Development Common Data Model for Adverse Drug Signal Detection based on multi-center EMR systems

Transcription

1 Dvlopmnt Common Data Modl for Advrs Drug Signal Dtction basd on multi-cntr EMR systms Mi Jung, Rho Graduat School of Halthcar Managmnt and Policy, Th Catholic Univrsity of Kora ) Sung Ho, Park Graduat School of Information Systm, Th Hanyang Univrsity of Kora ) Bum Joon, Park Graduat School of Information Systm, Th Hanyang Univrsity of Kora ) Si Ra, Kim Graduat School of Halthcar Managmnt and Policy, Th Catholic Univrsity of Kora ) Kwang Soo, Jang Graduat School of Information Systm, Th Hanyang Univrsity of Kora ) In oung Choi (Corrsponding author) Graduat School of Halthcar Managmnt and Policy, Th Catholic Univrsity of Kora ) Abstract Th drug safty monitoring basd on EMR systm is abl to collct mor objctiv pharmacovigilanc data and analyz advrs drug raction (ADR) arlir than spontanous ADR rporting This study dvlopd th Kora ADR common data modl (K-ADR CDM) for arly dtction of advrs drug raction which is fasibl for Koran EMR systms To do that, w analyzd prviously studid data modl from two prominnt drug safty survillanc rsarchs: Mini-Sntinl data modl and Obsrvational Mdical Outcoms Partnrship (OMOP) data modl Th K-ADR CDM of ight s which contain dmographic, drug, visit, procdur, diagnosis, dath, laboratory and organization Each consists of 5~12 filds In addition, controlld trminology will b applid to intgrat diffrnt EMR systms To validat th data modl of K-ADR, EMR data of S hospital was xportd and mappd with th K-ADR Furthr fforts for th standardization of procdur and laboratory will b ndd for multi-institutional pharmacovigilanc databas systm Th pharmacovigilanc activity basd EMR systm will b cost-ffctiv mthod to dtct ADR signals Kywords Advrs Drug Signal Dtction; Common Data Modl; EMR; Pharmacovigilanc I ITRODUCTIO Pharmacovigilanc is procss to dfin, assss, undrstand and prvnt advrs ffcts of mdicins [1] Food and Drug Administration (FDA) has opratd th Advrs Evnt Rporting systm (AERS) which is th world s largst databas for spontanous rporting of advrs drug ractions sinc th 1960's [2] In South Kora, spontanous advrs drug ractions rporting systm has bn usd sinc 1985 Spontanous rporting systm, howvr, has limitation to dtct advrs drug ractions ffctivly [3] Spontanous rporting systm nd to allow mor tim and cost for collcting th data In addition, bcaus this systm dpnds mainly on halth car providrs voluntary rport, it could postpon dtcting th advrs drug ractions Accordingly, drug safty monitoring basd on EMR is gtting mor important for arly dtction of possibl harmful advrs drug ractions Thr ar drug safty survillanc projcts using lctronic halth rcords (EHR) In th Unitd Stats, th Mini-Sntinl projct was initiatd to prform activ survillanc of th safty of marktd mdical products, including drugs, biologics, and mdical dvics in 2009 [4] Th Mini-Sntinl, rcivd th funding from th US FDA [5], is a collaborativ projcts including 25 institutions [6] In th fourth quartr of 2008, th Obsrvational Mdical Outcoms Partnrship (OMOP) bgan to improv th monitoring of drugs for safty and ffctivnss Th OMOP was a public-privat partnrship among th FDA, acadmia, data ownrs, and th pharmacutical industry [7] Th OMOP was fundd and managd through th Foundation for th ational Instituts of Halth In Europ, th EU-ADR projct startd to dvlop approach to th arly dtction of ADR from Fbruary 2008, fundd by th Europan Commission [8] Four Europan countris, which wr Italy, th thrlands, th Unitd Kingdom and Dnmark, participatd in th EU-ADR projct [9] Ths drug safty survillanc modls hav chosn a common data modl to crat harmonizd input fil from th distributd databas In South Kora, many hospitals hav htrognous EMR databass Thr ar a varity of data typs and htrognous information Thrfor, th EMR dat could not b analyzd consistntly [10] In addition to, thr was an incrasd dmand for dvlopmnt survillanc systm basd on EMR systm In ordr to build a drug safty survillanc systm, intgration with diffrnt EMR systms was To incorporat multi-cntr EMR systms, common data modl /13/$ IEEE

