Decision Support System

Using computerised decision-support systems
How to travel cheaper last minute
(TopTipsNews)
9 September, 2013
Computerised decision-support systems can help health professionals bring together complex
information and guide management and treatment
IN THIS ARTICLE…
 How computerised systems can help health professionals to make decisions
 Benefits and drawbacks of these systems
 Evaluating system effectiveness

Author
Dawn Dowding is professor of applied health research at the University of Leeds.
ABSTRACT
Dowding D (2013) Using computerised decision-support systems. Nursing Times; 109: 36, 23-
25.
Decision support is an extension of electronic health record or electronic patient record systems.
As well as enabling health professionals to look up information about individual patients stored
in the system and to consult evidence-based guidance, they give advice on the treatment and
management most appropriate for that patient. They are designed to help with the process of
clinical decision making.
Computerised decision-support systems match patient characteristics to a computerised
knowledge base to produce patient-specific assessments or recommendations. Decision support
can be paper-based, but computerised systems have the advantage of being able to quickly
process patient-specific information and match it to computerised decision rules or algorithms.
This article discusses the benefits and limitations of using decision-support technology, which is
becoming increasingly important as the use of health information technology systems becomes
more common across healthcare.
 This article has been double-blind peer reviewed
 Figures and tables can be seen in the attached print-friendly PDF file of the
complete article in the „Files‟ section of this page

5 KEY POINTS
1. Computerised decision-support systems match patient characteristics to a computerised
knowledge base to produce patient-specific assessments or recommendations
2. The systems give guidance based on current evidence and best practice guidelines
3. Current evidence does not give a clear indication of the benefits of using the systems
4. Too little consideration is being given to how the systems will be integrated into existing
work practices
5. It is essential to plan how the effectiveness of the system will be measured before it is
introduced

It can be argued that technology, and computerised decision-support systems (CDSSs) in
particular, takes away the ―art‖ of clinical judgement in nursing practice, and that standardised
approaches to decision making remove the individualised and holistic approach to care practised
by nurses. Contrary to this belief, CDSSs work by aiding clinical decision making; they cannot
make decisions for health professionals, but they can provide advice and guidance on the best
course of action.
Table 1 provides examples of how CDSSs can help to support clinical decision making. These
are based on categorisations developed by Garg and Adhikari (2005) following a systematic
review of the benefits of using CDSSs in healthcare settings. While the majority of these
examples refer to use by doctors, the systems are increasingly being used by nurses taking on
extended and specialist roles. A systematic review of their use in nursing carried out in 2007
found that formal evaluations were almost exclusively focused on telephone triage and assisting
with dosing and appointment recommendations in anticoagulation management (Randell et al,
2007). However, it is likely that nurses use CDSSs across a much wider spectrum; for example
many nurses are non-medical prescribers and may be using them to support drug dosing and
prescribing.
Evidence base
One of the problems with the evidence base that is associated with CDSSs is that often studies do
not measure patient outcomes, or sample sizes are too small to detect differences between groups
of patients. Currently, there is no firm indication as to whether CDSSs are worth investing in, in
terms of increased benefits in patient care processes or improved patient outcomes.
The trials reviewed by Garg and Adhikari (2005) indicated that some types of CDSSs clearly
improve care processes. For example, a reminder system designed to assist with the recall of
patients for screening and procedures improved recall rates by 75%. The authors also found the
use of a CDSS improved care processes around disease management in 62% of the studies
reviewed, including rates of assessments and examinations for long-term condition management
such as diabetes, heart disease and asthma. However, few studies indicate that using CDSSs
improves outcomes for patients. There is a similar picture for studies evaluating the benefits of
CDSSs used by nurses. Randall et al (2007) systematically reviewed only 13 studies and none of
these indicated improvements in either nursing care or outcomes for patients with any certainty.
At a local level, the benefits of CDSSs may be more obvious, which may explain why their use is
becoming more widespread and integrated into health information technology (HIT) systems.
The systems are used by nurses working for telephone triage services such as NHS Direct,
NHS24 (in Scotland) and the new NHS 111 service.
Selecting and implementing CDSS Suitability
Clearly, CDSSs are not suitable for every type of healthcare decision. They are most useful in
situations where health professionals need to pull together complex information from a variety of
sources (such as information about individual patients and physiological measures with complex
clinical guidance), and where there is enough time to make use of the system (Dowding, 2008).
Often the decision to use a particular CDSS is made at an operational level to give a ―safety net‖
of guidance to health professionals undertaking a new role or extending their practice. This type
of approach was found in a study evaluating how nurses use the systems in two areas - in one
area, nurses were expected to use a CDSS to help guide the management of patients receiving
warfarin therapy, in another to use a triage system to help them with decision making at a walk-
in centre. In both areas, the systems had been implemented at an organisational level as a way of
supporting an extended nurse role (Dowding et al, 2009a).
A number of factors must be considered when deciding whether to implement a CDSS, which
will influence how successfully the system is used (Box 1).
Rationale
It is important to identify at the outset exactly what decisions need to be supported and have a
clear rationale for why a CDSS will be helpful in this instance. Consideration needs to be given
to whether or not this type of decision could benefit from a CDSS, and the potential impact on
the patient if a decision was wrong (is it a rare but fatal event that would be prevented, or is it a
decision that is made frequently but does not have a serious impact if it is wrong?).
Any current evidence that there is an issue with current clinical practice also needs to be taken
into consideration, for example if local audit data shows health professionals are regularly taking
decisions that do not follow evidence-based guidelines.
It is also important to clarify who will be using the CDSS and why. One of the main reasons HIT
systems fail is because too little consideration is given to the needs of the system users and how a
system will be integrated into their existing work practices.
Integrating technology
The technology associated with the CDSS - both hardware (the equipment needed to run it) and
software (what the CDSS interface will look like) - must also be considered. Ideally CDSSs
should be integrated into existing HIT systems, such as electronic patient records, so the
information already stored in the database can be used as the basis for guidance and advice. This
may be particularly appropriate for systems focusing on drug dosing and prescribing, as medical
history, laboratory results and current prescriptions are needed.
Another consideration is how the CDSS actually interacts with the professionals using the
system. Kesselheim et al (2011) highlighted the problem of ―alert fatigue‖, where an electronic
system has too many alerts related to different decisions, or where the alert is sensitive and
triggered easily, leading to health professionals ignoring alerts. It is better to have fewer and
more important alerts related to decision making, rather than less important alerts that are easier
to ignore.
Organisational support
Implementing any technology effectively involves organisational support. This includes being
clear on why the CDSS is important, a well-defined strategy for implementing and supporting
the technology as it is introduced, and the provision of education and support to staff while they
are using it.
It also requires a recognition that introducing the system may lead to both intended and
unintended changes in the way health professionals work. Studies have shown that the way in
which CDSSs are used by nurses changes depending on their expertise (for example, Dowding et
al, 2009b); this highlights a need to recognise and be flexible in terms ofwhen it would be
appropriate to use a CDSS and when it would be reasonable to accept a health professional‘s
clinical judgement.
Evaluating effectiveness
Mechanisms in place to evaluate the system‘s effectiveness once it has been introduced are vital.
If the purpose of the CDSS is to improve either care processes (such as the number of patients
recalled for screening) or patient outcomes (such as reducing the number of pressure ulcers)
procedures must be in place to measure whether improvements are occurring. It may also be
useful to monitor if and how the CDSS is being used by staff, and whether it has changed to the
way individuals work in other areas that may threaten safety elsewhere in the care system.
Unintended consequences
Often the introduction of new technology can have unintended consequences such as behaviour
known as ―work arounds‖, these occur when the new technology does not fit with the way
people work, or actively increases their workload and they develop strategies to ―fix‖ the
problem or work around it. One example of this was highlighted in a study by Koppel et al
(2008); nurses were observed using an electronic medication administration system to make
copies of patients‘ bar codes and fixing them to their clipboards to save time, rather than
scanning patients at the bedside. This increased the likelihood of drugs being given to the wrong
patient if they had more than one patient bar code attached to the clip board at a time.
Conclusion
There are a number of benefits to introducing CDSSs to support decision making in practice,
including providing nurses and other health professionals with current research evidence to
inform their decisions. However, there needs to be a clear rationale for their introduction and
systems need to be in place to support implementation and monitor use once they have been
introduced.
References:
Dowding D et al (2009a) Nurses‘ use of computerised clinical decision support systems: a case
site analysis. Journal of Clinical Nursing; 18: 8, 1159-1167.
Dowding D et al (2009b) Experience and nurses use of computerised decision support systems.
Studies in health Technology and Informatics; 146, 506-510.
Dowding D (2008) Computerised decision support systems in nursing. In: Cullum N et al (eds)
Evidence-based Nursing: An Introduction. Oxford: Blackwell Publishing.
Garg A, Adhikari N (2005) Effects of computerised clinical decision support systems on
practitioner performance and patient outcomes. JAMA: Journal of the American Medical
Association; 293: 10, 1223-1238.
Kesselheim AS et al (2011) Clinical decision support systems could be modified to reduce ‗alert
fatigue‘ while still minimizing the risk of litigation. Health Affairs; 30: 12, 2310-2317.
Koppel R et al (2008) Workarounds to barcode medication administration systems: their
occurrences, causes, and threats to patient safety. Journal of the American Medical Informatics
Association; 15, 408-423.
Randell R et al (2007) Effects of computerised decision support systems on nursing
performance and patient outcomes: a systematic review. Journal of Health Services

