The adoption of new medical technologies - a focus on outcomes and value

Medical devices – is value-based healthcare a help or a hindrance?

Any innovative intervention in healthcare should demand us to ask of ourselves  the problem we are trying to solve, the desired outcome for this individual, and  the associated costs and ramifications for the wider healthcare system. Nowhere is this more true than in the development and adoption of diagnostic, monitoring and therapeutic medical devices.

An evidence-based path to adoption of novel devices which may be of very high value to patients has been problematic, as highlighted by the recent excellent article by Campbell et al, ‘Generating evidence for new high-risk medical devices’. Additionally there are differences between the type of data collection required and evidence needed to support HTA decision making between diagnostic and therapeutic devices.

What we are seeking to do is achieve value for patients (in terms of the outcomes that matter to them) and healthcare providers and governments who are under pressure to balance resource allocation to meet multiple needs of patients across the population they serve.

Therefore we want to adopt novel devices quickly when they show a lot of promise but also subsequently ensure that they deliver on that promise in the lives of real people. We can and should back up that decision making with ongoing outcome data capture including patient-reported outcomes as part of real world evidence generation. The push towards value-based healthcare is helpful in this regard as it encourages the embedding of patient-reported outcome data capture into direct care, thereby making it a more sustainable endeavour with the ability to create more robust data sets. The implementation of patient-reported outcome measurement should never be set up as a data collection exercise alone. Patient -reported outcomes can enhance the communication between patients and their clinical teams and therefore have a range of uses in supporting shared decision making, acting as triggers for key conversations and as a needs assessment, long before we have acquired larger datasets for value analysis.

Much has already been said about the need for a new relationship between industry and healthcare systems, particularly in considering new contracting models based on patient outcomes. This should not be misunderstood, as this new relationship with industry is not about driving the medical industrial complex in isolation without taking account of the impact of an intervention on the whole system of care for patients. We accept that industry has to be profitable. However, to achieve maximum value for patients we need to also ensure that we undertake two additional activities.

The first of these is of course to minimise unnecessary costs by adopting the most cost effective product on the market. Careful attention to outcomes is necessary in order to make this judgment.

The second is to ensure optimal positioning of devices in the patient pathway. Does evidence based medicine not achieve this? If we look at any atlas of variation on device utilisation it would appear not. For example, the use of implantable devices for patients with advanced heart failure often correlates only with the proximity of the patient’s home to the tertiary centre. Unwarranted variation, both over- and under-treatment exist and we must tackle both. If we look at the definition of evidence-based medicine as outlined by David Sackett, we may be able to guess at the answer why.

•       ‘Evidence-based practice is the conscientious, explicit and judicious use of current best evidence in making decisions about the care of the individual patient. It means integrating individual clinical expertise with the best available external clinical evidence from systematic research.’

•       ‘The patient brings to the encounter his or her own personal preferences and unique concerns, expectations, and values.’

Crucially, evidence-based guidelines tend to miss out the second part about the patient’s context and the shared understanding of their situation with their clinician. This in turn may subsequently influence choices made about the appropriateness of an intervention for that individual including whether to receive a medical device. This problem impacts on monitoring devices, for example CGM in diabetes or telemetry in heart failure; and therapeutic devices, for example implantable defibrillators or resynchronisation devices in heart failure patients.

Condition-specific patent-reported outcome data are essentially structured communications about symptoms and health status between an individual and their clinician. It therefore provides information both about need and about impact of an intervention on quality of life in a very specific way. Unlike generic measures of quality of life such as the EQ5D, it is generally harder to generate QALYs (quality adjusted life years), the comparable measure of cost effectiveness usually employed by health economists as part of the bases for a health technology assessment. However, I would argue that these measures give us much more information about the impact of an intervention.

If we can embed patient-reported outcome measures as a useful activity in the direct care of patients, and find ways to use this data including in the generation of QALYs from condition-specific tools, then we will enhance our ability to assess the value of medical devices and their true impact on the lives of patients.