Continuous Quality Improvement Initiative

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Copyright © 2020 e-Service Journal. All rights reserved. No copies of this work may be distributed in print or electronically without express written permission from Indiana University Press. doi: 10.2979/eservicej.12.1.02

An Evaluation of Medicare’s Hospital Compare as a

Decision-Making Tool for Patients and Hospitals

Sagnika Sen Pennsylvania State University, USA

AbStRACt

Medicare’s Hospital Compare aims to assist patients to make informed decisions about

their choice of care through its star rating systems and side-by-side comparison of

hospitals. Despite the use of the rating system by hospitals as an endorsement of quality,

it is not clear whether the information helps consumers make choices specific to certain

diseases. Moreover, the system also does not provide any guidance to hospitals as to

which quality improvements lead to better outcome and why. Using data from 4793

hospitals, this research explores the relationship using the triad of structure, process, and

outcome. Our results show that the star rating system is inadequate for making disease-

specific decision. More importantly, there is little evidence linking the structure and

process related variables with disease specific clinical quality outcomes.

Keywords: Medicare, Triple Aim Performance, Hospital Performance, Clinical Quality, Efficiency

INtRoduCtIoN

To bring transparency and efficiency in health services, Centers for Medicare and

Medicaid Services (CMS) provides consumers with a tool to assess the quality

of hospitals and other health care providers in their vicinity through its Hospital

Compare website. (Medicare, n.d.). The data in Hospital Compare originate from

An Evaluation of Medicare’s Hospital Compare

different quality and cost-effectiveness initiatives undertaken by CMS where indi-

vidual hospitals report on various outcome and process measures regarding mor-

tality, safety, readmissions, patient experience, and timeliness and effectiveness of

care (Kaye et al., 2017). Using a complex methodology, CMS assigns a star-rating

system on a scale of 1 through 5 (1: worst, 5:best) to individual hospitals (Hospital

Compare Overall Ratings Resources, n.d.). Upon entering a zip code or a hospital

name in the Hospital Compare website, a summary of nearby hospitals along with

their star-ratings are displayed. Up to three hospitals can then be selected to make

detailed side-by-side comparisons related to heart attack, heart failure, pneumonia,

surgery and other conditions. These comparisons are organized by patient satisfac-

tion, timeliness and effectiveness of care, readmissions and deaths, among others.

While the star rating system is widely used by patients, care providers,

insurance companies, and policymakers (Mehta et al., 2020), there is also a

considerable debate regarding its deviance from other quality rankings (Austin

et al., 2015). Furthermore, there is often little information explaining the relation-

ship between star ratings and a specific disease outcome (e.g. Acute Myocardial

Infarction, commonly known as heart attack) due to methodological reasons of

standardization and inability to use data from low-volume hospitals (George,

et al., 2017). Often, there is no underlying pattern of correlation among different

outcome measures, thus raising the concern whether consumers decision should

rely on global ranking systems (Hu et al., 2017).

Furthermore, there are limitations of the Hospital Compare database

regarding its ability to provide direction to the hospitals as to which quality

improvement and efficiency initiatives are yielding better outcomes (MacLean

& Shapiro, 2016). Only a handful of hospitals achieve “triple aim performance,”

i.e. scoring high on all three outcome dimensions measured by CMS—clinical

quality, patient experience, and efficiency (Roth, et al., 2019). Despite the vast

amount of data collected by CMS regarding hospitals’ technology capabilities,

quality, and cost effectiveness initiatives, there is a lack of comprehensive studies

to assess how these relate to different outcome measures.

In this regard, the current study explores the relationship of different classes

of outcome variables with technology capabilities and process related variables. The

objective of the study is two-fold. First, whether the CMS star ratings system provides

sufficient information to consumers towards choosing a hospital for a disease-specific

condition. Second, how and to what extent structural and process initiatives affect the

Sagnika Sen

44 e-Service Journal Volume 12 Issue 1

different outcome dimensions, such as patient satisfaction, cost efficiency, and qual-

ity. Using data from 4,793 hospitals included in 2018 Hospital Compare database,

we focus on general outcomes such as patient survey of hospital and spending per

beneficiary as well as readmission rates and excess days spent in care specific to acute

myocardial infarction (AMI), commonly known as heart attack.

While the CMS star rating is used by hospitals as an endorsement of

quality, there is a lack of understanding as to whether these ratings really help

and patients and family members in their choice of care. More importantly, to

the best of our knowledge there are no studies exploring the causal relationship

between structural and process variables to hospital performance.

