Clinical decision support

Klaus-Peter Adlassnig

Section for Medical Expert and Knowledge-Based SystemsCenter for Medical Statistics, Informatics, and Intelligent SystemsMedical University of ViennaSpitalgasse 23, A-1090 Vienna

www.meduniwien.ac.at/kpa

Einführung in die Medizinische Informatik, WS 2015/16, 04. November 2015

Computers in clinical medicine—steps of natural progression

• step 1: patient administration– admission, transfer, discharge, and billing

• step 2: documentation of patients’ medical data– electronic health record: all media, distributed, life-long (partially fulfilled)

• step 3: patient and hospital analytics– data warehouses, quality measures, reporting and research databases,

patient recruitment… population-specific

• step 4: clinical decision support (applying knowledge to data)– safety net, quality assurance, evidence-based

… patient-specific

Medical research

biomolecular researchmedical statistics

clustering and classification data and knowledge mining

consensus conferences

factual/causal, definitional, statistical, and heuristic knowledge

molecular biomedicine

medical knowledge

facts consensus generalization

medical studies

evidence-based medicine

Patient care

decision-oriented analysis and interpretation

of patient data

factual/causal, definitional, statistical, and heuristic knowledge

human-to-human human-to-humanclinical decision support

patient-physician encounter

patient-physician follow-up

medical knowledge modules

medical knowledge

Medical information and knowledge-based systems

symptomssigns

test resultsclinical findings

biosignalsimages

diagnosestherapies

nursing data

•••

standardizationtelecommunication

chip cards

anatomybiochemistryphysiology

pathophysiologypathologynosology

therapeutic knowledgedisease management

•••

subjective experienceintuition

knowledge-based systems

patient’s medical data physician’s medical knowledge

medical statisticsclustering & classificationdata & knowledge mining

machine learning

clinical decision supportmedical expert systems

manypatients

single patient

diagnosistherapy

prognosismanagement

generalknowledge

•••

generalknowledge

telemedicine telemedicineintegration

information systems

induction

deduction

Clinical medicine

medical guidelines

history data

symptoms

signs

laboratorytest results

biosignals

images

genetic data

prognosispatient patient

symptomatic therapy

differentialtherapy

differentialdiagnosis

symptoms

signs

test results

findings

examination subspecialities clinic

medication history

radiological diagnosis

laboratorydiagnosis

…

Clinical medicine

medical guidelines

history data

symptoms

signs

laboratorytest results

biosignals

images

genetic data

prognosispatient patient

symptomatic therapy

differentialtherapy

differentialdiagnosis

symptoms

signs

test results

findings

examination subspecialities clinic

medication history

radiological diagnosis

laboratorydiagnosis

…

QMRDxPlainCADIAG

MYCIN

…

ANNs

SVMs

…

…

personalizedmedicine

+

+

Clinical medicine: high complexity• sources of medical knowledge

‒ factual/causal‒ definitional‒ statistical‒ heuristic

• layers of medical knowledge‒ observational and measurement level‒ interpretation, abstraction, aggregation, summation‒ pathophysiological states‒ diseases/diagnoses, therapies, prognoses, management decisions

• imprecision, uncertainty, and incompleteness‒ imprecision (=fuzziness) of medical concepts

* due to the unsharpness of boundaries of linguistic concepts‒ uncertainty of medical conclusions

* due to the uncertainty of the occurrence and co-occurrence of imprecise medical concepts‒ incompleteness of medical data and medical theory

* due to only partially known data and partially known explanations for medical phenomena• “gigantic” amount of medical data and medical knowledge

‒ patient history, physical examination, laboratory test results, clinical findings‒ symptom-disease relationships, disease-therapy relationships, …‒ terminologies, ontologies: SNOMED CT, LOINC, UMLS, …

specialization, teamwork, quality management, computer support

Clinical medicine: Hidden challenges

• holistic diagnosis– logical conclusions, evidence-based knowledge, and practical experience– intuition and pattern matching– patient‘s non-formalizable/non-digitizable data

