Using a PICO(T) Framework and Evidence to Develop Care Practices

Introduction

Nurses must perform medical procedures according to the organization’s policies. Most healthcare organizations use evidence-based practice to ensure that patient-centered care is offered. PICO (T) is an acronym of a research framework that stands for patients/population, intervention/indicator, comparison, outcome, and time where applicable. Medication errors are one of the leading causes of death in the United States (Durham et al., 2016). They result in serious effects that can affect the patient’s quality of life, increase mortality, and length of hospitalization. Most medication errors are related to medication administration (Durham et al., 2016). They can be due to inadequate patient education and reduced attention of healthcare workers. This essay will define a practice issue related to medication errors. It will utilize the PICO (T) framework to locate literature that will answer the research question. It will further the relevance of the significant findings.

Use of the PICO(T) Approach Medication Errors

Medication errors can lead to significant adverse effects. It can be due to wrong medications, wrong dosages, or the processing of wrong orders. The PICO (T) question under investigation is “Is medication administration record technology used by nurses effective in reducing medication-related errors when compared to other interventions?” Hospitals with a larger nursing workforce have a lower rate of medication-related errors, while nurses who performed tasks outside their scope of practice had higher rates of medication errors (Kang et al., 2016). The population in the PICO (T) framework is nurses. The intervention is medication administration record technology; the comparison is other interventions such as patient education, and the outcome is the reduction of medication errors.

Identification of Sources of Evidence

Look-Alike Sound-Alike Drugs

Look-alike, sound-alike medication errors occur when the packaging for drugs looks similar or the names sound similar. Study reports indicate that medication-related errors due to this are common, lead to many deaths, and cost the United States healthcare system a lot of money (Shao et al., 2018). The drug administration process is long in hospitals and the healthcare industry. Relaying of the information occurs through different individuals and devices, hence increasing the chances for medication errors. A way of minimizing this error would be by pharma industries manufacturing drugs with distinct packages and names. The MAR system can also reduce these errors since it allows for faster retrieval of the relevant resources in the drug administration chain.

HALT Model

The HALT model was designed to aid nurses in realizing that they are all human and that common human behaviors can lead to medication errors. This model utilizes proactive strategies in identifying nurses at risk. This method opines that HALT models include having nurses take breaks from the ward to take deep breaths and reorganize the nurses’ workload to enable breaks. The HALT methods can result in nurses’ job satisfaction and the provision of quality healthcare, leading to a reduction of medication-related errors. Ragau et al. (2018) noted that this model reduced medication errors by 31% in two months, reduction of human errors by 25 %, and errors related to documentation and communication by 22 %.

MARs- Medication Administration Record

Technology is constantly changing, and the medical world has not been left behind. Healthcare professionals must stay current with new drugs and organizational policies. MAR technology helps prevent medication errors. A pharmacist is always just a phone call away in case of any inquiries. In MAR, each drug is scanned. If the nurse scans the wrong drug, the drug will not be ordered, and the system will display an error message, declining the order process (Durham et al., 2016). If a nurse attempts to give the drug early, a pop-up message will appear asking the nurse if they want to give the drug.MAR improves patient safety during medication administration by preventing medication errors.

Relevance of Findings from Articles

These articles offer valid information on how to reduce medication-related errors. Healthcare organizations must develop and implement policies that can safeguard patients’ safety and minimize medication-related errors. Human errors arise due to reduced attention or insufficient knowledge. Durham et al. (2016) note that technology is more successful in preventing medication errors. Due to this, the healthcare industry must be aware of any technological advancements and be constantly looking out to help prevent medication erro