JMIR MEDICAL INFORMATICS

Feldman et al

Review

Health Information Technology in Healthcare Quality and Patient
Safety: Literature Review
Sue S Feldman1*, RN, MEd, PhD; Scott Buchalter2*, MD; Leslie W Hayes3*, MD
1

Department of Health Services Administration, The University of Alabama at Birmingham, Birmingham, AL, United States

2

Pulmonary and Critical Care, The University of Alabama at Birmingham Medical Center, Birmingham, AL, United States

3

Department of Pediatrics, The University of Alabama at Birmingham Medical Center, Birmingham, AL, United States*

all authors contributed equally

Corresponding Author:
Sue S Feldman, RN, MEd, PhD
Department of Health Services Administration
The University of Alabama at Birmingham
1716 9th Avenue
SHPB #590K
Birmingham, AL, 35294
United States
Phone: 1 6616188805
Email: suefeldman1009@gmail.com
Related Article:
This is a corrected version. See correction statement in: https://medinform.jmir.org/2019/1/e11320/

Abstract
Background: The area of healthcare quality and patient safety is starting to use health information technology to prevent
reportable events, identify them before they become issues, and act on events that are thought to be unavoidable. As healthcareorganizations begin to explore the use of health information technology in this realm, it is often unclear where fiscal and humanefforts should be focused.
Objective: The purpose of this study was to provide a foundation for understanding where to focus health information technology
fiscal and human resources as well as expectations for the use of health information technology in healthcare quality and patient
safety.
Methods: A literature review was conducted to identify peer-reviewed publications reporting on the actual use of healthinformation technology in healthcare quality and patient safety. Inductive thematic analysis with open coding was used to categorize
a total of 41 studies. Three pre-set categories were used: prevention, identification, and action. Three additional categories wereformed through coding: challenges, outcomes, and location.
Results: This study identifies five main categories across seven study settings. A majority of the studies used health IT for
identification and prevention of healthcare quality and patient safety issues. In this realm, alerts, clinical decision support, andcustomized health IT solutions were most often implemented. Implementation, interface design, and culture were most often
noted as challenges.
Conclusions: This study provides valuable information as organizations determine where they stand to get the most “bang fortheir buck” relative to health IT for quality and patient safety. Knowing what implementations are being effectivity used by otherorganizations helps with fiscal and human resource planning as well as managing expectations relative to cost, scope, and outcomes.
The findings from this scan of the literature suggest that having organizational champion leaders that can shepherd implementation,
impact culture, and bridge knowledge with developers would be a valuable resource allocation to consider.
(JMIR Med Inform 2018;6(2):e10264) doi: 10.2196/10264
KEYWORDS
Health Information Technology; Healthcare Quality; Patient Safety

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Introduction
Background
It has long been known and accepted that healthcare in the US
is too expensive and the outcomes are less than predictable [1].
The turn of the century brought with it a realization that
healthcare, like other industries, could use data to increase ourawareness of seemingly uncontrollable costs and unpredictableoutcomes. With almost two decades of compiling, analyzing,mashing up data, and trying to make sense of how the data
inform multiple layers of healthcare, it is time to look beyond
the awareness that the data provide, and instead develop anunderstanding of how to use the data for predictable andactionable purposes, especially with regard to healthcare quality
and patient safety. The literature is mixed on the degree to which
health information technology (IT) as a valuable suite of tools,
applications, and systems that have contributed to actual savingsand efficiencies [1-4]. However, the area of healthcare quality
and patient safety lends itself to many of the same businessintelligence and predictability advantages that are seen in thecredit card industry [5-7].
Much like the Triple Aim of Healthcare, the credit card industry
is working toward decreased costs (fraud), increased quality(better transactions), and increased satisfaction (happiermerchants and happier cardholders). The credit card industry
began using business intelligence to predict behavior thatsuggested fraud, developed process maps for transaction
processing, and offered perks to merchants and cardholders.
Just as the credit card industry learned from healthcare,healthcare can borrow from the credit card industry to use
healthcare intelligence for prevention, identification, and action
related to healthcare quality and patient safety events.
The Institute for Healthcare Improvement (IHI) suggests that
reliability around healthcare is a three-part cycle of failure
prevention, failure identification, and process redesign anddefines reliability as “failure-free operation over time.” [8].
Other areas of healthcare have used information systems toFeldman et alprovide continuous monitoring with real-time, or near real-time
reporting as a means of achieving reliability [9]. As such, itmakes sense to think about the role of health IT in reliability as
it relates to healthcare quality and patient safety. A review ofthe literature suggests that healthcare organizations are using
health IT for healthcare quality and patient safety and that they
have replaced redesign in Figure 1 with action as shown in
Figure 2 [10-12]. Action, in this case, allows for health IT tobe implemented after a potential healthcare quality or patient
safety event has occurred and does not necessarily require aredesign. Ordering alerts in the electronic health record are an
example of action; the event has occurred (the order has beenentered) and health IT in the form of an alert is initiated to stop
the potentially unsafe order from being filled by the pharmacy.
Having an understanding of this cycle helps to create awarenessaround where various applications of health IT find their “bestfit” in improving the reliability of healthcare quality and patient
safety. A distinct advantage of this being a “cycle” is that there
is no defined beginning and ending point, but rather an insertion
point. This is all to say that the cycle should not be interpreted
as starting with prevention and ending with action.

