Clinical decision support system: Difference between revisions

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===Bayesian Belief Network===
===Bayesian Belief Network===
A Bayesian Belief Network (BBN) is a mathematical model for using AI (probabilistic) networks for predictive purposes. It is used in the CDSS module as an AI engine to help make probabilistic predictions based upon the observations made by the clinician. These could be used for diagnostics, treatment planning, and even predicting outcomes.
A Bayesian Belief Network (BBN) is a mathematical model for using AI (probabilistic) networks for predictive purposes. It is used in the CDSS module as an AI engine to help make probabilistic predictions based upon the observations made by the clinician. These could be used for diagnostics, treatment planning, and even predicting outcomes. An example is Pathfinder for surgical pathology.<ref name="pmid1635462">{{cite journal |author=Heckerman DE, Nathwani BN |title=Toward normative expert systems: Part II. Probability-based representations for efficient knowledge acquisition and inference |journal=Methods Inf Med |volume=31 |issue=2 |pages=106–16 |year=1992 |pmid=1635462 |doi=}}</ref><ref name="pmid1635470">{{cite journal |author=Heckerman DE, Horvitz EJ, Nathwani BN |title=Toward normative expert systems: Part I. The Pathfinder project |journal=Methods Inf Med |volume=31 |issue=2 |pages=90–105 |year=1992 |pmid=1635470 |doi=}}</ref>


==Effectiveness==
==Effectiveness==

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Clinical (or Diagnostic) Decision Support Systems (CDSS) are interactive computer programs that directly assist physicians and other health professionals with decision making tasks. These fall under the class of Decision support systems within medical informatics. CDSS offer a powerful medical informatics tool that can promote evidence based medicine.

Why do we need such systems at all? If diagnosis becomes the job of the computer program, what will the doctors do? Such questions overlook the key term support - i.e., they are intended to support the clinician, rather than replace them. Computers are not error or fatigue prone like human beings!

In medical diagnosis, there is scope for ambiguity in the inputs, such as the history (patient’s description of the diseased condition), physical examinations (especially in cases of uncooperative or less intelligent patients), and laboratory tests (faulty methods or equipment). Moreover, in treatment, there are the chance of drug reactions and specific allergies, and also of patients' non-compliance of the therapy due to cost or time or adverse reactions. All these factors may not be taken into account by a busy clinician attending hundreds of patients daily.

In all these areas, computers can help the clinician to reach an accurate diagnosis faster. Another new branch of medicine pharmacogenomics is the product of breeding between information technology and biology, leading to individualized treatment.

The basic components of a CDSS include a dynamic (medical) knowledge base and an inference mechanism (usually a set of rules derived from the experts and from the data provided by evidence-based medicine). It may be based on Expert systems or artificial neural networks or both (Connectionist expert systems) - all of which can be loosely termed as Artificial intelligence &endash; AI &endash; techniques.

CDSS may be linked to Electronic health records, for decision making and can also be used for assisting the practice of Evidence-based medicine (EBM) in a more structured way. However these systems are not meant to replace physicians, but rather to empower them to make better and more rational decisions.

The role of even an apparently simple search by Google in making diagnoses has been quantified.[1]

Methods of Decision Support

Various tools applied for Artificial intelligence have been used as decision support systems and data mining.

Rule-based (expert systems)

Rule-based expert systems use predefined rules in the form of {if-else if-then} guide the decision making. Rules may also be obtained by various forms of decision trees (e.g., Iterative Dichotomizer or ID variants) or Bayesian networks. The critical difficulty in constructing rule-based expert systems for medical decision support has been in recruiting experts with domain knowledge to create tge knowledge base and train the system. Training expert systems is time-consuming, and has only produced useable results in narrowly-scoped projects. Examples of rule-based systems are:

  1. Logical or deductive: branching logic (e.g., ID3 or iterative dichotomizer 3); e.g., MYCIN, NEOMYCIN [Sotos 1990]
  2. Probabilistic: Bayesian e.g., de Dombal [1995] and
  3. Hybrid: heuristic reasoning e.g., QMR (Quick Medical Reference), DXplain, ILIAD[2], ISABEL.[3]

Artificial Neural Networks

Artificial Neural Networks ANNs (also referred to as connectionist architectures, parallel distributed processing, and neuromorphic systems), are information-processing paradigm inspired by the way the densely interconnected, massively parallel structure of the mammalian brain processes information. ANNs are collections of mathematical models that emulate some of the observed properties of biological nervous systems and draw on the analogies of adaptive biological learning. ANN can perform supervised or unsupervised machine learning, depending on the categorical information maps available at the time of rule inferencing.

