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1 w wz 21«1y Kor. J. Clin. Pharm., Vol. 21, No » y t sƒv e z t ½ Á x Á xk w w w ( Á Á ) Comparison of Drug Prescriptions Before and AfterG Computerized Drug Utilization Review Program Installation in a Tertiary Hospital Setting Sang Mee Kim, Hyun Soon Sohn and Hyun Taek Shin College of Pharmacy, Sookmyung Women's University, Seoul , Korea (Received December 31, 2010ÁRevised March 11, 2011ÁAccepted March 13, 2011) Drug Utilization Review (DUR) is known to play an important role to improve appropriateness of drug prescriptions. This retrospective, observational study was conducted to compare prescription patterns after installation of Computerized DUR Program (Drug Information Framework-Korea ) (Jan-Mar 2008; After) to before DUR program (Jan-Mar 2007: Before). 8 physicians affiliated in the S University Hospital were enrolled in the study and their 3 months' prescription data were analysed for drug prescription trends and DUR conflict events per 7 DUR screening modules (drugdrug interaction, therapeutic duplication, allergy, dosing, disease contra-indication, geriatric contra-indication, pediatric contra-indication). Average rate of DUR modules usage in 2008 (After) were Average number of prescription drug per patient were reduced from 5.6 (Before) to 3.8 (After), and DUR program seemed to effect positively on physician's prescription related decision process. Overall DUR conflict events occurred by 8 physicians for 3 months were 17,923 Before and 20,057 After DUR program, and DUR conflict events per prescription were 2.8 Before and 2.9 After, respectively. Therapeutic duplication (37%), geriatric contra-indication (34%) and dosing (18%) were high ranked DUR conflicts. As the study was not sufficient to show a consistent trend to reduce DUR conflicts After, another study to confirm it's effectiveness would be recommended. This study would be of help to develop awareness of DUR program to healthcare providers. Key words - Computerized Drug Utilization Review Program, DUR module, DUR conflict t w ƒ v w û ƒ yw» w ƒ w. w w (Adverse Drug Events, ADE) y j x wš z, k š š š. IOM (Institute of Medicine) 1999 w š To Err is Human: Building a Safer Health System y 44,000-98,000 y ƒ y š(medical error) w š wš1,2) w y ƒ 10 w š. w 3) Harvard Medical Practice Study, 51 w y 30,195 19% Correspondence to : xk w w w q z ¼ 56 Tel: , Fax: ùkû š w. w 4), 1995 Johnson decision analysis x w, w y 766 wš, ) y š w 1,774 w. ù x t 6) ùš w % y sw š7), 2003» 15 x l y t sƒv (Computerized Drug Utilization Review Program) w, 7,866,409 2,527,025 conflict ƒ ùkù, 10 s³ 3 s ƒ(drug Utilization Review, DUR) conflict ƒ ùkù y w. ù t w 8) w y š ³ d w, 2006 z š» w 14

2 3» y t sƒv e z t 15 y 17,644 y š w š w. w w 9) w ƒw w w» w ù ƒ» š. ù 2000 z t mƒ w ³ ù, m wš w mƒ z wš û. x ¾ ü ƒ z y w DUR, w» t sƒw t z yw. 10,11) DUR e (therapeutic duplication), - y»(drug-disease contraindication), - y (drugdrug interaction), w (incorrect drug dosage), w n» (incorrect duration), -» y (drug-allergy interaction), û (clinical abuse/ misuse) š12), w w (prospective), zw (retrospective) (concurrent) DUR w. w 11,13) DUR y ù, y mw w w» w z ƒ. 14) ù, zw DUR z ù ql sƒw, DUR w sƒ w x z v w sƒw xk. 15) x ù š mw ƒ (»,»,», w t, t ) w 16) DUR š ù z w š š, ˆ mw x wš, w w w DUR xk š, y y w» w v w. x w zw DUR sƒ w xk š, z DUR w» e»» w y. y mv, v w w w wš mw. 17) Smith DH Pacific Northwest Health Maintenance Organization y mv z w, v z 22% w š w y non-preferred agent ùkû. 18) Gregoire JP y w DUR zw DUR w, z ƒ š DUR zw DUR { ƒ k ùkû. w mv 19) š y w v w. ù pilot study š t w š š, y v» sƒ» sw ü» y mv w».» w v w» w š. v w ù y d» v z y sƒw», ü w y t w y m v w» z ql w v w z ƒ sƒ w š., ü ƒ y wš 3» wù S, y mv ew» z x ql wš w.» S w ü 8 ü y mv e» 3 ( ) v e z 3 ( ) w y x, zw w. y m v w ( )r p ƒ œ w y m v (DIF-Korea ) w, v First DataBank, San Bruno, CA( w FDB) NDDF (National Drug Data File) Plus DUR MMARM (Minimum/Maximum Adult Daily Range of Dosing Module: / m ), MMGRM (Minimum/ Maximum Geriatric Daily Range of Dosing Module: / m ), PDM (Pediatric Dosing Module: m ), ACM (Age Contraindication Module: p»), DDIM (Drug-Drug Interaction Module: y m ) w t t (KFDA) xƒ w w KADM (KFDA Labeled Korea Adult Daily dosing Module:»

