Understanding physicians’ behavior toward alerts about nephrotoxic medications in outpatients: a cross-sectional analysis

BMC Nephrology

David W Bates³  Patricia C Dykes⁴  Nivethietha Maniam³  Diane L Seger³  Karen C Nanji¹  Sarah P Slight²  Insook Cho⁴ 

[1]Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, MA, USA

[2]Division of Primary Care, University of Nottingham, Nottingham, UK

[3]Partners Healthcare Systems, Inc., Wellesley, MA, USA

[4]Harvard Medical School, Boston, MA, USA

关键词: Chronic kidney disease;    Drug prescribing;    Renal insufficiency;    Clinical decision support system;    Medication safety;   

Others  :  1083480
DOI  :  10.1186/1471-2369-15-200

 received in 2014-07-01, accepted in 2014-12-11,  发布年份 2014

【 摘 要 】

Background

Although most outpatients are relatively healthy, many have chronic renal insufficiency, and high override rates for suggestions on renal dosing have been observed. To better understand the override of renal dosing alerts in an outpatient setting, we conducted a study to evaluate which patients were more frequently prescribed contraindicated medications, to assess providers’ responses to suggestions, and to examine the drugs involved and the reasons for overrides.

Methods

We obtained data on renal alert overrides and the coded reasons for overrides cited by providers at the time of prescription from outpatient clinics and ambulatory hospital-based practices at a large academic health care center over a period of 3 years, from January 2009 to December 2011. For detailed chart review, a group of 6 trained clinicians developed the appropriateness criteria with excellent inter-rater reliability (κ = 0.93). We stratified providers by override frequency and then drew samples from the high- and low-frequency groups. We measured the rate of total overrides, rate of appropriate overrides, medications overridden, and the reason(s) for override.

Results

A total of 4120 renal alerts were triggered by 584 prescribers in the study period, among which 78.2% (3,221) were overridden. Almost half of the alerts were triggered by 40 providers and one-third was triggered by high-frequency overriders. The appropriateness rates were fairly similar, at 28.4% and 31.6% for high- and low-frequency overriders, respectively. Metformin, glyburide, hydrochlorothiazide, and nitrofurantoin were the most common drugs overridden. Physicians’ appropriateness rates were higher than the rates for nurse practitioners (32.9% vs. 22.1%). Physicians with low frequency override rates had higher levels of appropriateness for metformin than the high frequency overriders (P = 0.005).

Conclusion

A small number of providers accounted for a large fraction of overrides, as was the case with a small number of drugs. These data suggest that a focused intervention targeting primarily these providers and medications has the potential to improve medication safety.

