Artificial intelligence triage in online consultations to reduce delays in urgent primary care Not Applicable
Conditions
Interventions
Study Parts:
1. Interrupted Time Series Analysis: the study will compare how quickly GP practices can complete urgent and emergency online consultations before and after introducing Al Triage.
2. Quan
Sponsors
University of Manchester
Eligibility
Sex/Gender
All
Inclusion criteria
Inclusion criteria: GP practices: GP practices must meet both the following criteria to be eligible for the study: 1. Currently actively using PATCHS without AI Triage 2. Actively used PATCHS without AI Triage for at least 12 months Patients: 1. Patients who use PATCHS in the intervention and control GP practices who have not opted out of sharing their anonymised data for research purposes with UoM. 2. Patients who are at least 16 years old, though carers can use PATCHS on behalf of patients under 16 years old if they have guardianship or parental responsibility (as manually verified by the GP practice in PATCHS).
Exclusion criteria
Exclusion criteria: Not meeting the participant inclusion criteria
Design outcomes
Primary
| Measure | Time frame |
|---|---|
| The proportion of delays in completing urgent and emergency online consultations at the patient level (delayed urgent online consultations + delayed emergency online consultations) ÷ (total urgent patient online consultations + total emergency online consultations) at baseline versus the intervention comparison | — |
Secondary
| Measure | Time frame |
|---|---|
| 1. The proportion of delayed emergency online consultations at the patient level (disaggregated primary outcome measure; denominator = number of emergency online consultations) at one timepoint 2. The proportion of delayed urgent online consultations at the patient level (disaggregated primary outcome measure; denominator = number of urgent online consultations) at one timepoint | — |
Countries
England, United Kingdom
Contacts
Public ContactBenjamin Brown
Outcome results
None listed