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The Impact of Visualization Dashboards on Quality of Care and Clinician Satisfaction: Integrative Literature Review

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Background: Intensive Care Units (ICUs) in the United States admit more than 5.7 million people each year. The ICU level of care helps people with life-threatening illness or injuries and involves close, constant attention by a team of specially-trained health care providers. Delay between condition onset and implementation of necessary interventions can dramatically impact the prognosis of patients with life-threatening diagnoses. Evidence supports a connection between information overload and medical errors. A tool that improves display and retrieval of key clinical information has great potential to benefit patient outcomes. The purpose of this review is to synthesize research on the use of visualization dashboards in health care. Objective: The purpose of conducting this literature review is to synthesize previous research on the use of dashboards visualizing electronic health record information for health care providers. A review of the existing literature on this subject can be used to identify gaps in prior research and to inform further research efforts on this topic. Ultimately, this evidence can be used to guide the development, testing, and implementation of a new solution to optimize the visualization of clinical information, reduce clinician cognitive overload, and improve patient outcomes. Methods: Articles were included if they addressed the development, testing, implementation, or use of a visualization dashboard solution in a health care setting. An initial search was conducted of literature on dashboards only in the intensive care unit setting, but there were not many articles found that met the inclusion criteria. A secondary follow-up search was conducted to broaden the results to any health care setting. The initial and follow-up searches returned a total of 17 articles that were analyzed for this literature review. Results: Visualization dashboard solutions decrease time spent on data gathering, difficulty of data gathering process, cognitive load, time to task completion, errors, and improve situation awareness, compliance with evidence-based safety guidelines, usability, and navigation. Conclusions: Researchers can build on the findings, strengths, and limitations of the work identified in this literature review to bolster development, testing, and implementation of novel visualization dashboard solutions. Due to the relatively few studies conducted in this area, there is plenty of room for researchers to test their solutions and add significantly to the field of knowledge on this subject.
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Understanding the Impact of Visualization Dashboards on Patient Safety: An Integrative
Literature Review
Aniesha Dukkipati, MSN, RN1, Heather Alico Lauria, BSN, RN1, Thomas Bice, MD2,
Debbie Travers PhD, RN1,3, Shannon Carson, MD2, Saif Khairat, PhD*1,3
1 School of Nursing, University of North Carolina- Chapel Hill, Chapel Hill, NC, USA
2 Pulmonary Diseases & Critical Care Medicine, School of Medicine, University of North
Carolina- Chapel Hill, Chapel Hill, NC, USA
3 Carolina Health Informatics Program, University of North Carolina- Chapel Hill, Chapel Hill,
NC, USA
*Corresponding Author:
Saif Khairat, PhD
Assistant Professor
Carolina Health Informatics Program (CHIP)
Division of Healthcare Systems - School of Nursing
University of North Carolina - Chapel Hill
Phone: 919-843-5413
Email: Saif@unc.edu
ICU VISUALIZATION DASHBOARD
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Abstract
Background: Intensive Care Units (ICUs) help people with life-threatening illness or injuries
and involve close attention by a team of specially-trained health care providers. Delay between
condition onset and implementation of necessary interventions can dramatically impact the
prognosis of patients. A tool that improves display and retrieval of key clinical information has
great potential to benefit patient outcomes. The purpose of this review is to synthesize research
on the use of visualization dashboards in health care.
Methods: Articles were included if they addressed the development, testing, implementation, or
use of a visualization dashboard solution in a health care setting. An initial search was
conducted of literature on dashboards only in the intensive care unit setting, but there were not
many articles found that met the inclusion criteria. A secondary follow-up search was conducted
to broaden the results to any health care setting. The initial and follow-up searches returned a
total of 17 articles that were analyzed for this literature review.
Results: Visualization dashboard solutions decrease time spent on data gathering, difficulty of
data gathering process, cognitive load, time to task completion, errors, and improve situation
awareness, compliance with evidence-based safety guidelines, usability, and navigation.
Conclusions: Researchers can build on the findings, strengths, and limitations of the work
identified in this literature review to bolster development, testing, and implementation of novel
visualization dashboard solutions. Due to the relatively few studies conducted in this area, there
is plenty of room for researchers to test their solutions and add significantly to the field of
knowledge on this subject.
Keywords: Intensive care unit, Visualization, Dashboard, Cognitive load, Information overload,
Usability, User interface design, Health information technology, Electronic health record
ICU VISUALIZATION DASHBOARD
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Background
State of the Problem
Critical Patient Population
Intensive Care Units (ICUs) in the United States admit more than 5.7 million people
each year [1]. The ICU level of care helps people with life-threatening illness or injuries and
involves close, constant attention by a team of specially-trained health care providers [2]. ICU
patients require frequent assessment and have a greater need for technological and clinical
support compared to non-ICU patients [1]. Important metrics in the ICU range from simple vital
sign monitoring and laboratory data to mechanical ventilator support, vasoactive medications,
and even complete circulatory support, depending on the unique needs of specific patients.
Although ICU patients receive care for a wide variety of disease states, the leading causes of
death in the ICU are multi-organ system failure, cardiovascular failure, and sepsis [2]. Delay
between condition onset and implementation of necessary interventions can dramatically impact
the prognosis of a patient with one of these life-threatening diagnoses,
Electronic Health Record Usability
EHR use has increased nationwide; however, the question remains whether EHRs are
actually being used in an effective and efficient way that improves clinical workflow and health
outcomes [3, 4]. A systematic review and meta-analysis intended to evaluate effects of health
information technology in the hospital and ICU on mortality, length of stay, and cost found
significant interstudy and intrastudy variability. The study demonstrated that more research is
needed with standardized interventions and endpoints to evaluate EHR use and implementation.
Currently, no conclusion can be made regarding the effect of health information technology on
inpatient and ICU outcomes such as mortality, length of stay, and cost [4].
Information Overload
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In 2013, Singh and et al. conducted a cross-sectional study of primary care providers to
evaluate predictors of missed test results in the setting of electronic health record (EHR) alerts.
Of the nearly 2,600 respondents, 87% perceived the quantity of alerts they received to be
excessive, 70% reported receiving more alerts than they could effectively manage, 56% reported
that the current EHR notification system made it possible for practitioners to miss test results,
and 30% reported having personally missed test results that led to care delays [5]. To address the
high volume of metrics used and the time-sensitive nature of responding to changes in a critically
ill patient's condition, a tool that improves ICU display and retrieval of key clinical information
has great potential to benefit patient outcomes.
Proposed Solution
Visualization is a field of study concerned with the transformation of data to visual
representations, where the goal is the effective and efficient cognitive processing of data [6].
