|
In this month's piece we briefly examine clinical decision-support
systems: their prevalence and role in improving the
quality of care and reducing the occurrence of medical
errors.
Background
The pursuit of quality in medicine is not but a "performance
gap" still exists between recognized best practices
and clinicians' practice patterns. Pervasive overuse,
underuse, and misuse of various interventions have been
demonstrated. Variations in care, the persistence of
medical errors, and escalating health care costs are
among the factors contributing to the emphasis on quality
measurement and improvement. (For more information on
quality of care, please visit THCI's online modules,
Definitions and Measurement of Quality, Quality Improvement
Techniques, and Quality Oversight in the Marketplace.)
Increasingly, information technology (IT) in the form
of clinical decision-support systems is seen as a solution
to improve clinical performance and potentially generate
cost savings. Clinical decision-support systems (CDSS)
have been described as "systems that can synthesize
and integrate patient-specific information, perform
complex evaluations, and present the results to clinicians
in a timely fashion" (Hunt, et al., 1998).
Systems that provide knowledge, logic or analysis for
the care process generally fall under the umbrella of
clinical decision support. CDSSs also offer help in
non-clinical areas such as administration and expense
management.
CDSSs offer the potential to improve the quality and
reduce the cost of care by influencing medical decisions
at the time and place decisions are made. By matching
patient-specific information with guidelines, they create
dynamic programs that offer real-time management advice.
CDSSs have been used in a variety of health care settings
to address clinical problems, aid physicians in diagnosis,
and manage therapeutic treatments.
Types of Systems
Clinical decision support systems (CDSSs) vary greatly
in their complexity, function, and application. In the
Journal of Healthcare Information Management
(Perreault, 1999), the four key functions of CDSSs were
outlined as follows:
- Administrative: Supporting clinical coding and documentation,
authorization of procedures, and referrals.
- Managing clinical complexity and details: Keeping
patients on research and chemotherapy protocols; tracking
orders, referrals follow-up, and preventive care.
- Cost control: Monitoring medication orders; avoiding
duplicate or unnecessary tests.
- Decision support: Supporting clinical diagnosis
and treatment plan processes; and promoting use of
best practices, condition-specific guidelines, and
population-based management.
CDSS applications can also be grouped into three types
of clinical decisions: preventive and monitoring tasks,
prescribing drugs, and diagnosis and management. Applications
in the first two categories are simpler to design and
implement, and consequently have shown the greater success
so far. The complexity of the CDSS depends on the system
design, the information that is entered and made available,
and the needs of the users. Simple CDSSs, such as drug-dosing
calculators, have no inherent logic systems; they calculate
appropriate doses of medications based upon minimal
input data. More complex CDSSs, such as diagnostic decision
support tools, require extensive patient-specific data
entry, and a detailed systems architecture with a complex
logic system.
The most successful use of CDSSs has been to improve
compliance with guidelines in many clinical areas. While
CDSSs have been show to improve drug dosing, the use
of CDSSs to improve the other aspects of prescribing
drugs (i.e., limiting interactions and adverse side
effects, proper drug selection) have been curtailed
by the lack of access to comprehensive electronic medical
records. Due to the limited implementation of computer-assisted
diagnostic and management tools so far, only a few evaluations
have been conducted and no definitive conclusions have
been reached regarding these form of CDSSs.
Implementation and Barriers
Payers, providers, and managers are advocating for
the dissemination and implementation of clinical decision
support systems.
- According to Modern Healthcare's 12th Annual Survey
of Information Trends, health care executives of single-hospital
and multiple-facility systems replied for the eighth
consecutive year that improving decision support for
clinicians was one of their top three Information
Technology priorities.
- In a March 2000 survey of hospital and health care
systems executives by Gartner, an information technology
research and advisory firm, nearly half of respondents
stated they intend to add a clinical decision-support
system to their health care IT infrastructure within
the next two years; nearly 60% intend to add a physician-order
entry system.
- Another recent health care IT survey conducted by
McKinsey & Co. indicates that overall hospital
spending on IT will increase 6% to 7% per year through
2004, and clinical IT spending will grow 13% to 15%
annually during the same period.
Despite this interest, diffusion of CDSS has been limited,
in large part because of the expense.
- A majority of respondents to the Modern Healthcare
survey reported that their organization's overall
IT spending remains at a modest 3.5% of their entire
operating yearly budget; most IT spending has focused
on upgrading and expanding basic business functions
such as billing and accounting for patient services.
