JMIR Publications

ABSTRACT

Background:

Clinical guideline development preferentially relies on evidence from randomised controlled trials (RCTs). RCTs are the gold-standard method to evaluate the efficacy of treatments with the highest internal validity but limited external validity, in the sense that their findings may not always be applicable to, or generalisable to clinical populations/population characteristics. The external validity of RCTs for the clinical population is constrained by the lack of tailored epidemiological data analysis designed for this purpose due to data governance, consistency of disease/condition definitions, re-duplicated effort in analysis code, etc.

Objective:

To develop a digital tool that characterises the overall population and differences between clinical trial eligible and ineligible populations from the clinical populations of a disease/condition regarding demography (in terms of groupings for e.g., age, sex, ethnicity), comorbidity, co-prescription, hospitalisation and mortality. Currently, the process is complex, onerous and time consuming whereas a real-time tool may be used to rapidly inform a guideline developer’s judgement about the applicability of evidence.

Methods:

The National Institute for Health and Care Excellence (NICE) – in particular the gout guideline development group - and the Scottish Intercollegiate Guidelines Network (SIGN) guideline developers were consulted to gather their requirements and evidential data needs when developing guidelines. An R shiny tool was designed and developed using electronic primary healthcare data linked with hospitalisation and mortality data built upon an optimised data architecture. Disclosure control mechanisms were built into the tool to ensure data confidentiality.

Results:

The tool supports 128 chronic health conditions as index conditions and 161 conditions as comorbidities (33 in addition to the 128 index conditions). It enables two types of analyses via the graphic interface: overall population and stratified by user-defined eligibility criteria. The analyses produce overview statistical tables (on e.g. age, gender) of the index condition population and, within the overview groupings, produce details on e.g. electronic Frailty Index (eFI), comorbidities, co-prescriptions. The disclosure control mechanism is integral to the tool limiting tabular counts to meet local governance needs. An exemplar result for gout as an index condition is presented. Guideline developers from NICE and SIGN provided positive feedback on the tool.

Conclusions:

Using the digital tool can potentially improve evidence-driven guideline development through the availability of real-world data in real time.