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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

Outcome of the Variability Explorer Tool on the full cluster of depression ICD-9 codes
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f1-2092363: Outcome of the Variability Explorer Tool on the full cluster of depression ICD-9 codes

Mentions: To illustrate VET’s variability plot, case examples of depression are presented. Depression is commonly tracked on patients seen in primary care and therefore offers good natural examples of variability in EHR data. ICD-9 codes for depression used in this study include 296.2× (Major Depressive Disorder, single episode), 296.3× (Major Depressive Disorder, recurrent), 300.4 (Dysthymic disorder), and 311 (Depressive Disorder, Not Otherwise Specified). Figure 1 shows VET’s visualization of the variability in the proportion of patients with any of the selected ICD-9 depression codes in each of the years for which data are available. The horizontal axis represents the time period in which depression codes are available in the database, 1990 to 2013. Blue boxes in a given year represent where approximately 95.4% of data points are distributed across clinics. The number of clinics providing data to the tool can vary from year to year. Therefore, a taller box represents more variation in prevalence of these depression diagnoses between clinics in the given year. Variability across years can be inferred from comparing the height of boxes over time.


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)

Outcome of the Variability Explorer Tool on the full cluster of depression ICD-9 codes
© Copyright Policy
Related In: Results  -  Collection

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

f1-2092363: Outcome of the Variability Explorer Tool on the full cluster of depression ICD-9 codes
Mentions: To illustrate VET’s variability plot, case examples of depression are presented. Depression is commonly tracked on patients seen in primary care and therefore offers good natural examples of variability in EHR data. ICD-9 codes for depression used in this study include 296.2× (Major Depressive Disorder, single episode), 296.3× (Major Depressive Disorder, recurrent), 300.4 (Dysthymic disorder), and 311 (Depressive Disorder, Not Otherwise Specified). Figure 1 shows VET’s visualization of the variability in the proportion of patients with any of the selected ICD-9 depression codes in each of the years for which data are available. The horizontal axis represents the time period in which depression codes are available in the database, 1990 to 2013. Blue boxes in a given year represent where approximately 95.4% of data points are distributed across clinics. The number of clinics providing data to the tool can vary from year to year. Therefore, a taller box represents more variation in prevalence of these depression diagnoses between clinics in the given year. Variability across years can be inferred from comparing the height of boxes over time.

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