Large-Scale Data Mining to Optimize Patient-Centered Scheduling at Health Centers.

Patient-centered appointment access is of critical importance at community health centers (CHCs) and its optimal implementation entails the use of advanced data analytics. This study seeks to optimize patient-centered appointment scheduling through data mining of Electronic Health Record/Practice Management (EHR/PM) systems. Data was collected from different EHR/PM systems in use at three CHCs across the state of Indiana and integrated into a multidimensional data warehouse.

Identifying Successful Practices to Overcome Access to Care Challenges in Community Health Centers: A "Positive Deviance" Approach.

Background: Despite health care access challenges among underserved populations, patients, providers, and staff at community health clinics (CHCs) have developed practices to overcome limited access. These "positive deviant" practices translate into organizational policies to improve health care access and patient experience.

Objective: To identify effective practices to improve access to health care for low-income, uninsured or underinsured, and minority adults and their families.

A Multidimensional Data Warehouse for Community Health Centers.

Community health centers (CHCs) play a pivotal role in healthcare delivery to vulnerable populations, but have not yet benefited from a data warehouse that can support improvements in clinical and financial outcomes across the practice. We have developed a multidimensional clinic data warehouse (CDW) by working with 7 CHCs across the state of Indiana and integrating their operational, financial and electronic patient records to support ongoing delivery of care. We describe in detail the rationale for the project, the data architecture employed, the content of the data warehouse, along with a description of the challenges experienced and strategies used in the development of this repository that may help other researchers, managers and leaders in health informatics.

Effects of data transformation methods on classification of patients diagnosed with myocardial infarction.

Large datasets may contain redundant data. Variable selection methods that select most relevant variables in the data set, fail to consider the interaction between the variables. Data transformation methods are used to transfer the original data to a new dimension and capture the most significant information within the data set.

Automated blood sampling and glucose sensing in critical care settings.

Background: Tight glycemic control (TGC) studies in intensive care units (ICU) have shown substantial improvements in clinical outcomes. However, implementation of TGC in ICU practice is partly constrained by the lack of automated continuous blood glucose monitoring systems that can facilitate clinically accurate feedback of glycemic data. The aim of this work is to develop a portable automated blood sampling system for integration with a glucose sensor for use in critical care settings.