Clinical Decision Support Systems HIMA 4160 Fall 2009

Outline Definitions Definitions Methodologies Methodologies Applications Applications Probabilistic reasoning Probabilistic reasoning Decision tree Decision tree 2

CDSS Providing clinicians or patients with clinical knowledge and patient- related information, intelligently filtered or presented at appropriate times, to enhance patient care Providing clinicians or patients with clinical knowledge and patient- related information, intelligently filtered or presented at appropriate times, to enhance patient care NOT just physicians …NOT just physicians … Not just rules and alerts …Not just rules and alerts … (NOT just computer-based …)(NOT just computer-based …) 3

Categories Generating alerts and reminders Generating alerts and reminders Diagnostic assistance Diagnostic assistance Therapy critiquing and planning Therapy critiquing and planning Image recognition and interpretation Image recognition and interpretation And many others … And many others … 4

Need for CDSS Limited resources - increased demand Limited resources - increased demand Need for systems that can improve health care processes and their outcomes in this scenario Need for systems that can improve health care processes and their outcomes in this scenario The marriage of medical and technological advances - to produce a child called Frugal Efficiency? The marriage of medical and technological advances - to produce a child called Frugal Efficiency? 5

Generalized Structure Knowledge Base Inference Engine 6

Knowledge base, inference engine, and interface 7

Application Areas 8

Workflow Opportunities 9

Possible Disadvantages of CDSS Changing relation between patient and the physician Changing relation between patient and the physician Limiting professionals’ possibilities for independent problem solving Limiting professionals’ possibilities for independent problem solving Legal implications - with whom does the onus of responsibility lie? Legal implications - with whom does the onus of responsibility lie? Information fatigue Information fatigue 10

Issues for success or failure Evaluation of User Needs Evaluation of User Needs Top management support Top management support Commitment of expert Commitment of expert Integration Issues Integration Issues Human Computer Interface Human Computer Interface Incorporation of domain knowledge Incorporation of domain knowledge Consideration of social and organizational context of the CDS Consideration of social and organizational context of the CDS 11

Evaluation of Clinical Decision Support Systems Criteria for success of CDSS Criteria for success of CDSS Aspects for consideration during evaluation Aspects for consideration during evaluation 12

Criteria for a clinically useful DSS Knowledge based on best evidence Knowledge based on best evidence Knowledge fully covers problem Knowledge fully covers problem Knowledge can be updated Knowledge can be updated Data actively used drawn from existing sources Data actively used drawn from existing sources Performance validated rigorously Performance validated rigorously System improves clinical practice System improves clinical practice Clinician is in control Clinician is in control The system is easy to use The system is easy to use The decisions made are transparent The decisions made are transparent 13

Aspects for Evaluation of a CDSS The clinician need that the CDSS is intended to address The clinician need that the CDSS is intended to address The process used to develop the system The process used to develop the system The system’s intrinsic structure The system’s intrinsic structure Evidence of accuracy, generality and clinical effectiveness Evidence of accuracy, generality and clinical effectiveness The impact of the resource on patients and other aspects of the health care environment The impact of the resource on patients and other aspects of the health care environment 14

MethodologyMajor UseKey developments Information RetrievalFinding information, answering questions Taxonomies, ontologies, text- based methods, automatic invocation Evaluation of logical conditions Alerts, reminders, constraints, inference system Decision tables, event-condition- action-rules, production rules Probabilistic and data driven classification or prediction Diagnosis, technology assessment, treatment selection, classification and prediction, prognosis estimation, evidence-based medicine Bayes theorem, decision theory, ROC analysis, data mining, logistic regression, artificial neural networks, belief networks, meta-analysis. Heuristic modeling and export systems Diagnostic and therapeutic reasoning, capturing nuances of human expertise Rule-based systems, frame- based reasoning Calculations, algorithms and multistep processes Execution of computational processes, flow-chart-based guideline and consultations, interactive dialogue control, biomedical image and signal processing Process flow and workflow modeling, guideline formalisms and modeling languages Associative groupings of elements Structured data entry, structured reports, order sets, other specialized presentations and data views Report generators and document construction tools, document architectures, templates, markup languages, ontology tools, ontology languages 15

