Using Quality Indicators in Anaesthesia
Integrating the lessons from the diverse literature described previously into coherent guidance for practice in the anaesthetic service area is a somewhat difficult task. Taken at face value, an effective feedback strategy should be timely, intensive, originate from a trustworthy and credible data source, be confidential and non-judgemental, be supported by the broader organization, supplied continuously over time, and integrated within a broader quality improvement framework.
From a clinical perspective, the modern practising anaesthetist rarely receives feedback on the patient's experience downstream of the intraoperative phase of care. Feedback on postoperative nausea or pain control often occurs on a periodic basis through successive audit cycles, but these information streams are discontinuous and may not be geared towards continuous improvement actions. Feedback upon the quality of recovery experienced by patients upon waking from surgery often occurs only on an ad hoc basis, through personal interactions with patients and recovery room staff or when anaesthetists have the opportunity to personally follow-up patients in between busy theatre list schedules or are requested to attend a patient by recovery room staff. There are few reported examples of systematic, routine monitoring of quality indicators in the recovery room to provide rapid, continuous feedback on the quality of anaesthetic care delivered. Similarly, few evaluative studies of personal professional monitoring programmes for anaesthetists or trainees exist.
Drawing upon the principles outlined above, what might an effective data feedback system for anaesthetic quality indicators look like? By way of an example, the key features of an initiative underway at a large academic hospital in London, UK, in which the authors are involved, may provide a potential model embodying many of the key lessons from this review (Box 2). The initiative is part of a research study to investigate how information from quality indicators can be used to effectively support quality improvement in anaesthesia. It adopts a continuous quality monitoring approach, in accordance with industrial process control practices and seeks to monitor a range of quality indicators for anaesthesia and perioperative services. Data are collected by PACU nurses against the following metrics: (i) temperature upon arrival in recovery (in accordance with NICE guidelines), (ii) patient-reported QoR score (Myles and colleagues, as described previously), (iii) patient-reported PONV (categorical), (iv) patient-reported pain scale score (categorical and continuous scales), and (vi) patient transfer efficiency as measured by ward wait time (WWT), the time interval between the ward being called and the patient leaving PACU.
The main feedback loops for dissemination of the data back to frontline service providers are depicted within Figure 3. Data are collected for all elective and emergency surgery cases passing through general theatres and PACU, excluding day surgery cases. On a monthly basis, tailored data feedback reports are produced for individual anaesthetists, each surgical ward, and for the PACU. Taking the anaesthetist reports as an example, each is based upon a single anaesthetist's personal case history, thus ensuring that feedback is directly relevant to the recipient. The report includes summary descriptive statistics providing both a cross-sectional and longitudinal perspective on the data and graphical presentations which compare the individual's scores with an anonymized distribution of peers' scores. The report provides the anaesthetist with a breakdown of case load by specialty and other key demographics. Bar charts provide comparative information concerning proportion of patients with temperature more than 36° upon arrival in recovery, proportion of patients free from PONV, and proportion of patients with pain scores <4 (considered to be the comfortable region on an 11-point continuous scale). The anaesthetist's pain scores are additionally subdivided by main specialty and compared with the aggregated score for that specialty across the whole department.
(Enlarge Image)
Figure 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.
Variation across anaesthetists on the majority of quality indicators is typically small. Figure 4 presents a comparative view of individual anaesthetists mean pain scores. Anaesthetist scores are not case mix adjusted to account for variations in procedure profile or other patient factors at the individual anaesthetist level. By providing regular feedback in a familiar format over time, however, anaesthetists recognize where they fall in the distribution and through the comparisons with historical data can determine longitudinal variations. In addition to the anaesthetist feedback reports, regular summaries of data on quality of recovery and patient transfer efficiency are provided to the PACU nursing team, the surgical wards, and perioperative service managers.
(Enlarge Image)
Figure 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.
Routine Monitoring and Feedback: A Case Example
Integrating the lessons from the diverse literature described previously into coherent guidance for practice in the anaesthetic service area is a somewhat difficult task. Taken at face value, an effective feedback strategy should be timely, intensive, originate from a trustworthy and credible data source, be confidential and non-judgemental, be supported by the broader organization, supplied continuously over time, and integrated within a broader quality improvement framework.
From a clinical perspective, the modern practising anaesthetist rarely receives feedback on the patient's experience downstream of the intraoperative phase of care. Feedback on postoperative nausea or pain control often occurs on a periodic basis through successive audit cycles, but these information streams are discontinuous and may not be geared towards continuous improvement actions. Feedback upon the quality of recovery experienced by patients upon waking from surgery often occurs only on an ad hoc basis, through personal interactions with patients and recovery room staff or when anaesthetists have the opportunity to personally follow-up patients in between busy theatre list schedules or are requested to attend a patient by recovery room staff. There are few reported examples of systematic, routine monitoring of quality indicators in the recovery room to provide rapid, continuous feedback on the quality of anaesthetic care delivered. Similarly, few evaluative studies of personal professional monitoring programmes for anaesthetists or trainees exist.
Drawing upon the principles outlined above, what might an effective data feedback system for anaesthetic quality indicators look like? By way of an example, the key features of an initiative underway at a large academic hospital in London, UK, in which the authors are involved, may provide a potential model embodying many of the key lessons from this review (Box 2). The initiative is part of a research study to investigate how information from quality indicators can be used to effectively support quality improvement in anaesthesia. It adopts a continuous quality monitoring approach, in accordance with industrial process control practices and seeks to monitor a range of quality indicators for anaesthesia and perioperative services. Data are collected by PACU nurses against the following metrics: (i) temperature upon arrival in recovery (in accordance with NICE guidelines), (ii) patient-reported QoR score (Myles and colleagues, as described previously), (iii) patient-reported PONV (categorical), (iv) patient-reported pain scale score (categorical and continuous scales), and (vi) patient transfer efficiency as measured by ward wait time (WWT), the time interval between the ward being called and the patient leaving PACU.
The main feedback loops for dissemination of the data back to frontline service providers are depicted within Figure 3. Data are collected for all elective and emergency surgery cases passing through general theatres and PACU, excluding day surgery cases. On a monthly basis, tailored data feedback reports are produced for individual anaesthetists, each surgical ward, and for the PACU. Taking the anaesthetist reports as an example, each is based upon a single anaesthetist's personal case history, thus ensuring that feedback is directly relevant to the recipient. The report includes summary descriptive statistics providing both a cross-sectional and longitudinal perspective on the data and graphical presentations which compare the individual's scores with an anonymized distribution of peers' scores. The report provides the anaesthetist with a breakdown of case load by specialty and other key demographics. Bar charts provide comparative information concerning proportion of patients with temperature more than 36° upon arrival in recovery, proportion of patients free from PONV, and proportion of patients with pain scores <4 (considered to be the comfortable region on an 11-point continuous scale). The anaesthetist's pain scores are additionally subdivided by main specialty and compared with the aggregated score for that specialty across the whole department.
(Enlarge Image)
Figure 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.
Variation across anaesthetists on the majority of quality indicators is typically small. Figure 4 presents a comparative view of individual anaesthetists mean pain scores. Anaesthetist scores are not case mix adjusted to account for variations in procedure profile or other patient factors at the individual anaesthetist level. By providing regular feedback in a familiar format over time, however, anaesthetists recognize where they fall in the distribution and through the comparisons with historical data can determine longitudinal variations. In addition to the anaesthetist feedback reports, regular summaries of data on quality of recovery and patient transfer efficiency are provided to the PACU nursing team, the surgical wards, and perioperative service managers.
(Enlarge Image)
Figure 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.