Clinical Data Mining for Physician Decision Making and Investigating Health Outcomes: Methods for Prediction and Analysis

Clinical Data Mining for Physician Decision Making and Investigating Health Outcomes: Methods for Prediction and Analysis

Patricia Cerrito, John Cerrito

Copyright: © 2010 |Pages: 370

ISBN13: 9781615209057|ISBN10: 1615209050|EISBN13: 9781615209064

DOI: 10.4018/978-1-61520-905-7

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MLA

Cerrito, Patricia, and John Cerrito. Clinical Data Mining for Physician Decision Making and Investigating Health Outcomes: Methods for Prediction and Analysis. IGI Global, 2010. https://doi.org/10.4018/978-1-61520-905-7

APA

Cerrito, P. & Cerrito, J. (2010). Clinical Data Mining for Physician Decision Making and Investigating Health Outcomes: Methods for Prediction and Analysis. IGI Global. https://doi.org/10.4018/978-1-61520-905-7

Chicago

Cerrito, Patricia, and John Cerrito. Clinical Data Mining for Physician Decision Making and Investigating Health Outcomes: Methods for Prediction and Analysis. Hershey, PA: IGI Global, 2010. https://doi.org/10.4018/978-1-61520-905-7

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The investigation of healthcare databases can be used to examine physician decisions and develop evidence-based treatment guidelines that optimize patient outcomes.

Clinical Data Mining for Physician Decision Making and Investigating Health Outcomes: Methods for Prediction and Analysis demonstrates how concern for detail in datasets and the use of data mining techniques can extract important and meaningful knowledge from healthcare databases. Basic information on processing data with step-by-step instructions is provided, allowing readers to use their own data and follow the instructions to find meaningful results.

Table of Contents

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Front Materials

Title Page

Copyright Page

Dedication

Preface

Acknowledgment

Chapters

Chapter 1

Preprocessing the Data (pages 1-10)

Patricia Cerrito, John Cerrito

In this book, we provide tools that are needed to investigate administrative and clinical databases that are routinely collected in the support of patient treatment. Often, these databases are large and require non-traditional...

Chapter 2

Errors and Missing Values in the Dataset (pages 11-18)

Patricia Cerrito, John Cerrito

Many of the datasets provided by the federal government have been well cleaned. However, like many other datasets collected for health outcomes research, these datasets contain errors and missing values. Both errors and missing...

Chapter 3

Introduction to the Use of MEPS (Medical Expenditure Panel Survey) (pages 19-56)

Patricia Cerrito, John Cerrito

We will begin with data from the Medical Expenditure Panel Survey and use it throughout the text. This dataset has been provided since 1996 and contains yearly information concerning every interaction with the healthcare profession...

Chapter 4

Preprocessing Medpar Data (pages 57-68)

Patricia Cerrito, John Cerrito

Medicare data provide information that hospitals submit for billing purposes, Medpar, or Medicare Provider Analysis and Review. It is publicly available (for a fee) at http://www.cms.hhs.gov/Limited-...

Chapter 5

Extracting Data from the National Inpatient Sample (pages 69-93)

Patricia Cerrito, John Cerrito

In the other type of health care database that we discuss in this chapter, there are multiple columns for each patient observation. It is more difficult to find both the most frequently occurring codes, or to find patients with...

Chapter 6

Creating a One-to-One Relationship in the Data from a Many-to-Many (pages 94-115)

Patricia Cerrito, John Cerrito

One of the biggest problems in dealing with healthcare data is that there can be multiple patient events for the same individual. Without finding some way to combine these many observations into one row in the dataset, it is very...

Chapter 7

Merging Different Datasets to Allow for a Complete Analysis (Inpatient, Outpatient, Physician Visits, Medications) (pages 116-153)

Patricia Cerrito, John Cerrito

If we want to gain a complete picture of patient treatment, we need to examine all patient encounters with the medical profession. Then, we need to divide them into episodes of care. For example, an encounter with a physician might...

Chapter 8

Introduction to Analysis Using Time Components (pages 154-192)

Patricia Cerrito, John Cerrito

The introduction of a time component requires the use of statistical methods that can utilize dependent data. The assumption of independence that is required for regression models is no longer applicable. In this section, we will...

Chapter 9

More Survival Data Mining of Multiple Time of Endpoints (pages 193-214)

Patricia Cerrito, John Cerrito

Survival analysis is almost always reserved for an endpoint of mortality or recurrence. (Mantel, 1966) However, it can be used for many different types of endpoints as the survival distribution is defined as the time to an event....

Chapter 10

Using the Data to Define Patient Compliance (pages 215-233)

Patricia Cerrito, John Cerrito

Patient compliance with treatment is essential. However, it is difficult to examine the issue of compliance from claims and administrative databases that include no direct input from patients. In order to measure compliance, we now...

Chapter 11

Compression of Diagnosis and Procedure Codes (pages 234-248)

Patricia Cerrito, John Cerrito

Each of the datasets has many different diagnosis and procedure codes to represent a patient’s condition. There are thousands of potential codes, and millions of potential combinations of codes. In order to use patient diagnosis and...

Chapter 12

Comparisons of Patient Severity Indices (pages 249-286)

Patricia Cerrito, John Cerrito

In this section, we will briefly discuss two methods of ranking patient severity. The first method we consider is the AHRQ comorbidities, which is a collection of 30 patient conditions that are used to define an index consisting of...

Chapter 13

Decision Trees and Their Development: Use of Data to Determine the Quality of Care (pages 287-304)

Patricia Cerrito, John Cerrito

Decision trees are developed to support physicians who must make treatment decisions. Risk estimates are used to find the optimal treatment pathway for a group of patients. Unfortunately, decision trees often are developed in the...

Chapter 14

Example of Diabetes Using CMS Data (pages 305-317)

Patricia Cerrito, John Cerrito

We want to examine the treatment of patients with diabetes, and the reasons these patients are in the hospital. In order to do this, we must consider a cohort of patients who have diabetes and who are treated with medication and with...

Chapter 15

Example of Breathing Illnesses, Asthma and COPD Using MEPS Data (pages 318-328)

Patricia Cerrito, John Cerrito

Asthma and COPD (chronic obstructive pulmonary disease) require maintenance and emergency medication. We want to examine the use of the required medications, as well as to examine the need for emergency treatment when the treatment...

Chapter 16

Example of Wound Care Using Medpar Data (pages 329-345)

Patricia Cerrito, John Cerrito

Medpar Data is used for billing Medicare; it typically is “packed” into different sheets of data. The first step required to use the data is to “unpack” it into its component sheets and then to merge the necessary data. Once that is...

Chapter 17

Discussion (pages 346-351)

Patricia Cerrito, John Cerrito

Now that the data are more readily available for outcomes research and the techniques to analyze that data are available, we need to use the tools to investigate the total complexity of patient care. We should no longer rely upon...

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