Comparison Of The Performance Of Several Data Mining Methods For Bad Debt Recovery In The Healthcare Industry

The healthcare industry, specifically hospitals and clinical organizations, are often plagued by unpaid bills and collection agency fees. These unpaid bills contribute significantly to the rising cost of healthcare. Unlike financial institutions, health care providers typically do not collect financial information about their patients.  This lack of information makes it difficult to evaluate whether a particular patient-debtor is likely to pay his/her bill.  In recent years, the industry has started to apply data mining tools to reduce bad-debt balance. This paper compares the effectiveness of five such tools - neural networks, decision trees, logistic regression, memory-based reasoning, and the ensemble model in evaluating whether a debt is likely to be repaid. The data analysis and evaluation of the performance of the models are based on a fairly large unbalanced data sample provided by a healthcare company, in which cases with recovered bad debts are underrepresented. Computer simulation shows that the neural network, logistic regression, and the combined model produced the best classification accuracy. More thorough interpretation of the results is obtained by analyzing the lift and receiver operating characteristic charts. We used the models to score all “unknown” cases, which were not pursued by a company. The best model classified about 34.8% of these cases into “good” cases. To collect bad debts more effectively, we recommend that a company first deploy and use the models, before it refers unrecovered cases to a collection agency.   &nbsp

Mentoring, Career Plateau Tendencies, Turnover Intentions And Implications For Narrowing Pay And Position Gaps Due To Gender Structural Equations Modeling

This study analyzed responses to career-related questions from a survey of experienced Canadian Certified Management Accountants (CMAs), relative experts in the field of management accounting, to address how mentoring affects turnover intentions and career plateau tendency of male and female accounting professionals in industry. In this regard, we used structural equations modeling to build and test a framework illustrating the impact of mentoring and career-related factors. Results indicate that fostering a mentoring environment within an organization can strengthen CMAs perceptions of their careers and employers. Mentoring has also been suggested to enhance womens opportunities to advance in organizations and help women break the glass ceiling. Analyses of data relating to compensation in 2007 and 2009 for a sample of female and male CEOs and operating performance of companies led by these CEOs for these years indicate that, that compensation gaps due to gender appear to be narrowing at the top management level

Mentoring, Career Plateau Tendencies, Turnover Intentions And Implications For Narrowing Pay And Position Gaps Due To Gender Structural Equations Modeling

This study analyzed responses to career-related questions from a survey of experienced Canadian Certified Management Accountants (CMAs), relative experts in the field of management accounting, to address how mentoring affects turnover intentions and career plateau tendency of male and female accounting professionals in industry. In this regard, we used structural equations modeling to build and test a framework illustrating the impact of mentoring and career-related factors. Results indicate that fostering a mentoring environment within an organization can strengthen CMAs perceptions of their careers and employers. Mentoring has also been suggested to enhance womens opportunities to advance in organizations and help women break the glass ceiling. Analyses of data relating to compensation in 2007 and 2009 for a sample of female and male CEOs and operating performance of companies led by these CEOs for these years indicate that, that compensation gaps due to gender appear to be narrowing at the top management level

The relationship between market orientation and performance in the hospital industry: A structural equations modeling approach

There is general consensus in the research literature that market orientation is related to organizational performance. This study examines this relationship in the hospital industry. One unique feature of this study is that both market orientation and performance are conceptualized as being multi-dimensional constructs. Hence the technique of Structural Equations Modeling (SEM) is used to examine the relationship. Analyses were based on market orientation and performance data obtained from 175 hospitals in a five-state region of the United States. The SEM results confirm the multi-dimensional nature of both market orientation and performance, and the strong relationship between the constructs. Interestingly, this relationship is found to be much stronger for smaller hospitals than for larger hospitals. Implications for the hospital industry are discussed. Copyright Kluwer Academic Publishers 2000market orientation, hospital performance, structural equations modeling, hospital size, customer orientation, customer service, internal quality, market development, responsiveness to customers, responsiveness to competition,