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

1www.cs.man.ac.uk/ai/modules/cds

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 http://www.cs.man.ac.uk/~rector/modules/cds/Notes-1-HI-general-cds-2006.ppt


The Hype of the Time

• Guidelines

• Evidence Based Medicine

• Clinical Errors (reducing)– Improving prescribing practice

– Reducing adverse drug reactions

• Protocols

• Knowledge Management

• ...


Clinical Judgement and Clinical Errors

• To Err is Human http://www.nap.edu/books/0309068371/html/

• Supporting a Humanly Impossible Task http://www.cs.man.ac.uk/~rector/papers/Humanly-Impossible-Task.pdf

• Johnson Articles - see resourceshttp://www.cs.man.ac.uk/~rector/modules/cds/cds_links.htm(NB some links may be broken because of University merger)

• OpenClinical Web sitehttp://www.openclinical.org/

• OpenEHR web sitehttp://www.openehr.org

http://www.nap.edu/books/0309068371/html/

http://www.nap.edu/books/0309068371/html/

http://www.cs.man.ac.uk/~rector/papers/Humanly-Impossible-Task.pdf

http://www.cs.man.ac.uk/~rector/papers/Humanly-Impossible-Task.pdf

http://www.openclinical.org/

http://www.openehr.org/


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: 698-705.

• Grimshaw J and Russell I (1993). Effect of clinical guidelines on medical practice: a systematic review of rigorous evaluations. Lancet 342: 1317-1322.

• 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: 135-142.


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): 209-215


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


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


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: 505-509.

• Meeting them effectively– “The user is always right…

but the user is usually wrong”

– The technology is still crude at best

• Implementing it successfully


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, 1992. http://smi-web.stanford.edu/pubs/SMI_Abstracts/SMI-92-0449.html

http://smi-web.stanford.edu/pubs/SMI_Abstracts/SMI-92-0449.html






Potted History (1)

• Bayesian stream– 1968 Ledley and Lusted: Diagnosis using ‘Idiot Bayes’

discriminant• Followed by Pauker Decision Support using utility theory

– 1970-1985 - 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


“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 ofAppendicitis, 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


Potted History (2)• Rule based stream

– 1972 - 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 97-0657. Methods of Information in Medicine 37:540-550, 1998.

• ProForma at ICRF• ASBRU• PRODIGY III

http://www-smi.stanford.edu/pubs/SMI_Reports/SMI-97-0657.pdf




Typical Mycin Rules

• IF the gram-stain is gram-negativeAND if the culture-site is sterileAND if the culture-site is bloodAND if the aerobicity is anerobic

THEN there is strong (.8) evidence that the organism is enterobacter

• Based on expert opinion rather than data


Potted History (3)

• Reminders– 1970 - 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).


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


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 295,000Yahoo 26, Netscape 2000

• Classic Information Retrieval and Librarianship – Digital Libraries

• Different fields with little contact


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


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!


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 10-100 times the power of the machine that

first ran Mycin!

– Why Web technology and XML are critical to this course• divorce content and presentation


What would you want from decision support?

• Discussion break


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’


The Interface of Three Technologies /Modelling Paradigms

• Terminology and Ontologies

• Electronic Patient Records

• Decision Support/Inferencing– including ‘abstraction’

• Plus Information Management/Information Retrieval

interface

interface

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)


A Protocol


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 UTIThe data suggest that short-term treatment of UTI should not be for <7 days. The data do not support treatment 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%.

FOOTNOTESThe 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.


Semi Structured in GEM as seen in Gem Cutter



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


Example Arden Syntax

• Data Slotcreatinine := 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

http://www.cpmc.columbia.edu/arden/documents.html#tutorial2


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


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


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 Clinica

l Terminology


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


The Link to Medical Records

• The Terminology provides the content for the boxes in the information model

www.cs.man.ac.uk/ai/modules/cds

has diagnosis has treatment

Disease

Surgical Procedure

Patient DiseaseSurgical

Procedure

has

com

plica

tion

Infection


has diagnosis has treatment

has

com

plica

tion

Disease

Surgical Procedure

Mrs Smith Melanoma Excision

Melanoma Infection

Excision

Infection


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 here

– And from GEM site here • 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

http://www.nice.org.uk/article.asp?a=16529

http://www.pediatrics.org/cgi/content/full/103/4/e54#Recommendation


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


Today’s Standards

• EHRs– HL7 v2 and v3

– OpenEHR / CEN 13606 / Ocean Informatics Archetypes

• Terminology– SNOMED-CT

– Clinical Terms V2

– ICD 9/10 (CM)

