Simulation and Modeling for Improving Access to Care for Underserved Populations
Indiana University-Purdue University Indianapolis (IUPUI) === This research, through partnership with seven Community Health Centers (CHCs) in Indiana, constructed effective outpatient appointment scheduling systems by determining care needs of CHC patients, designing an infrastructure for meaning...
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ndltd-IUPUI-oai-scholarworks.iupui.edu-1805-180952021-03-11T05:08:13Z Simulation and Modeling for Improving Access to Care for Underserved Populations Mohammadi, Iman Jones, Josette F. Berbari, Edward Liu, Xiaowen Wu, Huanmei Indiana University-Purdue University Indianapolis (IUPUI) This research, through partnership with seven Community Health Centers (CHCs) in Indiana, constructed effective outpatient appointment scheduling systems by determining care needs of CHC patients, designing an infrastructure for meaningful use of patient health records and clinic operational data, and developing prediction and simulation models for improving access to care for underserved populations. The aims of this study are 1) redesigning appointment scheduling templates based on patient characteristics, diagnoses, and clinic capacities in underserved populations; 2) utilizing predictive modeling to improve understanding the complexity of appointment adherence in underserved populations; and 3) developing simulation models with complex data to guide operational decision-making in community health centers. This research addresses its aims by applying a multi-method approach from different disciplines, such as statistics, industrial engineering, computer science, health informatics, and social sciences. First, a novel method was developed to use Electronic Health Record (EHR) data for better understanding appointment needs of the target populations based on their characteristics and reasons for seeking health, which helped simplify, improve, and redesign current appointment type and duration models. Second, comprehensive and informative predictive models were developed to better understand appointment non-adherence in community health centers. Logistic Regression, Naïve Bayes Classifier, and Artificial Neural Network found factors contributing to patient no-show. Predictors of appointment non-adherence might be used by outpatient clinics to design interventions reducing overall clinic no-show rates. Third, a simulation model was developed to assess and simulate scheduling systems in CHCs, and necessary steps to extract information for simulation modeling of scheduling systems in CHCs are described. Agent-Based Models were built in AnyLogic to test different scenarios of scheduling methods, and to identify how these scenarios could impact clinic access performance. This research potentially improves well-being of and care quality and timeliness for uninsured, underinsured, and underserved patients, and it helps clinics predict appointment no-shows and ensures scheduling systems are capable of properly meeting the populations’ care needs. 2021-12-21 2019-01-07T20:59:16Z 2019-12-21T10:30:14Z 2018-12 Dissertation http://hdl.handle.net/1805/18095 en_US |
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Indiana University-Purdue University Indianapolis (IUPUI) === This research, through partnership with seven Community Health Centers (CHCs)
in Indiana, constructed effective outpatient appointment scheduling systems by
determining care needs of CHC patients, designing an infrastructure for meaningful use of
patient health records and clinic operational data, and developing prediction and simulation
models for improving access to care for underserved populations. The aims of this study
are 1) redesigning appointment scheduling templates based on patient characteristics,
diagnoses, and clinic capacities in underserved populations; 2) utilizing predictive
modeling to improve understanding the complexity of appointment adherence in
underserved populations; and 3) developing simulation models with complex data to guide
operational decision-making in community health centers. This research addresses its aims
by applying a multi-method approach from different disciplines, such as statistics,
industrial engineering, computer science, health informatics, and social sciences. First, a
novel method was developed to use Electronic Health Record (EHR) data for better
understanding appointment needs of the target populations based on their characteristics
and reasons for seeking health, which helped simplify, improve, and redesign current
appointment type and duration models. Second, comprehensive and informative predictive
models were developed to better understand appointment non-adherence in community
health centers. Logistic Regression, Naïve Bayes Classifier, and Artificial Neural Network
found factors contributing to patient no-show. Predictors of appointment non-adherence
might be used by outpatient clinics to design interventions reducing overall clinic no-show rates. Third, a simulation model was developed to assess and simulate scheduling systems
in CHCs, and necessary steps to extract information for simulation modeling of scheduling
systems in CHCs are described. Agent-Based Models were built in AnyLogic to test
different scenarios of scheduling methods, and to identify how these scenarios could impact
clinic access performance. This research potentially improves well-being of and care
quality and timeliness for uninsured, underinsured, and underserved patients, and it helps
clinics predict appointment no-shows and ensures scheduling systems are capable of
properly meeting the populations’ care needs. === 2021-12-21 |
author2 |
Jones, Josette F. |
author_facet |
Jones, Josette F. Mohammadi, Iman |
author |
Mohammadi, Iman |
spellingShingle |
Mohammadi, Iman Simulation and Modeling for Improving Access to Care for Underserved Populations |
author_sort |
Mohammadi, Iman |
title |
Simulation and Modeling for Improving Access to Care for Underserved Populations |
title_short |
Simulation and Modeling for Improving Access to Care for Underserved Populations |
title_full |
Simulation and Modeling for Improving Access to Care for Underserved Populations |
title_fullStr |
Simulation and Modeling for Improving Access to Care for Underserved Populations |
title_full_unstemmed |
Simulation and Modeling for Improving Access to Care for Underserved Populations |
title_sort |
simulation and modeling for improving access to care for underserved populations |
publishDate |
2019 |
url |
http://hdl.handle.net/1805/18095 |
work_keys_str_mv |
AT mohammadiiman simulationandmodelingforimprovingaccesstocareforunderservedpopulations |
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