Send the link below via email or IMCopy
Present to your audienceStart remote presentation
- Invited audience members will follow you as you navigate and present
- People invited to a presentation do not need a Prezi account
- This link expires 10 minutes after you close the presentation
- A maximum of 30 users can follow your presentation
- Learn more about this feature in our knowledge base article
Do you really want to delete this prezi?
Neither you, nor the coeditors you shared it with will be able to recover it again.
Make your likes visible on Facebook?
Connect your Facebook account to Prezi and let your likes appear on your timeline.
You can change this under Settings & Account at any time.
Effective Nursing Informatics to Reduce Medication Errors
Transcript of Effective Nursing Informatics to Reduce Medication Errors
Within last 7 years
Keywords: Bar-code technology, computerized physician order entry, clinical decision support, medication errors, patient safety Methodology: Quasi-experimental pretest-post-test study Findings: Frequency of errors declined from 18.2% to 8.2%, a reduction in adjusted odds of 70%
Greatest reduction in odds occurred with illegibility (97%), inappropriate abbreviations (94%), and missing information (85%)
Significant reductions also occurred with wrong strength (81%), drug-disease interaction (79%), and drug-drug interaction (76%)
Non-significant wrong-drug (63%), drugs contraindicated in patients 65 or older (56%), and wrong direction errors (34%) Methodology: Quasi-Experimental Pretest-post-test study Findings: CPOE with CDS statistically significant reductions in errors related to Drug allergy detection 833 pre to 109 post (OR, 0.14; 95%CI 0.11-0.17; p<0.001) Excessive dose 1341 pre to 871 post (OR, 0.68; 95%CI 0.62-0.74; p < 0.001 Incomplete or unclear order 1976 pre to 663 post (OR, 0.35; 95%CI 0.32-0.38; p<0.001) BCMA with electronic charting intercepted 73 medication errors for every 100,000 charted doses (wrong patient 12.2, wrong drug/dose/route 5.8, and wrong time 55.3) Methodology: Quasi-experimental pretest-post-test study Findings: Error rates fell from 14.4% to 1.3% after electronic system was implemented (3,195 manual to 713 electronic) By Variable: Dose - 831 pre to 71 post electronic implementation
Administration frequency/time - 174 pre to 20 post electronic implementation
Route of administration - 490 pre to 5 post electronic implementation Methodology: Quasi-Experimental pretest-post-test study Findings Error rates fell by 58% excluding wrong time errors
Increase in wrong time administrations was noted after BCMA implementation on med-surg floors Fewer omitted medications and unavailable medication contributed to this effect No statistical difference was noted in the ICU setting Findings: Methodology: Quasi-Experimental before-and-after study Non-timing errors fell from 776 (11.5% error rate) before BCMA to 495 (6.8% error rate) a 41.4% relative reduction in errors (p<0.001) (OR -41.1; 95%CI, -34.2 to -47.6)
The rate of potential adverse drug events fell from 3.1% pre-BCMA to 1.6 post-BCMA, a 50.8% relative reduction (p<0.001)
Transcription errors occurred at a rate of 6.1% pre-BCMA to being completely eliminated post-BCMA implementation CDS with CPOE is more likely to prevent errors resulting from bad judgement, insufficient knowledge, and incomplete clinical information BCMA is more likely to prevent errors related to memory lapses or mental slips Methodology: Quasi-experimental pretest-post-test study Findings: The medication error rate was reduced by 56% after implementation of BCMA (19.7% pre-BCMA to 8.7% post-BCMA, p<0.001)
Wrong time errors decreased from 18.8% pre-BCMA to 7.5% post implementation (p<0.001)
No other error types showed significant differences Methodology: A retrospective evaluation of the frequency and preventability of adverse drug events Results: A total of 17 ADEs (1.4 per 100 admissions) and 146 PADEs (12.2 per 100 admissions) involving multiple drugs were identified
Documented events were related to drug duplication (n=126), drug-drug interaction (n=21), additive effects (n=14), and therapeutic duplication (n=7)
Majority of actual ADEs were due to drug-drug interactions (most common opioids, benzodiazepines, and cardiac meds)
The study determined that 5 (29.4%) of the ADEs and 131 (89.7) of the PADEs could have been detected through the use of the evaluated CDS tools Severity of Medication Administration Errors Detected by a Bar-Code Medication Administration System Julie Sakowski, Jeffrey M. Newman, and Krystin Dozier (2008) Methods: Findings: Only 1% of the errors reviewed were rated as having the potential to result in a severe or life-threatening adverse event
8% were judged to have the potential to produce moderate adverse effects
91% were expected to produce minimal, if any, clinical effects
Medication errors with no corresponding order in the computer were significantly more likely to produce moderate or severe outcomes than other error types