– Specialist terminologies


HL7 - A Very Brief Intro


HL7 Reference Information Model (The RIM)

Account

name : STbalanceAmt : MOcurrencyCode : CEinterestRateQuantity : RTO<MO,PQ>allowedBalanceQuantity : IVL<MO>

DeviceTask

parameterValue : LIST<ANY>

DiagnosticImage

subjectOrientationCode : CE

Diet

energyQuantity : PQcarbohydrateQuantity : PQ

FinancialContract

paymentTermsCode : CE

FinancialTransaction

amt : MOcreditExchangeRateQuantity : REALdebitExchangeRateQuantity : REAL

InvoiceElement

modifierCode : SET<CE>unitQuantity : RTO<PQ,PQ>unitPriceAmt : RTO<MO,PQ>netAmt : MOfactorNumber : REALpointsNumber : REAL

ManagedParticipation

id : SET<II>statusCode : SET<CS>

Observation

value : ANYinterpretationCode : SET<CE>methodCode : SET<CE>targetSiteCode : SET<CD>

PatientEncounter

preAdmitTestInd : BLadmissionReferralSourceCode : CElengthOfStayQuantity : PQdischargeDispositionCode : CEspecialCourtesiesCode : SET<CE>specialAccommodationCode : SET<CE>acuityLevelCode : CE

Procedure

methodCode : SET<CE>approachSiteCode : SET<CD>targetSiteCode : SET<CD>

PublicHealthCase

detectionMethodCode : CEtransmissionModeCode : CEdiseaseImportedCode : CE

SubstanceAdministration

routeCode : CEapproachSiteCode : SET<CD>doseQuantity : IVL<PQ>rateQuantity : IVL<PQ>doseCheckQuantity : SET<RTO>maxDoseQuantity : SET<RTO>

Supply

quantity : PQexpectedUseTime : IVL<TS>

WorkingList

ownershipLevelCode : CE

Container

capacityQuantity : PQheightQuantity : PQdiameterQuantity : PQcapTypeCode : CEseparatorTypeCode : CEbarrierDeltaQuantity : PQbottomDeltaQuantity : PQ

Device

manufacturerModelName : SCsoftwareName : SClocalRemoteControlStateCode : CE...alertLevelCode : CElastCalibrationTime : TS

LivingSubject

administrativeGenderCode : CEbirthTime : TSdeceasedInd : BLdeceasedTime : TSmultipleBirthInd : BLmultipleBirthOrderNumber : INTorganDonorInd : BL

ManufacturedMaterial

lotNumberText : STexpirationTime : IVL<TS>stabilityTime : IVL<TS>

Material

formCode : CENonPersonLivingSubject

strainText : EDgenderStatusCode : CE

Organization

addr : BAG<AD>standardIndustryClassCode : CE

Person

addr : BAG<AD>maritalStatusCode : CEeducationLevelCode : CEraceCode : SET<CE>disabilityCode : SET<CE>livingArrangementCode : CEreligiousAffiliationCode : CEethnicGroupCode : SET<CE>

Place

mobileInd : BLaddr : ADdirectionsText : EDpositionText : EDgpsText : ST

Access

approachSiteCode : CDtargetSiteCode : CDgaugeQuantity : PQ

Employee

jobCode : CEjobTitleName : SCjobClassCode : CEsalaryTypeCode : CEsalaryQuantity : MOhazardExposureText : EDprotectiveEquipmentText : ED

LicensedEntity

recertificationTime : TS

Patient

confidentialityCode : CEveryImportantPersonCode : CE

ActRelationship

typeCode : CSinversionInd : BLcontextControlCode : CScontextConductionInd : BLsequenceNumber : INTpriorityNumber : INTpauseQuantity : PQcheckpointCode : CSsplitCode : CSjoinCode : CSnegationInd : BLconjunctionCode : CSlocalVariableName : STseperatableInd : BL

