YAD-ASSIGMMENT 2

Medication Errors and Clinical Decision Support 1

Medication Errors and Clinical Decision Support

A PICOT Analysis

Medication Errors and Clinical Decision Support

Student’s name: Yadiana Hernandez

Florida National University

Instructor: Yesenia Osle

Course: Applied Nursing Research-DAX-DL01

Date: September 16, 2026

Introduction

A medication error constitutes a problem when discussing patient safety because errors can occur during the process of prescribing, transcription, dispensing, administration, and monitoring of a treatment plan. Electronic health records (EHRs) and clinical decision support systems (CDSSs) are capable of identifying any potentially adverse medication orders prior to their execution. Clinical decision support for medication enables issuing alerts on drug-drug interactions, allergies, duplicate therapy, contraindications, improper dosages and other medication order problems. The research demonstrates that clinical decision support regarding medication is capable of lowering the rates of medication errors; nonetheless, the issuance of unneeded and inappropriate alerts leads to alert fatigue, incorrect overriding and disruption of the clinical workflow. The objective of the paper is to explore the possibility of implementing a hypothetical project intended to reduce medication errors related to CDS alert optimization based on an electronic health record.

Problem Statement

Despite the existence of EHR-integrated CDSSs that help detect medication-related risks, medication errors remain one of the avoidable issues in patient safety. Inappropriate medication-related CDS alerts may disrupt the workflow of clinicians, resulting in their silencing, ignoring, or disabling alerts. In such circumstances, the effectiveness of the CDSS technology in reducing medication-related incidents is limited. Recent studies have shown the strengths and limitations of medication-related CDSS. The rapid review commissioned by the Agency for Healthcare Research and Quality showed the moderate strength evidence of the positive impact of computerized provider order entry coupled with medication-related CDSSs on reducing medication errors. In addition, medication-related CDSS interventions that targeted specific problems led to lower medication errors and adverse drug events. However, the review indicated the high rate of alert overrides, which entailed unintended consequences such as alert fatigue, inappropriate overrides, over-reliance on alerts, and technology errors (Syrowatka et al., 2024).

PICOT Question

In adult hospitalized patients receiving medications (P), does implementation of an optimized EHR-based medication clinical decision support alert system (I), compared with existing standard medication-related EHR alerts (C), reduce medication errors (O) over a 12-week implementation period (T)?

Population of Interest

The target population for this project will include adult hospitalized patients, 18 years and above, who get their medications using the EHR-based system. The proposed project will target adult medical and surgical patients due to the fact that they usually have multiple medications and therefore, multiple opportunities for prescription, transcription, dispensing, and administration errors (Raven et al., 2025). The project will involve medication orders that have been issued within the period of implementation of the intervention and medication administration records as well. Inpatients on routine medications, high-alert medications, antibiotics, anticoagulants, insulin, and other routinely administered medications could be involved in the project. Children would not be involved because they differ from adults in their medication dosing and safety concerns.

Intervention of Interest

The intervention will involve the optimization of an existing medication-related clinical decision support alert system integrated within the EHR system. Instead of increasing the number of alerts, this intervention will concentrate on enhancing the clinical utility and appropriateness of existing alerts. The intervention will involve a multidisciplinary team comprising nurses, physicians, pharmacists, informaticians, and patient safety advocates who would screen medication-related alerts and identify duplicative, too frequent, clinically meaningless, and ill-timed alerts.

High-value alerts would be preserved or made more effective in terms of clinically important risks like serious drug allergies, clinically significant drug interactions, contraindications, dosing issues, and other potentially harmful medication-related risks. Low-value alerts would be redesigned using evidence-based alert optimization techniques like suppression rules, enhanced clinical specificity, non-interruptive display methods, and others. Clinicians would be educated on the rationale behind the revised alerts.

The intervention is based on the existing evidence that medication-related CDSS targeting specific situations can prevent medication errors and ADEs (Syrowatka et al., 2024). The literature also proves the effectiveness of machine learning CDSS in intercepting inappropriate prescriptions and medication-related errors, including wrong drug and look alike/sound alike medications (Chen et al., 2024).

There is evidence that CDS alert optimization should take clinicians into account instead of being based only on technical changes in EHR.  used multidisciplinary team meetings, feedback from clinicians, alert data, literature, and drug references while optimizing CDS alerts. Their research showed a decrease in alert rate after using certain optimization methods (Bakker et al., 2024).

Comparison of Interest

The comparison will ensure that changes in the medication error rates can be compared to something practical, such as the organization's standard medication alerts. The comparison also makes it possible for the research project to find out if the process of optimizing the current CDSS provides any safety benefit beyond what the organization currently has in terms of electronic medication safety measures. Comparing the project against the current situation is important since CDSSs have been known to reduce medication errors; thus, the research project should not assume that any CDSS will make things better (Syrowatka et al., 2024).

