Reducing Medication Errors In Nursing Practice Discussion

With the high patient care demand, medication safety has gained increased attention due to high hospital admission and frequent premature deaths.  In united states, CDC data suggested that more than 1500 individuals died due to overdose of opioid. In 2020, more than 15 million American encountered health issues due to high medication errors followed by 40000 premature deaths (Green,  2018).  Medication errors are considered as the preventable errors that have drastic impacts on the patient health. The most common types   of the medication errors are similar name medication, unauthorized use of the drug, lack of appropriate admission of the drugs, wrong dose of medication or administrating medication to the wrong patient (Alqenae,  Steinke & Keers, 2020)Reducing Medication Errors In Nursing Practice Discussion.  Emerging literature highlighted that lack of awareness of appropriate medication dosages, lack of reviewing patient chart, high workload and frequent   interruptions during medication formulation play crucial role in initiating medications errors. Dirik et al. (2018), suggested that while medication errors identification and reporting essential, lack of reminders and gap in drug knowledge, limited support from experienced professionals, increased workload are major reason behind poor medication error identification and mandatory reporting.  The common impact of such medication errors are increased length of hospital stays, increased inaccurate medication administration, high length of hospital stays, increased workload and  burnout amongst nursing professionals (Shahin, 2019). Hence, it is crucial to conduct literature review   regarding medication errors to improve clinical practice.

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PICOT question is defined as roadmap of clinical research that enable professionals to comply with clinical practice. The major components of PICOT question are patient, intervention, comparison, outcome and Time. As discussed above, medication errors are considered as the preventable errors that   have drastic impacts on the patient health. In this case, the PICOT question is following:

In  nurses having experience less than 2 years, Clinical decision system with early warning system is suitable for  reducing medication errors compared to manual calculation of medication dose and  administration  within  3 months? The PICOT framework is following:

Population- The nurses  having experience less than 2 years

Intervention- Clinical decision system with early warning system.

Comparison- manual calculation of medication dose and administration

Outcome- Low medication errors and low hospital stays with improved patient satisfaction.

While registered nurses often calculate the drug dose and develop appropriate drug formulation in standard care procedure, clinical decision-support system is one such technology that   improve medical decision making with patient information and appropriate dose.  The proper warning in this electronic system will enhance self confidence of the nursing professionals to administrate appropriate medication. Hence,  the comparison of the effectiveness of two intervention will reduce prescription error and administration  rates  that contributed to the patient readmission and  premature  death Reducing Medication Errors In Nursing Practice Discussion.

Criteria Article 1 Article 2 Article 3
APA-Formatted Article Citation with Permalink Corny, J., Rajkumar, A., Martin, O., Dode, X., Lajonchère, J. P., Billuart, O., … & Buronfosse, A. (2020). A machine learning–based clinical decision support system to identify prescriptions with a high risk of medication error. Journal of the American Medical Informatics Association, 27(11), 1688-1694. https://doi.org/10.1093/jamia/ocaa154 Ancker, J. S., Edwards, A., Nosal, S., Hauser, D., Mauer, E., & Kaushal, R. (2017). Effects of workload, work complexity, and repeated alerts on alert fatigue in a clinical decision support system. BMC medical informatics and decision making, 17(1), 1-9. https://pubmed.ncbi.nlm.nih.gov/28395667/ Nanji, K. C., Garabedian, P. M., Shaikh, S. D., Langlieb, M. E., Boxwala, A., Gordon, W. J., & Bates, D. W. (2021). Development of a Perioperative Medication-Related Clinical Decision Support Tool to Prevent Medication Errors: An Analysis of User Feedback. Applied Clinical Informatics, 12(05), 984-995. DOI: 10.1055/s-0041-1736339 (
How Does the Article Relate to the PICOT Question?  Yes because article focused on effectiveness of clinical decision support system to identify prescriptions  errors and medication errors Yes because  article focused on   effect of workload , medication errors and fatigue in a clinical decision support Yes  because  article focused on   effectiveness of Perioperative Medication-Related clinical decision support system in improving medication errors
Quantitative, Qualitative (How do you know?) Quantitative study as researchers conducted cross sectional study   by gathering 18-month period Quantitative study as researchers conducted Retrospective cohort Quantitative study as researchers conducted descriptive analysis
Purpose Statement  The aim of the study is to assess  machine clinical decision support system to identify risk of  medication errors using hybrid model  To assess the effect of the workload, fatigue alert  on clinical decision support system  To assess the   impact of CDS on warn of medication errors
Research Question  Is  machine learning based  clinical decision support system  is effective to reduce medication errors  Is arising temporary clinical decision support systems able to   provide fatigue alert, workload and   cognitive overload  Is Medication-Related Clinical Decision Support is effective to improve medication doses,  patient specific dose information
Outcome  To improve patient safety and    accuracy of the prescription  improved   patient safety, accuracy of medication administration  Improved workflow considerations
Setting

