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The Participant-Reported Implementation Update and Score PRIUS: A Novel Method for Capturing Implementation-Related Data Over Time

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Integrating Technology Adoption Models Into Implementation Science Methodologies: A Mixed-Methods Preimplementation

Natasha L Gallant^1,2, Thomas Hadjistavropoulos^1,2, Rhonda J N Stopyn^1,2

¹Department of Psychology, University of Regina, Regina, Saskatchewan, Canada.

The Gerontologist

|July 10, 2022

Summary

Implementing automated pain behavior monitoring (APBM) systems in long-term care (LTC) requires understanding user expectations. Performance and effort expectancies are key determinants for successful technology adoption in healthcare settings.

Keywords:

Barriers Facilitators Health care settings Patient-oriented technology Technology adoption

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Area of Science:

Health Informatics
Implementation Science
Nursing Technology

Background:

Sustainable implementation of patient-oriented technologies in healthcare is challenging.
Preimplementation studies using frameworks like CFIR and technology adoption models are crucial.
This study focused on determinants for implementing an automated pain behavior monitoring (APBM) system in long-term care (LTC).

Purpose of the Study:

Identify determinants for successful implementation of an APBM system in an LTC facility.
Explore factors influencing nurses' behavioral intentions to use the APBM system.
Provide recommendations for implementing patient-oriented technologies in healthcare.

Main Methods:

Mixed-methods study design involving 164 LTC nurses and 68 LTC staff.
Quantitative data collected via questionnaires and analyzed using mediation analyses.
Qualitative data from interviews analyzed using directed content analysis.

Main Results:

Performance expectancy and effort expectancy mediated the influence of implementation readiness and technology readiness on behavioral intentions.
Findings suggest user expectations are critical for technology adoption in LTC.
Qualitative data informed intervention development grounded in the CFIR.

Conclusions:

User expectancies significantly influence the adoption of patient-oriented technologies like APBM systems.
Recommendations are provided for implementing such technologies in healthcare settings.
Integrating technology adoption frameworks with implementation science enhances successful technology integration.