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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Project description:ImportanceResponding to the substantial research on the relationship between social risk factors and health, enthusiasm has grown around social risk screening in health care settings, and numerous US health systems are experimenting with social risk screening initiatives. In the absence of standard social risk screening recommendations, some health systems are exploring using publicly available community-level data to identify patients who live in the most vulnerable communities as a way to characterize patient social and economic contexts, identify patients with potential social risks, and/or to target social risk screening efforts.ObjectiveTo explore the utility of community-level data for accurately identifying patients with social risks by comparing the social deprivation index score for the census tract where a patient lives with patient-level social risk screening data.Design, setting, and participantsCross-sectional study using patient-level social risk screening data from the electronic health records of a national network of community health centers between June 24, 2016, and November 15, 2018, linked to geocoded community-level data from publicly available sources. Eligible patients were those with a recorded response to social risk screening questions about food, housing, and/or financial resource strain, and a valid address of sufficient quality for geocoding.ExposuresSocial risk screening documented in the electronic health record.Main outcomes and measuresCommunity-level social risk was assessed using census tract-level social deprivation index score stratified by quartile. Patient-level social risks were identified using food insecurity, housing insecurity, and financial resource strain screening responses.ResultsThe final study sample included 36 578 patients from 13 US states; 22 113 (60.5%) received public insurance, 21 181 (57.9%) were female, 17 578 (48.1%) were White, and 10 918 (29.8%) were Black. Although 6516 (60.0%) of those with at least 1 social risk factor were in the most deprived quartile of census tracts, patients with social risk factors lived in all census tracts. Overall, the accuracy of the community-level data for identifying patients with and without social risks was 48.0%.Conclusions and relevanceAlthough there is overlap, patient-level and community-level approaches for assessing patient social risks are not equivalent. Using community-level data to guide patient-level activities may mean that some patients who could benefit from targeted interventions or care adjustments would not be identified.

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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