• Quality improvement
• Patient-centred care
• Quality improvement methodologies

Improving quality while maintaining or reducing costs requires balancing competing demands to bring value to healthcare. High-value reporting of quality improvement (QI) initiatives similarly requires balancing descriptions of improvements achieved with assessments of potential costs and unintended consequences. Using balanced QI metrics allows simultaneous measurement of intended improvements (eg, reduced length of stay (LOS)) and of processes or outcomes that might worsen as a result of a given intervention (eg, mortality, hospital readmission). In their initiative to improve the efficiency of inpatient care without compromising safety at a large teaching hospital in Edmonton, Alberta in Canada, McAlister et al1 report balanced measures, use a methodologically evaluative QI design, and describe the local contextual factors that influenced their success, thus creating generalisable knowledge.

Their intervention bundles a number of plausible improvements on inpatient units: daily interdisciplinary care rounds, geographical cohorting of patients—that is, placing general medicine patients and their doctors at one place in the hospital, strategies to optimise care transitions (eg, medication reconciliation) and use of best practice through care maps, order sets and decision support tools. Many would regard these changes as components of high-quality inpatient care and appropriate to all patients. In reality, limited evidence supports these interventions individually and the magnitude of their benefits (at least on their own) is probably not large. Hence, the reason for a multifaceted or bundled intervention—we do not know which component will generate important improvements, and, it is possible all are needed. Some components may even have synergistic effects. Such bundled interventions will be increasingly important to deal with highly complex healthcare problems, which typically have no single ‘magic bullet’ solution.

On the one hand, a bundled approach aims at the QI target quickly (as opposed to testing each component in turn or trying different combinations of possibly synergistic components). …