Graduation Year

2014

Document Type

Dissertation

Degree

Ph.D.

Degree Granting Department

Electrical Engineering

Major Professor

Wilfrido Moreno, Ph.D.

Co-Major Professor

Grisselle Centeno, Ph.D.

Committee Member

Paris Wiley, Ph.D.

Committee Member

Chung Seop Jeong. Ph.D.

Committee Member

Yaroslav Shtogun, Ph.D.

Keywords

Big Data, Control Systems, Modelling, System Design, Systems Engineering

Abstract

Healthcare is in urgent need of an effective way to manage the complexity it of its systems and to prepare quickly for immense changes in the economics of healthcare delivery and reimbursement. Centers for Medicare & Medicaid Services (CMS) releases policies affecting inpatient and long-term care hospitals policies that directly affect reimbursement and payment rates. One of these policy changes, a quality-reporting program called Hospital Inpatient Quality Reporting (IQR), will effect approximately 3,400 acute-care and 440 long-term care hospitals. IQR sets guidelines and measures that will contain financial incentives and penalties based on the quality of care provided. CMS, the largest healthcare payer, is aggressively promoting high quality of care by linking payment incentives to outcomes. With CMS assessing each hospital's performance by comparing its Quality Achievements and Quality Improvement scores, there is a growing need and demand to understand these quality measures under the context of patient care, data management and system integration. This focus on patient-centered quality care is difficult for healthcare systems due to the lack of a systemic view of the patient and patient care. This research uniquely addresses the hospital's need to meet these challenges by presenting a healthcare specific framework and methodology for translating data on quality metrics into actionable processes and feedback to produce the desired quality outcome. The solution is based on a patient-care level process ontology, rather than the technology itself, and creates a bridge that applies systems engineering principles to permit observation and control of the system. This is a transformative framework conceived to meet the needs of the rapidly changing healthcare landscape. Without this framework, healthcare is dealing with outcomes that are six to seven months old, meaning patients may not have been cared for effectively. In this research a framework and methodology called the Healthcare Ontology Based Systems Engineering Model (HOB-SEM) is developed to allow for observability and controllability of compartmental healthcare systems. HOB-SEM applies systems and controls engineering principles to healthcare using ontology as the method and the data lifecycle as the framework. The ontology view of patient-level system interaction and the framework to deliver data management and quality lifecycles enables the development of an agile systemic healthcare view for observability and controllability

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