Digital technologies and adherence in respiratory diseases: the road ahead

Abstract

Outcomes for patients with chronic respiratory diseases remain poor despite the development of novel therapies. In part, this reflects the fact that adherence to therapy is low and clinicians lack accurate methods to assess this issue. Digital technologies hold promise to overcome these barriers to care. For example, algorithmic analysis of large amounts of information collected on health status and treatment use, along with other disease relevant information such as environmental data, can be used to help guide personalised interventions that may have a positive health impact, such as establishing habitual and correct inhaler use. Novel approaches to data analysis also offer the possibility of statistical algorithms that are better able to predict exacerbations, thereby creating opportunities for preventive interventions that may adapt therapy as disease activity changes. To realise these possibilities, digital approaches to disease management should be supported by strong evidence, have a solid infrastructure, be designed collaboratively as clinically effective and cost-effective systems, and reflect the needs of patients and healthcare providers. Regulatory standards for digital interventions and strategies to handle the large amounts of data generated are also needed. This review highlights the opportunities provided by digital technologies for managing patients with respiratory diseases.

FIGURE 1

Four patterns of digitally monitored lung function, adherence and inhaler technique, as assessed by a digital audio recording device attached to the inhaler. a) A patient with well-controlled asthma with stable peak expiratory flow (PEF) and regular use of a twice daily preventer inhaler. Normal lung function (peak expiratory flow rate (PEFR)) is maintained by proficiently and regularly taken treatment (green dots on the lower graph in each panel indicate correct inhaler technique). b) A patient with poor lung function (PEFR recordings in red indicate lung function at 80% of baseline) due to poor inhaler technique (shown by orange squares) and missed doses (shown as red triangles). c) A patient with initial poor lung function who subsequently improves. Improved lung function is associated with regular and correct inhaler use. d) A patient with periods of intermittent inhaler use and poor lung function, followed by periods of improved adherence and improved lung function. Drops in lung function are associated with intermittant inhaler use and appear to prompt the patient to restart use. The absence of dots on the time graph indicates that no doses were taken.

FIGURE 2

Results of the Digital Maturity Self-Assessment survey in 2016, measuring how well secondary care providers in England are making use of digital technology to achieve a health and care system that is paper-free at the point of care. Readiness indicates how well providers are able to plan and deploy digital services, while capabilities indicate whether providers have staff with the digital skills needed. The infrastructure score is based on whether providers have the right technology in place. Data was from the National Health Service (NHS), England. Reproduced with permission of the rights holder, Royal College of Physicians from [93].