Routine use of the “ADE Scorecards”, an application for automated ADE detection, in a general...

Routine use of the ADE Scorecards, an application for automated ADE detection, in a general hospital Emmanuel Chazard a, Michel Luyckx b, Jean-Baptiste Beuscart c, Laurie Ferret a, Rgis Beuscart a a Public Health Department, Lille University Hospital; UDSL EA 2694; Univ Lille Nord de France; F-59000 Lille, France. b Hospital Pharmacy, Lille University Hospital; UDSL EA 4481; Univ Lille Nord de France; F-59000 Lille, France. c Geriatrics Department, Lille University Hospital; UDSL EA 2694; Univ Lille Nord de France; F-59000 Lille, France.

Introduction I. Adverse Drug Events II. The PSIP European Project III. The ADE Scorecards 2013-08-222 Routine use of the ADE Scorecards, an application for automated ADE detection, in a general hospital.

2013-08-22 Adverse drug events ADEs = Adverse Drug Events Several definitions. Institute of Medicine (2007): An injury resulting from the use of a drug An injury due to medication management rather than the underlying condition of the patient Epidemiological data: 98,000 deaths per year in the US An ADE would occur in 5-9% of inpatient stays Two fields of research: Retrospective ADE detection Prospective prevention of ADEs by CDSS (clinical decision support systems) 3 Routine use of the ADE Scorecards, an application for automated ADE detection, in a general hospital.

2013-08-22 Funded by the European Research Council, 7th framework program (agreement N216130) The PSIP European Project Patient Safety Through Intelligent Procedures in Medication 4 Routine use of the ADE Scorecards, an application for automated ADE detection, in a general hospital. Hospitals: Lille University Hospital (F) Rouen University Hospital (F) Denain general hospital (F) Hospitals from the Region Hovedstaden (Dk) Industrial partners: Oracle (Europe) IBM Denmark (Dk) Medasys (F) Vidal SA (F) KITE solutions (I) Ideea Advertising (Ro) Academic partners: Aristotle Thessaloniki University (Gr) Aalborg University (Dk) UMIT Innsbruck University (Au)

2013-08-22 The ADE Scorecards, a tool for automated ADE detection in EHR 5 Routine use of the ADE Scorecards, an application for automated ADE detection, in a general hospital. EHR Database ADE detection rules Computation step List of potential ADE cases Contextualized statistics Web-based display tool -Statistics on ADEs -Description of rules -Cases review Routinely- collected data (diags, lab, drugs, etc.). /!\ ADEs are not flagged. 236 validated complex rules Statistics and automated filters are site- dependant Real past inpatient stays Multilingual (En, Fr, Dk, Bu)

2013-08-226 Routine use of the ADE Scorecards, an application for automated ADE detection, in a general hospital.

2013-08-229 Routine use of the ADE Scorecards, an application for automated ADE detection, in a general hospital. Number of cases per month Histogram of appearance delay

2013-08-2215 Routine use of the ADE Scorecards, an application for automated ADE detection, in a general hospital. 83

16 Routine use of the ADE Scorecards, an application for automated ADE detection, in a general hospital.2013-08-22

The ADE Scorecards, a tool for automated ADE detection in EHR Usage: Installed in 5 hospitals (2 Danish, 2 French and 1 Bulgarian) Routinely used by the physicians and pharmacists of a French general hospital during three years Objective: show the results of its use in real-life situation 17 Routine use of the ADE Scorecards, an application for automated ADE detection, in a general hospital.

Material and methods I. Quantitative analysis II. Qualitative analysis 2013-08-2218 Routine use of the ADE Scorecards, an application for automated ADE detection, in a general hospital.

Quantitative & qualitative analysis Analyzed data: January 2007 to August 2012 Description: Patients, medical background (95% confidence intervals) Comparisons: Between medical departments (displayed here: Gynecology-Obstetrics vs. Cardiology) Chi-2 tests & Students t-tests (with =5%) Focus on 2 outcomes: Hyperkalemia: defined as K + >5.5mmol/l may induce lethal cardiac rhythm troubles INR increase: defined as INR5 (INR=international normalized ratio) due to VKA overdose or interaction (VKA=vitamin K antagonist) may induce a severe hemorrhage Observation of the daily use by a human-factors specialist and a pharmacist 2013-08-2219 Routine use of the ADE Scorecards, an application for automated ADE detection, in a general hospital.

