Overview
This study aims to evaluate the clinical utility and acceptability to patients and practitioners of running diabetes classification algorithms on primary care data to help improve diagnosis of diabetes subtypes in adults diagnosed with diabetes under the age of 50. The outputs from this research will help provide initial data on how best to use these algorithms in primary care and the optimal design of a decision support tool that could be taken forward to a full trial.
Description
Part 1:
Using a successful approach from previous research, and experience from existing online diabetes classification calculators, we will test the feasibility of developing a decision support tool that would run the algorithms in these calculators on electronic healthcare record data at participating GP sites. We will work with a company that will develop a decision support tool that will search and extract relevant healthcare data in GP systems, run our algorithms on these data, and produce a display, highlighting patient records where there is a potential misclassification of diabetes and/or records where there are potential data quality issues (e.g. mis-coding or missing information). The decision support tool will only run on extracted data (rather than being embedded in the GP system).
Participating GP sites will be offered an introductory education session on classification of diabetes subtypes and identification of MODY (Maturity Onset Diabetes of the Young) and training on running and interpreting the decision tool.
On receipt of the outputs from the decision support tool, practice staff will be advised to review the records of potentially misclassified patients to explore any mis-codings and to consider further testing/referrals as relevant and in line with the standard clinical care pathway for diabetes.
At the end of the study, the data extraction/decision support tool may be re-run to determine whether there have been changes and whether additional testing (eg C-peptide or islet autoantibody) or referral to a diabetes specialist team has been carried out.
Part 2:
To assess the acceptability of the diabetes classification tools to potential users, and to consider how best to implement them in clinical practice long term for maximum benefit, we will explore the views and experiences of general practice teams and people with diabetes on the use of the diabetes classification tools.
A sample of clinical and admin staff at participating GP sites, and diabetes patients flagged by the tool as misclassified, will be invited to take part in a semi-structured interview about their views & experience.
Eligibility
Eligibility criteria for qualitative interviews.
Inclusion Criteria:
Patients
- Able to give written informed consent
- 18 years of age or over at the time of study participation
- Diabetes diagnosed at or under the age of 50
- Registered with a participating GP practice
- Sufficient understanding of the English Language to enable completion of the interview
Staff
- Able to give written informed consent
- 18 years of age or over
- Working at a participating General Practice and directly involved with the study procedures (eg running the decision support tool, responsibility for clinical care of diabetes patients)
Exclusion Criteria:
Patients
- Unable to give written informed consent
- Under 18 years of age
- No diabetes or diabetes diagnosed over the age of 50.
- Not registered with a participating GP practice
- Insufficient understanding of the English Language to enable interview completion
- Have an opt-out code where patient has declined electronic medical records examined
- Considered by their General Practitioner(s) to be inappropriate to recruit due to psycho-social reasons, participating in another related clinical trial or significant health reasons, e.g. terminal illness/diagnosis.
Staff
- Unable to give written informed consent
- Under 18 years of age
- General Practice staff not involved with the study procedures.