ENHANCING PNEUMONIA MANAGEMENT IN LONG-TERM CARE THROUGH EARLY DETECTION AND AI-ENABLED CARE DECISIONS
AUTHOR(S) & CREDENTIALS: Poonam Sehgal and Cassy Hemphill, Communications and Engagement Coordinator
AFFILIATED INSTITUTION(S): University of Victoria and AGE-WELL National Innovation Hub: APPTA, AGE-WELL NCE.
Poonam Sehgal, a dedicated nurse practitioner in long-term care (LTC) for over a decade, has been a tireless advocate for the rights of elders to age in place while maintaining their autonomy. Her career is grounded in the belief that high-quality care should effectively manage both acute and chronic conditions while minimizing unnecessary hospitalizations, which can often lead to additional complications. Poonam’s commitment to providing care that allows seniors to remain at home, drives her to enhance the care systems within LTC facilities, ensuring elders receive the best possible care in the most beneficial setting.
Poonam’s inspiration for her latest project came from her firsthand experiences in LTC facilities, where she observed the critical impact of early detection on patient outcomes. Too often, early signs of decline in patients were missed, leading to delayed interventions. The main challenge addressed by this project is the need for improved early detection, accurate diagnosis, and timely treatment of pneumonia in LTC residents. Poonam aims to achieve this by using AI and Natural Language Processing (NLP) technologies to analyze clinician notes within Electronic Health Records (EHRs) for subtle health indicators.
This innovative project advocates for the integration of AI and NLP within LTC facilities to revolutionize pneumonia management. By leveraging the data contained in clinician notes, the system can identify early signs of deterioration and trigger alerts for potential health issues, thus enabling prompt and informed clinical decisions. This technology-forward approach is aimed at improving outcomes for the most vulnerable populations in healthcare.
Poonam believes that research in this area is crucial as pneumonia remains a leading cause of morbidity and mortality among LTC residents. By integrating advanced AI and NLP technologies into LTC facilities, early detection and personalized care can be enhanced, potentially reducing hospital admissions, and improving the quality of life for residents. The research outcomes could set a precedent for the application of technology in preventive healthcare and influence broader healthcare policies.
The policy implications of this project are significant. Poonam’s work proposes changes that include the ethical use of AI and the importance of privacy and consent in data use. Furthermore, her research highlights the necessity for government-backed financial and infrastructural support to enable the broad deployment of these technologies in LTC facilities.
An essential aspect of this project, not yet covered, is the engagement process with stakeholders, which is vital for its success. Involving LTC residents, their families, and staff through structured processes like focus groups and advisory panels ensures the technology meets real needs and addresses any concerns. Additionally, continuous ethical review and the development of a comprehensive training curriculum for staff are key to maintaining a high standard of care in the AI-enhanced healthcare landscape.
Poonam’s work represents a transformative approach to managing pneumonia in LTC facilities. Through her dedication and innovative use of AI and NLP, she is paving the way for a future where early detection and timely interventions improve the health outcomes and quality of life for elders, allowing them to age in place with dignity and autonomy.
Click here to read Poonam’s full policy brief!