Post reply #1
Diabetes is an illness prevalent in the US and affects the quality of health of the population. The CDSS is an essential tool that assists in the management of diabetes. The clinical scenario consists of caring for a diabetic patient to help improve the blood sugar levels. One of the ways to use the CDSS system in the management is by creating a diabetic dashboard that allows the display of patient vitals such as lipid (HDL and LDL), renal function (eGFR), and glycemic levels (HBA1c) (Conway et al., 2017). The nurse can use a graph summarized to analyze any trends in the patients’ vitals. The use of different color codes helps in making interpretation simple and easy. The approach is of high quality compared to the traditional laboratory reporting interface. The system helps decide on patient care quickly, given that the facility focuses on outpatients who need emergent care. Given the patients’ vital, it is critical to provide insulin therapy to help manage the blood sugar level and reduce the symptoms associated with abnormal blood sugar levels.
Pros and Cons
The CDSS is beneficial in the administrative functions of the hospital. It provides support by offering a system that could assist in implementing and delivering information. It helps in activities such as ordering procedures, tests, and diagnostic coding (Amirfar et al., 2011). Moreover, it is more accurate than the human implementation of the tasks bound by human error, such as forgetfulness or mistyping. The use of standardized tools also helps ensure that the hospital can plan ahead of time and avail the needed resources. It improves documentation quality, thus improving efficiency in the system and allowing the physicians to focus on care provision rather than administrative duties.
One of the cons of the CDSS is that sometimes it is hard to capture the related data mechanically. The aspect affects the quality of data and the effectiveness of the outcomes produced. Some of the limitations include the difficulty in recording the information accurately. Other types of data cannot get collected, which reduces the relevance of the system for use (Amirfar et al., 2011). It is essential to ensure that all the relevant data can get captured and translated to a form that is usable in the system.
One of the significant benefits of CDSS is its role in diagnostic support. The diagnostic decision support systems (DDSS). They have filters which help in offering a list of probable diagnosis given the patients symptoms. For example, Fuzzy logic gets used to diagnose peripheral neuropathy using the signs and diagnostic test output. The system has a 93% accuracy which is effective at clinical decision making (Amirfar et al., 2011). Such a role improves diagnosis and reduced misdiagnosis, which leads to medical errors and poor patient outcomes.
The other disadvantage of using the system is the lack of technological know-how on optimally using it. There is a need to empower the healthcare providers on specialized skills to allow them to use the technology well (Conway et al., 2017). Issues such as knowing the assumptions of using the technology are essential for applying the data to a patient situation. Without the knowledge of how the system assumptions work, the physician will not utilize the technology well. The issue affects the quality of care delivery.
Amirfar, S., Taverna, J., Anane, S., & Singer, J. (2011). Developing public health clinical decision support systems (CDSS) for the outpatient community in New York City: our experience. BMC Public Health, 11(1).
Conway, N., Adamson, K., Cunningham, S., Emslie Smith, A., Nyberg, P., & Smith, B. et al. (2017). Decision Support for Diabetes in Scotland: Implementation and Evaluation of a Clinical Decision Support System. Journal Of Diabetes Science And Technology, 12(2), 381-388.
Post Reply #2
This week we learned about the potential benefits and drawbacks to clinical decision support systems (CDSSs). Create a “Pros” versus “Cons” table with a column for “Pro” and a separate column for “Con”. Include at least 3 items for each column. Next to each item, provide a brief rationale as to why you included it on the respective list.
1) Safety: Prevention of medical errors and reduction of clinical variation
1) Safety comes from having measures in place to prevent unwanted outcomes. Reducing clinical variation results in decreased amount of unnecessary diagnostic testing and duplicate orders. Clinical decision support systems (CDSS) can sift through data and potentially alert a provider of a potential problem.
1) Alarm Fatigue
1) Too many alerts can create what is known as alarm fatigue: multiple alarms, including false alarms, caused by smart technology. This can result in workers to ignore or respond slowly.
2) Provide important clinical alerts
2) CDDS integration into Electronic health records allows for alerts and hard stops including drug interactions, allergies, and contraindications. The alerts come as the CDDS combines pertinent patient information with medication information and medical history.
2) Technology can be seen as a threat
2) Technology serves to enhance, not replace, clinical judgement. However, often times due to the benefits it provides, it can supersede direct care and can be seen as a threat to clinical judgement.
3) Improved care quality
3) Increased accessibility of documentation increases the time that providers can have face to face contact with patients. This results in improved care quality and satisfaction.
3) Not cost effective
3) Technology and technical medical integration comes with a large price. From design, to training, to implementation, the integration of CDDS is costly.
2. The primary goal of a CDSS is to leverage data and the scientific evidence to help guide appropriate decision making. CDSSs directly assist the clinician in making decisions about specific patients. For this discussion thread post, you are to assume your future role as an APN and create a clinical patient and scenario to illustrate an exemplary depiction of how a CDSS might influence your decision. This post is an opportunity for you to be innovative, so have fun!
Olivia is a 4 year old female. Her previous medical history includes intermittent asthma, rhinovirus, and multiple UTI’s. For the last three days, Olivia has had increasing temperatures which have now reached to a peak of 39.2, coughing, nasal congestion and abdominal discomfort. As the APRN caring for Olivia, after receiving report from her mom on her symptoms, I continue to ask further questions. It seems as though she is not eating and drinking nearly as often as normal. She had one episode of vomiting today and her cough has been less but continuous. I can gather that though she seems to have a cough, her lungs upon assessment are clear and her fever in indeed 39.2. At this point, I can make a clinical decision that I believe a UTI may be lurking. Upon transferring the information into the CDSs, I am alerted that Olivia has had three UTI’s in the most recent six months. The CDSs provides a potential UTI diagnosis. This secondary affirmation of the possible diagnosis allows me as the APRN to decide on diagnostic testing and the nudge to order a urinalysis, and also a kidney ultrasound due to the recurrent number of infections in such a short time frame.
Luckily, the results of the ultrasound come back negative, however the urinalysis does reveal that Olivia indeed has another UTI. Antibiotics and symptomatic care are the next steps. Prior to ordering the Augmentin (amoxicillin-clavulanate), the CDSS alerts me to the fact that Olivia has a penicillin allergy. After clarifying with Olivia’s mom, it appears Olivia was once given penicillin and she had a sever anaphylaxis after medication administration.
Therefore, the CDSs serves to best assist in my medication ordering decisions and advise me of potential risks such as fall risks for pediatric patients, which results improved quality of care and safety of the patients.
Moreira, M. W., Rodrigues, J. J., Korotaev, V., Al-Muhtadi, J., & Kumar, N. (2019). A Comprehensive Review on Smart Decision Support Systems for Health Care. IEEE Systems Journal, 13(3), 3536–3545.
Enaizan, O., Zaidan, A. A., Alwi, N. H., Zaidan, B. B., Alsalem, M. A., Albahri, O. S., & Albahri, A. S. (2018). Electronic medical record systems: decision support examination framework for individual, security and privacy concerns using multi-perspective analysis. Health and Technology, 10(3), 795–822.
Mazo, C., Kearns, C., Mooney, C., & Gallagher, W. M. (2020). Clinical Decision Support Systems in Breast Cancer: A Systematic Review. Cancers, 12(2), 369.

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