Medication safety alert tools

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Medication Safety: Adverse Drug Events (ADEs)

At least five of the meaningful use objectives set by the Health Information Technology for Economic and Clinical Health Act (HITECH) set standards for medications, including maintenance of active medication and drug allergy lists, implementation of drug formulary checks, implementation of drug-drug and drug-allergy interaction checks, and on a clinical level the generation and transmission of prescriptions electronically (Blumenthal & Tavenner, 2010).Errors in medication are associated with decreased patient safety and remain a significant health consequence (Wessell, et al., 2010; Zhan, et al., 2005). Bates and others first described an adverse drug event (ADE) as any injury suffered by a patient due to medication and further described a medication error as any error that occurs through the use of medication regardless of whether an injury occurs or not (Bates, Boyle, Vander Vliet, Schneider, & Leape, 1995; Bates et al., 1995).

There are relatively few medication errors that result in injury, though decreasing these errors is imperative to increasing patient safety (Bates, Cullen, et al., 1995). Bates and others reported ADEs occur at 6.5 per 100 hospital admissions, resulting in ADEs that were classified as fatal (1%), life-threatening (12%), serious (30%), and significant (57%) (Bates, Cullen, et al., 1995). Zhan and others report that ambulatory visits for treating ADEs (VADE) averaged 15 visits per 1000 population and there was an increase in an VADE rates from 1995 to 2001 (Zhan, et al., 2005). Elderly individuals (ages 65-74 years old) required the most treatment for ADEs (Zhan, et al., 2005). Injuries as a result of medication errors are preventable (Bates, Cullen, et al., 1995) and the implementation of medication safety-specific tools in EHR systems should lead to increased medication safety.

Studies that have investigated applications of medication ordering systems in EHR, determined that there was a reduction in prescribing errors (Shamliyan, Duval, Du, & Kane, 2008) and decreased ADE rates (Wolfstadt et al., 2008). Most medication prescribing occurs in the outpatient setting, and in an additional study by Smith and others following implementation of drug-specific alerts, there was a reduction in the exposure of elderly patients to 2 classes of non-preferred medications (Smith, et al., 2006).

Black-Box Warning Tools

An additional tool in medication safety specific to EHR, are black-box warnings issued by the U.S. Food and Drug Administration (FDA) to warn physicians about drug-drug, drug-disease, and drug-laboratory interactions for 30 drugs/drug classes (Murphy & Roberts, 2006). Yu and others evaluated prescribing violations before and after implementation of BBW medication safety alerts used by 51 ambulatory practices for one common EHR (Yu, et al., 2011). This study determined that implementation of BBW safety alerts did not improve adherence to BBWs by clinicians (Yu, et al., 2011).

ADE Reporting Tools

Physicians report less than 1% of ADEs to the FDA (Lopez-Gonzalez et al., 2009), comprising patient safety and quality of care. Linder and others developed an ADE spontaneous triggered reporting system (ASTHER) to submit ADE reports from EHR systems directly to the FDA (Linder et al., 2010). Prior to implementation, 90% of clinicians had not reported a single ADE in the 12 months before use of ASTHER (Linder, et al., 2010). Further, the report determined that this EHR-based ADE reporting system was efficient and effective to clinicians and provided detailed clinical information (Linder, et al., 2010). A modification of an ADE reporting tool linked to EHR systems has been developed and is described in an earlier study by Rozich and others (Rozich, et al., 2003). Though other described ADE tools have been successful and alleviate the required full-length reports, the other developed systems remain complicated and application of these tools in many hospitals have not been effective (Rozich, et al., 2003). This study describes a modified ADE reporting tool that is lower in cost and has limited technology compared to traditional reporting tools (Rozich, et al., 2003). Further Rozich and others demonstrate this modified ADE reporting tool has detected ADE incidents 50-fold compared to traditional ADE reporting tools (Rozich, et al., 2003).

