Download Centralized Clinical Decision Support System for Congestive Heart

406 GROUP PROJECT HAWKINS NARAYANAN ZUNAMON
CENTRAL CDSS FOR CHF
A standards-based centralized clinical decision support system
illustrated using decision support for Congestive Heart Failure
A group project submitted by
Nicole Hawkins, Ravi Narayanan and Alan Zunamon
for completion of coursework for MMI-406
(Decision Support Systems and Health Care)
on June 03, 2012
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Table of Contents
Introduction ................................................................................................................................2
Stakeholders, Goals and Objectives ...........................................................................................2
Stakeholders ...............................................................................................................................2
Project Goals & Objectives .......................................................................................................2
Identification of Appropriate Intervention and Workflow for CHF ..........................................2
Information Systems Architecture and Knowledge Management .............................................2
Conceptual Architecture ............................................................................................................2
Guideline Knowledge Management ...........................................................................................2
Decision Support Usage Data Aggregation and Analysis .........................................................2
Information System Inventory ....................................................................................................2
Information and Communication Technology Infrastructure ....................................................2
Intervention Specification ..........................................................................................................2
Workflow Design/Site-specific Customization .........................................................................2
Implementation and Change Management ................................................................................2
Plan and transition process .......................................................................................................2
Resource needs for testing, implementation, validation ............................................................2
Measurement of CDSS Effect on Clinical Practice ...................................................................2
Frequency of Evaluation and Communication of Results..........................................................2
Conclusion .................................................................................................................................2
References ..................................................................................................................................2
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Introduction
Congestive heart failure (CHF) is a relatively common condition that develops over time
where the heart cannot pump enough blood or fill with enough blood to meet the body’s
demands. Approximately 5.8 million people in the United States have heart failure, which can be
caused by high blood pressure, coronary heart disease, diabetes, cardiomyopathy or congenital
heart defects (National Heart Lung and Blood Institute, 2012). An estimated 400,000 to 700,000
new cases of heart failure are diagnosed each year and the number of deaths in the United States
from this condition has more than doubled since 1979, averaging 250,000 annually (Heart
Failure Society of America, n.d.). Furthermore, heart failure is more common in people 65 years
of age and older, African Americans, individuals who are overweight, individuals who have had
a heart attack, children with congenital heart defects and among men (National Heart Lung and
Blood Institute, 2012). The outlook for patients diagnosed with heart failure is bleak: less than
50% of patients are living five years after their initial diagnosis and less than 25% are living 10
years after diagnosis (Heart Failure Society of America, n.d.). In 2009, there were 3,041,000
physician office visits with a primary diagnosis of CHF, 668,000 emergency department visits
and 293,000 outpatient department visits for CHF (American Heart Association, 2012). In 2010,
CHF cost the United States $39.2 billion in healthcare services, lost productivity and medications
(Centers for Disease Control and Prevention, 2010). Projected total costs for healthcare related to
CHF are expected to increase from $44.6 billion in 2015 to $97 billion in 2030 (American Heart
Association, 2012).
The adverse impact of CHF on patients, their families, and the costs associated with
CHF-related healthcare can possibly be minimized via utilization of a clinical decision support
system (CDSS) intervention. An intervention can provide current and relevant information at the
point of care to support provider decision making to optimize treatment for CHF patients. While
this paper will focus on the decision support of CHF condition, the CDSS solution itself is
intended to support the decision support needs for multiple clinical conditions encounted by the
provider organizations. The providers can use this decision support intervention to make
adjustments in care management plans that will improve health outcomes for CHF patients. In
addition, scenarios for usage of the CDSS intervention, the guideline to be used in the proposed
CDSS intervention, the CDSS system architecture, potential benefits and challenges, the
guideline integration process, and measurement of the CDSS impact on clinical practice will also
be addressed in this paper.
Stakeholders, Goals and Objectives
Stakeholders
The centralized knowledge management module of the CDSS described in this paper
stores clinical CHF guidelines that can be utilized by providers in a large hospital healthcare
system, such as emergency room physicians, nurses, internists and primary care physicians.
