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Visualizing Anomalies in Electronic Health Record Data: The Variability Explorer Tool.

Estiri H, Chan YF, Baldwin LM, Jung H, Cole A, Stephens KA - AMIA Jt Summits Transl Sci Proc (2015)

Bottom Line: VET applies a simple statistical method to approximate probability distribution functions for the prevalence of any given diagnosis codes to visualize between-clinic and across-year variability.Even though data quality research often characterizes variability as an indicator for data quality, variability can also reflect real characteristics of data, such as practice-level, and patient-level issues.Researchers benefit from recognizing variability in early stages of research to improve their research design and ensure validity and generalizability of research findings.

View Article: PubMed Central - PubMed

Affiliation: Institute of Translational Health Sciences, University of Washington, Seattle, WA.

ABSTRACT
As Electronic Health Record (EHR) systems are becoming more prevalent in the U.S. health care domain, the utility of EHR data in translational research and clinical decision-making gains prominence. Leveraging primay· care-based. multi-clinic EHR data, this paper introduces a web-based visualization tool, the Variability Explorer Tool (VET), to assist researchers with profiling variability among diagnosis codes. VET applies a simple statistical method to approximate probability distribution functions for the prevalence of any given diagnosis codes to visualize between-clinic and across-year variability. In a depression diagnoses use case, VET outputs demonstrated substantial variability in code use. Even though data quality research often characterizes variability as an indicator for data quality, variability can also reflect real characteristics of data, such as practice-level, and patient-level issues. Researchers benefit from recognizing variability in early stages of research to improve their research design and ensure validity and generalizability of research findings.

No MeSH data available.


Related in: MedlinePlus

Variability in depression dato using ICD-9 code 311
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f3-2092363: Variability in depression dato using ICD-9 code 311

Mentions: In addition to visualizing variability between clinics and across years using a cluster of diagnoses (as shown in Figure 1), VET can be used to explore variability at the single diagnosis level. For example, the researcher can use VET plots to break down the cluster of diagnoses into a single diagnosis VET plot to compare variability across clinics, years, and the individual diagnoses. Figures 2 and 3 are VET plots using the 296.3× and 311 ICD-9 codes, respectively. Both between-clinic and across-year variability differ when data are pulled for these two different ICD-9 codes. It also appears that ICD-9 code 311 was a more prevalent depression diagnosis than ICD-9 code 296.3 in the dataset.


Visualizing Anomalies in Electronic Health Record Data: The Variability Explorer Tool.

Estiri H, Chan YF, Baldwin LM, Jung H, Cole A, Stephens KA - AMIA Jt Summits Transl Sci Proc (2015)

Variability in depression dato using ICD-9 code 311
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4525227&req=5

f3-2092363: Variability in depression dato using ICD-9 code 311
Mentions: In addition to visualizing variability between clinics and across years using a cluster of diagnoses (as shown in Figure 1), VET can be used to explore variability at the single diagnosis level. For example, the researcher can use VET plots to break down the cluster of diagnoses into a single diagnosis VET plot to compare variability across clinics, years, and the individual diagnoses. Figures 2 and 3 are VET plots using the 296.3× and 311 ICD-9 codes, respectively. Both between-clinic and across-year variability differ when data are pulled for these two different ICD-9 codes. It also appears that ICD-9 code 311 was a more prevalent depression diagnosis than ICD-9 code 296.3 in the dataset.

Bottom Line: VET applies a simple statistical method to approximate probability distribution functions for the prevalence of any given diagnosis codes to visualize between-clinic and across-year variability.Even though data quality research often characterizes variability as an indicator for data quality, variability can also reflect real characteristics of data, such as practice-level, and patient-level issues.Researchers benefit from recognizing variability in early stages of research to improve their research design and ensure validity and generalizability of research findings.

View Article: PubMed Central - PubMed

Affiliation: Institute of Translational Health Sciences, University of Washington, Seattle, WA.

ABSTRACT
As Electronic Health Record (EHR) systems are becoming more prevalent in the U.S. health care domain, the utility of EHR data in translational research and clinical decision-making gains prominence. Leveraging primay· care-based. multi-clinic EHR data, this paper introduces a web-based visualization tool, the Variability Explorer Tool (VET), to assist researchers with profiling variability among diagnosis codes. VET applies a simple statistical method to approximate probability distribution functions for the prevalence of any given diagnosis codes to visualize between-clinic and across-year variability. In a depression diagnoses use case, VET outputs demonstrated substantial variability in code use. Even though data quality research often characterizes variability as an indicator for data quality, variability can also reflect real characteristics of data, such as practice-level, and patient-level issues. Researchers benefit from recognizing variability in early stages of research to improve their research design and ensure validity and generalizability of research findings.

No MeSH data available.


Related in: MedlinePlus