• probable vs. possible diagnoses– suspected diagnosis, clinical diagnosis, pathological diagnosis– most probable diagnosis vs. possible diagnoses– limits of investigation, invasiveness, costliness

• terminology in context– not every diagnostic term is a diagnosis– surveillance vs. alert vs. clinical diagnosis– clinical diagnosis vs. discharge diagnosis

Clinical decision support: Definitions

• Foundational: Key origin of field of biomedical informatics– AIM = artificial intelligence in medicine– computer-based diagnosis in the heyday of AI

• Now: Intelligent assistant– support/assist human decision makers, not supplant them

• Core: Applying knowledge to data

Miller RA. Medical diagnostic decision support systems—past, present and future: a threaded bibliography and brief commentary. Journal of the American Medical Informatics Association 1994;1:8-27.

• studies in Colorado and Utah and in New York (1997)

– errors in the delivery of health care leading to the death of as many as 98,000 US citizens annually

• causes of errors– error or delay in diagnosis– failure to employ indicated tests– use of outmoded tests or therapy– failure to act on results of testing or

monitoring– error in the performance of a test, procedure,

or operation– error in administering the treatment – error in the dose or method of using a drug– avoidable delay in treatment or in responding

to an abnormal test– inappropriate (not indicated) care– failure of communication– equipment failure

• prevention of errors– we must systematically design safety into

processes of care

errors

prevention

Clinical decision support for patient safety and quality assurance

patients’ structured medical data: EHRs (local, national), Apps, …

hospital management and quality benchmarking• evidence-based reminders and processes• computerized clinical guidelines, protocols, standard

operating procedures• healthcare-associated infection surveillance

prognostic prediction• illness severity scores, prediction rules• trend detection and visualization

therapy advice• drug alerts, reminders, calculations

– indication, contraindications, redundant medications, substitutions

– dosage calculations, drug-drug interactions, adverse drug events

• management of antimicrobial therapies, susceptibility and resistance rates

• open- and closed-loop control systems

diagnostic support• alerts, reminders, to-do lists• clinical interpretation, (tele)monitoring• differential diagnostics

– rare diseases, rare syndromes– further or redundant examinations – diagnostic completeness (multi-morbidity)

• consensus-criteria-based evaluations– disease classification and surveillance criteria

structured medical knowledge: rules, tables, trees, guidelines, scores, algorithms, …

T

Clinical decision support: Five rights

• Framework for approaching & configuring CDS interventions

• “Rights” − right information delivered to the− right person in the− right intervention format through the− right channel at the− right point in workflow

Interpretation of

hepatitis serology test results

test results

interpretation

ORBIS Experter: Interpretation of hepatitis serology test results

Automated interpretation of

hepatitis serology test results

• includes frequent, rare, as well as inconsistent combinations

• complete coverage of the problem domains

• e.g., hepatitis B serology: about 150 rules in 3 layers for more than 61,000 possible combinations

Differentialdiagnose rheumatischerErkrankungen

ComputergestützteEntwöhnung vom

Respirator

University of Colorado Health—with Epic EMR

• patient follow-up and authorization of additional inpatient services (e.g., occupational and physical therapy)

© 2014 Epic Systems Corporation. Used with permission.

Example of e-mail from HFRRS MLM to HF nurse practitioners:

Heart failure readmission risk score (HFRRS)

Input:

• vital signs• lab data• demographics• ATD info• ICD codes

Integration into i.s.h.medat the

Vienna General Hospital

SOP checkingin melanoma patients

receiving chemotherapy

ArdenSuite integration with ICM by Dräger

• Data-, time-, and user-controlled execution of MLMs

• Application-specific viewer

by Stefan Kraus

by Stefan Kraus

Use Case: Hypoglycemia

DATA:

LET glucose BE READ {…glucose…};

LET physician_DECT BE DESTINATION {sms:26789};

LOGIC:

IF LATEST glucose IS LESS THAN 50 THEN

CONCLUDE true;

