Data Analytics and Modeling for Appointment No-show in Community Health Centers
Genre
Journal ArticleDate
2018-11-01Author
Mohammadi, IWu, H
Turkcan, A
Toscos, T
Doebbeling, BN
Subject
access to careappointment non-adherence
community health centers
electronic health records
predictive modeling
Adolescent
Adult
Appointments and Schedules
Bayes Theorem
Cell Phone
Child
Child, Preschool
Community Health Centers
Data Science
Electronic Health Records
Female
Humans
Infant
Logistic Models
Male
Medically Underserved Area
Middle Aged
Neural Networks, Computer
No-Show Patients
Primary Health Care
Smoking
Socioeconomic Factors
Time Factors
Young Adult
Permanent link to this record
http://hdl.handle.net/20.500.12613/4382
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Show full item recordDOI
10.1177/2150132718811692Abstract
© The Author(s) 2018. Objectives: 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.Citation to related work
SAGE PublicationsHas part
Journal of Primary Care and Community HealthADA compliance
For Americans with Disabilities Act (ADA) accommodation, including help with reading this content, please contact scholarshare@temple.eduae974a485f413a2113503eed53cd6c53
http://dx.doi.org/10.34944/dspace/4364
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