2 should b dvlopd Th common data modl (CDM) could intgrat th data from th diffrnt EMR systms Through th CDM, w could driv th rliabl rsults for arly dtction of advrs drug raction Although many studis hav bn conductd focusd on drug safty survillanc, thr wr limitd domstic rsarchs rlatd to drug safty survillanc systm [11-13] Prvious domstic rsarchs basd on singl EMR systm Thrfor, a varity studis, rlatd to dvlopmnt K-ADR CDM for drug safty survillanc systm basd on multi-cntr EMR systms, was This study attmptd to dvlop K-ADR CDM for multicntr EMR systms In addition to, w validatd th K-ADR CDM by xporting ral EMR data of an acadmic hospital and comparing th K-ADR CDM with EMR data II DRUG SAFET SURVEILLACE MODEL To dvlop K-ADR CDM for multi-cntr EMR systms, w analyzd prviously studid data modl from two prominnt drug safty survillanc rsarchs: Mini-Sntinl data modl and Obsrvational Mdical Outcoms Partnrship (OMOP) data modl A Mini-Sntinl data modl Mini-Sntinl usd a distributd databas Standardizd individual lvl data rmaind th local databas [14] Th distributd databas includd administrativ and claims data from 2000 to 2011 for ovr 300 million prson-yars, 24 billion ncountrs, 38 million inpatint hospitalizations, and 29 billion dispnsing [4] In Mini-Sntinl projct, data partnrs dvlopd th Mini-Sntinl distributd data systm such as Halth Cor, Inc; th HMO Rsarch twork; Humana; th Kaisr Prmannt Cntr for Effctivnss and Safty Rsarch; and Vandrbilt Univrsity [5] Th Mini-Sntinl CDM (MSCDM) containd many of th data lmnts ndd for mdical product safty valuations [5] Th MSCDM V10 consistd of ight s Th MSCDM V20 addd nw s such as laboratory, vitals, and summary s, in which rvisd dfinitions of ncountr variabls such as discharg disposition and discharg status Th MSCDM V20 consistd of fourtn s Th MSCDM V20 modifid in January 2012 Th MSCDM V21 consistd of tn s that rprsnt information for most of th ky data lmnts ndd for Mini-Sntinl activitis This study usd th MSCDM V21 for dvlopmnt K-ADR CDM for multi-cntr EMR systms Dtail of th MSCDM V20 was providd in Tabl 1 o 1 2 Tabl am Enrollm nt Dmogr aphic TABLE I MII-SETIEL CDM V21 TABLE LIST Ky Data Elmnts Patint ID, Enrollmnt_start, Enrollmnt _nd, Mdical car covrag, Drug covrag Patint ID, Birth dat, Sx, Hispanic, Rac Dispns ing Encount r Diagnos is Procdu r Patint ID, Dispnsing data, DC(national drug ), RxSup (umbr of days that th mdication supports), RxAmt (umbr of units: pills, ts, vials) Patint ID, Encountr ID, Encountr or admission data, Discharg data, Providr Cod, Facility_location, Encountr typ, Facility_, Discharg_disposition, Discharg_status Patint ID, Encountr ID, Encountr or admission Data, Providr, Enc typ, Diagnosis, Orig DX, Principal Diagnosis flag Patint ID, Encountr ID, Encountr or admission data, Providr, Enc typ, Procdur, Original procdur 7 Dath Patint ID, Dath Dt, DtImput, Sourc, Confidnc 8 9 Caus of Dath Laborat ory 10 Vitals Patint ID, Diagnosis, Cod typ, Caus typ, Sourc, Confidnc Patint ID, MS_tst_nam, MS_tst_sub_catgory, Spcimn_sourc, LOIC, LOIC_flag, Immdiacy of tst, Patints location, Location of tst rsult, Local rlatd to lab tst rsults, Battry_CD, Procdur, Cod typ flag, Tat data, Data spcimn collctd, Tim spcimn collctd, Rsult data, Tim of tst rsult, Rsult_C (Tst rsult as short string), Modifir, Rsult unit, ormal_low_c (Tst rsult as short string) Modifir_low (Modifir for valu