Clinical Decision Support Tools Did Not Reduce CVD Disparities
New research indicated that clinical decision support tools did not significantly reduce racial and sex disparities among CVD patients.
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By - According to a new study, using clinical decision support (CDS) tools in various care quality metrics, such as aspirin use and blood pressure control, did not reduce healthcare disparities among cardiovascular disease (CVD) patients. This finding prompted the need to test the different interventions.
The study defined CDS tools as electronic health records (EHR) prompts, standing orders, and clinical registries. The goal of CDS tools is to improve patient outcomes.
Often, CDS tools are associated with resourcefulness. However, their relationship with healthcare disparities is unclear. Researchers aimed to explore the value of population-based CDS tools in reducing racial and sex differences in cardiovascular care.
To do so, they conducted a cross-sectional study that considered practice-level data from the Agency for Healthcare Research and Quality-funded EvidenceNOW initiative. This initiative occurred between May 1, 2015, and April 30, 2021, across 12 states. It aimed to improve cardiovascular preventive care through quality improvement support for smaller primary care practices.
When drawing conclusions, researchers considered practice-level estimates of disparities between races and sexes.
In total, EvidenceNOW included 576 primary care practices that submitted survey data and EHR-derived ABCS data based on race and sex. Of this population, 219 contained patient panels with a White population exceeding half of the total. Also, 327 included panels with a female population exceeding half of the patient total.
Differences existed among patients who met various metrics involved in the study. For blood pressure and cholesterol management, White patients had a higher share relative to Black patients. Also, regarding aspirin use and cholesterol management, men had a higher share relative to women.
However, the opposite existed regarding blood pressure control and smoking cessation counseling. For these metrics, women exceeded men.
Excluding the smoking metric, the use of CDS tools did not correlate with differences in disparities related to race and sex. However, practices that used CDS tools had a higher share of men who met the smoking counseling metric. This was relative to women.
Despite the limited disparities within practices using CDS tools, resources did not lessen inequities. This conclusion emphasized the need to continue searching for interventions.
Incorporating CDS tools to enhance healthcare is becoming a dominant practice.
In November 2022, University of Houston researchers created an artificial intelligence (AI)-based CDS tool. With this tool, they used deep learning (DL) to determine patients’ diabetes complication risk.
Known as Primary Care Forecast, this tool relied on DL and patient history. With it, researchers intended to create the Diabetes Complication Severity Index (DCSI) Progression Tool.
“Our long-term goal is to help clinicians become more proactive and less reactive when treating diabetes. By leveraging the capabilities of artificial intelligence and machine learning, we can more effectively connect at-risk individuals with interventions before they become sicker,” said Winston Liaw, MD, the principal investigator of the project and chair of the Department of Health Systems and Population Health Sciences at the UH Tilman J. Fertitta Family College of Medicine, in the press release.
Both of these efforts display the evolution of healthcare. As the capabilities of CDS tools grow, researchers aim to use them to their advantage.
