PSIP β MIE Workshop - Goteborg β may 2008
PSIP
A European project
Patient Safety through Intelligent Procedures in medication
www.psip-project.eu
PSIP β MIE Workshop - Goteborg β may 2008
Agenda
β’β General Presentation of the European Project: R. BEUSCART
β’β Patient safety: The example of incident reporting in DK. J. EGEBART β’β Rationale : M.C. BEUSCART-ZEPHIR
ββ Presentation ββ Questions from the audience
β’β Identification of ADE: R. BEUSCART, E. CHAZARD, and S. DARMONI ββ Presentation ββ Questions from the audience
β’β Knowledge elicitation: M.C. BEUSCART ββ Presentation
β’β Questions and discussion with the audience
PSIP β MIE Workshop - Goteborg β may 2008
PROJECT PARTNERS
1. University Hospital of Lille and University of Lille2 (F) 2. University of Rouen (F) 3. Denain General Hospital (F) 4. 10 hospitals from the Β« Capital Region of Copenhagen Β» (DK) 5. Oracle (Europe) 6. IBM Danmark β division ACURE (DK) 7. Medasys (F) 8. Vidal SA (F) 9. KITE solutions (I) 10. Ideea Advertising (Romania) 11. Aristotle Thessaloniki University (Greece) 12. Aalborg University (DK) 13. UMIT βInnsbruck University (A)
PSIP β MIE Workshop - Goteborg β may 2008
The Project : A Research Project (7th PCRDT β IST)
β’β Context: ββ Adverse Drugs Events are too frequent and often severe ββ CPOE are developing and they are more and more coupled with
Decision Support Systems
β’β Objectives : ββ A better identification of ADE in Hospitals through the mining of
Patients Records ββ A better contextualisation of the Prescription-Dispensation-
Administration Process
PSIP β MIE Workshop - Goteborg β may 2008
Agenda of the Project
β’β Duration: 40 months β’β 13 partners β 13 workpackages β’β Signature : 19 December 2007 β’β Kick off: January 2008 β’β Experimentations : 1st quarter 2010 β’β End of the project : April 2011
β’βMay 08 = 4 months of work
PSIP β MIE Workshop - Goteborg β may 2008
Patient safety The example of incident reporting in DK
Jonas Egebart, MD Patient Safety Manager
Capital Region of Denmark Unit for Patient Safety
PSIP β MIE Workshop - Goteborg β may 2008
Capital Region of Denmark
β’β1,6 million inhabitants
β’β14 hospitals
β’β10,000 hospital beds
β’β31,000 staff
PSIP β MIE Workshop - Goteborg β may 2008
Incident reporting in Denmark
β’βFirst country to make incident reporting mandatory ββ 1 January 2004
β’βCurrently only hospital-based healthcare ββ Planned extension to cover primary care ββ Planned extension to let patients report
β’βA huge success! ββ More reports than expected ββ Reporting from doctors at a level corresponding to their proportion
of total staff
PSIP β MIE Workshop - Goteborg β may 2008
Principles for reporting
β’βMandatory ββ All licensed healthcare personnel working at hospitals must report
β’βConfidential ββ Reports excluded from freedom of information regulations
β’βNon-punitive ββ No action can be taken by employer, licensing authorities
(National Board of Health), or courts of law
PSIP β MIE Workshop - Goteborg β may 2008
What is reported?
β’βAll incidents, excluding
ββKnown complications
ββEvents that were prevented due to an effective safety culture
PSIP β MIE Workshop - Goteborg β may 2008
Scientific Background
Marie-Catherine Beuscart-ZΓ©phir Lille, F
PSIP β MIE Workshop - Goteborg β may 2008
The scientific questions addressed
β’β Β« To get a better knowledge of the prevalence of Adverse Drug Events (ADE) and of their characteristics per hospital, per Region, per Country Β» ββIdentification of ADE
β’β Β« To develop concepts and methods to achieve the contextualization of CDSS (alerting) functions Β» ββPrevention of ADE
PSIP β MIE Workshop - Goteborg β may 2008
Adverse Drug Event?