is Rsult_C), ormal_high_c, Modifir_high, Abnormal rsult indicator, Providr, Local, Local facility, Data partnr-spcific idntifir Patint ID, Masur_dat, Wight, Tabacco status, Tobacco typ, Diastolic blood prssur, Systolic blood prssur, Blood prssur typ, Position B OMOP data modl Th OMOP was initiatd to conduct rsarch activitis about th govrnanc, data accss, tchnology, and mthods for drug safty and bnfit monitoring in 2008 [7] Th goal of OMOP was to rfin th scondary us of multipl obsrvational databass [15] Th OMOP mthods wr dvlopd for distributd analysis, has applid an automatic mapping procdur using a standard vocabulary This projct had a dictionary of coding guidlins for halth outcoms of intrst (HOI) that could b applid to lctronic data such as halth insurr claims and EMRs [16] Th OMOP CDM V30 consistd of ightn s that includd prson, drug xposur, drug ra, condition occurrnc, condition ra, visit occurrnc, procdur occurrnc, obsrvation, obsrvation priod, dath, drug cost, procdur cost, location, providr, organization, car sit, payr plan priod, cohort [17] Dtail of th OMOP CDM V30 was providd in Tabl 2 TABLE II OMOP CDM V30 TABLE LIST o Tabl am Ky Data Elmnts 1 Prson 2 Drug Exposur Prson_id, Gndr_concpt_id, ar_of_birth, Month_of_birth, Day_of_birth, Rac_concpt_id, Ethinicity_concpt_id, Prson_location_id, Providr_id, Cas_sit_id, Prson_sourc_valu, Gndr_sourc_valu, Rac_sourc_valu, Ethnicity_sourc_valu Drug_xposur_id, Prson_id, Drug_concpt_id, Drug_xposur_start_dat, Drug_xposur_nd_dat, Drug_typ_concpt_id, Stop_rason, Rfills, Quantity, Days supply, Sig, Prscribing_providr_id,

3 3 Drug Era 4 Condition Occurrnc 5 Condition Era 6 7 Visit Occurrnc Procdur Occurrnc 8 Obsrvation 9 Obsrvation Priod 10 Dath 11 Drug Cost 12 Procdur Cost 13 Location 14 Providr 15 Organization 16 Car Sit 17 Payr Plan Priod 18 Cohort Visit_occuranc_id, Rlvant_condition_concpt, Drug_sourc_valu Drug_ra_id, Prson_id, Drug_concpt_id, Drug_ra_start_dat, Drug_ra_nd_dat, Drug_typ_concpt_id, Drug_xposur_count Condition_occuranc, Prson_id, Condition_concpt_id, Condition_start_dat, Condition_nd_dat, Condition_typ_concpt_id, Stop_rason, Associattd_providr_id, Visit_occuranc_id, Condition_sourc_valu Condition_ra_id, Prson_id, Condition_concpt_id, Condition_ra_start_dat, Condition_ra_nd_dat, Condition_typ_concpt_id, Condition_occuranc_count Visit_occuranc_id, Prson_id, Visit_start_dat, Visit_nd_dat, Plac_of_srvic_concpt, Car_sit_id, Visit_sourc_valu Procdur_occuranc, Prson_id, Procdur_concpt_id, Procdur_dat, Procdur_typ_concpt_id, Associattd_providr_id, Visit_occuranc_id, Rlvant_condition_concpt_id, Procdur_sourc_valu Obsrvation_id, Prson_id, Obsrvation_concpt_id, Obsrvation_dat, Obsrvation_tim, Valu_as_numbr, Valu_as_string, Valu_as_concpt_id, Unit_concpt_id, Rang_low, Rang_high, Obsrvation_typ_concpt_id, Associatd_providr_id, Visit_occuranc_id, Rlvant_condition_concpt_id, Obsrvation_sourc_valu, Units_sourc_valu Obsrvation_priod_id, Prson_id, Obsrvation_priod_start_dat, Obsrvation_priod_nd_dat Prson_id, Dath_dat, Dath_typ_concpt_id, Caus_of_dath_concpt_id, Caus_of_dath_sourc_valu Drug_cost_id, Drug_xposur_id, Paid_copay, Paid_coinsuranc, Paid_toward_dductibl, Paid_by_payr, Paid_by_coordination_bnfits, Total_out_of_pockt, Total_paid, Ingrdint_cost, Dispnding_f, Avrag_wholsal_pric, Payr_plan_priod_id Procsur_cost_id, Procdur_occuranc_id, Paid_copay, Paid_coinsuranc, Paid_toward_dductibl, Paid_by_payr, Paid_by_coordination_bnfits, Total_out_of_pockt, Total_paid, Disas_class_concpt_id, Rvnu concpt_id, Payr_plan_priod_id, Disas_class_sourc_valu, Rvnu sourc_valu Location_id, Addrss_1, Addrss_2, City, Stat, Zip, Country, Location_sourc_valu Providr_id, PI, DEA, Spciality_concpt_id, Car_sit_id, Providr_sourc_valu, Spciality_sourc_valu