Act

classCode : CSmoodCode : CSid : SET<II>code : CDnegationInd : BLderivationExpr : STtext : EDtitle : STstatusCode : SET<CS>effectiveTime : GTSactivityTime : GTSavailabilityTime : TSpriorityCode : SET<CE>confidentialityCode : SET<CE>...repeatNumber : IVL<INT>interruptibleInd : BLlevelCode : CEindependentInd : BLuncertaintyCode : CEreasonCode : SET<CE>languageCode : CE

0..n

1

outboundRelationship

0..n

source1

0..n

1

inboundRelationship 0..n

target

1

LanguageCommunication

languageCode : CEmodeCode : CEproficiencyLevelCode : CEpreferenceInd : BL

Participation

typeCode : CSfunctionCode : CDcontextControlCode : CS...sequenceNumber : INTnegationInd : BLnoteText : EDtime : IVL<TS>modeCode : CEawarenessCode : CEsignatureCode : CEsignatureText : EDperformInd : BLsubstitutionConditionCode : CE...

0..n

1

0..n

1

Entity

classCode : CSdeterminerCode : CSid : SET<II>code : CEquantity : SET<PQ>name : BAG<EN>desc : EDstatusCode : SET<CS>existenceTime : IVL<TS>...telecom : BAG<TEL>riskCode : CEhandlingCode : CE

1

0..n

1

0..n

RoleLink

typeCode : CSeffectiveTime : IVL<TS>...

Role

classCode : CSid : SET<II>code : CEnegationInd : BLaddr : BAG<AD>telecom : BAG<TEL>statusCode : SET<CS>effectiveTime : IVL<TS>certificateText : EDquantity : RTOpositionNumber : LIST<INT>...

0..n

1

0..n

10..n0..1

playedRole

0..n

player

0..1

0..n0..1

scopedRole

0..n

scoper

0..1

0..n

1

outboundLink 0..n

source

1

0..n

1

inboundLink

0..n

target1

RIM 2.01July 17,2003

ControlAct


HL7 RIM Backbone (UML)

ActRelationship

typeCode : CSinversionInd : BL

contextControlCode : CScontextConductionInd : BLsequenceNumber : INT

priorityNumber : INTpauseQuantity : PQcheckpointCode : CSsplitCode : CS

joinCode : CSnegationInd : BLconjunctionCode : CSlocalVariableName : STseperatableInd : BL

Act

classCode : CS

moodCode : CSid : SET<II>code : CDnegationInd : BLderivationExpr : STtext : EDtitle : STstatusCode : SET<CS>effectiveTime : GTSactivityTime : GTS

availabilityTime : TSpriorityCode : SET<CE>confidentialityCode : SET<CE>

repeatNumber : IVL<INT>interruptibleInd : BLlevelCode : CE

independentInd : BLuncertaintyCode : CEreasonCode : SET<CE>languageCode : CE

0..n

1

outboundRelationship

0..n

source1

0..n

1

inboundRelationship 0..n

target

1

Participation

typeCode : CSfunctionCode : CDcontextControlCode : CS...sequenceNumber : INTnegationInd : BLnoteText : EDtime : IVL<TS>modeCode : CEawarenessCode : CEsignatureCode : CE

signatureText : EDperformInd : BLsubstitutionConditionCode : CE...

0..n

1

0..n

1

Entity

classCode : CS

determinerCode : CSid : SET<II>code : CE

quantity : SET<PQ>name : BAG<EN>desc : EDstatusCode : SET<CS>

existenceTime : IVL<TS> ...telecom : BAG<TEL>riskCode : CE

handlingCode : CE

RoleLink

typeCode : CS

effectiveTime : IVL<TS>...

Role

classCode : CS

id : SET<II>code : CEnegationInd : BL

addr : BAG<AD>telecom : BAG<TEL>statusCode : SET<CS>

effectiveTime : IVL<TS>certificateText : EDquantity : RTOpositionNumber : LIST<INT>...

