ArticleApplied clinical informatics2016
A New Paradigm to Analyze Data Completeness of Patient Data.
Article in Applied clinical informatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed.
- A Review of Data Engineering in United States Healthcare Infrastructure.Healthcare (Basel, Switzerland) · 2026Review
- Incompleteness of Electronic Health Records: An Impending Process Problem Within Healthcare.Healthcare (Basel, Switzerland) · 2025Article
- Automating Electronic Health Record Data Quality Assessment.Journal of medical systems · 2023Review
- The effect of machine learning explanations on user trust for automated diagnosis of COVID-19.Computers in biology and medicine · 2022Article
- Key observations in terms of management of electronic health records from a mHealth perspective.mHealth · 2022Review
- An exploratory data quality analysis of time series physiologic signals using a large-scale intensive care unit database.JAMIA open · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThere is a need to develop a tool that will measure data completeness of patient records using sophisticated statistical metrics. Patient data integrity is important in providing timely and appropriate care. Completeness is an important step, with an emphasis on understanding the complex relationships between data fields and their relative importance in delivering care. This tool will not only help understand where data problems are but also help uncover the underlying issues behind them.
objectivesDevelop a tool that can be used alongside a variety of health care database software packages to determine the completeness of individual patient records as well as aggregate patient records across health care centers and subpopulations.
methodsThe methodology of this project is encapsulated within the Data Completeness Analysis Package (DCAP) tool, with the major components including concept mapping, CSV parsing, and statistical analysis.
resultsThe results from testing DCAP with Healthcare Cost and Utilization Project (HCUP) State Inpatient Database (SID) data show that this tool is successful in identifying relative data completeness at the patient, subpopulation, and database levels. These results also solidify a need for further analysis and call for hypothesis driven research to find underlying causes for data incompleteness.
conclusionDCAP examines patient records and generates statistics that can be used to determine the completeness of individual patient data as well as the general thoroughness of record keeping in a medical database. DCAP uses a component that is customized to the settings of the software package used for storing patient data as well as a Comma Separated Values (CSV) file parser to determine the appropriate measurements. DCAP itself is assessed through a proof of concept exercise using hypothetical data as well as available HCUP SID patient data.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.