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GUIDELINES FOR COMPARING INTERVENTIONS, PREDICTING HIGH-RISK PATIENTS, AND CONDUCTING OPTIMIZATION FOR EARLY HF READMISSION

Khasawneh, Ahmad Ali
http://rave.ohiolink.edu/etdc/view?acc_num=akron1499011064948037

Abstract Details

2017, Doctor of Philosophy, University of Akron, Mechanical Engineering.
Reducing 30-day readmission for certain chronic diseases has gained healthcare provider’s attentions especially when the Center for Medicare and Medicaid Services (CMS) started penalizing hospitals for excess readmissions. Hospital readmission reduction program (HRRP) was established by CMS in 2012 and released in 2013 with 1% penalty on the total CMS reimbursement. This penalty increased in 2014 and 2015 to be at maximum 2% and 3% respectively. This study focuses on Congestive Heart Failure (CHF) which has the highest readmission rate faced with the financial impact of this Program. Our research effort on reducing preventable readmission is divided into three main parts: comparing the effectiveness of intervention strategies, finding the characteristics of patients at high-risk to be readmitted, and combining the outcomes of the first two parts to target the right patient with the right and cost-effective actions. Regarding the effectiveness of the most widely used intervention strategies in reducing preventable early readmission rate, several techniques and approaches have been implemented in this work to investigate, analyze, and compare the role of those interventions including Analytical Hierarchy Process (AHP), descriptive model, visualization, statistical analysis, and Lean Six-Sigma (LSS). More than thirty-five studies were carefully collected and analyzed to get the needed data for this research. The overall results showed that educate patients/caregivers (focusing on “Teach Back”) as prior at discharge strategy and home visit as post-discharge strategy are the most recommended strategies followed by telephone and discharge planning and/or instructions (using clear instruction sheets) intervention strategies. Readmission predictive modeling is one of the main proposed readmission reduction methods that have been extensively researched in the recent years. However, little has been done to systematically synthesize and analyze the results from the existing literature. Therefore, in this research initiative, the results from more than 40 studies have been collected and used to identify the most significant variables in predicting readmissions for Congestive Heart Failure (CHF) patients. Furthermore, CHF readmission data from two community hospitals in Northeast Ohio were analyzed and compared with these findings. The outcomes of implementing numerous predictive models showed a good match. Multiple/univariate logistic regression and univariate chi-square tests were used to identify the characteristics of patients at high-risk for readmission. The results showed that “severity of illness”, “mortality risk”, “type of payer”, “previous admission”, and “diabetes” seem to be significant predictors for readmission. combining the finding of those areas of research is still unsearched or not being released clearly. Therefore, cost optimization model has been developed in this research to systematically study the effectiveness of readmission predictive model and its financial impacts on reducing readmissions through various intervention strategies. The cost optimization model considers few key factors, such as “revenue per readmission”, “national readmission rate”, “current readmission rate”, “CMS penalty”, and “the number of high and low-risk patients” that is extracted from the confusion matrix, an output from the predictive model. The results are summarized in a set of guidelines that help hospitals in selecting the intervention strategies with the target patient population for the optimal financial gain.
Shengyong Wang, Dr. (Advisor)
Chen Ling, Dr. (Committee Member)
Gregory Morscher , Dr. (Committee Member)
Yilmaz Sozer, Dr. (Committee Member)
Nao Mimoto, Dr. (Committee Member)
134 p.
Engineering; Health Care; Health Care Management; Industrial Engineering; Management; Statistics
HF Readmission, Comparing Interventions, Analytical Hierarchy Process AHP, Lean Six-Sigma LSS, Predictive Models, Data Analytics, Logistic Regression, Chi-Square, Optimization Model,

Recommended Citations

Citations

  • Khasawneh, A. A. (2017). GUIDELINES FOR COMPARING INTERVENTIONS, PREDICTING HIGH-RISK PATIENTS, AND CONDUCTING OPTIMIZATION FOR EARLY HF READMISSION [Doctoral dissertation, University of Akron]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=akron1499011064948037

    APA Style (7th edition)

  • Khasawneh, Ahmad. GUIDELINES FOR COMPARING INTERVENTIONS, PREDICTING HIGH-RISK PATIENTS, AND CONDUCTING OPTIMIZATION FOR EARLY HF READMISSION. 2017. University of Akron, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=akron1499011064948037.

    MLA Style (8th edition)

  • Khasawneh, Ahmad. "GUIDELINES FOR COMPARING INTERVENTIONS, PREDICTING HIGH-RISK PATIENTS, AND CONDUCTING OPTIMIZATION FOR EARLY HF READMISSION." Doctoral dissertation, University of Akron, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=akron1499011064948037

    Chicago Manual of Style (17th edition)

akron1499011064948037
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© 2017, some rights reserved.Creative Commons License GUIDELINES FOR COMPARING INTERVENTIONS, PREDICTING HIGH-RISK PATIENTS, AND CONDUCTING OPTIMIZATION FOR EARLY HF READMISSION by Ahmad Ali Khasawneh is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License. Based on a work at etd.ohiolink.edu.
This open access ETD is published by University of Akron and OhioLINK.