Using Computerized Provider Order Entry and Clinical Decision Support System Essay

Computerized provider order entry (CPOE) is an integral part of healthcare, greatly improving care provision. It is implemented to reduce prescription errors, which is a prevalent issue, and make health records more accessible, as patients may complain about undecipherable handwriting (Vélez-Díaz-Pallarés et al., 2017). CPOE is usually integrated with a clinical decision support system (CDSS), which offers the necessary supporting information (Vélez-Díaz-Pallarés et al., 2017). They contribute to care safety and cost reduction, although humans are still responsible for supplying accurate and relevant information on conditions, such as stroke, and their treatment (Vélez-Díaz-Pallarés et al., 2017). A sound design is essential for proper implementation and error prevention, as medicals ones are replaced by technical glitches.

CPOE Used to Design CDSS

A typical CPOE system will be employed for CDSS integration. Its interface will include a patient’s chart, an order list, which allows one to plan tasks, follow their progress, and see the results, and a trend tab showing one’s clinical data and important developments. The system will enable a healthcare employee to place orders, connecting to pharmacies. Direct input will be supported, meaning that it is possible to correct the existing data or add new entries without a third party. Lastly, a mobile version will be available for emergency cases and those preferring to work there.

Proposed CDSS

The CDSS will provide clinical information, patient data, and other relevant knowledge for a particular case. It will be connected to a database, matching an initial profile with the general hub, and offer suggestions, assessments, and interventions, including medicine (Bezemer et al., 2019). Based on the provided information, a clinician will be able to make balanced decisions (Zikos & DeLellis, 2018). Such convenience is achieved by combining and reusing data and imputing new feedback through the CPOE system, which is relevant for follow-up visits with additional tests (Zikos & DeLellis, 2018). Still, one is expected to apply critical thinking to carefully analyze the information and select the right approach (Zikos & DeLellis, 2018). The system will implement essential guidelines and offer various forms, including reminders (Zikos & DeLellis, 2018). They can be used to suggest cheaper drug alternatives and notify employees of important clinical events for a patient, prompting the staff to contact them in emergencies (Zikos & DeLellis, 2018). Overall, the CDSS will address multiple goals, one of which is connected with e-prescription.

Proposed CDSS Links for a Specific Medication

Non–vitamin K antagonist oral anticoagulants are gaining traction in stroke treatment due to having fewer side effects. However, the CDSS may not suggest them due to the database being flawed. Thus, a clinician should manually add rivaroxaban through the CPOE system and place an order on it. The process is especially important if the side effects of more traditional medications, for example, warfarin, put a patient at an increased risk (Yao et al., 2016). Consequently, a care receiver will be able to access the best available treatment due to the system removing most of the barriers to it.

Details of Clinical Issue

The clinical issue at hand is stroke, which is responsible for many deaths and disability cases. The focus will be on the ischemic one, which is associated with infarction of the brain caused by arterial occlusion (Campbell et al., 2019). It is rather prevalent and dependent on such factors as sex, age, and certain diseases, with hypertension considerably increasing its risk (Campbell et al., 2019). Stroke has many potential causes, which determine the treatment; for instance, atrial fibrillation would require the use of anticoagulants, slowing blood clot formation (Campbell et al., 2019). Warfarin is usually suggested for such a purpose, although patients may struggle with adherence (Yao et al., 2016). Newer anticoagulants, including rivaroxaban, address the problem and the cause, especially atrial fibrillation (Yao et al., 2016). Overall, despite the issue being prevalent, more advanced types of treatment exist to prevent it and serve as a post-event alleviation measure.

