Making the invisible visible: the availability and desirability of adherence data in routine CF care- findings from a national questionnaire survey.
Louisa RobinsonChin MaguireZhe Hui HooMartin J WildmanPublished in: F1000Research (2019)
Background: Inhaled medications for cystic fibrosis (CF) are effective but adherence is low. Clinicians find it difficult to estimate how much treatment people with CF (PWCF) take, whilst objective adherence measurement demonstrates that patients are poorly calibrated with a tendency to over-estimate actual adherence. The diagnostic approach to a PWCF with deteriorating clinical status and very low adherence is likely to be different to the approach to a deteriorating patient with optimal adherence. Access to objective adherence data in routine consultations could help to overcome diagnostic challenges for clinicians and people with CF. Attitudes of clinicians to the use and importance of routinely available adherence data is unknown. Methods: We conducted an online questionnaire survey with UK CF centres. We asked five questions relating to the current use and perception of objective measurements of adherence in routine care. Results: A total of eight CF centres completed the questionnaire. Few of the responding centres have adherence data readily available in routine clinics (13% of centres use medicines possession ratio; of centres with access to I-nebs® it was estimated that 17% of patients had I-neb data regularly available in clinics). All centres considered the availability of objectively measured adherence data to be important. Respondents identified that systems developed to provide adherence data in clinical practice must provide data to both clinicians and patients that is readily understood and easy to use. Conclusions: Centres perceived the availability of adherence data in routine care to be important but objective measures of adherence is rarely available at present.
Keyphrases
- cystic fibrosis
- clinical practice
- electronic health record
- big data
- end stage renal disease
- healthcare
- ejection fraction
- newly diagnosed
- primary care
- cross sectional
- pseudomonas aeruginosa
- quality improvement
- prognostic factors
- type diabetes
- metabolic syndrome
- data analysis
- patient reported outcomes
- artificial intelligence
- chronic obstructive pulmonary disease
- peritoneal dialysis
- general practice