This document discusses clinical decision support systems (CDSS). It begins by defining CDSS as systems that apply medical knowledge to patient data to generate recommendations. It then provides an example of how CDSS could help prevent drug interactions. The document outlines different types of CDSS, including knowledge-based and non-knowledge-based systems using machine learning. It also discusses the history and examples of CDSS, highlighting their role in improving healthcare quality and reducing errors.

1. Bhavithapulaparthi
Dept of
pharmacoinformatics
NIPER,S.A.S.Nagar.
CLINICAL DECISION-SUPPORT
SYSTEMS
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2. Clinical decision-support systems (CDSS) applies best-known
medical knowledge to patient data for the purpose of generating
case-specific decision-support advice
It is nothing but using health information technology (IT), including
CDS, to improve quality and cost of care
It provides clinicians, staff, patients, and other individuals with
knowledge and person-specific information, intelligently filtered and
presented at appropriate times, to enhance health and health care.
INTRODUCTION
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3. Suppose if doctor is out-of-town, an elderly asthma patient who has
developed severe knee pain sees by another physician
Unfortunately, within 2 months, the patient wound up in the emergency
room with a bleeding ulcer caused by interaction of the pain medicine
with the patient’s asthma medicine
This kind of problems occur frequently, as documented in reports from
the many hospitals.
PROBLEM WITH LACK OF
INFORMATION
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4. • Clinician enters clinical data of patient
• Matched with system’s knowledge base
• Software generates patient specific recommendations
• Provides evidence based decision support for the
clinician
HOW CDSS HELPS?
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5. • several types of CDS tools could have
prevented this patient’s drug interaction.
• a pop-up alert to the potential drug interaction when the
doctor prescribed the new medicine
• clinical prediction rules to assess the risks of the pain
medication for this patient
• reminders for timely follow up
• clinical guidelines for treatment
• Improves prescribing practices and reduces medication
errors
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6. • Started in the 1960s, the initial objective of introducing
computers into medical practice
• stream of research eventually developed into a dedicated
discipline, Artificial Intelligence in Medicine (AIM)
• CDSS is being increasingly mandated by regulatory bodies and
advocacy groups such as JACHO, the Leapfrog Group for Patient
Safety, and the Certification Commission for Healthcare
Information Technology
• The JACHO is a Management of Information section clearly
specifying the data requirements for enabling clinical decision
support
• Leapfrog, a voluntary group of large employers that ranks
hospitals based on their quality and safety performance and
whether a hospital is equipped with decision-support systems
capable of warning
HISTORY OF CLINICAL DECISION
SUPPORT
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7. CCHIT, a recognized certification body (RCB), incorporates the
clinical decision-support provision as a key component of its clinical
information system certification process
The essential component of CDSS is inference engine, which applies
the knowledge stored in a knowledge base to patient data to derive
case-specific recommendations.
CONTD……
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8. There are two main types of CDSS:
1. Knowledge-Based CDSS
2. NonKnowledge-Based CDSS
a) Artificial neural networks
b) Genetic algorithms
TYPES OF CLINICAL DECISION
SUPPORT
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9. • Most CDSS consist of three parts, the knowledge base, inference
engine, and mechanism to communicate.
• The knowledge base contains the rules and associations of
compiled data which most often take the form of IF-THEN
rules. If this was a system for determining drug interactions, then
a rule might be ,for example
• IF drug X is taken AND drug Y is taken THEN alert user. In such a way
using another interface, an advanced user could edit the knowledge base to
keep it up to date with new drugs.
• The inference engine combines the rules from the knowledge
base with the patient’s data.
• The communication mechanism will allow the system to show the
results to the user as well as have input into the system
Knowledge-Based CDSS
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10. • CDSS’s that do not use a knowledge base, but use a
form of artificial intelligence called machine learning,
which allow computers to learn from past experiences
and/or find patterns in clinical data.
