Developing and Evaluating Data Infrastructure and Implementation Tools to Support Cardiometabolic Disease Indicator Data Collection

Top Spinal Cord Inj Rehabil, 2023 · DOI: 10.46292/sci23-00018S · Published: January 1, 2023

Spinal Cord Injury Cardiovascular Science Healthcare

Simple Explanation

This study focuses on improving the collection of data related to cardiometabolic disease (CMD) risk factors in individuals with spinal cord injury or disease (SCI/D). An artificial intelligence (AI) tool was used to extract data from surveys, and educational resources were developed to help healthcare providers collect this data routinely. The goal is to identify and address gaps in care, such as assessing aerobic exercise levels and lipid profiles, to reduce CMD risk in this population.

Study Duration

Not specified

Participants

251 adults with SCI/D

Evidence Level

Not specified

Key Findings

1
An AI tool was effective for extracting data with a low error rate (<2%).
2
There are significant gaps in the teaching and assessment of aerobic exercise (AE) and lipid profiles among adults with SCI/D, especially in inpatient settings.
3
Implementation tools, including algorithms and education materials, were developed to address knowledge gaps in patient AE and lipid assessments.

Research Summary

This quality improvement (QI) project aimed to develop data infrastructure and implementation tools to support the collection of cardiometabolic disease (CMD) indicator data among individuals with spinal cord injury or disease (SCI/D). An AI tool was used to extract data from surveys, and the accuracy of data extraction was high, with an error rate of less than 2%. The study identified significant gaps in aerobic exercise (AE) teaching and lipid profile assessments, particularly in inpatient settings, and developed tools to address these gaps and promote CMD risk reduction.

Practical Implications

Improved Data Collection

The AI tool can be used to improve the efficiency and accuracy of data collection for CMD indicators, enabling better monitoring of CMD risk in individuals with SCI/D.

Targeted Interventions

The identified gaps in AE and lipid assessment highlight the need for targeted interventions to improve adherence to guidelines and reduce CMD risk in this population.

Enhanced Education and Awareness

The implementation tools, including algorithms and education materials, can be used to increase awareness and knowledge about CMD risk factors and promote behavior change among individuals with SCI/D.

Study Limitations

1
Cross-sectional design limits the ability to track changes over time.
2
Convenience sampling may not be representative of the broader SCI/D population.
3
Reliance on patient recall for AE levels and lipid profile assessments may introduce bias.

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