Health Information Technology for Prevention of
Quality and Safety Events
Health IT for prevention of quality and safety events involves
the use of health IT to prevent a quality and safety event fromeven happening. Automated reminders and alerts are useful inproviding essential information that supports safe and effective
clinical decisions [13]. Such alerts in the electronic health record(EHR) are a standard mechanism for the use of health IT for
prevention of potential missed quality and patient safety events.
For example, immunization alerts have led to a 12% increase
in well-child and a 22% increase in sick child immunization
administration [14] and drug alerts have been associated witha 22% decrease in medication prescription errors [15]. Soft-stops
can provide key information about a potential quality or patient
safety issue. They may offer choices but usually, require onlythat the user acknowledge the alert to proceed.

Figure 1. Improving the reliability of healthcare.

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Figure 2. Improving the reliability of healthcare quality and patient safety.

A hard-stop, on the other hand, prevents the user from moving
forward with an order or intervention that would be potentially
dangerous to a patient. Hard-stops may allow continuation of
the process, but only if significant required action is taken by
the user, such as a call to or consultation with an expert (suchas a pharmacist or a medical specialist). In some cases, soft-stops
might be ignored or overridden because of such issues as alert
fatigue, poor implementation, or poor interface design [16,17].
Hard-stops, when appropriately designed, have been shown tobe more successful in changing an unsafe plan or preventing a
potentially dangerous intervention [18,19].

Health Information Technology for Identification of
Quality and Safety Events
Health IT for identification of quality and safety events involves
health IT that is used to identify a quality and safety event when
it is about to occur. Health insurance providers increasingly
place pressure on healthcare systems to reduce the cost of caredelivery and improve patient outcomes. This pressure may exist
through tiered reimbursement structures, benefitting thosesystems which meet or exceed specific benchmarks ofperformance. Growing pressure from these payers takes theform of non-reimbursement for care determined by the payer
to be unnecessary or in excess of “standard care.” Health IT canbe used to find the EHR populations of patients for whom
reimbursement might be lower than expected. One such example
to consider is the length of stay for a particular procedure. Whilethe use of health IT can produce reports and dashboards thatare helpful for decision-making relative to reimbursement trends
and practices for lengths of stay for that diagnosis, it is crucial
that thoughtful consideration be given for appreciating any
unintended consequences. For example, when reducing thelength of stay, unintended readmissions are an important metricto follow.