Rationale

The integration of CPOE and the CDSS allows a clinician to resolve several tasks simultaneously. They include diagnosis prediction, concrete recommendations, physiological measurements observation, and medication suggestion and procurement (Zikos & DeLellis, 2018). The systems improve patient outcomes due to reducing medication errors by implementing dosage and other safeguards, setting reminders for both parties, optimizing clinical management, and saving costs (Zikos & DeLellis, 2018). For example, the CDSS will be able to predict the type of stroke from which an individual suffered, suggest a potential cause, and demonstrate the history of blood pressure measurements and other relevant values. The analysis will be followed by the most appropriate and cheapest medicine, allowing a clinician to place an order via the CPOE (Vélez-Díaz-Pallarés et al., 2017). The process will be less time-consuming and overcome the barriers of unavailability, medical errors, and the need to have additional visits, although they might be necessary. Altogether, the integrated CDSS benefits both the patient and the healthcare employee and helps combat such major diseases as stroke.

Implementation of CDSS

The implementation of the system may take a considerable amount of time considering its scope. It will require a collaboration of the staff, IT specialists, and pharmacists (Bezemer et al., 2019). Those responsible for the technical side will develop a database, link it to electronic health records, and install the necessary software on personal computers, which will be integrated with the other two. Then, the system will have to be adjusted to connect to pharmacies that should have similar programs to receive orders (Bezemer et al., 2019). Cybersecurity specialists will monitor the CDSS to ensure that all information is encrypted and only reaches the involved personnel. Lastly, the medical personnel will be responsible for the database’s accuracy, relevance, and scope, all of which directly impact patient outcomes. Generally, the process is challenging but fruitful in the long run.

Measured Outcomes

Several values will be relevant for determining whether the system is successful and addresses the persisting issues. One of them is patient outcomes, which should improve after the implementation; here, the target will be stroke cases. The medication error statistics are also likely to decrease, although the system cannot eliminate all potential causes. Medical costs deserve to be monitored, both the budget expenses and patient bills, which might see a reduction. Thus, positive clinical outcomes should increase, while errors and charges will ideally follow the opposite direction.

Challenges/Solutions

A potential challenge is the rise of technical errors, replacing medical ones. However, they are no less dangerous, impacting the overall network and affecting the ordering process (Vélez-Díaz-Pallarés et al., 2017). Moreover, wrong suggestions might appear, although an experienced specialist should be able to recognize them (Vélez-Díaz-Pallarés et al., 2017). Besides collaborating with technical support to fix the errors, one has more traditional ways of communication and a patient in front of them to be inspected manually.

Conclusion

A CDSS, in integration with the CPOE system, is a viable tool for providing care and address a patient’s needs. Their proper implementation eliminates numerous issues plaguing healthcare, making treatment more accessible and effective. Such diseases as stroke are no longer a significant threat, as one use of the CDSS can reveal all the relevant aspects and suggest the best therapy. Still, a clinician should not be too reliant on the system and remember more traditional methods.

References

Bezemer, T., De Groot, M. C., Blasse, E., Ten Berg, M. J., Kappen, T. H., Bredenoord, A. L., van Solinge W. W., Hoefer, I. E., & Haitjema, S. (2019). A human (e) factor in clinical decision support systems.Journal of Medical Internet Research, 21(3), e11732. Web.

Campbell, B. C. V., De Silva, D. A., Macleod, M. R., Coutts, S. B., Schwamm, L. H., Davis, S. M., & Donnan, G. A. (2019). Ischaemic stroke.Nature Reviews Disease Primers, 5(1), 70. Web.

Vélez-Díaz-Pallarés, M., Álvarez Díaz, A. M., Gramage Caro, T., Vicente Oliveros, N., Delgado-Silveira, E., Muñoz García, M., Cruz-Jentoft, A. J., & Bermejo-Vicedo, T. (2017). Technology-induced errors associated with computerized provider order entry software for older patients.International Journal of Clinical Pharmacy, 39(4), 729–742. Web.

Yao, X., Abraham, N. S., Alexander, G. C., Crown, W., Montori, V. M., Sangaralingham, L. R., Gersh, B. J., Shah, N. D., & Noseworthy, P. A. (2016). Effect of adherence to oral anticoagulants on risk of stroke and major bleeding among patients with atrial fibrillation.Journal of the American Heart Association, 5(2), e003074. Web.

Zikos, D., & DeLellis, N. (2018). CDSS-RM: A clinical decision support system reference model.BMC Medical Research Methodology, 18(1), 137. Web.