Posted: August 1st, 2023

One clinical decision support tool that is being used in my E.D

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Cyron Dalida
One clinical decision support tool that is being used in my E.D. is the sepsis screening tool. The sepsis screening tool identifies patients meeting SIRS (systemic inflammatory response syndrome) criteria. Once the criteria are aligned based on patient VS during intake/triage or reassessment of patients while in the emergency department (abnormal labs, abnormal vital signs), an alert is activated prompting the healthcare team with notifications delivered to them for action. Prompt recognition and early intervention of SEPSIS may reverse the inflammatory response of high-risk patient populations improving patient outcomes (Amland et al., 2015).

The C.D.S. that generates an alert in the E.H.R. however sometimes cause alert fatigue and sometimes interruption of workflow due to the detection algorithm. A slight change in patient status like increased heart rate or WBC count however will trigger the alert system without obvious signs of patient deterioration. Often nurses must huddle with providers before cancelling the prompt and proceed with standard patient care. If providers are not around, the alert continues causing alert fatigue for the nurse disrupting workflow in the process.

If I was to create a clinical decision support tool in my practice, it would be the recognition of patients who are “frequent flyers” in the emergency department. I often see the same patients come in multiple times with the same complaints but with different providers, approach to treatments can sometimes vary. The C.D.S. will alert providers about the patient and will provide clinical decisions on how to better care for that patient without ordering multiple tests. This will save money and time.

References:

Amland, R.C., Hahn-Cover K.K., (2019). Clinical decision support for early recognition of sepsis. American journal of medical quality, 34(5). https://doi.org/10.1177/1062860619873225

Carolyn Gaeckle
“Clinical decision support systems (CDSSs) are active knowledge systems that use two or more items of patient data to generate case-specific advice” (Kotze & Brdaroska, 2004, pg. 361). CDSs have the ability to improve patient care and put in place safety stops that can help prevent errors. They are meant to provide additional diagnostic information to clinicians to better improve the care that they give to their patients. One clinical decision support tool that I have seen in practice are red flags notifying critical lab results. This helps to make the lab result more easily identifiable and alerts the nurse and provider that something is off. In our old EHR system, Meditech, we (nurses) would receive medication specific alerts. For example, upon scanning a Lithium medication barcode, a patients lithium values would pop up if they were out of range. I found this to be helpful, as we could closely monitor the patients values and discuss with the doctor if symptoms we were seeing in a patient were indicative of a need to adjust medication. Another notification that we would receive is whether or not a patient had an allergy listed that coincided with a medication to be administered. However, in our new EHR, EPIC, we do not receive these notifications. This is something that I have brought up to the care team.

If I could create my own clinical decision support tool, thinking specifically about the hospital that I work at and the EHR that we are currently using, I would create a better admission flowsheet. The way that the admission process currently is, there is a lot of jumping back and forth between pages and there is a greater opportunity for missed information. Another possible clinical decision support tool relates to our use of care plans. When developing a care plan for a patient, we have a database to search that includes a variety of diagnoses and appropriate interventions. However, I have found that the database is quite limited in terms of psychiatric diagnoses.

Kotze, B., & Brdaroska, B. (2004). Clinical psychiatry: clinical decision support systems in psychiatry in the information age. Australasian Psychiatry, 12(4), 361–364.

Maria Houston
Based on the work of Jankovic, I., & Chen, J. H. (2020), 5 key factors should be considered when designing and implementing Clinical Design Support (CDS) to minimize the effects of clinician burnout: 1. Be relevant. CDS should solve problems that clinicians feel need to be solved. CDS alerts should incorporate as much patient-specific information as possible to maximize care and minimize the number of reminders. 2. Solicit feedback. End-users should be involved in all aspects of design, pre-testing, and implementation. 3. Customize. Whether allowing expert panels to tier alerts or clinicians to choose how and when to see CDS tools, customization can minimize alert burden and improve relevance as well as clinician satisfaction. 4. Measure outcomes. The effects on alert burden, override rates, workflow, efficiency, burnout, satisfaction, patient outcomes, etc. must be evaluated. Tools should either improve efficiency, patient outcomes, or both. Tools that do neither should be abandoned, especially if they add to alert burden or burnout, and 5. Iterate. CDS requires ongoing maintenance based on feedback and outcomes, as well as updates to clinical practice standards. Using these principles, future CDS tools can minimize their impacts on the problem of clinician burnout (Jankovic, I., & Chen, J. H., 2020)
If I could create my own Clinical Decision Support tool, I would use technology to create a psychiatric bed referral system that is electronically exchanged between health care facilities which would include assessments of behavioral health patients who wait in emergency rooms with cues for crisis clinicians or social services and health professionals to link systems and track individuals throughout the process, including medical clearance and current status, including how long they have been waiting, and what specifically is needed to advance them to either inpatient or other services such as socio-economic resources. Included would be safety planning cues and tools the patients could utilize for maintaining safety while they wait for a bed or if they are discharged from the ED prematurely due to a lack of psych beds.