Health Information Technology for Action in Quality
and Safety Events
Health IT for action of quality and safety events involves healthIT that is used to act on a quality and safety event once it has
already occurred. That is to say that these are actions that werereported in the literature that were taken as a result of an event.
Health IT for action differs from health IT for prevention in that
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the former is a reaction directly correlated to an event reported
in the article, whereas the latter is reported in the article as apreemptive measure, in advance of an event.
Because of their standardization, there are several clinical carepathways that lend themselves to clinical decision support. One
such clinical care pathway is sepsis. Despite nearly two decadesof advances in early sepsis care, sepsis outcomes persist to bepoor, and sepsis remains a leading cause of death worldwide
and accounts for significant morbidity and mortality [20]. In
light of this, there is a growing national push to increase early
identification and treatment of sepsis with a goal of improving
outcomes. Patients with sepsis are some of the most criticallyill patients admitted to hospitals, and survival depends heavily
upon timely and early administration of key interventions
followed quickly by assessing and acting on results of theseinterventions [21]. Some examples include administration of
IV antibiotics and aggressive IV fluids within one hour [21].
Examples of assessments of interventions include measuring
specific physical and laboratory values that provide crucial
information about the patient response. All too often, clinicians
are faced with an overabundance of data, that while allnecessary, may not be relevant to the issue at hand. For example,
lab results might be presented in their entirety, when in practice,there are only 3 or 4 tests that will drive decision-making. The
difficulty is how to separate the noise (non-essential at thatmoment) from the signal (essential at that moment). Health IT
solutions, such as dashboards and other solutions can be used
to ensure that essential data are in a primary viewing positionand non-essential data in a secondary viewing position (perhaps
on drill down, for example).
This paper will provide foundational knowledge andunderstanding for organizations of where to focus health IT
fiscal and human resources. It will also provide information
relative to some of the challenges that can be expected in
implementing health IT for quality and patient safety.

Methods
This review of the literature took a structured approach using
PubMed and a combination of keywords. Since PubMed indexes
peer-reviewed articles from biomedical information, it was felt
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Feldman et althat this was the most appropriate and inclusive source. A
healthcare-focused librarian, under the direction of all authors,
conducted the literature search. The articles for final selection
were discussed and decided upon among the authors. The
structured approach was guided by the model illustrated in
Figure 3.

identify the degree to which the article discussed health IT in
healthcare quality and patient safety. To be considered forinclusion, the study needed to report on the actual use of health
IT in healthcare quality and patient safety. Forty-one studies
met these criteria. Those studies with their contributions to theresults are shown in the results section of this paper.

The process to article inclusion involved three passes to collectpublications related to health IT in quality and patient safety
for peer-reviewed studies published between 2012-2017,
inclusive. The first pass, (shown as “1” in Figure 3), usedhigh-level keywords and returned 86 full-text articles. From thearticles gathered, additional keywords were added to the search.
After deduplication and citation review, the second pass (shownas “2” in Figure 3) added 67 unique full-text articles. After
deduplication and citation review, the third pass (shown as “3”in Figure 3) added 11 unique full-text articles, for a total of 164
unique full text articles. Each article was further analyzed to

Qualitative data analysis software (Atlas.ti 8 for Windows) wasused in directed content analysis as a method to categorize andcode the 41 studies relative to how health IT was used inhealthcare quality and patient safety. All 41 documents were
uploaded into the document manager in Atlas.ti as Primary
Documents (PD). During this process, the article title was usedas the PD name. Inductive thematic analysis with open coding
was used under the three pre-set categories of prevention,
identification, and action [22]. This allowed for capturing
descriptions of how health IT was used in each circumstance.

Figure 3. Literature search process.

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For example, prevention included descriptions of any use ofhealth IT to prevent quality issues or potential safety events,
identification included any descriptions of the use of health IT
to identify quality issues or safety events, and action included
any descriptions of the use of health IT to act on quality issues
or safety events that have occurred. When content was notedthat did not fit into the three pre-set categories, an additional
category was created. Additional categories were created tocapture challenges relative to the use of health IT in quality andpatient safety. Since some papers discussed how the use ofhealth IT impacted health outcomes, an additional category wascreated for outcomes. Lastly, an additional category was createdto capture the study settings or location.
The coding structure was agreed upon by all authors, and one
author conducted the coding. After all of the studies were coded,
two additional passes were made through the data. The firstpass was to ensure that all information from the studies thatshould be coded was actually coded and coded to the correct
code (ie, was a passage that described prevention actually coded
to prevention?). The second pass was to consider sub-categories
for consolidation. Six sub-categories were consolidated.
The purpose of examining co-occurrences is to understand what,
if any, relation exists between concepts [22,23]. Within Atlas.ti,
a co-occurrence table was run to find codes that co-occur acrossthe literature, the purpose of which was to illuminate the areas
most discussed. This table was then exported to Microsoft Excel
for further analysis.
Network maps are a means by which analysis can be visualized
in relationships to provide a different perspective on the codes,
categories, etc., and with that visualization, provide a mechanism
for moving codes around [22]. Those presented in the results
do not differ from the final coding structure, but instead areused to provide a visual representation.

