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 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.2021-12-2

A big data augmented analytics platform to operationalize efficiencies at community clinics

Indiana University-Purdue University Indianapolis (IUPUI)Community Health Centers (CHCs) play a pivotal role in delivery of primary healthcare to the underserved, yet have not benefited from a modern data analytics platform that can support clinical, operational and financial decision making across the continuum of care. This research is based on a systems redesign collaborative of seven CHC organizations spread across Indiana to improve efficiency and access to care. Three research questions (RQs) formed the basis of this research, each of which seeks to address known knowledge gaps in the literature and identify areas for future research in health informatics. The first RQ seeks to understand the information needs to support operations at CHCs and implement an information architecture to support those needs. The second RQ leverages the implemented data infrastructure to evaluate how advanced analytics can guide open access scheduling – a specific use case of this research. Finally, the third RQ seeks to understand how the data can be visualized to support decision making among varying roles in CHCs. Based on the unique work and information flow needs uncovered at these CHCs, an end to-end analytics solution was designed, developed and validated within the framework of a rapid learning health system. The solution comprised of a novel heterogeneous longitudinal clinic data warehouse augmented with big data technologies and dashboard visualizations to inform CHCs regarding operational priorities and to support engagement in the systems redesign initiative. Application of predictive analytics on the health center data guided the implementation of open access scheduling and up to a 15% reduction in the missed appointment rates. Performance measures of importance to specific job profiles within the CHCs were uncovered. This was followed by a user-centered design of an online interactive dashboard to support rapid assessments of care delivery. The impact of the dashboard was assessed over time and formally validated through a usability study involving cognitive task analysis and a system usability scale questionnaire. Wider scale implementation of the data aggregation and analytics platform through regional health information networks could better support a range of health system redesign initiatives in order to address the national ‘triple aim’ of healthcare