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Clinical Decision Support System and Medicine Titration Orders Essay

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Updated: Dec 11th, 2020

Introduction

Patient safety is one of the primary health care concerns globally. According to the World Health Organization (2018), one in ten patients get some harm in the process of receiving care. Therefore, about 43 million people suffer annually from incidences involving patient safety issues thus making unsafe medicine a global burden. However, in addition to patients being hurt due to improper care, healthcare facilities lose about 42 billion dollars a year because of medication errors (World health Organization, 2018). Consequently, there is a need in developing a system to support effective clinical decisions and improve patient outcomes due to the reduction of medication errors. One such opportunity is to design a Clinical Decision Support System (CDSS) using a Computerized Provider Order Entry (CPOE). This paper reviews the use of CDSS for improving patient outcomes by introducing medicine titration standards.

Clinical Issue Details

The clinical issue under analysis involves a problem of titrating the prescribed medicine, Fentanyl in particular. The issue implies the fact that due to the lack of a standard for titrating medicine, nurses were using different scales. This situation is unacceptable in conditions of a healthcare facility. It can lead to undesirable patient outcomes starting from no result in case a nurse applies a lower dose of medicine to lethal cases or a patient’s condition deterioration in case of overdose.

When it comes to the application of opioid medications, careful titration becomes crucial. For example, Fentanyl, which is a synthetic opioid, is both used to reduce pain and as one of the anesthesia medications. Therefore, the use of the appropriate dose for every patient is significant for the patient’s wellbeing. Improper titration can cause some common adverse effects such as confusion, sleepiness, or nausea, as well as more serious side effects. Moreover, an increase from 14 to 46 percent was observed in the percentage of fatal overdoses with synthetic opioids (Siemaszko, 2018). In fact, there are more deaths from prescription opioids than from heroin. One of the concerns of contemporary medical science related to fentanyl overdose is a rare form of amnesia (Aguirre, 2018). It is characterized by the inability to form new memories and damage of hippocampi. To avoid undesirable patient outcomes as a result of applying improper medication dose, the order entry needs to be specific and based on a standard protocol for titrating. Therefore, there is a need for developing a CDSS as a component of the Electronic Health Record (EGR) to empower clinical decision-making.

Rationale Behind the Design Development

The need for CDSS to provide better-informed clinical decisions is evident. CDSS as a component of EHR is a tool with the potential to improve diagnostic accuracy and, on the whole, achieve precision medicine (Castaneda et al., 2015). Moreover, CDSS is expected to minimize medication errors related to the titration of the prescribed medicine. Fentanyl is one of the medications usually applied in critical care units. Frequently, due to understaffing of these units, nurses responsible for medication intake lack qualifications or are not taught to titrate medication considering the peculiarities of every patient. Consequently, the risk of undesirable patient outcomes increases. Thus, the rationale for designing a CDSS is to stimulate the reduction of medication errors due to possible variations in titrating trough implementing titration standards. CDSS provides an opportunity to analyze patient data and calculate the optimal dosage of medication. For the case under consideration, the medical expert system type will be suitable (Sadegh-Zadeh, 2015). This type of system includes the functions important for empowering clinical decisions in titrating medications.

CDSS Implementation and Adoption

CDSS for empowering correct titration of Fentanyl and other medications is expected to be successfully implemented in healthcare settings. As a part of EHR, it can enhance patient safety and quality of care. Based on the CPOE, CDSS increases the efficiency of clinical decisions of all levels including medicine dosage (Middleton et al., 2013). Before CDSS implementation, it is necessary to organize staff training to make sure that all professionals involved in work with the system have enough skills and can use the system appropriately. The training should be provided for some staff representatives, who will train other nurses. During the implementation stage, strict control over the system is necessary to avoid possible technical mistakes. The staff of the healthcare facility is likely to willingly adopt the CDSS. Since the system is aimed to simplify the work of the staff and increase patient safety, clinicians are expected to be interested in its successful implementation and application. At the initial stage, some clinicians may be suspicious about the system, but after the implementation when the system proves its efficiency, it will be adopted by all staff members.

Measurement of Patient Outcomes

Measurement of patient outcomes is necessary to evaluate the efficiency of the implemented system for titrating Fentanyl. Data in such categories as mortality, the safety of care, adverse effects, patient experience, and effectiveness of care will be evaluated among the patients who were administered Fentanyl. The data will be taken from patients’ health records and obtained through interviews. The measurements will be taken before and after the CDSS implementation. In case mortality and adverse effects rate decrease while safety, effectiveness, and patients’ experience improve, the implementation of the system can be considered successful.

Assessment of Challenges and Solutions

Despite the evident benefits of CDSS for titrating medications, there can be some challenges. First of all, the technical equipment of the healthcare facility can be not ready for a new system. This challenge can be solved by upgrading hardware and software of the facility to enable the functioning of a new system. The second challenge can be related to the lack of experience of work with CDSS and insufficient training of the staff members. The solution to this challenge is to provide the training necessary for adopting the CDSS in conditions of the healthcare facility. Another challenge can be connected with the risk of information loss. It is a risk typical of all digital databases comprising personal information. The risk can be minimized by the use of protective software and the training of personnel responsible for the use of the system. One more challenge implies communication breakdown, which can lead to inappropriate information circulation and, as a result, mistakes in the system application. The challenge can be addressed by the use of effective communication strategies and careful personnel selection. Finally, there is a risk of technical mistake, which is unacceptable in case of CDS application for medicine titration. Thus, the system has to be tested on hypothetical patients and checked by the experienced clinicians.

Conclusion

On the whole, the CDSS is an effective tool which, in connection to EHR, has a potential to reduce medication errors and increase patient safety. One of its possible applications is the clinical issue of improper titration of prescribed medicine, which leads to undesirable patient outcomes. Errors in titration are particularly dangerous in case synthetic opioids such as Fentanyl are involved. Thus, the introduction of CDSS designed for titration of this medication is justified by the expected increase in patient safety and general improvement of patient outcomes. Still, there are some challenges that clinicians can face. However, they can be solved through the careful training of the staff members involved in the use of the new system as well as attention to technical details.

References

Aguirre, L. (2018). PBS. Web.

Castaneda, C., Nalley, K., Mannion, C., Bhattacharyya, P., Blake, P., & Pecora, A., … Suh, K. S. (2015). Clinical decision support systems for improving diagnostic accuracy and achieving precision medicine. Journal of Clinical Bioinformatics, 5(1), 1-16. Web.

Middleton, B., Bloomrosen, M., Dente, M., Hashmat, B., Koppel, R., Overhage, J., … Zhang, J. (2013). Enhancing patient safety and quality of care by improving the usability of electronic health record systems: Recommendations from AMIA. Journal of The American Medical Informatics Association, 20(e1), e2-e8. Web.

Sadegh-Zadeh, K. (2015). Handbook of analytic philosophy of medicine (2nd ed.). New York, NY: Springer.

Siemaszko, C. (2018). NBS News. Web.

World Health Organization. (2018). Web.

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