Bart, G. B., Saxon, A., Fiellin, D. A., McNeely, J., Muench, J. P., Shanahan, C. W., Huntley, K., & Gore-Langton, R. E. (2020). Developing a clinical decision support for opioid use disorders: a NIDA center for the clinical trials network working group report. Addiction science & clinical practice, 15(1), 4. https://doi.org/10.1186/s13722-020-0180-2

Jankovic, I., & Chen, J. H. (2020). Clinical Decision Support and Implications for the Clinician Burnout Crisis. Yearbook of medical informatics, 29(1), 145–154. https://doi.org/10.1055/s-0040-1701986
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Euridice Nobre
Clinical decision support systems (CDSS) are computer-based programs that analyze data within EHRs to provide prompts and reminders to assist health care providers in implementing evidence-based clinical guidelines at the point of care (Centers for Disease Control and Prevention [DC], 2020, para.1). Several studies have proven the effectiveness of CDSS in improving prescribing practice, reducing medication errors, and improving preventive care (Minian et al., 2019). For example, utilizing CDSS increases the probability of patients with nicotine and alcohol dependency accepting available treatment resources (Minian et al., 2019, p. 5).
One CDS tool that has been key in promoting patient safety at my current practice is a tool to reduce inappropriate medications (TRIM). It identifies older adults at high risk of receiving potentially problematic drugs and systematically evaluates the medication regimen for problems with both individual medications and the regimen (Rajeevan et al., 2017, p. 111). Another CDS tool that I find essential in enhancing patient care is the alert provided by the computerized patient record system (CPRS) when a patient is positive for COVID-19. This tool aids in preventing the spread of the virus.
A CDS tool I would create, and implement is an alert that “yellows or red” flags a patient’s medical record if the patient has not been COVID vaccinated or is presenting with COVID-19 symptoms. So, upon arrival on the unit, nurses would be aware of the patient’s status and would assign the patient to a designated room that follows COVID-19 hospital protocols.

References
Centers for Disease Control and Prevention (CDC). (2020, July 7). How to implement clinical decision support systems. https://www.cdc.gov/dhdsp/pubs/guides/best-practices/clinical-decision-support.htm
Minian, N., Baliunas, D., Noormohamed, A., Zawertailo, L., Giesbrecht, N., Hendershot, C. S., Le Foll, B., Rehm, J., Samokhvalov, A. V., & Selby, P. L. (2019). The effect of a clinical decision support system on prompting an intervention for risky alcohol use in a primary care smoking cessation program: A cluster randomized trial. Implementation Science: IS, 14(1), 85-10. https://doi.org/10.1186/s13012-019-0935-
Rajeevan, N., Niehoff, K. M., Charpentier, P., Levin, F. L., Justice, A., Brandt, C. A., Fried, T. R., & Miller, P. L. (2017). Utilizing patient data from the Veterans administration electronic health record to support web-based clinical decision support: Informatics challenges and issues from three clinical domains. BMC Medical Informatics and Decision Making, 17(1), 111-111. https://doi.org/10.1186/s12911-017-0501-x

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Dalida, Cyron

The sepsis screening tool is one clinical decision support tool that is used in my emergency department. The sepsis screening tool identifies patients who meet the criteria for SIRS (systemic inflammatory response syndrome). Once the criteria are aligned based on patient VS during intake/triage or reassessment of patients while in the emergency department (abnormal labs, abnormal vital signs), an alert is activated prompting the healthcare team with notifications delivered to them for action. Prompt recognition and early intervention of SEPSIS may reverse the inflammatory response of high-risk patient populations improving patient outcomes (Amland et al., 2015).

The C.D.S. that generates an alert in the E.H.R. however sometimes cause alert fatigue and sometimes interruption of workflow due to the detection algorithm. A slight change in

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