Community based clinics, private cardiology practice groups, and integrated managed care
organizations may also benefit from the CHF information contained in the CDSS. The proposed
CDSS will affect many stakeholders; the stakeholder title, role, high level goals and individual
clinical objectives related to the care of heart failure patients are in Table 1:
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Stakeholder
Role in the Project
High Level
Goals
CENTRAL CDSS FOR CHF
Clinical
Objectives
Chief Quality
Officer
Proponent, general
quality leader
Chief Nursing
Officer
Proponent or detractor, Patient and nursing
general quality leader education
Improve patient
understanding of diet and
lifestyle modifications
Chief Medical
Information
Officer
Proponent, advisor
Develop CDSS tools to
regarding development improve outcomes,
of CDSS
address info needs
Provide relevant clinical
information at point of
care
Chief of
Cardiology
Proponent, subject
matter expert,
custodian of sitespecific changes to
central guidelines
Improve care quality,
appropriate sources of
clinical guidelines
Improve performance of
individual clinicians
CHF Program
Director
Cardiology champion
Promote quality care,
Reduce admissions,
adherence to guidelines improve symptoms
Chief
Hospitalist
Detractor, concerned
about increased
workload
Improved quality of
care, appropriate
sources of clinical
guidelines
Reduce readmissions,
improve clinical outcomes
Internal
Medicine
Liaison
Proponent, CHF
education champion
Early recognition and
detection of disease,
improve outcomes
Increase patient awareness
of risk factors and
symptoms
Patient
Advocate
Proponent, medical
Increase patient
record access & patient awareness and
engagement
empowerment
Quality
Proponent, evaluation
Measurement and research studies
Representative
Promote quality care,
Reduce admissions,
adherence to guidelines improve symptoms,
reduce mortality
Participate in care process
& communicate with
provider
Determine impact of
Improve clinical outcomes
CHF guideline use on
via data analysis and
morbidity and mortality dissemination
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Stakeholder
Clinical
Knowledge
Manager
(Central
Knowledge
Repository)
Role in the Project
CENTRAL CDSS FOR CHF
High Level
Goals
Proponent, analyze
Ensure that the clinical
available evidence and guidelines represent the
the sources for
best available evidence
suitability to add to
repository
Clinical
Objectives
Assess quality of
guideline and its
representation in machineinterpretable format in the
knowledge repository
Table 1: Stakeholders impacted by a centralized CHF Decision Support System
These stakeholders will determine if the CDSS guideline intervention is accepted
and ultimately used within the organization. The CDSS team will actively seek
stakeholder input and feedback throughout the implementation process – from guideline
selection to interface design to final launch. Stakeholder suggestions will be used to make
changes to guideline content in the central knowledge engine and modify plans for sitespecific customization in order to limit interference with normal workflow and enhance
usability of the CDSS intervention.
Project Goals & Objectives
The specific clinical objectives are:
1. Use of beta-blockers in CHF with LV systolic dysfunction
2. Use of ACE-Inhibitors/ARBs in patients with CHF with LV systolic dysfunction
3. Measurement of ejection fraction in CHF patients
4. ICD counseling in patients with CHF with LV systolic dysfunction
5. Symptom assessment and management
6. Post-discharge followup appointment
7. Patient education
Some of these objectives occur in the inpatient environment, some in the outpatient, and
some in both. The goals are, in fact, measurable: e.g. percentage of patients receiving indicated
therapy pre- and post-intervention; number of readmissions pre- and post-intervention, etc.
One of the significant goals of the project is to explore the feasibility of using a central
knowledge repository to implement a clinical decision support system in a multi-hospital
environment. The objectives of this goal are:
1. Development of a computer-interpretable guideline management system that could be
shared by multiple sites
2. Ability to customize guidelines for site-specific changes
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3. Keeping guidelines current at all sites by updating the central repository of guidelines
4. Integration of the guidelines with the clinical workflow in a way that avoids interruptions
to the workflow to encourage productive use of the decision support aids
5. Use of existing and proposed standards for knowledge representation, validation,
execution and communication with clinical system
6. Understanding aggregate trends in guideline use to help continuously improve the
effectiveness of the guidelines
Identification of Appropriate Intervention and Workflow for CHF
We are proposing a decision support intervention that focuses on patients in a large
integrated healthcare system with a diagnosis of congestive heart failure due to left ventricular
systolic dysfunction. We have chosen this clinical group because it represents a large group of
patients who have significant symptoms that affect quality of life, increase their risk of dying,
result in frequent hospital admissions and use significant healthcare resources at great cost to our
respective communities. Moreover, there is an abundance of data that suggests utilization of
several interventions such as certain medications, procedures, and lifestyle changes can improve
quality of life, lower heart failure mortality, and save money via reduced hospitalizations.
