In its promise to contribute to considerable cost savings and improved patient care through efficient analysis of the tremendous amount of data stored in electronic health records (EHR), there is currently a strong push for the proliferation of artificial intelligence (AI) in health-care. We identify, through a study of AI being used to predict patient no-show's, that for the AI to gain full potential there lies a need to balance the introduction of AI with a proper focus on the patients and the clinicians' interests. We call for a Participatory Design (PD) approach to understand and reconfigure the socio-technical setup in health-care, especially where AI is being used on EHR data that are manually being submitted by health-care personnel. CCS Concepts: • Human-centered computing → Participatory design; Empirical studies in collaborative and social computing.

We present a method and prototype tool supporting participatory mapping of domain activities to event data recorded in information systems via the system interfaces. The aim is to facilitate responsible secondary use of event data recorded in information systems, such as process mining and the construction of predictive AI models. Another identified possible benefit is the support for increasing the quality of data by using the mapping to support educating new users in how to register data, thereby increasing the consistency in how domain activities are recorded. We illustrate the method on two cases, one from a job center in a danish municipality and another from a danish hospital using the healthcare platform from Epic.