Nyawa Taruhannya! Alasan Kenapa AI Medis Masih Suka 'Salah Diagnosis' Pasien

#sisiilmiah #faktailmiahterbaru #edukasi Jurnal : From real-world electronic health record data to realworld results using artificial intelligence Author & Afiliasi : Rachel Knevel ,1,2 Katherine P Liao3,4 1Department of Rheumatology, Leiden University Medical Center, Leiden, The Netherlands 2Newcastle University School of Clinical Medical Sciences, Newcastle upon Tyne, UK 3Division of Rheumatology, Immunology, and Allergy, Brigham and Women’s Hospital, Boston, Massachusetts, USA 4Harvard Medical School Center for Biomedical Informatics, Boston, Massachusetts, USA Abstrak : With the worldwide digitalisation of medical records, electronic health records (EHRs) have become an increasingly important source of real-world data (RWD). RWD can complement traditional study designs because it captures almost the complete variety of patients, leading to more generalisable results. For rheumatology, these data are particularly interesting as our diseases are uncommon and often take years to develop. In this review, we discuss the following concepts related to the use of EHR for research and considerations for translation into clinical care: EHR data contain a broad collection of healthcare data covering the multitude of real-life patients and the healthcare processes related to their care. Machine learning (ML) is a powerful method that allows us to leverage a large amount of heterogeneous clinical data for clinical algorithms, but requires extensive training, testing, and validation. Patterns discovered in EHR data using ML are applicable to real life settings, however, are also prone to capturing the local EHR structure and limiting generalisability outside the EHR(s) from which they were developed. Population studies on EHR necessitates knowledge on the factors influencing the data available in the EHR to circumvent biases, for example, access to medical care, insurance status. In summary, EHR data represent a rapidly growing and key resource for real-world studies. However, transforming RWD EHR data for research and for real-world evidence using ML requires knowledge of the EHR system and their differences from existing observational data to ensure that studies incorporate rigorous methods that acknowledge or address factors such as access to care, noise in the data, missingness and indication bias. Akses Jurnal (Url/ DOI) : doi:10.1136/annrheumdis-2022-222626