0..n

1

0..n

10..n0..1

playedRole

0..n

player

0..1

0..n0..1

scopedRole

0..n

scoper

0..1

0..n

1

outboundLink 0..n

source

1

0..n

1

inboundLink

0..n

target1


HL7 RIM Backbone as Block-Diagram

EntityclassCode *: <= ENTdeterminerCode *: <= INSTANCEid: SET<II> [0..*]code: CE CWE [0..1] <= EntityCodequantity: SET<PQ> [0..*]name: BAG<EN> [0..*]desc: ED [0..1]statusCode: SET<CS> CNE [0..*] <= EntityStatusexistenceTime: IVL<TS> [0..1]telecom: BAG<TEL> [0..*]riskCode: CE CWE [0..1] <= EntityRiskhandlingCode: CE CWE [0..1] <= EntityHandling

Entity

ActclassCode *: <= ACTmoodCode *: <= EVNid: SET<II> [0..*]code: CD CWE [0..1] <= ActCodenegationInd: BL [0..1]derivationExpr: ST [0..1]text: ED [0..1]statusCode: SET<CS> CNE [0..*] <= ActStatuseffectiveTime: GTS [0..1]activityTime: GTS [0..1]availabilityTime: TS [0..1]priorityCode: SET<CE> CWE [0..*] <= ActPriorityconfidentialityCode: SET<CE> CWE [0..*] <= ConfidentialityrepeatNumber: IVL<INT> [0..1]interruptibleInd: BL [0..1]levelCode: CE CWE [0..1] <= ActContextLevelindependentInd: BL [0..1]uncertaintyCode: CE CNE [0..1] <= ActUncertaintyreasonCode: SET<CE> CWE [0..*] <= ActReasonlanguageCode: CE CWE [0..1] <= HumanLanguage

0..* source

0..* target

typeCode *: <= ActRelationshipTypeinversionInd: BL [0..1]contextControlCode: CS CNE [0..1] <= ContextControlcontextConductionInd: BL [0..1]sequenceNumber: INT [0..1]priorityNumber: INT [0..1]pauseQuantity: PQ [0..1]checkpointCode: CS CNE [0..1] <= ActRelationshipCheckpointsplitCode: CS CNE [0..1] <= ActRelationshipSplitjoinCode: CS CNE [0..1] <= ActRelationshipJoinnegationInd: BL [0..1]conjunctionCode: CS CNE [0..1] <= RelationshipConjunctionlocalVariableName: ST [0..1]seperatableInd: BL [0..1]

sourceOf /targetOf

Act

0..1 playingEntity

0..1 scopingEntity

Role 0..* scopedRole

0..* playedRole

classCode *: <= ROLid: SET<II> [0..*]code: CE CWE [0..1] <= RoleCodenegationInd: BL [0..1]addr: BAG<AD> [0..*]telecom: BAG<TEL> [0..*]statusCode: SET<CS> CNE [0..*] <= RoleStatuseffectiveTime: IVL<TS> [0..1]certificateText: ED [0..1]quantity: RTO<QTY,QTY> [0..1]positionNumber: LIST<INT> [0..*]

0..* act

0..* participant

typeCode *: <= ParticipationTypefunctionCode: CD CWE [0..1] <= ParticipationFunctioncontextControlCode: CS CNE [0..1] <= ContextControlsequenceNumber: INT [0..1]negationInd: BL [0..1]noteText: ED [0..1]time: IVL<TS> [0..1]modeCode: CE CWE [0..1] <= ParticipationModeawarenessCode: CE CWE [0..1] <= TargetAwarenesssignatureCode: CE CNE [0..1] <= ParticipationSignaturesignatureText: ED [0..1]performInd: BL [0..1]

participant / participation


HL7 Data in XML

<act classCode=“ACT” moodCode=“…”><id root=“1.3.6.1.4.1.12009.3” extension=“A1234”/><code code=“...” codeSystem=“2.16.840.1.113883.6.1”/>

