From real-world electronic health record data to real-world results using artificial intelligence

Abstract

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.

Understanding the role and adoption of artificial intelligence techniques in rheumatology research: an in-depth review of the literature

“…These data often fall under the definition of Real-World Data (RWD). Although RWD has been shown to be a valuable source of information, some of its implications cannot be neglected, such as its less structured nature or the occurrence of biases (i.e., selection bias or informed consent bias) which may require additional processing [5]. In this regard, approaches based on Natural Language Processing (NLP) and topic modelling have been proposed to characterise the evolution of rare diseases in RMDs clinical narratives [6].…”

Blood self-sampling: a missing link for remote patient care

“… 1 Such platforms not only guarantee access to relevant health data but also enable the development of algorithms for predictive and cluster analyses as clinical decision support. 2 …”

“…1 Such platforms not only guarantee access to relevant health data but also enable the development of algorithms for predictive and cluster analyses as clinical decision support. 2 Examples outside medicine have shown how the 'point of care' can shift. Who would have thought some time ago that we would no longer conduct banking transactions or travel bookings in the bank or at the travel agency, but via an app?…”

“…All stakeholders agree that patients should be empowered and more involved in the treatment process, for example via digital disease management platforms 1. Such platforms not only guarantee access to relevant health data but also enable the development of algorithms for predictive and cluster analyses as clinical decision support 2…”

New developments in electronic health record analysis

“…Many ICTs are now entering clinical practice or are already part of standard care. For example, electronic health records (EHRs) and/or other patient documentation systems (such as in hospitals, practices and laboratories) offer a rich resource of data to advance our understanding of rheumatic conditions, and can complement traditional study designs because they capture almost the complete variety of patient journeys with real word data, leading to more generalizable results 1 . In addition, an increasing amount of data is being contained within these systems that might be used to analyse the epidemiological trends of inflammatory rheumatic diseases.…”