Asset Details
Do you wish to reserve the book?
From real-world electronic health record data to real-world results using artificial intelligence
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
From real-world electronic health record data to real-world results using artificial intelligence
Do you wish to request the book?
From real-world electronic health record data to real-world results using artificial intelligence
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
Journal Article
From real-world electronic health record data to real-world results using artificial intelligence
2023
Overview
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.
We currently cannot retrieve any items related to this title. Kindly check back at a later time.