Towards business intelligence in preoperative care: Choice, chance and communication

Abstract

BACKGROUND Each year millions of patients worldwide undergo elective surgery to correct a non-life-threatening health condition. For most patients, the risks of surgery and anaesthesia are low, however complications following surgery are an important cause of death. Determining the risk of death or disability is often the remit of the anaesthetist, so accurate information about these risks is needed during the preoperative anaesthetic assessment if informed decisions are to be made. There are limited data about the risk of dying that are relevant to the anaesthetist’s own clinical setting and, as a result, the risks of surgery can be poorly estimated and communicated. The application of routinely collected data (RCD) and business intelligence (BI) may provide anaesthetists with valuable information about perioperative outcomes, and in doing so, further improve the process of shared surgical decision-making during the preoperative assessment. AIM Using RCD, this pragmatic and interpretive study sought to gain actionable insights into perioperative outcomes for patients coming forward for elective surgery and to make these insights available to anaesthetists to support shared decision-making during the preoperative assessment. METHOD A two-year participatory action research (PAR) study was conducted in the Department of Anaesthesia and Perioperative Medicine at Auckland City Hospital, New Zealand. The first of three PAR cycles was undertaken to investigate the appropriateness of an information systems development (ISD) methodology, Multiview2, to inform the development of a BI prototype in the healthcare sector. The second PAR cycle used qualitative interviews with specialist anaesthetists to explore the work of risk communication during the preoperative assessment. The third PAR cycle conducted a single-centre, retrospective cohort study to describe 30-day and 1-year perioperative mortality rates for adult patients who underwent non-cardiac surgery at Auckland City Hospital from 2002 to 2012. The mixed and varied nature of these cycles reflected the interdisciplinary nature of the research. FINDINGS Over the course of the three PAR cycles, the objectives of this study were investigated. First, the stereotypical roles and outcomes for BI development were elicited, which led to a revised Multiview2 framework that is considered appropriate for BI development in the healthcare sector. Second, the interactional and circumstantial influences on anaesthetists’ communications with patients, as part of the shared decision-making that occurs prior to surgery, were found to be varied and complex. Third, 30-day and 1-year perioperative mortality rates for adult patients who underwent non-cardiac surgery at Auckland City Hospital from 2002 to 2012 were found to be comparable to those published internationally. Throughout these research cycles and through the building of a BI prototype, this research shows how one might go about using RCD to provide actionable insights into perioperative outcomes, and how the Multiview2 methodology, with its emphasis on the social and technical aspects of ISD, can be used to support that journey. CONCLUSIONS Shared decision-making and early discussion of the risks, benefits and alternatives to surgery are required to help patients to make the choices about surgery and anaesthesia that best meet their needs. This research has shown that RCD can be used to provide anaesthetists with valuable insights about patient outcomes that are relevant to their own clinical setting to support risk benefit assessment before elective surgery. The BI journey to acquiring these insights requires a unique mix of organisational analysis, sociotechnical analysis, data modelling and technical development and can be facilitated by the Multiview2 methodology. Choice, chance and communication lie at the heart of patient-centred preoperative care, and therefore the value of accurate, timely and relevant information about perioperative outcomes that are derived from RCD and BI should not be underestimated.