What Are the Characteristics That Explain Hospital Quality? A Longitudinal Pridit Approach

• DOI: http://doi.org/10.1111/rmir.12017
• Author: Dominique M. Comer,Robert D. Lieberthal
• Date: 01 March 2014
• Published date: 01 March 2014

Risk Management and Insurance Review

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Risk Management and Insurance Review, 2013, Vol.17, No. 1, 17-35

DOI: 10.1111/rmir.12017

INVITED ARTICLE

WHAT ARE THE CHARACTERISTICS THAT EXPLAIN

HOSPITAL QUALITY?ALONGITUDINAL PRIDIT APPROACH

Robert D. Lieberthal

Dominique M. Comer

ABSTRACT

Health outcomes vary substantially between high- and low-quality institutions,

meaning the difference between life and death in some cases. The prior litera-

ture has identified a number of variables that can be used to determine hospital

quality, but methodologies for combining variables into an overall measure of

hospital quality are not well developed. This analysis builds on the prior in-

vestigation of hospital quality by evaluating a method originally developed for

the detection of health-care fraud, Pridit, in the context of determining hospital

quality. We developed a theoretical model to justify the application of Pridit to

the hospital quality setting and then applied the Pridit method to a national,

multiyear data set on U.S. hospital quality variables and outcomes. The results

demonstrate how the Pridit method can be used predictively,in order to predict

future health outcomes based on currently available quality measures. These

results inform the use of Pridit, and other unsupervised learning methods, in

fraud detection and other settings where valid and reliable outcomes variables

are difficult to obtain. The empirical results obtained in this study may also be

of use to health insurers and policymakers who aim to improve quality in the

hospital setting.

Robert D. Lieberthal is an Assistant Professor at the Thomas Jefferson University, Jefferson

School of Population Health, 901 Walnut St, 10th Floor, Philadelphia, PA 19107; phone: (215)

503–3852; fax: (215) 923–7583; e-mail: robert.lieberthal@jefferson.edu. Dominique M. Comer is

a Health Economics and Outcomes Research Fellow at the Thomas Jefferson University, Jeffer-

son School of Population Health, 901 Walnut St, 10th Floor, Philadelphia, PA 19107; e-mail:

dominique.comer@jefferson.edu. The Society of Actuaries provided funding for this work

through their Health Section. Part of Dr. Comer’s time spent on this research was funded by

a Postdoctoral Fellowship award on Health Outcomes from the PhRMA Foundation. Wewould

like to thank the Society of Actuaries Project Oversight Group (POG), which provided advice

and guidance during the course of the project. Katie O’Connell also provided valuable research

assistance. A version of this work was originally published as a report by the Society of Actuaries

under the title “Validatingthe PRIDIT Method for Determining Hospital Quality with Outcomes

Data” and presented at the 2013 ARIA Annual Meeting. Muhammed Altuntas provided valuable

comments and feedback.

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18 RISK MANAGEMENT AND INSURANCE REVIEW

INTRODUCTION

Background

Hospitals are a critical setting for health-care quality improvement in the United States;

31 percent ($814 billion) of the $2.6 trillion of health care delivered in 2010 was spent in

the hospital (Martin et al., 2012). Quality of care is quite variable throughout the United

States, with much variation in services provided. The overuse and underuse of such

services have been identified as critical problems within the U.S. health-care system

(Agency for Healthcare Research and Quality, 2002). Medical errors in the hospital

setting that may result from poor quality of care account for approximately $17 billion

each year (Van Den Bos et al., 2011). Thus, any methodology that can provide evidence

about the overall quality of hospitals, their trends in quality over time, and the variables

that indicate high-quality care have the potential to improve the quality and lower the

costs of U.S. health care.

One major challenge in the study of overall hospital quality is that general hospitals

provide a wide variety of services and perform a number of different functions therein.

Hospitals care for patients with a range of chronic and acute conditions. Further,adding

in the complexity of the U.S. health-care system, investigating the quality and the out-

comes of health care has become substantially more difficult.

Health insurers have historically played a limited part in the push to improve quality,

but their efforts are growing. An example of this is the growing aspect of “pay-for-

performance” programs as a part of many managed care contracts. A number of quality

improvement efforts are also rapidly appearing at the national level, such as the National

Quality Forum’s listing of “Never Events”—medical errors that should never occur—

and policy recommendations to stop paying for these largely preventable occurrences.

Medicare has used risk-sharing arrangements to redistribute withheld money from

hospitals to those that meet certain benchmarks in mortality and readmissions rates.

Despite these changes, payors still frequently pay for care that is substandard; it is a

common industry practice to reimburse hospitals for corrective care, which reduces the

incentive for hospitals to increase their quality of care. Only recentlyhave insurers begun

to define specific “Never Events” that they will not pay for (Milstein, 2009).

Literature Review

When the hospital is the unit of observation, determining “high quality” is a challenge.

Objectifying the quality of hospital has proved to be a difficult and controversial topic.

Multiple methodologies exist for creating measures for hospital processes and outcomes

(i.e., Shahian et al., 2010; Lovaglio, 2012). Despite this disagreement in measuring qual-

ity, multiple programs and interventions exist that attempt to improve hospital quality.

Programs such as “pay for performance” and “meaningful use” utilize financial incen-

tives and disincentives in an attempt to improve the quality of care. Organizations such

as the Leapfrog Group (The Leapfrog Group, 2010) create public report cards to allow

for direct comparisons between hospitals and specialty clinics. The critical piece that

is missing from all of these initiatives is that they do not quantify the degree to which

different factors contribute to overall quality. In other words, while many analyses focus

on quality by hospital type, on improving the processes of care delivery, or on improving

health-care outcomes, few prior studies have combined these types of analyses into an

overall picture of hospital quality.