Can we nudge to reduce the perioperative low value care? Decision making factors influencing safe practice implementation.

PURPOSE OF THE REVIEW: Highlight sources of low-value care (LVC) during the perioperative period help understanding the decision making behind its persistence, the barriers for change, and the potential implementation strategies to reduce it. RECENT FINDINGS: The behavioural economics science spread of use through aligned strategies or nudge units offer an opportunity to improve success in the LVC reduction. SUMMARY: LVC, such as unneeded surgeries, or preanaesthesia tests for low-risk surgeries in low-risk patients, is a relevant source of waste and preventable harm, most especially in the perioperative period. Despite the international focus on it, initial efforts to reduce it in the last decade have not clearly shown a sustainable improvement. Understanding the shared decision-making process and the barriers to be expected when tackling LVC is the first step to build the change. Applying a structured strategy based on the behavioural science principles may be the path to increasing high value care in an effective an efficient way. It is time to foster nudge units at different healthcare system levels.

Extension of patient safety initiatives to perioperative care.

PURPOSE OF REVIEW: Patient safety has significantly improved during the intraoperative period thanks to the anesthesiologists, surgeons, and nurses. Nowadays, it is within the perioperative period where most of the preventable harm happened to the surgical patient. We aim to highlight the main issues and efforts to improve perioperative patient safety focusing and the relation to intraoperative safety strategies. RECENT FINDINGS: There is ongoing research on perioperative safety strategies aiming to initiate multidisciplinary interventions on early stages of the perioperative period as well as an increasing focus on preventing harm from postoperative complications. SUMMARY: Any patient safety strategy to be implemented needs to be framed beyond the operating room and include in the intervention the whole perioperative period.

Automatic real-time analysis and interpretation of arterial blood gas sample for Point-of-care testing: Clinical validation.

BACKGROUND: Point-of-care arterial blood gas (ABG) is a blood measurement test and a useful diagnostic tool that assists with treatment and therefore improves clinical outcomes. However, numerically reported test results make rapid interpretation difficult or open to interpretation. The arterial blood gas algorithm (ABG-a) is a new digital diagnostics solution that can provide clinicians with real-time interpretation of preliminary data on safety features, oxygenation, acid-base disturbances and renal profile. The main aim of this study was to clinically validate the algorithm against senior experienced clinicians, for acid-base interpretation, in a clinical context. METHODS: We conducted a prospective international multicentre observational cross-sectional study. 346 sample sets and 64 inpatients eligible for ABG met strict sampling criteria. Agreement was evaluated using Cohen's kappa index, diagnostic accuracy was evaluated with sensitivity, specificity, efficiency or global accuracy and positive predictive values (PPV) and negative predictive values (NPV) for the prevalence in the study population. RESULTS: The concordance rates between the interpretations of the clinicians and the ABG-a for acid-base disorders were an observed global agreement of 84,3% with a Cohen's kappa coefficient 0.81; 95% CI 0.77 to 0.86; p < 0.001. For detecting accuracy normal acid-base status the algorithm has a sensitivity of 90.0% (95% CI 79.9 to 95.3), a specificity 97.2% (95% CI 94.5 to 98.6) and a global accuracy of 95.9% (95% CI 93.3 to 97.6). For the four simple acid-base disorders, respiratory alkalosis: sensitivity of 91.2 (77.0 to 97.0), a specificity 100.0 (98.8 to 100.0) and global accuracy of 99.1 (97.5 to 99.7); respiratory acidosis: sensitivity of 61.1 (38.6 to 79.7), a specificity of 100.0 (98.8 to 100.0) and global accuracy of 98.0 (95.9 to 99.0); metabolic acidosis: sensitivity of 75.8 (59.0 to 87.2), a specificity of 99.7 (98.2 to 99.9) and a global accuracy of 97.4 (95.1 to 98.6); metabolic alkalosis sensitivity of 72.2 (56.0 to 84.2), a specificity of 95.5 (92.5 to 97.3) and a global accuracy of 93.0 (88.8 to 95.3); the four complex acid-base disorders, respiratory and metabolic alkalosis, respiratory and metabolic acidosis, respiratory alkalosis and metabolic acidosis, respiratory acidosis and metabolic alkalosis, the sensitivity, specificity and global accuracy was also high. For normal acid-base status the algorithm has PPV 87.1 (95% CI 76.6 to 93.3) %, and NPV 97.9 (95% CI 95.4 to 99.0) for a prevalence of 17.4 (95% CI 13.8 to 21.8). For the four-simple acid-base disorders and the four complex acid-base disorders the PPV and NPV were also statistically significant. CONCLUSIONS: The ABG-a showed very high agreement and diagnostic accuracy with experienced senior clinicians in the acid-base disorders in a clinical context. The method also provides refinement and deep complex analysis at the point-of-care that a clinician could have at the bedside on a day-to-day basis. The ABG-a method could also have the potential to reduce human errors by checking for imminent life-threatening situations, analysing the internal consistency of the results, the oxygenation and renal status of the patient.