Active Medication Lists

An additional tool to prevent medication errors in EHR systems is maintenance of an active medication lists for all patients. Inaccuracies in medication lists in EHR systems have been reported (Wagner & Hogan, 1996). The accuracy of medication lists further contributes to improved patient safety and increased level of patient care (Staroselsky, et al., 2008). The evaluation of medication lists in EHR systems have been investigated. In a study by Staroselsky and others, a secure web-based patient portal and its ability to produce more accurate medication lists in an EHR system compared to non-portal users were evaluated (Staroselsky, et al., 2008). The clinicians received a notification message if their patient’s medication lists changed, and then clinicians’ response in updating this list was measured (Staroselsky, et al., 2008). Patients that used the portal system had a similar amount of discrepancies in their medication lists than those patients who did not use the portal system (Staroselsky, et al., 2008) . The clinical message received by the clinicians in response to their patient’s reports did not result in the updating of their patient’s records (Staroselsky, et al., 2008).

Summary

Many tools have been developed to identify DDIs and ADEs with both failures and successes. As tools are implemented and researchers identify the successful attributes of these tools, modifications can be made to allow more hospitals and clinicians to adopt these tools into their EHR systems, resulting in increased patient safety.

References

  1. Bates, D. W., Boyle, D. L., Vander Vliet, M. B., Schneider, J., & Leape, L. (1995). Relationship between medication errors and adverse drug events. J Gen Intern Med, 10(4), 199-205.
  2. Bates, D. W., Cullen, D. J., Laird, N., Petersen, L. A., Small, S. D., Servi, D., et al. (1995). Incidence of adverse drug events and potential adverse drug events. Implications for prevention. ADE Prevention Study Group. JAMA, 274(1), 29-34.
  3. Linder, J. A., Haas, J. S., Iyer, A., Labuzetta, M. A., Ibara, M., Celeste, M., et al. (2010). Secondary use of electronic health record data: spontaneous triggered adverse drug event reporting. Pharmacoepidemiol Drug Saf, 19(12), 1211-1215.
  4. Murphy, S., & Roberts, R. (2006). "Black box" 101: How the Food and Drug Administration evaluates, communicates, and manages drug benefit/risk. J Allergy Clin Immunol, 117(1), 34-39.
  5. Rozich, J. D., Haraden, C. R., & Resar, R. K. (2003). Adverse drug event trigger tool: a practical methodology for measuring medication related harm. Qual Saf Health Care, 12(3), 194-200.
  6. Shamliyan, T. A., Duval, S., Du, J., & Kane, R. L. (2008). Just what the doctor ordered. Review of the evidence of the impact of computerized physician order entry system on medication errors. Health Serv Res, 43(1 Pt 1), 32-53.
  7. Smith, D. H., Perrin, N., Feldstein, A., Yang, X., Kuang, D., Simon, S. R., et al. (2006). The impact of prescribing safety alerts for elderly persons in an electronic medical record: an interrupted time series evaluation. Arch Intern Med, 166(10), 1098-1104.
  8. Staroselsky, M., Volk, L. A., Tsurikova, R., Newmark, L. P., Lippincott, M., Litvak, I., et al. (2008). An effort to improve electronic health record medication list accuracy between visits: patients' and physicians' response. Int J Med Inform, 77(3), 153-160.
  9. Wagner, M. M., & Hogan, W. R. (1996). The accuracy of medication data in an outpatient electronic medical record. J Am Med Inform Assoc, 3(3), 234-244.
  10. Wessell, A. M., Litvin, C., Jenkins, R. G., Nietert, P. J., Nemeth, L. S., & Ornstein, S. M. (2010). Medication prescribing and monitoring errors in primary care: a report from the Practice Partner Research Network. Qual Saf Health Care, 19(5), e21.
  11. Wolfstadt, J. I., Gurwitz, J. H., Field, T. S., Lee, M., Kalkar, S., Wu, W., et al. (2008). The effect of computerized physician order entry with clinical decision support on the rates of adverse drug events: a systematic review. J Gen Intern Med, 23(4), 451-458.
  12. Yu, D. T., Seger, D. L., Lasser, K. E., Karson, A. S., Fiskio, J. M., Seger, A. C., et al. (2011). Impact of implementing alerts about medication black-box warnings in electronic health records. Pharmacoepidemiol Drug Saf, 20(2), 192-202.
  13. Zhan, C., Arispe, I., Kelley, E., Ding, T., Burt, C. W., Shinogle, J., et al. (2005). Ambulatory care visits for treating adverse drug effects in the United States, 1995-2001. Jt Comm J Qual Patient Saf, 31(7), 372-378.

Submitted by Tara Macey

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