Use of visualization techniques in the clinical setting have the potential to improve data display
and cognitive processing of data, reducing cognitive overload among clinicians [6]. Information
visualization involves the transformation from lower-level data to visual representations of
meanings extracted from the data [6]. Extraction is by either a computational process or a
human transcription process, the aim of which is to explore data and create new insights [6].
Some guidelines for the development of an information visualization solution include:
use realistic techniques to enhance mapping of data elements to visual objects
minimize user actions to accomplish a goal
provide flexibility in the ways to achieve the same goal
provide functionality to represent additional information
ICU VISUALIZATION DASHBOARD
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spatially organize the visual layout
consistently apply design choices
place minimal cognitive load on the user
provide the user with information on alternatives when several actions are available
remove extraneous or distracting information
consider means to reduce the data set [6].
A dashboard is a data-driven clinical decision support tool capable of querying multiple
databases and providing a visual representation of key performance indicators in a single report
[7]. The utility of a dashboard comes from its ability to provide a concise overview of key
information [7]. Applied to the intensive care unit, a dashboard allows clinicians to quickly
identify changes in the patient's condition that require intervention. The clinician can choose
then either to dive deeper into the EHR data or refer back to the dashboard at a later point to
review changes. Depending on the design of the dashboard, features such as alerts and
documentation reminders can help clinicians improve compliance with best practice guidelines
and organizational standards [7].
Purpose of this Literature Review
The purpose of conducting this literature review is to present previous research on the use
of visualization dashboards to improve efficiency, clinician satisfaction, patient safety and
accuracy in the clinical setting. This evidence can be used to guide the development, testing and
implementation of new solutions to optimize the visualization of clinical information.
Methods
ICU VISUALIZATION DASHBOARD
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Inclusion Criteria
Articles were included if they addressed the development, testing, implementation, or use
of a visualization dashboard solution in a health care setting. An initial search was conducted of
literature on dashboards only in the intensive care unit setting, but there were limited articles
found that met the inclusion criteria. A secondary follow-up search was conducted to broaden
the results to any health care setting. Ideally, the article would compare outcomes with the novel
solution to outcomes prior to or without the novel solution. However, articles were not excluded
simply due to lack of a specific comparison. Articles should contain quantitative or qualitative
outcomes related to clinician satisfaction, cognitive overload, or patient outcomes. Initially,
abstracts were scanned to identify if articles were relevant to the specified research questions.
Exclusion Criteria
Due to the specificity and novel nature of this topic, no filters were applied to the query.
This means that no articles were excluded solely based on type, publication date, or country of
origin. However, articles were excluded if there was not an English version of the article
available. Articles were excluded if review of the abstract and full text revealed the article did
not address at least one of the specified research questions and meet the inclusion criteria.
Databases
Databases selected for this search were PubMed, PMC, CINAHL, and EMBASE, all of
which are health sciences journal article databases.
Initial Search Terms - ICU Only
In order to capture alternative ways of denoting the terms of interest, the query of
("electronic medical record" OR "electronic health record" OR EMR OR EHR) AND
(visualization OR dashboard OR design OR interface) AND ("intensive care" OR ICU OR
"critical care" OR CCU) was used for the initial search. Abstracts were screened for relevance to
the intended investigation. Articles with relevant abstracts were then read in entirety to further
screen the relevance and quality of the data.
ICU VISUALIZATION DASHBOARD
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Searching the above query into PubMed returned 151 results, 14 of which were analyzed,
and 8 of which were relevant, quality results for the final analysis. PMC returned 3,405 results,
13 of which were analyzed, 5 of which were excluded due to duplication, and 3 of which were
included in the final analysis. CINAHL returned 86 results, 7 of which were analyzed, 2 of
which were excluded due to duplication, and 0 included in the final analysis. EMBASE returned
646 results, 18 of which were analyzed, 6 of which were excluded due to duplication, and 1 of
which was included in the final analysis. Therefore, a total of 12 articles were obtained from this
primary search towards the final analysis.
Follow-up Search Terms - Any Health Care Setting
Due to the limited number of results obtained with the initial search, a secondary search
was completed using the query of visualization AND dashboard. The purpose of the secondary
search was to broaden the search to a visualization dashboard solution in any health care setting,
as opposed to only the intensive care unit setting. The same databases and process of screening
articles were maintained from the initial search process.
Searching the above query into PubMed returned 24 results, 7 of which were analyzed, 1
of which was excluded due to duplication, and 4 of which were relevant, quality results for the
final analysis. PMC returned 311 results, 10 of which were analyzed, 5 of which were excluded
due to duplication, and 1 of which was included in the final analysis. CINAHL returned 3
results, 2 of which were analyzed, 0 of which were excluded due to duplication, and 0 of which
were included in the final analysis. EMBASE returned 30 results, 4 of which were analyzed, 3
of which were excluded due to duplication, and 0 of which were included in the final analysis.
Therefore, a total of 5 articles were obtained from the secondary search towards the final
analysis, Figure 1.
Implications of Query Results
The initial and follow-up searches returned a total of 17 articles that were analyzed for
this literature review. The limited results reflect the novel status of this area of research.
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ICU VISUALIZATION DASHBOARD
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Supporting information from information and library science databases will be useful in the
analysis steps as much of this work involves development, testing, and implementation of a
novel software solution.
Figure 1a. Literature Review Process (Primary Search)
Figure 1b. Literature Review Process (Secondary Search)
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ICU VISUALIZATION DASHBOARD
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Results
The dashboard solutions that we identified in the 17 articles are presented with
organization by study findings related to efficiency, quality/safety, accuracy, and user
satisfaction. Some solutions were discussed in multiple articles, whereas others were unique to a
single article. Table 1 presents the sample size, metrics of interests, results and findings for each
study.
Table 1. Study characteristics and results
Study Metric of Interest Sample (n) Result Findings
Ahmed et. al
(2011) Accuracy, Efficiency 160
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Farri et al. Accuracy, Efficiency 8
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Satisfaction 12
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ICU VISUALIZATION DASHBOARD
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Pageler et al. Efficiency,
Quality/Safety 64
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#(
Efficiency
The Ambient Warning and Response Evaluation (AWARE) system was tested in two articles
included in this literature review [8,9]. AWARE is an ICU-specific patient viewer and
monitoring system that was developed at Mayo Clinic [8]. AWARE is a superstructure for
existing EHR. The development of this tool was guided by clinicians based their information
ICU VISUALIZATION DASHBOARD
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needs [8]. Pickering et al. (2015) used a step wedge cluster randomization trial to demonstrate a
decrease in time spent on pre-round data gathering using the AWARE system [9]. Compared to
the existing EHR, AWARE was reported to improve information management (data presentation
format and efficiency of data access) and make the task of gathering data for rounds significantly
less difficult and mentally demanding [9].
