Dataset Information

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

ABSTRACT:

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.

SUBMITTER: Mohammadi I

PROVIDER: S-EPMC6243417 | biostudies-literature | 2018 Jan-Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

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

Mohammadi Iman I Wu Huanmei H Turkcan Ayten A Toscos Tammy T Doebbeling Bradley N BN

Journal of primary care & community health 20180101

<h4>Objectives</h4>Using predictive modeling techniques, we developed and compared appointment no-show prediction models to better understand appointment adherence in underserved populations.<h4>Methods and materials</h4>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 crit ...[more]

PMID: 30451063

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Dataset Information

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

Objectives

Methods and materials

Results

Discussion

Conclusion

Publications

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

Similar Datasets

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