ENDIF;

ACTION:

WRITE „Warning…“ AT physician_DECT; by Stefan Kraus

CONCLUDE TRUE Do something

Hypoglycemia alert via DECT cordless telecommunications

Event monitors are

“tireless observers,constantly monitoringclinical events”

George Hripcsak

by Stefan Kraus

Arden Syntax: HL7- and ANSI-approved

• A standard language for writing situation-action rules, procedures, or knowledge bases that compute results based on clinical events detected in patient data

continuous development since 1989

• Each module, referred to as a medical logic module (MLM), contains sufficient knowledge to make a single decision

• Medical knowledge packages (MKPs) consist of interconnected MLMs for complex clinical decision support

• The Health Level Seven Arden Syntax for Medical Logic Systems, version 2.9—including fuzzymethodologies—was approved by Health Level Seven (HL7) International and the American National Standards Institute (ANSI) in 2013

• Version 2.10—including ArdenML, an XML-based representation of Arden Syntax MLMs—was approved in 2014

⇒ healthcare industry and academic users

General MLM LayoutMaintenance Category Library Category Knowledge Category Resources Category

Identify an MLMData TypesOperators

Basic OperatorsCurly Braces List OperatorsLogical OperatorsComparison OperatorsString OperatorsArithmetic OperatorsOther Operators

Control StatementsCall/Write Statements and Trigger

Sample MLM (excerpt)

ArdenSuite: Arden-Syntax-based genuine technology platform for clinical decision support (CDS)

Interface possibilities:1) Web services for MLM calling and for data transfer2) Web services for MLM calling and server/database

connector for data access3) Data warehouse + ArdenSuite server =

autonomous CDS system

ArdenSuite server and software components• web-services-based

ArdenSuite server including‒ ArdenSuite engine‒MLM manager‒ XML-protocol-based

interfaces, e.g., SOAP, REST, and HL7

‒ a project-specific dataand knowledge servicescenter

• Java libraries ‒ ArdenSuite compiler ‒ ArdenSuite engine

• ArdenSuite integrated development and test environment (IDE) including‒Medical logic module

(MLM) editor and authoring tool

‒ ArdenSuite compiler(syntax versions 2.1, 2.5, 2.6, 2.7, 2.8, 2.9, and 2.10)

‒ ArdenSuite engine‒MLM test environment‒MLM export component

• command-line ArdenSuite compiler data warehouse

– selected data and results, e.g., ICU & NICU, microbiology, MONI– reporting, quality measures, and benchmarking– study support and recruitment– App docking station (e.g., through FHIR server) – data and knowledge mining (big data)

Fuzzy Arden Syntax: Modeling uncertainty in medicine

• linguistic uncertainty

‒ due to the unsharpness (fuzziness) of boundaries of linguistic concepts; gradual transition from one concept to another

‒ modeled by fuzzy sets (e.g., fever, increased glucose level, hypoxemia)

• propositional uncertainty

‒ due to the incompleteness of medical conclusions; uncertainty in definitional, causal, statistical, and heuristic relationships

‒ here: modeled by truth values between zero and one (e.g., 0.6, 0.9)

Crisp sets vs. fuzzy sets

age

1

χY young

0 threshold

0 age

1

µY young

threshold0

0

“arbitrary” yes/no decisions• cause of unfruitful

discussions• often simply wrong

“intuitive” gradual transitions

Examples of fuzzy sets as they are applied in Moni-ICU

DoCfever

0.81.0

37.5 38.0 ºC0

37.9

DoC

4,000 5,000 11,000 12,000 WBC/mm3

leukopenia leukocytosis

0

1.0

11,500

0.5

DoC

1.0 1.3 shock index =systolic blood pressure/

heart rate

shock present1.0

0

0.9

Clinical concepts and relationships between them

( ) Dt3S2S1S →¬∨∧

truth value

degree of compatibility

𝑆𝑆1: fever𝑆𝑆2: hypotension𝑆𝑆3: leukopenia𝑆𝑆4: leukocytosis𝑆𝑆5: increased CRP𝑆𝑆6: inflammatory signs (with sepsis)

Uncertainty in conclusions I: through linguistic uncertainty in premises

Example:

𝑆𝑆1 ∨ 𝑆𝑆2 ∨ 𝑆𝑆3 ∨ 𝑆𝑆4 ∨ 𝑆𝑆51.0

𝑆𝑆6 is true is 0.8.