http://www.nursingtimes.net/nursing-practice/healthcare-it/using-computerised-decision-support-
systems/5063003.article
The Five Rights of Clinical Decision Support
CDS Tools Helpful for Meeting Meaningful Use
By Robert Campbell, EdD, CPHIMS, CPEHR
A 75-year-old man sits uncomfortably on an examination table as his physician informs him that
he needs to get a colonoscopy. The patient, an ex-cop, barks at the physician, ―Why after all
these years do I need to get a colonoscopy?‖ The physician coolly responds, ―With a paper
record, I never realized that you had a family history of colon cancer, and now that your health
information is being recorded in an electronic health record (EHR) system, I am being alerted
that it is time for you to have a colonoscopy.‖ The man relents and has the procedure. A small
walnut-shaped tumor is discovered in his colon, which turns out to be cancerous. The man
undergoes successful chemotherapy treatment and has just celebrated another birthday. This is a
best case scenario of what can happen in a world with clinical decision support.
With the advent of the ―meaningful use‖ EHR Incentive Program, healthcare organizations,
along with healthcare providers, are required to integrate clinical decision support (CDS) into
their federally certified EHR systems. To provide a foundation for understanding how to develop
a systematic CDS program, one should note the five ―rights‖ of clinical decision support. In
framing the discussion, CDS will be defined in terms of its relationship to meaningful use, with
the most common forms outlined, and evidence provided showing where CDS has been most
effective in improving quality of care.
Clinical Support Meets Meaningful Use
CDS has been defined as a ―process for enhancing health-related decisions and actions with
pertinent, organized clinical knowledge and patient information to improve healthcare and
healthcare delivery.‖
1
To understand the relationship between CDS and meaningful use, it is
important to comprehend, in an unconventional way, why the concept of meaningful use has
come into existence. Few, if any, remember the era when the concept of standardized time zones
did not exist. In that era, a person could start a journey in one town at 8 a.m. and arrive in a town
two miles away at 7:50 a.m., and arrive in another four miles away at 7:55 a.m. Time was
relative to location. With the creation of the concept of a time zone, time became more absolute.
Fast forward to the Pre-HITECH Meaningful Use Era, where if you asked a physician what they
wanted in an electronic health record, they might respond by saying, ―I would like a system that
allows me to create SOAP notes.‖ Or ―How about a system that keeps track of patients‘
demographics and a list of medications they are currently taking.‖ Further confounding the
matter was electronic health record systems developers‘ insistence that their systems were just
what the doctor ordered, in terms of their patient documentation needs. Meaningful use is a
much-needed attempt to standardize the functionality of an electronic health record system so
that, according to former ONC coordinator Farzad Mostashari, MD, the electronic record
becomes less of an ―electronic filing cabinet, and more of an indispensable tool by which to
deliver better healthcare.‖
2

Under the rules for stage 2 meaningful use, both hospitals and eligible professionals must
―implement five clinical decision support interventions‖ directly linked to four or more clinical
quality measures published by the Centers for Medicare and Medicaid Services. If relevant
clinical quality indicators are not applicable, a hospital or provider must implement support
measures that monitor high priority health conditions such as cancer, diabetes, hypertension, and
stroke. Returning to the example above, an entity wishing to adhere to this meaningful use
standard can develop a clinical decision support intervention that will alert physicians when a
particular patient is a candidate for colorectal screening. This type of intervention would
correspond directly with the clinical quality measure NQF-0034 Colorectal Cancer Screening,
which measures the percentage of adults 50 to 75 years of age who had appropriate screening for
colorectal cancer. In the above example, having a colonoscopy performed would qualify as an
appropriate screening measure, with the ultimate goal of having a high percentage of the
practice‘s patients—who are at risk for colon cancer—screened for this disease. Additionally, to
demonstrate meaningful use, hospitals and eligible professionals must implement drug-drug and
drug-allergy interaction checks to comply with the clinical decision support standard.
CDS Research Shows Care Improved by Tools
Over the past two decades there have been a number of studies investigating the role
clinical decision support has played in improving the quality of healthcare in the
United States. In the last five years, the Agency for Healthcare Research and Quality
has sponsored literature reviews examining the results of these studies. One review
looked at the impact CDS had on process outcomes, clinical outcomes (morbidity,
mortality, length of stay, health-related quality of life), and economic outcomes.
10
This
literature review revealed that there is evidence showing that CDS has the greatest
impact on process outcomes such as the ordering of preventive, clinical, and treatment
services, along with the enhancement of user‘s knowledge pertaining to a medical
condition. This led the authors of Improving Outcomes with Clinical Decision Support:
An Implementer‘s Guide to conclude that ―strong evidence now shows that CDS is
effective in improving process measures across diverse academic and nonacademic
settings using both commercially and locally developed systems.‖
11

Types of Clinical Decision Support
Although alerts are one of the most common forms of CDS, it must be noted there are many
interventions that make up the current CDS toolkit. In the book ―Improving Outcomes with
Clinical Decision Support: An Implementer‘s Guide‖ the authors state that CDS interventions
fall into one of four categories: data entry, data review, assessment and understanding, and
triggered by user task.
3
For example, one of the more innovative interventions—smart forms—
falls in the category of data entry, and ―integrate(s) decision support into the normal tasks of
seeing a patient and documenting a note.‖
4

Another more widely used data entry tool is the order set. An order set is a ―collection of pre-
formed orders‖
5
used ―to manage a disease state or a specific procedure‖
6
like a hip replacement.
Order sets are a key tool in the CDS arsenal because they are thought to reduce medical errors,
develop a safer healthcare environment, improve outcomes, and enhance workflow. Other
interventions in the data entry category include parameter guidance, and immediate alerts.
In the ―data review‖ category, an intervention known as the Virtual ICU is used to monitor
patients in intensive care units across multiple facilities. This intervention allows remote nurses
and intensivists to observe patients in real time, check vital signs, and work closely with bedside
providers. Furthermore, Virtual ICU technology can make sure that doctors and nurses follow
endorsed guidelines by prompting them when a ―lifesaving therapy‖ needs to be incorporated
into the patient‘s care plan.
7

