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1 Clinical Decision Support Lecture Brief History and State of the Art of Clinical Decision Support and relation to Terminology.

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Presentation on theme: "1 Clinical Decision Support Lecture Brief History and State of the Art of Clinical Decision Support and relation to Terminology."— Presentation transcript:

1 1 Clinical Decision Support Lecture Brief History and State of the Art of Clinical Decision Support and relation to Terminology and Electronic Healthcare Records (EHRs) Available at

2 2 The Hype of the Time Guidelines Evidence Based Medicine Clinical Errors (reducing) –Improving prescribing practice –Reducing adverse drug reactions Protocols Knowledge Management...

3 3 Clinical Judgement and Clinical Errors To Err is Human Supporting a Humanly Impossible Task Task.pdf Task.pdf Johnson Articles - see resources (NB some links may be broken because of University merger) OpenClinical Web site OpenEHR web site

4 4 Computer Aided Decision Support Works (sometimes) Evidence of effectiveness growing –25 years since Clem McDonald’s Protocol-based computer reminders, the quality of care and the non-perfectability of man Use still limited Meta studies and reviews a decade old Elson R E and Connelly D P (1995). Computerized patient records in primary care: Their role in mediating guideline-driven physician behaviour change. Archives of Family Medicine 4: Grimshaw J and Russell I (1993). Effect of clinical guidelines on medical practice: a systematic review of rigorous evaluations. Lancet 342: Johnston M, Langton K, Haynes R and Mathieu A (1994). Effects of computer-based clinical decision support systems on clinical performance and patient outcome. A critical appraisal of research. Archives of Internal Medicine 120:

5 5 Important Recent Study Cristina Tural, Lidia Ruiz, Christopher Holtzer, Jonathan Schapiro, Pompeyo Viciana, Juan Gonzàlez, Pere Domingo,Charles Bouche, C. Rey- Jol. BonaventuraClotet and the Havana Study Group (2002) Clinical utility of HIV-1 genotyping and expert advice: Havana trial, AIDS 16(2):

6 6 Types of Decision Support: Information Tasks Informative –Guidelines e.g. eBNF, BMJ Clinical Evidence,... –Literature search - DxPlain Information structuring –intelligent records (EPRs) PEN&PAD, Medcin vocabulary,... Triggers and warnings –MLMs, McDonald’s original work, HELP,... Critiquing - Perry Miller Advising

7 7 Types of Decision Support: Clinical Tasks Management Protocols (often effective, Johnston et. al 1994) –Prescribing –Protocol based care Oncocin, T-Helper, etc. –Referral Diagnosis (rarely effective, Johnston et. Al 1994) Mycin Internist I Knowledge Couplers

8 8 Reasons for success and failure(1) Understanding of problem –Meeting real and recognised needs Forsythe D E (1992). Using ethnography to build a working system: rethinking basic design assumptions. Sixteenth Annual Symposium on Computer Applications in Medical Care (SCAMC-92), Baltimore, MD, Baltimore, MD: Meeting them effectively –“The user is always right… but the user is usually wrong” –The technology is still crude at best Implementing it successfully

9 9 Reasons for success and failure(2) Most projects fail at implementation! The technology only works if people want it and use it –Requires emphasis on participation, ownership, training, respect for practicalities ‘Implementation’ begins with design Evaluation begins with design –Formative evaluation essential See Shortliffe Shortliffe: The Adolescence of AI in Medicine: Will the Field Come of Age in the 1990's? Artificial Intelligence in Medicine, 5:93-106, html html

10 10 Potted History (1) Bayesian stream –1968 Ledley and Lusted: Diagnosis using ‘Idiot Bayes’ discriminant Followed by Pauker Decision Support using utility theory – de Dombal: ‘Idiot Bayes’ abdominal pain and other surgical diagnostic problems Meanwhile RCP Computer Workshop refined discriminants and then stimulated Spiegelhalter to come up with practical algorithms for belief nets in early 1990s –1980s Society for Medical Decision Making formed and statistical work largely separated from rule based work

11 11 “Idiot Bayes” A simple statistical means to use databases to determine weights. –Collect a sample of patients with each disease, e.g. Acute Abdominal Pain patients 100 each of Appendicitis, Cholecystitis, Pancreatitis, Perforated Ulcer, Obstruction, GI Cancer, Tubal pregnancy (in women only) –Add a catch-all for everything else “Non specific Abdominal pain” –Assume that all symptoms are caused independently by each disease –e.g. that the mechanisms for rebound tenderness and nausea are different. –Derive a table of probabilities to be combined using the “Idiot Bayes’ formula –Proved much more robust than less “idiotic” methods

12 12 Potted History (2) Rule based stream – Shortliffe Mycin: First rule based system –1970s US AIM Workshop produced “Big 4” Mycin/Oncocin/Puff - Backwards chaining ‘shells’ Interist I - NEJM CPCs from a large network –Became QMR as a general reference Casnet - Multilayer causal reasoning (glaucoma) Abel - Complex causal networks (acid-base metabolism) –1990s Protocol based reasoning Protégé/Eon successors to Mycin/Oncocin at Stanford –Musen MA. Domain ontologies in software engineering use of Protégé with the EON architecture. SMI Technical Report Methods of Information in Medicine 37: , 1998.Musen MA. Domain ontologies in software engineering use of Protégé with the EON architecture. SMI Technical Report Methods of Information in Medicine 37: , 1998 ProForma at ICRF ASBRU PRODIGY III