Results I. Characteristics of the patients II. Potential ADEs with INR increase III. Potential ADEs with hyperkalemia 2013-08-2220 Routine use of the ADE Scorecards, an application for automated ADE detection, in a general hospital.

Characteristics of the patients ( n=73,836 inpatient stays) 2013-08-2221 Routine use of the ADE Scorecards, an application for automated ADE detection, in a general hospital. ParameterOverallCardiologyGyn. Obs.p value (all) Age (years)60.267.628 p5 => risk of hemorrhage The tree attempts to explain INR>5

2013-08-22 Results Example of decision tree (2) 1. VKA & butyrophenone discontinuation P=0.4 2. VKA & no butyrophenone discontinuation & hypoalbuminemia P=0.5 3. VKA & no butyrophenone discontinuation & no hypoalbuminemia P=0.05 4. no VKA P=0 We obtain 4 rules. 2 of them are associated with increased risk of hemorrhage => 2 ADE detection rules 4 3 2 1 35 Routine use of the ADE Scorecards, an application for automated ADE detection, in a general hospital.

2013-08-22 Results Example of decision tree (3) Butyrophenone: neuroleptic drugs, may accelerate the intestinal transit VKA Step 1: normal VKA intake INR VKA Step 2: butyrophenone => transit acceleration => too low INR Buty. INR VKA Step 3: increased intake of VKA => normal INR Buty. INR VKA Step 4: buty. discontinuation => normal transit => too high INR INR 36 Routine use of the ADE Scorecards, an application for automated ADE detection, in a general hospital.

2013-08-22 Results Example of decision tree (4) Albumine = plasmatic protein to which VKA bind. Only the non- bound part is biologically active. Serum albuminVKA Normal state: 99% of the VKA bind to albumin. Only 1% of VKA are biologically active. The intake is based on it. Hypoalbuminemia: decrease of the bound fraction, increase of the non-bound fraction => too high INR (with constant intake) 37 Routine use of the ADE Scorecards, an application for automated ADE detection, in a general hospital.

2013-08-22 Complete 2010 year of one hospital Number of stays :14,747 Number of hyperkalemia cases :117 (7.93) exhaustive review Result Precision39/75=52.0% Recall39/41=95.1% Harmonic mean67.2% Number of reported cases0/41=0% 52.0% 95.1% Results Evaluation of the ADE retrospective detection 38 Routine use of the ADE Scorecards, an application for automated ADE detection, in a general hospital.

2013-08-22 Retrospective detection of ADEs Retrospective identification of past ADEs, although no explicit signal exists in the data Inpatient stay Prescription Hospital information system, databases 39 Routine use of the ADE Scorecards, an application for automated ADE detection, in a general hospital.

2013-08-22 Prospective prevention of ADEs Inpatient stay Hospital information system Prescription Alert method Change of the drug prescription Alert generation, before the ADE occurs, in order to prevent it. 40 Routine use of the ADE Scorecards, an application for automated ADE detection, in a general hospital.

2013-08-22 Statistiques sur les EIM dans la base de donnes PSIP *: le nombre dvnements est rapport au nombre total de sjours, alors que les nombres suivants sont rapports aux sjours de plus de 2 jours uniquement **: ces nombres sont extrapols depuis un chantillon 41 Routine use of the ADE Scorecards, an application for automated ADE detection, in a general hospital.

2013-08-22 Pour chaque rgle, les statistiques contextualises sont calcules dans chaque service X all departments X surgery X gyneco-obstetrics X medicine A X medicine B X pneumology Y all departments Y apoplexy Y cardio & endocrinology Y geriatrics Y gynecology Y intensive care unit Y internal medicine Y obstetrics Y orthopedics Y rheumatology Y urology Z all departments W all departments X all dpts 43 Routine use of the ADE Scorecards, an application for automated ADE detection, in a general hospital.

Prvention prospective des EIM 44 Routine use of the ADE Scorecards, an application for automated ADE detection, in a general hospital.2013-08-22

Prvention prospective des EIM Approche de PSIP Ex : AVK & IPP risque hmorragique Implmentation classique : Implmentation PSIP : Service AService BService C AVK&IPP alerte Service AService BService C Probabilit mesure 10% AVK&IPP alerte Probabilit mesure 0.01% AVK&IPP silence Circonstance inconnue AVK&IPP alerte 45 Routine use of the ADE Scorecards, an application for automated ADE detection, in a general hospital.

