Asynchronous virtual clinics: A solution to waiting times?

Outpatient appointments across the UK account for 85% of all hospital activity (excluding A&E) and demand continues to rise. Return outpatient appointments account for 55% of consultant led outpatient activity and 85% of nurse-led clinics.

Improvements have been made to meet the rising demand including the introduction of nurse-led outpatient clinics, use of SMS reminders to reduce DNA (did not attend) rates and widespread trialing of video conferencing to reduce patient travel.

Nevertheless there is a case for introducing a new service model for outpatients that virtualises the delivery of routine return appointments. One which empowers patients and leverages cloud, APIs and targets mobile devices.

What is an asynchronous virtual clinic?

A clinical exchange completed through a secure digital service where a patient can submit structured information about their condition for review and response by a clinician.

Storm worked in collaboration with NHS clinicians and patients to develop an asynchronous virtual clinic model to replace routine return outpatient appointments. The service utilised the Storm ID Lenus Health platform and offered patients the option of a digital appointment as a replacement for a face-to-face one. The service targeted Dermatology as the exemplar but was developed to work across multiple condition types.

Key features of the service included:

• Limited changes to administrative workflow and reporting by integrating with the existing patient management system
• Introduced the concept of an appointment window where a patient has a set number of days prior to the appointment date to provide a response
• Support for a range of data types including PROMs (patient reported outcome measures), questions, images, video and physiology data
• Two-way messaging between the clinician and the patient as part of the appointment
• Integration with EPR (electronic patient record) to meet information governance requirements

The service objectives were focused on driving productivity by reducing the clinical hours required for return appointments and, by removing the scheduling conflict, allow patients to respond to a virtual appointment at a time that was more convenient to them.

The service can also be used to deliver a patient-led opt-in approach. In this scenario patients are offered an open digital appointment and can choose whether they wish to use the appointment or not within a set period of time.

The case for adoption

To support a case for adoption of asynchronous virtual clinics we have developed an interactive dashboard that models the potential productivity and environmental benefits of adoption across nine specialties:

• Ophthalmology
• Orthopaedics
• Dermatology
• Ear nose and throat
• Gastroenterology
• Urology
• Respiratory
• Rheumatology
• Plastic surgery and burns

The dashboard is based on Scottish outpatient data using ISD Scotland analysis of outpatients and references the Office of National Statistics report on regional productivity

Dashboard 1: Asynchronous appointment replacing face-to-face return appointments

The first dashboard models the potential clinical time saved for both consultants and nurses across each specialty if a percentage of return appointments are migrated to an asynchronous virtual appointment model, freeing up time to work with new referrals or those return patients where a face to face consultation is required. A variable for clinical time saved per asynchronous virtual clinic is available to support further analysis.

The model shows that if 25% of return appointments are migrated to asynchronous and the time for an asynchronous appointment is 80% of a normal appointment then almost 22,000 hrs of clinical time can be diverted to meet the rising demand.

Dashboard 2: Patient-led opt-in to virtual asynchronous appointment

The second dashboard models the clinical time saved for consultants and nurses across each specialty if a percentage of return appointments are migrated to a patient-led opt-in asynchronous virtual appointment model.

In this approach a patient is provided with an open virtual appointment over a set period and can choose to opt in and use the appointment if it is required. A variable for percentage of appointments offered and not opted in is provided for analysis.

The model shows that if 25% of return appointments are migrated to asynchronous approach and the time for an asynchronous appointment is 80% of a normal appointment and only 80% of patients choose to opt in and use the appointment then almost 40,000 hrs of clinical time can be diverted to meet the rising demand.

Dashboard 3: GVA productivity

As an asynchronous appointment can be responded to by the patient at a time convenient to them it reduces the need to take time off work. This model forecasts labour productivity gains from reduced absenteeism by replacing a percentage of face-to-face appointments with asynchronous virtual appointments. GVA (gross value added) per hour worked is the metric used.

The model accounts for employment rate, attendances from those on annual leave and not of working age and offers variables around time required to travel and attend an appointment.

The model forecasts that migrating 25% of appointments from these nine specialities to an asynchronous delivery model could deliver a £35m increase in GVA to the Scottish economy.

Dashboard 4: Carbon footprint

This dashboard models CO² savings where a percentage of face-to-face appointments are migrated to virtual interactions. Unlike the previous scenarios this applies equally to outpatient appointments delivered using video conferencing technology.

Based on an average journey of 18km to an appointment and an estimate of 70% of journeys by car, the model forecasts a saving of 670,000 KG of CO² if 25% of appointments across these nine specialties are migrated.

Dashboard 5: Potential impact of AI on outpatient demand

The potential for machine learning in healthcare has been written about extensively but its impact in outpatient waiting times is less well researched. This model explores the potential impact of introducing machine learning to automate screening and triage of new outpatient appointments. As an exemplar, the impact of introducing an AI-powered diagnostic service in primary care to offer decision support to GPs in diagnosing skin lesions has been modelled.

A Kings Fund report estimated that skin lesions account for around 40% of new referrals to dermatologists in secondary care across the UK. A report from British Association of Dermatologists in 2015 stated that for every melanoma diagnosed a dermatologist could expect to see 20-40 benign lesions.

This model forecasts a potential saving of 43,000 new consultant led appointments per year in Scotland if such a service was developed, regulated and operationalised successfully.

More information

To find out more about how to deploy asynchronous virtual clinics in your organisation please get in touch.