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Bhavithapulaparthi
Dept of
pharmacoinformatics
NIPER,S.A.S.Nagar.
CLINICAL DECISION-SUPPORT
SYSTEMS
1
Clinical decision-support systems (CDSS) applies best-known
medical knowledge to patient data for the purpose of generatin...
Suppose if doctor is out-of-town, an elderly asthma patient who has
developed severe knee pain sees by another physician
U...
• Clinician enters clinical data of patient
• Matched with system’s knowledge base
• Software generates patient specific r...
• several types of CDS tools could have
prevented this patient’s drug interaction.
• a pop-up alert to the potential drug ...
• Started in the 1960s, the initial objective of introducing
computers into medical practice
• stream of research eventual...
CCHIT, a recognized certification body (RCB), incorporates the
clinical decision-support provision as a key component of i...
There are two main types of CDSS:
1. Knowledge-Based CDSS
2. NonKnowledge-Based CDSS
a) Artificial neural networks
b) Gene...
• Most CDSS consist of three parts, the knowledge base, inference
engine, and mechanism to communicate.
• The knowledge ba...
• CDSS’s that do not use a knowledge base, but use a
form of artificial intelligence called machine learning,
which allow ...
• Artificial neural networks use nodes and weighted
connections between them to analyze the patterns
found in the patient ...
12
• Genetic Algorithms are based on simplified evolutionary
processes using directed selection to achieve optimal CDSS
resul...
• The use of computerized clinical information systems to support
hospital operation as well as clinical activities starte...
In 2006, the American Medical Informatics Association (AMIA)
published Roadmap for National Action on Clinical Decision
Su...
Zynx Health – the most prominent organization in the CDSS
marketplace, whose CDSS is linked to a statistically significant...
• The clinical reminder system (CRS) was jointly developed by the
H. John Heinz III School of Public Policy and Management...
• Visual representations, with embedded medical decision-making
logic are stored in XML files that theoretically can be sh...
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Clinical decision support systems

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INFORMATION TECHNOLOGY ON HEALTH AND CLINICAL CARE

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Clinical decision support systems

  1. 1. Bhavithapulaparthi Dept of pharmacoinformatics NIPER,S.A.S.Nagar. CLINICAL DECISION-SUPPORT SYSTEMS 1
  2. 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 2
  3. 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 3
  4. 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? 4
  5. 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 5
  6. 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 6
  7. 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…… 7
  8. 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 8
  9. 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 9
  10. 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 10
  11. 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 11
  12. 12. 12
  13. 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 13
  14. 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 14
  15. 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…. 15
  16. 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 16
  17. 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 17
  18. 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…. 18

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