2013-08-22 Prvention prospective des EIM Approche de PSIP Dveloppement de plusieurs systmes daide la dcision : Prototype IBM Prototype Medasys Simulation web dordonnance Caractristiques majeures : Alertes filtres statistiquement, contextualises moins dalertes, plus pertinentes Rgles raffines (segmentation statistique) prdiction du risque plus pertinente Mthodes dalerte moins interruptives, plus acceptables 46 Routine use of the ADE Scorecards, an application for automated ADE detection, in a general hospital.

Data reuse in insurance companies Routine activities: Reuse of the data: 2013-08-22 Transactional activities. The company: How much should Mr Smith pay for his car insurance? Nearly-custom database Model for predicting individual risk Statistical analysis Daily feeding and updating Accidents database (outcomes) Contributions data (incomes) Demographic data Data transformation Recruits and follows customers Banks insurance premiums Pays out claims Personalized insurance premiums Decision 47 Routine use of the ADE Scorecards, an application for automated ADE detection, in a general hospital.

big data is generally a property of the routinely collected data that can be reused Big can be understood through 5 dimensions: idagegenderdiagnosis Definition of big data 2013-08-22 idagegenderdiagnosis 12323MI10 12578MK37 24513FM61.2 27824MI41 32465FI48 35034FF20.2 1-Many records 2-Many variables IdParVal 123K+4.5 123K+4.8 123K+5.2 3-Many possible values for qualitative variables 4-Many tables & relationships 5-Variables with repeated measurements 48 Routine use of the ADE Scorecards, an application for automated ADE detection, in a general hospital.

Challenges in data reuse Where is the secret of a successful data reuse? 2013-08-22 Scientific question Nearly-custom database Results Statistical analysis Transactional database 3 Transactional database 2 Transactional database 1 Data transformation Interpretation Knowledge Here? Not really Data mining techniques ( statistical methods) are used, but not specific. Here? Not really Data mining techniques ( statistical methods) are used, but not specific. Here? Partially Significant tests are nearly always observed in Big Data: correct the risk, consider the effect size. Cf. post. Here? Partially Significant tests are nearly always observed in Big Data: correct the risk, consider the effect size. Cf. post. Here? Yes, mainly! The decisions that are taken for the data transformation process have a critical effect. Here? Yes, mainly! The decisions that are taken for the data transformation process have a critical effect. 49 Routine use of the ADE Scorecards, an application for automated ADE detection, in a general hospital.

Data reuse of EHR (Electronic Health Records) 2013-08-22 Demo. & Admin Age80 Man?0 Dead?0 Length of Stay9 () Diagnoses E119Diabetes I251Athrosclrosis I10Arterial hypertension N300Cystitis Lab results NPU03230 Potassium Administered drugs Free-text reports Medical devices?? Procedures ZZBQ002Thorax radiography 50 Routine use of the ADE Scorecards, an application for automated ADE detection, in a general hospital.

Data quality assessment 2013-08-2251 Routine use of the ADE Scorecards, an application for automated ADE detection, in a general hospital.

Data quality assessment Getting reliable data is a challenge => an iterative quality control is mandatory Extraction format, basic requirements (tables, fields) Single value validity. E.g.: Incorrect type: Age=old Impossible value: age=141, diagnosis=HHFA001 Out-of-terminology value: diagnosis=B99.0 Univariate validity. E.g.: Each value is possible, but Mean(age)=85 Contextual: mean(age)=21 in Pediatrics Bivariate validity. E.g.: Length of stay=2, admission=2013-05-14, discharge=2013-05-14 Age=21 in a Geriatrics Unit Age=21 with diagnosis=Alzheimer disease 2013-08-2252 Routine use of the ADE Scorecards, an application for automated ADE detection, in a general hospital.

Data transformation, data aggregation 2013-08-2253 Routine use of the ADE Scorecards, an application for automated ADE detection, in a general hospital.

Data transformation: what for? 2013-08-22 Mandatory before using statistical methods Enables to transform data into information: how would a human comment/summarize the raw data? Suppresses 3 over 5 dimensions of bigness that are not compatible with statistical analysis 54 Routine use of the ADE Scorecards, an application for automated ADE detection, in a general hospital.

Data transformation, example 1: suppression repeated measurements (1) 2013-08-22 Potassium: time0 Id stayDateParameterValue 1230Potassium4 1231Potassium4 123 0Sodium140 123 5280Potassium3.2 528 Sodium: time0 Potassium: time0 Stay n123 Stay n528 Several parameters may be measured several times during a given inpatient stay. => One curve per {id_patient*parameter} 55 Routine use of the ADE Scorecards, an application for automated ADE detection, in a general hospital.

