Peer Response

Giovanna

Clinical decision support systems have demonstrated effectiveness across various healthcare scenarios, including COVID-19, normal newborn screening, sepsis detection, and obstetrical screening. In chapter 19, there are effective interventions and their evaluation measures, along with insights on their qualitative or quantitative nature and potential improvements in evaluation strategies.

Case Study: COVID-19

Clinical decision support systems were employed to triage patients based on COVID-19 symptoms and risk factors. Quantitative measures included reduced wait times for testing and increased testing capacity. For example, Ameri et al. (2024) reported a 30% increase in appropriate triage decisions. These interventions are primarily quantitative, focusing on metrics like patient throughput and testing accuracy (Chen et al., 2022). Additionally, including qualitative feedback from healthcare providers about usability could enhance the effectiveness of these tools and identify areas needing improvement.

Case Study: Normal Newborn Order Sets

Clinical decision support systems that incorporate standardized screening guidelines for newborns will be the intervention. Quantitative data showed improved screening rates for conditions like congenital hypothyroidism and phenylketonuria, with some studies noting adherence rates as high as 90% (Rao & Palma, 2022). These interventions are mainly quantitative, with some qualitative assessments through parent and clinician satisfaction surveys (Chen et al., 2022). Finally, adding qualitative evaluations to assess parental understanding of the screening process could provide deeper insights into the system’s impact.

Case Study: Sepsis Detection (Think Sepsis)

Clinical decision support systems that utilize algorithms to flag potential sepsis cases based on vital signs and lab results. Quantitative metrics included reduced time to treatment, with studies reporting a 25% decrease in mortality rates due to timely interventions (Wulff et al., 2019). These interventions are primarily quantitative, but qualitative feedback from clinicians regarding the system’s alerts and workflow integration could enhance understanding of its real-world application (Chen et al., 2022). Gathering qualitative data on clinician experiences could help refine the alert thresholds and reduce alarm fatigue.

Case Study: Obstetrical Screening

Clinical decision support systems that provide reminders for screenings and help stratify risk in pregnant patients. Quantitative outcomes included increased screening rates for gestational diabetes, with compliance rates rising to over 80% in some settings (Cockburn et al., 2024). This intervention is largely quantitative, focusing on screening compliance and patient outcomes, supplemented by qualitative feedback from staff (Chen et al., 2022). A mixed-methods approach could be beneficial, combining quantitative data with qualitative insights from staff on workflow impacts.

In conclusion, while clinical decision support systems (CDSS) interventions have shown significant effectiveness across these areas, predominantly through quantitative measures, integrating qualitative evaluation strategies could enhance understanding of user experiences and system impacts. Improving evaluation strategies could involve a mixed-methods approach, ensuring that both numerical data and personal insights are considered to refine CDSS functionality and usability.