Organization_id, Organization_typ_concpt_id, Location_id, Organization_sourc_valu, Organization_typ_sourc_valu Car_sit_id, Location_id, Organization_id, Plac_of_srvic_concpt_id, Car_sit_sourc_valu, Plac_of_srvic_sourc_valu Payr_plan_priod_id, Prson_id, Payr_plan_priod_start_dat, Payr_plan_priod_nd_dat, Payr_sourc_valu Plan_sourc_valu, Family_sourc_valu Cohort_id, Cohort_concpt_id, Cohort_start_dat, Cohort_nd_dat, Prson_id, Providr_id, Visit_occurrnc_id, III Stop_rason K-ADR COMMO DATA MODEL A Dvlopmnt of K-ADR CDM and compar with EMR systms K-ADR CDM consistd of ight s including dmographics, visit typ, procdur, dath, diagnosis, drug, laboratory, organization Figur 1 is th ntity rlationshipdiagram (ER-Diagram) for K-ADR CDM Fig 1 ER-Diagram for K-ADR CDM First, dmographics includd patint ID, birth data, gndr, rac, and nationality Scond, visit typ consistd of ight columns: patint ID, visit-typ, visit start data, visitnd dat, admission-dat, discharg-dat, dpartmnt, and discharg-disposition Third, procdur includ patint ID, procdur-dat, procdur-, and procdur-nam Forth, dath consistd of patint ID, dath-dat, dathdiagnosis-, and dath-diagnosis nam Fifth, diagnosis includd lvn columns: patint ID, diagnosis-dat, diagnosis-nam, diagnosis-, diagnosis-dpt, allrgy-, allrgy-nam, allrgy-count, mdical history, mdical history nam, mdical history count TABLE III Tabl Dmographics Visit typ TABLES OF DEMOGRAPHICS, VISIT TPE, PROCEDURE, DEATH AD DIAGOSIS Column nam Valus EMR Patint ID umbr of patints Birth_dat /MM/DD SEX Mal/ Fmal ➀Whit ➁Asian Rac ➂Black ➃ Othrs ationality ationality Patint ID umbr of Patints Visit_typ Inpatint Outpatint Emrgncy visit othr visit typs Visit_start_dat Visit start dat Rfrnc

4 Procdur Dath Diagnosis Visit_nd dat Visit nd dat Admission_da t Discharg_dat Admission dat Discharg dat Dpt Dpartmnt Discharg_Di sposition A=Aliv(Discharg a patint from th hospital) E=Dath U=Unknown Patint ID umbr of patints Procdur_dat Procdur_co d Procdur_na m Procdur dat Procdur Procdur nam Patint ID umbr of patints Dath_dat Dath dat Dath_dig_co Dath diagnosis d Dath_dig_na m Dath diagnosis Patint ID umbr of Patints Diagnosis_dat Diagnosis_na m Diagnosis_co d Diagnosis_d pt Diagnosis dat Diagnosis nam Diagnosis Diagnosis dpartmnt Allrgy_ Allrgy Allrgy_nam Allrgy nam Allrgy_count Allrgy dtails Patint_mdhi Patint mdical story_ history Patint_mdhi Patint mdical story_nam history nam Patint_mdhi Patint mdical story_count history count *ICD-10 *ICD-10 Sixth, drug consistd of sixtn columns: patint ID, drug-nam, drug-ingrdints, drug-ingrdints nam, drug chmistry, ATC, drug-group, drug-count, drug-unit, drugrout, dosag-start dat, dosag-nd dat, dosag-dat, dosagpurpos, dosag-stop flag, and dosag flag Svnth, laboratory wr lvn columns: patint ID, ordr dat, ordr nam, lab typ, lab data, rsult dat, obsrvation rsult, rsult unit, rang low, rang high, abnormal chck Eighth, organization includd thr columns: organization ID, organization typ, and organization sourc valu TABLE IV TABLES OF DRUG, LABORATOR AD ORGAIZATIO Tabl nam Column nam Valus EMR Drug umbr of Patint ID patints Drug_nam Drug nam Drug_ingr_ Drug_ingr_nam Drug ingrdints Drug ingrdints nam Drugs Drug_chm chmistry ATC ATC Drug_group Drug_count Drug_unit Drug_rout Dosag_start_dat Dosag_nd_dat Dosag_dat Dosag_purpos Dosag_stop_fla g Dosag_flag Patint ID Ordr_dat Ordr_nam Lab_typ ➀ Main catgory ➁ Middl catgory ➂ Small catgory Amount of drug pr 1 day Drug unit Drug rout Dosag start dat Dosag nd dat Mdication priod Dosag purpos Dosag stop flag Advrs vnts chck for r-dosing umbr of patints ordr data ordr nam typ Lab_dat data Rsult_dat Rsult dat Rsult Rsult unit Rsult valu Rsult unit Rfrnc unit drug rout laboratory ordr nam rsult valu rsult unit