β’β A drug given to a patient had a negative consequence on his health.
PSIP β MIE Workshop - Goteborg β may 2008
Medication errors?
β’β In the context of the medication use process: a gap between what has been done and what should have been done
ββ WRONG (drug, dose, route, etc.) at any stage (prescription, dispensing, administration)
β’β A medication error may result or not in patient harm
Failure of a planned action to be completed as intended (i.e., error of execution), or the use of a wrong plan to achieve an aim (i.e., error of planning).
An error may be an act of commission or an act of omission. Preventing Medication Errors, IOM, Quality Chasm Series, 2007
PSIP β MIE Workshop - Goteborg β may 2008
Gandhi TK, Seger DL, Bates DW. 2000. Identifying drug safety issues: From research to practice. /International Journal for Quality in Health Care/ 12(1):69β76.
PSIP β MIE Workshop - Goteborg β may 2008
Identifying ADE: reporting systems
β’β Prevailing method
β’β Reporting systems focused on ADE: ββ Pharmaco surveillance (ex: MedDRA) ββ Difficult to tell adverse DRUG events from Β« normal Β» negative
outcomes due to patientsβ illness (old people, multiple pathologies, complex treatments)
ββ Underreporting
β’β Reporting systems for medication errors ββ Similar to error reporting systems in other domains (aviation) ββ Identify and fix causes of errors and accidents ββ Difficulties: fear of blame ββ Underreporting
PSIP β MIE Workshop - Goteborg β may 2008
Identifying ADE: other methods
β’β Medical records review and analysis ββ Focused on medication errors ββ Time consuming ββ Easier with computerized records and CPOE ββ Extrapolation to larger populations:
β’β risk of overestimation
β’β Direct observations of work procedures ββ Focused on medication errors ββ Extremely time consuming ββ Extrapolation to larger populations:
β’β risk of overestimation
PSIP β MIE Workshop - Goteborg β may 2008
Identifying ADE: heterogeneous results
β’β ADE: probably a major public health problem
β’β Most of the cited numbers are estimates (France10 000 up to 25000 deaths)
β’β Lack of epidemiological results at each level: ββ Europe ββ Countries ββ Regions ββ Hospitals
PSIP β MIE Workshop - Goteborg β may 2008
Identifying ADE: the PSIP approach
β’β To track back ADE from their outcomes
β’β Data and semantic mining of large healthcare repositories ββ To identify atypical hospital stays potentially due to ADE ββ To characterize these atypical stays along a number of
dimensions to support the interpretation
β’β To provide epidemiological results at each level: ββ Europe ββ Countries ββ Regions ββ Hospitals
PSIP β MIE Workshop - Goteborg β may 2008
Preventing ADE: the Β« quality Β» approach
β’β Example: HFMEA (Healthcare Failure Mode and Effect Analysis)
β’β A systematic approach to identify and prevent product and process problems before they occur
β’β Efficient but: ββ Highly time consuming ββ Requires a strong commitment from institutions and HC
professionals
PSIP β MIE Workshop - Goteborg β may 2008
Preventing ADE: CPOE
β’β Electronic prescribing and CPOE systems are efficient
β’β But most of the systems (++ commercial ones) suffer from inexistent and inefficient alerting and decision support systems (Alert fatigue)
β’β Less than 10% of hospitals are equipped with a complete CPOE system
β’β HC professionals need contextualized and task-oriented decision support functions
PSIP β MIE Workshop - Goteborg β may 2008
Preventing ADE: the PSIP approach
β’β The data and semantic mining procedures should provide us with the context of occurring ADE: ββ Characteristics of <patient> and <disease> and <drugs> and
<medical department> and β¦
β’β The question of the content of the alert is a difficult one: ββ Adopt a user-centred approach ββ Use the knowledge issued from the reporting system ββ Otherβ¦
PSIP β MIE Workshop - Goteborg β may 2008
Data Models
β’β Data are imported from 4 groups of Hospitals (Fr, Dk), with different medical records, different CPOEs, different Lab Medical Systems.
