The Model-based Approach to Medical Decision Support Why and How - PowerPoint PPT Presentation

The Model-based Approach to Medical Decision Support Why and How ... Peter Lucas peterl@cs.ru.nl Model-based System Development Section Institute for Computing and Information Sciences Radboud University Nijmegen CIHC 21-9-2010 – p. 1/47

Medicine ∼ Engineering? Bridge building Medicine Engineering principles Clinical principles Consequence of failure Consequence of failure CIHC 21-9-2010 – p. 2/47

Is Support Needed? Obstetric clinics at Vienna General Hospital mid 1800s Doctors (1st clinic) versus midwives (2nd clinic): Ignaz Semmelweis (1818–1865): infection after child birth can be drastically cut by hand washing CIHC 21-9-2010 – p. 3/47

Today · · · Hand hygiene in the intensive care unit: prospective observations of clinical practice Pol Arch Med Wewn, 2008; 118 (10): 543-547 Ismael A. Qushmaq, Diane Heels-Ansdell, Deborah J. Cook, Mark B. Loeb, Maureen O. Meade Abstract. INTRODUCTION: Adherence to hand hygiene recommendations in the intensive care unit (ICU) is variable and moderate, at best. OBJECTIVES: To measure adherence to hand hygiene recommendations among ICU clinicians in a prospective observational study in 6 multidisciplinary ICUs among 4 hospitals. . . . RESULTS: The rate of adherence to current recommendations was 20%. . . . CIHC 21-9-2010 – p. 4/47

Other Facts From a recent study (Arch Intern Med. 2010; 170(12): 1015-1021): Diagnostic errors often result in patient harm Structured review study of 7926 patient records of 21 hospitals across the Netherlands Results: diagnostic adverse events occurred in 0.4% of hospital admissions (6.4% of all adverse events) 83.3% were judged to be preventable. Human failure was the main cause (96.3%) the consequence was a higher mortality than for other adverse events (29.1% vs 7.4%) CIHC 21-9-2010 – p. 5/47

Protocols and Guidelines 2002 Centers for Disease Control and Prevention Guidelines for the prevention of intravascular catheter-related infections: Wash your hands before inserting a central venous catheter Clean the skin with chlorhexidine Use of full-barrier precautions during CVC insertion Avoid the femoral site Remove unnecessary central venous catheters ⇒ This guideline is clearly ignored CIHC 21-9-2010 – p. 6/47

Clinical Guidelines Definition: clinical (practice) guidelines: systematically developed statements to assist practitioners and patients decisions about appropriate health care in specific clinical circumstances Characteristics: Guidelines are based on scientific evidence (results from RCTs for example — evidence-based medicine) In conjunction with considerations such as safety, availability, and cost effectiveness Aim: improving health-care outcomes and reduce costs of care CIHC 21-9-2010 – p. 7/47

NICE (www.nice.org.uk) National Institute for health and Clinical Excellence CIHC 21-9-2010 – p. 8/47

Example: NICE DM2 Guideline DM2 GL: ORAL GLUCOSE CONTROL THERAPIES (2): Thiazolidinediones (glitazones) R40 If glucose concentrations are not adequately controlled (to HbA1c <7.5% or other higher level agreed with the individual), consider, after discussion with the person, adding a thiazolidinedione to: the combination of metformin and a sulfonylurea where insulin would otherwise be considered but is likely to be unacceptable or of reduced effectiveness because of: employment, social or recreational issues related to putative hypoglycaemia barriers arising from injection therapy or . . . ... CIHC 21-9-2010 – p. 9/47 a sulfonylurea if metformin is not tolerated

Which Decision Support is Best? Protocols and guidelines: Evidence based (reflect scientific evidence) Have been shown to have a positive effect on quality of care in some cases Non-interactive, often very lengthy textual documents (with fixed structure) Are hard to personalise Decision-support systems: Interactive, and allow exploring clinical problems of individual patients Offer one or more problem solving modes Can incorporate ideas from guidelines CIHC 21-9-2010 – p. 10/47

Computer-based Guidelines CIHC 21-9-2010 – p. 11/47

Start: Clinical Reasoning test patient patient diagnostic therapy data data signs process selection diagnosis therapy prediction medical disease knowledge progress prognosis CIHC 21-9-2010 – p. 12/47

Its Computerisation: Not Easy Early academic AI attempts, e.g.: Diagnosis and treatment of sepsis using rule-based system: MYCIN (1974–1979) Diagnosis of disorders in internal medicine (e.g., gastrointestinal, rheumatoid, endocrine disorders): INTERNIST-I (1975–1985) Diagnosis of glaucoma by Causal ASsociationel NETwork: CASNET (1971–1978) Commercial AI attempts: Quick Medical Reference (QMR) – based on INTERNIST-I (discontinued 2001) DXplain (1984–) – http://dxplain.org CIHC 21-9-2010 – p. 13/47

Why Failure? Focus on diagnostic systems: after entering set of findings ⇒ differential diagnosis First generation programs: immature technology, PhD projects Don’t offer the support clinicians want to have Computational infrastructure too primitive until 2000 Clinicians had little computer literacy until ± 1995 No integration with electronic patient record systems (still not generally available) Bad computer inferface CIHC 21-9-2010 – p. 14/47

Knowledge Formalisation Ingredients (knowledge representation): Uncertainty (probability theory) and decision theory Intuitive qualitative notions, such as: causal relations associations actions outcomes justification · · · ⇒ Probabilistic graphical models, such as Bayesian networks, and influence diagrams offer a good start CIHC 21-9-2010 – p. 15/47

