Clinical decision support

Transcription

1 Clinical decision support Klaus-Peter Adlassnig Section for Medical Expert and Knowledge-Based Systems Center for Medical Statistics, Informatics, and Intelligent Systems Medical University of Vienna Spitalgasse 23, A-1090 Vienna Einführung in die Medizinische Informatik, WS 2015/16, 04. November 2015

2 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

3 Medical research medical knowledge factual/causal, definitional, statistical, and heuristic knowledge facts consensus generalization molecular biomedicine evidence-based medicine medical studies biomolecular research medical statistics clustering and classification data and knowledge mining consensus conferences

4 Patient care human-to-human patientphysician encounter clinical decision support decision-oriented analysis and interpretation of patient data human-to-human patientphysician follow-up medical knowledge modules factual/causal, definitional, statistical, and heuristic knowledge medical knowledge

5 Medical information and knowledge-based systems patient s medical data medical statistics clustering & classification data & knowledge mining machine learning physician s medical knowledge symptoms signs test results clinical findings biosignals images diagnoses therapies nursing data standardization telecommunication chip cards information systems single patient many patients induction clinical decision support medical expert systems deduction general knowledge general knowledge diagnosis therapy prognosis management anatomy biochemistry physiology pathophysiology pathology nosology therapeutic knowledge disease management subjective experience intuition knowledge-based systems telemedicine integration telemedicine

6 Clinical medicine medication history symptomatic therapy history data patient symptoms signs laboratory test results biosignals images genetic data laboratory diagnosis radiological diagnosis symptoms signs test results findings differential diagnosis differential therapy prognosis patient medical guidelines examination subspecialities clinic

7 Clinical medicine medication history symptomatic therapy history data patient symptoms signs MYCIN laboratory laboratory test results diagnosis biosignalsanns QMR DxPlain symptoms CADIAG signs test results findings + differential diagnosis differential therapy prognosis patient images SVMs radiological diagnosis genetic data personalized medicine + medical guidelines examination subspecialities clinic

8 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

9 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

10 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.

11 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 failureerrors 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 prevention failure of communication equipment failure prevention of errors we must systematically design safety into processes of care

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13 Clinical decision support for patient safety and quality assurance patients structured medical data: EHRs (local, national), Apps, 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 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 hospital management and quality benchmarking evidence-based reminders and processes computerized clinical guidelines, protocols, standard operating procedures healthcare-associated infection surveillance structured medical knowledge: rules, tables, trees, guidelines, scores, algorithms, T

14 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

15 Interpretation of hepatitis serology test results

16 test results interpretation

17 ORBIS Experter: Interpretation of hepatitis serology test results

18 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

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20 Differentialdiagnose rheumatischer Erkrankungen

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22 Computergestützte Entwöhnung vom Respirator

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24 University of Colorado Health with Epic EMR Heart failure readmission risk score (HFRRS) Input: vital signs lab data demographics ATD info ICD codes 2014 Epic Systems Corporation. Used with permission. Example of from HFRRS MLM to HF nurse practitioners: patient follow-up and authorization of additional inpatient services (e.g., occupational and physical therapy)

25 Integration into i.s.h.med at the Vienna General Hospital SOP checking in melanoma patients receiving chemotherapy

26 ArdenSuite integration with ICM by Dräger Data-, time-, and usercontrolled execution of MLMs Application-specific viewer by Stefan Kraus

27 by Stefan Kraus

28 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; CONCLUDE TRUE Do something ACTION: WRITE Warning AT physician_dect; by Stefan Kraus

29 Hypoglycemia alert via DECT cordless telecommunications Event monitors are tireless observers, constantly monitoring clinical events George Hripcsak by Stefan Kraus

30 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 fuzzy methodologies 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

31 General MLM Layout Maintenance Category Library Category Knowledge Category Resources Category Identify an MLM Data Types Operators Basic Operators Curly Braces List Operators Logical Operators Comparison Operators String Operators Arithmetic Operators Other Operators Control Statements Call/Write Statements and Trigger

32 Sample MLM (excerpt)

33 ArdenSuite: Arden-Syntax-based genuine technology platform for clinical decision support (CDS) Interface possibilities: 1) Web services for MLM calling and for data transfer 2) Web services for MLM calling and server/database connector for data access 3) Data warehouse + ArdenSuite server = autonomous CDS system

34 ArdenSuite server and software components 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) web-services-based ArdenSuite server including ArdenSuite engine MLM manager XML-protocol-based interfaces, e.g., SOAP, REST, and HL7 a project-specific data and knowledge services center Java libraries ArdenSuite compiler ArdenSuite engine

35 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)

36 Crisp sets vs. fuzzy sets χ Y young threshold age arbitrary yes/no decisions cause of unfruitful discussions often simply wrong µ Y young 1 intuitive gradual transitions 0 0 threshold age

37 Examples of fuzzy sets as they are applied in Moni-ICU DoC fever ºC 37.9 DoC 1.0 leukopenia leukocytosis DoC 1.0 shock present ,000 5,000 11,000 12,000 WBC/mm 3 11, shock index = systolic blood pressure/ 0.9 heart rate

38 Clinical concepts and relationships between them truth value ( ) t S S S D 1 2 degree of compatibility 3

39 Uncertainty in conclusions I: through linguistic uncertainty in premises Example: SS 1 SS 2 SS 3 SS 4 SS SS6 is true is 0.8. degree of compatibility fever SS 1 : fever SS 2 : hypotension SS 3 : leukopenia SS 4 : leukocytosis SS 5 : increased CRP SS 6 : inflammatory signs (with sepsis) C

40 Uncertainty in conclusions II: through uncertainty in propositions Example 1: 0.8 SS 1 DD1 SS 1 : highly increased amylase DD 1 : acute pancreatitis Example 2: 0.8 II 1 SS1 II 1 : thermoregulation (cooling) SS 1 : fever Example 3: at least 4/11: SS 1 SS DD1 SS 1 : morning stiffness lasting at least one hour SS 5 : symmetric joint involvement SS 9 : positive serum rheumatoid factor DD 1 : rheumatoid arthritis

41 Two different hyperglycemia definitions Hyperglycemia (surveillance) is true is Hyperglycemia (alerting) is true is 0.75.

42 Towards a science of clinical medicine patient s medical data and healthcare processes for human processing patient s medical data and healthcare processes for machine processing observations measurements e.g., temperature chart e.g., CRP skin color (jaundice, livid, ) color measurement Measure what is measurable, and make measurable what is not so. Crucial point in clinical medicine: Digitize what is digitizable, and make digitizable what is not so. Galileo Galilei Klaus-Peter Adlassnig

43 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.

44 Literature on Clinical Decision Support : 36,211 publications Max Plischke,

45 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):

46 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

47 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:

48 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.