Scripps Clinic and Green Hospital used a rapid-cycle evaluation process to develop the
algorithms, alert systems, and interfaces intended to facilitate patient-provider interactions and
determination of treatment plans [10]. Brooke's Standardized Usability Tool was used to
evaluate usability and two independent appraisers reviewed the think aloud sessions for usability
themes [10]. Results pointed to positive results regarding usability and efficiency to identify
pertinent components in the patient's plan of care with use of the prototype [10].
Ahmed et al. (2011) evaluated a novel .NET based application by conducting a randomized
crossover study [11]. This study demonstrated improved workload (using NASA-task load
index), decreased time to task completion, and decreased number of errors of cognition, [11].
Additionally, the standard EHR contained a much larger data volume compared with the novel
user interface[11].
Koch et al. (2013) evaluated nurses’ situation awareness and task completion time using
an integrated information display compared to traditional displays [12]. Task completion time
(response time from seeing the question to submitting the answer) was measured using paper
prototypes of both displays [12]. Task completion times were nearly half with integrated
displays compared to traditional displays[12].
Farri et al. (2012) carried out three iterations of planning, risk analysis, design, and
evaluation of an EHR prototype. This user interface contained specific functionalities for
clinical documents [13]. They used a spiral model for software development and the EHR
ICU VISUALIZATION DASHBOARD
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system user interface framework of the Veterans Affairs computerized patient record system
(VistA CPRS) [13].
The researchers used a mixed methods approach to evaluate a sample of eight medical
interns as they synthesized EHR clinical documents in four pre-formed clinical scenarios [13].
Despite the non-significant difference in total times to task completion the researchers observed
shorter times for two scenarios with the visualization tool. This may suggest that the timesaving
benefits may be more evident with certain clinical processes [13]
Dolan et al. (2013) used a mixed quantitative and qualitative evaluation process to
evaluate their dashboard prototype [14]. The researchers observed the time participants spent
using the dashboard before choosing a preferred drug, ease of use, acceptability, decisional
conflict, and an open-ended qualitative analysis [14]. Qualitative findings were positive,
suggesting potential for informed decision making and patient centered care [14].
Medical Information Visualization Assistant, v.2 (MIVA 2.0) is an EHR dashboard
technology that uses a visualization engine to deliver multivariate biometric data by transforming
it into temporal resolutions [15]. ICU clinicians are able to use selection menus to control the
viewability of data in various time periods to assist with diagnosis and treatment [15]. The
usability speed test identified no significant difference in time-on-task between the control group
and the experimental group [15]. However, a significant difference was noted in speed with use
of MIVA 2.0 [15].
Clinician Satisfaction
Dziadzko et al. (2016) studied the before- and-after- implementation experience and
satisfaction of ICU providers at two hospitals using the AWARE system [8]. Providers agreed
that data gathering using the existing EHR system was difficult and time-intensive [8]. In a
survey analysis, researchers found that prescribers were significantly more satisfied with the
ICU VISUALIZATION DASHBOARD
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delivery of content and information output with AWARE due to the improvement of the
presentation of information [8]. Bakos et al. (2012) showed an increased use of the dashboard
tool at Virginia Commonwealth University Health System throughout the first year of
implementation, demonstrating clinician satisfaction with usage. Interviews further confirmed
the benefit and helpfulness of using the tool as staff confirmed its usefulness in their workflow
[16].
Quality and Safety
Pageler et al. (2013) discuss use of a checklist enhanced by the EHR and a unit-wide
dashboard to improve compliance with an evidence-based, pediatric-specific catheter care bundle
[17]. The researchers performed a cohort study with historical controls that included all patients
with a central venous catheter at a 24-bed Pediatric ICU (PICU) in an academic children's
hospital [17].
Central line associated bloodstream infection (CLABSI) rates decreased after the
checklist intervention [17]. Analysis of specific bundle elements demonstrated decreased
compliance with insertion bundle documentation. However, there was an increase in compliance
with daily documentation of line necessity, dressing changes, cap changes, and port needle
changes.
Shaw et al. (2015) evaluated a real-time visual display that showed data on presence of
consent for treatment, restraint orders, presence of urinary catheters, deep venous thrombosis
(DVT) prophylaxis, Braden Q score, and medication reconciliation [18]. An automated EHR
querying tool was created to assess compliance with a PICU safety bundle, and querying of the
EHR for compliance and updating of the dashboard automatically occurred every five minutes
[18].
Baseline compliance and duration of noncompliance was established during three time
periods: before activation of the dashboard, at one month following activation of the dashboard,
ICU VISUALIZATION DASHBOARD
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and at three months after activation [18]. There was no difference between the three periods in
presence of restraint orders, DVT prophylaxis, or development or worsening of pressure ulcers
[18]. Between the first and third time periods, the median time from PICU admission to
obtaining treatment consent decreased [18]. The number of patients with urinary catheters in
place > 96 hours decreased significantly after the intervention [18]. The researchers concluded
that a unit-wide dashboard could increase awareness for potential interventions, thereby affecting
patient safety in a dynamic manner [18].
Although Bakos et al. (2012) speculate that their visualization dashboard will contribute
to having zero events of preventable harm to patients, employees and visitors; there is no
quantifiable data to support this at this time [16]. Similarly, Hagland et al. (2010) discuss the
potential to improve patient safety, communication and clinician workflow using a new clinical
dashboard without quantifiable results [19].
Accuracy
Koch et al. (2013) used the paper prototypes of their displays to measure situation
awareness (accuracy of the participants’ answer). Nurses had a higher situation awareness and
accuracy when using the integrated display versus the traditional display [12].
To evaluate the accuracy of, MIVA 2.0, Faiola et al. (2015) used quantitative clinical
decision-making task questions. The clinical decision-making accuracy test identified an overall
significant improvement in accuracy of the eight-question test between the experimental versus
control groups. Qualitative results were obtained from seven open-ended interview questions,
wherein participants acknowledged the potential impact of MIVA 2.0 for reducing cognitive load
and enabling more accurate decision-making [15]. Overall, a significant difference was noted in
accuracy with use of MIVA 2.0 [15].
Dziadzko et al. (2016) surveyed healthcare providers who reported an improvement in
the accuracy of decision-making using AWARE but no quantifiable data is available [8]. Using
ICU VISUALIZATION DASHBOARD
15
the .NET based application, Ahmed et al. (2011) found that the median number of errors per
provider decreased significantly for the novel user interface compared to the standard electronic
medical record interface [13].
