AI Tool Reveals Pain Management Gaps in Long-Term Care
The implementation of PainChek in long-term care has revealed that AI can effectively identify pain in residents who cannot self-report, marking a pivotal shift in pain management practices.
Key Facts
- AI tool identified pain in 20% of assessments, highlighting unmet needs in long-term care.
- Nurses' confidence in tech reliability decreased post-implementation, revealing potential vulnerabilities.
- Follow-up assessments increased from 69% to 78%, indicating growing trust in AI for pain detection.
- Pain score of zero dropped from 14% to 6.2%, suggesting improved pain recognition and management.
- Operational barriers like limited tablet availability could hinder broader adoption and impact outcomes.
Summary
Summary
A Canadian long-term care home implemented the PainChek AI-powered pain assessment application across four units to enhance pain detection in residents unable to self-report. The deployment identified mild-to-severe pain in 20 percent of assessments conducted from June to October 2024, demonstrating the tool's effectiveness in improving pain management. Nurses reported high satisfaction and frequent use of the application, despite some operational challenges.
Background
The customer is a long-term care home in Canada with four units and 160 beds. Before deploying the PainChek application, the facility faced challenges in accurately assessing pain in residents, particularly those with cognitive impairments who could not communicate their discomfort.
Challenge
The primary challenge was the underrecognition and undertreatment of pain in residents who were unable to self-report their pain levels, leading to inadequate pain management.
Solution
The PainChek application was implemented after conducting 18 training sessions for clinical staff. Nurses completed 3,826 assessments over a five-month period using the AI tool. A pre-post study design was employed to evaluate nurses’ confidence in pain assessment and the technology's impact on care. Surveys were administered to 65 nurses before and 12 weeks after implementation to gauge confidence, satisfaction, and perceived care impacts.
Results
The implementation resulted in identifying pain in 20 percent of assessments. Nurses reported high satisfaction with the application, with median scores of 4 or higher in confidence and support for the technology. The proportion of follow-up assessments increased from 69 percent to 78 percent over three months, while the percentage of assessments recording a pain score of zero decreased from 14 percent to 6.2 percent. However, there was a slight decrease in nurses' confidence in the technology's reliability at the 12-week follow-up.
Key Insights
The deployment illustrated that AI-enabled technology can enhance pain assessment with minimal disruption to existing workflows. It highlighted the importance of addressing operational constraints, such as tablet availability and time required for assessments, to maximize the effectiveness of technology in clinical settings.
Customer Testimonial
No direct quote was provided in the source material.
Entities Mentioned
Products
Technologies
People
Organizations
Key Concepts
Definitions
- PainChek
- A commercially available AI-powered application designed to assess pain in individuals who cannot self-report.
- artificial intelligence
- The simulation of human intelligence processes by machines, particularly computer systems, used here for pain assessment.
- Likert scale
- A psychometric scale commonly used in surveys to measure attitudes or opinions, ranging from 1 to 5 in this study.
- pre-post study design
- A research design that compares outcomes before and after an intervention to assess its effectiveness.
- responsive behaviours
- Actions or reactions exhibited by individuals, often in response to pain or discomfort, particularly in cognitively impaired residents.
Use Cases
- →Automated pain assessment in long-term care settings
- →Training nursing staff on new technology
- →Enhancing confidence in pain assessment among nurses
- →Improving resident care through targeted pain detection
- →Monitoring technology adoption and usage patterns
- →Evaluating impacts on nursing workflows
Frequently Asked Questions
What was the main objective of the study?
The primary objective was to evaluate nurses’ confidence in assessing resident pain and the factors influencing technology adoption in a long-term care setting.
How did nurses respond to the PainChek application?
Nurses reported high satisfaction with the PainChek application, rating it as easy to use and expressing strong support for its integration into existing workflows.
What were some barriers to using the PainChek application?
Common barriers included limited tablet availability and the time required to complete assessments, which were noted by a significant percentage of respondents.
Did the implementation of PainChek change nurses' confidence in pain assessment?
While nurses initially reported confidence in assessing pain, there was a slight decrease in confidence regarding the reliability of the technology after 12 weeks.
What were the findings regarding pain scores recorded?
The analysis showed a decrease in the proportion of assessments recording a pain score of zero, indicating that the AI-enabled technology may enhance pain detection among residents.