Human centered AI design for clinical monitoring and data management. (30th September 2020)

• Record Type: Journal Article
• Authors: Adnan, H S; Matthews, S; Hackl, M; Das, P P; Manaswini, M; Gadamsetti, S; Filali, M; Owoyele, B; Santuber, J; Edelman, J
• Is Part Of: European journal of public health. Volume 30(2020)Supplement 5
• Publication Date: 2020-09-30
• Subjects: Epidemiology -- Europe -- Periodicals; Public health -- Europe -- Periodicals; 362.109405
• DOI: 10.1093/eurpub/ckaa165.225
• Languages: English
• ISSNs: 1101-1262

Abstract

Abstract: Background: In clinical settings, significant resources are spent on data collection and monitoring patients' health parameters to improve decision-making and provide better care. With increased digitization, the healthcare sector is shifting towards implementing digital technologies for data management and in administration. New technologies offer better treatment opportunities and streamline clinical workflow, but the complexity can cause ineffectiveness, frustration, and errors. To address this, we believe digital solutions alone are not sufficient. Therefore, we take a human-centred design approach for AI development, and apply systems engineering methods to identify system leverage points. We demonstrate how automation enables monitoring clinical parameters, using existing non-intrusive sensor technology, resulting in more resources toward patient care. Furthermore, we provide a framework on digitization of clinical data for integration with data management. Methods: Activities of Daily Living (ADLs) are essential parameters, necessary for evaluating patients in mental health wards. Ideally logging the parameters should take place at hourly intervals; however, time constraints and lack of resources restrict the nursing staff to consolidating the overall impression during the day, relying on what they recall. Using design methods, sensors (e.g. infrared, proximity, pressure) are used to automate the acquisition of data for machine learning that correspond to the ADLs, considering privacy and other medical requirements. Results: We present a concept of a room with sensors that can be deployed in clinical settings. Sensor data log ADLs, and provide machine learning data. A theoretical framework demonstrates how collected data can be used in electronic/medical health records. Conclusions: Data acquisition of the ADLs with automation enable variable specificity and sensitivity on-demand. It further facilitates interoperability and provides data for machine learning. Key messages: Our research demonstrates automated data acquisition techniques for clinical monitoring. Human centered AI design approach enables on-demand analysis of ADLs for mental health treatment.