Abstract
Pain management is often considered lower priority than many other aspects of health management in hospitals. However, there is potential for Quality Improvement (QI) teams to improve pain management by visualising and exploring pain data sets. Although dashboards are already used by QI teams in hospitals, there is limited evidence of teams accessing visualisations to support their decision making. This study aims to identify the needs of the QI team in a UK Critical Care Unit (CCU) and develop dashboards that visualise longitudinal data on the efficacy of patient pain management to assist the team in making informed decisions to improve pain management within the CCU. This research is based on an analysis of transcripts of interviews with healthcare professionals with a variety of roles in the CCU and their evaluation of probes. We identified two key uses of pain data: direct patient care (focusing on individual patient data) and QI (aggregating data across the CCU and over time); in this paper, we focus on the QI role. We have identified how CCU staff currently interpret information and determine what supplementary information can better inform their decision making and support sensemaking. From these, a set of data visualisations has been proposed, for integration with the hospital electronic health record. These visualisations are being iteratively refined in collaboration with CCU staff and technical staff responsible for maintaining the electronic health record. The paper presents user requirements for QI in pain management and a set of visualisations, including the design rationale behind the various methods proposed for visualising and exploring pain data using dashboards.
Highlights
One of the most difficult tasks associated with big data is that of organising, analysing, and presenting data to users in a manner that supports sensemaking (Venkatraman and Venkatraman, 2019)
Our analysis of the data led us to identify three key tasks the Quality Improvement (QI) team can perform in planning and monitoring quality improvement for pain management
To indicate to the QI team how accurately data is being captured by the bedside staff, we developed a task, see Figure 5, that will present details relating to the timing of pain scores to allow for easy lookup and identification of the Critical Care Unit (CCU)’s progress and allow the QI team to identify areas of pain scoring that can be addressed
Summary
One of the most difficult tasks associated with big data is that of organising, analysing, and presenting data to users in a manner that supports sensemaking (Venkatraman and Venkatraman, 2019). Through the use of visualisation tools, data can be abstracted into meaningful visual representations (Blandford et al, 2014) that enable “ah HA!” moments to occur (Spence, 2007). Conceptual structures of the domain in which the users work promotes sensemaking, as understanding the way in which users think about their activities makes it possible to develop visualisations that capture this structure (Blandford et al, 2014). When patients are in severe pain, additional comorbidities can occur including confusion, delirium, compromised respiratory and cardiac function, and sleeplessness (Devlin et al, 2018). If pain is over treated compromised respiratory and cardiac function can still occur, as well as reduced consciousness and depression (Dahan and Teppema, 2003). Evaluating and exploring pain data within the critical care unit can help to identify processes and actions to alleviate these issues
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