Make a follow-up of a student's weekly discussion and respond with your opinion regarding to her post
——You don't have to post this in APA format necessarily, it's just giving feedback to the student .
Lili Ketema
Clinical Decision Support Systems
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Pros
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Cons
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Patient Safety. Clinical Decision Support Systems empower Advanced Practice Nurses to make decisions in a timely and informed manner by detecting diseases early and managing them effectively (Ayed Aloufi, 2020). CDSS has reminder systems for medical events different from the ones related to medicine. For example, CDSS for measuring blood glucose in the ICU can decrease the frequency of hypoglycemia events (Sutton et al., 2020). This CDSS automatically prompts nurses to take glucose measurements with respect to the local glucose monitoring protocol that specifies particular patient demographics and previous glucose trends.
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Overreliance. CDSS may increase patient safety but increase reliance on the system, resulting in a decrease in critical thinking capabilities since the APN does not feel impelled to utilize their clinical judgment capabilities. This development is undesirable because the APN becomes less equipped for a task that they can execute in the absence of a CDSS. Sutton et al. (2020) compare overreliance on CDSS to using a calculator in math; the authors indicate that the user’s mental math skills decline with extended use. Therefore, APNs may end up less equipped to execute the services they should execute with ease.
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Improved Accuracy and Efficiency. CDSS can process significant quantities of patient data swiftly and precisely, empowering providers of care to effectively diagnose and plan for treatment (Ayed Aloufi, 2020). This decreases the possibility of errors by providing computerized consultation. The Diagnostic Decision Support Service provides data/user selections and then outputs a list of possible diagnoses (Sutton et al., 2020). These developments enhance EHR-integration as well as standardized vocabulary such as Snomed Clinical Terms.
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System and Content Maintenance. Maintenance is an often neglected aspect of the lifecycle of the CDSS. Maintenance encompasses technical and content of the systems that power the CDSS. The applications and knowledge-base of the CDSS should always be apace with the shifting nature of clinical guidelines and medical practice. Failure to stay updated may limit the CDSS’ capacity to maintain the desired levels of accuracy and efficiency. Sutton et al. (2020) assert that even the healthcare institutions that are highly advanced experience challenges keep9ing abreast with keeping their systems updated due to the inevitability of changes in medical knowledge bases.
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Cost Containment. The capacity of CDSS to decrease the length of stay for in-patients, provide clinical interventions, decrease test duplication, and suggest cheaper alternatives of medicine makes the systems more efficient (Sutton et al., 2020). For example, a CPOE-integrated has the capacity to limit the scheduling of blood count to a 24-hr interval when implemented in a paediatric cardiovascular intensive care unit. This laboratory resource utilization cost-reduction has a predictable cost discount of $717,538 every year, minus increasing mortality or length of stay. These advantages reveal the highly capabe nature of the CDSS to contain costs associated with hospital procedures and the overall ROI associated with CDSSs.
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The system is predicated on computer literacy. Decreased proficiency in technology can be limiting when a person is engaging with CDSS. The high design details associated with CDSS may be exceedingly complicated, decreasing the capacity of some APNs to use them to reach the advantages associated with the implementation of the system within a hospital setting (Sutton et al., 2020). Although some systems stay as close to close functionality as possible, every new system has a learning period, meaning the baseline of the technological competence of users is appropriate. Further training for APNs increase on the costs that the institution was aiming at cutting in the first place.
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Future role as an APN and clinical patient and scenario
A 68 year old man who has a history of diabetes, hypertension, and chronic renal disease shows up at the clinic complaining of fatigue, increased thirst, and frequent urination. Since I feel the patient's symptoms might be brought on by uncontrolled diabetes, I have made the decision as a prospective APN healthcare professional to ask for a blood test to confirm the diagnosis.
Impact of CDSS: Before prescribing any new medications, the system alerts me about the patient's current medications, which include metformin and lisinopril. The CDSS also prompts to consider the patient's renal status.
After noting the CDSS alert, I decide to review the patient's most recent lab results, particularly the estimated glomerular filtration rate (eGFR). The patient has substantial renal impairment, as seen by their eGFR, which is less than 30 mL/min/1.73m2, as I learned after examining the lab results.
In light of the CDSS alert and the patient's test results, I decide to alter the patient's prescription regimen. As opposed to providing a conventional oral anti-diabetic prescription like sulfonylureas, which may be contraindicated in patients with severe renal impairment, as APN i would consider alternate choices such insulin treatment or a newer family of anti-diabetic pharmaceuticals that are safe for patients with renal impairment.
Based on the patient's renal function and the medications they were taking at the time, the CDSS made recommendations. This let the medical practitioner make a more informed decision and avoid any side effects or drug interactions.
This scenario demonstrates how a CDSS might influence a provider's decision by providing timely reminders and cautions based on the patient's specific clinical data. It guarantees that the healthcare provider considers all relevant information and selects the best course of action for the patient's unique needs.
References
Ayed Aloufi, M. (2020). Effect of clinical decision support systems on quality of care by nurses.
International Journal for Quality Research,
14(3), 665–678. https://doi.org/10.24874/ijqr14.03-01
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). https://doi.org/10.1038/s41746-020-0221-y