Connectionist expert systems

Connectionist expert system are the "inferencing" methods of the ANNs can be backtracked and "rules generation" is possible. This might actually lead to the enhancement and enrichment of the medical knowledge base itself. This system architecture corresponds to the concept of "unsupervised" machine learning, where the algorithm looks for patterns in data without being instructed about the actual categories of information. This is also known as data mining

Rule-based CDSS

Rule-based CDSS are the ones that are mostly found in the commercially available clinical informatics applications. Alerts for allergies and possible drug interactions, prompts for drug doses corrected for weight, height, sex, age and underlying clinical condition are the ones that are most commonly touted as CDSS. Arden Syntax was designed and developed for this very purpose. However, due to some issues related to its acceptance and standardization, it has not really been able to deliver on its immense promise. However, use of Evidence-based medicine (EBM) and Outcomes Analysis (OA) coupled with Bayesian Belief Networks (BBN) can allow for a highly accurate diagnostic tool and treatment planner to be made available in the hands of the healthcare providers.

Bayesian Belief Network

A Bayesian Belief Network (BBN) is a mathematical model for using AI (probabilistic) networks for predictive purposes. It is used in the CDSS module as an AI engine to help make probabilistic predictions based upon the observations made by the clinician. These could be used for diagnostics, treatment planning, and even predicting outcomes. An example is Pathfinder for surgical pathology.[4][5]

Effectiveness

A systematic review of studies published through September, 2004 concluded "many CDSSs improve practitioner performance. To date, the effects on patient outcomes remain understudied and, when studied, inconsistent".[6]

A more recent trial found that a clinical decision support system could improve antimicrobial prescribing.[7]

In internal medicine, a study of four diagnostic support systems diagnosed a collection of tests cases with accuracy ranging from 0.52 to 0.71.[8] Another study of ISABEL found it included the correct diagnosis in its list of 30 proposed diagnoses for a collection of tests cases in 96% of the cases.[3] A study of QMR found that it could correctly list the final diagnosis among the first five diagnoses it suggested for 36% to 40% of 1144 consecutive inpatients.[9]

In pediatrics, a systematic review by the Cochrane Collaboration concluded "there are very limited data from randomised trials on which to assess the effects of clinical decision support systems in neonatal care."[10]