3 16 Kor. J. Clin. Pharm., Vol. 21, No. 1, 2011 m ), KDCM (Korea Drug Contraindication Module : n» ), KACM (Korea Age Contraindication Module : p n» m ) mw. x ƒƒ w y y,,,,»,, sww 7ƒ w DUR conflict event w sƒ» w. s ƒ y 18, 60 w. ù e w t 2ƒ n w. y conflict 3ƒ ù : 1 wì w», 2 ƒw y, 3 y. DIF-Korea v l Sequential Access Method file xk x g. š l l (Windows 2000 and Database Management System: MS-SQL) ƒƒ l DUR j l kw 7ƒ w DUR sƒ» w» DUR conflicts ƒ. DUR conflict l m w Microsoft Excel spreadsheet w. DUR m v z y, y w, š w w., m v w š, v z 7ƒ m w DUR conflict event w. Microsoft Excel 2007 w» m w. p 8 ü ƒ wš ww w y » (v, Before) 8,209, » (v z, After) 8,264. y Before After ƒƒ 51.0% 51.5% š, Before After ƒƒ 65.8% 63.9% wš 2 1% w 51.5 (Table 1). Before w After 3 y 2 w 3 Table 1. y p ( : ) 2007 (Before) 2008 (After) û 4,022 (49.0%) 4,008 (48.5%) 4,187 (51.0%) 4,256 (51.5%) 8,209 (100.0%) 8,264 (100.0%) ù (1.0%) 70 (0.9%) ,385 (65.8%) 5,259 (63.9%) 65 2,720 (33.2%) 2,896 (35.2%) 8,190 (100.0%) 8,225 (100.0%) l 183 ¾ ƒw 55 ƒ š, 8 ƒ ww y ƒ w 2 sww 3 wš ù ƒw ù 30 w (Table 2). 8 ƒ 3 w Before After ƒƒ 46,285 32,381 š, 3 Before 5,786 After 4,048 w š w ùkü (Fig. 1). wr, y Before s³ 5.6 After 3.8 s³ 1.8 ƒ š w ùkü (Fig. 2). mv mv w ƒ y w», 7ƒ DUR ( y, n,, m,»,, ) ( 91 ) w 1.00 w ƒ 3 š, ƒ û s³ 0.76 š, y, n,, m 0.95», Table 2. 3 y y A B C 1,651 1, ,695 1, D E 182 1, ,895 1, F , G 1, ,201 1, H w 8,209 8, ,038 9,008-30

4 3» y t sƒv e z t 17 Fig Fig. 2. y w û (Table 3). 8 s³ 0.72 ùkü (Fig. 3). %63DPOGMJDUFWFOU mv w mw 3 DUR conflict event ƒ Before 17,923 After 20,057 Before 2,134 ƒw (Table 4). After conflict s, n ƒ ƒ 37% ƒ š conflict ƒ 34%, conflict ƒ 18% w. w Before s j ƒ ù (Table 4, Fig. 6). After 3 conflict ƒ w w C, E H ƒw. After s³ 2,507 Before 267 ƒw C E 1,000 conflict ƒ ùkù s³ conflict w e (Fig. 4). w w s³ DUR conflict w s³e Before 2.8

5 18 Kor. J. Clin. Pharm., Vol. 21, No. 1, 2011 Table 3. DUR v module 1) DUR module y n» A B C D E F G H s³ 3) ) = ƒ / (91 ) 2) s³ = ƒ w / 7 ( w w) 3) s³ = ƒ w / 8 ( ƒ w w) s³ 2) Table 4. DUR conflict Fig. 3. DUR v w After ƒw w (Fig. 5). DUR conflict ƒ Before After ƒw ùkù, conflict C E x q w wš, Before After ƒ š w w. š y w mv» ew z y w t ql DUR conflict events y w Before (2007 ) After (2008 ) (%) (%) 46,285 32,381 y , , , , , , , , , , » , , , , w 17, , DUR v e (Before) z(after) 3 ƒƒ 8,200 y w 9,000 w w, e z y 1 ƒ w š ƒ s³ 1,700 w, computerized