<participant typeCode=“…”>

<participant classCode=“ROL”>

<id root=“1.3.6.1.4.1.12009.4” extension=“1234567-8”/><code code=“…” codeSystem=“2.16.840.1.113883.6.21”/>

<playingEntity classCode=“ENT”>

<name>...</name>

</playingEntity>

<scopingEntity classCode=“ENT”>

<name>...</name>

</scopingEntity>

</participant>

</participant>

<sourceOf typeCode=“REL”>

<target classCode=“ACT”>

<id root=“1.3.6.1.4.1.12009.3” extension=“A1235”/>

</target>

</sourceOf>

</act>


Refined Model – Observation on Patient

PersonclassCode *: <= PSNdeterminerCode *: <= INSTANCEid: SET<II> [0..*]code: CE CWE [0..1] <= EntityCodename: BAG<EN> [0..*]riskCode: CE CWE [0..1] <= EntityRiskhandlingCode: CE CWE [0..1] <= EntityHandlingadministrativeGenderCode: CE CWE [0..1] <= AdministrativeGenderbirthTime: TS [0..1]deceasedTime: TS [0..1]maritalStatusCode: CE CWE [0..1] <= MaritalStatuseducationLevelCode: CE CWE [0..1] <= EducationLeveldisabilityCode: SET<CE> CWE [0..*] <= PersonDisabilityTypelivingArrangementCode: CE CWE [0..1] <= LivingArrangementreligiousAffiliationCode: CE CWE [0..1] <= ReligiousAffiliationraceCode: SET<CE> CWE [0..*] <= RaceethnicGroupCode: SET<CE> CWE [0..*] <= Ethnicity

OrganizationclassCode *: <= ORGdeterminerCode *: <= INSTANCEname: BAG<EN> [0..*]standardIndustryClassCode: CE CWE [0..1] <= OrganizationIndustryClass

ObservationEventclassCode *: <= OBSmoodCode *: <= EVNid*: II [1..1]code*: CD CWE [1..1] <= ObservationTypetext: ED [0..1]statusCode*: CS CNE [1..1] <= completedeffectiveTime*: IVL<TS> [1..1]confidentialityCode: SET<CE> CWE [0..*] <= Confidentiality

0..* observationEventtypeCode *: <= COMP

component

ObservationEventclassCode *: <= OBSmoodCode *: <= EVNid*: II [1..1]code*: CD CWE [1..1] <= ObservationTypestatusCode: CS CNE [1..1] <= completedeffectiveTime*: IVL<TS> [0..1]confidentialityCode: SET<CE> CWE [0..*] <= Confidentiality

0..1 patientPerson

0..1 providerOrganization

Patient 0..* patient

0..* healthCareProvider

classCode *: <= PATid: SET<II> [0..*]code: CE CWE [0..1] <= RoleCodeaddr: BAG<AD> [0..*]telecom: BAG<TEL> [0..*]statusCode: SET<CS> CNE [0..*] <= RoleStatuseffectiveTime: IVL<TS> [0..1]confidentialityCode: CE CWE [0..1] <= ConfidentialityveryImportantPersonCode: CE CWE [0..1] <= PatientImportance

0..* patient

typeCode *: <= SBJawarenessCode: CE CWE [0..1] <= TargetAwareness

subject


Observation on Patient in XML

<observationEvent classCode=“OBS” moodCode=“EVN”><id root=“1.3.6.1.4.1.12009.3” extension=“A1234”/><code code=“...” codeSystem=“2.16.840.1.113883.6.1”/>