Results
Overview
Literature reviews can be conducted using a qualitative approach[24,25] with the results displayed in a variety of ways to supportmodels and show connections [22]. As such, this review presents
qualitative findings to support the “improving the reliability ofhealthcare quality and patient safety” model introduced earlier
in this paper and shows connections via network mappings in
Figure 6 through Figure 7 and co-occurrences in Table 2.
Table 1 provides a listing of the articles and their contribution
in this results section to support the model (Figure 2), network
maps (Figure 4 through Figure 7), and co-occurrences (Table
2).
From the 41 studies that fit the inclusion criteria, any elementin which the authors discussed the use of health IT for healthcare
quality and patient safety was identified, even if it did not fit
into the three previously determined categories. This process
yielded a total of 50 codes across five categories: action (7/41,
17.1%), challenges (12/41, 29.3%), identification (10/41,
24.4%), outcomes (5/41, 12.2%), and prevention (16/41, 39.0%)

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Feldman et al
across seven study settings. Just under a quarter of the studies
identified a study setting: anesthesia (2/41, 4.9%), behavioral
health (1/41, 2.4%), emergency department (2/41, 4.9%), any
intensive care unit (3/41, 7.3%), clinical diagnostic laboratory(1/41, 2.4%), pediatrics (2/41, 4.9), surgery (1/41, 2.4%).
Across all of the articles, there were 63 and 92 descriptions of
the use of health IT for identification and prevention ofhealthcare quality and patient safety issues, respectively. Health
IT for action and the challenges associated with health IT for
healthcare quality and patient safety was described 41 and 43
times, respectively.
The findings from the literature review are presented by thecategories outlined in the previously introduced model for
improving the reliability of healthcare quality and patient safety.

Prevention
The first exploration was across the literature that discussed
health IT for prevention of quality and patient safety issues tosee exactly how organizations were reporting health IT use toprevent a quality and safety event from even happening. The
greatest areas of use were around alerts [30,31,44,56,58], clinicaldecision
support[39,44,47,56],
implementation[10,32,37,38,56], interface design [26,34,42,45,56,59], andcustomized health IT solutions [29,30,32,34,46-50,56,58,59].
Customized health IT solutions were anything that described
the use of health IT but lacked any specificity beyond that
described in this section. For example, this could be somethingas simple as checklists or as complex as algorithmic diagnostic
trees. To clarify, alerts are a subset of clinical decision support.
Since so many of the occurrences specified alerts and clinicaldecision support separately, these were coded separately.
Clinical decision support, by definition, includes alerts, clinicalcare guidelines, condition-specific orders sets, clinical reports
and/or summaries, documentation templates, diagnostic support,
and clinical reference support. Implementation and interface
design were each described in terms of having been poorly
implemented or poorly designed and having implications on
utility in healthcare quality and safety.

Identification
The next exploration was across the literature that discussed
health IT for identification of quality and patient safety issues;in other words, how health IT was used to identify a quality andsafety event when it is about to occur. In this regard, similar toprevention (but described differently in the included studies),
alerts [26,30,31,44,56,58], clinical decision support[30,31,39,44,56,58], implementation [10,32,38,56], andcustomized health IT solutions [10,30,31,34,46-49,52,56,58]
were most prominent. For example, alerts, clinical decision
support, and customized health IT solutions were all describedin the literature as having been implemented to identify apotential quality or patient safety issue, yet the literature also
described how the implementation of these could have been
better in terms of providing more training to those on thereceiving end of the alerts, clinical decision support, or othercustomized health IT solutions.

