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Non-participant direct observation of healthcare processes offers a rich method for understanding safety and performance improvement. As a prospective method for error prediction and modelling, observation can capture a broad range of performance issues that can be related to higher aspects of the system.1–5 It can help identify underlying and recurrent problems6 that may be antecedents to more serious situations.7 It is also a way to understand the complexity of healthcare work that might otherwise be poorly understood or ignored,8 9 how workarounds influence work practices and safety,10 and is of fundamental importance to practitioners wishing to understand resilience in the face of conflicting workplace pressures.11 12 In some cases it will lead to the direct observation of near-misses or precursor events that might otherwise not be reported,13 14 while in others the observation process may lead to, or be a specific part of, improvement methodologies.15
Observation allows us to move from ‘work as imagined’ (ie, what should happen, what we think happens or what we are told happens) to ‘work as done’ (what really happens).16–18 This also creates a set of unique technical challenges, from the initial question of what should be observed, the role of the observer, supporting the observer in data collection and protecting human subjects, to the non-linear relationships between outcomes, accidents and their deeper systemic causes. The design of observation studies within a clinical context requires a range of trade-offs that need to be carefully considered, yet little has been formalised about how those decisions are made.
This viewpoint paper considers those design parameters and their impact on reliability, results and outcomes, and is specifically focused on researchers and quality improvement specialists seeking to design and conduct their own quantitative observational work, particularly, but not exclusively, …
Contributors KC conceived the paper. All authors discussed content and participated in writing and editing.
Funding This paper was supported by grant number P30HS0O24380 from the Agency for Healthcare Research and Quality.
Competing interests None declared.
Provenance and peer review Not commissioned; externally peer reviewed.
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