5 Organization Rang_low Rang_high Abnormal Organization_ID Organization_typ _ Organization_sou rc_valu Lowr limit of normal Uppr limit of normal Abnormal chck umbr of organization ➀ First mdical institution ➁ Scondary mdical institution ➂ Trtiary mdical institution ➃ tc ( ) Abbrviatd information about organization * Standard B Data xtraction of EMR systm to map with K-ADR CDM To validat th K-ADR CDM for advrs drug signal dtction, EMR data of S hospital was xportd and mappd with th K-ADR First, in dmographics, thr wr thr columns such as patint ID, birth data and gndr in EMR systm of S hospital Rac and nationality, howvr, wr not includd in EMR systm of S hospital Scond, all columns of visit typ wr EMR systm of S hospital In cas of visit typ, thr wr diffrnt dpnding on admission, outclinic, mrgncy mdicin, and othrs In column of discharg disposition, thr wr only data rlatd to dath in hospital If patint did aftr discharging from hospital, it was difficult to trac of drugs Third, all columns of procdur wr in EMR systm of S hospital Th standard for procdur would b ssntial to us K-ADR CDM Forth, all columns of dath, which wr patint ID, diagnosis-dat, diagnosis-nam and diagnosis-, also wr EMR systm of S hospital Rfrnc for dath-dig- was ICD-10 Fifth, among th lvn columns of diagnosis, fiv columns such as patint ID, diagnosis-dat, diagnosis-nam, diagnosis-, diagnosis-dpt wr in EMR systm of S hospital Rfrnc for diagnosis- was ICD-10 Sixth, thirtn columns of drug such as patint ID, drug-nam, drug-ingrdints, drug-ingrdints nam, drug chmistry, ATC, drug-group, drug-count, drug-unit, drug-rout, dosag-start dat, dosag-nd dat, dosag-dat wr in EMR systm of S hospital Dosag-purpos, dosag-stop flag, dosag flag wr not includd in EMR systm of S hospital Although thr wr standard trms of drug, it was diffrnt of drug nam dpnding on ach hospital In cas of drug group, thr was no standard of classification Svnth, although all columns of laboratory wr in EMR systm of S hospital, standard and unit wr Finally, organization was important to us data in multipl- cntr EMR systms Howvr, all columns of organization wr not in EMR systm of S hospital C Data rsult from EMR systm xtraction W slctd six drugs which is most popular mdication in clinical practic; ranitidin, clopidogrl, rosuvastatin, ciprofloxacin, fluorouracil, and clcoxibm Rlvant data wr xtractd from EMR systm of S hospital Th dscriptiv analysis was conductd using xtractd drugs data 1) Ag Th total numbr of prscription was 672,753 First, among total prscription, 60% (total 402,659) was prscribd for th fiftis -svntis Ranitidin was prscribd mor for th fiftis (total 87,908, 20 %) Clopidogrl was prscribd mor for th svntis (total 15,983, 35%) Rosuvastatin was prscribd mor for th svntis (total 5,691, 30%) Ciprofloxacin was prscribd mor for th fiftis (total 24,827, 24%) Fluorouracil was prscribd mor for th sixtis (total 16,125, 33%) Finally, clcoxib was prscribd mor for th svntis (total 5,350, 32%) ATC Ag TABLE V THE UMBER OF PRESCRIPTIO DEPEDIG O AGE Ranitidin Clopidogrl Rosuvastatin Ciprofloxacin Fluorouracil Clcoxib Total , , , , , , ,849 1, , ,681 1,816 1,901 18,136 4,757 1,304 58, ) 87,908 6,125 3,021 1) 24,827 12,624 2,707 79, ,260 11,819 4,423 20,066 1) 16,125 3, , ,304 1) 15,983 1) 5,691 10,389 12,455 1)5, ,130 Uppr 80 37,384 9,636 3,009 5,212 1,755 3, ,212 Total 437,176 46,006 19, ,676 49,576 16, ,493 2) Gndr Among total prscription, 49 % (total 328,086) was prscribd for fmal, 51% (total 344,667) th rst was prscribd for mal Ranitidin and ciprofloxacin wr prscribd similarly for fmal and mal Clopidogrl was prscribd mor for mal (total 28,960, 63%) Rosuvastatin was prscribd mor for mal (total 11,033, 58%) Fluorouracil was prscribd mor for mal (total 32,530, 66%) Finally, clcoxib was prscribd mor for fmal (total 10,994, 65%) TABLE VI THE UMBER OF PRESCRIPTIO DEPEDIG O GEDER Total ATC Ranitidin Clopidogrl Rosuvastatin Ciprofloxacin Fluorouracil Clcoxib Gndr Fmal 223,398 7,046 8,142 51,352 17,046 10, ,978 mal 213,778 28,960 11,033 52,324 32,530 5, ,515 Total 437,176 46,006 19, ,676 49,576 16, ,493 3) Dpartmnt Th most prscribd drug was ranitidin Th numbr of ranitidin prscription was 437,176(65%) Thr wr rsults about drug prscriptions dpnding on diagnostic dpartmnt Th total numbr of drug prscriptions dpnding on diagnostic dpartmnt was 2,872,382 Th top fiv diagnostic dpartmnts wr gnral surgry, hmato-oncology, mrgncy mdicin,

6 gyncology, and blood & marrow transplantation Th total numbr of prscription that blongd to th top fiv diagnostic dpartmnts was 1,959,596 (68%) In hmato-oncology, ranitidin was prscribd mor (total 285,032, 18%), clcoxib was prscribd mor in hmato-oncology (total 24,870, 32%) Clopidogrl was prscribd mor in cardiology (total 59,227, 41 %) Rosuvastatin was prscribd mor in cardiology (total 17,403, 25%) Ciprofloxacin was prscribd mor in mrgncy mdicin (total 113,209, 29%) In gnral surgry, fluorouracil was prscribd mor (total 368,393, 60%) TABLE VII THE UMBER OF PRESCRIPTIO DEPEDIG O DIAGOSTIC DEPARTMET ATC o Diagnostic Ranitidin Clopidogr Rosuvasta Ciproflox Fluoroura Clcoxib l tin acin cil dpartmnt Total 1 Gnral surgry 151,101 7,219 4,801 36, ,393 3, ,923 2 Hmatooncology 1) 285,032 9,848 5,067 41, ,426 2) 24, ,368 3 Emrgncy mdicin 257,124 14,693 11,682 1) 113,209 1) 20,351 9, ,264 4 Gyncology 205, ,097 3,172 1, ,938 5 Blood & marrow transplantation 82, , , , , uclar mdicin Total 1,562, ,306 70, , ,987 78,899 2,872,382 4) tst Finally, th svn drugs wr chckd by 41 laboratory tsts Th total numbr of prscription was 10,688,268 Th top four laboratory tsts wr hmoglobin (total 569,502, 53%), hmotocrit (total 569,396, 53%), platlt (total 566,056, 53%), whit blood cll (total 565,099, 53%) and osinophil (total 544,548, 51%) TABLE VIII THE UMBER OF PRESCRIPTIO DEPEDIG O LABORATOR TEST o Cod Tst nam Total 1 LHR102 Hmoglobin 569,502 2 LHR103 Hmotocrit 569,396 3 LHR104 Platlt 566,056 4 LHR100 Whit blood cll 565,099 5 LHR10505 Eosinophil 544, LEE101 Myogloin 199 Total 10,688,268 IV COCLUSIO W dvlopd th K-ADR CDM for arly dtction of advrs drug raction which was fasibl for Koran EMR systms Th K-ADR CDM includd ight s which contain dmographic, drug, visit, procdur, diagnosis, dath, laboratory and organization Each consistd of 5~12 filds In addition, trminology standard such as ICD-10 and WHO- ART would b providd to intgrat multipl EMR systms Aftr comparing th K-ADR common data modl with EMR data of S hospital, it should b concludd as follows First, in dmographics, rac and nationality wr not includd in EMR of S hospital In cas of drug, ffct or sid ffct may b diffrnt dpnding on rac and nationality In addition, as many forignrs immigratd to South Kora, South Kora bcam multicultural socity Thrfor, rac and nationality should b collctd from EMR systm in hospital Th K-ADR CDM has dvlopd to dtct advrs drug signal basd on multi-institutional pharmacovigilanc databas systm For multi-institutional pharmacovigilanc databas systm, th rfrnc for rac and nationality will b Scond, in cas of visit typ, thr ar diffrnt dpnding on admission, out-clinic, mrgncy mdicin, othrs Thrfor, diffrnt visit typ must b unifid In cas of patints dath aftr discharging from hospital, mthod is ndd to trac of drugs Third, although all four columns of procdur wr includd in EMR of S hospital, th standard for procdur- will b ssntial to us K-ADR CDM Accordingly, furthr fforts for dvlopmnt of th standardizd guidlins about procdur would b ndd for multi-institutional