Overall the majority of medication administration errors detected by BCMA were judged to pose minimal threats CPOE - Computerized Physician Order Entry CDS - Clinical Decision Support BCMA - Bar-Code-assisted Medication Administration Devine et al. (2009) - Uses a significant sample size (5,016 prescriptions pre and 5,153 post), high inter-rater reliability (93% agreement on error found and 97% on severity level), Power analysis, CPOE with limited CDS support Strengths Poon et al. (2010) - Significant sample size (14,041 medication administrations observed), direct observation method, power analysis and effect size Mahoney et al. (2007) - Tested the effect of an integrated clinical information system, detailed description of technology used, sufficient sample size, results consistent with other studies, and power analysis Weaknesses For all studies used there was no use of a control group, and none of the studies used a randomized sampling method Sakowski et al. (2008) - Used only errors detected by BCMA system, and patient specific information (comorbidities, diagnosis, concomitant meds, etc.) were not known Helmons et al. (2009) - Used fourth year graduate students as observers, the majority of medication administrations occurred at 9 a.m., and the observer who conducted 50% of the observations was responsible for data entry. Silviera et al. (2007) - Allowed for only a 1 month pilot period before data collection, and did not account for extraneous variables Wright et al. (2012) - Lacked power analysis, and used a small sample size Recommendations for Practice: Implement CPOE, CDS, and BCMA together to obtain maximum benefits Have the systems installed and managed by a professional multihospital medication information technology team (MITT) to ensure proper training, use, and maintenance of the system Implementation Highly encourage the use of CPOE, CDS, and BCMA based on the findings of my literature review Create as many barriers to medication errors as possible through the use of technology Recommendations for Future Research Problems associated with the use of technology Workarounds Reducing the sensitivity of alerts to reduce alert fatigue The benefit of using all three forms of technology in combination Sample size: Pre (n=5,016) Post (n=5,153) Sample size: pre (n=1,452,346 orders) post (n=1,390,789 orders) Sample size: 172 patients pre and 138 post Setting: 2 medical surgical units and 2 ICU units Sample size: 14,041 medication administrations and 3,082 order transcriptions Direct observation for 24 hrs 1 month pre and 4 months post Sample Size: 1,465 medication administrations A quantitative study using expert evaluation of medication error scenarios using a previously validated likert scale Jaron M. Licatovich References
Devine, E.B., Hansen, R.N., Wilson-Norton, J.L., Lawless, N.M., Fisk, A.W., Blough, D.K., . . . Sullivan, S.D. (2010). The impact of computerized provider order entry on medication errors in a multispecialty group practice. Journal of the American Medical Informatics Association, 17(1), 78-84. doi: 10.1197/jamia.M3285
DeYoung, J.L., VanderKooi, M.E., & Barletta, J.F. (2009). Effect of bar-code-assisted medication administration on medication error rates in an adult medical intensive care unit. American Journal of Health-System Pharmacy, 66(12), 1110-1115. doi: 10.2146/ajhp080355
Helmons, P.J., Wargel, L.N., & Daniels, C.E. (2009). Effect of bar-code-assisted medication administration on medication administration errors and accuracy in multiple patient care areas. American Journal of Health-System Pharmacy, 66(13), 1202-1210. doi: 10.2146/ajhp080357
Hidle, U. (2008). Implementing technology to improve medication safety in healthcare facilities: a literature review. Journal of the New York State Nurses Association, 38(2), 4-9. Retrieved from http://web.ebscohost.com/ehost/detail?
Kohn, T.L., Corrigan, J.M., & Donaldson, S.M. (Eds.). (2000) To err is human: building a safer health system. Washington, D.C.: Institute of Medicine.
Mahoney, C.D., Berard-Collins, C.M., Coleman, R., Amaral, J.F., & Cotter C.M. (2007). Effects of an integrated clinical information system on medication safety in a multi-hospital setting. American Journal of Health-System Pharmacy, 64(18), 1969-1977. doi: 10.2146/ajhp060617
Poon, E.G., Keohane, C.A., Yoon, S.C., Ditmore, M., Bane, A., Levtzion-Korach, O., . . . Gandhi, T.K. (2010). Effect of bar-code technology on the safety of medication administration. The New England Journal of Medicine, 362(18), 1698-1707. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed
Sakowski, J., Newman, J.M., & Dozier, K. (2008). Severity of medication administration errors detected by a bar-code medication administration system. American Journal of Health-System Pharmacy, 65(17), 1661-1665. doi: 10.2146/ajhp070634
Silveira, E.D., Vigil, M.S., Menendez-Conde, C.P., Tellez de Cepeda, L.D., & Vicedo, T.B. (2007). Prescription errors after the implementation of an electronic prescribing system. Farmicia Hospitalaria: Organo Oficial de Expresion Ceientificia fe la Sociedad Espanola de Farmacia Hospitalaria, 31(4), 223-230. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed
Jhyla, V., Saranto, K., & Bates, W.D. (2011) Preventable adverse drug events and their causes and contributing factors: the analysis of register data. International Journal for Quality In Healthcare, 23(2), 187-197. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed?
Wrights, A., Feblowitz, J., Phansalkar, S., Liu, J., Wilcox, A., Keohane, C.A., . . . Bates, D.W. (2012). Preventability of adverse drug events involving multiple drugs using publicly available clinical decision support tools. American Journal of Health-System Pharmacy, 69(3), 221-227. doi: 10.2146/ajhp110084