Effects of a computerized provider order entry and a clinical decision support system to improve cefazolin use in surgical prophylaxis: a cost saving analysis

Main Article Content

Lucas M. Okumura http://orcid.org/0000-0003-3607-619X
Izelandia Veroneze
Celia I. Burgardt
Marta F. Fragoso

Keywords

Cost Savings, Clinical Pharmacy Information Systems, Decision Support Systems, Clinical, Medical Order Entry Systems, Anti-Bacterial Agents, Hospitals, Pharmacists, Brazil

Abstract

Background: Computerized Provider Order Entry (CPOE) and Clinical Decision Support System (CDSS) help practitioners to choose evidence-based decisions, regarding patients’ needs. Despite its use in developed countries, in Brazil, the impact of a CPOE/CDSS to improve cefazolin use in surgical prophylaxis was not assessed yet.

Objective: We aimed to evaluate the impact of a CDSS to improve the use of prophylactic cefazolin and to assess the cost savings associated to inappropriate prescribing.

Methods: This is a cross-sectional study that compared two different scenarios: one prior CPOE/CDSS versus after software implementation. We conducted twelve years of data analysis (3 years prior and 9 years after CDSS implementation), where main outcomes from this study included: cefazolin Defined Daily Doses/100 bed-days (DDD), crude costs and product of costs-DDD (cost-DDD/100 bed-days). We applied a Spearman rho non-parametric test to assess the reduction of cefazolin consumption through the years.

Results: In twelve years, 84,383 vials of cefazolin were dispensed and represented 38.89 DDD/100 bed-days or USD 44,722.99. Surgical wards were the largest drug prescribers and comprised >95% of our studied sample. While in 2002, there were 6.31 DDD/100 bed-days, 9 years later there was a reduction to 2.15 (p<0.05). In a scenario without CDSS, the hospital would have consumed 75.72 DDD/100 bed-days, which is equivalent to USD 116 998.07. It is estimated that CDSS provided USD 50,433.39 of cost savings.

Conclusion: The implementation of a CPOE/CDSS helped to improve prophylactic cefazolin use by reducing its consumption and estimated direct costs.

Abstract 2111 | PDF Downloads 1179

References

1. Bohmer RM. The four habits of high-value health care organizations. N Engl J Med. 2011 Dec 1;365(22):2045-7. doi: 10.1056/NEJMp1111087

2. Sim I, Gorman P, Greenes RA, Haynes RB, Kaplan B, Lehmann H, Tang PC. Clinical decision support systems for the practice of evidence-based medicine. J Am Med Inform Assoc. 2001;8(6):527-534.

3. O’Sullivan D, Fraccaro P, Carson E, Weller P. Decision time for clinical decision support systems. Clin Med (Lond). 2014;14(4):338-341. doi: 10.7861/clinmedicine.14-4-338

4. Holstiege J, Mathes T, Pieper D. Effects of computer-aided clinical decision support systems in improving antibiotic prescribing by primary care providers: a systematic review. J Am Med Inform Assoc. J Am Med Inform Assoc. 2015;22(1):236-242. doi: 10.1136/amiajnl-2014-002886

5. Bratzler DW, Dellinger EP, Olsen KM, Perl TM, Auwaerter PG, Bolon MK, Fish DN, Napolitano LM, Sawyer RG, Slain D, Steinberg JP, Weinstein RA; American Society of Health-System Pharmacists; Infectious Disease Society of America; Surgical Infection Society; Society for Healthcare Epidemiology of America. Clinical practice guidelines for antimicrobial prophylaxis in surgery. Am J Health Syst Pharm. 2013;70(3):195-283. doi: 10.2146/ajhp120568

6. Appleby DH, John JF Jr. Use, misuse, and cost of parenteral cephalosporines at a county hospital. South Med J. 1980;73(11):1473-1475.

7. Katz E, Schlamowitz S. Savings achieved through cephalosporin surveillance. Am J Hosp Pharm. 1978;35(12):1521-1523.

8. Rana DA, Malhotra SD, Patel VJ. Inappropriate surgical chemoprophylaxis and surgical site infection rate at a tertiary care teaching hospital. Braz J Infect Dis. 2013;17(1):48-53. doi: 10.1016/j.bjid.2012.09.003

9. Harbarth S, Samore MH. Antimicrobial resistance determinants and future control. Emerg Infect Dis. 2005;11(6):794-801.

10. Howard DH, Scott RD 2nd. The economic burden of drug resistance. Clin Infect Dis. 2005 Aug 15;41(Suppl 4):S283-S286.

11. World Health Organization Collaborating Center for Drug Statistics Methodology. Guidelines for ATC classification and DDD assignment 2013. WHO: Oslo; 2012. Available at: http://www.whocc.no/atcddd/ (accessed September 24, 2014).

12. Okumura LM, Silva MM, Veroneze I. Effects of a bundled antimicrobial stewardship program on mortality: a cohort study. Braz J Infect Dis. 2015;19(3):246-252. doi: 10.1016/j.bjid.2015.02.005

14. Bozkurt F, Kaya S, Gulsun S, Tekin R, Deveci Ö, Dayan S, Hoşoglu S. Assessment of perioperative antimicrobial prophylaxis using ATC/DDD methodology. Int J Infect Dis. 2013 Dec;17(12):e1212-e1217. doi: 10.1016/j.ijid.2013.08.003

14. Pestotnik SL, Classen DC, Evans RS, Burke JP. Implementing antibiotic practice guidelines through computer-assisted decision support: clinical and financial outcomes.. Ann Intern Med. 1996;124(10):884-890.

15. Huh K, Chung DR, Park HJ, Kim MJ, Lee NY, Ha YE, Kang CI, Peck KR, Song JH. Impact of monitoring surgical prophylactic antibiotics and a computerized decision support system on antimicrobial use and antimicrobial resistance. Am J Infect Control. 2016. [Epub ahead of print] doi: 10.1016/j.ajic.2016.01.025

16. Jonkers D, Swennen J, London N, Driessen C, Stobberingh E. Influence of cefazolin prophylaxis and hospitalization on the prevalence of antibiotic-resistant bacteria in the faecal flora. J Antimicrob Chemother. 2002;49(3):567-571.

17. Stevens DL, Bisno AL, Chambers HF, Dellinger EP, Goldstein EJ, Gorbach SL, Hirschmann JV, Kaplan SL, Montoya JG, Wade JC; Infectious Diseases Society of America. Practice guidelines for the diagnosis and management of skin and soft tissue infections: 2014 update by the Infectious Diseases Society of America. Clin Infect Dis. 2014;59(2):e10-e52. doi: 10.1093/cid/ciu444

Most read articles by the same author(s)