Abstract
INTRODUCTION: Forgetting shapes learning in two different ways. It impedes learning when important lessons are forgotten. Equally, it can be difficult to enact new lessons if we do not let go of old beliefs and practices that are no longer useful. A learning health system (LHS) that wishes to improve health service delivery will need to find ways to remember processes that shape quality and safety - using data that often resides beyond electronic health records. An LHS will also need to "forget", or programmatically decommission, obsolete practices, whose persistence otherwise leads to unnecessary system complexity and inertia to change. DISCUSSION: New forms of data needed to improve health services include process metrics extracted from digital systems; human-level metrics that capture workflow patterns and clinician behaviors; and multivariate process patterns that can identify service "syndromes." To avoid inertia to change, system complexity must be reduced by retiring (or forgetting) inefficient or unhelpful work practices. Biological models of programmed cell death provide a rich set of mechanisms to decommission elements of health services. These models suggest health service elements should be able to detect the end of their useful life and should contain internal mechanisms to orchestrate decommissioning-in contrast to current service decommissioning, which is an externally initiated, top-down down-driven process. CONCLUSIONS: An LHS should take advantage of digital infrastructure to bring together people, sensors, analytics, and quasi-autonomous mechanisms for service adaptation. By drawing inspiration from biology, we can design LHSs that do not just remember but also actively forget.