Transformational Change in Health Care Systems: An Organizational Model

Abstract

Background:
The Institute of Medicine's 2001 report Crossing the Quality Chasm argued for fundamental redesign of the U.S. health care system. Six years later, many health care organizations have embraced the report's goals, but few have succeeded in making the substantial transformations needed to achieve those aims.

Purposes:
This article offers a model for moving organizations from short-term, isolated performance improvements to sustained, reliable, organization-wide, and evidence-based improvements in patient care.

Methodology:
Longitudinal comparative case studies were conducted in 12 health care systems using a mixed-methods evaluation design based on semistructured interviews and document review. Participating health care systems included seven systems funded through the Robert Wood Johnson Foundation's Pursuing Perfection Program and five systems with long-standing commitments to improvement and high-quality care.

Findings:
Five interactive elements appear critical to successful transformation of patient care: (1) Impetus to transform; (2) Leadership commitment to quality; (3) Improvement initiatives that actively engage staff in meaningful problem solving; (4) Alignment to achieve consistency of organization goals with resource allocation and actions at all levels of the organization; and (5) Integration to bridge traditional intra-organizational boundaries among individual components. These elements drive change by affecting the components of the complex health care organization in which they operate: (1) Mission, vision, and strategies that set its direction and priorities; (2) Culture that reflects its informal values and norms; (3) Operational functions and processes that embody the work done in patient care; and (4) Infrastructure such as information technology and human resources that support the delivery of patient care. Transformation occurs over time with iterative changes being sustained and spread across the organization.

Practice implications:
The conceptual model holds promise for guiding health care organizations in their efforts to pursue the Institute of Medicine aims of fundamental system redesign to achieve dramatically improved patient care.

Full text

R E S E A R C H A R T I C L E Open Access
Hospital implementation of health information
technology and quality of care: are they related?
Joseph D Restuccia
1,2*
, Alan B Cohen
1,2
, Jedediah N Horwitt
1
and Michael Shwartz
1,2
Abstract
Background: Recently, there has been considerable effort to promote the use of health information technology
(HIT) in order to improve health care quality. However, relatively little is known about the extent to which HIT
implementation is associated with hospital patient care quality. We undertook this study to determine the
association of various HITs with: hospital quality improvement (QI) practices and strategies; adherence to process of
care measures; risk-adjusted inpatient mortality; patient satisfaction; and assessment of patient care quality by
hospital quality managers and front-line clinicians.
Methods: We conducted surveys of quality managers and front-line clinicians (physicians and nurses) in 470
short-term, general hospitals to obtain data on hospitals’extent of HIT implementation, QI practices and strategies,
assessments of quality performance, commitment to quality, and sufficiency of resources for QI. Of the 470
hospitals, 401 submitted complete data necessary for analysis. We also developed measures of hospital
performance from several publicly data available sources: Hospital Compare adherence to process of care measures;
Medicare Provider Analysis and Review (MEDPAR) file; and Hospital Consumer Assessment of Healthcare Providers
and Systems HCAHPS
W
survey. We used Poisson regression analysis to examine the association between HIT
implementation and QI practices and strategies, and general linear models to examine the relationship between
HIT implementation and hospital performance measures.
Results: Controlling for potential confounders, we found that hospitals with high levels of HIT implementation
engaged in a statistically significant greater number of QI practices and strategies, and had significantly better
performance on mortality rates, patient satisfaction measures, and assessments of patient care quality by hospital
quality managers; there was weaker evidence of higher assessments of patient care quality by front-line clinicians.
Conclusions: Hospital implementation of HIT was positively associated with activities intended to improve patient
care quality and with higher performance on four of six performance measures.
Background
Interest in the role of health information technology
(HIT) for improving health care quality and patient
safety has grown dramatically in recent years, spurred by
the Institute of Medicine’s 2001 report, Crossing the
Quality Chasm, that emphasized “the critical role of in-
formation technology in the design of health care sys-
tems”to meet six aims of care, i.e., care “that is safe,
effective, efficient, timely, equitable and patient-
centered”[1]. The report recommended establishing a
healthcare information infrastructure that would lead to
the elimination of most handwritten clinical data by the
end of the decade. Since then, the federal government
has established an Office of the National Coordinator
for Health Information Technology (ONC) within the
Department of Health and Human Services; various pri-
vate organizations, such as the Institute for Healthcare
Improvement (IHI) and the Leapfrog Group, have made
HIT adoption a central theme within their quality im-
provement (QI) campaigns; and numerous healthcare
providers have invested substantially in acquiring various
HITs. The passage of the Health Information Technol-
ogy for Economic and Clinical Health Act (HITECH), as
part of the American Recovery and Reinvestment Act of
* Correspondence: jres@bu.edu
1
Health Policy Institute, Boston University School of Management, 53 Bay
State Road, Boston, MA 02215, USA
2
VA Boston Healthcare System, 150 South Huntington Avenue, Boston, MA
02130, USA
© 2012 Restuccia et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative
Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and
reproduction in any medium, provided the original work is properly cited.
