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. 2024 Jan 11:2023:1175-1182.
eCollection 2023.

Outliers in diagnosis ratios: A clue toward possibly absent data

Dmitry Morozyuk  1 , Mark G Weiner  1
Affiliations

Outliers in diagnosis ratios: A clue toward possibly absent data

Dmitry Morozyuk et al. AMIA Annu Symp Proc. .

Abstract

The evaluation of completeness of real-world data is a particularly challenging component of data quality assessment because the degree of truly versus erroneously absent data is unknown. Among inpatient data sets, while absolute counts of admissions having specific categories of diagnoses in the principal or any position may vary depending on hospital size, we hypothesized that the ratio of these parameters will be preserved across sites, with outliers suggesting the potential for erroneously absent data. For several categories of clinical conditions assigned to inpatient admissions, we analyzed the ratio of their recording as the principal diagnosis versus any diagnosis across several hospitals and compared the ratios against a national benchmark. Our analysis showed ratios that matched clinical expectations, with reasonable preservation of ratios across sites. However, some conditions exhibited more variability in the ratios and some sites had many outliers possibly reflecting data quality issues that warrant further attention.

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Figures

Figure 1.
Figure 1.
Diagnosis ratios by condition and by site for 2018
Figure 2a.
Figure 2a.
Diagnosis ratios for Diabetes by site 2018-2020
Figure 2b.
Figure 2b.
Diagnosis ratios for COPD by site 2018-2020
Figure 2c.
Figure 2c.
Diagnosis ratios for Stroke by site 2018-2020
Figure 2d.
Figure 2d.
Diagnosis ratios for CHF by site 2018-2020
Figure 2e.
Figure 2e.
Diagnosis ratios for MI by site 2018-2020
See this image and copyright information in PMC

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