NR 599 Discussion Clinical Decision Support Systems
NR 599 Discussion Clinical Decision Support Systems
As stated, CDSSs may some disruptions in patient care workflows, they were initially intended to improve the delivery of healthcare by facilitating the enhancement of clinical decisions that are targeted within clinical knowledge, patient information, and other health information as they were created to directly aid in clinical decision-making that is geared towards clinicians’ knowledge-based and patient-specific assessments or recommendations for implementations (Sutton et al., 2020). As CDSS alerts nurses to access real-time patient data, this helps to facilitate and enhance patient safety, medication accuracy, and pertinent specific patient-centered clinical decision-making plan of care, which is a critical benefit to advance practice nurses to direct, diagnose, and implement a specific patient-centered plan of care, holistic nursing, and collaboration of interdisciplinarity as needed.
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), 17–17. https://doi.org/10.1038/s41746-020-0221-yLinks to an external site.
I enjoyed you scenario and how you related it to use of CDSS. In you PROs list you have workload and burnout. In my reading and research through material it was interesting to me that earlier CDSS models could actually increase workloads. Baumgart, et al. reported that some of the source information providers needed for their documentation was outside of their normal workspace (An overview, 2020). This led to providers having to exhaust more effort to complete tasks within the CDSS (Baumgart, et al., 2020). While this technology continues to advance by leaps and bounds, I have to wonder how many of these less adequate systems are still in circulation? I have worked with a few. It seems that small, stand-alone facilities with less revenue to invest in the newer, better systems, may be inclined to go this route even if just for financial reasons. While it makes them compliant for implementing a CDSS, it still leaves practitioners with a burdensome task load because the usability is lacking.
Baumgart, D., Fedorak, R., Kroeker, K., Pincock, D., Sadowski, D. & Sutton, R. (2020). An overview of clinical decision support systems: benefits, risks, and strategies for success. NPJ Digital Medicine, 3(17). doi: 10.1038/s41746-020-0221-y. Retrieved on 04/04/2023 from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7005290/Links to an external site.
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While providers cannot fully rely on CDS tools to make decisions, when they are designed in alignment with the provider’s preferences, they can be useful in increasing positive health outcomes for those that are at increased risk for diabetes (Obrien et al, 2022). Obrien et al’s (2022) study showed that providers that used CDS tool for pre-diabetes had increased rates of prescriptions for metformin and lab tests orders for hemoglobin A1C. There were also increased rates of referrals for health counseling for these patients. Taking into consideration the time to learn the system, once providers are competent and have incorporated it into their workflow, it certainly can help initiate the conversation with the patient about their diagnosis and early interventions to reduce the progression of diabetes. Shi’s randomized controlled trial showed that while modest, when comparing team-based care alone to care given with CDS “significantly reduced cardiovascular risk factors in patients with diabetes,” (Shi et al 2023). Without it, most patients could possibly not be even informed that they have prediabetes. I think utilizing this technology appropriately, absolutely makes CDSS a benefit to the care we provide.
O’Brien, M. J., Vargas, M. C., Lopez, A., Feliciano, Y., Gregory, D. L., Carcamo, P., Mohr, L., Mohanty, N., Padilla, R., Ackermann, R. T., Persell, S. D., & Feinglass, J. (2022). Development of a novel clinical decision support tool for diabetes prevention and feasibility of its implementation in primary care. Preventive Medicine Reports, 29, 101979. https://doi.org/10.1016/j.pmedr.2022.101979Links to an external site.