Restuccia et al. BMC Medical Informatics and Decision Making 2012, 12:109
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2009, included over $20 billion for HIT, and provided
further indication of the growing consensus regarding
the potential salutary effect of HIT [2].
HITs intended to improve patient care quality and
safety encompass an array of technologies, most notably
electronic medical records (EMRs), computerized pro-
vider order entry (CPOE) systems, medication manage-
ment systems (MMS), and picture archival and
communications systems (PACS), all designed to im-
prove the accuracy, accessibility, and timeliness of stor-
age and transmission of patients’medical information.
Two other technologies—bar coding and radio fre-
quency identification (RFID) systems—are used to track
the location and disposition of pharmaceuticals, medical
equipment, surgical supplies, and patients to help en-
sure, for example, that medications are administered
safely and correctly.
Despite the growing interest in information technol-
ogy, relatively little is known about the extent to which
HIT implementation is associated with hospital patient
care quality. A systematic review of 257 studies of the
impact of HIT found few studies that have shown an im-
pact on quality [3]. Of these, the most important positive
impact was on adherence to guideline-based or
protocol-based care through use of decision support sys-
tems providing computerized reminders for preventive
care, such as vaccinations and blood tests. Moreover,
most such studies involved a single technology and a
single site, often in academic medical centers, thus limit-
ing their generalizability to broad-based use of HIT or to
other types of healthcare provider organizations. We
found six other articles investigating the relationship be-
tween HIT and quality of care in multiple sites that have
been published since 2006. Amarasingham et al. [4]
reported a study involving a sample of 41 Texas hospi-
tals that found that the extent of automation of clinical
information processes was associated at statistically sig-
nificant levels with lower inpatient mortality and fewer
patient complications. In a study involving 2,707 hospi-
tals, Parente and McCullough [5] investigated the associ-
ation between three HITs (EMRs, nurse charts, and
PACS) and three patient safety indicators (infection due
to medical care, postoperative haemorrhage or
hematoma, and pulmonary embolism or deep vein
thrombosis). The only statistically significant association
found was between EMRs and reduced infections due to
medical care. McCullough et al. [6] found that, among
3,401 hospitals classified into those with both an EMR
and CPOE and those without either of these HITs, the
former showed small but statistically significant im-
provement between 2004 and 2007 for two of six
process measures of quality (pneumococcal vaccine ad-
ministration and use of the most appropriate antibiotic
for pneumonia). Himmelstein et al. [7] developed a
“computerization score”for 4,000 hospitals and found
that it was weakly related to process measures for acute
myocardial infarction but not for heart failure, pneumo-
nia, or a composite of the three conditions. Mollon et al.
[8] conducted a systematic review of studies to evaluate
the effect of prescribing decision support systems on pa-
tient outcomes. Only five of the 41 studies that met their
inclusion criteria, primarily that the study design was a
randomized controlled trial, reported improvements in
patient outcomes. Encinosa and Bae [9] studied the rela-
tionship between hospital EMR use and the outcomes
and cost of hospital care in a sample of 2,619 institu-
tions. They found that EMRs had no impact on the rate
of patient safety events, although having an EMR
assisted in responding to an event, reducing deaths,
readmissions, and expenditures.
In this paper, we report findings from a study involv-
ing 401 U.S. hospitals that examined the relationship be-
tween the level of hospital HIT implementation and use
of QI practices and strategies as well as with perform-
ance on five sets of quality of care measures: 1) adher-
ence to the Hospital Compare process of care measures
for acute myocardial infarction (AMI), heart failure (HF)
and pneumonia; 2) risk-adjusted inpatient mortality; 3)
patient satisfaction, as derived from the Hospital Con-
sumer Assessment of Healthcare Providers and Systems
(HCAHPS
W
) survey; 4) hospital quality managers’assess-
ments of patient care quality; and 5) front-line clinicians’
assessments of patient care quality.
Methods
Sample design
We designed and conducted a survey in 2006 of all
4,237 short-term, non-federal, general service hospitals
in the United States that had at least 25 beds, according
to the 2004 AHA Annual Survey of Hospitals. Pediatric,
psychiatric, rehabilitative, orthopedic, and chronic dis-
ease hospitals were excluded from the sample.
