Evidence map›Paper›PMID 34946374›Full record

ArticleHealthcare (Basel, Switzerland)2021

Real-World Evidence of COVID-19 Patients' Data Quality in the Electronic Health Records.

Samar Binkheder, Mohammed Ahmed Asiri, Khaled Waleed Altowayan, Turki Mohammed Alshehri, Mashhour Faleh Alzarie, Raniah N Aldekhyyel, Ibrahim A Almaghlouth, Jwaher A Almulhem

Abstract read
In one paragraph

Article in Healthcare (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

12 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Electronic health record data quality assessment and tools: a systematic review.Journal of the American Medical Informatics Association : JAMIA · 2023
    Pooled it
  2. Observational
  3. Article
  4. Article
  5. Review
  6. Article
  7. Article
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  11. Health Informatics: The Foundations of Public Health.Healthcare (Basel, Switzerland) · 2023
    Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Samar BinkhederMedical Informatics and E-Learning Unit, Medical Education Department, College of Medicine, King Saud University, Riyadh 12372, Saudi Arabia.ORCID 0000-0003-0400-823X
Mohammed Ahmed AsiriMedical Informatics and E-Learning Unit, Medical Education Department, College of Medicine, King Saud University, Riyadh 12372, Saudi Arabia.
Khaled Waleed AltowayanMedical Informatics and E-Learning Unit, Medical Education Department, College of Medicine, King Saud University, Riyadh 12372, Saudi Arabia.
Turki Mohammed AlshehriMedical Informatics and E-Learning Unit, Medical Education Department, College of Medicine, King Saud University, Riyadh 12372, Saudi Arabia.
Mashhour Faleh AlzarieMedical Informatics and E-Learning Unit, Medical Education Department, College of Medicine, King Saud University, Riyadh 12372, Saudi Arabia.
Raniah N AldekhyyelMedical Informatics and E-Learning Unit, Medical Education Department, College of Medicine, King Saud University, Riyadh 12372, Saudi Arabia.
Ibrahim A AlmaghlouthDepartment of Medicine, College of Medicine, King Saud University, Riyadh 12372, Saudi Arabia.ORCID 0000-0002-8176-287X
Jwaher A AlmulhemMedical Informatics and E-Learning Unit, Medical Education Department, College of Medicine, King Saud University, Riyadh 12372, Saudi Arabia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Despite the importance of electronic health records data, less attention has been given to data quality. This study aimed to evaluate the quality of COVID-19 patients' records and their readiness for secondary use. We conducted a retrospective chart review study of all COVID-19 inpatients in an academic healthcare hospital for the year 2020, which were identified using ICD-10 codes and case definition guidelines. COVID-19 signs and symptoms were higher in unstructured clinical notes than in structured coded data. COVID-19 cases were categorized as 218 (66.46%) "confirmed cases", 10 (3.05%) "probable cases", 9 (2.74%) "suspected cases", and 91 (27.74%) "no sufficient evidence". The identification of "probable cases" and "suspected cases" was more challenging than "confirmed cases" where laboratory confirmation was sufficient. The accuracy of the COVID-19 case identification was higher in laboratory tests than in ICD-10 codes. When validating using laboratory results, we found that ICD-10 codes were inaccurately assigned to 238 (72.56%) patients' records. "No sufficient evidence" records might indicate inaccurate and incomplete EHR data. Data quality evaluation should be incorporated to ensure patient safety and data readiness for secondary use research and predictive analytics. We encourage educational and training efforts to motivate healthcare providers regarding the importance of accurate documentation at the point-of-care.

Indexed as

case identificationclinical documentationCOVID-19data qualityelectronic health recordmedical informatics

Identifiers

PMID34946374
PMCPMC8701465

What OpenQuestion holds

Textmetadata
LicenceCC BY
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Registered trials

None linked

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.