Data Mining to Identify Quality of Care Factors Associated with Liability Claims and Risk Management Strategies in Florida Nursing Homes

Author

Ernande Fortune, Lynn University

Date of Award

7-2009

Document Type

Dissertation

Granting Institution

Lynn University

Degree Name

Doctor of Philosophy (PHD)

Degree Program

Global Leadership - with a specialization in Corporate and Organizational Management

Department

College of Business and Management

First Advisor

Jeanette Francis

Second Advisor

Ralph Norcio

Third Advisor

James Miller

Abstract

The challenges facing the nursing home industry are increasingly important to the population of the United States. As the population grows older, the number of people that will require services from a nursing home will increase. In today's environment the nursing home business is facing many challenges that will define the future of the industry. Among them is the plaintiff attorney lawsuit against nursing homes, rising liability costs and vulnerability to lawsuits. A reduction in liability claims should allow nursing homes in Florida to remain solvent and stay in business to take care of those who cannot take care of themselves. This demographic shift has to be supported by a vibrant, efficient, and high-quality nursing home system.

The purpose of this study was to examine the influence that quality of care factors and risk management strategies have on liability claims in nursing homes, and to create a risk management model. Four research questions and a hypothesis were tested. The research design was an exploratory and predictive quantitative design using data mining of secondary data. The study analyzed the quality of care factors associated with liability claims and model risk management in order to predict and generate strategies that can decrease claims in Florida nursing homes. The data sets that were used in the study consisted of data from 106 nursing homes from 67 counties in Florida. The study used data mining software application to conduct data mining analysis and create risk management models. The data models developed were used to identify quality of care factors associated with liability claims in Florida nursing homes.

Findings indicated that (a) there was a strong correlation between quality of care indicators and the incidents that led to liability claims; (b) various risk management strategies have been used in Florida, of which the most common seem to be methods for training staff; (c) while various risk management strategies such as training and educating staff do have an effect on the number and severity of lawsuits, they are not necessarily sufficient to decrease nursing homes' exposure to risk substantially; and, (d) the success of the measurements indicated that there are indeed diagnostic tools that can identify areas of risk, but the external factors noted in the answer to the previous question still apply.

The implications and recommendations were essentially that the solution to the problems facing the nursing home industry requires a holistic focus on the legal and financial context of that industry. That holistic focus, in conjunction with efforts to further improve the nursing home industry itself, could help ensure that as millions of Americans begin to retire, they have the necessary resources and infrastructure to support them.

Recommended Citation

Fortune, E. (2009). Data Mining to Identify Quality of Care Factors Associated with Liability Claims and Risk Management Strategies in Florida Nursing Homes [Doctoral dissertation, Lynn University]. SPIRAL. https://spiral.lynn.edu/etds/243