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. 2013 Oct;46(5):830-6.
doi: 10.1016/j.jbi.2013.06.010. Epub 2013 Jun 29.

Defining and Measuring Completeness of Electronic Health Records for Secondary Use

Free PMC article

Defining and Measuring Completeness of Electronic Health Records for Secondary Use

Nicole G Weiskopf et al. J Biomed Inform. .
Free PMC article


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.

Keywords: Completeness; Data quality; Electronic health records; Secondary use.


Figure 1
Figure 1. An EHR Completeness Model
Each square point denotes an observed and recorded data point, stars are unobserved but desired data points, and the boxes indicate all data points that are required for a given task.
Figure 2
Figure 2. Documentation Completeness Improvement Over Time
The documentation completeness of records has improved as documentation practices have changed and EHR adoption has increased.
Figure 3
Figure 3. Documentation Completeness of Records
Shows the number of patients who have been present in the hospital for a certain number of days, as well as the number of patients whose records have narrative notes or reports associated with a certain number of days that they have been present.
Figure 4
Figure 4. Breadth Completeness of Records
The number of patients with laboratory results, medication orders, and diagnoses on the same day as compared to the number of days when they were present in the hospital. Below, the number of patients with zero, one, two, or all of these data types present in their record on the same day.
Figure 5
Figure 5. Density Completeness of Records
The number of patients with a given number of days with recorded visit events, laboratory results, or medication orders. The raw number of days, the number of days adjusted for variance, and the number of days adjusted for variance and time period are shown.
Figure 6
Figure 6. Comparison of Completeness Definition Results
Subsets of patients with complete records according to the density (medication orders and laboratory tests over time with Sperrin’s adjustment), breadth (record includes date of birth, sex, and at least one medication order, laboratory test, and diagnosis), documentation (at least one visit accompanied by a note), and predictive (a gap of 180 days can be correctly predicted) completeness definitions.

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