Unfortunately, a large percentage of patients with congestive heart failure are not in compliance
with recommended interventions for a wide variety of reasons.
We start with a clinical group of patients identified from inpatient or outpatient
encounters: discharge diagnosis, office visit diagnosis, or problem lists. The relevant diagnosis is
congestive heart failure for all patients who have had documentation of their ejection fraction,
evaluation and management of symptoms, education, and follow-up appointments. Those
patients whose ejection fraction is less than 40% (the subgroup known as LVSD or left
ventricular systolic dysfunction) are candidates for medications (certain beta-blockers and either
ACE-inhibitors or ARBs), in addition to appropriate ICD counseling.
Hospitalists and cardiologists are targeted as providers who must make sure the
appropriate tests and interventions are ordered. Nurses are targeted primarily for educational
interventions. Patients are targeted with surveys via the electronic medical record to test their
understanding of their condition, the goals of therapy, adherence to therapy, and in the cases of
non-compliance, what barriers exist.
A central source of knowledge is chosen ideally because it represents the distillation of
best practices based on clinical studies that are reviewed by experts in the respective fields. One
such practical example is the ACCF/AHA/AMA-PCPI 2011 Performance Measures for Adults
With Heart Failure. This document represents the work of multiple organizations including
cardiologists, nurses, pharmacists, electrophysiologists, family physicians, and hospice/palliative
care physicians. Thus many stakeholders have vetted the measures that are based on solid
evidence, and have the support of the American Medical Association’s Physician Consortium for
Practice Improvement.
The knowledge content should be customizable for use by specific sites. For example, in
a site that uses Epic Systems EHR, providers can use the “infobutton” to access more details
about the interventions proposed.
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For inpatients, the hospitalist prepares the discharge instructions which include
medications, followup appointments, and appropriate testing. Patients with CHF on their
problem list or discharge diagnosis can be evaluated by the system for relevant medications,
measurement of ejection fraction, etc. Those patients who are deficient in one or more measures,
would cause the system to trigger a reminder to the clinician. Nurses would be measured on how
often they provide education to the patient at discharge on the importance of keeping follow-up
appointments, medication adherence and other factors that will impact the patient’s health in the
future. Furthermore, this CDSS intervention can be used outside of the regular patient visit to
alert the clinician about patient deficiencies that may lead the clinician to either order or cancel
orders for recommended tests and medications. Patients can be contacted via the EHR portal
(assuming they are registered and have Internet access) after hospitalizations or office visits to
survey satisfaction and compliance with these performance measures.
Ultimately, most of these measures can be calculated prior to the initiation of this
intervention and then after an appropriate interval, recalculated to look for evidence of any
impact both in terms of the healthcare processes and actual outcomes, such as hospital
admissions, readmissions and mortality.
Information Systems Architecture and Knowledge Management
Conceptual Architecture
The core of the shared clinical decision support system consists of a central decision
engine that can be accessed from any authorized clinic or facility through their clinical
information systems or electronic medical records. The components of this system can be
grouped into two parts – the knowledge engine and the knowledge adapter. Figure 1 provides a
conceptual view of this architecture.
The system architecture was influenced by the principles articulated in the review of the
evolution of clinical decision support architectures (Wright & Sittig, 2008). One of the
observations in that review was that earlier generations of decision support systems were limited
in functionality because they were either stand-alone systems (resulting in manual re-entry of
data) or were tightly integrated into clinical information systems (causing extensive changes to
the system when guidelines needed to be revised). The optimal design would then be the
separation of the clinical information system from that of the actual clinical guidelines and
knowledge but retaining interoperability through standards based interfaces using service
oriented architecture (SOA).
There are generally two approaches for implementing such an architecture - one approach
will expose a set of application programming interfaces (APIs) and let the clinical information
systems interface with the knowledge repository using the APIs (e.g. SAGE model), and the
other approach will let any clinical system interface with the knowledge repository through
clinical system specific adapters (e.g. SEBASTIAN). There are challenges with either of these
models as outlined by Wright and Sittig (2008), but with the progress made in adopting key HL7
standards for decision support interfaces, the SAGE model offers more promise and it is also not
constrained by patents (as compared to SEBASTIAN).