A third category, ―assessment and understanding,‖ is concerned with satisfying the information
needs of physicians and patients as they formulate, debate, and discuss treatment options and
care plans. One innovative tool in this category is the Health Level Seven (HL7) Infobutton.
8
An
infobutton can be used in an electronic health record and can appear next to a condition listed in
a patient‘s problem list, or medication in the medication list. To learn more about the condition,
the physician clicks on the button and is immediately linked to an information source presenting
detailed, evidence-based knowledge regarding the disease and its treatment. The same
functionality applies to medications and their contraindications with other substances.
The final category, ―triggered by user task,‖ raises awareness about events that occur outside of
routine patient-specific workflows.
9
These types of interventions range from an alert in an EHR,
text message, or electronic mail notification regarding an abnormal test result, a prompt
regarding the need for an influenza or pneumonia vaccination, or a reminder that the patient is
due for a preventive test like a colonoscopy examination.
The Five “Rights” of Clinical Decision Support
With a firm understanding of clinical decision support, its various forms, and its relationship to
meaningful use, the focus can turn to the five ―rights‖ of CDS. These five rights can be used as a
framework when planning to implement CDS interventions within a facility or practice, or when
creating an extensive CDS program.
The five rights include:
 the right information,
 to the right person,
 in the right intervention format,
 through the right channel,
 at the right time in workflow.
The Right Information
The information presented to the end-user—or in some cases, the patient—should be evidence-
based, derived from a set of recognized guidelines, or based on a national performance measure.
In the case of the 75-year-old colonoscopy patient, an alert is generated informing the physician
that the patient needs to be screened for colon cancer. The alert is based on the performance
measure NQF-0034, which is a national measure developed by the National Committee for
Quality Assurance. Furthermore, this performance measure is based on a set of guidelines
developed by the American Cancer Society that stipulates who, from the general population,
should be screened for colon cancer on a regular basis.
The intervention, in this instance an alert, should contain only enough information for the end
user to act on. If the end user is given too much information, this may induce cognitive overload
and cause them to disregard the alert. In the current example, the physician is alerted to the fact
that the patient has a family history of colon cancer and they are within the threshold—patients
age 50-75—of who should be screened for colon cancer. In instances where the physician would
like to read the performance measure or the guidelines on which the alert is based, the channel
(EHR) used to deliver the alert should make this information available via a URL or portable
document format file. As a result of the alert, the physician advises the patient to have a
colonoscopy performed.
Some experts recommend that healthcare organizations and practitioners who find themselves in
the early stages of CDS intervention development refrain from basing interventions solely on
expert opinion.12 In some cases expert opinion can be contentious. Because it may not be
universally agreed upon as best practice, it may negatively influence whether an end user
complies with the recommended actions forming the basis of the CDS intervention.
The Right Person
As healthcare becomes more of a team approach, it is important to make sure that the right
information gets to the right person that can then take action. The right person can be a nurse,
physician, physical therapist, or in some cases, a significant other. In the example above, the
right person is the physician who receives the alert and advises the patient to get a colonoscopy.
However, it is important to note that CDS interventions can sometimes change care team roles.
For example, if the patient is resistant to advice from their physician, the information, in the form
of an alert, may be best conveyed by a significant other or sibling who can use persuasion to help
gain patient compliance. The important takeaway is to present information only to individuals
who can take action. A common example in the health informatics literature is one where a nurse
receives an order to adjust medication dosing for a patient. This type of information is
problematic because the nurse has no way of knowing whether the medication dosing has already
been adjusted.
The Right Intervention Format
As previously discussed, CDS may be implemented in various formats—alerts, order sets,
protocols, patient monitoring systems, and infobuttons. Consequently, it becomes important for
implementers to identify the issues and problems they are trying to solve and choose the best
format to resolve the problem at hand. Furthermore, when developing a CDS program,
implementers should create an inventory of current systems to determine which CDS tools are
available, which tools need to be developed in-house, and which tools need to be purchased
through a vendor. In the opening example, a practice wishes to identify patients at risk for major
illnesses, and get them to adopt preventive measures. The simplest solution is an alert that non-
intrusively informs the physician of a patient‘s predisposition to an illness—in this case colon
cancer.
The Right Channel
In healthcare, CDS interventions can be delivered through an EHR, PHR, computerized
physician order entry, an app running on a smartphone, and—if necessary—in paper form via
flow-sheets, forms, and labels. In the example above, if the physician is the right person, then the
EHR may be the best platform for delivering the alert. However, if a significant other is the right
person, then the right platform may be a text messaging app running on a smartphone. The alert
would inform the individual of the patient‘s need to have a colonoscopy performed.
The Right Time in Workflow
A common problem in health information management is the desire to overlay new technology
onto current clinical processes. One negative outcome of this practice is that information may be
delivered to a clinician at the wrong time, or it may not be available when it is needed. A
common example of this problem occurs when a physician is treating a patient who is taking
aspirin. The physician temporarily loses track of this fact and begins the process of prescribing
Coumadin for the patient. After entering in all the pertinent information for the prescription, the
physician attempts to send the script to the pharmacy. An alert appears on the screen informing
the physician that the patient is already taking aspirin and prescribing Coumadin could generate
an adverse outcome.
This is an example of where information is presented at the wrong time in the clinical workflow
process. It would be more advantageous for the physician to be alerted when they began typing
the word Coumadin at the very start of the prescription process, not after the prescription had
been entered. This highlights a very fundamental fact in the CDS implementation process, that to
successfully create an intervention, the clinical processes involved must be thoroughly
understood and documented so that the right information is delivered to the right person at the
right time.
Closing the loop in the example from above, workflow analysis performed on the clinical
process of a physical examination may reveal that a passive alert found in the patient‘s electronic
health record informing the physician of the patient‘s need for colon cancer screening may be the
best intervention to employ. An alert appearing when the physician opens the patient‘s health
record, and requires the physician to actively acknowledge that they have seen the alert—which
requires them to click on an alert window—may not be the best intervention. This could disrupt
the physician‘s current workflow and consequently may not be processed at all.
Passive alerts can appear in a prominent place on the health record—a decision based on the
results of the workflow analysis—and can be processed once the physician completes the
physical examination. An alternative method would be when the physician closes the patient
record they are given a prompt informing them that they have five patient alerts that need to be
processed.
CDS Goals and Objectives
In the early stages of developing a CDS intervention, an organization would be advised to choose
a goal to focus their efforts. The goal can be an organizational priority (patient satisfaction,
safety, or prevention) or it can be a national priority, such as meaningful use or the CMS quality
initiatives. Whatever goal is chosen, a direct link needs to be made between the goal and its
potential impact on illness, death, and clinical outcomes. Once the goal is determined, a set of
objectives can be defined that when achieved will provide evidence that the goal has been
successfully realized.
For example, a goal could be a reduction in major illnesses in the practice‘s patient census. An
objective to reach this goal could be a 90 percent screening rate for patients at risk of developing
colon cancer. In addition, for each objective that is defined, a baseline rate needs to be identified
to determine how successful the practice has been in meeting the objective. By defining a set of
goals and objectives for the development of a CDS intervention, a practice can make use of the
five rights to determine the what (information), who (recipient), how (intervention), where
(format), and when (workflow) for a proposed intervention.
HIM Professionals‘ Role in CDS When developing a CDS intervention for an organization or
practice, one of the most important tasks to be performed by an HIM professional is workflow
analysis on the related clinical processes. A workflow analysis will examine the process and
determine where the process can be modified and what information is needed at each step within
the process. Also, the analysis would determine which intervention(s) are candidates for
enhancing the process, making it safer and more efficient while providing a higher level of
quality care.
Another area where an HIM professional can be involved is the implementation and use of
vocabularies and classification systems used to code family histories, problem lists, symptoms,
diagnoses, and medication lists. If patient information is not properly coded, CDS interventions
such as alerts and reminders will not be activated if a patient meets a specific criterion.
For example, a patient who is coded as having a family history of colon cancer, whose age falls
in the range of 50-75 years old, will automatically activate an alert informing their physician that
they are a candidate for a colon cancer screening. If the information regarding the patient‘s
family is entered as a note or unstructured data, then the alert will not be activated, and the
patient may not be deemed as having a greater chance of succumbing to colon cancer.
Through the use of the five rights of CDS, workflow analysis, and clinical vocabularies and
coding systems, HIM professionals can play a major role in the development of a CDS
intervention and in turn help improve patient care.
Notes
1. Osheroff, J.A., Teich, J.A., D. Levick et al. Improving Outcomes with Clinical Decision Support: An
Implementer’s Guide. 2nd Edition. Chicago, IL: HIMSS, 2012: p. 15.
2. Ibid.
3. Ibid.
4. Schnipper, J.L. et al. “‘Smart Forms in an Electronic Medical Record: Documentation-based
Clinical Decision Support to Improve Disease Management.” Journal of the American Medical
Informatics Association 15, no. 4 (2008): 520.
5. Bobb, A.M., Payne, T.H., and P.A. Gross. “Viewpoint: controversies surrounding use of order sets
for clinical decision support in computerized provider order entry.” Journal of the American
Medical Informatics Association 14, no. 1 (2007): 130-1.
6. Ash, J.S., Stavri P.Z., and G.J. Kuperman. “A Consensus Statement on Considerations for a
Successful CPOE Implementation.” Journal of the American Medical Informatics Association 10,
no. 3 (2003): 232.
7. Garlock, K., J. Price. “Virtual ICU lets doctors monitor patients from afar.” Charlotte Observer
(June 10, 2013).
8. Del Fiol, G. et al. “Implementations of the HL7 Context-Aware Knowledge Retrieval (‘Infobutton’)
Standard: Challenges, strengths, limitations, and uptake.” Journal of Biomedical Informatics 45,
no. 4 (2012): 726-35.
9. Osheroff, J.A. et al. Improving Outcomes with Clinical Decision Support: An Implementer’s Guide.
2nd Edition. Chicago, IL: HIMSS, 2012, p. 170.
10. Lobach, D. et al. Enabling Health Care Decisionmaking Through Clinical Decision Support and
Knowledge Management: Evidence Report/Technology Assessment No. 203. Rockville, MD.
Agency for Healthcare Research and Quality, April 2012.
http://www.ncbi.nlm.nih.gov/books/NBK97318/.
11. Osheroff, J.A. et. al. Improving Outcomes with Clinical Decision Support: An Implementer’s Guide,
p. 20.
12. Ibid.
Robert Campbell ([email protected]) is an assistant professor, health services and
information management, at East Carolina University.

Article citation:
Campbell, Robert. "The Five Rights of Clinical Decision Support: CDS Tools Helpful for
Meeting Meaningful Use." Journal of AHIMA 84, no.10 (October 2013): 42-47.

http://library.ahima.org/xpedio/groups/public/documents/ahima/bok1_050385.hcsp?dDocName=bok1
_050385




Effects of computerized clinical decision
support systems on practitioner performance
and patient outcomes: Methods of a decision-
maker-researcher partnership systematic
review
How to save money on your plane tickets
(TopTipsNews)
R Brian Haynes
*
, Nancy L Wilczynski and the Computerized Clinical Decision Support
System (CCDSS) Systematic Review Team
 * Corresponding author: R Brian Haynes [email protected]
Author Affiliations
Health Information Research Unit, Department of Clinical Epidemiology and Biostatistics,
McMaster University, Health Sciences Centre, 1280 Main Street West, Hamilton, Ontario,
Canada
For all author emails, please log on.
Implementation Science 2010, 5:12 doi:10.1186/1748-5908-5-12

The electronic version of this article is the complete one and can be found online at:
http://www.implementationscience.com/content/5/1/12

