Intelligent use and clinical benefits of electronic health records in rheumatoid arthritis

Abstract

In the past 10 years, electronic health records (EHRs) have had growing impact in clinical care. EHRs efficiently capture and reuse clinical information, which can directly benefit patient care by guiding treatments and providing effective reminders for best practices. The increased adoption has also lead to more complex implementations, including robust, disease-specific tools, such as for rheumatoid arthritis (RA). In addition, the data collected through normal clinical care is also used in secondary research, helping to refine patient treatment for the future. Although few studies have directly demonstrated benefits for direct clinical care of RA, the opposite is true for EHR-based research – RA has been a particularly fertile ground for clinical and genomic research that have leveraged typically advanced informatics methods to accurately define RA populations. We discuss the clinical impact of EHRs in RA treatment and their impact on secondary research, and provide recommendations for improved utility in future EHR installations.

Keywords:

• clinical decision support systems
• electronic health records
• genomics
• pharmacogenetics
• rheumatoid arthritis

Financial & competing interests disclosure

This work was supported by the National Library of Medicine: 5T15 LM007450; National Institute of General Medical Sciences: U01 GM092691, R01 GM105688, and R01 GM103859. AE Eyler is the recipient of an Arthritis Foundation Clinical to Research Transition Award. The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.

No writing assistance was utilized in the production of this manuscript.

Key issues

• Electronic health records (EHRs) are growing in adoption and complexity due in part to national incentives for adoption. EHR use is typically associated with improved outcomes and reduced cost, though specific studies in rheumatoid arthritis (RA) are lacking.

• RA-specific tools exist to help track and use patient disease activity.

• Clinical decision support is available to help clinicians treat patients, including assisting in dosing and alerting for possible drug–drug interactions.

• Identifying quality indicators and disease activity scores are helped by codified data, for example, joint counts, and EHRs are moving to collect more of this information.

• RA has been an active area for secondary clinical and genetic research using EHR data. Genetic studies in EHR have demonstrated results in multiethnic cohorts, evaluated the role of genetics to predict autoantibody status and contributed to finding new RA genetic loci.

• Rule-based and machine learning methods exist for identifying RA patients and their disease activity automatically from the EHR. These available algorithms can be used to create cohorts for research or surveillance.

• Research networks provide opportunities for collaborations in studying hypotheses. Growing populations of EHR-linked biobanks will enable greater RA and RA pharmacogenetic research.

Notes