Data transformation, example 1: suppression repeated measurements (2) Objective: 1 table with 1 row per stay Example of simple transformation (without a priori knowledge): 2013-08-22 Potassium: time0 Sodium: time0 Potassium: time0 Stay n123 Stay n528 Id stay Potassium.Min Potassium.Max Sodium.Min Sodium.Max 1233.86.2136142 5282.83.2NA 56 Routine use of the ADE Scorecards, an application for automated ADE detection, in a general hospital.

Data transformation, example 1: suppression repeated measurements (3) Another example of transformation, with knowledge Uses the range of normal values according to the parameters, summarizes the anomalies 2013-08-22 Id stay Hyperkalemia Hypokalemia Hypernatremia Hyponatremia 1231000 52800NA Potassium: time0 Sodium: time0 Potassium: time0 Stay n123 Stay n528 57 Routine use of the ADE Scorecards, an application for automated ADE detection, in a general hospital.

Data transformation, example 2: interpretation of a qualitative variable (1) 2013-08-22 Id stayProcedure.codeProcedure.wording 123LMMC004Bilateral treatment of inguinal hernia without prosthesis, by video surgery 123GLHF001Arterial blood collection for blood gas and pH sampling 528LMMC020Treatment of abdominal hernia with prosthesis, by laparoscopy 528ZZBQ002Thorax radiography Each stay may have 0, 1 or several procedures. The terminology used (CCAM) has more than 5,000 possible codes. In this case, we only interest on hernia treatment. 58 Routine use of the ADE Scorecards, an application for automated ADE detection, in a general hospital.

Data transformation, example 2: interpretation of a qualitative variable (2) 2013-08-22 Id stayProcedure.code 123LMMC004 123GLHF001 528LMMC020 123ZZBQ002 New qualitative variables are created: Each variable has few possible values A mapping is necessary (with overlaps) Requires a strong medical knowledge about codes Id stay H.type H.prosthesis H.approach H.bilateral 123Inguinal0Videosurgery1 528Abdominal1Laparoscopy0 59 Routine use of the ADE Scorecards, an application for automated ADE detection, in a general hospital.

Data transformation, example 2: interpretation of a qualitative variable (3) Example of mapping used for hernia FROM (raw data): 18 codes TO (information): Type (n=3) Prosthesis (n=2) Approach (n=6) Bilateral (n=2) 2013-08-2260 Routine use of the ADE Scorecards, an application for automated ADE detection, in a general hospital.

Problems of interpretation with big data I. Many variables (columns) => type I error correction II. Many records (rows) => effect size => over fitting of the models 2013-08-2261 Routine use of the ADE Scorecards, an application for automated ADE detection, in a general hospital.

1- Type I error inflation with numerous statistical tests Reminder: principle of statistical tests (1) Example : we want to test whether variables A & B are independent. A null hypothesis is supposed: H 0 : A & B are independent Alternative hypothesis H 1 : A & B are not independent. A test statistic is computed: Its behavior under H 1 is unknown! Under H 0, this test statistics follows a known distribution 2013-08-22 Example : A & B are qualitative Test = Khi test Test statistic = 62 Routine use of the ADE Scorecards, an application for automated ADE detection, in a general hospital.

Example : The Test statistic follows a distribution The reject zone is represented in red: Under H 0, this test statistics follows a known distribution Zone of reject of H 0 : extreme values of the test statistic, corresponding to a chosen probability (generally 5%) 2013-08-22 Distribution and zone of reject of H 0 in a Khi test 1- Type I error inflation with numerous statistical tests Reminder: principle of statistical tests (2) 95%5% 63 Routine use of the ADE Scorecards, an application for automated ADE detection, in a general hospital.

1- Type I error inflation with numerous statistical tests Reminder: principle of statistical tests (3) Use of the test: we suppose that H 0 is true. If the result of the test is an improbable value (associated probability p < ), we decide that H 0 is not true: we reject H 0. But, if H 0 is really true, there exists a risk to reject it wrongly: the risk = the Type 1 error (generally 5%) 2013-08-2264 Routine use of the ADE Scorecards, an application for automated ADE detection, in a general hospital.