The Model-based Approach Management (diagnosis, treatment, prognosis) can be formalised: meta-model, e.g., What is a diagnosis? What is a prognosis, etc. Medical knowledge is also modelled (object model) Deployment of: probabilistic graphical models, in particular Bayesian networks logical methods CIHC 21-9-2010 – p. 16/47

Problem Solving – Probabilistic A diagnosis d ∗ is maximum a posteriori assignment d ∗ = argmax d P ( d | e ) , where e observed evidence (symptoms, test results) Prognostic reasoning; determine outcome o : P ( o | e, a ) , with a a sequence of treatment actions Optimal treatment: a ∗ ∈ argmax a � o P ( o | e, a ) u ( a, o, e ) Pretreatment Treatments observations Pretreatment Treatments observations Outcome Outcome U CIHC 21-9-2010 – p. 17/47

Have you got Mexican Flu? P ( m, c, s ) = 0 . 009215 P ( ¯ m, ¯ c, ¯ s ) = 0 . 97912 P ( m, ¯ c, s ) = 0 . 000485 M : mexican flu; C : chills; S : sore throat P ( m, c, ¯ s ) = 0 . 000285 s ) = 1 . 5 · 10 − 5 Probability of mexican P ( m, ¯ c, ¯ flu and sore throat? m, c, s ) = 9 . 9 · 10 − 6 P ( ¯ P ( ¯ m, ¯ c, s ) = 0 . 0098901 Probability of mexican P ( ¯ m, c, ¯ s ) = 0 . 0009801 flu given sore throat? CIHC 21-9-2010 – p. 18/47

Have you got Mexican Flu? P ( m, c, s ) = 0 . 009215 P ( ¯ m, ¯ c, ¯ s ) = 0 . 97912 P ( m, ¯ c, s ) = 0 . 000485 M : mexican flu; C : chills; S : sore throat P ( m, c, ¯ s ) = 0 . 000285 s ) = 1 . 5 · 10 − 5 Probability of mexican P ( m, ¯ c, ¯ flu and sore throat? m, c, s ) = 9 . 9 · 10 − 6 P ( ¯ 0.0097 P ( ¯ m, ¯ c, s ) = 0 . 0098901 Probability of mexican P ( ¯ m, c, ¯ s ) = 0 . 0009801 flu given sore throat? 0.495 CIHC 21-9-2010 – p. 18/47

Probabilistic Reasoning Joint probability distribution P ( X 1 , X 2 , . . . , X n ) marginalisation: � P ( Y ) = P ( Y, Z ) , with X = Y ∪ Z Z conditional probabilities: P ( Y | Z ) = P ( Y, Z ) P ( Z ) Bayes’ theorem: P ( Y | Z ) = P ( Z | Y ) P ( Y ) P ( Z ) CIHC 21-9-2010 – p. 19/47

Probabilistic Reasoning (cont) Examples: P ( m, s )= P ( m, c, s )+ P ( m, ¯ c, s )=0 . 009215+0 . 000485=0 . 0097 P ( m | s )= P ( m, s ) /P ( s )=0 . 0097 / 0 . 0196=0 . 495 Note that: Mainly interested in conditional probability distributions: P ( Z | E ) = P E ( Z ) for (possibly empty) evidence E (instantiated variables) Tendency to focus on conditional probability distributions of single variables Many efficient reasoning algorithms exist CIHC 21-9-2010 – p. 20/47

Bayesian Networks P ( CH , FL , RS , DY , FE , TEMP ) P ( FE = y | FL = y, RS = y ) = 0 . 95 P ( FE = y | FL = n, RS = y ) = 0 . 80 P ( FE = y | FL = y, RS = n ) = 0 . 88 P ( FE = y | FL = n, RS = n ) = 0 . 001 P ( FL = y ) = 0 . 1 flu (FL) fever (FE) TEMP ( y es/ n o) ( y es/ n o) ( ≤ 37 . 5 / > 37 . 5 ) P ( TEMP ≤ 37 . 5 | FE = y ) = 0 . 1 P ( RS = y | CH = y ) = 0 . 3 P ( TEMP ≤ 37 . 5 | FE = n ) = 0 . 99 P ( RS = y | CH = n ) = 0 . 01 SARS (RS) P ( DY = y | RS = y ) = 0 . 9 ( y es/ n o) P ( DY = y | RS = n ) = 0 . 05 dyspnoea (DY) VisitToChina (CH) P ( CH = y ) = 0 . 1 ( y es/ n o) ( y es/ n o) CIHC 21-9-2010 – p. 21/47

Evidence Propagation Nothing known: FLU NO YES FEVER TEMP no <=37.5 yes >37.5 SARS DYSPNOEA no no yes yes VisitToChina no yes Temperature > 37 . 5 ◦ C: FLU NO YES FEVER TEMP no <=37.5 yes >37.5 SARS DYSPNOEA no no yes yes VisitToChina no yes CIHC 21-9-2010 – p. 22/47

Project I: Pneumonia in ICU ICU at Utrecht MC Diagnosis and antimicrobial treatment of patients with ventilator-associated pneumonia (VAP) About 15-20% of ICU patients develop VAP Mortality rate: up to 40% Up to 50% of antibiotics in ICUs are prescribed for airway infections CIHC 21-9-2010 – p. 23/47