OBJECTIVES: Healthcare systems are increasingly using ‘precision medicine’ to ensure that patients receive the most appropriate treatment. Whilst the benefits of precision medicine are widely discussed, there is less discussion around the circumstances where precision medicine is most likely to reap benefits, and even less discussion around the potential downsides of precision medicine. This study aims to address these points.

METHODS: A hypothetical case study was developed that with two available treatments: T1 (with an effectiveness rate of 90%) and T2 (effectiveness = 60%). Different scenarios are run, where the individual patients who would benefit from T2 are (i) entirely ‘within’ the 90% that would also benefit from T1, (ii) covering the 10% who would not benefit from T1 but also including some would benefit from T1, and (iii) an ‘overlapping’ case in-between. For each scenario, two options were assessed: ‘With precision medicine’ (i.e. the decision maker can identify which patients will benefit from each treatment) and ‘no precision medicine’.

RESULTS: In all scenarios, total population health increased when precision medicine would be applied. Costs were reduced in all scenarios, although this did not include the cost of implementing the precision medicine approach itself. Decision making was shown to be more complex, because precision medicine would introduce multiple layers of decisions. Data requirements were greatly increased with precision medicine, and decision uncertainty was also greater (due to smaller sample sizes within smaller subgroups). In a health system that uses value-based pricing, value was shown not to increase, since the price of treatments would likely increase in line with the improved effectiveness due to precision medicine methods.

CONCLUSIONS: In the case study presented, precision medicine methods delivered increase population health, but the value for money varied considerably between scenarios. Better understanding of disease areas in which precision medicine will benefit patients is essential.