(Where did the study take place?)

   large, private  and nonprofit hospital in Paris American health care setting Massachusetts, United States
Sample   Patient data collected for  18-month  112 ambulatory primary care 35 participants   for  gaining  patient  feedback
Method  Researchers conducted cross sectional study   by gathering 18-month period ( jan 2017- august 2018) Collecting data from  electronic health record data from January 2010 to June 2013  Collecting  two groups and eight individual design feedback  for 35 participants
Key Findings of the Study  The findings reported that  it is accurate existing  techniques   because  current process unable to  address urgent need of  improvement  . in this case, this will enable  professionals to detection medication errors through CDS alert system and priorities  appropriate medication   It can provide alert from medication errors, wrong medication  dose, , higher work complexity and low informational value  The finding suggested that it  improve  patient-specific dosing information  as  alert.
Recommendations of the Researcher  To conduct research in  multiple  hospital setting  Loss of follow up cases Limited sample
Criteria Article 4 Article 5 Article 6
APA-Formatted Article Citation with Permalink  Hajesmaeel Gohari, S., Bahaadinbeigy, K., Tajoddini, S., & R Niakan Kalhori, S. (2021). Effect of Computerized Physician Order Entry and Clinical Decision Support System on Adverse Drug Events Prevention in the Emergency Department: A Systematic Review. The Journal of pharmacy technology : jPT : official publication of the Association of Pharmacy Technicians, 37(1), 53–61. https://doi.org/10.1177/8755122520958160 Taheri Moghadam, S., Sadoughi, F., Velayati, F., Ehsanzadeh, S. J., & Poursharif, S. (2021). The effects of clinical decision support system for prescribing medication on patient outcomes and physician practice performance: a systematic review and meta-analysis. BMC medical informatics and decision making, 21(1), 1-26. DOIhttps://doi.org/10.1186/s12911-020-01376-8 Sutton, R. T., Pincock, D., Baumgart, D. C., Sadowski, D. C., Fedorak, R. N., & Kroeker, K. I. (2020). An overview of clinical decision support systems: benefits, risks, and strategies for success. NPJ digital medicine, 3(1), 1-10.
How Does the Article Relate to the PICOT Question? Yes, because article focused effect of Clinical Decision Support on Adverse Drug Events in emergency care  Yes, cause this article focused on clinical decision support system on  medication safety and  physician performance   Yes because it  focused on overview of clinical decision support systems
Quantitative, Qualitative (How do you know?) Qualitative study as researchers conducted systematic review  It is qualitative study because researchers conducted  systematic review It is qualitative study because researchers conducted systematic review
Purpose Statement To   assess the effect of Clinical Decision Support in assessing side effects of medication errors in emergency care  To review the effectiveness of  system  lower  occurrence of ADEs  To review the benefit, risks and strategies
Research Question  Is Clinical Decision Support able to assess   side effects of medication errors in emergency care  Is Clinical Decision Support able to assess   side effects of medication   What is the benefit, risks and strategies
Outcome  Improve administration of correct dose  Improve formulation and provide alert for medication safety    Reduce hospitalization and high dosing
Setting

(Where did the study take place?)