The rest of the paper is organized as follows. We present a brief review

of the literature in the next section, followed by a description of our data and

methodology. Analysis and discussion of the results are presented next. Finally,

we discuss the limitations of the study and concluding remarks.

lItERAtuRE REVIEw

In a seminal article, Donabedian (1966) proposed using the triad of structure,

process, and outcome to evaluate the quality of health care. Ever since its intro-

duction, the Donabedian framework has been the most cited in health services

research, especially regarding the theory and practice of quality assurance in

healthcare (Ayanian & Markel, 2016).

According to the Donabedian framework, structure is defined as the set-

tings where healthcare takes place and includes provider qualifications and organ-

izational characteristics. Process includes the functions surrounding the delivery

of care such as diagnosis, treatment, prevention. Finally, outcome relates to the

effect of healthcare service on the patient and population. These concepts were

further extended to identify different dimensions of quality (Donabedian, 1990)

and still constitutes the foundation of quality assessment. In the following, we

briefly describe the extant literature on each of the three dimensions of structure,

process, and outcome as it relates to healthcare research.

Structural Measures

One of the most important structural measures arguably revolves around a hospi-

tal’s technology capabilities. While the Donabedian framework includes provider

An Evaluation of Medicare’s Hospital Compare

qualification, we feel that hospitals participating in CMS programs such as

Medicare and Medicaid have standard qualification rules for their doctors and

nurses, and as such would have similar effect on all hospitals. However, since the

introduction of the HITECH (Health Information Technology for Economic

and Clinical Health) act in 2010, considerable emphasis has been placed on hos-

pital capabilities regarding electronic healthcare records (EHR), especially the

ability to collect, receive, and transmit patient healthcare records in standardized

format. Hospitals were incentivized to achieve “meaningful use” of EHR with

respect to healthcare quality (Gholami et al., 2015).

A significant body of academic research has explored the relationship of

technology and healthcare quality (Chaudhry et al., 2006). A longitudinal study

of hospitals in the US have shown that healthcare technology usage is not only

is associated with increase in healthcare quality but also reducing operating costs

(Bardhan & Thouin, 2013). Also, investments in technology leads hospitals to

disclose quality measures voluntarily (Angst et al., 2014).

While extant literature predominantly have shown positive effect of health-

care technology (Buntin et al., 2011), a recent article also cites the existence of

“productivity paradox” seen earlier in the manufacturing sector (Bui et al., 2018).

Their study of hospitals in the state of New York show only mixed outcomes after

a considerable investment in technology, especially since their research found no

evidence of relationship between technology use and patient satisfaction, mortal-

ity, and readmission rates. The authors of this paper call for further research to

explore the causal linkage between technology use and specific outcomes such as

patient satisfaction, spending, mortality, and readmission rates.

Process Measures

The quality improvement literature has long recognized the role of process man-

agement in impacting outcomes. Quality initiatives such as Six Sigma aim to

improve quality through a rational modularization and streamlining of workflows

followed by the implementation of standardized best practices (McCormack

et al., 2009). Healthcare organizations have embraced various process improve-

ment initiatives towards improving hospital efficiency, clinical outcomes, and

patient experience (Roth et al., 2019). In general, these programs have resulted

in improved outcomes (Zheng et al., 2018).

Sagnika Sen

46 e-Service Journal Volume 12 Issue 1

In order to reduce the number of preventable medical errors, CMS devel-

oped a set of best practices to improve care delivery. These processes are specif-

ically aimed to improve the care for acute myocardial infarction (heart attack),

heart failure, pneumonia, as well as surgical processes and infections. It has been

shown that participating in process improvement initiatives for heart attack

resulted in reducing clinical outcomes of mortality and readmission rates (Ding,

2015). However, other studies have shown that hospitals’ emphasis on process

management leads to increases in clinical quality but reduction in patient satis-

faction (Chandrasekaran et al., 2012).

Measuring healthcare Service outcomes

Effectiveness and efficiency are inherent indicators of process performance and

have been captured in the literature as quality and efficiency (Melville et al.,

2004). Quality can be measured in terms of process results and is determined

by how well a process meets the customer’s needs. In the context of healthcare,

quality can be measured by customer perceptions, and/or ranking and rating pro-

vided by insurance agencies (e.g. Medicare) and independent third parties (e.g.

US News and World Report).

Efficiency, on the other hand, is a simple ratio of output to input and is

representative of how well the results are achieved. Recent literature in healthcare

services have emphasized on the triple aim performance—clinical quality, patient

satisfaction, and reduction in cost (Roth et al., 2019; Zheng et al., 2018). We

adopt all three outcome measures in our analysis described below.

dAtA ANd MEthodology

This research utilizes data from CMS Hospital Compare (Medicare, n.d.) for

the year 2018. A total of 4,793 acute care hospitals registered with Medicare

are included in the database. Hospital Compare reports information on vari-

ous performance metrics such as spending, quality and efficiency of care, HIT

implementation, and customer satisfaction collected from the hospitals. In addi-

tion, CMS also provides ranking and benchmarking for each of the hospitals.

