Beyond metrics? Utilizing 'soft intelligence' for healthcare quality and safety. (October 2015)

• Record Type: Journal Article
• Authors: Martin, Graham P.; McKee, Lorna; Dixon-Woods, Mary
• Is Part Of: Social science & medicine. Volume 142(2015)
• Page Start: 19
• Page End: 26
• Publication Date: 2015-10
• Subjects: Patient safety -- Healthcare quality metrics -- Knowledge management -- England; Social medicine -- Periodicals; Medical anthropology -- Periodicals; Public health -- Periodicals; Psychology -- Periodicals; Medicine -- Periodicals; Medicine -- Periodicals; Médecine sociale -- Périodiques; Anthropologie médicale -- Périodiques; Santé publique -- Périodiques; Psychologie -- Périodiques; Médecine -- Périodiques; Electronic journals; 362.105
• DOI: 10.1016/j.socscimed.2015.07.027
• Languages: English
• ISSNs: 0277-9536

Abstract

Abstract: Formal metrics for monitoring the quality and safety of healthcare have a valuable role, but may not, by themselves, yield full insight into the range of fallibilities in organizations. 'Soft intelligence' is usefully understood as the processes and behaviours associated with seeking and interpreting soft data—of the kind that evade easy capture, straightforward classification and simple quantification—to produce forms of knowledge that can provide the basis for intervention. With the aim of examining current and potential practice in relation to soft intelligence, we conducted and analysed 107 in-depth qualitative interviews with senior leaders, including managers and clinicians, involved in healthcare quality and safety in the English National Health Service. We found that participants were in little doubt about the value of softer forms of data, especially for their role in revealing troubling issues that might be obscured by conventional metrics. Their struggles lay in how to access softer data and turn them into a useful form of knowing. Some of the dominant approaches they used risked replicating the limitations of hard, quantitative data. They relied on processes of aggregation and triangulation that prioritised reliability, or on instrumental use of soft data to animate the metrics. The unpredictable, untameable, spontaneous quality of soft data could be lost in efforts to systematize their collection and interpretation to render them more tractable. A more challenging but potentially rewarding approach involved processes and behaviours aimed at disrupting taken-for-granted assumptions about quality, safety, and organizational performance. This approach, which explicitly values the seeking out and the hearing of multiple voices, is consistent with conceptual frameworks of organizational sensemaking and dialogical understandings of knowledge. Using soft intelligence this way can be challenging and discomfiting, but may offer a critical defence against the complacency that can precede crisis. Highlights: There are calls for greater use of 'soft' intelligence around quality and safety. Little research examines the challenges and opportunities soft data present. Our study in the English NHS found clinicians and managers saw utility in soft data. But dominant approaches to interpretation risked obscuring their greatest value. Soft data might better be used to disrupt understanding and challenge consensus.