Antidotes to overdiagnosis and overtreatment

Sir,

Sharma and Cotton ¹ wrote an insightful editorial on the twin epidemic of overdiagnosis and overtreatment, which disproportionately affect low- and middle-income countries where there are already significant limitations in healthcare resources and financing. We suggest that a combination of cognitive and behavioural interventions may help to effectively counter the underlying drivers of overdiagnosis and overtreatment.^2,3

Firstly, physicians should apply a ‘testing and treatment threshold’ model to clinical decision-making.^4,5 Within such a framework, testing is guided by pre-test disease probability (base rate prevalence and patient-specific risk factors), and similarly, treatment decision is guided by post-test probability. ⁴ The thresholds may shift based on the severity of clinical diagnosis, as well as the relative benefit and risk of testing and treatment specific to the individual patient. Recently, an ‘ABC’ heuristic (assessing the right patient, right test, and right timing, being aware of dangerous diagnoses and high-risk patients, and care centricity of diagnostic testing) was described as a tool to optimise diagnostic stewardship. ³ Furthermore, de-biasing interventions to avoid commission bias (the tendency to perform actions such as testing or treatment as opposed to watchful waiting), status quo bias (performing ‘routine’ tests even if they are not evidence-based), or recency bias (where adverse events in recent memory lead to over-testing) are also helpful. ³ To resist the intuitive tendency to over-test when there is clinical uncertainty, physicians could practise mindfulness, demonstrate humility, and learn to communicate uncertainty openly and in an empathic manner to their patients. ²

Secondly, behavioural interventions in the clinical practice environment are also helpful in rationalising tests and treatments prescribed. This could include behavioural nudges such as computerised decisional support systems, default options, reflex testing, and even individualised performance feedback on testing habits. ³ It is also beneficial to implement high-value, cost-conscious care models that focus more on qualitative (e.g. patient-reported) metrics in place of traditional fee-for-service models that are based on clinical volume. ⁶ In low- and middle-income countries, the concept of ‘frugal innovation in healthcare’ has gained prominence by providing cost-effective and good-quality alternatives to high-tech clinical services.^7,8 Such disruptive innovations can further be extended to the development of targeted diagnostic tools (e.g. point-of-care tests) and treatment algorithms that optimise healthcare utilisation.