Algorithmic Recommendation Tools and Experiential Learning in Clinical Care

This study examines the relationship between the adoption of algorithmic recommendation tools and experiential learning. We argue that the adoption of an algorithmic recommendation tool will harm experiential learning in organizations by limiting knowledge retention and retrieval. We further argue that the adverse relationship between algorithmic tool adoption and experiential learning will be stronger in organizations operating in low-task-difficulty environments than those in high-task-difficulty ones because organizational members in such organizations are likely to rely more on algorithmic recommendations, experiencing higher skill erosion. In addition, the relationship will be stronger in organizations facing low task variety than in those with high task variety, as these organizations are likely to have more rigid routines and in turn experience higher routine disruption after adopting an algorithmic tool. We utilize data on the adoption of an algorithmic tool called a clinical decision support system (CDSS) in a sample of emergency departments in California and utilize a fixed-effects panel regression with control function to test our arguments. We find that the relationship between cumulative experience and mortality becomes significantly weaker after CDSS adoption, suggesting flatter learning curves. We also find evidence that the effect is moderated by task difficulty and task variety.