Fig 1 The healthcare system as a basic cybernetic feedback loop based upon monitoring quality indicators. Information concerning outputs from the current.

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Enhanced feedback from perioperative quality indicators: Studying the impact of a complex QI intervention Jonathan Benn Centre for Patient Safety and Service.

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Presentation transcript:

Fig 1 The healthcare system as a basic cybernetic feedback loop based upon monitoring quality indicators. Information concerning outputs from the current system is fed back to an earlier stage to modify processes in order to achieve enhanced future performance. Feedback may target multiple levels of the system: the organization, the clinical unit, and/or the individual clinician within the clinical unit, the specific information requirements of each end-user group being different. From: Using quality indicators in anaesthesia: feeding back data to improve care Br J Anaesth. 2012;109(1):80-91. doi:10.1093/bja/aes173 Br J Anaesth | © The Author [2012]. Published by Oxford University Press on behalf of the British Journal of Anaesthesia. All rights reserved. For Permissions, please email: journals.permissions@oup.com

Fig 2 Statistical process control chart (P chart) plotting the weekly proportion of patients arriving in PACU with temperature below the recommended 36°C. Aggregating the data over the full duration of the timeline suggests that overall, 5% of patients arrive in recovery cold. This is a poor representation of the performance of perioperative warming processes, however, which process control charts reveal. In reality, the reliability of the process varies over time as a function of a range of routine fluctuation and significant special causes. Evidence of special causes may be detected in real time by violation of control chart rules associated with data points which fall beyond control limits or which contribute to statistically significant trends or shifts in the process level (see Benneyan and colleagues<sup>47</sup> for further discussion). In the figure, the circular/green data points represent such anomalies that warrant further investigation to either control detrimental effects or understand what is driving sustained improvement. From: Using quality indicators in anaesthesia: feeding back data to improve care Br J Anaesth. 2012;109(1):80-91. doi:10.1093/bja/aes173 Br J Anaesth | © The Author [2012]. Published by Oxford University Press on behalf of the British Journal of Anaesthesia. All rights reserved. For Permissions, please email: journals.permissions@oup.com

Fig 3 PACU-based quality monitoring and feedback Fig 3 PACU-based quality monitoring and feedback. The schematic depicts the data flows linked to the main stages of the perioperative pathway. Data are collected at the patient bedside during the recovery period by trained PACU nurses. The metrics collected include: patient temperature upon arrival in recovery, QoR score, PONV, pain scale score, and WWT, the interval between the surgical ward being contacted and patient handover. The data are cleaned and validated by a researcher through comparison with the theatre administration system and patient logbook before being entered into a database repository. In order to produce the feedback, templates are applied to the database which break down the data according to the required parameters. On a monthly basis, data feedback reports are produced for individual anaesthetists, each surgical ward, and for the PACU. From: Using quality indicators in anaesthesia: feeding back data to improve care Br J Anaesth. 2012;109(1):80-91. doi:10.1093/bja/aes173 Br J Anaesth | © The Author [2012]. Published by Oxford University Press on behalf of the British Journal of Anaesthesia. All rights reserved. For Permissions, please email: journals.permissions@oup.com

Fig 4 Example of the distribution of individual anaesthetist's patient-reported pain scores within a single anaesthetics department which undertakes a broad range of surgical procedure types. 95% confidence intervals for the mean pain scores and the overall sample level mean are depicted. The sample level mean score was 1.65 on an 11-point rating scale ranging from 0 (no pain) to 10 (severe pain). Variation in mean scores is attributable to case mix differences between individual anaesthetists in addition to a range of process and patient-related factors. In the feedback that anaesthetists receive, pain scores are presented as the proportion of patients with scores below 4. From: Using quality indicators in anaesthesia: feeding back data to improve care Br J Anaesth. 2012;109(1):80-91. doi:10.1093/bja/aes173 Br J Anaesth | © The Author [2012]. Published by Oxford University Press on behalf of the British Journal of Anaesthesia. All rights reserved. For Permissions, please email: journals.permissions@oup.com