References

  1. ↑ Tang H, Ng JH (2006). "Googling for a diagnosis--use of Google as a diagnostic aid: internet based study". BMJ 333 (7579): 1143–5. DOI:10.1136/bmj.39003.640567.AE. PMID 17098763. Research Blogging.
  2. ↑ Friedman CP, Elstein AS, Wolf FM, et al (1999). "Enhancement of clinicians' diagnostic reasoning by computer-based consultation: a multisite study of 2 systems". JAMA 282 (19): 1851–6. PMID 10573277. [e]
  3. ↑ 3.0 3.1 Graber ML, Mathew A (2008). "Performance of a web-based clinical diagnosis support system for internists". J Gen Intern Med 23 Suppl 1: 37–40. DOI:10.1007/s11606-007-0271-8. PMID 18095042. Research Blogging.
  4. ↑ Heckerman DE, Nathwani BN (1992). "Toward normative expert systems: Part II. Probability-based representations for efficient knowledge acquisition and inference". Methods Inf Med 31 (2): 106–16. PMID 1635462. [e]
  5. ↑ Heckerman DE, Horvitz EJ, Nathwani BN (1992). "Toward normative expert systems: Part I. The Pathfinder project". Methods Inf Med 31 (2): 90–105. PMID 1635470. [e]
  6. ↑ Garg AX, Adhikari NK, McDonald H, et al (2005). "Effects of computerized clinical decision support systems on practitioner performance and patient outcomes: a systematic review". JAMA 293 (10): 1223–38. DOI:10.1001/jama.293.10.1223. PMID 15755945. Research Blogging.
  7. ↑ McGregor JC, Weekes E, Forrest GN, et al (2006). "Impact of a computerized clinical decision support system on reducing inappropriate antimicrobial use: a randomized controlled trial". J Am Med Inform Assoc 13 (4): 378–84. DOI:10.1197/jamia.M2049. PMID 16622162. Research Blogging.
  8. ↑ Berner ES, Webster GD, Shugerman AA, et al (1994). "Performance of four computer-based diagnostic systems". N. Engl. J. Med. 330 (25): 1792–6. PMID 8190157. [e]
  9. ↑ Lemaire JB, Schaefer JP, Martin LA, Faris P, Ainslie MD, Hull RD (1999). "Effectiveness of the Quick Medical Reference as a diagnostic tool". CMAJ 161 (6): 725–8. PMID 10513280. [e]
  10. ↑ Tan K, Dear PR, Newell SJ (2005). "Clinical decision support systems for neonatal care". Cochrane Database Syst Rev (2): CD004211. DOI:10.1002/14651858.CD004211.pub2. PMID 15846701. Research Blogging.

Further reading

  1. Bergman LG, Fors UG, Computer-aided DSM-IV-diagnostics – acceptance, use and perceived usefulness in relation to users’ learning styles. BMC Med Inform Decis Mak. 2005; 5:1
  2. Coiera E. (2003) Chapter 25 - Clinical Decision Support Systems. In Guide to health informatics. London: Arnold. ISBN 0-340-76425-2.
  3. de Dombal FT, Computer-aided diagnosis and medical decision support are not synonymous. Methods Inf Med. 1995; 34: 369-370
  4. Kavitha S, Sarbadhikari SN, Rao Ananth N, Implementation of Decision Tree Classifier Using Classification Algorithm for Some Inborn Errors of Metabolism, Proc. Global Convention and Exposition on Telemedicine and eHealth, New Delhi 17-22 August, 2006.
  5. Kunnamo I, et al, 2005, National Decision support database based on computer-readable guidelines and using structured data from electronic patient records http://www.terveysportti.fi/pls/kotisivut/docs/f1917377807/gin_poster_decision_support_v2.pdf (Accessed July 2006)
  6. Mendelson D, Carino TV, Evidence-Based Medicine In The United States—De Rigueur Or Dream Deferred? Health Affairs, 2005, 24: 133 – 136. DOI 10.1377/hlthaff.24.1.133
  7. Pradhan M, The Crystal Ball - The Future of Informatics and Decision Making, http://www.informatics.adelaide.edu.au/topics/DS/MP-CrystalBallTalk.html 2001 (Accessed July 2006)
  8. Sarbadhikari SN, A CDSS for diagnosing amenorrhea http://www.geocities.com/drsupten 2006
  9. Sarbadhikari, SN Automated diagnostic systems. Indian Journal of Medical Informatics, 2004b, 1: 25-28. (Accessible at http://openmed.nic.in/218/])
  10. Smith S, The Classification Algorithm http://www.cs.mdx.ac.uk/staffpages/serengul/The.Classification.algorithm.htm (Accessed July 2006).
  11. Sotos G, MYCIN and NEOMYCIN: two approaches to generating explanations in rule-based expert systems. Aviat Space Environ Med. 1990; 61: 950-954.
  12. Trowbridge et al. (2001) Chapter 53: Clinical Decision Support Systems. In Making Health Care Safer: A Critical Analysis of Patient Safety Practices. Evidence Report/Technology Assessment: Number 43. AHRQ Publication No. 01-E058, July 2001. Agency for Healthcare Research and Quality, Rockville, MD. http://www.ahrq.gov/clinic/ptsafety/

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