<subject typeCode=“…”>

<patient classCode=“ROL”>


<patientPerson classCode=“PSN”>

<name><given>John</given><family>Doe</family></name>

</patientPerson>

<providerOrganization classCode=“ORG”>

<name>St., Josephs Hospital</name>

</providerOrganization>

</patient>

</subject>

<component typeCode=“REL”>

<observationEvent classCode=“ACT”>


</observationEvent>

</component>

</observationEvent>


Refined Model – Observation on Trial Subject

PersonclassCode *: <= PSNdeterminerCode *: <= INSTANCEid: SET<II> [0..*]code: CE CWE [0..1] <= EntityCodename: BAG<EN> [0..*]riskCode: CE CWE [0..1] <= EntityRiskhandlingCode: CE CWE [0..1] <= EntityHandlingadministrativeGenderCode: CE CWE [0..1] <= AdministrativeGenderbirthTime: TS [0..1]deceasedTime: TS [0..1]maritalStatusCode: CE CWE [0..1] <= MaritalStatuseducationLevelCode: CE CWE [0..1] <= EducationLeveldisabilityCode: SET<CE> CWE [0..*] <= PersonDisabilityTypelivingArrangementCode: CE CWE [0..1] <= LivingArrangementreligiousAffiliationCode: CE CWE [0..1] <= ReligiousAffiliationraceCode: SET<CE> CWE [0..*] <= RaceethnicGroupCode: SET<CE> CWE [0..*] <= Ethnicity

OrganizationclassCode *: <= ORGdeterminerCode *: <= INSTANCEname: BAG<EN> [0..*]standardIndustryClassCode: CE CWE [0..1] <= OrganizationIndustryClass

ObservationEventclassCode *: <= OBSmoodCode *: <= EVNid*: II [1..1]code*: CD CWE [1..1] <= ObservationTypetext: ED [0..1]statusCode*: CS CNE [1..1] <= completedeffectiveTime*: IVL<TS> [1..1]confidentialityCode: SET<CE> CWE [0..*] <= Confidentiality

0..* observationEventtypeCode *: <= COMP

component

ObservationEventclassCode *: <= OBSmoodCode *: <= EVNid*: II [1..1]code*: CD CWE [1..1] <= ObservationTypestatusCode: CS CNE [1..1] <= completedeffectiveTime*: IVL<TS> [0..1]confidentialityCode: SET<CE> CWE [0..*] <= Confidentiality

0..1 subjectPerson

0..1 researchSponsor

ResearchSubject 0..* sponsoredSubject

0..* subjectOf

classCode *: <= RESBJid: SET<II> [0..*]code: CE CWE [0..1] <= RoleCodeaddr: BAG<AD> [0..*]telecom: BAG<TEL> [0..*]statusCode: SET<CS> CNE [0..*] <= RoleStatuseffectiveTime: IVL<TS> [0..1]

0..* researchSubject

typeCode *: <= SBJsubject


Observation on Trial Subject in XML

<observationEvent classCode=“OBS” moodCode=“EVN”><id root=“1.3.6.1.4.1.12009.3” extension=“A1234”/><code code=“...” codeSystem=“2.16.840.1.113883.6.1”/>

<subject typeCode=“…”>

<researchSubject classCode=“ROL”>


<subjectPerson classCode=“PSN”>

<name><given>John</given><family>Doe</family></name>

</subjectPerson>

<researchSponsor classCode=“ORG”>

<name>Eli Lilly</name>

</researchSponsor>

</researchSubject>

</subject>

<component typeCode=“REL”>

<observationEvent classCode=“ACT”>


</observationEvent>

</component>

</observationEvent>


Archetypes

• Find on openEHR web site– Google “OpenEHR”


OpenEHR: http://www.openehr.org

http://www.openehr.org/


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?


Combinatorial Explosion: 20 questions

Q

yes no

yes no yes no

yes no yes no yes no yes no

1

2

4

8

...The legend of the Persian chess board


Combinatorial Explosion!

• 264 = 2106.4 = 1019

• 1019 milliseconds = 1011 days 109 years– ‘1 billion years’

• 1019 nanoseconds = 1000 years

• 1019 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)


The human brain

• How big? How fast?– 1010 neurons

– 105 connections per neuron

– 103 firings per second

1018 floating point operations / second to simulate and 1018 memory locations equivalent

• Probably a gross under estimate


Computers

• Current computers: around 109-1012

– At least 106 to go!• 106 220

• Moore’s law says power doubles every 1.5 years– Roughly 30 years to go!

• And then what? • See recent controversy http://www.tecsoc.org/innovate/focusbilljoy.htm

(click here)

– “Heuristics” • Rules of thumb• As opposed to “Algorithms” - procedures with guaranteed solutions

http://www.tecsoc.org/innovate/focusbilljoy.htm


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

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

Documents