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Hence the centralized knowledge management module of our Clinical Decision Support
(CDSS) system is influenced by the Shareable Active Guideline Environment (SAGE) Guideline
Model (Tu et. al, 2007). Extensions to the SAGE model include additional workflow integration,
site-specific customization of guidelines and guideline usage analytics capabilities.
The knowledge engine consists of a central repository of guidelines, rules, standard order
sets & calculators and a knowledge base to support clinicians with optimal and relevant
information for informed decision making during diagnosis or treatment. The knowledge engine
uses a central platform for representing clinical guidelines in machine interpretable format.
Figure 1. System Architecture - Conceptual Diagram
The knowledge adapter module interfaces with the Clinical Information System/EMR at
each site for obtaining the patient’s clinical data and the workflow context to help identify the
appropriate guideline through the knowledge engine. The knowledge adapter is essentially a
workflow engine with standards-based interface to EMR and other clinical information systems
at the local site. The knowledge adapter communicates with the knowledge engine through web
services. A workflow event is invoked based on user input (e.g. infobutton) or a step in the
workflow that requires evaluation of available appropriate guidelines. The (de-identified)
patient-specific data needed to interpret the guideline are gathered by the knowledge adapter colocated within a clinical information system and translated into a virtual Medical Record (vMR),
a proposed HL7 standard, and then sent to the knowledge engine for interpretation. vMR is a
simplified version of the HL7 RIM model. The knowledge engine could then use this to execute
guidelines and pre-populate standard or site-specific order sets.
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Site-specific customization of a guideline and the context of its workflow can be done by
any authorized user of the system and the knowledge engine maintains that version of the
guideline.
Guideline Knowledge Management
There are four processes involved in the management of the central repository of
knowledge to support clinical decision support - identification of (new or changes to) a
guideline, its validation, translation into machine-interpretable format (including standard order
sets), and notification of changes to custodians of site-specific customization of guidelines.
Clinical experts (the Clinical Knowledge Manager role) identify and assess the available
guidelines using standard criteria like the AGREE instruments. The accepted guidelines are then
translated into computer interpretable format through a guideline editor. The centralized model
allows the clinical experts to periodically evaluate and update the shared guideline repository
with new evidence in a cost-effective way. Any site-specific modifications to the guidelines are
versioned and maintained separately so that customized guidelines affected by any new evidence
could be identified and their custodians notified.
As mentioned earlier in this paper, this project has chosen to illustrate the functionality of
the centralized decision support system through the American College of Cardiology Foundation
(ACCF) guideline for heart failure. As defined in the system architecture, the interaction between
the central decision support system and the clinical information system/Electronic Medical
Record is managed through a knowledge adapter component at each site that implements the
CDSS solution. This adapter is responsible for interfacing with the central knowledge engine at
the appropriate workflow step to share the patient’s data necessary for execution of the guideline
and providing the results in the form of alerts and modified/annotated order set items based on
patient-specific information. In addition, context-aware infobuttons (Collins, 2012) will help the
clinician look up additional relevant information on the guideline, e.g. CardioCompass for heart
failure (Cardiosource, n.d.), or access risk calculators and patient education tools.
Decision Support Usage Data Aggregation and Analysis
The results of the workflow steps before and after the execution of a guideline or rule
could be aggregated into a central repository and used along with relevant local data warehouse
content to analyze the patterns of usage and correlate them to the outcomes or performance
measures. This will help in refining the workflow and the guideline rules as well as educate
clinicians on the effective use of the decision support tools. This process could be achieved
without storage of personally identifiable information or provider organization proprietary data
at the central repository through the use of cross-referenced record linkages generated at the local
site.
Information System Inventory
Table 2 describes the components required to implement this clinical decision support
system.
