Prevention of medication errors

CDSS PROS CDSS CONS Prevention of medication errors.
Having systems in place to support the healthcare staff is important for a multitude of reasons. Doublechecking doses, allergies, potential interactions, and right patient are just a few of the protections offered. This not only adds an extra layer of protection to the patient, but it helps to save costs and time to the hospital and staff (Westerbeek et al., 2021).
Overwhelming amount of information.
Alarm and alert fatigue are common issues seen as CDSS continues to evolve. Annual training and spot updates require time away from patient care. If alarm parameters are too sensitive, or information is used incorrectly, the result will be alarm fatigue. The same problem can happen with CDSS alerts in the EHR. The APN might start to ignore alerts if they are not applicable to the situation. Ultimately, constant input from users will be needed to keep the programs running smooth.
Cost savings to patient.
The CDSS system will help to prevent costly repetition of labs, imaging, prescriptions and billing. Certain items may also be accomplished remote or virtual versus being seen in person at the clinic or hospital. Although some of these items seems small, they can easily add up to a substantial cost to the patient. Data collection.
Not all data can be utilized in a beneficial way. Certain data might be impossible to collect, or categorize. There can also be errors with data entered by the healthcare staff. If this incorrect data goes unnoticed, it can lead to other inconsistencies.
Failsafe to ensure patient’s timely care. CDSS enables reminders in patient charts for preventative care such as screenings, labs, imaging and vaccines. This saves time for the staff by eliminating some aspects of chart review. This same application helps with accuracy of completing these items (Zare et al., 2021). An example of this would be annual flu shot or colonoscopy when the appropriate age is reached. IT cost for implementation and maintenance.
The CDSS systems can be expensive to purchase and to maintain. There are constant security and functional updates to keep everything running smoothly. The IT department is also costly and most hospitals have 24-hour coverage to help with any issues. This is all necessary though to keep the systems problem free (Sulley, 2018).

Billy Bob is a 45-year-old male being seen for his annual physical. Reviewing his record shows no problems in his medical history. The plan is to draw some standard labs and to check vital signs, height, weight, BMI and ask about diet and exercise. While prepping to see the patient, the APN is alerted by the use of CDSS to check if patient is in need of a colonoscopy due to him recently turning 45. While visiting with the patient, Billy mentions that his grandfather died of colorectal cancer. Billy mentions that he did not think this information was important because his father was never sick, and he exercises regularly and eats a healthy diet. A colonoscopy is scheduled by the APN and performed just to be on the safe side. Billy has a small localized change in his colon tissue and subsequently removed before it can become cancerous. Due to the accuracy of CDSS, the APN was able to use the alert and their own critical thinking and assessment skills to identify the need for further testing. This scenario is to point out the many uses of CDSS, but critical thinking by the APN has no replacement.
References
Sulley, S. (2018). Impact of Clinical Decision Support Systems (CDSS) on Health Outcomes Improvement. International Journal of Health Sciences Research, 8(5), 292–298.
Westerbeek, L., Ploegmakers, K. J., de Bruijn, G.-J., Linn, A. J., van Weert, J. C., Daams, J. G., van der Velde, N., van Weert, H. C., Abu-Hanna, A., & Medlock, S. (2021). Barriers and facilitators influencing medication-related cdss acceptance according to clinicians: A systematic review. International Journal of Medical Informatics, 152, 104506. https://doi.org/10.1016/j.ijmedinf.2021.104506 (Links to an external site.)
Zare, S., Meidani, Z., Shirdeli, M., & Nabovati, E. (2021). Laboratory test ordering in inpatient hospitals: A systematic review on the effects and features of clinical decision support systems. BMC Medical Informatics and Decision Making, 21(1). https://doi.org/10.1186/s12911-020-01384-8 (Links to an external site.)
ReplyReply to Comment

Pro Con
Cost-saving
o Reduce duplicate testing such as radiology imaging, labs, EKG (Comandé et al., 2014)
o Decrease the rate of unnecessary coronary care admissions and hospital by 15% (Garg et al., 2005)
o Reduce hospital length of stay with multi general medical diagnoses (Garg et al., 2005) Alert fatigue
o Clinicians received 56 alerts per day (McCoy et al., 2014)
o Clinicians spent 49 minutes per day processing those alerts (McCoy et al., 2014)
Ensure patient safety
o Create alerts about drug-drug interaction, allergy interaction (McCoy et al., 2014)
o “Improve the time to diagnosis of acute bowel obstructions” (Garg et al., 2005) False-positive alert
o Divert clinician attention and time to process the advice (McCoy et al., 2014)
Avoid complication
o Reduce adverse cardiac events when patients receive thrombolysis treatment from heparin (Garg et al., 2005)
o 28% of adverse drug events were prevented (McCoy et al., 2014)
o Decrease medication error by 81% (McCoy et al., 2014) False-negative alert
o can leave patients at risk for adverse reactions and events (McCoy et al., 2014)

An Asian- American woman came to my primary care office to have an annual check-up. She has not seen a doctor since 2018 since she lost her insurance. We obtained her vital signs, weight, height, and EKG. She reported that she had both Covid vaccination in March 2021 and her flu shot was in October 2020. I completed her physical and history and entered them into our electronic medical record (EMR). She is 65 years old and both of her parents are deceased. Her maternal mother and brother both had diabetes.
We then drew blood works, including complete basic chemistry, complete blood count, ferritin level, vitamin D, vitamin B12, thyroid function, HA1C, fasting lipid panel. The computer also notified me that she needed colorectal screening so we gave her a kit to submit her sample to the lab when it is convenient for her. Base on her age, the EMR recommends a mammogram, bone scan, flu shot, and Covid booster shot, which she agreed to get.
Several days later, the patient’s lab results came back and alert with a high level of lipid panel and her A1C level. I notified the patient about her results and place her on Atorvastatin 10 mg daily. About her A1C level, she is prediabetic, I recommended she modify her diet, reduce her sugar intake, increase her exercise routine, and come back in three months to recheck her level. I provided the patient with educational material about her condition, medication in her language through our built-in database in other languages.
Reference
Comandé, G., Nocco, L., & Peigné, V. (2014). An empirical study of healthcare providers and
patients׳ perceptions of electronic health records. Computers in Biology and Medicine, 59, 194–201. https://doi.org/10.1016/j.compbiomed.2014.01.011Links to an external site.
Garg, A. X., Adhikari, N. K. J., McDonald, H., Rosas-Arellano, M. P., Devereaux, P. J., Beyene,
J., Sam, J., & Haynes, R. B. (2005). Effects of Computerized Clinical Decision Support Systems on Practitioner Performance and Patient Outcomes: A Systematic Review. JAMA : the Journal of the American Medical Association, 293(10), 1223–1238. https://doi.org/10.1001/jama.293.10.1223
McCoy, A. B., Thomas, E. J., Krousel-Wood, M., & Sittig, D. F. (2014). Clinical decision
support alert appropriateness: a review and proposal for improvement. The Ochsner Journal, 14(2), 195–202.

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