  United Kingdom   Small hospital in Iran   Canada
Sample  6 articles  11 articles  No specification
Method  The researchers conducted 6 articles from PubMed, Embase, Cochrane Library The researchers conducted 11 articles from  Researchers conducted limited data
Key Findings of the Study   The positive impact is improved health record data, real time alert system, compliance with clinical guidelines,  It has beneficial impact on  reducing medication errors,  prescription errors while  improving practice.  It has beneficial role in variety of decisions and patient care tasks by  providing reminders  for medication errors
Recommendations of the Researcher  Primary research for improving risk of bias  Conducting primary  research Conducting primary research

References:

Alqenae, F. A., Steinke, D., & Keers, R. N. (2020). Prevalence and nature of medication errors and medication-related harm following discharge from hospital to community settings: a systematic review. Drug safety, 43(6), 517-537. https://link.springer.com/article/10.1007/s40264-020-00918-3

Ancker, J. S., Edwards, A., Nosal, S., Hauser, D., Mauer, E., & Kaushal, R. (2017). Effects of workload, work complexity, and repeated alerts on alert fatigue in a clinical decision support system. BMC medical informatics and decision making, 17(1), 1-9.

Corny, J., Rajkumar, A., Martin, O., Dode, X., Lajonchère, J. P., Billuart, O., … & Buronfosse, A. (2020). A machine learning–based clinical decision support system to identify prescriptions with a high risk of medication error. Journal of the American Medical Informatics Association, 27(11), 1688-1694. https://doi.org/10.1093/jamia/ocaa154

Dirik, H. F., Samur, M., Seren Intepeler, S., & Hewison, A. (2019). Nurses’ identification and reporting of medication errors. Journal of clinical nursing, 28(5-6), 931-938. https://onlinelibrary.wiley.com/doi/abs/10.1111/jocn.14716

Green, C. (2018). Contemporary issues: The pre-licensure nursing student and medication errors. Nurse education today, 68, 23-25. https://www.sciencedirect.com/science/article/abs/pii/S0260691718302089

Hajesmaeel Gohari, S., Bahaadinbeigy, K., Tajoddini, S., & R Niakan Kalhori, S. (2021). Effect of Computerized Physician Order Entry and Clinical Decision Support System on Adverse Drug Events Prevention in the Emergency Department: A Systematic Review. The Journal of pharmacy technology : jPT : official publication of the Association of Pharmacy Technicians, 37(1), 53–61. https://doi.org/10.1177/8755122520958160 Reducing Medication Errors In Nursing Practice Discussion

Nanji, K. C., Garabedian, P. M., Shaikh, S. D., Langlieb, M. E., Boxwala, A., Gordon, W. J., & Bates, D. W. (2021). Development of a Perioperative Medication-Related Clinical Decision Support Tool to Prevent Medication Errors: An Analysis of User Feedback. Applied Clinical Informatics, 12(05), 984-995. DOI: 10.1055/s-0041-1736339

Shahin, M. A. H. (2019). Improving intravenous medication administration and reducing medication errors among critical care nurses at Jordan University Hospital. Journal of Bioscience and Applied Research, 5(3), 352-366. https://journals.ekb.eg/article_147401.html

Sutton, R. T., Pincock, D., Baumgart, D. C., Sadowski, D. C., Fedorak, R. N., & Kroeker, K. I. (2020). An overview of clinical decision support systems: benefits, risks, and strategies for success. NPJ digital medicine, 3(1), 1-10.

Taheri Moghadam, S., Sadoughi, F., Velayati, F., Ehsanzadeh, S. J., & Poursharif, S. (2021). The effects of clinical decision support system for prescribing medication on patient outcomes and physician practice performance: a systematic review and meta-analysis. BMC medical informatics and decision making, 21(1), 1-26. DOIhttps://doi.org/10.1186/s12911-020-01376-8 Reducing Medication Errors In Nursing Practice Discussion