Big data in anaesthesia: a narrative, nonsystematic review

Data generation is growing with the use of 'anaesthesia information management systems' (AIMS), but the appropriate use of data for scientific purposes is often wasted by a lack of integration. This narrative review aims to describe the use of routinely collected data and its potential usefulness to improve the quality of care, first by defining the six levels of integration of electronic health records as proposed by the National Health Service (NHS) illustrated by examples in anaesthesia practice. Secondly, by explaining what measures can be taken to profit from those data on the micro-system level (for the patient), the meso-system (for the department and the hospital institution) and the macro-system (for healthcare and public health). We will next describe a homemade AIMS solution and the opportunities which result from his integration on the different levels and the research prospects implied. Opportunities outside of high-income countries will also be presented. All lead to the conclusion that a core dataset for peri-operative global research may facilitate a framework for the integration of large volumes of data from electronic health records. It will allow a constant re-evaluation of our practice as anaesthesiologists to offer the best care for patients. In this regard, the training of some anaesthesiologists in data science and artificial intelligence is of paramount importance. We must also take into account the ecological footprint of data centres as these are energy-consuming. It is essential to prepare for these changes and turn the speciality of anaesthesia, collaborating with data scientists, into a more prominent role of peri-operative medicine.

Determining the Minimum Dataset for Surgical Patients in Africa: A Delphi Study

BACKGROUND

It is often difficult for clinicians in African low- and middle-income countries middle-income countries to access useful aggregated data to identify areas for quality improvement. The aim of this Delphi study was to develop a standardised perioperative dataset for use in a registry.

METHODS

A Delphi method was followed to achieve consensus on the data points to include in a minimum perioperative dataset. The study consisted of two electronic surveys, followed by an online discussion and a final electronic survey (four Rounds).

RESULTS

Forty-one members of the African Perioperative Research Group participated in the process. Forty data points were deemed important and feasible to include in a minimum dataset for electronic capturing during the perioperative workflow by clinicians. A smaller dataset consisting of eight variables to define risk-adjusted perioperative mortality rate was also described.

CONCLUSIONS

The minimum perioperative dataset can be used in a collaborative effort to establish a resource accessible to African clinicians in improving quality of care.

Publishing quality improvement studies: learning to share and sharing to learn

This editorial welcomes the decision of BJA Open to publish quality improvement (QI) studies. It summarises the current problems with conducting, evaluating, and publishing QI studies. It highlights existing guidance for prospective authors to follow regarding the reporting of QI interventions, their context(s), underlying theories, and evaluation. In so doing, we hope to encourage the publication of more QI studies of sufficient quality to facilitate learning or replication elsewhere.

Improving perioperative brain health: an expert consensus review of key actions for the perioperative care team

Delirium and postoperative neurocognitive disorder are the commonest perioperative complications in patients more than 65 yr of age. However, data suggest that we often fail to screen patients for preoperative cognitive impairment, to warn patients and families of risk, and to take preventive measures to reduce the incidence of perioperative neurocognitive disorders. As part of the American Society of Anesthesiologists (ASA) Perioperative Brain Health Initiative, an international group of experts was invited to review published best practice statements and guidelines. The expert group aimed to achieve consensus on a small number of practical recommendations that could be implemented by anaesthetists and their partners to reduce the incidence of perioperative neurocognitive disorders. Six statements were selected based not only on the strength of the evidence, but also on the potential for impact and the feasibility of widespread implementation. The actions focus on education, cognitive and delirium screening, non-pharmacologic interventions, pain control, and avoidance of antipsychotics. Strategies for effective implementation are discussed. Anaesthetists should be key members of multidisciplinary perioperative care teams to implement these recommendations.

