Defining and measuring completeness of electronic health records for secondary use

By Nicole Gray Weiskopf, George Hripcsak, S. Swaminathan, Chunhua Weng. Journal of Biomedical Informatics, 2013

347 citations recorded by the source corpus.

We demonstrate the importance of explicit definitions of electronic health record (EHR) data completeness and how different conceptualizations of completeness may impact findings from EHR-derived datasets. This study has important repercussions for researchers and clinicians engaged in the secondary use of EHR data. We describe four prototypical definitions of EHR completeness: documentation, breadth, density, and predictive completeness. Each definition dictates a different approach to the measurement of completeness. These measures were applied to representative data from NewYork-Presbyterian Hospital’s clinical data warehouse. We found that according to any definition, the number of complete records in our clinical database is far lower than the nominal total. The proportion that meets criteria for completeness is heavily dependent on the definition of completeness used, and the different definitions generate different subsets of records. We conclude that the concept of completeness in EHR is contextual. We urge data consumers to be explicit in how they define a complete record and transparent about the limitations of their data.

Subfields: Health Information Management, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/cb4680865fa065de6a1cf1b082e1c939662c5f6c

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Citations in the corpus

DeepCare: A Deep Dynamic Memory Model for Predictive Medicine (2016, background): “The wide adoption of EMRs has led to calls for meaningful use [3, 20].”
Deep Patient: An Unsupervised Representation to Predict the Future of Patients from the Electronic Health Records (2016, background): “EHR data is challenging to represent and model due to its high dimensionality, noise, heterogeneity, sparseness, incompleteness, random errors, and systematic biases 7,10,11 .”
The National COVID Cohort Collaborative (N3C): Rationale, design, infrastructure, and deployment (2020, background): “DQ measures, including consistency, correctness, concordance, currency, and plausibility, are important to support analysis and computation.(73,74) Many large-scale data aggregation projects benefit from focusing on a set of contextual use cases or a defined population research domain.”