pharmacovigilanc databas systm Fourth, among th diagnosis, although thr wr itms for allrgy- and allrgy-nam, it is difficult to collct data Thrfor, allrgy- and allrgy-nam must b collctd Th patint_mdhistory_nam and patint_mdhistory_count hav bn collctd in th form of fr txt, it should b r-d to analysis Accordingly, ths columns wr ndd coding work to standardiz In addition, whn w xtractd th data from EMR, w found that thr was column without diagnosis nam during th data prprocssing for inpatints first diagnosis nam Column without diagnosis nam should b rconfirmd by principal diagnosis In addition to, thr wr multipl diagnosis s or principal diagnosis to th on patint To solv ths problms, guidlin for ths problms would b prpard Fifth, in drug, thr wr many issus Although thr wr standard trms of drug, it was diffrnt of drug nam dpnding on ach hospital Accordingly ach hospital must chang from drug nam to drug-ingrdints Th standard for drug-ingrdints, drug-unit, drugrout and drug-group would b ncssary to us K-ADR CDM Espcially, whn drug-rout was wll rcordd, it was asy to map th data To standard for drug-ingrdints, drug-unit, drug-rout and drug-group, rgulation would b In addition to, although dosag stop flag and dosag flag wr important columns, thr wr not in EMR systm of S hospital In cas of Japan, EMR systm providd pop-up window srvic to notic dosag stop flag Accordingly, systms for dosag stop flag and dosag flag must b dvlopd Whil w xtractd th data from EMR, it was difficult to idntify dosag data xactly, bcaus dosag data wr diffrnt dpnding on prscription data and nursing rcords data From th advic of doctors, w could confirm that nursing rcords data was mor rasonabl In addition, thr wr som problms whn data rlatd to drug was

7 xtractd, bcaus hospitals had a diffrnt drug-nam, drugingrdints, drug-ingrdints nam, drug chmistry Som drugs did not map on th ATC Thrfor, drug should b standardizd, map on th ATC Sixth, in cas of laboratory, bcaus ach hospital has usd diffrnt and unit such as ordr nam, laboratory typ, rsult, rsult unit and abnormal chck would b Although th standard for limit of normal was vry important, ach hospital hav usd diffrnt standard Thrfor, to dtct ADR signals on multipl-cntr EMR systms, cntral laboratory should manag diffrnt standard Dvlopmnt of th standardizd guidlins about laboratory would b ndd for multi-institutional pharmacovigilanc databas systm In addition, thr wr charactr typ data in many columns whn th data rlatd to obsrvation rsult was xtractd Accordingly, data claning was positivly ncssary To do that, ffctiv data claning tool should b dvlopd Svnth, Organization was important to us data in multipl-cntr EMR systms Howvr, all columns of organization wr not in EMR systm of S hospital Accordingly, th standard for organization typ would b ssntial to us K-ADR CDM In addition, thr wr som problms whn w xtractd th data about hospitalization history, bcaus hospitals had a diffrnt nam of mdical dpartmnts In addition to, th on somtims has mappd on svral mdical dpartmnts Thrfor, th standard for organization typ would b to us K-ADR CDM basd on multi-cntr EMR systms This study had som limitation Ovrhang t al (2012) convrtd tn diffrnt datast into th OMOP CDM, to validat th OMOP CDM [17] W compard th K-ADR CDM with EMR data of S hospital, xtractd data from EMR systm to validat th K-ADR CDM Howvr, th K-ADR common data modl was for advrs drug signal dtction basd on multi-cntr EMR systms Thrfor, futur study should b conductd focusd on multi-cntr EMRs such as Ovrhang t al (2012) This study would provid guidlin to dvlop th mthod in ordr to dtct ADR signals Th pharmacovigilanc activity basd EMR would b cost-ffctiv mthod to dtct ADR signals REFERECES [1] World Halth Organization, Th