Int J Med Inform - Physician productivity and the ambulatory EHR in a large academic multi-specialty physician group.

Tópicos

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Resumo

RPOSE: The impact of the ambulatory electronic health record (EHR) on physician productivity is poorly understood. Fear of productivity loss remains a major concern for practitioners and health care delivery organizations and inhibits system adoption. This study describes the changes in physician productivity after the implementation of a commercially available ambulatory EHR system in a large academic multi-specialty physician group.METHODS: Weill Cornell faculty members implemented on the EpicCare (Epic Systems) EHR between 2001 and 2007 were identified as potential study participants. Monthly visit volume, charges, and work relative value units (wRVUs) were compared pre and post each provider's EHR implementation go-live date. Practitioners who lacked at least 6 months of pre- and post-implementation visit volume and charge data were excluded. Practitioners who did not meet pre-determined system proficiency metrics were additionally identified and became the basis of a non-adopter comparison group.RESULTS: 203 physicians met criteria for the analysis. The eligible providers were divided into an adopter and non-adopter cohort based on system proficiency benchmarks. Those practitioners who adopted the EHR had a statistically significant increase in average monthly patient visit volume of 9 visits per provider per month. The non-adopter cohort's visit volume was statistically unchanged. Both the EHR adopters and non-adopters had statistically significant increases (22% and 16% respectively) in average monthly charges in the post-implementation period. Average monthly wRVUs were statistically unchanged in the non-adopter cohort, but showed a positive and statistically significant increase of 12 wRVUs per provider per month for the adopter group. The EHR adoption group showed an incremental increase in productivity once practitioners achieved 6 or more months experience with the EHR, consistent with a "ramp-up" period. A multivariable regression model did not reveal any association between the post-EHR implementation change in wRVUs and several potential confounding variables, including baseline provider average monthly visit volume and wRVUs, date of system adoption, and specialty categorization.CONCLUSION: Provider productivity, as measured by patient visit volume, charges, and wRVUs modestly increased for a cohort of multi-specialty providers that adopted a commercially available ambulatory EHR. The productivity gain appeared to become even more pronounced after several months of system experience. This objective data may help persuade apprehensive practitioners that EHR adoption need not harm productivity. The baseline differences in productivity metrics for the adopters and non-adopters in our study suggest that there are fundamental differences in these groups. Further characterizing these differences may help predict EHR adoption success and guide future implementation strategies.

Resumo Limpo

rpose impact ambulatori electron health record ehr physician product poor understood fear product loss remain major concern practition health care deliveri organ inhibit system adopt studi describ chang physician product implement commerci avail ambulatori ehr system larg academ multispecialti physician groupmethod weill cornel faculti member implement epiccar epic system ehr identifi potenti studi particip month visit volum charg work relat valu unit wrvus compar pre post provid ehr implement goliv date practition lack least month pre postimplement visit volum charg data exclud practition meet predetermin system profici metric addit identifi becam basi nonadopt comparison groupresult physician met criteria analysi elig provid divid adopt nonadopt cohort base system profici benchmark practition adopt ehr statist signific increas averag month patient visit volum visit per provid per month nonadopt cohort visit volum statist unchang ehr adopt nonadopt statist signific increas respect averag month charg postimplement period averag month wrvus statist unchang nonadopt cohort show posit statist signific increas wrvus per provid per month adopt group ehr adopt group show increment increas product practition achiev month experi ehr consist rampup period multivari regress model reveal associ postehr implement chang wrvus sever potenti confound variabl includ baselin provid averag month visit volum wrvus date system adopt specialti categorizationconclus provid product measur patient visit volum charg wrvus modest increas cohort multispecialti provid adopt commerci avail ambulatori ehr product gain appear becom even pronounc sever month system experi object data may help persuad apprehens practition ehr adopt need harm product baselin differ product metric adopt nonadopt studi suggest fundament differ group character differ may help predict ehr adopt success guid futur implement strategi

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