Received: 4 December 2009
Accepted: 5 February 2010
Published: 5 February 2010
© 2010 Haynes et al; licensee BioMed Central Ltd.
This is an Open Access article distributed under the terms of the Creative Commons Attribution
License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use,
distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract
Background
Computerized clinical decision support systems are information technology-based systems
designed to improve clinical decision-making. As with any healthcare intervention with claims to
improve process of care or patient outcomes, decision support systems should be rigorously
evaluated before widespread dissemination into clinical practice. Engaging healthcare providers
and managers in the review process may facilitate knowledge translation and uptake. The
objective of this research was to form a partnership of healthcare providers, managers, and
researchers to review randomized controlled trials assessing the effects of computerized decision
support for six clinical application areas: primary preventive care, therapeutic drug monitoring
and dosing, drug prescribing, chronic disease management, diagnostic test ordering and
interpretation, and acute care management; and to identify study characteristics that predict
benefit.
Methods
The review was undertaken by the Health Information Research Unit, McMaster University, in
partnership with Hamilton Health Sciences, the Hamilton, Niagara, Haldimand, and Brant Local
Health Integration Network, and pertinent healthcare service teams. Following agreement on
information needs and interests with decision-makers, our earlier systematic review was updated
by searching Medline, EMBASE, EBM Review databases, and Inspec, and reviewing reference
lists through 6 January 2010. Data extraction items were expanded according to input from
decision-makers. Authors of primary studies were contacted to confirm data and to provide
additional information. Eligible trials were organized according to clinical area of application.
We included randomized controlled trials that evaluated the effect on practitioner performance or
patient outcomes of patient care provided with a computerized clinical decision support system
compared with patient care without such a system.
Results
Data will be summarized using descriptive summary measures, including proportions for
categorical variables and means for continuous variables. Univariable and multivariable logistic
regression models will be used to investigate associations between outcomes of interest and
study specific covariates. When reporting results from individual studies, we will cite the
measures of association and p-values reported in the studies. If appropriate for groups of studies
with similar features, we will conduct meta-analyses.
Conclusion
A decision-maker-researcher partnership provides a model for systematic reviews that may foster
knowledge translation and uptake.
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Background
Computerized clinical decision support systems (CCDSSs) are information technology-based
systems designed to improve clinical decision-making. Characteristics of individual patients are
matched to a computerized knowledge base, and software algorithms generate patient-specific
information in the form of assessments or recommendations. As with any healthcare intervention
with claims to improve healthcare, CCDSSs should be rigorously evaluated before widespread
dissemination into clinical practice. Further, for CCDSSs that have been properly evaluated for
clinical practice effects, a process of 'knowledge translation' (KT) is needed to ensure appropriate
implementation, including both adoption if the findings are positive and foregoing adoption if the
trials are negative or indeterminate.
The Health Information Research Unit (HIRU) at McMaster University has previously
completed highly cited systematic reviews of trials of all types of CCDSSs [1-3]. The most
recent of these [1] included 87 randomized controlled trials (RCTs) and 13 non-randomized trials
of CCDSSs, published up to September 2004. This comprehensive review found some evidence
for improvement of the processes of clinical care across several types of interventions. The
evidence summarized in the review was less encouraging in documenting benefits for patients:
only 52 of the 100 trials included a measure of clinical outcomes and only seven (13%) of these
reported a statistically significant patient benefit. Further, most of the effects measured were for
'intermediate' clinical variables, such as blood pressure and cholesterol levels, rather than more
patient-important outcomes. However, most of the studies were underpowered to detect a
clinically important effect. The review assessed study research methods and, fortunately, found
study quality improved over time.
We chose an opportunity for 'KT synthesis' funding from the Canadian Institutes of Health
Research (CIHR) to update the review, partnering with our local hospital administration and
clinical staff and our regional health authority. We are in the process of updating this review and,
in view of the large number of trials and clinical applications, split it into six reviews: primary
preventive care, therapeutic drug monitoring and dosing, drug prescribing, chronic disease
management, diagnostic test ordering and interpretation, and acute care management. The timing
of this update and separation into types of application were auspicious considering the
maturation of the field of computerized decision support, the increasing availability and
sophistication of information technology in clinical settings, the increasing pace of publication of
new studies on the evaluation of CCDSSs, and the plans for major investments in information
technology (IT) and quality assurance (QA) in our local health region and elsewhere. In this
paper, we describe the methods undertaken to form a decision-maker-research partnership and
update the systematic review.
Methods
Steps involved in conducting this update are shown in Figure 1.
Figure 1. Flow diagram of steps involved in conducting this review.
Research questions
Research questions were agreed upon by the partnership (details below). For each of the six
component reviews, we will determine whether the accumulated trials for that category show
CCDSS benefits for practitioner performance or patient outcomes. Additionally, conditional on a
positive result for this first question for each component review, we will determine which
features of the successful CCDSSs lend themselves to local implementation. Thus, the primary
questions for this review are: Do CCDSSs improve practitioner performance or patient outcomes
for primary preventive care, therapeutic drug monitoring and dosing, drug prescribing, chronic
disease management, diagnostic test ordering and interpretation, and acute care management? If
so, what are the features of successful systems that lend themselves to local implementation?
CCDSSs were defined as information systems designed to improve clinical decision-making. A
standard CCDSS can be broken down into the following components. First, practitioners,
healthcare staff, or patients can manually enter patient characteristics into the computer system,
or alternatively, electronic medical records can be queried for retrieval of patient characteristics.
The characteristics of individual patients are then matched to a computerized knowledge base
(expert physician opinion or clinical practice guidelines usually form the knowledge base for a
CCDSS). Next, the software algorithms of the CCDSS use the patient information and
knowledge base to generate patient-specific information in the form of assessments
(management options or probabilities) and/or recommendations. The computer-generated
assessments or recommendations are then delivered to the healthcare provider through various
means, including a computer screen, the electronic medical record, by pager, or printouts placed
in a patient's paper chart. The healthcare provider then chooses whether or not to employ the
computer-generated recommendations.
Partnering with decision-makers
For this synthesis project, HIRU partnered with the senior administration of Hamilton Health
Sciences (HHS, one of Canada's largest hospitals), our regional health authority (the Hamilton,
Niagara, Haldimand, and Brant Local Health Integration Network (LHIN)), and clinical service
chiefs at local hospitals. The partnership recruited leading local and regional decision-makers to
inform us of the pertinent information to extract from studies from their perspectives as service
providers and managers. Our partnership model was designed to facilitate KT, that is, to engage
the decision-makers in the review process and feed the findings of the review into decisions
concerning IT applications and purchases for our health region and its large hospitals.
The partnership model has two main groups. The first group is the decision-makers from the
hospital and region and the second is the research staff at HIRU at McMaster University. Each
group has a specific role. The role of the decision-makers is to guide the review process. Two
types of decision-makers are being engaged. The first type provides overall direction. The names
and positions of these decision-makers are shown in Table 1. The second type of decision-maker
provides specific direction for each of the six clinical application areas of the systematic review.
These decision-makers are shown in Table 2. Each of these clinical service decision-makers
(shown in Table 2) is partnered with a research staff lead for each of the six component reviews.
The role of the research staff is to do the work 'in the trenches,' that is, undertake a
comprehensive literature search, extract the data, synthesize the data, plan dissemination, and
engage in the partnership. This group is comprised of physicians, pharmacists, research staff,
graduate students, and undergraduate students. The partners will continue to work together
throughout the review process.
Table 1. Name and position of decision-makers providing overall direction
Table 2. Name and position of decisions makers for each of the six clinical application areas
Both types of decision-makers were engaged early in the review process. Their support was
secured before submitting the grant application. Each decision-maker partner was required by the
funding agency, CIHR, to sign an acknowledgement page on the grant application and provide a
letter of support and curriculum vitae. Research staff in HIRU met with each of the clinical
service decision makers independently, providing them with copies of the data extraction form
used in the previous review and sample articles in their content areas, to determine what data
should be extracted from each of the included studies. Specifically, we asked them to tell us what
information from such investigations they would need when deciding about implementation of
computerized decision support.
Engaging the decision-makers at the data extraction stage was enlightening, and let us know that
decision-makers are interested in, among other things:
1. Implementation challenges, for example, how was the system put into place? Was it too
cumbersome? Was it too slow? Was it part of an electronic medical record or computerized
physician order entry system? How did it fit into existing workflow?
2. Training details, for example, how much training on the use of the CCDSS was done, by
whom, and how?
3. The evidence base, for example, if and how the evidence base for decision support was
maintained?
4. Customization, for example, was the decision support system customizable?
All of this led to richer data extraction to be undertaken for those CCDSSs that show benefit.
We continued to engage the decision-makers throughout the review process by meeting with
them once again before data analysis to discuss how best to summarize the data and to determine
how to separate the content into the six component reviews. Prior to manuscript submission,
decision-makers will be engaged in the dissemination phase, engaging in manuscript writing and
authorship of their component reviews.
Studies eligible for review
As of 13 January 2010 we started with 86 CCDSS RCTs identified in our previously published
systematic review [1] (one of the 87 RCTs from the previous review was excluded because the
CCDSS did not provide patient-specific information), and exhaustive searches that were
originally completed in September 2004 were extended and updated to 6 January 2010.
Consideration was given only to RCTs (including cluster RCTs), given that participants in
CCDSS trials generally cannot be blinded to the interventions and RCTs at least assure
protection from allocation bias. For this update, we included RCTs in any language that
compared patient care with a CCDSS to routine care without a CCDSS and evaluated clinical
performance (i.e., a measure of process of care) or a patient outcome. Additionally, to be
included in the review, the CCDSS had to provide patient-specific advice that was reviewed by a
healthcare practitioner before any clinical action. CCDSSs for all purposes were included in the
review. Studies were excluded if the system was used solely by students, only provided
summaries of patient information, provided feedback on groups of patients without individual
assessment, only provided computer-aided instruction, or was used for image analysis.
The five questions answered to determine if a study was eligible for inclusion in the review were:
1. Is this study focused on evaluating a CCDSS?
2. Is the study a randomized, parallel controlled trial (not randomized time-series) where patient
care with a CCDSS is compared to patient care without a CCDSS?
3. Is the CCDSS used by a healthcare professional-physicians, nurses, dentists, et al.-in a clinical
practice or post-graduate training (not studies involving only students and not studies directly
influencing patient decision making)?
4. Does the CCDSS provide patient-specific information in the form of assessments
(management options or probabilities) and/or recommendations to the clinicians?
5. Is clinical performance (a measure of process of care) and/or patient outcomes (on non-
simulated patients) (including any aspect of patient well-being) described?
A response of 'yes' was required for all five questions for the article to be considered for
