1 Clinical Decision Support Lecture 1b Brief History and State of the Art of Clinical Decision Support.

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

Clinical Decision Support Lecture 1bBrief History and State of the Art of Clinical

Decision Support


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

• Supporting a Humanly Impossible Task

• Johnson Articles - see resources

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



http://www.cs.man.ac.uk/mig/ftp/bibliography/temp/Humanly-Impossible-Task.pdf


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.


Examples of Protocols –See handouts


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. al1994)

– Prescribing

– Protocol based care• Oncocin, T-Helper, etc.

– Referral

• Diagnostics(rarely effective, Johnston et. al1994)

• 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


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


Bayes Methods

• Probablistic– If you think the patient has one of a set of diseases:

• e.g. for Acute Abdominal Pain: Appendicitis, Obstruction, Perforating Ulcer, Pancreatitis, Gallbladder Inflammation, Tubal preganancy (if female), or ‘Other’

– If you know • a) That a patient has an indicant• a) The prior probability of each disease• b) The probability that a patient with each disease has the indicant

– You can calculate• c) the posterior probability that the patient has each disease given that

they have the indicant.

– And you can do so for all of a set of indicants.


Bayes: simple example

• Prior probabilities: appendicitis 70% obstruction 10% other 20%

• Probability of indicant given diagnosis: (“vomiting without nausea”):

– 1% of patients with Appendicitis have indicant

– 25% of patients with obstruction have indicant

– 5% of patients with ‘other’ have indicant

• Posterior probabilities of appendicitis given indicant– appendicitis70*1/correction 17% of pts with indicant have appe’is

– obstruction 1*25/correction 60% of pts with indicant have obstr

– other 2*5/correction 23% of pts with indicant have other

Easy to collect


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




Rules: Example from MYCIN

• IF the site of culture is bloodAND the method of culture is sterileAND the aerobicity of the organism is anaerobic

THEN there is good (.65) evidence that the Diagnosis of the Organism is Enterobacter


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).


Examples

• CONDITION: the serum potassium is over 4.8 & the patient is on any digitalis derivative

• ACTION: issue alarm


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 (PubMed) 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’ & the Semantic Web– 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


Potted History 7:Cuing and Intelligent Medical Records

• PEN&PAD, MedCin, …

• Almost took off

• Where we came from…


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


Potted History 6:Re-use, Terminologies and Ontologies

• Transferring the logic is easy

• Transferring 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


Why isn’t decision support in routine use?

• Hypothesis one: “Pearls before swine”– Doctors are ‘resistant’

• Hypothesis two: “The Emperor’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


A modern ViewThe Tripod

• Electronic Patient Records

• Decision Support

• Terminology and Ontologies

Plus

• Knowledge Management/Information Retrieval

interface

interface

interface

Concept Model(Ontology)

Information Model(Patient Data Model)

Inference Model(Guideline Model)

Dynamic Guideline Knowledge

Static Domain Knowledge (2a)

Patient Specific Records


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.


The Technologies

• Semi Structured text and XML – GEM/GEMCUTTER– Systematising what is needed

– XML is now the generic syntax for everything• Find out about it from any handy book or web tutorial

• Knowledge representation, Ontologies, terminologies, – Protégé, OilEd and OWL

• Rule based systems - Tallis/ProForma

• Bayesian inference MicroHugin


Exercises/Lab

• Follow links or your own knowledge to find a range of “guidelines” on the Web. – Compare what is on offer

• Who are they for? What are they for? • Who has supplied them?• How might you use them as a clinician treating patients?• How might you author one?• How might you provide computer support to follow one?

• Pick a disease – Look it up in Google with “protocol”, “guideline”, “systematic review”

• Answer the questions above.

• Find some protocols in your institution, if you work in the NHS – answer the questions above.


Apology…We hope this is fixed…

• Illogical order due to technical difficulties– Due to the wrong kind of .dll in the MS automatic upgrade, we

will discuss & demonstrate GEM later when the software is has been upgraded to fit the new ‘standard’. Microsoft is unlikely to apologise for the inconvenience this will cause to your module, but we apologise and will do our best to cope.

1 Clinical Decision Support Lecture 1b Brief History and State of the Art of Clinical Decision Support.

Documents

pauker decision support

clinical performance

clinical errorsto

effect of clinical guidelines

bmj clinical evidence

computer applications

effects of computer

johnston et