Development and evaluation of a low-cost database solution for the Community Paramedicine at Clinic (CP@clinic) database

• Published in: PLOS digital health Vol. 3; no. 12; p. e0000689
• Main Authors: Angeles, Ricardo, Adamczyk, Krzysztof, Marzanek, Francine, Pirrie, Melissa, Plishka, Mikayla, Agarwal, Gina
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 01.12.2024; Public Library of Science (PLoS)
• Subjects: Adults; Algorithms; Automation; Electronic health records; Family physicians; Health risk assessment; Medical records; Medical referrals; Older people; Paramedics; Personal health; Risk assessment; Risk factors; Usability; Volunteers
• ISSN: 2767-3170; 2767-3170
• DOI: 10.1371/journal.pdig.0000689

The Community Paramedicine at Clinic (CP@clinic) program is a community program that utilizes community paramedics to support older adults in assessing their risk factors, managing their chronic conditions, and linking them to community resources. The aim of this project is to design a low-cost, portable, secure, user-friendly database for CP@clinic sessions and pilot test the database with paramedics and older adult volunteers. The CP@clinic program database using the Microsoft Access software was first developed through consultation with the CP@clinic research team. Next, the database was pilot tested with two sets of older adults and one set of paramedics to assess user experience. Volunteers completed a survey regarding their perceptions of the level of difficulty when using the database. A computer-based database was the best option as it provided flexibility while reducing costs. The final database should perform calculations and summarize risk assessment data, provide recommended resources, generate automated reports, capture changes in medical and medication history, and ensure that the sensitive information is secure. During pilot testing, the older adult participants and the paramedics indicated that the database was easy to use. This low-cost, user-friendly and secure database captures initial and follow-up data, incorporates algorithms that guide the paramedics, and calculates risk factor scores for the participants. This solution to a healthcare database is translatable to other health research studies in which ongoing patient data is collected electronically and longitudinally.