1- Type I error inflation with numerous statistical tests What if several tests are realized? Scenario with several tests: k independent tests are realized on a dataset A Signal is observed if at least one of the k tests is significant (p inflation of the risk. 2013-08-2265 Routine use of the ADE Scorecards, an application for automated ADE detection, in a general hospital.

1- Type I error inflation with numerous statistical tests What if several tests are realized? => inflation of the risk. 2013-08-22 Number of tests (0-100) Total alpha risk if every test is computed with a 5% individual alpha risk 66 Routine use of the ADE Scorecards, an application for automated ADE detection, in a general hospital.

1- Type I error inflation with numerous statistical tests How to correct this inflation? In order to get the total wished, the individual threshold indiv must be modified: idks correction: Use indiv =1-(1- total ) 1/k Reminder: a b = e b.ln(a) Bonferronis correction, very popular: For usual values of total (1 to 10%), use simply: indiv =( total )/k in practice: indiv =(0.05)/k Threshold very close (and inferior to) idks threshold: more conservative, easy to compute Usable even when the k tests are not independant! 2013-08-2267 Routine use of the ADE Scorecards, an application for automated ADE detection, in a general hospital.

1- Type I error inflation with numerous statistical tests Example of use (1) A dataset of inpatient stays contains X binary variables (e.g. administration of Spironolactone, hyperkalemia, etc.). We want to discover associations by testing every couple of variables. For X=2, 10, 100, 1000, 10000: How many tests are performed? What is the value of total if indiv =0.05? What indiv should be used to obtain total =0.05? idk:Bonferroni: 2013-08-2268 Routine use of the ADE Scorecards, an application for automated ADE detection, in a general hospital.

Number of variables Number of tests total if indiv =5% Sidaks indiv Bonferronis indiv 210.055.000E-02 10450.90061.139E-031.111E-03 10049501.01.036E-051.010E-05 1000499 5001.01.027E-071.001E-07 1000049 995 0001.01.026E-091.000E-09 1- Type I error inflation with numerous statistical tests Example of use (2) A dataset of inpatient stays contains X binary variables (e.g. administration of Spironolactone, hyperkalemia, etc.). We want to discover associations by testing every couple of variables. For X=2, 10, 100, 1000, 10000: 2013-08-2269 Routine use of the ADE Scorecards, an application for automated ADE detection, in a general hospital.

2- Significance versus size of the effect Formulation of the result of a statistical test A decision variable is first computed. Then it is tested. Two equivalent ways to formulate the result (examples with =5%): The p value (significance) : We test whether the decision variable is different from the value expected under H 0 (generally 0 or 1) H 0 is rejected if p < 5% The confidence interval: The test returns the 95% confidence interval of the decision variable H 0 is rejected if this confidence interval does not contain the value expected under H 0 (generally 0 or 1) 2013-08-2270 Routine use of the ADE Scorecards, an application for automated ADE detection, in a general hospital.

2- Significance versus size of the effect Example: comparing proportions We want to compare 2 proportions with =5%. Examples of equivalent ways: Way #1: = 1 - 2 is the effect size H 0 : =0 Test => p value: H 0 is rejected if p < 0.05 Way #2: Relative risk RR= P(A/B) / P(A/B) is the effect size H 0 : RR=1 Test => confidence interval CI 0.95 : H 0 is rejected if CI 0.95 does not contain 1 2013-08-22 0 Significance Effect size 1 RR Confidence interval of RR Significance Effect size 71 Routine use of the ADE Scorecards, an application for automated ADE detection, in a general hospital.

2- Significance versus size of the effect Example of proportions comparison In a hospital database (n=500,000 patients) we want to test whether the intake of Drug X is associated with the occurrence of cardiac insufficiency Relative risk with 95% confidence interval: 1.071 [1.008 ; 1.137] => statistically significant, but weak effect size (+7.1%) 2013-08-22 CI+CI- (%CI+) drug+1,07024,000 4.2% drug-18,930456,000 3.9% 72 Routine use of the ADE Scorecards, an application for automated ADE detection, in a general hospital.

3- Overfitting of predictive models Why does it happen in Big Data? In Big Data and Data Reuse, it seems easy to discover good predictive models: data-driven approaches: without hypothesis Absence of Bonferronis correction Big sample sizes => high significance (even if poor size effect) Overfitting of the model: The models seems to be good in the sample: it is optimized for this particular sample But it could be fortuitous: perhaps another model would have been discovered on another sample 2013-08-2273 Routine use of the ADE Scorecards, an application for automated ADE detection, in a general hospital.

