetite (e.g., cancer) were excluded. This study sought to examine the proportion of patients who received ED and psychiatric diagnoses within 5 years of the index visit and patient characteristics associated with these diagnoses. Results: Of 884 patients diagnosed with symptoms of unexplained weight loss, underweight, or poor appetite, 662 patients (M age = 15.8; SD = 1.6; 66.0% female) met study criteria. Within 5 years of the index visit, the lifetime prevalence of all psychiatric disorders was 70% (n = 461) and of EDs was 21% (n = 141). For both psychiatric disorders and EDs, sex and race were significantly associated with receiving a diagnosis within 5 years. Decrease in body mass index (BMI) percentile was associated with receiving an ED diagnosis, whereas the highest historical BMI percentile was associated with receiving a psychiatric diagnosis. Conclusion: Patients presenting with symptoms of unexplained weight loss, underweight, or poor appetite are at risk not only for EDs but also for other psychiatric disorders that may require further assessment and follow-up.