• Two types of non-knowledge-based systems are
• artificial neural networks and
• genetic algorithms.
NON-KNOWLEDGE-BASED CDSS
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11. • Artificial neural networks use nodes and weighted
connections between them to analyze the patterns
found in the patient data to derive the associations
between the symptoms and a diagnosis.
• This eliminates the need for writing rules and for
expert input.
• However since the system cannot explain the reason it
uses the data the way it does, most clinicians don’t use
them for reliability and accountability reasons
ARTIFICIAL NEURAL NETWORKS
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13. • Genetic Algorithms are based on simplified evolutionary
processes using directed selection to achieve optimal CDSS
results. The selection algorithms evaluate components of random
sets of solutions to a problem.
• The solutions that come out on top are then recombined and
mutated and run through the process again. This happens over
and over till the proper solution is discovered.
• They are the same as neural networks in that they derive their
knowledge from patient data.
• Non-knowledge-based networks often focus on a narrow list of
symptoms like ones for a single disease as opposed to the
knowledge based approach which cover many different diseases to
diagnosis
GENETIC ALGORITHMS
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14. • The use of computerized clinical information systems to support
hospital operation as well as clinical activities started in the early
1990s
• Besides the significant technological insight, the availability of
enterprise-level database management systems (DBMS) and health
data standards such as ICD and HL7, new legislation and
advocacy agencies also played a key role
• In the United States, ICD-9-CM is widely used to codify
diagnostic data for administrative (such as billing) purposes.
• HL7 (Health Level Seven ) is an all-volunteer, not-for-profit
organization. It oversees the development of international health
data exchange standards.
NEW GENERATION OF
GUIDELINE-BASED CDSS
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15. In 2006, the American Medical Informatics Association (AMIA)
published Roadmap for National Action on Clinical Decision
Support, which identifies three key objectives for CDSS research and
practice:
1) Best Knowledge Available When Needed, clinical decision support should be
based on up-to-date medical knowledge and should occur as part of clinician
workflow and at the time and location of decision-making
2) High Adoption and Effective Use,
3) Continuous Improvement of Knowledge and CDS Methods
CONTD….
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16. Zynx Health – the most prominent organization in the CDSS
marketplace, whose CDSS is linked to a statistically significant
percentage of hospital discharges nationwide
MYCIN, one of the first expert systems to be developed in the
1970s, it does ethiological diagnoses of bacterial diseases
CADUCEUS, a medical expert system that could diagnose 1000
diseases
Internist-I, is a rule-based expert system for solving complex
diagnostic problems in general internal medicine
Dxplain, developed at Massachusetts General Hospital, uses a
modified form of Bayesian logic to produce a ranked list of
diagnoses that might explain or be associated with the clinical
manifestations.
EXAMPLES OF CDSS
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17. • The clinical reminder system (CRS) was jointly developed by the
H. John Heinz III School of Public Policy and Management at
Carnegie Mellon University and the Western Pennsylvania
Hospital (WPH).
• Since February 2002, CRS has been deployed in two WPH
medical practices treating real patients.
• CRS uses evidence-based clinical guidelines to support the
management of four chronic health conditions (asthma, diabetes,
hypertension, and hyperlipidemia), and five preventive care
procedures (breast cancer, cervical cancer, influenza, pneumonia,
and steroid-induced osteoporosis)
CASE STUDY: THE CLINICAL
REMINDER SYSTEM
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18. • Visual representations, with embedded medical decision-making
logic are stored in XML files that theoretically can be shared with
other CDSS systems if they implement the same underlying
guideline representation model
• In order to generate case-specific reminders, CRS stores and
manages patient information, such as patient descriptors,
symptoms, and orders
• Standard medical vocabularies are used whenever possible. For
example, ICD-9-CM is for encoding diagnoses, CPT4 for
procedural treatments, and the National Drug Code (NDC) for
medication prescriptions
CONTD….
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