Using CPOE and CDS-Nursing Paper Examples

The US has close to 6,800 prescription drugs and innumerable over-the-counter medicines. As the general public regularly uses thousands of health supplements, herbs, potions, and lotions to cure their health issues, it complicates a practitioner’s role during patient treatment (Tariq et al., 2023) (Using CPOE and CDS).

It is plausible that errors can be committed when doctors prescribe or dispense medications, given the variety of available substances. The high likelihood of substance interactions is an additional factor, as 7,000 to 9,000 persons each year. More so, in the United States alone pass away due to drug mistakes (Tariq et al., 2023).

Using CPOE and CDS
Using CPOE and CDS

Millions of people have adverse reactions or other pharmaceutical issues but frequently fail to disclose them. Care associated with medication errors costs more than $40 billion a year (Tariq et al., 2023). Moreover, patients suffer psychologically and physically due to drug errors, leading to poor health outcomes (Using CPOE and CDS).

A significant effect of pharmaceutical mistakes remain lowering patient satisfaction. In addition, foster a rising mistrust of the healthcare system. In this case, clinical decision support systems can help minimize medication errors. This paper discusses a patient safety issue associated with medication administration and reviews the literature on CDSSs that can help address the patient safety issue (Using CPOE and CDS).

Patient Safety Issue (Using CPOE and CDS)

Adverse drug events remain attributed to medication errors, including medication administration errors. For patients with numerous disorders, advances in clinical therapies have significantly improved their health, but rising risks have also offset them. Any commission or omission error occurring between the time a clinician prescribes a drug and when the patient receives it is a medication error (Skelly et al., 2022).

A patient suffering injury after exposure to a medication remain said to have suffered an adverse drug event (ADE). Adverse drug events can include any form of physical harm, psychological distress, or even death (Skelly et al., 2022). The presence of an ADE, like the more generic term adverse event, does not always denote a mistake or subpar medical care (Using CPOE and CDS).

An incorrectly administered medication that harms the patient in any way remain perceived as a preventable adverse drug event. Most experts agree that around half of the ADEs remain avoidable (Skelly et al., 2022). Potential adverse drug events (ADEs) remain medication-related errors that do not pose any harm. Moreover, because they remain avoidable before they affect the patient or through good fortune (Skelly et al., 2022).

A medication-related adverse event (ADE) can be minimized even though not entirely avoidable and causes harm to the patient. Finally, a certain proportion of patients will still develop ADEs or nonpreventable ADEs or side effects, regardless of whether drugs are prescribed and provided correctly (Using CPOE and CDS) .

Search Strategy

The search utilized PubMed, Medline, Embase, and Cochrane Library databases, limiting the search to articles published in English. Quantitative research papers, systematic reviews metanalysis, and case reports remain included in the search. The search focused on articles reporting on the use of CDSS in preventing medication errors and associated patient safety issues, focusing on adverse drug events (ADE). Keywords and key phrases used in the search included clinical decision support systems (CDSSs), medication administration errors, adverse drug events, use of CDSSs in addressing medication administration errors and adverse drug events, and patient safety.

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The search also utilized limiters to narrow the search focus to CDSSs that help address adverse drug events. Mesh terms utilized in the search included Clinical Decision Support Systems, Clinical Decision Support System, Clinical Decision Support, Clinical Decision Supports, Decision Supports, Clinical, Support, Clinical Decision, Supports, Clinical Decision, Decision Support, and Clinical (Using CPOE and CDS).

The researcher reviewed the abstracts for the identified articles to determine whether the outcomes supported the research topic and question before accessing full articles for studies selected from this step. The researcher undertook narrative summaries of research findings and meta-analysis of primary study data. From the search, 17 unique articles were identified, and their abstracts were reviewed. Of the 17, only two were selected as providing the most valuable evidence to support the research topic, and full articles were obtained (Using CPOE and CDS).

Synthesis and Evaluation of Literature

Clinical decision support systems come in two categories, knowledge-based and non-knowledge-based CDSSs. Multiple CDSSs were adopted to streamline and facilitate decision-making, increasing healthcare effectiveness and efficiency and reducing room for error. CDSS has indicated effectiveness in minimizing medication administration errors. This review focuses on two effective CDSSs addressing adverse drug events; alert-based CDSS and mobile-based CDSS can help address ADEs (Chien et al., 2022) (Using CPOE and CDS).

Alert-based CDSSs adopt reminders to help with medication prescriptions by generating medication recommendations and using alerts to interact with the practitioners. Chien et al. (2022) indicate the increased importance of alert usability to be clinically relevant. Alerts, also known as notifications or warnings, effectively highlight a potential risk (Using CPOE and CDS).

The alerts are used to inform clinicians of a potential or actual medication administration error that can potentially increase the risk of ADEs. Chien et al. (2022) show alerts are efficient approaches to streamlining clinical workflow and preventing medication errors and associated consequences, such as ADEs. However, alert-based CDSS can lead to alert fatigue, which has detrimental effects on patient outcomes. Therefore, optimizing the alert mechanism and robustness and improving alert appropriateness to minimize alert fatigue is critical.

On the other hand, mobile-based CDSS are deployed on mobile devices to increase portability and access. Such a CDSS is possible with the increased adoption of mobile technology in the healthcare environment. Clinicians can use CDSS on handheld devices at the point of care, increasing healthcare delivery, especially in disadvantaged areas like middle-income communities and rural areas where the complete CDSS system adoption is challenging (Using CPOE and CDS).