inclusion in the review.
Finding Relevant Studies
We have previously described our methods of finding relevant studies until 2004 [1]. An
experienced librarian developed the content terms for the search filters used to identify clinical
studies of CCDSSs. We pilot tested the search strategies and modified them to ensure that they
identified known eligible articles. The search strategies used are shown in the Appendix. For this
update, we began by examining citations retrieved from Medline, EMBASE, Ovid's Evidence-
Based Medicine Reviews database (includes Cochrane Database of Systematic Reviews, ACP
Journal Club, Database of Abstracts of Reviews of Effects (DARE), Cochrane Central Register
of Controlled Trials (CENTRAL/CCTR), Cochrane Methodology Register (CMR), Health
Technology Assessments (HTA), and NHS Economic Evaluation Database (NHSEED)), and
Inspec bibliographic database from 1 January 2004 to 6 January 2010. The search update was
initially conducted from January 1, 2004 to December 8, 2008, and subsequently to January 6,
2010. The numbers of citations retrieved from each database are shown in the Appendix. All
citations were uploaded into an in-house literature evaluation software system.
Pairs of reviewers independently evaluated the eligibility of all studies identified in our search.
Disagreements were resolved by a third reviewer. Full-text articles were retrieved for articles
where there was a disagreement. Supplementary methods of finding studies included a review of
included article reference lists, reviewing the reference lists of relevant review articles, and
searching KT+ http://plus.mcmaster.ca/kt/ webcite and EvidenceUpdates
http://plus.mcmaster.ca/EvidenceUpdates/ webcite, two databases powered by McMaster PLUS
[4]. The flow diagram of included and excluded articles is shown in Figure 2.
Figure 2. Flow diagram of included and excluded studies for the update
January 1, 2004 to December 8, 2008 as of January 13, 2010 (Number for the further
update to January 6, 2010 will appear in the individual clinical application results papers).
Reviewer agreement on study eligibility was quantified using the unweighted Cohen κ [5]. The
kappa was κ = 0.84 (95% confidence interval [CI], 0.82 to 0.86) for pre-adjudicated pair-wise
assessments of in/in and in/uncertain versus out/out, out/uncertain, and uncertain/uncertain.
Disagreements were then adjudicated by a third observer.
Data Extraction
Pairs of reviewers independently extracted the following data from all studies meeting eligibility
criteria: study setting, study methods, CCDSS characteristics, patient/provider characteristics,
and outcomes. Disagreements were resolved by a third reviewer or by consensus. We attempted
to contact primary authors of all included studies via email to confirm data and provide missing
data. Primary authors were sent up to two email messages where they were asked to review and
amend, if necessary, the data extracted on their study. Primary authors were presented with a
URL in the email message. When they clicked on the URL, they were presented with an on-line
web-based data extraction form that showed the data extracted on their study. Comments buttons
were available for each question and were used by authors to suggest a change or provide
clarification for a data extraction item. Upon submitting the form, an email was sent to a research
assistant in HIRU summarizing the author's responses. Changes were made to the extraction
form noting that the information came from the primary author. We sent email correspondence to
the authors of all included trials (n = 168 as of January 13, 2010) and, thus far, 119 (71%)
provided additional information or confirmed the accuracy of extracted data. When authors did
not respond or could not be contracted, a reviewer trained in data extraction reviewed the
extraction form against the full-text of the article as a final check.
All studies were scored for methodological quality on a 10-point scale consisting of five
potential sources of bias. The scale used in this update differs from the scale used in the
previously published review because only RCTs are included in this update. The scale we used is
an extension of the Jadad scale [6] (which assesses randomization, blinding, and accountability
of all patients), and includes three additional potential sources of bias (i.e., concealment of
allocation, unit of allocation, and presence of baseline differences). In brief, we considered
concealment of allocation (concealed, score = 2, versus unclear if concealed, 1, versus not
concealed, 0), the unit of allocation (a cluster such as a practice, 2, versus physician, 1, versus
patient, 0), the presence of baseline differences between the groups that were potentially linked
to study outcomes (no baseline differences present or appropriate statistical adjustments made for
differences, 2, versus baseline differences present and no statistical adjustments made, 1, versus
baseline characteristics not reported, 0), the objectivity of the outcome (objective outcomes or
subjective outcomes with blinded assessment, 2, versus subjective outcomes with no blinding but
clearly defined assessment criteria, 1, versus, subjective outcomes with no blinding and poorly
defined, 0), and the completeness of follow-up for the appropriate unit of analysis (>90%, 2,
versus 80 to 90%, 1, versus <80% or not described, 0). The unit of allocation was included
because of the possibility of group contamination in trials in which the patients of an individual
clinician could be allocated to the intervention and control groups, and the clinician would then
receive decision support for some patients but not others. Contamination bias would lead to
underestimating the effect of a CCDSS.
Data Synthesis
CCDSS and study characteristics predicting success will be analyzed and interpreted with the
study as the unit of analysis. Data will be summarized using descriptive summary measures,
including proportions for categorical variables and means (±SD, standard deviation) for
continuous variables. Univariable and multivariable logistic regression models, adjusted for
study methodological quality, will be used to investigate associations between the outcomes of
interest and study specific covariates. All analyses will be carried out using SPSS, version 18.0.
We will interpret p ≤ 0.05 as indicating statistical significance; all p-values will be two-sided.
When reporting results from individual studies, we will cite the measures of association and p-
values reported in the studies. If appropriate for groups of studies with similar features, we will
conduct meta-analyses using standard techniques, as described in the Cochrane Handbook
http://www.cochrane.org/resources/handbook/ webcite.
Conclusion
A decision-maker-researcher partnership provides a model for systematic reviews that may foster
KT and uptake.
Competing interests
The authors declare that they have no competing interests.
Authors' contributions
This paper is based on the protocol submitted for peer review funding. RBH and NLW
collaborated on this paper. Members of the Computerized Clinical Decision Support System
(CCDSS) Systematic Review Team reviewed the manuscript and provided feedback. All authors
read and approved the final manuscript.
Appendix
Databases searched from 1 January 2004 to 6 January 2010:
Medline - Ovid
Search Strategy
1. (exp artificial intelligence/NOT robotics/) OR decision making, computer-assisted/OR
diagnosis, computer-assisted/OR therapy, computer-assisted/OR decision support systems,
clinical/OR hospital information systems/OR point-of-care systems/OR computers, handheld/ut
OR decision support:.tw. OR reminder systems.sh.
2. (clinical trial.mp. OR clinical trial.pt. OR random:.mp. OR tu.xs. OR search:.tw. OR meta
analysis.mp,pt. OR review.pt. OR associated.tw. OR review.tw. OR overview.tw.) NOT
(animals.sh. OR letter.pt. OR editorial.pt.)
3. 1 AND 2
4. limit 3 to yr = '2004-current'
Total number of citations downloaded as of January 13, 2010 = 7,578 (6,430 citations retrieved
when conducting the search from January 1, 2004 to December 8, 2008; 1,148 citations retrieved
when further updating the search to January 6, 2010)
EMBASE - Ovid
Search Strategy
1. computer assisted diagnosis/OR exp computer assisted therapy/OR computer assisted drug
therapy/OR artificial intelligence/OR decision support systems, clinical/OR decision making,
computer assisted/OR hospital information systems/OR neural networks/OR expert systems/OR
computer assisted radiotherapy/OR medical information system/OR decision support:.tw.
2. random:.tw. OR clinical trial:.mp. OR exp health care quality
3. 1 AND 2
4. 3 NOT animal.sh.
5. 4 NOT letter.pt.
6. 5 NOT editorial.pt.
7. limit 6 to yr ='2004-current'
Total number of citations downloaded as of January 13, 2010 = 5,165 (4,406 citations retrieved
when conducting the search from January 1, 2004 to December 8, 2008; 759 citations retrieved
when further updating the search to January 6, 2010)
All EBM Reviews - Ovid - Includes Cochrane Database of Systematic Reviews, ACP Journal Club,
DARE, CCTR, CMR, HTA, and NHSEED
Search Strategy
1. (computer-assisted and drug therapy).mp.
2. (computer-assisted and diagnosis).mp.
3. (expert and system).mp.
4. (computer and diagnosis).mp
5. (computer-assisted and decision).mp.
6. (computer and drug-therapy).mp.
7. (computer and therapy).mp.
8. (information and systems).mp.
9. (computer and decision).mp.
10. decision making, computer-assisted.mp.
11. decision support systems, clinical.mp.
12. CDSS.mp.
13. CCDSS.mp.
14. clinical decision support system:.mp.
15. (comput: assisted adj2 therapy).mp.
16. comput: assisted diagnosis.mp.
17. hospital information system:.mp.
18. point of care system:.mp.
19. (reminder system: and comput:).tw.
20. comput: assisted decision.mp.
21. comput: decision aid.mp.
22. comput: decision making.mp.
23. decision support.mp.
24. (comput: and decision support:).mp.
25. 1 OR 2 OR 3 OR 4 OR 5 OR 6 OR 7 OR 8 OR 9 OR 10 OR 11 OR 12 OR 13 OR 14 OR 15
OR 16 OR 17 OR 18 OR 19 OR 20 OR 21 OR 22 OR 23 OR 24
26. limit 25 to yr = '2004-current'
Total number of citations downloaded as of January 13, 2010 after excluding citations retrieved
from Cochrane Database of Systematic Reviews, DARE, CMR, HTA, and NHSEED = 1,964
(1,573 citations retrieved when conducting the search from January 1, 2004 to December 8,
2008; 391 citations retrieved when further updating the search to January 6, 2010)
INSPEC - Scholars Portal
Search Strategy
1. EXPERT
2. SYSTEM?
3. 1 AND 2
4. EVALUAT?
5. 3 AND 4
6. MEDICAL OR CLINICAL OR MEDIC?
7. 5 AND 6
8. PY = 2004:2010
9. 7 AND 8
Total number of citations downloaded as of January 13, 2010 = 87 (84 citations retrieved when
conducting the search from January 1, 2004 to December 8, 2008; 3 citations retrieved when
further updating the search to January 6, 2010)
Acknowledgements
The research was funded by a Canadian Institutes of Health Research Synthesis Grant:
Knowledge Translation KRS 91791. The members of the Computerized Clinical Decision
Support System (CCDSS) Systematic Review Team are: Principal Investigator, R Brian Haynes,
McMaster University and Hamilton Health Sciences (HHS), [email protected]; Co-
Investigators, Amit X Garg, University of Western Ontario, [email protected] and K Ann
McKibbon, McMaster University, [email protected]; Co-Applicants/Senior Management
Decision-makers, Murray Glendining, HHS, [email protected], Rob Lloyd, HHS,
[email protected], Akbar Panju, HHS, [email protected], Teresa Smith, HHS,
[email protected], Chris Probst, HHS, [email protected] and Wendy Gerrie, HHS,
[email protected]; Co-Applicants/Clinical Service Decision-Makers, Rolf Sebaldt, McMaster
University and St Joseph's Hospital, [email protected], Stuart Connolly, McMaster
University and HHS, [email protected], Anne Holbrook, McMaster University and
HHS, [email protected], Marita Tonkin, HHS, [email protected], Hertzel Gerstein,
McMaster University and HHS, [email protected], David Koff, McMaster University and
HHS, [email protected], John You, McMaster University and HHS, [email protected] and
Rob Lloyd, HHS, [email protected]; Research Staff, Nancy L Wilczynski, McMaster
University, [email protected], Tamara Navarro, McMaster University,
[email protected], Jean Mackay, McMaster University, [email protected], Lori Weise-
Kelly, McMaster University, [email protected], Nathan Souza, McMaster University,
[email protected], Brian Hemens, McMaster University, [email protected], Robby
Nieuwlaat, McMaster University, [email protected], Shikha Misra, McMaster
University, [email protected], Jasmine Dhaliwal, McMaster University,
[email protected], Navdeep Sahota, McMaster University,
[email protected], Anita Ramakrishna, McMaster University,
[email protected], Pavel Roshanov, McMaster University,
[email protected], Tahany Awad, McMaster University, [email protected], Chris
Cotoi, McMaster University, [email protected] and Nicholas Hobson, McMaster University,
[email protected].
References
1. Garg AX, Adhikari NK, McDonald H, Rosas-Arellano MP, Devereaux PJ, Beyene J,
Sam J, Haynes RB: Effects of computerized clinical decision support systems on
practitioner performance and patient outcomes: a systematic review.
JAMA 2005, 293:1223-1238. PubMed Abstract | Publisher Full Text
2. Hunty DL, Haynes RB, Hanna SE, Smith K: Effects of computer-based clinical
decision support systems on physician performance and patient outcomes: a
systematic review.
JAMA 1998, 280:1339-1346. PubMed Abstract | Publisher Full Text
3. Johnstony ME, Langton KB, Haynes RB, Mathieu A: Effects of computer-based
clinical decision support systems on clinical performance and patient outcomes. A
critical appraisal of research.
Ann Intern Med 1994, 120:135-142. PubMed Abstract | Publisher Full Text
4. Haynes RB, Cotoi C, Holland J, Walters L, Wilczynski N, Jedraszewski D, McKinlay J,
Parrish R, McKibbon KA, McMaster Premium Literature Service (PLUS) Project:
Second-order peer review of the medical literature for clinical practitioners.
JAMA 2006, 295:1801-1808. PubMed Abstract | Publisher Full Text
5. Fleiss J: Statistical methods for rates and proportions. 2nd edition. New York: Wiley-
Interscience; 1981.
6. Jadad AR, Moore RA, Carroll D, Jenkinson C, Reynolds DJ, Gavaghan DJ, McQuay HJ:
Assessing the quality of reports of randomized controlled trials: is blinding
necessary?
Control Clin Trials 1996, 17:1-12. PubMed Abstract | Publisher Full Text