β’β But the items on which the mining will be performed must be identical
β’β Types of data: ββ Computerised medical records, including DRG systems ββ Biological results ββ Drugs (commercial name + ATC code) ββ Anonymised reports and letters
PSIP β MIE Workshop - Goteborg β may 2008
Data Model agreed upon and used by all the partners
Complete documentation
on the data fields
General Presentation
PSIP β MIE Workshop - Goteborg β may 2008
Problems Difficulties
β’β Main Problems ββ Technical problems in the hospitals to extract data from their
Hospital Information System -> delays in the delivery of the anonymised files
ββ Some data are only exported. Some have to be calculated by scripts. The management of the quality of data is a high level challenge
β’β Solved problems ββ Complete agreement on the coding systems and catalogs to be
used ββ After 2 months of work, all the hospitals have agreed on the data
model, which is now stable ββ It is an iterative process
PSIP β MIE Workshop - Goteborg β may 2008
Data Quality Analysis and Controls
Hospital
files
SFTP
Quality analysis
Automatic report on the quality of
the data
Errors report
Validation and corrections
PSIP β MIE Workshop - Goteborg β may 2008
Perspectivesβ¦
Generalisation: ββ This model was approved by 4 hospitals ββ Applied in 2 hospitals with CPOEs, 2 hospitals without CPOEs
(but reports, letters) ββ Same data should be available from every European hospital (to
be validated)
Other data bases to be explored: ββ Vigilance databases ββ Data from information providers (Vidal)
PSIP β MIE Workshop - Goteborg β may 2008
Methods
β’β Methods to be used: statistical methods ββ Decision Trees ββ Association Rules ββ Logistic Regression Analysis ββ Multi-factorial Analysis: Principal Component Analysis, Multiple
Component Analysis
β’β It is currently a preliminary phase: ββ On currently available databases and items
PSIP β MIE Workshop - Goteborg β may 2008
Assumptions
β’β Coding Systems: ββ ICD 10: 18 000 possible Diagnosis codes ββ Drugs data bases: 9 000 possible drugs
β’β Information has to be aggregated to allow statistical analyses ββ Objective: to reduce the number of variables; to increase the
number of patients ββ Examples concerning AVK
β’β 9 ICD -> 1 ATC category β’β Gastric Ulcer -> possible bleeding lesion β’β Hemarthrosis -> hemorrhages β’β Too high INR -> coagulation disorders
β’β All kinds of data have to be filtered : ββ Possible effect of an ADE ββ Possible context or cause of an ADE
PSIP β MIE Workshop - Goteborg β may 2008
Data Aggregation
Stays
External declaration of mapping policies
Diagnosis mapping policy
Biology mapping policy
Drug
Drugs mapping policy
Agg. Engine
PSIP β MIE Workshop - Goteborg β may 2008
Available Data
Available data have to be classified: cause or context of an ADE possible ADE manifestation !!!
