Staff training and cultural adaptation strategies for AI adoption in healthcare RCM
Karunasena, Niraj (2025)
2025
All rights reserved. This publication is copyrighted. You may download, display and print it for Your own personal use. Commercial use is prohibited.
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:amk-2025110326914
https://urn.fi/URN:NBN:fi:amk-2025110326914
Tiivistelmä
Healthcare revenue cycle management process is modernized by the fast integration of artificial intelligence into revenue cycle management platforms, which has created a need to understand how human resource management influences the success of hybrid human AI workflows. This study focuses on understanding how effective staff training frameworks and cultural adaptation strategies increase successful transition.
The study was facilitated by a descriptive research design to investigate the impact of training quality, technology perceptions, and leadership strategies on AI adoption in revenue cycle management. A quantitative survey was conducted to assess the perceptions, readiness, and confidence of personnel, which were theoretically associated with Knowles’s Andragogy theory, Davis’s Technology Acceptance Model, and Kotter’s Change Management and Leadership theory.
The survey results indicated varying perspectives on training effectiveness as managerial staff rated the overall training quality at a mean of 4.1, whereas other operational staff rated the training at a mean of 3.1, with more desire expressed for further training, scoring 4.0 on the need for additional training. The training quality correlated positively with staff confidence in using AI, and perceptions of accuracy and usefulness. Technology perceptions, such as usefulness, positively contribute to staff confidence, job satisfaction, and adoption intentions, while simplicity and ease of use increase the trust in AI, especially for operational staff. Operational staff emphasized that their adoption intentions are positively correlated with open communication by leaders, involvement opportunities in implementation decisions, and allowing sufficient time for transition in contrast to managerial staff, who mainly highlighted communication aspects.
The research highlights the significance of training, technology acceptance, and leadership in transitioning to hybrid human AI workflows, providing a framework for policymakers to encourage a continuous AI learning culture in healthcare revenue cycle management organizations in the USA.
The study was facilitated by a descriptive research design to investigate the impact of training quality, technology perceptions, and leadership strategies on AI adoption in revenue cycle management. A quantitative survey was conducted to assess the perceptions, readiness, and confidence of personnel, which were theoretically associated with Knowles’s Andragogy theory, Davis’s Technology Acceptance Model, and Kotter’s Change Management and Leadership theory.
The survey results indicated varying perspectives on training effectiveness as managerial staff rated the overall training quality at a mean of 4.1, whereas other operational staff rated the training at a mean of 3.1, with more desire expressed for further training, scoring 4.0 on the need for additional training. The training quality correlated positively with staff confidence in using AI, and perceptions of accuracy and usefulness. Technology perceptions, such as usefulness, positively contribute to staff confidence, job satisfaction, and adoption intentions, while simplicity and ease of use increase the trust in AI, especially for operational staff. Operational staff emphasized that their adoption intentions are positively correlated with open communication by leaders, involvement opportunities in implementation decisions, and allowing sufficient time for transition in contrast to managerial staff, who mainly highlighted communication aspects.
The research highlights the significance of training, technology acceptance, and leadership in transitioning to hybrid human AI workflows, providing a framework for policymakers to encourage a continuous AI learning culture in healthcare revenue cycle management organizations in the USA.