Moving towards a sensor-based patient monitoring system: Evaluating its impact on data and information quality

For future healthcare systems to take advantage of sensor-based patient monitoring devices, careful consideration must be taken in respect to the impact they can have on existing workflow processes. In association with this, data and information quality dimensions need to be incorporated to help ensure a successful outcome. This paper will explore the utilisation of a paper-based patient assessment scorecard and the transition to an electronic version with a view to the future adoption of sensor-based devices. To evaluate the transition from paper-based to sensor-based solutions, the Modified Early Warning Scorecard (MEWS) is the primary exemplar within this paper. MEWS has a defined set of protocols and guidelines that assist the healthcare providers in classifying a patient's status of health in detecting patient deterioration. Paper-based MEWS are already deployed within Medical Assessment Units (MAU). Thus, the MAU is an ideal test bed for the evaluation of sensor-based solutions. The Socio-Technical Information Systems Design (STISD) science research framework is the methodology employed to address a practical problem (i.e. frequent capturing of patient vital signs) raised by the healthcare providers in relation to MEWS. In accordance with the STISD framework, a review of the extant theories, knowledge and data reveal an Event-driven Process Chain (EPC) diagram for the paper-based MEWS. Based on these findings an electronic version of the paper-based MEWS (eMEWS) is proposed and tested. Further refinements are required to explore the full capabilities of sensorbased solutions and the role they can play within MEWS. Presented is a conceptual model which examines the relationship between the content and system quality measures and their associated independent variables. Alpha and gamma tests are conducted to evaluate the eMEWS against the desired outcomes and provide the foundation for the development of the sensor-based eMEWS solution. Results show that the eMEWS prototype addresses key data quality dimensions, with sensor-based eMEWS indicting potential enhancement to the timeliness and frequency of data capturing.