Development of a Clinical Prediction Model for In-hospital Mortality from the South African Cohort of the African Surgical Outcomes Study

BACKGROUND

Data on the factors that influence mortality after surgery in South Africa are scarce, and neither these data nor data on risk-adjusted in-hospital mortality after surgery are routinely collected. Predictors related to the context or setting of surgical care delivery may also provide insight into variation in practice. Variation must be addressed when planning for improvement of risk-adjusted outcomes. Our objective was to identify the factors predicting in-hospital mortality after surgery in South Africa from available data.

METHODS

A multivariable logistic regression model was developed to identify predictors of 30-day in-hospital mortality in surgical patients in South Africa. Data from the South African contribution to the African Surgical Outcomes Study were used and included 3800 cases from 51 hospitals. A forward stepwise regression technique was then employed to select for possible predictors prior to model specification. Model performance was evaluated by assessing calibration and discrimination. The South African Surgical Outcomes Study cohort was used to validate the model.

RESULTS

Variables found to predict 30-day in-hospital mortality were age, American Society of Anesthesiologists Physical Status category, urgent or emergent surgery, major surgery, and gastrointestinal-, head and neck-, thoracic- and neurosurgery. The area under the receiver operating curve or c-statistic was 0.859 (95% confidence interval: 0.827-0.892) for the full model. Calibration, as assessed using a calibration plot, was acceptable. Performance was similar in the validation cohort as compared to the derivation cohort.

CONCLUSION

The prediction model did not include factors that can explain how the context of care influences post-operative mortality in South Africa. It does, however, provide a basis for reporting risk-adjusted perioperative mortality rate in the future, and identifies the types of surgery to be prioritised in quality improvement projects at a local or national level.

Can quality improvement improve the quality of care? A systematic review of reported effects and methodological rigor in plan-do-study-act projects

Background

The Plan-Do-Study-Act (PDSA) method is widely used in quality improvement (QI) strategies. However, previous studies have indicated that methodological problems are frequent in PDSA-based QI projects. Furthermore, it has been difficult to establish an association between the use of PDSA and improvements in clinical practices and patient outcomes. The aim of this systematic review was to examine whether recently published PDSA-based QI projects show self-reported effects and are conducted according to key features of the method.

Methods

A systematic literature search was performed in the PubMed, Embase and CINAHL databases. QI projects using PDSA published in peer-reviewed journals in 2015 and 2016 were included. Projects were assessed to determine the reported effects and the use of the following key methodological features; iterative cyclic method, continuous data collection, small-scale testing and use of a theoretical rationale.

Results

Of the 120 QI projects included, almost all reported improvement (98%). However, only 32 (27%) described a specific, quantitative aim and reached it. A total of 72 projects (60%) documented PDSA cycles sufficiently for inclusion in a full analysis of key features. Of these only three (4%) adhered to all four key methodological features.

Conclusion

Even though a majority of the QI projects reported improvements, the widespread challenges with low adherence to key methodological features in the individual projects pose a challenge for the legitimacy of PDSA-based QI. This review indicates that there is a continued need for improvement in quality improvement methodology.

Electronic supplementary material

The online version of this article (10.1186/s12913-019-4482-6) contains supplementary material, which is available to authorized users.

Improvement Science in Anaesthesia

PURPOSE OF REVIEW

This article offers an overview of the history and features of Improvement Science in general and some of its applications to Anaesthesia in particular.

RECENT FINDINGS

Improvement Science is an evolving discipline aiming to generate learning from quality improvement interventions. An increasingly common approach to improving Anaesthesia services is to employ large-scale perioperative data measurement and feedback programmes. Improvement Science offers important insights on questions such as which indicators to collect data for; how to capture that data; how it can be presented in engaging visual formats; how it could/should be fed back to frontline staff and how they can be supported in their use of data to generate improvement.

SUMMARY

Data measurement and feedback systems represent opportunities for anaesthetists to work with multidisciplinary colleagues to help improve services and outcomes for surgical patients. Improvement Science can help evaluate which approaches work, and in which contexts, and is therefore of value to healthcare commissioners, providers and patients.