The Modern Outpatient: A Collaborative Approach 2017-2020
Transforming the patient experience by optimising the roles of all clinicians, utilising new technologies and putting the patient at the centre of care.
9. Managing and Understanding Variation in Return Demand
9.1 The number of return outpatient appointments in each specialty is subject to variability. This can be considered as being natural variation, the number of patients who have a clinical need for an outpatient appointment varies over time, and artificial variation, such as automatic appointments when there is no clinical need for them. It is widely recognised that many outpatient appointments offer little or no value to the patients attending them, but the extent and precise nature of the issue is not well understood. Evidence from across the country points to areas where clinical variation can be reduced.
9.2 Published research indicates that many follow-up appointments could be in primary care and better discharge arrangements would support this transition. Moreover, a Scottish study of cardiology patients concluded 'a substantial proportion of current cardiology return outpatients do not require regular outpatient review1. On the other hand the benefits of virtual clinics for many cases is supported by an increasing evidence base.
9.3 A paper on fracture redesign using virtual clinics concluded 'The pathway reduced unnecessary re-attendance of patients at face-to-face fracture clinics for a review of stable, self-limiting injuries'.
9.4 The Whole System Patient Flow Programme will apply operations management techniques (Exhibit 3) to develop an approach to reduce variation in outpatient return appointments by reducing artificial variation and by managing natural variation better. This will build on existing work to better manage variability in acute inpatient flow and will be developed through collaborative working with one of the pilot sites for that work, Glasgow Royal Infirmary.
9.5 The aim is to:
- understand variation;
- develop urgency classification for returns;
- standardise booking processes; and
- use data to model optimal capacity against demand and implement optimised capacity.
Exhibit 3
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