38.037.9

37.5

fever

°C

1.0

0.8

0.0

degreeof

compatibility

Uncertainty in conclusions II: through uncertainty in propositions

Example 1:

𝑆𝑆10.8

𝐷𝐷1 𝑆𝑆1 : highly increased amylase𝐷𝐷1: acute pancreatitis

Example 2:

𝐼𝐼10.8

𝑆𝑆1 𝐼𝐼1 : thermoregulation (cooling)𝑆𝑆1: fever

Example 3:

at least 4/11: 𝑆𝑆1 … 𝑆𝑆110.75

𝐷𝐷1 𝑆𝑆1: morning stiffness lasting at least one hour

𝑆𝑆5: symmetric joint involvement

𝑆𝑆9: positive serum rheumatoid factor

𝐷𝐷1: rheumatoid arthritis

……

…

Two different hyperglycemia definitions

Hyperglycemia (surveillance) is true is 1.00.Hyperglycemia (alerting) is true is 0.75.

Towards a science of clinical medicine

patient’s medical data and

healthcare processes for

machine processing

patient’s medical data and

healthcare processes for

human processing

≠

“Measure what is measurable, and make measurable what is not so.”Galileo Galilei

1564–1642

Crucial point in clinical medicine:

“Digitize what is digitizable, and make digitizable what is not so.”Klaus-Peter Adlassnig

observations measurementse.g., temperature chart e.g., CRP

skin color (jaundice, livid, …) color measurement… …

Challenges to clinical decision support (CDS)

• mental– necessity or imperative not recognized (fatalistic attitude towards risk/suffering)– factual incomprehension (don’t understand it)– emotional refusal (don’t want it)– insufficient endorsement (don’t do it)

• clinical– too simplistic or insufficient quality (lack of content quality)– lack in workflow integration (lack of process quality)

• technical– lack in structured patient data (documentation)– insufficient data/semantic interoperability (data and terminology standards)

• financial– insufficient funds (often not true!)

⇒ How to overcome these barriers? By clinically useful solutions.

Literature on “Clinical Decision Support”: 36,211 publications

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1960 1970 1980 1990 2000 2010 2020Max Plischke, 2015

Challenge for CDS: Explosion in data + knowledge

Stead WW, Searle JR, Fessler HE et al. Biomedical informatics: Changing What Physicians Need to Know and How They Learn. Academic Medicine 2011; 86(4):429-434.

A “holy grail” of clinical informatics is scalable, interoperable clinical decision support.

according to

Kensaku Kawamoto

HL7 Work Group Meeting,

San Diego, CA, September 2011

Clinical decision support: Ten commandments

• Speed is everything• Anticipate needs & deliver in real time (alert)• Fit into user workflow (five rights)• Little things make a big difference (e.g., screen design, used terms)• Recognize that MDs will resist stopping (e.g., medication)• Changing direction is easier than stopping (e.g., dosing)• Simple interventions work best• Ask for information only if you really need it (avoid additional data input)• Monitor impact, get feedback and respond (keep the user informed)• Manage & maintain your KBS (continues improvement)

Bates DW, Kuperman GJ, Wang S et al. Ten Commandments for Effective Clinical Decision Support: Making the Practice of Evidence-Based Medicine a Reality. Journal of the American Medical Informatics Association 2003;10:523-30.

Regulatory framework for clinical decision support software: Present uncertainty and prospective proposition

From Y. Tony Yang and Bradley Merrill Thompson (2015) Journal of the American College of Radiology.