Medical institutions are adopting electronic health records (EHR) in accordance with Meaningful Use. This affords the opportunity for standardizing and capturing patient data to build the backbone for registry information serving Learning Health Systems (LHS). LHS leverage clinical data to generate new evidence and knowledge improving clinical practice; ensuring quality, safety, and value; and driving innovation in health care.

The Pediatric Rheumatology Care and Outcomes Improvement Network (PR-COIN) is a 17 center learning network designed to improve the outcomes of rheumatic disease care. Teams collect and analyze point of care data on process and outcomes that guide improvement activities. Data collection at most centers currently involves manual data entry that is duplicative: (1) patients and providers fill out paper forms; (2) providers document clinical visits in the EHR; (3) staff complete paper case report forms (CRF); and (4) staff enter data from CRFs into the registry database. This time consuming process increases the risk for data entry errors. In addition, patient level EHR data can appear in different areas interfering with timely viewing, evaluating, and responding.

We describe a PR-COIN and EHR vendor (Epic Systems Corporation) collaboration to design, build, and implement a technical architecture to make clinical documentation more efficient, standardize data collection, improve data display methods, and enable easy access to more informed clinical visits.

Methods:

Development entailed direct interaction of the EHR vendor with three institutions’ information systems departments. Twice a month meetings occurred to discuss collection form and note template builds, data element standardization, and process workflows. Our goals were: (1) to support a “data-in-once” strategy for registry data collection minimizing re-work and integrating data capture into routine patient clinical visit documentation; (2) to develop automated pre-visit planning reports embedded in the EHR supporting QI measure compliance and evidence based chronic care patient management.

Results:

Components of the “data-in-once” build developed include: (1) point of care seamless discrete standardized data exchange between the clinical documentation and PR-COIN registry data collection elements within the EHR; (2) capability for ongoing electronic transfer of the EHR PR-COIN registry data to the external PR-COIN registry. Automated pre-visit planning reports are strategically localized in various areas of the EHR for ease of access and use. Four institutions are piloting system implementation with subsequent roll-out planned to other Epic users in the learning network.

Conclusion:

Learning health systems can be the foundation for improved quality of clinical care and patient outcomes. Using the EHR platform to obtain registry data directly from the patient care process, we developed a method to efficiently and unobtrusively collect patient data accelerating the rate of useful data accumulated for analysis with enhanced presentation of clinically meaningful data to users.