- Decision support systems usage in ambulatory practice
settings is also extremely low, as revealed by a 2000
survey of nonhospital-based specialty and nonspecialty
groups with five or more physicians (FitzHenry et
al).
Robust computerized decision-support systems remain
quite costly and may remain out of the reach of smaller
hospitals and physician offices. Even large integrated
delivery systems have a limited IT budget and must carefully
scrutinize their computerized decision-support system
purchase. Considerations include ensuring that the system
will last at least 3-5 years, return a certain level
of investment, and be capable of integrating within
the provider's existing IS system. In addition, systems
must comply with the patient confidentiality regulations
in the Health Insurance Portability and Accountability
Act (HIPAA).
A recent article (Sim et al, 2002) recommends the following
technologies for a computerized decision-support system
to improve workflow and be methodologically rigorous:
- Computer-understandable clinical research databases
- Electronic medical records (EMRs) and other clinical
systems that use a
standardized clinical vocabulary to ensure that systems
are able to communicate
with one another
- Standardized interfaces among clinical and practice
management systems that
facilitate communication among multiple systems
- New and higher-performance technologies (e.g., speech
recognition and wireless computers) to make it easier
for physicians, clinicians, and administrators to
enter data and enable better workflow compatibility.
Unfortunately, a number of these technologies are not
yet widely available and many electronic data information
standards not yet been adopted industry-wide. One of
the most difficult barriers is the development of a
standardized EMR. HIPAA defined a set of recommendations
for an EMR but did not legislate an industry-wide standard
EMR.
Sample CDSS products
A number of vendors and organizations, ranging from
software companies to professional societies, are offering
CDSS products. Systems and sponsors include:
- American College of Physicians-American Society
of Internal Medicine (ACP-
ASIM): The ACIP-ASIM has recently developed a new
Web-based decision-support tool, The Physicians' Information
and Education Resource (PIER). PIER's authors comb
through the medical literature to provide distilled
bullet lists under six different topics: diseases,
screening and prevention, complementary/ alternative
medicine, ethical and legal issues, and procedures
and drug resources. Each topic is further subdivided
into specific modules that can be easily searched,
and relevant evidence-based treatment recommendations
are ranked on a "letter grade" basis by
PIER's authors. PIER is currently in a prototype version,
and access is restricted to ACP-ASIM members.
http://pier.acponline.org/index.html?jhp
- Institute for Medical Knowledge Implementation (IMKI):
IMKI is a non-
profit organization that is developing and maintaining
a library of medical knowledge applications for use
in clinical information systems. IMKI's Medical Knowledge
Content Library™ will use well-established and
defined data structures to enable vendors or organizations
to incorporate Library content in its operational
CDSSs. Any interested organization or individual may
contribute, use the Library, and assist in its maintenance.
Recently, the Robert Wood Johnson Foundation awarded
a grant to the IMKI to develop a process for writing,
evaluating, and disseminating Clinical Decision Support
rules. http://www.imki.org/
- MedicaLogic: MedicaLogic's Logicianâ is an
electronic medical record system that documents clinical
information, supports administrative functions, and
also serves as a decision support tool. Besides medication
checking and formulary compliance, Logician aids physicians
by reminding them when patients are due for a certain
procedure or test, providing patients with educational
handouts, and having a component that guides clinicians
through a patient encounter in accordance with the
Center for Medicare and Medicaid Services guidelines.
http://www.medicalogic.com/products/logician/
- Zynx Health Inc.: Zynx Health's Clinical Pathway
Constructor™ is a Web-based,
systematic clinical content tool with a compendium
of evidence-based guidelines focusing on 23 different
inpatient conditions. For each condition, all peer-reviewed
medical literature for the past ten years was summarized
and graded using specific criteria. Within each condition,
there are numerous topic headings with guideline recommendations;
if the evidence does not indicate a definite superior
intervention, the guideline statement defers to the
users' clinical judgement. Access is available to
institutions through a subscription fee. http://www.zynx.com/Products/products-cpc.htm
Summary
There is growing evidence that computerized decision-support
systems can enhance quality, reduce errors, particularly
with drug dosing issues, and achieve cost-savings. As
computerized decision-support systems are more widely
adopted by health care providers, they will serve as
a tool that supplements clinicians' decision-making
processes. Still, clinicians will serve as the final
authority on care decisions and certain limitations
will exist with decision-support systems, especially
with diagnosis-related tools.
|