Farri et al. (2012) evaluated the accuracy of using their spiral model software. The resulting
differences in unretrieved patient information and accurate inferences were not statistically
significant, but suggested some improvement with the new information visualization tool [13].
Other observed effects of the tool included more intuitive navigation between patient details and
increased effort towards methodical synthesis of clinical documents [13].
Scripps Clinic and Green Hospital demonstrated an improved accuracy of the healthcare provider
“Heart Team” in clinical decision-making using 15 mock patients. However, a complete data
analysis was not performed [10].
Tool Development
Five articles did not focus on efficiency, quality/safety, accuracy and satisfaction
outcomes but discussed their process of tool development. Their findings during visualization
tool development are included in the discussion section below [20, 21,22, 23, 24].
Discussion
Table 1.
Overview
The 17 articles included in this literature review demonstrate how efficiency,
quality/safety, clinician satisfaction and accuracy can be improved using a visualization
ICU VISUALIZATION DASHBOARD
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dashboard. These 17 articles share many themes regarding how the each dashboard was
designed and what user-friendly features are available when using the dashboard. These themes
are discussed below. With each idea outlined, a discussion of its application to prior
visualization dashboard solutions and its implications for future studies follows. Application of
these approaches, methods, and features may serve useful in future efforts related to this subject
matter. A summary of findings from the aforementioned articles is depicted in Figure 1.
Figure 2. Summary of findings from the literature review.
Human- Centered
Design
A
The IView dashboard was developed for use on three ICU's at the Children's Hospital at
Pittsburgh, and resulted from intensive clinician-IT team-based work and a collaborative
relationship with the hospital's clinical IT vendor [15]. Qualitative measures regarding perceived
ICU VISUALIZATION DASHBOARD
17
patient safety, clinician workflow, and physician-nurse communications pointed to positive
outcomes in all three categories [15].
Swartz et al. (2014) discuss the creation of iNYP, a Java-based service-oriented web
application, to meet the specific information needs of emergency medicine clinicians [21]. A
combination of survey and structured interview were used to inform the development of this
specialty-specific clinical dashboard [20].
Hartzler et al. (2015) discuss use of human-centered design methods to create visual displays of
patient reported outcomes [21]. Targeted, iterative design activities were used to inform
development of a dashboard that visually displays patient-reported pain and disability outcomes
following spine surgery [21]. The Multi-signal Visualization of Physiology (MVP) was
developed at the Neuroscience ICU of the National Neuroscience Institute in Singapore to
provide a more visual, straightforward, and intuitive diagnosis process [23]. The MVP makes
use of a polygram that incorporates live readings of physiological signs and colors to highlight
different patient statuses [23].
Overview
With each idea outlined, a discussion of its application to prior visualization dashboard solutions
and its implications for future studies follows. Application of these approaches, methods, and
features may serve useful in future efforts related to this subject matter. Interdisciplinary
Approach
Nine articles mentioned use of an interdisciplinary approach in developing, testing, and
implementing their visualization solution. The benefit of an interdisciplinary approach is that the
varied professional perspectives and skills that come with different disciplines are integrated into
each step of the process [8, 10, 11, 15, 16, 19-21, 24]
Use of an Interactive Prototype
Prototyping is a useful process as it allows developers to strategize product design and
obtain feedback from end users without the expansive investment of resources required to make
changes in the EHR format [10, 12- 15, 21, 22]. While there are viable electronic prototyping
ICU VISUALIZATION DASHBOARD
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options available, paper-based prototyping can be a useful, cost-effective solution in early stages
of product design [25]. A mixed evaluation process of quantitative and qualitative measures can
be used to direct feedback from end user interaction with the prototype and improve design on
subsequent revisions [10, 12-15, 21, 22].
Using Open-Source Technology vs. Adapting a Third-Party Vendor's EHR System
A team with limited resources may not be able to invest financial, temporal, and staff
resources into developing a suitable product [26]. Those teams with limited resources may have
to wait for a solution to stem from others using open-source technology or for the third-party
vendor to provide an option that will be suitable [22, 27]. Use of an open-source technology can
allow more freedom for the user to develop and share their tool with others than when making
adjustments to an EHR developed by a third-party vendor [22]. Ultimately, each team at a
specific organization will decide which route aligns better with their own resources and goals,
but the distinct opportunities and risks inherent with each option are important to consider.
Adapting a vendor's EHR system will require continual consultation with the vendor and
there may be significant limitations imposed by the contract between the organization and the
vendor [27].
Application of Evidence-Based, Clinical Practice Guidelines to the EHR
Clinical practice guidelines (CPGs) are intended to improve the quality, consistency, and
effectiveness of care by applying evidence-based medicine [28]. A review of physician
adherence to clinical practice guidelines suggested that as many as 38% of physicians consider
clinical practice guidelines as inconvenient or too difficult to use [28]. Incorporation of clinical
practice guidelines into the structure and display of the EHR may help improve convenience of
access to practice guidelines and increase use in clinical decision making [16-18].
Clinician Controlled Selection Menus
Allowing the clinician to adjust the data displayed in alignment with the preference and
needs of that individual may further improve clinician satisfaction with the system [15, 24]. This
capability can also help meet the goal of reducing cognitive overload [15, 24]. If clinicians are
ICU VISUALIZATION DASHBOARD
19
able to filter out information that is not pertinent to them, the remaining information will have
improved visibility without obstruction from extraneous information [15, 24]. The capability to
filter information by location, service lines, and specific diagnoses may also serve useful to
improve efficiency, accuracy and user satisfaction of clinicians managing many patients [15].
Improved Display of Trends in Physiological Signs
In a setting such as an intensive care unit, the stability of a patient's condition can quickly
deteriorate [2]. While clinicians have primary responsibility to assess their patient's condition
and intervene appropriately, adding features to the EHR that can assist with this process can
expedite these steps; improving efficiency [23]. With the vast array of physiological parameters
under continuous monitoring in the ICU setting, improved display of data trends may improve
the clinician's responsiveness in adding or weaning interventions based on the patient's changing
condition [23].
Classification of Data by Body System
Classification of data using a body system approach was a common decision for the EHR
designs in this literature review [8, 11]. By using a body system approach, clinicians are able to
follow a systematic approach to optimizing the patient's holistic health. The design choice of
matching the body system approach used by intensive care unit clinicians allows for congruency
between the EHR display and cognitive organization of clinical information [8, 11].