13 13 Typical Mycin Rules IF the gram-stain is gram-negative AND if the culture-site is sterile AND if the culture-site is blood AND if the aerobicity is anerobic THEN there is strong (.8) evidence that the organism is enterobacter Based on expert opinion rather than data

14 14 Potted History (3) Reminders – Homer Warner, HELP, LDS 1980s - Arden Syntax 1990s - MLMs - standardised Arden –1970s - Clem McDonald - ‘…reminders and the nonperfectability of man” Regenstrief laboratory systems –Many variations PRODIGY II Systematic Review: Johnston M, Langton K, Haynes R and Mathieu A (1994).

15 15 Potted History (4) Offshoots and Idiosyncratics –Critiquing - Perry Miller Also Johan van der Lei –Quick Medical Reference - Chip Masari –Intelligent Records - Alan Rector and Anthony Nowlan –Knowledge Couplers - Larry Weed

16 16 Potted History (5) Knowledge Management and the Web –1980s Grateful Med and DxPlain Quick access to Medline abstracts and related –1990s “The Web with everything” Rise of Evidence Based Medicine –Cochrane, NICE, NELH, Health on the Web (HoN),… Indexing and ‘meta data’ –How do you find it Portals and certification –How do you know if it is any good Information for Public and Patients –Its an open world out there Type “Diabetes Support” at Google 776,000 hits, AllTheWeb 295,000 Yahoo 26, Netscape 2000 Classic Information Retrieval and Librarianship –Digital Libraries Different fields with little contact

17 17 Examples of Web Based Initiatives DxPlain PaperChase Health on the Net (HoN) OpenClinical Baby CareLink Guardian Angels … and of course PubMed and the NLM initiatives

18 18 Why isn’t decision support in routine use? Hypothesis one: “Pearls before swine” –Doctors are ‘resistant’ Hypothesis two: “The Emporer’s new clothes” –Systems are not clinically worthwhile Not clinically useful Too time consuming - too hard to learn Too expensive Too inaccessible Too sparse –How many diabetic patients does a GP see per week? Easier ways to get help –The technology is still primitive Developers misunderstand medicine –They think it is rational!

19 19 Why isn’t decision support in routine use? Hypothesis 3: “The invisible computer” –When it works, no one notices ECG interpretation Alerts and reminders NHS Direct –Simple but effective? –Junior doctors’ PDAs Convergence of communication and computing Upmarket PDAs have times the power of the machine that first ran Mycin! –Why Web technology and XML are critical to this course divorce content and presentation

20 20 What would you want from decision support? Discussion break

21 21 Some Technical Issues Technical –Re-use, transfer, and Terminology –Links to medical records –Protocols and Problem Solving Methods Combinatorial explosions Context and common sense Cognitive utility –The demise of the ‘oracle’ –The difficulty of ‘mixed initiative systems’

22 22 The Interface of Three Technologies / Modelling Paradigms Terminology and Ontologies Electronic Patient Records Decision Support/Inferencing –including ‘abstraction’ Plus Information Management/Information Retrieval

23 interface Concept Model (Ontology) Information Model (Patient Data Model) Inference Model (Guideline Model) Dynamic Guideline Knowledge (2b) Static Domain Knowledge (2a) Patient Specific Records (1)

24 24 A Protocol

25 25 Who Should Be Evaluated for UTI? Under the assumptions of the analysis, all febrile children between the ages of 2 months and 24 months with no obvious cause of infection should be evaluated for UTI, with the exception of circumcised males older than 12 months. Minimal Test Characteristics of Diagnosis of UTI To be as cost-effective as a culture of a urine specimen obtained by transurethral catheter or suprapubic tap, a test must have a sensitivity of at least 92% and a specificity of at least 99%. With the possible exception of a complete UA performed within 1 hour of urine collection by an on-site laboratory technician, no other test meets these criteria. Performing a dipstick UA and obtaining a urine specimen by catheterization or tap for culture from patients with a positive LE or nitrite test result is nearly as effective and slightly less costly than culturing specimens from all febrile children. Treatment of UTI The data suggest that short-term treatment of UTI should not be for 14 days if an appropriate clinical response is observed. There are no data comparing intravenous with oral administration of medications. Evaluation of the Urinary Tract Available data support the imaging evaluation of the urinary tracts of all 2- to 24-month-olds with their first documented UTI. Imaging should include VCUG and renal ultrasonography. The method for documenting the UTI must yield a positive predictive value of at least 49% to justify the evaluation. Culture of a urine specimen obtained by bag does not meet this criterion unless the previous probability of a UTI is >22%. FOOTNOTES The recommendations in this statement do not indicate an exclusive course of treatment or serve as a standard of medical care. Variations, taking into account individual circumstances, may be appropriate.