3- Overfitting of predictive models Which solutions? Those solutions should be applied together when possible: Apply Bonferronis correction Consider the effect size as a stop criterion in some algorithmes Trade-off between the complexity of the model and the goodness of fit of the model using criterion such as Akaike Information Criterion Build the model and test it on 2 different databases 2013-08-2274 Routine use of the ADE Scorecards, an application for automated ADE detection, in a general hospital.

3- Overfitting of predictive models Which solutions? Example of procedure for predictive models: 2013-08-22 Original database Random sampling Learning setEvaluation set 20%80% Predictive model Statistical procedure Evaluation (prediction error computation) Learning phase Evaluation phase Final predictive model Statistical procedure 100% Final learning 75 Routine use of the ADE Scorecards, an application for automated ADE detection, in a general hospital.

Data reuse of HER Some data sources Drug (distinguish drug prescription and drug administration). Dont forget to add some sources: Some procedures to map, e.g.: Scanner with iodine => iodine Surgery with anesthesia => anesthetic drug Perfusion, that are often provided without terminology code The drugs the patient brings with him Medical devices (implanted or not): Raw data: hudge amount Aggregated data Results of the interpretation automatically done by the device 2013-08-2276 Routine use of the ADE Scorecards, an application for automated ADE detection, in a general hospital.

Data reuse of HER Terminologies Vocabulary, support of semantic interoprability: Couples of codes and wordings Codes are sometimes included in a hierarchy Enable to associate each concept to a code Contrary to free-text, unambiguous and usable for statistical analysis Example : rysiple = rsiple = dermo-hypodermite = A46 in ICD10 77 Routine use of the ADE Scorecards, an application for automated ADE detection, in a general hospital.2013-08-22

Data reuse of HER Drug terminologies Commercial name Ex : Dafalgan CID Common international denomination Ex : Paractamol / Acetaminophen Various terminology Among them, the ATC classification ATC = Anatomical Therapeutic and Chemical classification Codes, wordings and hierarchy (principally based on the therapeutic indication) 78 Routine use of the ADE Scorecards, an application for automated ADE detection, in a general hospital.

2013-08-22 Example of the Aspirin in the ATC classification 79 Routine use of the ADE Scorecards, an application for automated ADE detection, in a general hospital.

2013-08-22 Laboratory results Biochemistry, hematology, bacteriology, virology, immunology Examples terminologies: LOINC (recommended) IUPAC But most of the time, each laboratory produces its own terminology: No semantic interoperability: Impossible to pool 2 hospital databases In the same hospital, data are sometimes heterogeneous according to the date of the measure Need for producing ad hoc mappings 80 Routine use of the ADE Scorecards, an application for automated ADE detection, in a general hospital.

2013-08-22 Introduction Le projet europen PSIP 81 Routine use of the ADE Scorecards, an application for automated ADE detection, in a general hospital.

2013-08-22 Patient XXX Age80 Homme ?0 Dcd ?0 Dure sjour9 () Diagnostics E119Diabte I251Athrosclrose I10HTA N300Cystite NPU03230 Potassium Mdicaments administrs Courriers et compte-rendus Administered drugs Dtection rtrospective des EIM Modle de donnes 82 Routine use of the ADE Scorecards, an application for automated ADE detection, in a general hospital.

2013-08-22 Vue gnrale des rgles dEIM 83 Routine use of the ADE Scorecards, an application for automated ADE detection, in a general hospital.

2013-08-22 Nombre croissant de sjours analyss 84 Routine use of the ADE Scorecards, an application for automated ADE detection, in a general hospital.

2013-08-22 Facult des rgles dtecter rtrospectivement des EIM Revue de cas experte : valuation de la prcision des rgles Cas valids / cas revus Prcision Intervalle de confiance 95% Hyperkalimie (K+>5.3mmol/l) 54 / 10153.5% [43.7;63.2] Surdosage en AVK (INR>4.9) 5 / 955.6% [23.1;88.0] Insuffisance rnale (creat.>135 mol/l or ure>16.6 mmol/l) 35 / 7546.7% [35.4;58.0] Autres types dvnements14 / 5326.4% [14.5;38.3] TOTAL108 / 23845.4% [39.1;51.7] 85 Routine use of the ADE Scorecards, an application for automated ADE detection, in a general hospital.

Routine use of the “ADE Scorecards”, an application for automated ADE detection, in a general...

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