http://www.implementationscience.com/content/5/1/12

What Is Clinical Decision Support?
Posted on May 29, 2012 by Mary Griskewicz MS, FHIMSS
Many clinicians are still unclear what clinical decision support (CDS) means. Eligible
Professionals (EPs) in medical practices who have attested, or are in process of attesting, to the
Medicare and Medicaid EHR Incentive Programs, and thus, demonstrate meaningful use
of certified EHR technology, should already know that they must attest to meeting the Stage 1
menu criteria related to CDS. That means they must implement one clinical decision support
rule, and be able to track compliance.
For example, I am often asked if e-prescribing is considered as clinical decision support. The
short answer to the question is ―no.‖
According to a recent study (April 23, 2012) conducted by the Agency for Research Health and
Quality, the authors concluded, “Both commercially and locally developed Clinical Decision
Support Systems (CDSSs) are effective at improving health care process measures across diverse
settings, but evidence for clinical, economic, workload, and efficiency outcomes remains
sparse.”
That is helpful, but it‘s still unclear what really makes up CDS.
__________________________________________________________________
According to HIMSS, ―Clinical Decision Support” is a process for enhancing health-related
decisions and actions with pertinent, organized clinical knowledge and patient information to
improve health and healthcare delivery.
Information recipients can include:
 Patients,
 Clinicians, and
 Others involved in patient care delivery.
Information delivered can include:
 General clinical knowledge and guidance,
 Intelligently processed patient data, or
 A mixture of both.
Information delivery formats can be drawn from a rich palette of options that includes:
 Data and order entry facilitators,
 Filtered data displays,
 Reference information, alerts and others.
__________________________________________________________________
HIMSS provides the following example of CDS in its CDS Scenarios 101 site for a primary care
physician‘s office: You are a primary care physician in a large group practice that uses an
electronic health record. At the beginning of each visit, you view a dashboard of preventive care
measures – like flu vaccine, colon cancer screening, cholesterol tests – that are due for your
patient, based on age, medical history (problem list), and medication list stored in the EHR.
If you‘d like to learn more, the HIMSS website has built some valuable resources for clinicians
and the industry with case studies and an updated book on CDS.
The second edition of this authoritative, best-seller, I mproving Outcomes with Clinical Decision
Support: An Implementer’s Guide, has been substantially enhanced with expanded and updated
guidance on using CDS interventions to improve care delivery and outcomes in diverse care
settings. The book is available at the HIMSS online store.
The new edition is more reader-friendly than the earlier version with sections that help:
1. Set up (or refine) a successful CDS program in a hospital, health system or physician
practice; and
2. Configure and launch specific CDS interventions that recipients appreciate and
measurably improve targeted outcomes.
Two detailed case studies illustrate key points showing how a ―real-life‖ CDS program—and
specific CDS interventions—might evolve in a hypothetical community hospital and small
physician practice.
I like the book because it provides readers with enhanced worksheets—including sample data—
that help readers document and use information needed for their CDS program and
interventions. Sections in each chapter present considerations for health IT software suppliers to
effectively support their clients implementing CDS.
HIMSS resources provide eligible professionals with actual examples to help them not only meet
the Stage 1 meaningful use criteria, but also, to build the needed evidence for improved clinical,
economic, workload and efficiency outcomes.
Now, back to my original question – and answer – on defining clinical decision support. I hope
you will agree with me, if done correctly, CDS can help providers deliver safe and efficient care
for patients.
http://blog.himss.org/2012/05/29/what-is-clinical-decision-support/


CLINICAL DECISION SUPPORT SYSTEMS
Top tips to choose the right bank for your
student loan (TopTipsNews)
Fahhad Farukhi


INTRODUCTION

The health care market is the largest industry in the United States, with expenditures expected
to reach $2.6 trillion, or 15.9% of the national GDP by 2010 [1]. In addition to the rapid increase in
health care expenditures, technology has developed to provide support to health care delivery staff.
The primary care delivery entity is the physician or the specialist. Throughout the evolution of medical
technology, the development of an efficient, useful, and practical clinical information system has
become a significant focus in the vendor market. However, the resistance to the implementation of
helpful technology that is common within the health care market has limited the maximization of the
potential of clinical information systems. Through the satisfaction of aggregate physician needs in
conjunction with the needs of health care managers, the implementation of a clinical information
system can be advantageous to the health care delivery process.

Why create a CIS?
The creation of a clinical information system
requires immense financial and intellectual
investment. Therefore, the justifications for
such an investment must be quantitatively and
qualitatively measurable upon implementation.
The development of the clinical information
system arose from needs within the health care
field, from health care managers, quality
control, and care providers, such as physicians.
The needs that motivated the development of
clinical information systems include; clinical
task support, clinical management control,
competition support, and clinical decision
support [2].
The primary source of support needed is in the clinical task support domain. Through the
development of the electronic medical record, the maintenance of the patient record will be simplified,
providing information support to the clinician. The electronic medical record will allow for a longitudinal
source of information regarding patient history, previous encounter history, drug allergies, and other
relevant information. The development and use of such a system will allow for a significant decrease in
medical errors. The National Academy of Sciences’ Institute of Medicine estimates that the deaths
caused by medical mistakes are greater than deaths caused by AIDS, breast cancer or car accidents. The
number of deaths caused by medical error has been estimated to be 98,000 individuals per year [3].
Through effective data entry and use of the electronic medical record, the amount of deaths caused by
lack of data or erroneous data entry will be significantly reduced.
Furthermore, the development of an effective clinical information system should provide the
clinician process support, or the ability to share information. Through the creation of a standard
electronic medical record, numerous users involved in the care delivery process can utilize the
information stored on the record[1]. This accessibility will allow for all users, nurses, administrative
staff, laboratory staff, pharmacists, and physicians to update and extract information for their specific
usage needs.
A significant contributor to the creation of a universal electronic medical record is the
development of SNOMED, or the Systemized Nomenclature of Medicine, and Arden Syntax[2]. SNOMED
allows for a consistent compilation of clinical information that allows various specialists, researchers,
and even patients to share their knowledge based on a common linguistic description of health. This
knowledge sharing can be conducted across care sites and differing computer systems. Furthermore,
privacy is maintained through the use of authorization privileges. This expandable nomenclature system
can be customized for individual clinicians or nation-wide health care delivery organizations [4]. Arden
Syntax is a medical language system that is specifically designed for medical logic systems, or medical
logic modules. Each medical logic module possesses ample information to create a single medical
decision based on information entered. The use of medical logic modules allows for the constant
maintenance of patient status and clinician alert of any changes in the status of the patient [5].
In addition, the development of the clinical information system arose due to a need to maintain
clinical management control. This management of clinical practice is applicable to individual patient
management, intra-practice management, and inter-practice management. On an individual patient
basis, the measurement of quality of care, the monitoring of care provided, and feedback regarding the
care that has been provided allows for improved quality care provision. The clinical information system
would allow for the maintenance of a standard practice pattern, which includes the provision of care
based on specified care paths and/or flow sheets. Furthermore, the treatments and protocols chosen
for care provision can be compared to an industry standard, or a “best practice” methodology.
This clinical management support also applies to inter-practice management situations. By
standardizing “best practice” care methodologies; the clinician has a database against which their care
path decision may be measured. Also, the integration of the numerous care contributors into a single
information system will allow for cost management and quality control across care sites. Such inter-site
care monitoring is especially significant for large care delivery organizations such as Kasier-Permanante,
where cost control, patient health, practice pattern variation, and quality control is integral to the
business operations.
In terms of competition support, the development of an effective clinical information
system will place health care organizations in an advantageous position relative to other
competitors. The benefits of a clinical information system are evident in terms of the reduction
of medical error, the standardization of medical protocol, knowledge sharing, cost control,
quality control, and decision support. Therefore, firms that are able to develop facilities in these
areas will have a competitive edge over other care delivery organizations. Furthermore, a
clinical information system will allow for the comparison of numerical data from numerous
fields with the data obtained from other firms in the industry. Such comparisons will allow
delivery organizations to increase the quality of care accordingly, learn from other firms, adjust
price according to competitor levels, and adjust to community demand levels.
However, the domain within which the clinical information system may provide the greatest
support is within the clinical decision support domain. Through the development of the electronic
medical record and the increased SNOMED usage, clinicians will be able to share knowledge more
rapidly on an interactive level. Furthermore, the clinical environment requires an extensive knowledge
of rare complications and revolutionary research that could dramatically alter the diagnosis of diseases
in patients. However, the breadth of information and the specialization of practice prevent physicians
from understanding or even learning that such findings may exist. Through the collaboration
technologies that would be available within a clinical information system, clinicians may be able to
rapidly contact “experts” within a specific field who would be better able to assess the patient situation
through a brief analysis of the patient’s electronic medical record and other information as provided by
the physician.
Currently, the adaptation of new technology within the health care sector is fairly slow, with
change hinging more on cultural features of the organization or entity than on the technological benefits
that are produced by the implemented system. Therefore, the clinical information system technology
has become increasingly demand driven, in that the usage of the systems will only increase as long as
there is an acceptance of the new technology within the environment by the physicians. In order to
ensure successful integration, the organization must ensure that the key players, mainly the physicians,
are informed of the benefits to the individual physician, the health care organization, and health care as
a whole [6].