Computerized Physician/Provider Order Entry

The Two Sides of Errors 44,000+ hospital deaths due to medical error 50 adverse events/1000 outpatient pt-years (Gurwitz 2003) Patients receive 55% of recommended care (McGlynn, 2003) 17

Our Solution to Safety BMJ 2000;320:768–70 physician nurse pharmacis t Bedside team 18

What is CPOE? Computer application which replaces traditional paper order sheets Computer application which replaces traditional paper order sheets Care / computerized provider is a key part of the name Care / computerized provider is a key part of the name 19

Motivators for Adoption of CPOE The Six Aims of Health Care Safe Safe Timely Timely Effective Effective Efficient Efficient Equitable Equitable Patient-centered Patient-centered IOM,

Key Advantages to CPOE Data aggregated for clinical use Data aggregated for clinical use Clinician can interact with medical record away from the bedside Clinician can interact with medical record away from the bedside Immediate routing of orders and requisitions to ancillary departments Immediate routing of orders and requisitions to ancillary departments Smart prompts and checks can enhance safety and quality of care Smart prompts and checks can enhance safety and quality of care 21

Important OE work 1.El Camino Hospital, 1971 First clinician order entry system, (TDS) 2. Warner, Pryor, Clayton, Gardner, et al HELP System, LDS Hospital, (3M) 3. McDonald, Tierney et al, Regenstrief order entry / reminders / (~SMS) 4. Glaser, Teich, Bates, Kuperman et al, Brigham & Women’s order entry (~Eclipsys) 22

Commercial Order Entry (80s) 23

CPOE Integration Decision support Pharmacy External Knowledge Sources Terminology Lab System ADT Data Server Or Interface CPOE System EHR (documents)+ Internal Knowledge Sources 24

Copyright (C) 2003 Vanderbilt University Medical Center WizOrder Main Screen Layout: Simple, fixed format: functionally oriented, designed with users Physician enters order for antibiotic, Gentamicin, by partially typing its name 1) Active orders 2) Common useful orders based on patient location 3) What to do next in WizOrder 4) Buttons for commonly used features 25

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TimeSavings:Newmethodforsummarizing“active” orders & currentinformation “What you need to know about patient” printed on one piece of paper Active orders Recent Labs Copyright (C) 2003 Vanderbilt University Medical Center 27

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Issues People People Process Process 30

Bayesian Network 31

Review of Probability P(A) = p, P(not A) = 1 – p P(A) = p, P(not A) = 1 – p P(A, B) = P (A | B)* P(B) P(A, B) = P (A | B)* P(B) P(A, B) = P (A | B) * P(B) = P(A) * P(B) P(A, B) = P (A | B) * P(B) = P(A) * P(B) P(A) = P(A) = 32

Probability Frequentist Frequentist Bayesian Bayesian 33

Bayes’ Theorem Posterior Prior Probability of Evidence Likelihood Probability of an hypothesis, h, can be updated when evidence, e, has been obtained. 34

A Simple Example Consider two related variables: 1. Disease (D) with values y or n 2. Test (T) with values +ve or –ve And suppose we have the following probabilities: P(D = y) = P(T = +ve | D = y) = 0.8 P(T = +ve | D = n) = 0.01 These probabilities are sufficient to define a joint probability distribution. Suppose an athlete tests positive. What is the probability that he has the disease? 35

Sensitivity, Specificity, Prevalence and Probabilities Consider two related variables: 1. Disease (D) with values y or n 2. Test (T) with values +ve or –ve And suppose we have the following probabilities: P(D = y) = (Prevalence) P(T = +ve | D = y) = 0.8(Sensitivity) P(T = +ve | D = n) = 0.01(1-specificity) These probabilities are sufficient to define a joint probability distribution. Suppose an athlete tests positive. What is the probability that he has taken the drug? 36

Bayesian Network Demo

Decision Tree