Shi, X., He, J., Lin, M., Liu, C., Yan, B., Song, H., Wang, C., Xiao, F., Huang, P., Wang, L., Li, Z., Huang, Y., Zhang, M., Chen, C.-S., Obst, K., Shi, L., Li, W., Yang, S., Yao, G., & Li, X. (2023). Comparative Effectiveness of Team-Based Care With and Without a Clinical Decision Support System for Diabetes Management : A Cluster Randomized Trial. Annals of Internal Medicine, 176(1), 49–58. https://doi.org/10.7326/M22-1950Links to an external site.
|Provide clinicians with filtered knowledge to enhance healthcare (Zikos & Delillis,||CDS tools could have unintended consequences such as leading a clinician to think there are no other alternatives than what is suggested by the tool.|
|Improve patient Safety – alerts to reduce errors, promotes adherence to clinical initiatives like DVT prophylaxis and cardiac mortality prevention strategies||Provider resistance – implementing new CDS technology that a provider may feel affects their timing and autonomy, affecting their workflow.|
|Support clinician workflow – Encourages providers to do the right thing at the right time with the correct interventions||Affects autonomy of providers if the alerts in the CDS are “hard stops”, and prevent them from moving forward in the system until an alert is addressed.|
|Promote patient education- quick access to education tools and referral links increase patient engagement in diagnosis||Legal Implications- Malpractice risk and legal implications to providers using CDS and not acting on an alert.|
Pratt et al (2022) writes that there is a 40% lifetime risk of diabetes development in one’s lifetime. As primary care providers, our role is to engage patient in interventions to help reduce their risk of developing diabetes. Regularly these patients are missed in screening when they have risk factors to diabetes. CDS could be a powerful tool to collect data and alert providers to patients increased risk. This clinical example below highlights an example of utilizing this CDS tool in practice:
A 47-year-old, female patient presents to her primary care office for her annual physical examination. Prior to her appointment she has basic labs, hemoglobin A1c, lipid panel drawn, and results uploaded into this system. She fills out a questionnaire about her current health habits in the waiting room and then gets checked into her exam room after getting her height, weight and vital signs checked. The EHR information in the system flags this patient as eligible for the Pre-DM CDS algorithm. (Pratt et al, 2022). A best practice alert appears on the screen that the patient displays information to the provider. This includes her last three measurements of weight, BMI, hemoglobin A1C, fasting glucose, creatinine and random glucose. The CDS guides the provider to add prediabetes code to the problem list, prescribe metformin if appropriate, and order additional labs as needed (Obrien et al, 2022). The tool also suggests a link to be clicked on to refer the patient to a health educator to discuss healthy lifestyle changes.
According to the CDC (2022), only 15.3% of patients with prediabetes report being told by a provider that they have this condition. Utilizing this CDS will help to initiate the conversation between the provider and the patient and ideal promote early interventions to reduce the development of diabetes. After following the Pre-DM CDS tool to completion, patient education generated from the EHR with patient specific information on it, can be handed to the patients or sent electronically during this appointment to begin the conversation and treatment plan.
Centers for Disease Control and Prevention. National Diabetes Statistics Report, Estimates of Diabetes and Its Burden in the United States. 2022; https://www.cdc.gov/diabetes/data/statistics-report/index.htmlLinks to an external site.. Accessed April 8, 2022.
O’Brien, M. J., Vargas, M. C., Lopez, A., Feliciano, Y., Gregory, D. L., Carcamo, P., Mohr, L., Mohanty, N., Padilla, R., Ackermann, R. T., Persell, S. D., & Feinglass, J. (2022). Development of a novel clinical decision support tool for diabetes prevention and feasibility of its implementation in primary care. Preventive Medicine Reports, 29, 101979. https://doi.org/10.1016/j.pmedr.2022.101979Links to an external site. Pratt, R., Saman, D. M., Allen, C., Crabtree, B., Ohnsorg, K., Sperl-Hillen, J. A. M., Harry, M., Henzler-Buckingham, H., O’Connor, P. J., & Desai, J. (2022). Assessing the implementation of a clinical decision support tool in primary care for diabetes prevention: A qualitative interview study using the Consolidated Framework for Implementation Science. BMC Medical Informatics and Decision Making, 22(1). https://doi.org/10.1186/s12911-021-01745-xLinks to an external site.