Search Articles

View query in Help articles search

Search Results (1 to 10 of 13 Results)

Download search results: CSV END BibTex RIS


Quality of Answers of Generative Large Language Models Versus Peer Users for Interpreting Laboratory Test Results for Lay Patients: Evaluation Study

Quality of Answers of Generative Large Language Models Versus Peer Users for Interpreting Laboratory Test Results for Lay Patients: Evaluation Study

He was able to explain in details the results. He provides inside on the different differential diagnosis. And provide alternative a management. He shows empathy.” LLa MA 2: “Very thorough and thoughtful.” ORCA_mini: “It was a great answer. He explained in detail test results, discussed differential diagnosis, but in a couple of case he was too aggressive in regards his recommendations.” ORCA_mini: “Standard answers, not the most in depth.”

Zhe He, Balu Bhasuran, Qiao Jin, Shubo Tian, Karim Hanna, Cindy Shavor, Lisbeth Garcia Arguello, Patrick Murray, Zhiyong Lu

J Med Internet Res 2024;26:e56655

Patient Challenges and Needs in Comprehending Laboratory Test Results: Mixed Methods Study

Patient Challenges and Needs in Comprehending Laboratory Test Results: Mixed Methods Study

It doesn't have to be fancy, but a little button that says “please push F to read this for them”, and then they could hear it. [...] or maybe the doctor gives a specialized URL and he like “hey, go to this URL, and it will be for blind assist” [...] I would say it needs to be a little bit more use-friendly as far as older folks go. [...] So since the world is having a national aging problem, it should be geared more for grandma to understand it.

Zhan Zhang, Daniel Citardi, Aiwen Xing, Xiao Luo, Yu Lu, Zhe He

J Med Internet Res 2020;22(12):e18725

Prevalence of Multiple Chronic Conditions Among Older Adults in Florida and the United States: Comparative Analysis of the OneFlorida Data Trust and National Inpatient Sample

Prevalence of Multiple Chronic Conditions Among Older Adults in Florida and the United States: Comparative Analysis of the OneFlorida Data Trust and National Inpatient Sample

He et al [7] used the National Health and Nutrition Examination Survey data and a public clinical trial registry—Clinical Trials.gov—to analyze the gap between the prevalence of MCCs and the clinical trials on the prevalent MCCs. They found that the current and past clinical trials rarely investigate the prevalent MCCs. Recent years have witnessed a wide adoption of electronic health record (EHR) systems driven by the Health Information Technology for Economic and Clinical Health (HITECH) Act of 2009 [8].

Zhe He, Jiang Bian, Henry J Carretta, Jiwon Lee, William R Hogan, Elizabeth Shenkman, Neil Charness

J Med Internet Res 2018;20(4):e137