NR 599 Clinical Decision Support System

NR 599 Clinical Decision Support System

NR 599 Clinical Decision Support System

 

Pros  

Cons 

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:

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A 47-year-old, female patient presents to her primary care office for her annual physical examination. Prior to her

NR 599 Clinical Decision Support System
NR 599 Clinical Decision Support System

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 Reports29, 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 Making22(1). https://doi.org/10.1186/s12911-021-01745-xLinks to an external site.

I truly enjoyed reading through your post. The use of CDSS helps the clinician to make the best clinical decision in order to provide the best option for care.  According the study done by Shi (2023) it was discovered that the use of CDSS in the management of patients with  diabetes proved very effective as it helps the provider  determine patients who are mostly at risk for cardiovascular diseases associated with diabetes and  proffer options for better management.

 

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 Medicine176(1), 49–58. https://doi.org/10.7326/M22-1950Links to an external site.

The disconnection between providers and their patients can result in a disrupted workflow, where providers depend on CDSS and lose sight that they do need to integrate that face-to-face time with their patients (Sutton et al., 2020). Despite this issue, which is something that could easily be fixed, there is “provider and patient satisfaction” (HealthIT.gov). Errors are decreased, clinicians have information and decision-making improvements, and patients can access portals to collaborate with their providers and view results and upcoming immunizations, etc. After listing the pros and cons, do you feel CDSS are a benefit, or do you think they impair the care we deliver?

 

References:

HealthIT.gov. (2018, April 10). Clinical Decision Support. https://www.healthit.gov/topic/safety/clinical-decision-support. 

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, 17. https://doi.org/10.1038/s41746-020-0221-y 

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 Reports29, 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 Medicine176(1), 49–58. https://doi.org/10.7326/M22-1950Links to an external site.