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System Name
Functionality
CENTRAL CDSS FOR CHF
Information Type Typical Users
Notes
Central Clinical Guideline System
Knowledge
Engine
Knowledge
acquisition,
translation to
computerinterpretable
guidelines
Guideline
Interchange
Format (GLIF),
GELLO Guideline
Expression
Language, HL7
RIM, Diagnosis
(ICD-9), Procedure
(CPT), Clinical
(SNOMED-CT),
Lab (LOINC),
Drugs (RxNorm)
Medical
Informatician,
Knowledge
Engineer
Initial focus on
guideline
related to
Heart Failure
Knowledge
Adapter
Interfacing EMR and
Hospital Information
Systems with
Knowledge Engine
HL7, ICD-9, CPT,
SNOMED-CT,
LOINC, RxNorm,
vMR (Virtual
Medical Record),
XPDL (XML
Process Definition
Language)
Application
Programmer
Tighter
integration of
guideline with
clinical
workflow will
help targeted,
user specific
interactions
GLIF, GELLO,
Workflow editor
Medical
Informatician,
Clinicians
Guideline
Site-specific
Customization customization user
Console
interface
Decision
Aggregated reporting
Support Usage of guideline usage for
Analytics
measuring
effectiveness of
guidelines and
refining
guideline/workflow
Medical
Informatician,
Clinicians,
Reporting
Analyst
Clinical Guidelines
National
Repository of
Guideline
guidelines
Clearinghouse
Guideline
definition
Medical
Informatician,
Clinicians
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System Name
American
College of
Cardiology
Foundation
Functionality
Heart Failure related
clinical guidelines
CENTRAL CDSS FOR CHF
Information Type Typical Users
Guideline
definition
Notes
Medical
Informatician,
Clinicians
Clinic/Hospital and Patient Management
EpicCare/Epic Patient’s EMR,
enter
CPOE and order
management
Patient Record
Clinicians
Clinicare
Hospital Information
System
Billing, Provider
information
Clinicians,
Administrators
Clarity
Epic Reporting
Module
Patient Record
Medical
Informatician,
Reporting
Analyst,
Application
Programmer
EDW
Enterprise Data
Warehouse that
supports performance
and quality reporting
Integrated database
of all clinical and
administrative,
financial data at
the hospital
Medical
Informatician,
Reporting
Analyst
EpicRx
Inpatient Pharmacy
System
RxNorm, NDC
Clinicians,
Pharmacists
EpicLab
Clinical Laboratory
Information System
LOINC, Radiology Clinicians, Lab
Reports (DICOM) Technicians
Table 2: Information Systems Inventory
Information and Communication Technology Infrastructure
The computing environment at the hospitals and clinics include redundant, commodity
servers based on open-source software stack (e.g. LAMP - Linux Operating System, Apache
Web Server, MySQL Database, Perl/PHP Scripting Language), for fault-resilient and costeffective operations.
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The centralized clinical decision support intervention that provides current evidencebased guidelines for treatment of CHF can be utilized in healthcare systems that have satellite
hospitals or clinics. Satellite facilities must have the ability to connect with the main hospital in
order to send and transmit data and access clinically relevant content and guidelines as proposed
in this paper. A network will serve as the telecommunications link connecting the hospital and its
satellite facilities. This network will consist of local area networks (LANs) at each satellite
facility that are connected to the main hospital via a wide area network (WAN) and will use the
Ethernet standard.
The main hospital LAN will have a combination of wireless (to facilitate syncing of
mobile devices and tablet PCs, if approved for use in the future) and wired connectivity (a
redundant feature to ensure continued network operations in the event of a wireless failure). Each
satellite LAN will be wireless and consist of a main server, port switches, printers, computers
(desktop, laptop or both), and a router to connect to the main hospital and other satellite
facilities. The number and type of server, switches, printers and computers will be determined by
the number of staff at each facility, type of clinical services provided, operational requirements
and budgetary allowances. Also, the WAN will utilize leased line circuits to connect satellite
routers to the Internet Service Provider, other satellite facilities and the main hospital.
Overseeing the network maintenance and modifications will be the network administrator
located at the main hospital and two additional IT staff (number may vary depending on the size
of the healthcare system and IT budget). The network administrator will be responsible for
updating email and Internet anti-virus programs are updated regularly, perform monthly (or
weekly) data backups, grant/deny user access to certain files/functions and remind users to
change system passwords.
Intervention Specification
The application will improve compliance with performance measures that have been
identified as being important for the care of patients with congestive heart failure due to left
ventricular systolic dysfunction (CHF-LVSD). Clinicians will be notified regarding patients
admitted to the hospital with the diagnosis of CHF. Order sets will be created that are linked to
key performance measures allowing appropriate tests, medications, and educational interventions
to be ordered. At discharge, a checklist will be provided which will allow the clinician to make
additional orders, continue key medications, and arrange for appropriate out-patient testing and
followup. In the office setting, these same types of patients will be identified and the clinician
will have the opportunity to reinforce the importance of these measures. There will be links to
information sources which can provide key references and support for these measures.
Additionally, patients can be identified through the data warehouse who are not in compliance
with the measures by linking diagnoses from their problem list with relevant medications.