【 授权许可】

   
2014 Cho et al.; licensee BioMed Central.

【 图 表 】

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【 参考文献 】

• [1]Weingart SN, Toth M, Sands DZ, Aronson MD, Davis RB, Phillips RS: Physicians' decisions to override computerized drug alerts in primary care. Arch Intern Med 2003, 163(21):2625-2631.
• [2]Salgado TM, Arguello B, Martinez-Martinez F, Benrimoj SI, Fernandez-Llimos F: Clinical relevance of information in the summaries of product characteristics for dose adjustment in renal impairment. Eur J Clin Pharmacol 2013, 69(11):1973-1979.
• [3]Coresh J, Selvin E, Stevens LA, Manzi J, Kusek JW, Eggers P, Van Lente F, Levey AS: Prevalence of chronic kidney disease in the United States. Jama J Am Med Assoc 2007, 298(17):2038-2047.
• [4]Kuhn K: A survey of USA acute care hospitals’ computer-based provider order entry system infusion levels. In Proceedings of the 12th World Congress on Health (Medical) Informatics. Edited by MEDINFO 2007. Amsterdam: IOS Press; 2007:252.
• [5]Yap C, Dunham D, Thompson J, Baker D: Medication dosing errors for patients with renal insufficiency in ambulatory care. Joint Comm J Qual Patient Saf 2005, 31(9):514-521.
• [6]Kidney Disease Statistics for the United States http://kidney.niddk.nih.gov/kudiseases/pubs/kustats/#4 webcite
• [7]Wright A, Pang J, Feblowitz JC, Maloney FL, Wilcox AR, Ramelson HZ, Schneider LI, Bates DW: A method and knowledge base for automated inference of patient problems from structured data in an electronic medical record. J Am Med Inform Assoc 2011, 18(6):859-867.
• [8]Chertow GM, Lee J, Kuperman GJ, Burdick E, Horsky J, Seger DL, Lee R, Mekala A, Song J, Komaroff AL: Guided medication dosing for inpatients with renal insufficiency. JAMA 2001, 286(22):2839-2844.
• [9]Helmons PJ, Grouls RJ, Roos AN, Bindels AJ, Wessels-Basten SJ, Ackerman EW, Korsten EH: Using a clinical decision support system to determine the quality of antimicrobial dosing in intensive care patients with renal insufficiency. Qual Saf Health Care 2010, 19(1):22-26.
• [10]Shah NR, Seger AC, Seger DL, Fiskio JM, Kuperman GJ, Blumenfeld B, Recklet EG, Bates DW, Gandhi TK: Improving acceptance of computerized prescribing alerts in ambulatory care. J Am Med Inform Assoc 2006, 13(1):5-11.
• [11]Matzke GR, Aronoff GR, Atkinson AJ, Bennett WM, Decker BS, Eckardt K-U, Golper T, Grabe DW, Kasiske B, Keller F: Drug dosing consideration in patients with acute and chronic kidney disease—a clinical update from Kidney Disease: Improving Global Outcomes (KDIGO). Kidney Int 2011, 80(11):1122-1137.
• [12]Aronoff G: Drug prescribing in renal failure. Philadelphia: ACP Press; 2007.
• [13]Farag A, Garg AX, Li L, Jain AK: Dosing errors in prescribed antibiotics for older persons with CKD: a retrospective time series analysis. Am J Kidney Dis 2014, 63(3):422-428.
• [14]Prajapati A, Ganguly B: Appropriateness of drug dose and frequency in patients with renal dysfunction in a tertiary care hospital: A cross-sectional study. J Pharm Bioallied Sci 2013, 5(2):136-140.
• [15]Papaioannou A, Clarke J-A, Campbell G, Bédard M: Assessment of adherence to renal dosing guidelines in long-term care facilities. J Am Geriatr Soc 2000, 48(11):1470-1473.
• [16]Field TS, Rochon P, Lee M, Gavendo L, Subramanian S, Hoover S, Baril J, Gurwitz J: Costs associated with developing and implementing a computerized clinical decision support system for medication dosing, for patients with renal insufficiency in the long-term care setting. J Am Med Inform Assoc 2008, 15(4):466-472.
• [17]Terrell KM, Perkins AJ, Hui SL, Callahan CM, Dexter PR, Miller DK: Computerized Decision Support for Medication Dosing in Renal Insufficiency: A Randomized. Control Trial Ann Emerg Med 2010, 56(6):623-629.
• [18]Aronoff GR, Aronoff JR: Drug Prescribing in Kidney Disease: Can't We Do Better? Am J Kidney Dis 2014, 63(3):382-383.
• [19]McCoy AB, Waitman LR, Lewis JB, Wright JA, Choma DP, Miller RA, Peterson JF: A framework for evaluating the appropriateness of clinical decision support alerts and responses. J Am Med Inform Assoc 2012, 19(3):346-352.
• [20]Huang DL, Abrass IB, Young BA: Medication safety and chronic kidney disease in older adults prescribed metformin: a cross-sectional analysis. BMC Nephrol 2014, 15:86. BioMed Central Full Text
• [21]Galanter WL, Didomenico RJ, Polikaitis A: A trial of automated decision support alerts for contraindicated medications using computerized physician order entry. J Am Med Inform Assoc 2005, 12(3):269-274.
• [22]McCoy AB, Waitman LR, Gadd CS, Danciu I, Smith JP, Lewis JB, Schildcrout JS, Peterson JF: A computerized provider order entry intervention for medication safety during acute kidney injury: a quality improvement report. Am J Kidney Dis 2010, 56(5):832-841.
• [23]Phansalkar S, Desai AA, Bell D, Yoshida E, Doole J, Czochanski M, Middleton B, Bates DW: High-priority drug–drug interactions for use in electronic health records. J Am Med Inform Assoc 2012, 19(5):735-743.