Year: 2020
Type: article
Abstract: Health systems are increasingly testing interventions to reduce costs and improve patient care.1 By leveraging their ability to deploy interventions across a network of providers and their data infrastructure, health systems have capacity to test new models of care delivery and to assess their effects.
What differentiates health systems from learning health systems, however, is how systems combine this infrastructure with rigorous research methods to generate evidence that can inform decision-making.1 2 Critical to this is isolating the causal effects of interventions from potential sources of bias (internal validity) and providing evidence that generalises to broader, real-world settings (external validity). As Walkey et al demonstrate in this issue of BMJ Quality and Safety , natural experiments that arise in the design and implementation of health system interventions can provide valuable opportunities to learn about the effectiveness of interventions, yielding findings that may offer both high internal and external validity.3
Natural experiments arise when the design or implementation of a programme generates variation in exposure to an intervention that is ‘as good as random’, conditional on observed confounders. For example, patients may be allocated to different treatments based on an arbitrary eligibility threshold (Walkey et al exploit this variation); health systems may stagger the roll-out of interventions across sites4; or patients may be entered into a lottery to allocate a number of intervention slots.5 When well conceived and thoroughly vetted, natural experiments retain many of the appealing statistical features of …
Source: BMJ Quality & Safety
Authors: Sunita Desai, Eric Roberts
Institutions: New York University, University of Pittsburgh
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: bronze
Award: G2574417593
OpenAlex ID: W3004796451