Applicability of a Visualization Dashboard to Non-ICU Clinical Settings
While this literature review focused primarily on the application of a visualization
dashboard to the intensive care unit setting, the same intervention could have benefit in other
clinical settings as well [10, 13, 14, 16 20-22]. The emergency department could be well-suited
for this intervention as the EHR could then assist in alerting clinicians to new results and a
change in the patient's clinical status that modifies the plan of care [20]. Step-down units and
inpatient floors may not have the same extent of clinical data as the intensive care unit setting but
clinicians may still find benefit from features related to improved display of clinical information.
ICU VISUALIZATION DASHBOARD
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Once a visualization dashboard is successfully implemented in the ICU setting, the dashboard
can be modified, tested, and implemented in non-ICU clinical settings; working towards similar
goals [22].
Conclusions
. The findings, strengths, and limitations discussed in this section can drive future research
efforts on visualization dashboard solutions.
Information needs varied based on patient population and clinical role. Key findings regarding
clinician needs for the solution included the following:
application of evidence-based, clinical practice guidelines
clinician controlled selection menus
use of color coded visual indicators
classification of data by body system
match of EHR design to process of interdisciplinary rounds
Strengths and Limitations of Solutions
This literature review includes information on several visualization dashboards that have
been tested with positive results from quantitative and qualitative analysis. These positive results
support the potential benefits of a visualization dashboard solution to clinical practice
environments.
Limitations were noted in the following areas:
The interpretation of what a visualization dashboard solution entails varied widely among
the researchers of the different studies included.
ICU VISUALIZATION DASHBOARD
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Many of the visualization dashboard solutions were evaluated with a solely qualitative
approach, rather than with a quantitative or mixed methods approach.
Some articles included details about the design, implementation, and evaluation
processes, but did not include full detail on the data obtained.
Some studies used a simulated setting in lieu of a live clinical setting, which means that
results may differ when the solution is applied to a live clinical setting.
Most studies tested a single solution in a single implementation setting, which limits the
generalizability of the findings to other solutions and other implementation settings.
Future Direction
Researchers can build on the findings, strengths, and limitations of the work identified in
this literature review to bolster development, testing, and implementation of a novel visualization
dashboard solution. Due to the relatively few studies conducted in this area, there is plenty of
room for researchers to test their solutions and add significant information to the field of
knowledge on this subject. An effective solution in this area can drive process improvement and
improved patient outcomes for not only the initial setting of implementation, but also to any
further clinical units and organizations that adopt the intervention.
Conclusions
Overall, successful visualization dashboards utilized an interdisciplinary approach to
develop a human-centered design. Dashboards were flexible and could be adjusted to the users’
preferences as well as organized based on body system, color-coded and adapted for clinician
team rounding. These features are important due to the variety in patient population and the
diverse way that clinicians interpret information. Utilizing these common themes to develop
ICU VISUALIZATION DASHBOARD
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visualization tools for patient care has shown to improve efficiency, quality/safety, clinician
satisfaction and accuracy in a variety of patient settings.
This section synthesizes the major findings of the 17 articles. As discussed, visualization
tools have the potential to impact accuracy, efficiency, user satisfaction and quality/safety of care
in the ICU and other settings. Numerous factors such as clinician controlled displays,
organization by body system, an interdisciplinary design team and using open-source technology
can result in successful implementation of a visualization dashboard. The findings, strengths,
and limitations discussed in this section can drive future research efforts on visualization
dashboard solutions.
Design Recommendation based on Clinician Needs
Information needs varied based on patient population and clinical role. Key findings regarding
clinician needs for the solution included the following:
application of evidence-based, clinical practice guidelines
clinician controlled selection menus
use of color coded visual indicators
classification of data by body system
match of EHR design to process of interdisciplinary rounds
ICU VISUALIZATION DASHBOARD
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As demonstrated in the results section of this paper, the combination of the above components
can allow for user-friendly dashboard designs that have the potential to impact accuracy,
efficiency, user satisfaction and quality/safety of care.
Declarations
Ethics Approval and Consent to Participate
Not applicable.
Consent for Publication
Not applicable.
Availability of Data and Material
Not applicable.
Competing Interests
The authors declare that they have no competing interests.
Funding
Not applicable.
Authors' Contributions
AD and carried out the literature review and drafted the manuscript under the direct
supervision of SK and DT. AD, HL, SK, DT, TB, and SC analyzed and interpreted the results,
revised the manuscript, and support this research. All authors contributed to, read, and approved
the final manuscript.
ICU VISUALIZATION DASHBOARD
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Abbreviations
EHR: Electronic Health Record
ICU: Intensive Care Unit
AWARE: Ambient Warning and Response Evaluation
CPGs: Clinical Practice Guidelines
MVP: Multi-signal Visualization of Physiology
DVT: Deep Vein Thrombosis
ICU VISUALIZATION DASHBOARD
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... To create an effective dashboard, developers must make multiple complex decisions. End users' information needs are highly contextual and depend on the clinical setting, professional roles, and the patient population, which impact selection of appropriate data elements, visualizations, and interactivity [13][14][15]. Although health care executives may prefer to see graphic performance trends over weeks or months, clinicians working with vulnerable patient groups may require real-time, patient-level health data so they can intervene quickly if needed. ...
... This scoping review will provide a narrative overview of design elements and characteristics of health care dashboards, including where they exist geographically, the intended end users, information presented, whether/how the end user and setting impact dashboard design, and the processes used for development, implementation, and evaluation. Although previous reviews on health care dashboards have focused on identifying important design features and effectiveness of dashboards in improving patient outcomes and clinician satisfaction [11,14,15,21,22], an updated review of how dashboard tools are used, and by whom will provide meaningful insight into how intended end user and setting impact the design, development and implementation of the dashboard (ie, the relationship of form and function). This information is essential to provide insights into (1) how and why dashboards work in different settings for different users, to allow relevant stakeholders to make more informed decisions about where to implement, and (2) how to effectively design dashboards based on their intended purpose and target audience. ...
... Given the rapidly evolving field of health informatics, the scoping review will also provide insight into the latest trends in dashboards, from initial conception and development through implementation and evaluation. Previous reviews of dashboards have included articles published only as recently as 2017 [11,14,[21][22][23]. ...