Patients can be notified via electronic messaging, letters, and phone calls to educate them
regarding these measures and arrange for necessary testing and follow-up treatment.
Sources such as the Agency for Healthcare Research and Quality(AHRQ), National
Quality Forum(NQF), and the Heart Failure Society of America were reviewed. Ultimately the
primary source for information chosen was the ACCF/AHA/AMA-PCPI 2011 Performance
Measures for Adults With Heart Failure (ACCF/AHA 2011). This document was chosen because
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it is the most recent and comprehensive source of information based on the input of multiple
stakeholders as previously noted and reflects the standards of care in the United States rather
than other countries. These performance measures were based on clinical guidelines published
in 2005 and then updated in 2009 (ACCF/AHA 2009).
Workflow Design/Site-specific Customization
One example of how the intervention can work is via order sets used during a
hospitalization. At the time of admission and/or discharge, order sets that include relevant tests,
medications, educational activities, and follow-up can be created. While the knowledge that
drives the order sets is linked to the central guidelines used as previously described, site-specific
customization is allowed. A paper from the 1996 AMIA annual symposium addresses the issues
related to using generic guidelines, but suggests that the guidelines can be made site-specific for
a variety of reasons (Fridsma et al, 1996). Factors such as availability of specialists, testing
facilities, nursing educators, costs, and others may all lead to some modifications, additions, or
deletions to the generic guidelines. An example of what such an order set may look like is shown
below. This example includes educational activity, medications, and testing that are all relevant
for the patient with CHF. As the physician needs to write orders on admission and discharge, this
approach is more desirable than annoying “best practice” alerts which interrupts the workflow or
often leads to an override of the alert. The order set provides a tool that is in line with the
physician’s workflow rather than forcing him/her to deviate from it.
According to Kawamoto et al., 2005, the following four traits are highly desirable in
gaining physcian acceptance and usability:
1. “Computer-based decision support is more effective than manual processes for decision
support.
2. CDSS interventions that are presented automatically and fit into the workflow of the
clinicians are more likely to be used.
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3. CDSS that recommends actions for the user to take are more effective than CDSS that
simply provides assessments.
4. CDSS interventions that provide information at the time and place of decision-making are
more likely to have an impact.”
Regarding the maintenance of the central knowledge repository, Eta Berner included the
following in a 2009 summary on CDSS:
“What SOA (service-oriented architecture) allows is for the central site to maintain the
knowledge but for local sites to develop systems that, in the background, can access it when
needed. Ideally users should not be able to tell that they are getting information any differently
Figure 3: CHF Order Set Example 2
Figure 2: CHF Order Set Example 1
than they would get information residing on their own computers. While this approach makes
updating easier since it is done centrally, it is also likely to require expertise at the local level to
integrate the CDS. In addition, obtaining consensus as to what should be included in a
centralized system can be a challenge. Given the expense of knowledge management, and to
some extent duplication of effort when one looks at the aggregate effort across health care
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facilities, it has been advocated that some sort of national repository of knowledge that can be
incorporated into a variety of CDS be developed.” (Berner, 2009)
Thus there is an obvious advantage of depending on a centrally developed set of
guidelines in order not to duplicate and “reinvent the wheel” in organizations across the country.
However, there will need to be some type of local adaptation or the knowledge and guidelines
will simply be unusable. The challenge is for each institution to figure out what their own
resources and values are and to make the necessary choices to optimize guideline usage.
Figures 2 and 3 are examples of possible configuration of Congestive Heart Failure Order
Set.
Implementation and Change Management
Plan and transition process
To integrate the current CHF guidelines into existing electronic medical record
(EMR) systems, a change management plan is required. The plan will review
technological capabilities, minimize disruption to clinician workflow, provide end-user
training and education about the CHF intervention and facilitate and maintain
communication between the implementation team and stakeholders throughout the
integration process.
After consulting with stakeholders and securing their support for the CHF
guidelines integration project, an assessment of the current IT infrastructure and capacity,
availability of IT staff, clinical staffing requirements and schedules, provider workflows,
and end-user satisfaction with existing EMR systems and decision support interventions
will be conducted. Much of this information will be obtained via meetings and individual
discussions with the stakeholders involved (chief quality officer, chief nursing officer,
chief medical information officer, chief of cardiology, cardiologists, hospitalists, nurses)
and others (operations and quality improvement staff) who will be affected by this
project. The assessment will determine if the healthcare organization has the
technological resources to handle the integration and identify potential obstacles (from
workplace culture, established procedures and/or legal issues) that could negatively
impact the process.