Information regarding Veterans Administration hospitals, children’s hospitals,

and critical access hospitals are also included in Hospital Compare but was not

part of the current study.

An Evaluation of Medicare’s Hospital Compare

Details of the variables used in this study are provided in Table 1. As

previously mentioned, the triple aim performance goals are used. For patient

satisfaction, we use the aggregate scores from Hospital Consumer Assessment

of Healthcare Providers and Systems (HCAHPS) patient experience survey.

In addition, the CMS overall star rating is also used. For cost reduction/effi-

ciency, the Medicare Spending Per Beneficiary (MSPB) is used. MSPB is a

price-standardized, risk-adjusted measures of spending efficiency (Trzeciak

et al., 2017). It assesses the cost of services performed by hospitals and other

healthcare providers during the period immediately prior to, during, and

following a beneficiary’s hospital stay compared to a median national hos-

pital. The measure adjusts for geographic differences, patient severity, and

age (Medicare Spending Per Beneficiary (MSPB) Measure Methodology, n.d.).

For clinical quality, the heart attack measures are chosen. Since hospital per-

formance varies across different disease and treatment conditions, we chose

to focus on one disease (Hu et al., 2017). In the past, disease specific mor-

tality and readmission rates were used as standard clinical quality outcomes.

However, these measures sometimes created skewed incentives for hospitals

(Psotka et al., 2020). Consequently, more recent measures by CMS include

Excess Days in Care instead or mortality which measures unplanned patient

encounters such as observation stays, emergency department visits 30 days

post discharge (Horwitz et al., 2018).

The process variables are a combination of heart attack specific measures

(e.g. percentage of patients who were admitted with complaints of chest pain and

received aspirin) and general emergency department (ED) throughput measures

(e.g. time spent in ED). We have also included emergency department volume as

one of the control variables.

Structural measures included health information technology (HIT) related

measures, as well as safety measures. Descriptive statistics of all variables are pro-

vided in Table 2.

Sagnika Sen

48 e-Service Journal Volume 12 Issue 1

T ab

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An Evaluation of Medicare’s Hospital Compare

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Sagnika Sen

50 e-Service Journal Volume 12 Issue 1

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An Evaluation of Medicare’s Hospital Compare

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p =

0. 01

Sagnika Sen

52 e-Service Journal Volume 12 Issue 1

These different pieces of data reside in separate reports within Hospital

Compare indexed by each hospital. Once data from these different sources are

combined, separate regression models were run for each outcome variable. For

categorical variables, the “not available” group was used as the baseline. Results

of the regression are provided in Table 3.

RESultS

First glance at the results reveal that not all outcome variables are equally impacted

by the structure and process variables. Survey-based patient satisfaction and CMS

computed star rating outcomes are the ones best explained, as is evidenced from

the adjusted R2 values of 21.1% and 17.7% respectively. The efficiency meas-

ure, Medicare Spending Per Beneficiary (MSPB), and one of the heart attack

related measures (excess days of care) have moderate values of adjusted R2 values,

whereas heart-attack readmission rates are not at all impacted by the structure

and process related variables. In the following section, the structure-outcome and

process-outcome relationships are discussed in detail.

Structure-outcome Relationships

Interestingly, the two HIT variables did not have any effect on any of the

five outcomes despite about two-thirds of the hospitals reporting both

capabilities. While it seems counterintuitive, recent research suggests that

electronic health care capabilities cannot be fully harnessed unless the organ-

ization’s capabilities are built to exploit those technologies (Jena et al., 2020).

Hospitals that did not have an inpatient safe surgery checklist (compared to

the ones that did not report on this measure) highly impacted excess days

of care. Not having a safe surgery checklist increased the excess days of care

considerably. However, this measure did not have any effect on the other four

outcomes. The outpatient safe surgery checklist on the other hand, resulted

in reduced patient satisfaction (compared to hospitals that did not report on

the surgery checklist). A possible explanation may be that it increased the

time taken for outpatient procedures. Also, hospitals that did not have an

outpatient safe surgery checklist had reduced excess days. Finally, hospitals

that used a survey of patient safety culture led to both an increase in spending

and excess days of care.

An Evaluation of Medicare’s Hospital Compare

Process-outcome Relationships

For the process variables specific to heart attack care, administering aspirin has a

positive effect both on CMS hospital rating as well as in reducing spending per

beneficiary. Surprisingly, it also slightly increases excess days in acute care.. The

average time it takes for a probable heart attack patient to get an ECG reduces

patient satisfaction but does not have any effect on the other outcome variables.