MD benefits of the Clinical Information System
The primary benefit of the clinical information system will be within the domains of task support
and decision support[3]. The task support field has been revolutionalized through the implementation
of the electronic medical record in conjunction with SNOMED. The universalization of the electronic
medical record will increase the accessibility of patient information to clinicians as well as increase the
amount of data available for clinical use, reducing medical error significantly.
However, the greatest tool to increase the standardization of care, reduction of practice pattern
variation, successful and effective diagnosis, and correct care path choice will result from the
development of the clinical decision support domain of the clinical information system. Clinical decision
support software offers the possibility to improve the quality and reduce the cost of care by influencing
medical decisions at the time and place that these decisions are made.
An ideal clinical information system would alert physicians when outlier results are returned from
data entry of laboratory testing. The data attained for a specific patient can then be compared to the
general population to indicate whether the data is within the normal fit or is an outlier that may require
further analysis. Such a practice would induce the physician to notice certain data that may otherwise
go unnoticed, and therefore, alter the diagnosis of the patient. Also, the physician may interact with the
system on a hypothesis-testing basis. A physician may enter a possible diagnosis into the system and
then receive feedback from the system regarding the plausibility of such a diagnosis being true. This
allows physicians to receive guided feedback during their consideration of similar diagnoses, which may
be significantly different based on their appropriate care path.
The development of an effective clinical decision support system will have a significant impact on
practice methodology. Clinical decision support systems are intended to receive patient data and utilize
that data to propose a series of possible diagnoses and a course of action. The advent of such a system
will provide physicians with a guideline through which the physician can model their decision.
Furthermore, the clinical decision support system can lead to a reduction of the practice pattern
variation that plagues the health care delivery process. By reducing practice pattern variation, the
overall cost of healthcare may be reduced. Also, an effective clinical decision support system can
recognize drug-drug interaction and patient complications that would otherwise be unrecognized by the
physician to provide a valid, efficient, and “best practice” solution to the patient diagnosis process.
The dynamic environment surrounding patient diagnosing complicates the process of diagnosis.
Numerous significant input variables, special patient circumstances, and the basic complexity involved in
the diagnosis process limits the accuracy of a given clinical decision support system. However, the
potential advantages that are introduced through a successful system are significant. Primarily, possible
diagnoses could save numerous patient lives, since an information system may be better able to
synthesize vast amounts of information. Furthermore, the diagnosis suggestion may allow for better
standardization of care delivery and the care path followed.
Ever since computers were first coming into use, it was believed that computers could model
the clinical problem solving techniques used by physicians. As early as 1970, William Schwartz of Tufts
University School of Medicine wrote: “Computing science will probably exert its major effects by
augmenting and, in some cases, largely replacing the intellectual functions of the physician.” *7]
However, the dynamic, complex, and unique health care environment has hindered the
development of a standard clinical decision support system that would enable the universalization of
any clinical decision support system. Several factors including lack of investment, lack of leadership
from practicing physicians, medical schools are responsible for the dreams of an ideal clinical decision
support from being realized.

DEVELOPMENT OF DECISION SUPPORT SYSTEM DOMAIN
Medical diagnosis is a complex human process that is difficult to represent in an
algorithmic model. Not only does medical diagnosing require the understanding of symptoms,
drug-drug interactions, and patient history, the diagnosing process requires knowledge of
diseases in general as well as the general population. Furthermore, the system would have to be
updateable to constant changes that accompany the scientific development that is a result of the
extensive research within the medical field. Also, the system would have to be able to utilize
varying levels of data in order to diagnose an individual. While one patient may have data
showing high cholesterol, chest pain, higher blood pressure within an arterial section, and
previous heart attack history within the family, another patient may only show high cholesterol
and chest pain. While both patients may require a catheterization, the limited data of the second
patient may limit the ability of the diagnosis, and therefore, could lead to the misdiagnosis of the
patient.
Furthermore, it is imperative that the diagnosing systems provide reasoning for the
medical diagnosis provided. Such a process would allow the physician to understand the reasons
the system may have had for a specific decision that may have been made.
Clinical information systems have developed from rudimentary data entry and retrieval
on an intra-hospital basis, to real-time data retrieval, multi-user data entry, multi-access data
retrieval, knowledge sharing, sophisticated consultation, patient and inter-practice management,
competition support, and enhanced decision support functionality. With the advent of the
physician workstation, hand-held data entry systems[4], voice recognition systems, and real-time
clinical data retrieval and electronic medical record update, the clinical information system is
becoming a comprehensive system integrating many aspects of the care delivery process.
Innovative point-of-care support, such as vital sign monitoring, medication administration
monitoring, basic chart maintenance, lab and drug orders administration, and alerting, are
reducing labor needs while increasing accuracy and quality through the continuous update of the
electron medical record. The development of technology that has led to greater ―alerting and
protocol support, utilization control, case management, outcome management, and executive
decision support‖ [8], have enhanced the care delivery process, particularly the decision support
aspect of the clinical information system.
Clinical decision support systems have evolved from a foundation based upon statistical
algorithms to complex artificial neural networks. The early decision support systems, also
termed medical diagnostic decision systems, were based on Bayesian statistical theory[5],
providing crude probability diagnoses based on certain critical variables [9]. In 1994 Berner et al
published the results of a study in which four commercially available medical diagnostic systems
were challenged to diagnose a series of 105 patients each of whom had been referred to a
consultant and in which of whom a diagnosis had been established. The programs studied
included Dxplain, Iliad, Meditel and QMR. The proportion of correct diagnosis ranged from
52% to 71% and the relevant diagnoses ranged from 19% to 37%. Thus it appeared that
computer aided diagnostic systems had failed to deliver on their promise [10].
The complexity of the health care environment requires significant adaptations in order to
maintain legitimate diagnoses. Clinicians do not combine clinical data using probability used by the
computer but use case specific knowledge and heuristics based on their experience. Hence earlier
systems that attempted to replace the clinician were largely unsuccessful. Ralph Engle, one of the
pioneers of computer assisted diagnosis wrote: “ Our experience confirms the great difficulty and even
impossibility, of incorporating the complexity of the human thought into a system that can be handled
by a computer. We concluded that we should stop trying to make a computer act like a diagnostician
and concentrate instead on ways of making computer-generated relevant information available to
physicians as they make decisions.” *11+

Current Practices in Decision Support
Systems
Previous decision support systems have utilized the Arden syntax in conjunction with
HL7 standards and medical logic modules to create decision systems ranging from single
decision models to complex, sequenced decision models. Each medical logic module contains,
within itself, the ability to make one single medical decision based on data entry. However,
through the sequencing of various medical logic modules, fairly complex models may be
created. Numerous clinical information system vendors have developed such decision support
systems, including; HBOC, IBM, Siemens Medical Systems, and Health VISION. These
systems have been implemented within numerous care delivery sites, including; Columbia-
Presbyterian Medical Center, JFK Medical Center, Ohio State University, and Meridian Health
Systems.

Recent Shifts in CDSS:
In recent years, clinical decision support systems have begun utilizing artificial neural networks.
However, the shift to the artificial neural network continues to utilize the Bayesian statistical theory, but
is able to integrate greater decision variable than previously possible. Previously, medical diagnosis
systems were effective in evaluating outcomes for a group of patients, yet failed to perform as
successfully on a patient-by-patient basis. Through the application of the artificial neural network
system, this success rate on an individual patient basis may be increased significantly. The neural
network is able to accept numerous input variables, including demographic information, admission
information, previous diagnosis information, and patient history to rapidly create possible diagnoses
[12]. Rather than provide a single diagnosis for a specific patient, the system returns a set of possible
diagnoses from which the clinician may choose based on their own discretion. Advanced systems are
able to return probability estimates of the likeliness of a particular diagnosis. Furthermore, these
advanced systems are able to propose “best practice” methodologies for care delivery or flow sheets
that dictate action steps in the care delivery process.
However, in order to effectively determine “best practice” methodologies, clinical practice
guidelines must be set. Clinical practice guidelines that define what steps are necessary in order to
ensure quality care provision can be separated into decisions, actions, and processes. The decision
model includes selection of which variables to consider and at what weights, selection of diagnosis, and
consideration of alternative diagnoses. By utilizing such a flexible system, the patient and the physician
become the “chooser” in the environment, being able to control what information is relevant and which
result to act upon. Furthermore, the action model specifies which actions need to be performed. These
actions include the specification of type of action and temporal limitation (i.e. take dose for 3 months)
through the standardized medical terminology available. Finally, the process model organizes actions
sequentially and hierarchically in order to determine which actions are crucial to the care process and in
what order the care should be delivered [13]. The creation of clinical practice guidelines is necessary in
order to have a template with which the system may prescribe diagnoses, actions, and processes.

Regulation
With the increasing number of vendors producing such systems there is increasing
variability in their quality. This is a cause for concern as a simple mistake in a clinical decision
support can lead to the loss of a life. Hence the need for enhanced oversight and regulation is
needed. The FDA has regulated that CDSS‘s are similar to medical devices. However the legal
responsibility for the treatment and advice given to the patient will rest with the clinician
regardless of whether he was assisted by the CDSS [14].

XML
XML is a type of computer language that stands for eXtensible Markup Language. It is
considered to be the future of computer applications in health care. Its acceptance and widespread use
as a method for defining clinical content in text documents depends on the establishment of standard
vocabularies so that healthcare organizations can exchange electronic information with one another.
The design of XML is to highlight the content of information rather than its appearance, which is the
case with HTML (Hyper Text Markup Language). This technology allows uses to create queries and
defined structures of information based on relevant content. This means that health care providers and
look at a chart, discharge summary, or other hospital records and isolate only the information they
need. Users can search for information from a number of sources and bring it together in one place.
They can sort the relevant information and group it by a wide array of classification systems as befits
their needs. This data can then be extracted to fit any type of order, form, or document that the user
requires.
XML can also be used to create documents that pursue set pathways according to criteria such
as content, intent, and origin. One example of this potential is having a document system that requires
an attending physician’s signature. Any chart signed by a resident without the attending’s signature
would be routed back to the attending physician for review. Thus, this technology creates efficiency
and speed by creating a system that minimizes errors and checks for complete and full workup in clinical
documentation.
On the technical side, XML files can typically be used in conjunction with HTML and with other
computer systems. This flexibility will allow users to uniformly obtain all the information they need
from a wide range of sources in order to filter out what information they precisely need. Hence, XML
appears to be the binding force that culminates clinical data from multiple systems and presents it in a
form that is easy to filter and utilize [15].

Increasing Acceptance
The trend for the future shows increased dependence on clinical decision support systems by
physicians. Constant contact with such systems will ensure that the most optimal level of care is
provided utilizing both physician judgment and technological innovativeness. Such a future will
mean that physicians and other health care providers will have to change the way they collect,
sort, and use health care data. There are many benefits to such systems as well as barriers to
use. It is important to look at such barriers in order to understand how to increase support and
acceptance to ensure successful implementation. It is only a matter of time before these systems
are common place in all health settings.