Article
Full-text available
Background: Health care organizations increasingly depend on business intelligence tools, including "dashboards," to capture, analyze, and present data on performance metrics. Ideally, dashboards allow users to quickly visualize actionable data to inform and optimize clinical and organizational performance. In reality, dashboards are typically embedded in complex health care organizations with massive data streams and end users with distinct needs. Thus, designing effective dashboards is a challenging task and theoretical underpinnings of health care dashboards are poorly characterized; even the concept of the dashboard remains ill-defined. Researchers, informaticists, clinical managers, and health care administrators will benefit from a clearer understanding of how dashboards have been developed, implemented, and evaluated, and how the design, end user, and context influence their uptake and effectiveness. Objective: This scoping review first aims to survey the vast published literature of "dashboards" to describe where, why, and for whom they are used in health care settings, as well as how they are developed, implemented, and evaluated. Further, we will examine how dashboard design and content is informed by intended purpose and end users. Methods: In July 2020, we searched MEDLINE, Embase, Web of Science, and the Cochrane Library for peer-reviewed literature using a targeted strategy developed with a research librarian and retrieved 5188 results. Following deduplication, 3306 studies were screened in duplicate for title and abstract. Any abstracts mentioning a health care dashboard were retrieved in full text and are undergoing duplicate review for eligibility. Articles will be included for data extraction and analysis if they describe the development, implementation, or evaluation of a dashboard that was successfully used in routine workflow. Articles will be excluded if they were published before 2015, the full text is unavailable, they are in a non-English language, or they describe dashboards used for public health tracking, in settings where direct patient care is not provided, or in undergraduate medical education. Any discrepancies in eligibility determination will be adjudicated by a third reviewer. We chose to focus on articles published after 2015 and those that describe dashboards that were successfully used in routine practice to identify the most recent and relevant literature to support future dashboard development in the rapidly evolving field of health care informatics. Results: All articles have undergone dual review for title and abstract, with a total of 2019 articles mentioning use of a health care dashboard retrieved in full text for further review. We are currently reviewing all full-text articles in duplicate. We aim to publish findings by mid-2022. Findings will be reported following guidance from the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist. Conclusions: This scoping review will provide stakeholders with an overview of existing dashboard tools, highlighting the ways in which dashboards have been developed, implemented, and evaluated in different settings and for different end user groups, and identify potential research gaps. Findings will guide efforts to design and use dashboards in the health care sector more effectively. International registered report identifier (irrid): DERR1-10.2196/34894.
... Between 2014 and 2018, seven reviews were identified that were relevant to the implementation of dashboards within health care organizations. [12][13][14][15][16][17][18] These reviews encompassed 148 individual studies published between 1996 and 2017. ...
... Although our review focused on the use of real-time, EHR data that was not a prerequisite for prior reviews, we did not C4: Organizational culture [16] C5: Lack of clinical guidelines/benchmarks [17] C6: Changing implementation environment [15,28,29] C7: Implementation time constraints [17] C8: Difficult to assess [35] Information C9: Quantity of data [18,19] C10: Complexity of data [19] C11: Uncertainty of data [20] C12: Quality of data [21] C13: Missing required data [36] C14: Normalization/regularization of data [22,25] [20] ...
... C15: Additional manual data entry [23] C16: Lack of nomenclature standardization [24] C17: Need for bioinformatician to extensively code [21] [20,24] C24: Sourcing patient outcome information [33] C25: Handling rare events/small data sets [34] C26: Clinicians having enough info on dashboard [5] C27: Tech teething problems turns off users [13] C28: Support diverse users/workflows/screens [28,29,31,33] C29: Support change to environment [30] Horizon 3: clinical model C31: Negative impact of dashboard on patient [1][2][3][4][5] C32: Clinician resistance [6,9] [ 1,11] C33: Resource concerns [12] C34: Integrating clinician thinking with dashboard [3] C35: Lack of clinician time [9] C36: Understanding variability of data [26] Process C37: Ethical concerns over data usage [13] C38: Different needs in different clinical settings [15] [2,28] ...
Article
Objective A learning health care system (LHS) uses routinely collected data to continuously monitor and improve health care outcomes. Little is reported on the challenges and methods used to implement the analytics underpinning an LHS. Our aim was to systematically review the literature for reports of real-time clinical analytics implementation in digital hospitals and to use these findings to synthesize a conceptual framework for LHS implementation. Methods Embase, PubMed, and Web of Science databases were searched for clinical analytics derived from electronic health records in adult inpatient and emergency department settings between 2015 and 2021. Evidence was coded from the final study selection that related to (1) dashboard implementation challenges, (2) methods to overcome implementation challenges, and (3) dashboard assessment and impact. The evidences obtained, together with evidence extracted from relevant prior reviews, were mapped to an existing digital health transformation model to derive a conceptual framework for LHS analytics implementation. Results A total of 238 candidate articles were reviewed and 14 met inclusion criteria. From the selected studies, we extracted 37 implementation challenges and 64 methods employed to overcome such challenges. We identified common approaches for evaluating the implementation of clinical dashboards. Six studies assessed clinical process outcomes and only four studies evaluated patient health outcomes. A conceptual framework for implementing the analytics of an LHS was developed. Conclusion Health care organizations face diverse challenges when trying to implement real-time data analytics. These challenges have shifted over the past decade. While prior reviews identified fundamental information problems, such as data size and complexity, our review uncovered more postpilot challenges, such as supporting diverse users, workflows, and user-interface screens. Our review identified practical methods to overcome these challenges which have been incorporated into a conceptual framework. It is hoped this framework will support health care organizations deploying near-real-time clinical dashboards and progress toward an LHS.
... Although available urban dashboards include some of the environmental indicators deemed key to monitoring EH, they focus on urban management instead of health monitorization. For this reason, the engagement of experts as potential end-users in an integrated approach to design a dashboard reflects its unique ability to help stakeholders and decision-makers focusing on both health needs and environmental drivers (65,66). Engaging end-users from organizations with different perspectives and roles regarding monitorization of EH in Lisbon allowed us to gather the different needs and to identify the issues about data collection. ...
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Dashboards are being increasingly used in the health field, and literature points out that accurate and efficient dashboards require not only dealing with data issues, but also ensuring that dashboards are user-friendly and that incorporate users' views and needs. The integration of evidence and data into decision aiding tools, such as dashboards, to assess and monitor environmental health (EH) in urban settings requires careful design. Departing from EH evidence and making use of the views of EH stakeholders and experts, this study aimed at defining requirements for a dashboard to help decision-makers analyzing and visualizing EH information in the Lisbon urban context. In order to set those requirements, it was combined a user-centered with a design card approach to engage EH potential end-users so as to collect their visualization preferences and gather information related to dashboard requirements. Specifically, three online group semi-structured interviews, involving 11 potential end-users from different organizations, were conducted; design cards with a set of visualization options regarding 17 indicators of built and natural environment determinants were used in the interviews to capture participants' preferences and their rationale; questions about other dashboard features were also asked; and the results from the interviews were synthesized into four separate, but interrelated features, and operationalized into 11 requirements for a dashboard to monitor EH in Lisbon. This study contributes to EH literature by producing knowledge to inform dashboard construction, by highlighting issues related with the usability, analysis, and visualization of data to inform EH decision-making in urban contexts, and by designing an approach that can be replicated to other EH dashboard contexts.