Survey content
We developed and administered two surveys, the Qual-
ity Improvement Activities Survey (QAS) and the Clini-
cians’Perceptions of Quality Survey (CPS). The QAS
instrument was intended for completion by the hospital’s
chief quality officer (CQO) or designated lead quality
manager, and was designed to gather information about
the nature and extent of QI activities undertaken and
their impact on patient care quality. The CPS was
intended for completion by physicians and nurses to
elicit their assessment of patient care quality at their
hospital. The questionnaires contained mostly new and
unique items, but also included some questions adapted
from established surveys, such as the National Survey of
Efforts to Improve Quality [10] and the Leapfrog
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Group’s Hospital Quality and Safety Survey [11], as well
as questions regarding several QI activities endorsed by
the Institute for Healthcare Improvement in its 100,000
Lives Campaign. Some questions also were adapted from
the first-wave survey instrument developed by members
of our team, in collaboration with colleagues from Bos-
ton University and the VA Boston Healthcare System,
for an evaluation of the Robert Wood Johnson Founda-
tion’sPursuing Perfection Program [12]. The final ver-
sions of the QAS and CPS were derived based on pilot
testing in a small sample of hospital CQOs and physi-
cians and nurses, respectively, and on feedback from
experienced health services researchers with expertise in
survey research. The final version of the QAS contained
173 questions and took approximately 45 minutes to
complete while the CPS contained 74 questions and
required about 20 minutes to complete. The study de-
sign, instruments, and informed consent procedures
were approved by the Institutional Review Boards of
Boston University and the Health Research & Educa-
tional Trust (HRET).
Hospital quality managers were asked to indicate the
extent to which eight HITs had been implemented in
their hospitals, using a six-point scale with the following
response categories: “not under consideration;”“under
active discussion but not yet budgeted;”“budgeted but
not yet in place;”“in testing;”“implemented in one or
more units;”and “implemented hospital-wide.”The HITs
included: 1) inpatient Electronic Medical Record (EMR)
System, 2) outpatient EMR System, 3) inpatient Compu-
terized Provider Order Entry (CPOE) System, 4) out-
patient CPOE System, 5) Medication Management
System (MMS), 6) Picture Archival and Communica-
tions System (PACS), 7) bar coding, and 8) Radio Fre-
quency Identification (RFID) technology. In addition to
assessing the extent of HIT implementation in hospitals,
the QAS included questions on the extent of implemen-
tation of specific quality practices and clinical strategies
used throughout the hospital, asked on a 5-point scale
anchored by “not used at all”and “used hospital-wide,”
eight of which could be expected to be facilitated by
HIT [Table 1]. Two questions asked of clinicians in the
CPS, on a five-point scale anchored by “strongly dis-
agree”and “strongly agree,”were the extent to which
“the hospital is committed to delivering the highest qual-
ity patient care”and whether “the hospital provides suffi-
cient resources and support for improving patient care.”
(We shall subsequently refer to these as the “commit-
ment question”and the “resources question.”) A ques-
tion common to both surveys asked respondents how
they would rate patient care today at their hospital com-
pared to what they think it should be on a five-point
scale ranging from “well below expectations”to “well
above expectations.”
In the analyses in this paper, we recoded responses to
questions asking extent of agreement so that “agree”or
“strongly agree”were coded as 1 and the other three re-
sponse categories were coded as 0. Similarly, we recoded
responses to questions about implementation so that
“used hospital-wide”and “used widely”were coded as 1
and the other three categories were coded as 0. The CPS
was administered to a random sample of physicians and
nurses in each hospital based on hospital bed size, ran-
ging from 6 in small hospitals to 12 in large hospitals.
General findings from the QAS and a detailed descrip-
tion of the survey’s complex methodology are reported
elsewhere [13].
Final sample
The sample contained 470 hospitals that submitted sur-
veys, representing 11 percent of the 2004 population
from which they were drawn. Eight survey responses
failed to provide complete answers to questions regard-
ing HIT implementation. In addition, we included in our
analysis only hospitals that had a response from the
CQO and responses to the CPS from at least three
front-line clinicians. This reduced the final sample size
to 401 hospitals. The length and complexity of the ques-
tionnaires contributed to the lower-than-desired re-
sponse rate. However, as reported in Cohen et al. [13],
the sample of 470 hospitals was similar to the population
of hospitals (2005 AHA Annual Survey, n = 4,222) along
a number of dimensions including Census region (Mid-
west, Northeast, South, and West), network affiliation,
system affiliation, Medicare disproportionate share hos-
pital status, and location in a metropolitan or non-
metropolitan county (as is the current study’s sample of
401 hospitals of these 470 hospitals). The main differ-
ences between the population and the sample in the
current study were the higher percentages in our sample
of large hospitals (19.5% with over 400 beds vs. 9.7% in
the population) and teaching hospitals (15.4% with
membership in the Council of Teaching Hospitals vs.
