TY - JOUR
T1 - Implementation of Outpatient Infectious Diseases E-Consults at a Safety Net Healthcare System
AU - Medford, Richard J.
AU - Granger, Madison
AU - Pickering, Madison
AU - Lehmann, Christoph U.
AU - Mayorga, Christian
AU - King, Helen
N1 - Publisher Copyright:
© The Author(s) 2022.
PY - 2022/7
Y1 - 2022/7
N2 - Background. Safety net healthcare systems have high patient volumes and significant demands for specialty care including infectious diseases (ID) consultations. Electronic ID consults (E-consults) can lessen this burden by providing an alternative to face-to-face ID referrals and decreasing financial, time, and travel constraints on patients. This system could increase access to ID care for patients in limited-resource settings. Methods. We described characteristics of all outpatient ID E-consults at Parkland Health in Dallas, Texas, from March 2018 to February 2021. We used modeling to determine which characteristics influenced conversion of E-consults to clinic visits and integrated these data into a predictive model for face-to-face conversion. Results. For 725 E-consults, common E-consult topics included 118 (16%) latent tuberculosis, 116 (16%) syphilis, and 76 (10%) gastrointestinal infections. Nearly two-thirds of E-consults (456 [63%]) were requested by primary care providers. The majority (78%) were resolved without a face-to-face ID visit. Osteomyelitis, nontuberculous mycobacterial, and gastrointestinal questions frequently required face-to-face visits at rates of 65%, 49%, and 32%, respectively. Our logistic regression model predicted the need for a face-to-face visit with 80% accuracy and an area under the receiver operating characteristic curve of 0.72. Conclusions. An outpatient ID E-consult program at a safety net healthcare system was an effective tool to provide timely input on common ID topics. E-consults were requested by a range of providers, and most were completed without a face-to-face visit. Predictive modeling identified important characteristics of E-consults and predicted conversion to face-to-face visits with reasonable accuracy.
AB - Background. Safety net healthcare systems have high patient volumes and significant demands for specialty care including infectious diseases (ID) consultations. Electronic ID consults (E-consults) can lessen this burden by providing an alternative to face-to-face ID referrals and decreasing financial, time, and travel constraints on patients. This system could increase access to ID care for patients in limited-resource settings. Methods. We described characteristics of all outpatient ID E-consults at Parkland Health in Dallas, Texas, from March 2018 to February 2021. We used modeling to determine which characteristics influenced conversion of E-consults to clinic visits and integrated these data into a predictive model for face-to-face conversion. Results. For 725 E-consults, common E-consult topics included 118 (16%) latent tuberculosis, 116 (16%) syphilis, and 76 (10%) gastrointestinal infections. Nearly two-thirds of E-consults (456 [63%]) were requested by primary care providers. The majority (78%) were resolved without a face-to-face ID visit. Osteomyelitis, nontuberculous mycobacterial, and gastrointestinal questions frequently required face-to-face visits at rates of 65%, 49%, and 32%, respectively. Our logistic regression model predicted the need for a face-to-face visit with 80% accuracy and an area under the receiver operating characteristic curve of 0.72. Conclusions. An outpatient ID E-consult program at a safety net healthcare system was an effective tool to provide timely input on common ID topics. E-consults were requested by a range of providers, and most were completed without a face-to-face visit. Predictive modeling identified important characteristics of E-consults and predicted conversion to face-to-face visits with reasonable accuracy.
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U2 - 10.1093/ofid/ofac341
DO - 10.1093/ofid/ofac341
M3 - Article
C2 - 35903155
AN - SCOPUS:85159367168
SN - 2328-8957
VL - 9
JO - Open Forum Infectious Diseases
JF - Open Forum Infectious Diseases
IS - 7
M1 - ofac341
ER -