Examples on diagnoses: chronic diseases Acute events
Examples on biology : Existing abnormality at the entry of the patient Biological abnormality occurring during the hospitalisation
Examples on drugs : Prescription Antidotes
Atypical stays of which ADE
1- identification of atypical stays
(0/1 or βhow muchβ)
2- Identification of a link (predictors)
5- Alert output
Rules engine
4- Rules production
3- Knowledge production
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PSIP β MIE Workshop - Goteborg β may 2008
First results obtained from logistic regression analysis
β’β Example 1 ββ Circumstances : Heparin, liver insufficiency, existing
thrombopenia ββ Effect : ICU transfer after some days of hospitalisation
β’β Example 2 ββ Circumstances : heparin, renal insufficiency, normal coagulation
when admitted ββ Effect : thrombopenia
β’β Example 3 ββ Circumstances : elderly patient, no renal insufficiency, too low
INR, AVK prescription, diuretics, enzyme inhibitor, high number of associated diagnosis
ββ Effect : renal insufficiency occurring during the stay
PSIP β MIE Workshop - Goteborg β may 2008
First Results (2)
β’β Example 4 ββ Effect : too low INR during the stay ββ Circumstances : renal insufficiency, too high INR, AVK, anti-
thrombinic agent
β’β Example 5 ββ Effect : rapid re-hospitalization ββ Circumstances : heparin or anti-thrombinic agent
β’β Example 6 ββ Effect : ADE declaration ββ Circumstances : high INR at the entry
PSIP β MIE Workshop - Goteborg β may 2008
Perspectives
β’β To complete the work and obtain new results ββ Identification of atypical hospitalizations that could be linked with
ADE ββ Identification of the drugs associated with these hospitalizations
β’β Provide understandable Results ββ Worksheets ββ Medical Records susceptible to be associated with an ADE
β’β Validate this research ββ Probabilities of abnormal events ββ Rules weight ββ Queries in the databases
PSIP β MIE Workshop - Goteborg β may 2008
Why Semantic mining in PSIP
β’β Extract from discharge summaries ββ Medical terms in various health terminologies ββ Drugs (commercial or international names)
β’β Completion to data mining, useful when no CPOE in HIS
PSIP β MIE Workshop - Goteborg β may 2008
Modeling the medical terminologies in PSIP
β’β SNOMED CT & SNOMED Int. β’β ICD10 β’β MeSH β’β CCAM (French equivalent to US CPT; ) β’β MEDDRA, WHO ART (for adverse effects) (not yet) β’β ATC classification β’β CAS number β’β Commercial names, ATC codes, specific codes in France
(CIP, CIS, UCD) provided by Vidal β’β Generic model to allow interoperability between
terminologies β’β Draft delivrable on the PSIP Web site
PSIP β MIE Workshop - Goteborg β may 2008
How semantic mining is generic
β’β Semantic mining is closely related to languages => difficulty in Europe with so many different languages
β’β Modelling of the terminologies is not language-dependant
β’β Need to translate main terminologies in the various languages in Europe
β’β Need to develop semantic mining tools in each language
PSIP β MIE Workshop - Goteborg β may 2008
Knowledge Elicitation
Marie-Catherine Beuscart-ZΓ©phir Lille, Fr
PSIP β MIE Workshop - Goteborg β may 2008
Knowledge elicitation: objectives
β’β Select the most relevant abnormal stays ββ Validate the list of variables describing the abnormal outcomes ββ [Provide additional outcomes variables] ββ Confirm that these abnormal stays are probable ADE
β’β Characterize the abnormal stays on a number of dimensions (medical, pharmaceutical, biochemical and human factors expertise) ββ predictors of ADE
β’β Validate association rules between predictors and outcomes
PSIP β MIE Workshop - Goteborg β may 2008
Knowledge elicitation: methods
β’β Involve different categories of experts (pharmacists and pharmacologists, physicians, nurses, patient safety and HF experts)
β’β 1st step: individual review of the atypical cases β’β 2nd step: debriefing session
ββ Harmonisation of results ββ Editing of association rules
PSIP β MIE Workshop - Goteborg β may 2008
Knowledge elicitation: expert review
β’β Each expert is presented with: ββ A set of abnormal stays characterized along all the dimensions of
the data model ββ The association rules statistically characterizing these stays
<age over 70> AND <severe renal insufficiency> AND <anticoagulant>
β’β For each case, the expert answers the following questions β’β Abnormal stay? β’β ADE? β’β Drug (s) involved? β’β [interpretation] β’β Consistent with association rule? β’β Other information required?
β’β Experts reasoning is monitored
PSIP β MIE Workshop - Goteborg β may 2008
Knowledge elicitation: experts debriefing
β’β Clear potential inconsistencies between expertsβ conclusions
β’β Edit the knowledge rule for input in CDSS rule ββ Ex: expert validation of the rule
IF <age over 70> AND <severe renal insufficiency> AND <anticoagulant> THEN [ADE] send an alert
PSIP β MIE Workshop - Goteborg β may 2008
The content of the alert: a difficult question
β’β The alert must incorporate professional knowledge and be contextualized
β’β Data mining will provide information about the context
β’β Professional knowledge will be incorporated by adopting a cooperative user-centred approach for the design of the alerts