26 26 Semi Structured in GEM as seen in Gem Cutter

27 27

28 28 Terminology, Medical Records, and “the curly bracket problem” Re-use –Why should everyone start from scratch? –Attempts to transplant HELP complete did not work Could we transfer fragments of Help? Workshop at IBM centre at Arden near New York City produced generalisation of HELP syntax: –The Arden Syntax - now renamed Medical Logic Modules, MLMs

29 29 Example Arden Syntax Data Slot creatinine := read {'dam'="PDQRES2"}; last_creat := read last {select "OBSRV_VALUE" from "LCR" where qualifier in ("CREATININE","QUERY_OBSRV_ALL")}; Items in curly brackets {…} are institution specific Source: MLM Tutorial AMIA 2001 here

30 30 Arden Syntax - Next bit but from another institution data: creatinine_storage := event {'32506','32752'; /* isolated creatinine */...'32506','33801'; /* chem 20 */}; evoke: creatinine_storage;; Items in curly brackets {…} are institution specific

31 31 The ‘Curly Bracket Problem’ Transfering the logic is easy Transfering the access rules in curly brackets is hard –And it takes your most skilled people Subtle dependencies and system indiosyncracies The need for a common vocabulary

32 32 Where we come from Best Practice Clinical Terminology Data Entry Clinical Record Decision Support Best Practice Data Entry Electronic Health Records Decision Support & Aggregated Data GALEN Clinical Terminology

33 33 Controlled Vocabularies and ‘Ontologies’ A common theme –Affects Protégé/Eon, ProForma, ASBRU etc –Protégé/Eon based on Shared Problem Solving Methods (PSM) and shared Ontology A library of PSMs. No reused ontologies! The glue to link Medical Records and Clinical Decision Making –But only half the problem Systems must have the same concepts Doctors must use the same concepts –But made worse because most vocabulary is so awful to use

34 34 The Link to Medical Records The Terminology provides the content for the boxes in the information model

35 has diagnosis has treatment Disease Surgical Procedure Patient Disease Surgical Procedure has complica tion Infection

36 has diagnosis has treatment has complica tion Disease Surgical Procedure Mrs Smith Melanoma Excision Melanoma Infection Excision Infection

37 37 Protocols and Problem Solving Methods Machines and people –If it is easy for people it is hard to specify logically and program and vice versa –A real Guideline from NICE herehere – And from GEM site herehere What do you do with one of these? What does it mean operationally? –See next page for extract from GEM site protocol on UTI in children

38 38 How might we do it? Can you make a simple “Clinical Algorithm” from the previous? Can you scale this up to a cancer chemotherapy protocol

39 39 Today’s Standards EHRs –HL7 v2 and v3 –OpenEHR / CEN / Ocean Informatics Archetypes Terminology –SNOMED-CT –Clinical Terms V2 –ICD 9/10 (CM) –Specialist terminologies

40 40 HL7 - A Very Brief Intro

41 41 HL7 Reference Information Model (The RIM)

42 42 HL7 RIM Backbone (UML)

43 43 HL7 RIM Backbone as Block-Diagram

44 44 HL7 Data in XML......

45 45 Refined Model – Observation on Patient

46 46 Observation on Patient in XML John Doe St., Josephs Hospital

47 47 Refined Model – Observation on Trial Subject

48 48 Observation on Trial Subject in XML John Doe Eli Lilly

49 49 Archetypes Find on openEHR web site –Google “OpenEHR”

50 50 OpenEHR:

51 51 Ex: General Biochemistry

52 52 Ex: Blood Lipids

53 53 Computers can do anything you can tell them to How to write a perfect chess programme –List all the possible first move For each first move, list all the possible answering moves –For each answering move list all the replies... A 10 line programme –So why don’t computers play perfect chess?

54 Combinatorial Explosion: 20 questions Q yes no yesno yesno yes noyesnoyesnoyesno The legend of the Persian chess board

55 Combinatorial Explosion! 2 64 = = milliseconds = days 10 9 years –‘1 billion years’ nanoseconds = 1000 years grains of wheat = 1000 million metric tons of wheat –Predicted world wheat production for 2001: 567 million metric tons Brute force does not always work! (But don’t underestimate brute force cleverly applied - consider Google)

56 56 The human brain How big? How fast? –10 10 neurons –10 5 connections per neuron –10 3 firings per second  floating point operations / second to simulate and memory locations equivalent Probably a gross under estimate

57 57 Computers Current computers: around –At least 10 6 to go! 10 6  2 20 Moore’s law says power doubles every 1.5 years –Roughly 30 years to go! And then what? See recent controversy (click here) (click here) –“Heuristics” Rules of thumb As opposed to “Algorithms” - procedures with guaranteed solutions

58 58 Technologies Problem Solving Methods require –Knowledge Representation Semantic nets, frames, description logics, ontologies –Inference Rule based systems Planning –Skeletal Plan Refinement Bayesian Reasoning Belief Nets Logic Engines Programming by Search

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