The Role of Physicians and Barriers to Use
It is extremely important for physicians, caregivers, staff, administrators, and technical experts
to work in collaboration in the design, implementation, and improvement of decision support systems.
The physician role has expanded significantly in today’s managed care environment. Physicians and
other clinicians need to be involved in throughout the whole process of finding, designing,
implementing, and improving such a system. Their input and support is critical because of the
significant role that physicians play in the health care delivery process [16].
The acceptance of these systems has not been as easy as most other healthcare technologies.
One reason for this is the fact that these systems including decision support systems affect the long
history of traditional medical practices. New systems are changing the ways physicians think and
behave [17]. Failure to accept these systems among physicians occurs when implementation does not
provide direct benefits to their users and the process of implementation itself changes the traditional
practices of the clinical environment [18].

Organizational Environment and Barriers to Use
The culture in a health care setting can be very complex. This is because there are a many types
of people involved in the system. Not only are there patients and physicians, but there is also the
administration, technical experts, and other staff that add to the web of issues, tensions, and interests
that exist. There are many values and mode of practice that exist in such a web that often complicate
the culture of the health care environment as a whole. It is natural to conclude that with such a large
group of diverse people, there are often a difference of values and interests. Therefore, adopting a
major acquisition such as a clinical decision support system requires that there is overall acceptance and
support from all relevant parties to ensure successful implementation. The benefits and how people
evaluate them with regards to how they fit within their practices are the key factors indicative of success
[19].
Despite evidence that clinical information systems can improve patient care, there have been a
number of instances where implementation has not been successful [20]. Barriers here are due to
organizational stigmas that the systems are not noticeable benefits to the practice of care. Others argue
the cost benefits of certain systems within their operating margins. Like physicians, those medical staff
and administrators who are not properly acquainted with new systems argue that they negatively affect
care in areas of patient waiting times and work load. However, negative results in these areas occur
when those using the system do not have adequate training and support for such a system whereby the
use of it turns out to be negative rather than positive. [21, 22, 23]
Since the acceptance of decision support systems depends heavily on physicians, it’s the values
that they hold which determine if they view new systems as beneficial. These values concern the
patient-physician relationship, the quality of patient care, the balance between clinical guidelines and
decision support technology, and physician autonomy.


Quality of Treatment
Physicians’ attitudes about what a piece of technology can do has more of an effect on
acceptance of that technology than what it can do in a realistic setting. This belief is confined towards
the physician’s own practices and how they operate. If there is a direct benefit here, then a system has
improved the quality of care.

Clinical Procedures and Decision Support Technology
The balance between the science of medicine and the art of it is a pressing issue that will
influence acceptance. There is natural tension between the capabilities of technology and the
method of medical practices that have existed for years. Physicians struggle over whether to let
technology based on population characteristics guide them in their decision making as opposed
to making decisions based on their own experience and the novelty of each patient‘s case. The
advent of providing accurate information through clinical information has been essential in the
improvement of patient care. However, just because information is provided does not mean that
it is used. Physicians may ignore information offered up by some clinical decision support
systems. Those who do use it may not use it to its fully capability or may only use it for a short
time before discontinuing its use. Physicians are worried about the legal ramifications of not
following the advice of decision support systems. However, the issue that decision support
systems are only a tool for advice and not a computer making the final decision has increased
physicians‘ comfort level with decision support systems [21].


Relationship between Patient and Physician
The relationship between a physician and his/her patient is crucial to quality care. The
acceptance of decision support systems depends on their ability to cater to this need. Systems have to
make the physician feel more equipped to provide better care while instilling trust and confidence in the
patient that the technology is not a replacement of the physician, but a tool that enhances the
treatment process.


Physician Autonomy
Medicine is characterized by a simple, but long lasting philosophy. The ability to treat patients
requires not only individual expertise, but experience in being able to effectively address the needs of
patients. Therefore, physicians have favored their own clinical judgment in determining the needs of
particular patients over broad policies, administrative guidelines, and the reliance on technology.
Therefore, promoting awareness about decision support systems capabilities has to be done in a way to
break down social barriers and stigma that are associated with physicians’ egos *24+.

CONCLUSIONS
Current trends in the United States will not only lead to increased spending in the health care
arena but also an accelerated growth of the acceptance and spending of health care information
technology. The necessity for innovative and dependable clinical information systems with decision
support capabilities is crucial. Increasing systems’ acceptance depends on the culture of the hospital as
well as the involvement of physicians. The involvement of all health care professionals especially
physicians in the selection and implementation of the system from the outset is essential. Not only will
this ensure support, but as a result the frequency of communication is likely to increase amongst one
another. In turn, this frequent consultation and communication is likely to better the quality of patient
care as well as the patient-physician relationship [25].
Secondly, it is essential to consider in advance the new system’s effects on the culture,
practices, and attitudes of the people in the organization. Explicitly identifying how people and groups
in the organization will benefit specifically will lend support for its implementation. The use of
information systems by physicians will occur if that system will allow them to provide better care for
their patients. Benefits to the organization, in general, will not be as successful to motivate or even
inspire physicians to alter the way they have practiced medicine.
Finally, healthcare organizations must anticipate and be prepared to handle a diverse array of
changes that will occur during the implementation process itself. Therefore the organization needs to be
able to operate normally on schedule while operating the implementation process as well. By phasing in
the system's implementation and anticipating problems proactively, the hospital should be able to
reduce the number of negative experiences associated with the introduction of a new system [26].

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(http://www.cwru.edu/med/epidbio/mphp439/Clinical_Decision.htm)-Notes







Computerised decision support systems in
order communication for diagnostic,
screening or monitoring test ordering:
systematic reviews of the effects and cost-
effectiveness of systems.
Top tips to choose the right bank for your
student loan (TopTipsNews)
Main C
1
, Moxham T, Wyatt JC, Kay J, Anderson R, Stein K.
Author information
Abstract
BACKGROUND:
Order communication systems (OCS) are computer applications used to enter diagnostic and
therapeutic patient care orders and to view test results. Many potential benefits of OCS have
been identified including improvements in clinician ordering patterns, optimisation of clinical
time, and aiding communication processes between clinicians and different departments. Many
OCS now include computerised decision support systems (CDSS), which are information
systems designed to improve clinical decision-making. CDSS match individual patient
characteristics to a computerised knowledge base, and software algorithms generate patient-
specific recommendations.
OBJECTIVES:
To investigate which CDSS in OCS are in use within the UK and the impact of CDSS in OCS
for diagnostic, screening or monitoring test ordering compared to OCS without CDSS. To
determine what features of CDSS are associated with clinician or patient acceptance of CDSS in
OCS and what is known about the cost-effectiveness of CDSS in diagnostic, screening or
monitoring test OCS compared to OCS without CDSS.
DATA SOURCES:
A generic search to identify potentially relevant studies for inclusion was conducted using
MEDLINE, EMBASE, Cochrane Controlled Trials Register (CCTR), CINAHL (Cumulative
Index to Nursing and Allied Health Literature), DARE (Database of Abstracts of Reviews of
Effects), Health Technology Assessment (HTA) database, IEEE (Institute of Electrical and
Electronic Engineers) Xplore digital library, NHS Economic Evaluation Database (NHS EED)
and EconLit, searched between 1974 and 2009 with a total of 22,109 titles and abstracts screened
for inclusion.
REVIEW METHODS:
CDSS for diagnostic, screening and monitoring test ordering OCS in use in the UK were
identified through contact with the 24 manufacturers/suppliers currently contracted by the
National Project for Information Technology (NpfIT) to provide either national or specialist
decision support. A generic search to identify potentially relevant studies for inclusion in the
review was conducted on a range of medical, social science and economic databases. The review
was undertaken using standard systematic review methods, with studies being screened for
inclusion, data extracted and quality assessed by two reviewers. Results were broadly grouped
according to the type of CDSS intervention and study design where possible. These were then
combined using a narrative synthesis with relevant quantitative results tabulated.
RESULTS:
Results of the studies included in review were highly mixed and equivocal, often both within and
between studies, but broadly showed a beneficial impact of the use of CDSS in conjunction with
OCS over and above OCS alone. Overall, if the findings of both primary and secondary
outcomes are taken into account, then CDSS significantly improved practitioner performance in
15 out of 24 studies (62.5%). Only two studies covered the cost-effectiveness of CDSS: a Dutch
study reported a mean cost decrease of 3% for blood tests orders (639 euros) in each of the
intervention clinics compared with a 2% (208 euros) increase in control clinics in test costs; and
a Spanish study reported a significant increase in the cost of laboratory tests from 41.8 euros per
patient per annum to 47.2 euros after implementation of the system.
LIMITATIONS:
The response rate from the survey of manufacturers and suppliers was extremely low at only
17% and much of the feedback was classified as being commercial-in-confidence (CIC). No
studies were identified which assessed the features of CDSS that are associated with clinician or
patient acceptance of CDSS in OCS in the test ordering process and only limited data was
available on the cost-effectiveness of CDSS plus OCS compared with OCS alone and the
findings highly specific. Although CDSS appears to have a potentially small positive impact on
diagnostic, screening or monitoring test ordering, the majority of studies come from a limited
number of institutions in the USA.
CONCLUSIONS:
If the findings of both primary and secondary outcomes are taken into account then CDSS
showed a statistically significant benefit on either process or practitioner performance outcomes
in nearly two-thirds of the studies. Furthermore, in four studies that assessed adverse effects of
either test cancellation or delay, no significant detrimental effects in terms of additional
utilisation of health-care resources or adverse events were observed. We believe the key current
need is for a well designed and comprehensive survey, and on the basis of the results of this
potentially for evaluation studies in the form of cluster randomised controlled trials or
randomised controlled trials which incorporate process, and patient outcomes, as well as full
economic evaluations alongside the trials to assess the impact of CDSS in conjunction with OCS
versus OCS alone for diagnostic, screening or monitoring test ordering in the NHS. The
economic evaluation should incorporate the full costs of potentially developing, testing, and
installing the system, including staff training costs.
http://www.ncbi.nlm.nih.gov/pubmed/21034668