Data Analytics and Modeling for Appointment No-show in Community Health Centers

dc.contributor.authorMohammadi, Iman
dc.contributor.authorWu, Huanmei
dc.contributor.authorTurkcan, Ayten
dc.contributor.authorToscos, Tammy
dc.contributor.authorDoebbeling, Bradley N.
dc.contributor.departmentBioHealth Informatics, School of Informatics and Computingen_US
dc.date.accessioned2018-12-20T16:12:36Z
dc.date.available2018-12-20T16:12:36Z
dc.date.issued2018
dc.description.abstractObjectives: Using predictive modeling techniques, we developed and compared appointment no-show prediction models to better understand appointment adherence in underserved populations. Methods and Materials: We collected electronic health record (EHR) data and appointment data including patient, provider and clinical visit characteristics over a 3-year period. All patient data came from an urban system of community health centers (CHCs) with 10 facilities. We sought to identify critical variables through logistic regression, artificial neural network, and naïve Bayes classifier models to predict missed appointments. We used 10-fold cross-validation to assess the models’ ability to identify patients missing their appointments. Results: Following data preprocessing and cleaning, the final dataset included 73811 unique appointments with 12,392 missed appointments. Predictors of missed appointments versus attended appointments included lead time (time between scheduling and the appointment), patient prior missed appointments, cell phone ownership, tobacco use and the number of days since last appointment. Models had a relatively high area under the curve for all 3 models (e.g., 0.86 for naïve Bayes classifier). Discussion: Patient appointment adherence varies across clinics within a healthcare system. Data analytics results demonstrate the value of existing clinical and operational data to address important operational and management issues. Conclusion: EHR data including patient and scheduling information predicted the missed appointments of underserved populations in urban CHCs. Our application of predictive modeling techniques helped prioritize the design and implementation of interventions that may improve efficiency in community health centers for more timely access to care. CHCs would benefit from investing in the technical resources needed to make these data readily available as a means to inform important operational and policy questions.en_US
dc.eprint.versionFinal published versionen_US
dc.identifier.citationMohammadi, I., Wu, H., Turkcan, A., Toscos, T., & Doebbeling, B. N. (2018). Data Analytics and Modeling for Appointment No-show in Community Health Centers. Journal of Primary Care & Community Health, 9, 2150132718811692. https://doi.org/10.1177/2150132718811692en_US
dc.identifier.issn2150-1327en_US
dc.identifier.urihttps://hdl.handle.net/1805/18022
dc.language.isoen_USen_US
dc.publisherSAGEen_US
dc.relation.isversionof10.1177/2150132718811692en_US
dc.relation.journalJournal of Primary Care & Community Healthen_US
dc.rightsAttribution-NonCommercial 3.0 United States
dc.rights.urihttp://creativecommons.org/licenses/by-nc/3.0/us/
dc.sourcePublisheren_US
dc.subjectAccess to Careen_US
dc.subjectCommunity Health Centersen_US
dc.subjectPredictive Modelingen_US
dc.subjectAppointment Non-Adherenceen_US
dc.subjectElectronic Health Recordsen_US
dc.titleData Analytics and Modeling for Appointment No-show in Community Health Centersen_US
dc.typeArticleen_US
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