The average time spent in the emergency department (ED) for patients

who were ultimately admitted as inpatients reduces patient and CMS rating,

increases spending per beneficiary, and increases readmission rates. Overall time

spent in ED for all patients increases both patient satisfaction and CMS ratings.

The percentage of people who left the ED before being seen reduces both patient

satisfaction and CMS rating.

A hospital’s emergency department volume seems to play a significant role

for most outcomes. In general, higher volume hospitals had less satisfaction,

lower ratings, more spending, and higher amount of excess days. Not all volume

categories have the same impact on the outcome variables though. It is only the

very high-volume hospitals that resulted in more excess days. For both spending

per beneficiary and CMS rating, the ED volume, which can serve as a proxy for

hospital size, resulted in increased spending and lower rating.

dISCuSSIoN

One of the key findings from our analysis is that the CMS overall rating pro-

vides a broad overview of hospital performance. All outcomes show an improving

trend towards the higher star rated hospitals. However, while the structure and

process variables explain quite a bit about patient satisfaction and CMS com-

puted hospital ratings, they provide less information regarding spending effi-

ciency, and even less for disease-specific clinical outcomes. In other words, while

the current structure and process-related variables demonstrably improve patient

performance, their impact on reducing unplanned visits and readmission rates

is not evident. A closer look at the distribution on excess days and readmission

rates show a significant overlap of these measures across hospital ratings (Figure

1), implying that hospitals even in high-star rating category may have less-than-

standard outcome for heart attack patients. Interestingly, hospitals that were not

assigned a star rating by CMS had worse performance than those that received

Sagnika Sen

54 e-Service Journal Volume 12 Issue 1

star ratings of 4 and 5, but at par or slightly better than those with ratings 1–3.

It should be noted here that consumers do not have ready access to the clinical

quality scores through the Hospital Compare website, and are shown the per-

formance of the hospital as compared to national median. In order to access the

actual scores, patients have to look through the enormous number of data files

in the archives.

Figure 1: Heart Attack Readmission Rates and Excess Days In Care Across Hospital Rating

An Evaluation of Medicare’s Hospital Compare

In summary, the CMS star ratings, while providing a general overview of

a hospital’s performance, may not be the best way to choose care for a specific

disease. More importantly, the structure and process variables, currently captured

by the CMS, fail to provide hospitals with any insights as to which initiatives

result in better clinical and spending outcomes.

CoNCluSIoNS ANd futuRE RESEARCh

In this study, we assess of the utility of the Hospital Compare star rating ser-

vice in helping patients make informed decision for the choice of their care. We

also explore which structure and process variables impact different dimensions of

hospital performance and how. Our analysis highlights the shortcomings of the

current service for both patients and providers.

At this point, the limitations of our study should be recognized. This is a

cross-sectional study of hospitals reporting on many of their process and quality

related initiatives. Since CMS does not report any data where the number of

cases are very small, some methodological issues are raised regarding the under-

estimation of quality risks at low-volume hospitals (George et al., 2017). More

information regarding the variation in patient demographics as well as hospital

characteristics (size, urban/rural location) should be included in future studies to

appropriately assess the clinical quality. Apart from a low volume of cases, some

hospitals did not report performance data on excess days and quality of care,

although they reported other process and structural measures. Further longitu-

dinal studies may investigate if the proportion of hospitals reporting these meas-

ures increase over time, and whether such changes explain the causal relationship

between process initiatives and quality measures.

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Nidhi Singh is Assistant Professor and Dean (Students Affairs) at Jaipuria

Institute of Management, Noida. She is an active researcher enrolled with IP

University, Delhi. She has qualified UGC Net also. She has presented many

papers in various Seminars & Conferences including IIMR, IICA, NLSIU etc.

and published papers in journals of National & International Repute like the

International Journal of Information Management, Elsevier, Journal of Retailing

and Consumer Services, Elsevier, International Journal of Bank Marketing,

Emerald, Decision-Springer publication, Management and Labour Studies

-Sage Publication, International Journal of Sustainable Strategic Management

-Inderscience publication, FIIM, SERD, GSCCR etc.

Dr. Sagnika Sen is an Associate Professor of Information Systems in the School

of Graduate Professional Studies at Pennsylvania State University. She received

her Ph.D. from Arizona State University. Her research focuses on process per-

formance, metrics and incentive design in organizations, mainly the design of

effective decision-making frameworks and the use of data-driven decision models

to obtain analytical insights on processes and performance measures. She has

published in top academic journals in the field of Information Systems such as

Information Systems Research and Journal of Management Information Systems. Her

work has also appeared in other prestigious academic outlets such as Decision

Support Systems, Information and Management, Communications of the ACM,

Human Resources Management, Service Sciences, Journal of Managerial Psychology,

etc.

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