Text Mining Techniques for Healthcare Provider Quality Determination: Methods for Rank Comparisons

ISBN13: 9781605667522|ISBN10: 1605667528|EISBN13: 9781605667539|ISBN13 Softcover: 9781616924515

DOI: 10.4018/978-1-60566-752-2

MLA

Cerrito, Patricia. Text Mining Techniques for Healthcare Provider Quality Determination: Methods for Rank Comparisons. IGI Global, 2010. https://doi.org/10.4018/978-1-60566-752-2

APA

Cerrito, P. (2010). Text Mining Techniques for Healthcare Provider Quality Determination: Methods for Rank Comparisons. IGI Global. https://doi.org/10.4018/978-1-60566-752-2

Chicago

Cerrito, Patricia. Text Mining Techniques for Healthcare Provider Quality Determination: Methods for Rank Comparisons. Hershey, PA: IGI Global, 2010. https://doi.org/10.4018/978-1-60566-752-2

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The quest for quality in healthcare has led to attempts to develop models to determine which providers have the highest quality in healthcare, with the best outcomes for patients.

Text Mining Techniques for Healthcare Provider Quality Determination: Methods for Rank Comparisons discusses the general practice of defining a patient severity index in order to make risk adjustments to compare patient outcomes across multiple providers with the intent of ranking the providers in terms of quality. This innovative reference source, valuable to medical practitioners, researchers, and academicians, brings together research from across the globe focusing on how severity indices are generally defined when determining the best outcome for patient care.

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Front Materials

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Title Page

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Dedication

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Preface

Chapters

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Chapter 1

Introduction to Ranking Models (pages 1-34)

Patricia Cerrito

Risk adjustment models only consider patient condition and not patient compliance with treatment.(Rosen, Reid, Broemeling, & Rakovski, 2003) This paper suggests that health status is dependent upon health behaviors and psychosocial...

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Chapter 2

Data Visualization and Data Summary (pages 35-72)

Patricia Cerrito

Patients in a clinic or geographic area are very heterogeneous. Therefore, the distribution of patient factors will not have a normal distribution. Generally, the distribution will have a heavy tail since every patient population...

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Chapter 3

Statistical Methods (pages 73-109)

Patricia Cerrito

Ultimately, a patient severity index is used to compare patient outcomes across healthcare providers. If the outcome is mortality, logistic regression is used. If the outcome is cost, length of stay, or some other resource...

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Chapter 4

Predictive Modeling Versus Regression (pages 110-152)

Patricia Cerrito

Predictive modeling includes regression, both logistic and linear, depending upon the type of outcome variable. It can also include the generalized linear model. However, there are other types of models also available, including...

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Chapter 5

The Charleston Comorbidity Index (pages 153-201)

Patricia Cerrito

In this chapter, we consider the Charlson Comorbidity Index (CCI). This index is published, and the weights used to define risk adjustment in the logistic regression model are clearly identified as well. (Sundararajan et al., 2004)...

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Chapter 6

The All Patient Refined Diagnosis Related Group (pages 202-238)

Patricia Cerrito

In this chapter, we will discuss the APRDRG, or all patient refined diagnosis related group. It is another type of coding system that, unlike the Charlson Index, is proprietary and developed by the 3M Healthcare Company in...

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Chapter 7

Risk Adjustment Based Upon Resource Utilization (pages 239-286)

Patricia Cerrito

Resource utilization is based upon the assumption that patients with more severe problems will utilize more resources, and the most severe patients will require the most resources. This type of index assumes that no unnecessary...

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Chapter 8

Text Mining and Patient Severity Clusters (pages 287-340)

Patricia Cerrito

Text mining diagnosis codes takes advantage of the linkage across patient conditions instead of trying to force the assumption of independence. Combinations of diagnoses are used to define groups of patients. For example, patients...

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Chapter 9

Working from Claims Data (pages 341-356)

Patricia Cerrito

Claims data are more difficult to work with to extract the necessary information about patient conditions in relationship to costs. There can be multiple claims for the same patient episode from different sources. A physician visit...

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Chapter 10

Use of Risk Adjustment Models for Provider Reimbursements (pages 357-385)

Patricia Cerrito

In this chapter, we will focus on the use of patient severity indices to determine the reimbursement to healthcare providers. In order to do this, we must first examine the standard practice of reimbursing hospitals for specific DRG...

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Chapter 11

How to Check Measures for Adequacy (pages 386-392)

Patricia Cerrito

Perhaps the biggest problem when checking measures for adequacy, in addition to overlooking the fact that model assumptions are invalid, is the need to examine the model for reliability, and also to generalize a reliable result to an...

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Text Mining Techniques for Healthcare Provider Quality Determination: Methods for Rank Comparisons

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