... They combine visual representations and other graphical embellishments to provide layers of abstraction and simplification for numerous related data points, so that viewers get an overview of the most important or relevant information, in a time-efficient way. Their ability to provide insight at a glance has led to dashboards being widely used across many application domains, such as business [20,38], nursing and hospitals [8,17,30,37,53,53], public health [35], learning analytics [12], urban analytics [36], personal analytics, energy [23] and more, summarized elsewhere [20,41,44,54]. These examples, designed mainly for domain experts, have since been complemented by dashboards for public health or political elections, designed for a more general audience and disseminated through news media [55] or dedicated dashboard and tracker websites [5,14,48]. ...
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This paper introduces design patterns for dashboards to inform their design processes. Despite a growing number of public examples, case studies, and general guidelines there is surprisingly little design guidance for dashboards. Such guidance is necessary to inspire designs and discuss tradeoffs in screenspace, interaction, and information shown. Based on a systematic review of 144 dashboards, we report on eight groups of design patterns that provide common solutions in dashboard design. We discuss combinations of these patterns in dashboard genres such as narrative, analytical, or embedded dashboard. We ran a 2 week dashboard design workshop with 23 participants of varying expertise working on their own data and dashboards. We discuss the application of patterns for the dashboard design processes, as well as general design tradeoffs and common challenges. Our work complements previous surveys and aims to support dashboard designers and researchers in co-creation, structured design decisions, as well as future user evaluations about dashboard design guidelines. Detailed pattern descriptions and workshop material can be found online: https://dashboarddesignpatterns.github.io
... 4,5 ICU teams must review, interpret, and take action on these data points when managing multiple patients in a time-constrained environment. Since its implementation, the EHR has demonstrated capability in improving quality and efficiency of ICU care processes, 6 improving communication, and becoming a platform for clinical decision support. ...
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As a knowledge-based field of medicine, critical care medicine has benefited from the use of the electronic health records (EHRs) in daily practice, as intensive care unit (ICU) patients generate thousands of pieces of clinical data each day.1 ICU teams must review, interpret, and take action on these data points when managing multiple patients in a time-constrained environment. The increasing number of available data facts to be processed by ICU clinicians for decision-making surpasses human cognitive capacity. ICU physicians described the current display and representation of patient data in the EHR as suboptimum. Performance dashboards are an information delivery system that display the most important information about performance objectives to ICU directors, allowing them to monitor and manage their ICU performance more effectively. The development of visualization dashboards that monitor ICU performance will still need to adhere to usability principles such as Jakob Nielsen's heuristics. The goal of improving EHR interfaces will directly enhance provider well-being, patient outcomes, and quality of care.
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Background Multidisciplinary rounds (MDRs) are scheduled, patient-focused communication mechanisms among multidisciplinary providers in the intensive care unit (ICU). Objective i-Dashboard is a custom-developed visualization dashboard that supports (1) key information retrieval and reorganization, (2) time-series data, and (3) display on large touch screens during MDRs. This study aimed to evaluate the performance, including the efficiency of prerounding data gathering, communication accuracy, and information exchange, and clinical satisfaction of integrating i-Dashboard as a platform to facilitate MDRs. Methods A cluster-randomized controlled trial was performed in 2 surgical ICUs at a university hospital. Study participants included all multidisciplinary care team members. The performance and clinical satisfaction of i-Dashboard during MDRs were compared with those of the established electronic medical record (EMR) through direct observation and questionnaire surveys. Results Between April 26 and July 18, 2021, a total of 78 and 91 MDRs were performed with the established EMR and i-Dashboard, respectively. For prerounding data gathering, the median time was 10.4 (IQR 9.1-11.8) and 4.6 (IQR 3.5-5.8) minutes using the established EMR and i-Dashboard (P<.001), respectively. During MDRs, data misrepresentations were significantly less frequent with i-Dashboard (median 0, IQR 0-0) than with the established EMR (4, IQR 3-5; P<.001). Further, effective recommendations were significantly more frequent with i-Dashboard than with the established EMR (P<.001). The questionnaire results revealed that participants favored using i-Dashboard in association with the enhancement of care plan development and team participation during MDRs. Conclusions i-Dashboard increases efficiency in data gathering. Displaying i-Dashboard on large touch screens in MDRs may enhance communication accuracy, information exchange, and clinical satisfaction. The design concepts of i-Dashboard may help develop visualization dashboards that are more applicable for ICU MDRs. Trial Registration ClinicalTrials.gov NCT04845698; https://clinicaltrials.gov/ct2/show/NCT04845698
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Background As technology enables new and increasingly complex laboratory tests, test utilization presents a growing challenge for healthcare systems. Clinical decision support (CDS) refers to digital tools that present providers with clinically relevant information and recommendations, which have been shown to improve test utilization. Nevertheless, individual CDS applications often fail, and implementation remains challenging. Content We review common classes of CDS tools grounded in examples from the literature as well as our own institutional experience. In addition, we present a practical framework and specific recommendations for effective CDS implementation. Summary CDS encompasses a rich set of tools that have the potential to drive significant improvements in laboratory testing, especially with respect to test utilization. Deploying CDS effectively requires thoughtful design and careful maintenance, and structured processes focused on quality improvement and change management play an important role in achieving these goals.
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Background: Static nature of performance reporting systems in health care sector has resulted in inconsistent, incomparable, time consuming, and static performance reports that are not able to transparently reflect a round picture of performance and effectively support healthcare managers’ decision makings. So, the healthcare sector needs interactive performance management tools such as performance dashboards to measure, monitor, and manage performance more effectively. The aim of this article was to identify key issues that need to be addressed for developing high-quality performance dashboards in healthcare sector. Methods: A literature review was established to search electronic research databases, e-journals collections, and printed journals, books, dissertations, and theses for relevant articles. The search strategy interchangeably used the terms of “dashboard”, “performance measurement system”, and “executive information system” with the term of “design” combined with operator “AND”. Search results (n=250) were adjusted for duplications, screened based on their abstract relevancy and full-text availability (n=147) and then assessed for eligibility (n=40). Eligible articles were included if they had explicitly focused on dashboards, performance measurement systems or executive information systems design. Finally, 28 relevant articles included in the study. Results: Creating high-quality performance dashboards requires addressing both performance measurement and executive information systems design issues. Covering these two fields, identified contents were categorized to four main domains: KPIs development, Data Sources and data generation, Integration of dashboards to source systems, and Information presentation issues. Conclusion: This study implies the main steps to develop dashboards for the purpose of performance management. Performance dashboards developed on performance measurement and executive information systems principles and supported by proper back-end infrastructure will result in creation of dynamic reports that help healthcare managers to consistently measure the performance, continuously detect outliers, deeply analyze causes of poor performance, and effectively plan for the future.
