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A 2018 study showed that virtually 0% of EHRs are error free. Fortunately, most of the errors are harmless such as when a data entry clerk incorrectly encodes “no information”. However, some errors can seriously affect patient safety, such as encoding the wrong diagnosis or treatment. The study, called “Interoperability Progress and Remaining Data Quality Barriers of Certified Health Information Technologies”, evaluated 401 EHRs from 52 health information technologies for data quality and compliance with the Consolidated Clinical Document Architecture (C-CDA) standard. This paper received the 'Distinguished Paper Award' at AMIA 2018!


Without clean standardized data in C-CDA documents, EHRs cannot exchange data consistently with precise meaning. Even with C-CDAs, interoperability remains a major challenge due to incomplete data, incorrect data, missing data, and correct data in the wrong place – all of which results in poor data quality. Doctors complain of receiving too many CDA’s, even hundreds for one patient, which are mostly empty.


Why is the Data Bad?


Implementing the CDA standard is difficult and requires years of specialized training. The HL7 C-CDA standard definition is complicated and runs more than 1,000 pages. This leads to a substantial variability in how and where clinical content is encoded in EHRs. For example, is it “Tylenol” or “acetaminophen”? Does a flu shot go under “procedure” or “immunization”? People are rushed and enter incomplete and incorrect data. Information such as the medication sig, status, dose, route, patient instructions, dispense, and fill quantities were sometimes left out. One record showed the patient weighing 194 lb. in the narrative and 194 kg. in the data field. One of these must be incorrect and could result in dangerous drug dosing.


Reference terminology coding standards such as RxNorm and LOINC are not being included along with local terms and codes inside these clinical documents. Medications should be encoded using RxNorm, and vital signs and results should use LOINC codes with Unified Code of Units of Measure (UCUM) for physical values. Most of the EHRs did not use these standards. Adoption of industry-wide data standards decreases the cost of health information exchange while improving data quality.


How Do We Improve Data Quality?


EHRs are an essential fixture in modern healthcare with more than 500 million C-CDAs exchanged annually in the United States. Healthcare needs teams regularly dedicated to reviewing clinical data quality using both automated tools and manual methods. A variety of tools reliably flag known issues, but no suite is 100% effective in enabling high quality and the safe exchange of clinical data. Consequently, manual evaluation is still required. Outsourcing of these regular CDA data reviews are a viable option for many hospitals.


Policy makers and hospital CIOs should require the inclusion of standard international codes in EHR records to give the data a precise meaning that the whole world has agreed upon for true interoperability. “The data inside of CDA documents must be reviewed and corrected, not just exported out of an EHR system without examination.” says Jackie Mulrooney, President of J P Systems, Inc. Once problems are identified, then a data quality team can work on finding the source of the errors. Maturity and strengthening of CDA Implementation Guides specific to the U.S. must pave the road the way to clinical interoperability.


J P Systems, Inc. is a Healthcare IT consulting firm which specializes in clinical data standards, data quality improvement, data modeling, standard reference terminologies and HL7 document standards.


Sandra Mitchell, Eric Hwang, Marie Swall and Eric LaChance contributed to the study in the link above.





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