To minimize changes to the way work is conducted, the CDSS team will engage
nurses, hospitalists and cardiologists as part of a workflow analysis to determine: the
optimal point in the care process to deliver the guidelines, how much content should be
displayed, the feasibility of utilizing an infobutton, mechanisms for overriding
guidelines, the preferred format for reminder alerts when patients are not in compliance
with one or more of the recommended CHF guidelines, the timing and type of reminder
alerts delivered to the clinician outside of regular office hours and whether the patient
population is likely to use an EMR portal to communicate with providers about guideline
compliance.
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To further mitigate impact on workflow and minimize unintended consequences,
stakeholders will assist with the development of CHF use cases to pilot test the EMR
system’s ability to properly identify patients not in compliance with CHF guidelines and
provide information to the clinician when establishing a patient’s care management plan.
During pilot testing, the CDSS team will observe clinician interaction with the decision
support intervention, document recommendations for improvement and make additional
changes prior to finalizing the design and securing approval to formally launch the CHF
guidelines intervention.
Potential challenges that may arise during the integration of the CHF guidelines
include interoperability problems at the interface between the EMR system and the
central Knowledge Engine that contains the guidelines, users that have no experience
with the Guide Analytics system (or any other application) that generates aggregate
reports of guideline usage and the possibility of system failure (inability to retrieve the
CHF guideline information). The CDSS team will discuss interoperability with IT staff at
the sites, who have knowledge of existing coding schemes in use, and make
enhancements to the interface based on their recommendations. Meetings with quality
measurement and/or clinical analytics staff are necessary to determine their experience
with reporting and analytical applications. If staff have more experience using other
applications, the CDSS team will review the options with administrative and finance staff
to determine feasibility of using alternate reporting software applications. Finally, the
CDSS team will create a contingency plan to handle system failure (due to electrical or
hardware problems) that will likely consist of having a backup server for the knowledge
engine, use of surge protectors and encouraging IT staff to review the demands on
switches and routers in the transmission network as components are removed or added.
In the initial phases of the integration project, monthly meetings with stakeholders
and staff will be used for project updates and to elicit feedback that will be used to
modify project activities and timelines, if necessary. As the implementation date
approaches, communications will include weekly system wide emails, information flyers
posted in break rooms, clinical departments, and administrative offices to remind staff of
the impending CHF guidelines integration. The CDSS team, in collaboration with
organizational leadership, will also conduct information sessions to discuss the purpose
of the CHF guidelines integration project and how the decision support intervention will
support the organizational mission to provide effective healthcare and the quality
improvement objectives for cardiac patients.
The CDSS team will work with operations staff to determine the best time to
integrate the CHF guidelines into the central knowledge engine. In addition, one CDSS
staff member will be responsible for site-specific guideline customization; this will
ensure consistency in communications and guideline modification activities. The CDSS
team will also survey end-users to identify problems with reminder alerts, data retrieval
and guideline triggers and will make appropriate adjustments to the guideline model.
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Resource needs for testing, implementation, validation
Resources needed to integrate the CHF guidelines into the centralized knowledge engine
include:
1. A medical/clinical informatics specialist who is has experience with the Guideline
2.
3.
4.
5.
6.
7.
Interchange Format (GLIF) or equivalent computer-interpretable guidelines and
execution environments, HIT standards (HL7), workflow markup languages XML
Process Definition Language (XPDL);
Clinicians who can validate guideline quality for conformance with AGREE
guideline definition standards and clinicians familiar with the workflow
processes, especially related to CHF;
Software application developers with experience in programming environments
(Java/J2EE or ASP/.Net), web services development, Epic application
programming interfaces, cache data access, or Info Button configurations;
Database Administrators that can manage the various databases an organization
uses to collect and store patient information;
Reporting analysts and developers that can create reports from Clarity, EDW and
the database repository that provides information to the knowledge engine;
Project Manager with expertise in managing complex, multi-stakeholder projects
to plan and coordinate the activities of the CHF guideline integration project; and
Technology infrastructure specialists to install and administer servers, manage
hardware inventory and connections to the transmission network and monitor
peripheral connections and functions.
