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01.12.2012 | Research article | Ausgabe 1/2012 Open Access

BMC Medical Informatics and Decision Making 1/2012

Implementation of automated reporting of estimated glomerular filtration rate among Veterans Affairs laboratories: a retrospective study

Zeitschrift:
BMC Medical Informatics and Decision Making > Ausgabe 1/2012
Autoren:
Rasheeda K Hall, Virginia Wang, George L Jackson, Bradley G Hammill, Matthew L Maciejewski, Elizabeth M Yano, Laura P Svetkey, Uptal D Patel
Wichtige Hinweise

Electronic supplementary material

The online version of this article (doi:10.​1186/​1472-6947-12-69) contains supplementary material, which is available to authorized users.

Competing interests

The authors declare that they have no competing interests.

Authors' contributions

RH was responsible for study design, data collection, statistical analysis, interpretation of results, and manuscript preparation. VW participated in study design, interpretation of results, and revision of the manuscript. GJ participated in study design, interpretation of results, data acquisition, and revision of the manuscript. BH assisted with interpretation of results. MLM participated in study design and interpretation of results. EMY participated in study design, data acquisition, and interpretation of results. LS participated in study design and revision of the manuscript. UDP participated in study design, interpretation of results, and revision of the manuscript. All authors read and approved the final manuscript.

Abstract

Background

Automated reporting of estimated glomerular filtration rate (eGFR) is a recent advance in laboratory information technology (IT) that generates a measure of kidney function with chemistry laboratory results to aid early detection of chronic kidney disease (CKD). Because accurate diagnosis of CKD is critical to optimal medical decision-making, several clinical practice guidelines have recommended the use of automated eGFR reporting. Since its introduction, automated eGFR reporting has not been uniformly implemented by U. S. laboratories despite the growing prevalence of CKD. CKD is highly prevalent within the Veterans Health Administration (VHA), and implementation of automated eGFR reporting within this integrated healthcare system has the potential to improve care. In July 2004, the VHA adopted automated eGFR reporting through a system-wide mandate for software implementation by individual VHA laboratories. This study examines the timing of software implementation by individual VHA laboratories and factors associated with implementation.

Methods

We performed a retrospective observational study of laboratories in VHA facilities from July 2004 to September 2009. Using laboratory data, we identified the status of implementation of automated eGFR reporting for each facility and the time to actual implementation from the date the VHA adopted its policy for automated eGFR reporting. Using survey and administrative data, we assessed facility organizational characteristics associated with implementation of automated eGFR reporting via bivariate analyses.

Results

Of 104 VHA laboratories, 88% implemented automated eGFR reporting in existing laboratory IT systems by the end of the study period. Time to initial implementation ranged from 0.2 to 4.0 years with a median of 1.8 years. All VHA facilities with on-site dialysis units implemented the eGFR software (52%, p<0.001). Other organizational characteristics were not statistically significant.

Conclusions

The VHA did not have uniform implementation of automated eGFR reporting across its facilities. Facility-level organizational characteristics were not associated with implementation, and this suggests that decisions for implementation of this software are not related to facility-level quality improvement measures. Additional studies on implementation of laboratory IT, such as automated eGFR reporting, could identify factors that are related to more timely implementation and lead to better healthcare delivery.
Zusatzmaterial
Authors’ original file for figure 1
12911_2011_514_MOESM1_ESM.jpeg
Authors’ original file for figure 2
12911_2011_514_MOESM2_ESM.doc
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