Agarwal et al. (2021) confirm the effectiveness of mobile-based CDSS on various outcomes, such as adherence and appropriate practice. Mobile-based CDSS increases ease of data entry and use, minimizing fatigue associated with using computer devices. Agarwal et al. (2021) also show the multi-functionality capability of mobile apps for clinical decision support and more user involvement in decision development and evaluation. However, more research is needed on their impact on patient behavior and data security (Using CPOE and CDS). 

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Most Feasible CDSS

Alert-based CDSS has been a norm in most hospitals, but due to increasing concern about alert fatigue, alternative adoptions of CDSS are necessary. There has been a rise in customized CDSS to address alert fatigue, tailoring data to specific patient physiological characteristics to ensure high specificity and unnecessary alerts (Chien et al., 2022) (Using CPOE and CDS).

Despite the improvements, the advancement in mobile technology and increased adoption in healthcare make mobile-based CDSS more favorable in addressing adverse drug events by first preventing medication errors. It is easier and less costly to integrate mobile devices with the EHR. Although more research is needed on mobile-based CDSS, multiple advantages, including increased acceptability and satisfaction, ease of use and increased portability, increased user involvement in decision-making, and multi-functionality make it an interesting area to indulge in and make it more workable for the future (Agarwal et al., 2021) (Using CPOE and CDS).

Mobile devices for decision support also include notifications on the mobile device, hence performing the same task as alert-based CDSS. An additional advantage would be the easy customizability of mobile-based CDSS, perceived as a solution to alert fatigue. Moreover, mobile-based CDSS allows “anytime, anywhere” access to patient information and enhances provider communication. Generally, it enhances timely decision-making through real-time retrieval of patient information.   

Plan for Implementation of CDSS

Implementing mobile-based CDSS begins with establishing a mobile platform for the healthcare organization. The platform has an analytical engine, transaction engine, supplemental engine, and a security module. The solution architecture for the mobile platform has different levels and interfaces. The background or hidden level includes the hospital information system (HIS) and the data warehouse connected to the next level through the HIS and data retrieval interfaces (Chakravarti & Bhattacharyya, 2020).

The second level includes alerts, warnings, recommendations, data display, search, and processing components. This level also includes the analytical, supplemental, and transaction engines, which progress to the next level containing the security module and the network interface connected to the mobile devices via the wireless network (Chakravarti & Bhattacharyya, 2020). Generally, the healthcare organization can adopt appropriate mobile devices and develop a network architecture to connect the mobile apps for decision support to the current EHR. The implementation extends the current system to a mobile-enabled platform (Using CPOE and CDS).

Evaluation of Effects on Patient Safety

Mobile-based CDSS indicates high interoperability and information exchange due to the ability for real-time retrieval of patient information from the HIS. It is compelling for healthcare providers who are always on the move, and interacting with a computer becomes an interruption. When integrated with the EHR, mobile-based CDSS provides an all-round all-time access to the HIS and allows providers to take recommendations and utilize them at the point of care through handheld devices like PDAs and mobile phones, allowing efficient decisions, which reduces waiting times and room for error (Carter et al., 2019).

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A Public Key Infrastructure (PKI) or Wireless PKI remain adequate to secure end-to-end communication. Hence, patient health information is safe and can be shared seamlessly among providers due to higher confidence (Chakravarti & Bhattacharyya, 2020). The solution increases customer satisfaction and facilitates value-added services anytime, anywhere. Conclusively, mobile-based CDSS can improve patient safety by increasing the efficiency and accuracy of the decision-making process (Using CPOE and CDS).

Conclusion

Medication errors threaten patient safety due to associated patient safety issues like drug interactions, adverse drug reactions, and adverse drug events. Adverse drug events can lead to complications, rehospitalization, medication comorbidities, and death. Clinical decision support systems have indicated effectiveness in improving medication administration decision-making, reducing the potential for ADEs (Using CPOE and CDS).

Alert-based CDSS and mobile-based CDSS are among many different types of CDSSs, with the former using reminders to warn practitioners of potential or actual error and the latter being deployed on mobile devices like palm, PDA, and WAP. Per the evaluation, mobile-based CDSS is more suitable when integrating with EHR (Using CPOE and CDS).

References

Agarwal, S., Glenton, C., Tamrat, T., Henschke, N., Maayan, N., Fønhus, M. S., Mehl, G. L., & Lewin, S. (2021). Decision-support tools via mobile devices to improve quality of care in primary healthcare settings. The Cochrane Database of systematic reviews7(7), CD012944. https://doi.org/10.1002/14651858.CD012944.pub2

Carter, J., Sandall, J., Shennan, A. H., & Tribe, R. M. (2019). Mobile phone apps for clinical decision support in pregnancy: A scoping review. BMC Medical Informatics and Decision Making19(1). https://doi.org/10.1186/s12911-019-0954-1

Chakravarti, B., & Bhattacharyya, S. B. (2020). Mobile-based clinical decision support system. Provider & Public Health Group17(1), 154.

Chien, S., Chen, Y., Chien, C., Chin, Y., Yoon, C. H., Chen, C., Yang, H., & Li, Y. (. (2022). Alerts in clinical decision support systems (CDSS): A bibliometric review and content analysis. Healthcare10(4), 601. https://doi.org/10.3390/healthcare10040601

Skelly, C. L., Cassagnol, M., & Munakomi, S. (2022). Adverse events. In StatPearls [Internet]. StatPearls Publishing. https://www.ncbi.nlm.nih.gov/books/NBK558963/

Tariq, R. A., Vashisht, R., Sinha, A., & Scherbak, Y. (2023). Medication dispensing errors and prevention. In StatPearls [Internet]. StatPearls Publishing. https://www.ncbi.nlm.nih.gov/books/NBK519065/

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