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Patient daily goal sheets have been shown to improve compliance with hospital policies but might not represent the dynamic nature of care delivery in the pediatric ICU (PICU) setting. A study was conducted at Children's National Health System (Washington, DC) to determine the effect of a visible, unitwide, real-time dashboard on timeliness of compliance with quality and safety measures. An automated electronic health record (EHR)- querying tool was created to assess compliance with a PICU Safety Bundle. Querying of the EHR for compliance and updating of the dashboard automatically occurred every five minutes. A real-time visual display showed data on presence of consent for treatment, restraint orders, presence of urinary catheters, deep venous thrombosis (DVT) prophylaxis, Braden Q score, and medication reconciliation. Baseline compliance and duration of noncompliance was established during three time periods: the first, before activation of the dashboard; the second, at one month following activation of the dashboard; and the third, at three months after activation. A total of 450 patients were included in the analysis. Between the first and third time periods, the median time from PICU admission to obtaining treatment consent decreased by 49%, from 393 to 202 minutes (p = .05). The number of patients with urinary catheters in place > 96 hours decreased from 16 (32%) in Period 1 to 11 (19%) for Periods 2 and 3 combined (p = .01). Completion of medication reconciliation improved from 80% in the first time period to 93% and 92%, respectively, in the subsequent two periods (p = .002). There was no difference between the three periods in presence of restraint orders, DVT prophylaxis, or development or worsening of pressure ulcers. A unitwide dashboard can increase awareness for potential interventions, affecting patient safety in the PICU in a dynamic manner.
Heart Team meetings are becoming the model of care for patients undergoing transcatheter aortic valve implantations (TAVI) worldwide. While Heart Teams have potential to improve the quality of patient care, the volume of patient data processed during the meeting is large, variable, and comes from different sources. Thus, consolidation is difficult. Also, meetings impose substantial time constraints on the members and financial pressure on the institution. We describe a clinical decision support system (CDSS) designed to assist the experts in treatment selection decisions in the Heart Team. Development of the algorithms and visualization strategy required a multifaceted approach and end-user involvement. An innovative feature is its ability to utilize algorithms to consolidate data and provide clinically useful information to inform the treatment decision. The data are integrated using algorithms and rule-based alert systems to improve efficiency, accuracy, and usability. Future research should focus on determining if this CDSS improves patient selection and patient outcomes.
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Advances in intensive care unit bedside displays/interfaces and electronic medical record (EMR) technology have not adequately addressed the topic of visual clarity of patient data/information to further reduce cognitive load during clinical decision-making. We responded to these challenges with a human-centered approach to designing and testing a decision-support tool: MIVA 2.0 (Medical Information Visualization Assistant, v.2). Envisioned as an EMR visualization dashboard to support rapid analysis of real-time clinical data-trends, our primary goal originated from a clinical requirement to reduce cognitive overload. In the study, a convenience sample of 12 participants were recruited, in which quantitative and qualitative measures were used to compare MIVA 2.0 with ICU paper medical-charts, using time-on-task, post-test questionnaires, and interviews. Findings demonstrated a significant difference in speed and accuracy with the use of MIVA 2.0. Qualitative outcomes concurred, with participants acknowledging the potential impact of MIVA 2.0 for reducing cognitive load and enabling more accurate and quicker decision-making.
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Objective: Failure to rapidly identify high-value information due to inappropriate output may alter user acceptance and satisfaction. The information needs for different intensive care unit (ICU) providers are not the same. This can obstruct successful implementation of electronic medical record (EMR) systems. We evaluated the implementation experience and satisfaction of providers using a novel EMR interface-based on the information needs of ICU providers-in the context of an existing EMR system. Methods: This before-after study was performed in the ICU setting at two tertiary care hospitals from October 2013 through November 2014. Surveys were delivered to ICU providers before and after implementation of the novel EMR interface. Overall satisfaction and acceptance was reported for both interfaces. Results: A total of 246 before (existing EMR) and 115 after (existing EMR+novel EMR interface) surveys were analyzed. 14% of respondents were prescribers and 86% were non-prescribers. Non-prescribers were more satisfied with the existing EMR, whereas prescribers were more satisfied with the novel EMR interface. Both groups reported easier data gathering, routine tasks & rounding, and fostering of team work with the novel EMR interface. This interface was the primary tool for 18% of respondents after implementation and 73% of respondents intended to use it further. Non-prescribers reported an intention to use this novel interface as their primary tool for information gathering. Conclusion: Compliance and acceptance of new system is not related to previous duration of work in ICU, but ameliorates with the length of EMR interface usage. Task-specific and role-specific considerations are necessary for design and successful implementation of a EMR interface. The difference in user workflows causes disparity of the way of EMR data usage.
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Recent health care policies have supported the adoption of Information and Communication Technologies (ICT) but examples of failed ICT projects in this sector have highlighted the need for a greater understanding of the processes used to implement such innovations in complex organizations. This study examined the interaction of sociological and technological factors in the implementation of an Electronic Medical Record (EMR) system by a major national hospital. It aimed to obtain insights for managers planning such projects in the future and to examine the usefulness of Actor Network Theory (ANT) as a research tool in this context. Case study using documentary analysis, interviews and observations. Qualitative thematic analysis drawing on ANT. Qualitative analyses revealed a complex network of interactions between organizational stakeholders and technology that helped to shape the system and influence its acceptance and adoption. The EMR clearly emerged as a central ‘actor’ within this network. The results illustrate how important it is to plan innovative and complex information systems with reference to (i) the expressed needs and involvement of different actors, starting from the initial introductory phase; (ii) promoting commitment to the system and adopting a participative approach; (iii) defining and resourcing new roles within the organization capable of supporting and sustaining the change and (iv) assessing system impacts in order to mobilize the network around a common goal. The paper highlights the organizational, cultural, technological, and financial considerations that should be taken into account when planning strategies for the implementation of EMR systems in hospital settings. It also demonstrates how ANT may be usefully deployed in evaluating such projects.