Table 1 Hospital Quality Practices and Strategies
Potentially Facilitated by HIT Implementation and Use
1 Progress toward achieving hospital-wide quality goals is tracked and
communicated to clinical staff
2 Quality improvement project results are regularly communicated to
clinical staff
3 The hospital regularly communicates achievement of hospital-wide
quality goals to the general public
4 Patient care processes are standardized, where and when appropriate
5 Evidence-based practice guidelines/clinical pathways
6 Chronic disease registries
7 Standing orders
8 Medication reconciliation
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6.5% in the population) and the smaller percentage of
for-profit hospitals (3.6% vs. 15.7% in the population).
In addition, to understand the extent to which hospi-
tals responding to the survey may be different in terms
of their commitment to QI, we compared sample hospi-
tals to the population of hospitals in terms of their per-
formance on the 15 Hospital Compare process measures
for acute myocardial infarction (AMI), congestive heart
failure (CHF) and pneumonia (described in more detail
in the next section). Using the approach described in the
next section, we calculated composite measures of both
overall performance on the process measures and
condition-specific performance. When the overall com-
posite measure was divided into deciles, the average hos-
pital in the population fell into the fifth decile, while the
average hospital in the sample was one decile higher in
terms of quality. Similar results were obtained when
analyses were conducted separately for each of the three
conditions. Thus, while the differences were not large,
the better performance on Hospital Compare measures
among hospitals responding to the survey suggested that
they may be in the vanguard of QI efforts (i.e., more
likely to have embraced QI aims and to have engaged
more extensively in QI activities) than non-participating
hospitals.
Quality of care measures - hospital compare
Process of care measures
We developed a composite measure of hospital pro-
cesses of care based on the Hospital Compare data for
three conditions: acute myocardial infarction, heart fail-
ure, and pneumonia. We used data available from the
Centers for Medicare and Medicaid Services (CMS)
Hospital Compare website for calendar year 2005 on ad-
herence to the evidence-based processes of care from
hospitals that had at least 100 patients eligible for the
sum of the following 15 process measures for the three
conditions: AMI (6 measures): aspirin at arrival; aspirin
prescribed at discharge; ACE inhibitor or angiotensin re-
ceptor blocker (ARB) for left ventricular systolic dys-
function (LVSD); beta blocker prescribed at discharge;
beta blocker at arrival; and adult smoking cessation ad-
vice/counsel; HF (4 measures): left ventricular function
assessment; angiotensin-converting enzyme (ACE) in-
hibitor or ARB for LVSD; discharge instructions; and
adult smoking cessation advice/counselling; Pneumonia
(5 measures): oxygenation assessment; pneumococcal
vaccination status assessment; initial antibiotic received
within 4 hours of hospital arrival; blood culture per-
formed in emergency department before first antibiotic
received in hospital; and adult smoking cessation advice/
counselling.
To calculate a composite measure across all 15 process
measures, we used the approach recommended by CMS
in its Premier demonstration pay-for-performance pro-
gram for aggregating across measures within condition:
sum the numerators, sum the denominators, and then
calculate the ratio of summed numerators to summed
denominators [14]. This is equivalent to calculating a
weighted average of the proportion eligible for each
intervention that receives the intervention, where the
weight applied to each proportion is the ratio of the
number eligible for the specific intervention to the sum
of the numbers of eligibles for all interventions. These
weights are called opportunity-based weights. We calcu-
lated the composite measure for all hospitals where the
sum of the numbers of those eligible for each of the
interventions was greater than 100.
Inpatient mortality rates
We applied the 3 M™Health Information Systems’All
Patient Refined Diagnosis Related Groups (APR-DRGs)
software to the CMS Medicare Provider Analysis and
Review (MEDPAR) File to measure patient severity. The
APR-DRG software adds four subclasses to each DRG
based on mortality risk. Using a reference population of
4.5 million Medicare patients from approximately 1,000
hospitals (including the 401 hospitals in this study) [15],
we calculated the risk of in-hospital mortality for each
subclass in each DRG and then assigned each patient in
our sample of 401 hospitals an expected mortality risk
based on their DRG subclass. The expected number of
deaths in each hospital was calculated by summing the
expected mortality risks of all patients in that hospital.