The CDSS team will be responsible for testing and validating the CHF guidelines
in the knowledge engine with assistance from clinicians, application developers and
technology infrastructure specialists. It is imperative that the correct guideline is
delivered to the individual requesting it and that the decision rules or algorithms are
correctly engineered to identify patients that are or are not in compliance with the CHF
guideline.
Measurement of CDSS Effect on Clinical Practice
Measurement of specific elements related to the purpose and function of the
CDSS intervention is required in order to determine how the intervention affects
clinicians and patients and what modifications need to be made to improve the
intervention and ensure its operation in accordance with organizational goals and
objectives. Prior to implementation of the CHF guideline intervention, healthcare
organizations should verify that they have the quantity and type of patient data necessary
for pre and post intervention data analysis. In this scenario, organizations should have
enough cardiac patients to meet statistical power requirements and generate analyses for
cardiac-related data elements (beta blockers, ACE inhibitors, ARBs, ejection fraction,
provider visits, hospital admissions, hospital readmissions, co-morbid conditions, etc).
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Suggested structure, process, outcome and end user measures are described
below; healthcare organizations may use these or construct additional measures relevant
to their particular practice environment:
Structure Measures
1. Types of healthcare organizations that implement CHF guideline (private practice,
etc)
2. Which CHF guideline was implemented and why
3. Which healthcare organizations utilize infobuttons and which do not utilize
infobuttons
4. Where and how infobuttons are displayed
Process Measures
1. Which healthcare providers (nurse, cardiologist, internist, etc) access the
guideline
2. When and how often healthcare providers access the guideline
3. Provider rationale for using (or not using) the guideline
Outcome Measures
1. Use of beta blockers in CHF patients with LV systolic dysfunction (pre/post
intervention)
2. Use of ACE inhibitors/ARBs in CHF patients with LV systolic dysfunction
(pre/post intervention)
3. Number of readmissions (pre/post intervention)
4. Measurement of ejection fraction in CHF patients (pre/post intervention)
5. Frequency of patient discharge education provided by nurses (pre/post
intervention)
End User Measures
1. Survey physicians about impact on workflow, time spent reviewing guideline
during patient visit
2. Survey nurses about usefulness/quality of guideline in patient discharge process
and education efforts
3. Survey patients about satisfaction with care, understanding of condition and
treatment options
Frequency of Evaluation and Communication of Results
Most healthcare organizations generate either quarterly or annual reports that
summarize financial and operational activities, community health status and notable
achievements for stakeholders. Depending on data analysis and quality measurement
staffing resources, quarterly assessments of the measures previously described would be
beneficial for administrative, management and clinical staff. Printing the measurement
data in a quarterly report and/or presenting the measurement data at quarterly or annual
healthcare system meetings is recommended. This information can also be posted on
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internal employee websites. Evaluating CDSS performance will help organizations with
their quality improvement efforts, specifically: determining if the CDSS intervention
achieves targeted objectives, identifying any unintended consequences and establishing
the value of the CDSS in terms of improving healthcare delivery and patient outcomes
(Osheroff et al., 2012). Stakeholders that receive timely reports of CDSS impact on
providers, patients, and outcomes and understand the methodology related to the
evaluation and analyses are more likely to realize the importance of CDSS in the overall
context of the organization’s mission and may be more supportive of future CDSS
projects.
Conclusion
The primary advantage of the decision support intervention described in this
report is that providers can select the CHF guideline appropriate for their practice that
aligns with their clinical goals and objectives towards improving patient cardiovascular
health. This site-specific customization confers a great degree of flexibility and allows
providers to periodically evaluate and update the shared guideline repository with new
evidence in a cost-effective way. Because CHF affects people of all ages, from children
and young adults to the middle-aged and the elderly, there is a need for a targeted CDSS
intervention (like the one described in this paper) that can distribute evidence-based CHF
guidelines to a variety of healthcare organizations in a format that can be integrated into
existing EMR systems to improve patient outcomes. As the American health care system
continues to struggle with the rising costs from treating chronic, long-term diseases, more
organizations will adopt CDSS to assist with the identification, management and
treatment of patients, and many of these patients may have some form of cardiovascular
disease, such as CHF. The conceptual architecture described in this paper for a
centralized CHF guideline engine can be useful for CDSS teams considering guideline
based interventions aimed at reducing the morbidity and mortality associated with CHF
and improving health outcomes within their own organizations and communities.
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