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). 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.
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.
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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 ( |
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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 |
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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 |
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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 |
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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 |
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Outcome |
To improve patient safety and accuracy of the prescription |
improved patient safety, accuracy of medication administration |
Improved workflow considerations |
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Setting (Where did the study take place?) |
large, private and nonprofit hospital in Paris |
American health care setting |
Massachusetts, United States |
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Sample |
Patient data collected for 18-month |
112 ambulatory primary care |
35 participants for gaining patient feedback |
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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 |
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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. |
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Recommendations of the Researcher |
To conduct research in multiple hospital setting |
Loss of follow up cases |
Limited sample |
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Criteria |
Article 4 |
Article 5 |
Article 6 |
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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. |
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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 |
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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 |
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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 |
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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 |
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Outcome |
Improve administration of correct dose |
Improve formulation and provide alert for medication safety |
Reduce hospitalization and high dosing |
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Setting (Where did the study take place?) |
United Kingdom |
Small hospital in Iran |
Canada |
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Sample |
6 articles |
11 articles |
No specification |
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Method |
The researchers conducted 6 articles from PubMed, Embase, Cochrane Library |
The researchers conducted 11 articles from |
Researchers conducted limited data |
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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 |
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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
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
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