We then calculated the ratio of observed deaths to
expected deaths (O/E) considering only those patients
who had one of the conditions that comprise the AHRQ
Mortality Inpatient Quality Indicators (https://www.
qualityindicators.ahrq.gov), as these conditions have
been judged to be ones in which in-hospital mortality is
sensitive to the quality of patient care provided.
Patient satisfaction
We downloaded from the CMS website HCAHPS
W
data
for sampled hospitals. We considered two questions
from the survey:
1) How do you rate the hospital overall?
2) Would you recommend the hospital to friends and
family?
For the first question, we focused on the percentage of
respondents who gave the hospital a rating of 9 or 10
(the two highest ratings). For the second question, we
focused on the percentage of respondents who said they
would definitely recommend the hospital. We used the
average response for the two questions as the measure
of patient satisfaction.
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Hospital quality Managers’assessments of patient care
quality
This measure consisted of the response by the hospital
quality manager to the question, “How would you rate
patient care today at your hospital compared to what
you think it should be?”
Front-line Clinicians’assessments of patient care quality
This measure consisted of the average of responses by
front-line clinicians (physicians and nurses) to the ques-
tion above that was asked of quality managers. We first
calculated the mean assessment in each hospital and
then used these means in the analysis. We preferred this
approach to one that uses the individual front-line clin-
ician response as the unit of analysis because it weights
each hospital equally, as opposed to giving more weight
to larger hospitals with greater numbers of front-line
clinician responses. We have shown that in this sample
it is reasonable to aggregate individual responses to the
hospital level [15].
Statistical analysis
For each performance measure, we used one-way
ANOVA to examine the difference in the average of the
performance measure by the following HIT categories: 0
or 1 (low), 2 to 4 (medium), and 5 or more (high). To
identify pairs of means that differed across HIT category,
we used Tukey’s HSD (honestly significant difference)
test.
When performing the statistical analyses for mortality,
which is in the form of an O/E ratio, we took the log of
the ratio before performing the analysis. When examin-
ing the performance measure “assessment of quality by
front-line clinicians,”we first calculated the mean assess-
ment in each hospital and then used these means in the
analysis.
To investigate the relationship between extent of HIT
implementation and the performance measures, we used
a General Linear Model (GLM) with the following inde-
pendent variables: the HIT categories defined above, hos-
pital structural characteristics, and the mean clinician
response by hospital for the commitment and resource
questions. We included the following four structural
characteristics: bed-size category (25–99 beds, 100–399
beds, >400 beds), ownership type (government, not-for-
profit, for-profit), urban/rural location (metropolitan
county or non-metropolitan county), and teaching status
(accredited member or non-member of the Council of
Teaching Hospitals and Health Systems). In addition, we
included as independent variables clinicians’responses to
the commitment question and to the resources question,
both of which might seriously confound the relationship
between HIT and the performance measures. The as-
sumption underlying inclusion of these two variables is
that commitment and resources are the drivers of quality;
HIT is one of the important means by which commit-
ment and resources are translated into improved per-
formance. This leads to our specific hypothesis: among
hospitals with the same level of commitment and
resources, those that have more completely implemented
HIT will have higher levels of performance. However,
there is an alternative hypothesis one might reasonably
make: survey responders believe commitment and
resources are higher when HIT is more fully implemen-
ted. That is, assessed levels of commitment and resources
reflect the extent of HIT implementation. Under this as-
sumption, commitment and resources should not be
included as covariates in the model. We think the first
hypothesis is the most likely and, hence, for our main
analyses, we include commitment and resources in the
model. Since these variables are positively correlated with
extent of HIT implementation, inclusion of the variables
decreases the chance of finding a statistically significant
relationship between HIT and performance. When extent
of HIT implementation was not statistically significant,
we reran the model without these variables.
To examine the relationship between extent of QI
practices and strategies used in the hospital and extent
of HIT implementation, we ran a Poisson regression
model with the number of practices and strategies as the
dependent variable and the same independent variables
as above.
We interpreted p-values of less than 0.05 as indicating
statistically significant differences. Survey data were ana-
lyzed using SPSS version 16.0.
Results
Table 2 shows the unadjusted means for each of the per-
formance measures. For all of the performance mea-
sures, there was a statistically significant difference by
HIT category and between the low HIT category and the
high HIT category. For some measures, there were also
statistically significant differences between the low cat-
egory and the medium category, or between the medium
category and the high category.