6 3» y t sƒv e z t 19 Fig DUR conflict Fig. 5. DUR conflict DUR program w e. ù m k w w DUR v š w» w. wr, DUR v ü e z x t DUR v w»¾ v w» e z 3» DUR v š š j» w, DUR v s³ 0.72 š š sƒ. w DUR conflict d DUR v e ( 18,000 ) z( 20,000 ) ƒw w ùkü. e z w wš DUR conflict ƒ ƒw ùkù, r» w d wz

7 20 Kor. J. Clin. Pharm., Vol. 21, No. 1, 2011 Fig. 6. DUR conflict s w w ƒ v w., DUR» 30% conflict ƒ ùkû, wz DUR w ƒ m š w w w. DUR conflict w DUR v e z ( ) w w ù 2 1,000 ƒ w x ùkù, ü DUR v ew DUR v y w w š d. DUR v e w, wwš z y w w, DUR v y z yw w. w ƒ š w., y mv w y DUR conflict w w», conflict w w» w., ü 1 w 8 DUR v w» ù x š w t k yw»., DUR v ew z v w w w v e z e w» z yw y w» w ƒ. e z w 1-2 v x k š» w z sƒw ww y v w., 8 w» w ƒ DUR v w š š y ƒ w w ƒ» w ƒ w ƒ w., DUR v w e w y w» w DUR v ql w e ƒ y m w v w w w w š,, wz w w v w. w» w, mw x k q w š DUR v w m conflict alert ƒ w y w š w. DUR v e z ƒ w y w, wz» t w ü x t w mƒ z» w wš, w d mv y w w e»ƒ» w. š x 1. National Academy of Sciences Institute of Medicine. To err is human: Building a Safer Health System. Washington, DC: National Academy Press; Available at nap.edu/books/ /html/. Accessed August Malone DC, Abarca J, Hansten PD, Grizzle AJ, Armstrong EP, Van Bergen RC, Duncan-Edgar BS, Solomon SL and Lipton RB. Identification of Serious Drug-Drug Interactions: Results of the Partnership to Prevent Drug-Drug Interactions. J Am Pharm Assoc 2004; 44(2):

8 3» y t sƒv e z t Lazarou J, Pomeranz BH and Corey PN. Incidence of adverse drug reactions in hospitalized patients: a metaanalysis of prospective studies. JAMA 1998; 279: Leape LL, Brennan TA, Laird N, Lawthers AG, Localio AR, Barnes BA, Hebert L, Newhouse JP, Weiler PC and Hiatt H. The nature of adverse events in hospitalized patients. N Engl J Med 1991; 324: Johnson JA and Bootman JL. Drug-related morbidity and mortality. A cost-of-illness model. Arch Intern Med 1995; 155: Ernst FR and Grizzle AJ. Drug-related morbidity and mortality: updating the cost-of-illness model. J Am Pharm Assoc 2001; 41: ½,,, wx,, y, û. y ùkù y k. J Kor Hosp Pharm 1999; 16(2): xk. y DURv mw x z w w. x sƒ š xk. y š ³ q w š ƒ Drug Use Evaluation w. J Kor Soc Health-Syst Pharm 2001; 18(4): Alan L, Betsy S, Thomas R. F, Theodore M. C. Ambulatory Drug Utilization Review: Opportunities for Improved Prescription Drug Use. AJMC 2001; 7(1): t sƒ(dur) w. w z Soumerai SB and Lipton HL. Computer-Based Drug- Utilization Review-Risk, Benefit, or Boondoggle? N Engl J Med 1995; 332: ½. k -2 ¼w sƒ mw z sƒ. w w w The U.S. Pharmacopeia Drug Utilization Review Advisory Panel. Drug Utilization Review: Mechanisms to Improve Its Effectiveness and Broaden Its Scope. J Am Pharm Assoc 2000; 40(4): x sƒ. -.Available at = HIRAF Accessed on April, Forni A, Chu HT, Fanikos J. Technology utilization to prevent medication errors. Curr Drug Saf Jan; 5(1): David H. S, Nancy P, Adrianne F, Xiuhai Y, Daniel K, Steven R. S, Dean F. S, Richard Platt, Soumerai S. B. The Impact of Prescribing Safety Alerts for Elderly Persons in an Electronic Medical Record. Arch Intern Med. 2006; 166: Jean-Pierre G, Jocelyne M, Louise P, Isabelle C, Rene V, Alain M. Effect of drug utilization reviews on the quality of in-hospital prescribing: a quasi-experimental study. BMC Health Medical Research. 2006; 6: 1-11.

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