Patricia Cerrito

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...

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

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

With the healthcare industry becoming increasingly more competitive, there exists a need for medical institutions to improve both the efficiency and the quality of their...

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

In this case, we provide some basic information concerning the statistical methods used throughout this text. These methods include visualization by kernel density estimation...

The study presents the analysis of the results of a health survey that focuses on the health risk behaviors and attitudes in adolescents that result in teenage obesity....

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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....

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...

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...

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...

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...

The purpose of this chapter is to demonstrate how text mining can be used to reduce the number of levels in a categorical variable to then use the variable in a predictive model....