Hospitals in the high HIT implementation category
used an average of 4.20 practices and strategies while
those in the medium category used 3.63 and those in the
low category used 2.44 (p < 0.000 for differences in un-
adjusted means). As seen in Table 3, which contains the
parameter estimates of the multivariable models for each
of the six performance measures, after controlling for
covariates, we still found that hospitals with high levels
of HIT implementation, engaged in significantly greater
numbers of HIT-related QI practices and strategies
(p = 0.003 for differences in adjusted means).
Risk-adjusted inpatient mortality was higher for hospi-
tals with low HIT implementation compared to those
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Table 2 Relationships between HIT Implementation and Mean Number of QI Strategies and Practices, and between HIT
Implementation and Mean of the Hospital Performance Measures (numbers of hospitals in parentheses)
Measure HIT Category
P-value
Low Medium High
QI Strategies and Practices 2.44 (79) 3.63 (264) 4.20 (61) 0.000 †
}
Composite HQA Process of Care 81.0 (51) 82.6 (241) 85.2 (61) 0.009 †
{
Observed to Expected Mortality Rate* 1.29 (70) 1.06 (263) 1.07 (61) 0.000†
}
HCAHPS Patient Satisfaction 60.5 (31) 66.0 (187) 67.9 (48) 0.000†
}
Quality Manager Assessment of Patient Care Quality 3.11 (81) 3.19 (266) 3.56 (62) 0.001†
{
Front-line Clinicians’Assessment of Patient Care Quality 3.22 (82) 3.31 (269) 3.40 (62) 0.032†
†Significant difference between Low and High.
{
Significant difference between Medium and High.
}
Significant difference between Low and Medium.
*: ln(O/E) was used when conducting the statistical tests.
Table 3 Multivariable Model Parameter Estimates
Covariate*
QI Strategies and Practices Composite HQA Process of Care Observed to Expected Mortality Rate
Beta 95% CI P Beta 95% CI P Beta 95% CI P
bed-size category 0.285 0.022 0.972
25-99 beds −0.177 −0.398, 0.045 0.119 −0.04 −0.07, -0.010 0.009 0.007 −0.123, 0.137 0.916
100-399 beds −0.057 −0.205, 0.091 0.453 −0.011 −0.032, 0.010 0.308 −0.004 −0.103, 0.095 0.940
ownership type 0.438 0.001 0.626
government −0.159 −0.404, 0.086 0.204 −0.003 −0.046, 0.039 0.881 −0.017 −0.205, 0.170 0.856
not-for-profit −0.122 −0.334, 0.090 0.260 0.034 −0.006, 0.073 0.093 −0.053 −0.229, 0.124 0.557
urban 0.079 −0.078, 0.236 0.323 −0.009 −0.029, 0.010 0.353 −0.098 −0.181, -0.014 0.022
non-teaching 0.046 −0.113, 0.205 0.569 0.006 −0.017, 0.029 0.607 0.050 −0.056, 0.156 0.350
HIT category 0.003 0.257 0.005
low −0.378 −0.610, -0.146 0.001 −0.021 −0.049, 0.008 0.157 0.065 −0.061, 0.191 0.312
medium −0.084 −0.236, 0.068 0.277 −0.016 −0.037, 0.004 0.122 −0.076 −0.172, 0.020 0.121
commitment 0.123 −0.091, 0.336 0.261 0.03 0.002, 0.058 0.037 −0.029 −0.155, 0.096 0.646
resources 0.090 −0.085, 0.265 0.312 0.007 −0.015, 0.029 0.545 −0.010 −0.109, 0.089 0.842
R
2
0.109 0.136 0.072
HCAHPS Patient Satisfaction Quality Manager Assessment of Patient Care
Quality
Front-line Clinicians’Assessment of Patient Care
Quality
Covariate* Beta 95% CI P Beta 95% CI P Beta 95% CI P
bed-size category 0.002 0.153 0.006
25-99 beds 2.390 −1.493, 6.273 0.227 0.119 −0.186, 0.424 0.443 0.112 0.004, 0.220 0.042
100-399 beds −2.256 −4.977, 0.466 0.104 −0.082 −0.316, 0.153 0.493 −0.011 −0.094, 0.072 0.797
ownership type 0.001 0.613 0.094
government 8.952 3.467, 14.437 0.001 0.207 −0.221, 0.634 0.343 0.168 0.015, 0.320 0.031
not-for-profit 9.373 4.400, 14.347 0.000 0.199 −0.203, 0.601 0.331 0.130 −0.013, 0.274 0.075
urban −0.149 −2.569, 2.271 0.903 0.056 −0.137, 0.249 0.570 −0.021 −0.089, 0.048 0.557
non-teaching −1.220 −4.030, 1.590 0.393 −0.030 −0.280, 0.220 0.813 −0.097 −0.186, -0.008 0.032
HIT category 0.000 0.006 0.392
low −7.208 −11.047, -3.680 0.000 −0.439 −0.730, -0.147 0.003 −0.067 −0.170, 0.036 0.202
medium −1.177 −3.776, 1.421 0.370 −0.342 −0.567, -0.117 0.003 −0.022 −0.102, 0.058 0.586
commitment 2.686 −0.889, 6.261 0.140 0.337 0.050, 0.623 0.021 0.370 0.268, 0.473 0.000
resources 2.814 0.029, 5.599 0.048 0.022 −0.209, 0.253 0.851 0.354 0.271, 0.436 0.000
R
2
0.228 0.078 0.579+
* The reference category is excluded for each covariate, e.g. estimates for non-teaching hospitals are relative to teaching hospitals.
