Knowledge-based monitoring of hospital-acquired infections in adult intensive care patients Klaus-Peter Adlassnig 1 , Alexander Blacky 2 , Walter Koller 2 1 Section on Medical Expert and Knowledge-Based Systems Medical University of Vienna Spitalgasse 23 A-1090 Vienna, Austria www.meduniwien.ac.at/mes 2 Division of Hospital Hygiene Clinical Institute for Hygiene and Medical Microbiology Medical University of Vienna Waehringer Guertel 18–20 A-1090 Vienna, Austria Einführung in Medizinische Informatik, WS 2008/09, 5 November 2008
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Knowledge-based monitoring of hospital-acquired infections in adult intensive care patients
Klaus-Peter Adlassnig1, Alexander Blacky2, Walter Koller2
1 Section on Medical Expert and Knowledge-Based SystemsMedical University of ViennaSpitalgasse 23A-1090 Vienna, Austria www.meduniwien.ac.at/mes
2
Division of Hospital HygieneClinical Institute for Hygiene and Medical MicrobiologyMedical University of ViennaWaehringer
Guertel
18–20A-1090 Vienna, Austria
Einführung in Medizinische Informatik, WS 2008/09, 5 November 2008
Computers in clinical medicine—steps of natural progression
•
step 1: patient administration–
admission, transfer, discharge, and billing of medical services
•
step 2: documentation of patients’
medical data–
electronic health record: life-long, multimedia
•
step 3: patient data retrieval and analysis–
medical research databases, medical studies–
quality assurance in the medical institution
•
step 4: knowledge-based software systems for clinical decision support–
safety net, quality assurance and improvement: …
for the individual patient … and the physician …
and the medical institution
•
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
Nosocomial, or hospital-acquired, infections
ESBL -
extended spectrum beta-lactamase
VRE -
vancomycin-resistant
enterococcus
MDR-TB
-
multidrug-resistant
tuberculosis
increaseddisposition by low immunity
MRSA -
methicillin-resistant
Staphylococcus aureus
exposure to pathogens
entry sites
Potential for reducing the rate of hospital-acquired infections
hospital-acquired (nosocomial) infections:
% reduction:
∗
wound infections
35∗
urinary tract infections
31 ∗
pneumonias
28 ∗
bloodstream infections
35
through continuous surveillance
Haley, R.W., Culver, D.H., White, J.W., Morgan, W. M., Emori, T.G., Munn, V.P., and Hooton, T.M. (1985) The efficacy of infection surveillance and control programs in preventing nosocomial infections in US hospitals. American Journal of Epidemiology 121(2), 182–205. (SENIC study)
Moni/Surveillance-ICU
knowledge-based identification and monitoring of nosocomial infections
patient-specific alerts
infection control
natural-language definitions of nosocomial
infections
Fuzzy theories
Artificial intelligence
Monitoring
of nosocomial infections
knowledge-based systems
fuzzy sets and logic
ICUICU
microbiology
cockpit surveillance remote
clinical data
Medicine
data on microorganisms
cockpit surveillance at ward ICU
Bloodstream infection with clinical signs and growth of same skin contaminant from two separate blood samples
Cockpit surveillance at the infection control unit: Three criteria- based definitions in two patients are completely fulfilled (100%), backtracking of the logical chain of reasoning is provided …
… until the detailed medical data are reached
Data sources and integration
HIS: hospital information system (here: HIS of the City of Vienna)PDMS: patient data management systems (here: CareVue by Philips)CDA: clinical data archiveISM: information support martLIS: laboratory information system of the microbiology (here: HIS of the City of Vienna)HIS DB: relational data base of medical data
6 definitions of bloodstream infections (BSI and BX)–
9 definitions of ICU-acquired pneumonias (PN)–
6 definitions of urinary tract infections (UTI)–
3 definitions of central venous catheter-related infections (CRI)•
Moni/Surveillance-ICU is operated at 12 ICUs at the Vienna General Hospital (96 beds); being extended to neonatal ICU patients, …
•
Moni/Surveillance-ICU is connected to HIS, LIS, and PDMS•
cockpit surveillance for infection control unit–
automated daily and/or manual activation•
Evaluation over a period of 2 months (2 ICUs)–
24 out of 28 patients TP (detected and correct), 0 FPs, 4 FNs
(technical reasons: missing data, missing in rule condition, …), many TNs
–
manual evaluation of criteria: each episode of infection > 2 hours–
with Moni: < 5 min per episode
Arden syntax
•
A standard language for writing situation-action rules that can trigger alerts based on abnormal clinical events detected by a clinical information system.
van Bemmel, J.H., Musen, M.A. (eds.) (1997) Handbook of Medical Informatics, Springer-
Verlag, Heidelberg, Glossary, p. 559.
•
A language to encode actions within a clinical protocol into a set of situation-action rules, for computer interpretation, and also to facilitate exchange between different institutions.
•
The Arden syntax resembles the Pascal computer programming language, and is procedural in its design.
Coiera, E. (2003) Guide to Health Informatics, Arnold, London, 2nd ed., p. 165.
Arden and Health Level Seven (HL7)
•
A standard language for writing situation-action rules that can trigger alerts based on abnormal clinical events detected by a clinical information system.
van Bemmel, J.H., Musen, M.A. (eds.) (1997) Handbook of Medical Informatics, Springer-Verlag, Heidelberg, Glossary, p. 559.
•
Each module, referred to as a Medical Logic Module (MLM), contains sufficient knowledge to make a single decision.extended by packages of MLMs for complex clinical decision support
•
Contraindication alerts, management suggestions, data interpretations, treatment protocols, and diagnosis scores are examples of the health knowledge that can be represented using MLMs.
extended by single and differential diagnosis, temporal monitoring, control systems, selective computerized processing of clinical pathways and management guidelines
•
The first version of the ARDEN syntax was administered and issued by the American Society for Testing and Materials ASTM (1992, version 1.0; today
2.5). Since 1998, an Arden Syntax Special Interest Group (SIG) is part of the HL7 organization (www.hl7.org).
functionality
integration
HIS, MIS, PDMS, LIS, medical practice SW, web-based EHR, telemedicine applications,health portals,…
reminders and alerts, monitoring, surveillance, diagnostic andtherapeutic decision support, …
*
* harmonized input data +collected reasoning data +knowledge application statistics
data services center
Arden, ArdenServer, and health care information systems
Moni/Surveillance-ICU and -NICU
•
identification and monitoring of nosocomial infections according
to KISS (German) and HELICS (European)–
47 MLMs–
data-to-symbol conversion done by MLMs–
fuzzy sets and operators simulated by MLMs
•
for NICU–
>100 MLMs
•
towards FuzzyArden
Contributions to success in CDSS
0% 50% 75% 100%
availability and significance of medical data, structuralization of
medical knowledge, standardization of medical work processes