Outcome of Interest

The principal outcome measure will be the medication error rate per 1,000 medication administrations over the implementation period. A medication error will refer to any discrepancy noted between the ordered medication and the medication administration standards, such as wrong medication, wrong dose, wrong route, omission, and other clinically significant errors.

The measurement is possible due to the quantification of medication errors through the review of medication orders and medication administration records in relation to the total number of medication administrations. In addition, direct observation may be included in periodic validation reviews. Recent observations have indicated the usefulness of structured observation and comparison of the administered medications with the prescription for detecting medication administration errors (Jessurun et al., 2023). Another study described the use of a validated technique for assessing the potential impact of medication administration errors using multidisciplinary expert evaluation for reliable severity classification (Assunção-Costa et al., 2023).

One of the secondary outcomes can be medication alert override rate, since too many overrides mean that the alerts are not adequately relevant or useful. This would help assess whether the proposed intervention will improve the quality of alerts and not just change the quantity of medication errors. Alert overrides are significant since current research has demonstrated considerable variation in alert management and established alert characteristics as one of the determinants of clinician response to alerts (Mäkinen et al., 2025).

Timeline

The suggested project will have a 12-week implementation period. The first two weeks will be allocated for measuring baseline medication error rate, reviewing existing medication alerts with the interdisciplinary team, and developing the optimal configuration of the CDS system. The third and fourth weeks will be used for completing the configuration of the CDS system, testing, educating personnel, and implementing the project. Weeks 5 to 12 will represent the evaluation period. Medication error rate will be assessed during the baseline and intervention periods. Regular monitoring on a weekly or biweekly basis would allow detection of unexpected increases in the number of medication errors, alerting issues, or any other problem related to the work process.

Conclusion

Though there is an extensive use of electronic health records and decision support systems, medication errors still remain an important issue in relation to patients' safety. The available evidence proves that medication-focused decision support systems can be effective in reducing the number of medication errors; however, excessive or poorly designed alerts can lead to problems with alert fatigue, alert override, and workflow interference. The proposed PICOT project will consider this problem through the evaluation of optimized EHR-based medication alerts in the case of hospitalized adults who take medications. The existing standards of medication alerts will act as a control group, while the number of medication errors per 1,000 medication administrations will serve as the primary measurable outcome.

References

Assunção-Costa, L., Pinto, C. R., Machado, J. F. F., Valli, C. G., & De Souza, L. E. P. F. (2023). Assessing the severity of medication administration errors identified in an observational study using a valid and reliable method. Journal of Pharmaceutical Policy and Practice, 16(1), 143. https://doi.org/10.1186/s40545-023-00653-x

Bakker, T., Klopotowska, J. E., Dongelmans, D. A., Eslami, S., Vermeijden, W. J., Hendriks, S., ... & Duyvendak, M. (2024). The effect of computerised decision support alerts tailored to intensive care on the administration of high-risk drug combinations, and their monitoring: a cluster randomised stepped-wedge trial. The Lancet, 403(10425), 439-449. https://doi.org/10.1016/S0140-6736(23)02465-0

Chen, C. Y., Chen, Y. L., Scholl, J., Yang, H. C., & Li, Y. C. J. (2024). Ability of machine-learning based clinical decision support system to reduce alert fatigue, wrong-drug errors, and alert users about look alike, sound alike medication. Computer Methods and Programs in Biomedicine, 243, 107869. https://doi.org/10.1016/j.cmpb.2023.107869

Jessurun, J. G., Hunfeld, N. G. M., de Roo, M., van Onzenoort, H. A. W., van Rosmalen, J., van Dijk, M., & van den Bemt, P. M. L. A. (2023). Prevalence and determinants of medication administration errors in clinical wards: A two‐centre prospective observational study. Journal of clinical nursing, 32(1-2), 208-220. https://doi.org/10.1111/jocn.16215

Raven, K., Zhang, J., Vagliano, I., Jaspers, M. W., Peute, L. W., & Medlock, S. (2025). Determinants influencing medication-related alert handling in the electronic health record: A systematic review and meta-analysis. International journal of medical informatics, 203, 106011. https://doi.org/10.1016/j.ijmedinf.2025.106011

Syrowatka, A., Motala, A., Lawson, E., & Shekelle, P. (2024). Computerized clinical decision support to prevent medication errors and adverse drug events: Rapid review. Making Healthcare Safer IV: A Continuous Updating of Patient Safety Harms and Practices. Agency for Healthcare Research and Quality. https://doi.org/10.23970/AHRQEPCCER285