+ High R
2
is due to the strong relationship between the commitment question and the quality performance measure question.
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with medium or high HIT implementation, with the O/E
ratio for the former being 1.29, compared to 1.06 and
1.07 for the latter two, respectively (p < 0.000 unadjusted;
p = 0.005 adjusted).
The HCAHPS
W
-based measure of patient satisfaction
showed a similar finding, with low HIT implementation
hospitals having a 60.5% average satisfaction score and
medium and high HIT implementation hospitals having
average scores of 66.0% and 67.9%, respectively
(p < 0.000 unadjusted; p < 0.000 adjusted).
Quality managers’assessments of patient care quality
(i.e., responses to the question of how they would rate
patient care today at their hospital compared to what
they think it should be) were higher for hospitals that
had higher levels of HIT implementation. The average
scores, on a five-point scale, for high, medium, and low
HIT implementation hospitals were 3.56, 3.19, and 3.11,
respectively (p = 0.001 unadjusted; p = 0.006 adjusted).
For front-line clinicians’assessments of quality, differ-
ences between unadjusted means (with average scores of
3.40, 3.31, and 3.22, respectively) were significant
(p = 0.032). After covariate adjustment, they were not
significant (p = 0.392). However, if the commitment and
resource variables were not included in the multivariable
adjustment model, the difference in means by HIT cat-
egory was statistically significant (p < 0.000).
The percent adherence to the composite Hospital
Compare process of care measure increased with greater
HIT implementation, with low HIT implementation hos-
pitals at 81.0% adherence, medium HIT implementation
hospitals at 82.6% adherence, and high HIT implementa-
tion hospitals at 85.23% adherence (p = .009). However,
the differences were not statistically significant in the
multivariable model either with or without inclusion of
the commitment and resource as covariates.
Discussion
We found a statistically significant association between
the extent of HIT implementation and individual hos-
pital quality practices and strategies that could be facili-
tated by HIT, plus a statistically significant association
between HIT implementation and hospital performance
on four of five measures of quality (though in one case,
front-line clinicians’assessment of quality, the results
were statistically significant only when the commitment
and resources questions were not included in the
model).
It is likely that HITs are enablers of quality practices
and clinical QI strategies through enhanced communica-
tion, documentation, information transfer, performance
monitoring, and error prevention, thus, leading to
improved quality performance.
A limitation of the study is that of the performance
measures associated with HIT implementation, one was
based on the quality manager survey in which respon-
dents were asked about both HIT implementation and
patient care quality. This creates a common methods
bias and makes it difficult to draw conclusions about
causality. It is possible that respondents may have
believed that patient care quality was better in their hos-
pitals simply because their hospitals had implemented
quality-enhancing HITs. However, the two publicly-
available measures that showed a relationship to HIT
implementation, mortality rate and patient satisfaction,
are not subject to common methods bias. It is unlikely
that knowledge of the hospital’s performance on these
measures influenced survey respondents to indicate a
particular level of HIT implementation.
Another limitation is the survey response rate of 11
percent for the two survey-assessed measures of patient
care quality. We cannot rule out the possibility that un-
measured, complex motivational factors may have con-
tributed to the selective response by hospitals to
participate in the surveys. As described in the Methods
section, teaching hospitals were overrepresented in the
sample and for-profit and non-metropolitan hospitals
were underrepresented. In addition, sample hospitals, on
average, performed better on Hospital Compare mea-
sures [13]. Although the differences were not large, the
sample hospitals’higher performance levels on these
measures suggested that they may have been in the van-
guard of QI efforts than non-participating hospitals.