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Table 1. Article contribution to results (in alphabetical order). An “X” indicates the area of the results contribution and “—” indicates no contribution.
Citation

ActionChallenges

Identification

Outcomes

Prevention

Ancker et al [10]

X

X

X

—

X

Arabi et al [26]

—

—

X

X

X

Asch et al [27]

—

—

—

—

X

Badrick et al [28]

—

—

—

—

X

Coiera et al [29]

—

X

—

—

X

Colicchio et al [11]

X

—

—

—

—

El Morr et al [30]

—

—

X

—

X

Every et al [31]

—

—

X

—

X

Farzandipour et al [32]

—

X

X

—

X

Gupta and Kaplan [12]

X

—

—

—

—

Hoonakker et al [33]

—

—

X

—

X

Jensen [34]

X

X

X

—

X

Khullar et al [35]

X

—

—

—

—

Kim et al [36]

—

X

—

—

X

Koppel [37]

X

X

—

—

X

Lassere et al [38]

X

X

X

—

X

Levesque et al [39]

X

—

X

X

X

Magrabi et al [40]

X

—

—

—

—

Martin et al [41]

—

X

—

—

—

Mazur et al [42]

X

—

—

—

X

Nakhleh [43]

-—

—

—

—

X

Peters [44]

X

X

X

—

X

Popovici [45]

—

X

—

—

X

Rizzato et al [46]

X

X

X

—

X

Seblega et al [47]

X

—

X

X

X

Shy et al [48]

X

—

X

—

X

Skyttberg et al [49]

—

X

X

X

X

Stanton [50]

X

—

—

—

X

Strickland [51]

—

—

—

—

X

Suresh [52]

—

X

X

—

X

Wang et al [53]

—

X

—

—

X

Weiner [54]

—

—

—

—

X

Whipple et al [55]

X

—

—

—

—

Whitt et al [56]

X

X

X

—

X

Yermak, et al [57]

—

—

—

—

X

Yu et al [58]

—

X

X

—

X

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Figure 4. ACTION Network Diagram (G=groundedness, D=density).

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Figure 5. OUTCOMES Network Diagram (G=groundedness, D=density).

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Figure 6. IDENTIFICATION Network Diagram (G=groundedness, D=density).

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Figure 7. PREVENTION Network Diagram (G=groundedness, D=density).

Table 2.
Code

Co-occurrences

Implementation

Prevention, Identification, Action, Challenges

AlertsPrevention, Identification

Clinical decision support

Prevention, Identification

Interface design

Prevention, Challenges

CultureAction, Challenges (tattling)a

Customized health IT solutionsaPrevention, Identification

IT: information technology.

ActionThe third exploration was across the literature that discussed
health IT for action on a quality and safety event once it has
already occurred. That is to say that these are actions that werereported in the literature that were taken as a result of an event.
In regards to action, the major areas were documentation[10,32,37,41,46,56,58], implementation [10,32,37,58], andculture [10,29,41,53,58] relative to the use of health IT.
The findings from the review of the literature show that
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action. Implementation in general has been demonstrated in theliterature as a challenge, and that was revealed in this literature
review also. Culture was most often referred to as needing tocreate a culture of quality and patient safety in order for health
IT to be embraced. Organizations that started working on culturechange before implementation of health IT solutions suggested
that health IT for acting on quality and patient safety eventswas more favorable. Therefore, the analysis was run withchallenges which suggests the major areas are: culture,
implementation, and interface design.