Importanc of Pharmacovigilanc? Safty Monitoring of Mdicinal Products Gnva: World HalthOrganization; 2002 Availabl from: Accssd March 6, 2013 [2] EM Rodriguz, JA Staffa, DJ Graham, Th rol of databass in drug postmarkting survillanc, Pharmacopidmiol Drug Saf, vol 10, no 5, pp , 2001 [3] AK Jha, G J Kuprman, J M Tich, L Lap, B Sha, R Ev, E Burdick, D L Sgr, M V Vlit, and D W Bats, Idntifying Advrs Drug Evnts Dvlopmnt of a Computr-basd Monitor and Comparison with Chart Rviw and Stimulatd Voluntary Rport, J Am Md Inform Assoc, vol 5, pp , 1998 [4] R Platt, R M Carnahan, J S Brown, E Chrischills, L H Curtis, S Hnnssy, J C lson, J A Racoosin, M Robb, S Schnwiss, S Toh, and M G Winr, Th US Food and Drug Administration's Mini-Sntinl program: status and dirction, Pharmacopidmiol Drug Saf, vol 21, pp 1-8, 2012 [5] L H Curtis, M G Winr, D M Boudrau, W O Coopr, G W Danil, V P air, M A Rabl, U Bauliu, R Rosofsky, T S Woodworth, and J S Brown, Dsign considrations, architctur, and us of th Mini-Sntinl distributd data systm, Pharmacopidmiol Drug Saf, vol 21, pp 23 31, 2012 [6] S Forrow, DM Campion, L J Hrrinton, V P air, M A Robb, M Wilson, and R Platt, Th organizational structur and govrning principls of th Food and Drug Administration's Mini-Sntinl pilot program, Pharmacopidmiol Drug Saf, vol 21, pp 12 17, 2012 [7] P E Stang, P B Ryan, J A Racoosin, J M Ovrhag, A G Hartzma, C Rich, E Wlbob, T Scarnccia, and J Woodcock, Advancing th scinc for activ survillanc: rational and dsign for th Obsrvational Mdical Outcoms Partnrship, Ann Intrn Md, vol 153, pp , 2010 [8] G Trifiro, A Fourrir-Rglat, M C Sturknboom, C Díaz Acdo, and J Van Dr Li, Th EU-ADR projct: prliminary rsults and prspctiv, Stud Halth Tchnol Inform, vol 148, pp 43-9, 2009 [9] P M Coloma, M J Schumi, G Trifirò, R Gini, R Hrings, J Hippisly-Cox, G Mazzaglia, C Giaquinto, G Corrao, L Pdrsn, J Van Dr Li, and M Sturknboom, Combining lctronic halthcar databass in Europ to allow for larg-scal drug safty monitoring: th EU-ADR Projct, Pharmacopidmiol Drug Saf, vol 20, pp1 11, 2011 [10] H S Kim, H Cho, and I K L, Dsign and Dvlopmnt of an EHR Platform Basd on Mdical Informatics Standards, Journal of Fuzzy Logic and Intllignt Systms, vol 21, pp , 2011 [11] SH Hwang, E Kim, S L, S Jung, M L, K H Son, K U Choi, S H L, and Kim, Implmntation and Evaluation of th Computrizd Survillanc Systm to Idntify Advrs Drug Evnts: pilot study, J Kor Soc Halth-Syst Pharm, vol 22, no 2, pp , 2005 [12] H L, M oon, B M L, H J Hwang, and UG Kang, Dvlopmnt of Mining modl through rproducibility assssmnt in Advrs drug vnt survillanc systm, Th Kora Socity of Computr and Information, vol 1, no 3, pp , March 2009a [13] H L, UG Kang, and R W Park, Dvlopmnt of Advrs Drug Evnt Survillanc Systm using BI Tchnology, Journal of Kora Contnts, vol 9, no 2, pp , 2009b [14] AJ Cook, R C Tiwari, R D Wllman, S R Hckbrt, L Li, P Hagrty, T Marsh, and J C lson, Statistical approachs to group squntial monitoring of postmarkt safty survillanc data: currnt stat of th art for us in th Mini Sntinl pilot, Pharmacopidmiol Drug Saf, vol 21, pp 72-81, 2012 [15] PE Stang, PB Ryan,SB Dustzina, AG Hartzma, C Rich, J M Ovrhag,J A Racoosin, "Halth Outcoms of Intrst in Obsrvational Data: Issus in Idntifying Dfinitions in th Litratur," Halth Outcoms Rsarch in Mdicin, vol 3, Iss 1, pp 37 44, 2012 [16] S Murphy, V Castro, J Colcchi, A Duby, V Gainr, C Hrrick, and M Sordo,"Partnrs HalthCar OMOP Study Rport," Foundation for th ational Instituts of Halth Obsrvational Mdical Outcoms Partnrship Partnrs HalthCar, Jan 10, 2011 [17] J M Ovrhag, P B Ryan, C G Rich, A G Hartzma, and P E Stang, Validation of a common data modl for activ safty survillanc rsarch, Am Md Inform Assoc, vol 19, pp 54-60, 2012