Thus, our findings are not necessarily representative of
all short-term, non-federal, general service hospitals with
25 or more beds. However, given that the study includes
over 400 hospitals, study findings nevertheless provide
important information on the relationship between HIT
implementation and quality of care. Furthermore, be-
cause the observed levels of HIT implementation and
performance in sample hospitals still fell well below tar-
gets set by the Institute of Medicine and other QI pro-
ponents, our results suggest that there is substantial
room for improvement even in hospitals that appear to
be more advanced than many.
Further research is needed to determine the
generalizability of the relationship between HIT imple-
mentation and quality of care, and to ascertain the par-
ticular features of health information systems that lead
to effective QI activities and quality performance. How-
ever, it is clear that, for the 401 hospitals in our study,
those with higher levels of HIT implementation were
more likely to engage in practices and strategies
intended to improve the quality of patient care and also
exhibited better performance on important measures
reflecting different dimensions of quality: a clinical out-
comes measure (risk-adjusted mortality); a publicly-
available measure of patient satisfaction (HCAHPS
W
);
assessment of patient care quality by hospital quality
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managers; and, though the evidence was weaker, assess-
ment of quality by front-line clinicians.
Conclusions
For many years, the federal government and private
organizations, such as the Institute of Medicine and the
Leapfrog Group, have encouraged increased investment
in information technologies, most notably EMR and
CPOE systems, to improve patient care quality and
safety. Numerous barriers to HIT implementation have
been posited, among them high cost, technological com-
plexity, decreased physician productivity, and uncertain
return on investment [16]. Clearly, these barriers must
be overcome if nationwide levels of HIT implementation
are to increase substantially, especially in small, non-
teaching, non-metropolitan hospitals, which lag behind
their larger, academic, urban counterparts [17]. Our
study provides empirical evidence that such efforts may
be warranted.
Abbreviations
AMI: Acute Myocardial Infarction; AHRQ: Agency for Healthcare Research and
Quality; APR-DRGs: All Patient Refined Diagnosis Related Groups;
AHA: American Hospital Association; ARB: Angiotensin Receptor Blocker;
ACE: Angiotensin-Converting Enzyme; CMS: Centers for Medicare and
Medicaid Services; CQO: Chief Quality Officer; CPS:Clinicians’Perceptions of
Quality Survey; CPOE: Computerized Provider Order Entry; EMRs: Electronic
Medical Records; GLM: General Linear Model; HITs: Health Information
Technologies; HITECH: Health Information Technology for Economic and
Clinical Health Act; HRET: Health Research & Educational Trust; HF: Heart
Failure; HCAHPS: Hospital Consumer Assessment of Healthcare Providers and
Systems; IHI: Institute for Healthcare Improvement; IOM: Institute of Medicine;
LVSD: Left Ventricular Systolic Dysfunction; MEDPAR: Medicare Provider
Analysis and Review; MMS: Medication Management Systems; ONC: Office of
the National Coordinator for Health Information Technology; PACS: Picture
Archival and Communications Systems; QI: Quality Improvement;
QAS:Quality Improvement Activities Survey; RFID: Radio Frequency
Identification; HSD: Tukey’s Honestly Significant Difference.
Competing interests
The authors declare that they have no competing interests.
Authors’contributions
JR conceived the study. JR, AC, and MS contributed to the research design.
JR and AC obtained funding. MS, JH, and JR were involved in the data
analysis. All authors were involved in the interpretation of data and have
read and given final approval of paper.
Acknowledgements
This work was supported by a grant from the Commonwealth Fund. We are
indebted to Anthony Shih and Anne-Marie Audet of the Fund for their
advice, support, and constructive suggestions throughout the design and
conduct of the study. We thank our colleagues –Raymond Kang, Peter
Kralovec, Sally Holmes, Frances Margolin, and Deborah Bohr –for their
valuable contributions to the development of the QAS, the CPS, and the
database on which the analytic findings reported here were based. We also
thank 3 M™Health Information Systems’for use of its All Patient Refined
Diagnosis Related Groups (APR-DRGs) software. We especially wish to thank
Jennifer Drake for her contributions not only to survey development, but
also to earlier analysis of survey findings relevant to this paper.
Received: 26 October 2011 Accepted: 23 June 2012
Published: 27 September 2012
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doi:10.1186/1472-6947-12-109
Cite this article as: Restuccia et al.:Hospital implementation of health
information technology and quality of care: are they related?. BMC
Medical Informatics and Decision Making 2012 12:109.
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http://www.biomedcentral.com/1472-6947/12/109