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Co-occurrences
Employing the Improving the reliability of healthcare quality
and patient safety model introduced in Figure 2 and adding
challenges, six critical co-occurrences emerged (see Table 2).
As described earlier, co-occurrences expose relationships exists
between concepts [22,23]. The top co-occurring codes in Table
2 create a macro level view of how health IT was mostcommonly used for quality and patient safety relative to the“improving the reliability of healthcare quality and patient
safety” model introduced in Figure 2. However, it is also
important to understand the universe of ways in whichorganizations used health IT for quality and patient safety; inother words, the art of the possible when using health IT for
quality and patient safety. Network maps provide a mechanism
by which to visualize the connectedness of all data coded acrossall 41 articles included in this analysis. These maps, along withsome quantitative information increase understanding at thisuniverse level (macro and micro views).
In the network diagrams that follow (which also represent thecoded categories and sub-categories), G signifies the level ofgroundedness of the particular code. Groundedness, in this case,indicates the frequency of the code relative to the code category.
D signifies the level of density or connectedness of the particularcode. Density, in this case, indicates the number of other codes
to which this code is connected. For example, under ACTION,
Figure 4, the code action: culture shows G6, D2. ACTION is
the code category and action: culture is the code “culture” underthe ACTION code category (this coding structure helps tomaintain alpha order). This can be read as the following:“Culture was described six times across all 41 papers relative
to ACTION and is connected to two code categories total.”
Because it would make the network diagrams unwieldy, notshown in the exhibits is the specificity around the groundedness
or the density. See Figures 4 through Figure 7.

Discussion
Principal Findings
This scan of the literature is intended to inform practice. The
information from this study could be useful as organizations
determine where they stand to get the most “bang for their buck”
relative to health IT for quality and patient safety. Centered

Feldman et alaround the Improving the Reliability of Healthcare Quality and
Safety model introduced in Figure 2 and the macro level usesof health IT for quality and patient safety outlined in Table 2,organizations in the planning stages may want to begin withalerts and clinical decision support, understanding that alerts
are a subset of clinical decision support. This information also
helps with resource planning. For example, implementation
appeared in all three categories of the Improving the Reliabilityof Healthcare Quality and Safety model. Additionally, culture
was shown to be a challenge. Organizational leaders know thatchanging culture can be a long and intensive process. The
findings from this scan of the literature suggest that havingorganizational champion leaders that can shepherd
implementation, impact culture, and bridge knowledge with
developers would be a valuable resource allocation to consider.
Health IT must meet quality improvement at the intersection
with care delivery. From a clinical perspective, this isexperienced on several levels, and the solution depends, in part,on the clinical problem to be addressed. Some typical examplesof health IT interventions illuminated in the findings include:(1) reminders and alerts, (2) decision support tools, (3) checklists(including order sets and protocols), and (4) soft- and hard-stops.
As noted, this scan of the literature is provided as a means to
inform practice. It does not consider further model modification,and this represents an area of future research in the applicationof health IT for quality and patient safety.

Limitations
This study is limited in that it used PubMed as a single source
for the searching and one coder coded all studies. A morecomprehensive and systematic review would include multiple
databases and multiple coders. Although all authors reviewed
the codes, multiple coders would ensure intercoder reliability,
which cannot be assured in this study. Additionally, since allstudies reviewed did not include locations, generalizability to
all areas of clinical care cannot be certain.

Conclusion
A review of the literature for this study concluded thatorganizations in the planning stages of using health IT to
improve quality and safety may want to begin with reminders
and alerts, decision support tools, and checklists.

Acknowledgments
The authors acknowledge and appreciate the careful, detailed, and thoughtful comments by the reviewers whose suggestions
strengthened this paper.

Conflicts of Interest
None declared.

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Abbreviations
IT: information technology
IHI: Institute for Healthcare Improvement
EHR: Electronic Health Records
PD: Primary Documents

Edited by G Eysenbach; submitted 28.02.18; peer-reviewed by K Blondon, WC Su, K Fuji, Y Gong; comments to author 30.03.18;
revised version received 01.04.18; accepted 19.05.18; published 04.06.18
Please cite as:
Feldman SS, Buchalter S, Hayes LW
Health Information Technology in Healthcare Quality and Patient Safety: Literature Review
JMIR Med Inform 2018;6(2):e10264
URL: http://medinform.jmir.org/2018/2/e10264/
doi: 10.2196/10264
PMID:©Sue S Feldman, Scott Buchalter, Leslie W Hayes. Originally published in JMIR Medical Informatics (http://medinform.jmir.org),
04.06.2018. This is an open-access article distributed under the terms of the Creative Commons Attribution License(https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium,provided the original work, first published in JMIR Medical Informatics, is properly cited. The complete bibliographic information,a link to the original publication on http://medinform.jmir.org/, as well as this copyright and license information must be included.

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