Self-driven healthcare: Estimating waiting times in A&E
Lead Participant:
HEALTH INFORMATICS LTD
Abstract
**vision for the project**
The vision of this project is to empower patients to better estimate (1) _how_ long they must wait in A&E and (2) _which_ A&E is currently the best one regarding waiting times. This is also beneficial for the local provision of healthcare as demand is equally shared between hospitals and demands are more balanced.
Most people had to go at least once in their life to A&E either with a family member or as a patient. Often it is unclear, how long they must wait, and which A&E might be the best one. The main assumption is that this is not for life-threatening events which require immediate attention but for patients who use self-presentation to the hospital.
**key objectives**
The key objective is to provide a website or a mobile app which can provide an estimate of waiting time on a self-entered medical complaint code. The user (prospective patient) needs to go through a self-guided triage process. However, our current groundwork shows that we can only do this for a frequent medical complaint code but not for all. It is still possible that the actual waiting time is shorter or longer based on the real triage once they enter the A&E. The final product is a prototype. No real patients will use the service. The legal ownership of the prototype passes to NHS East Kent at the end of the project.
**main areas of focus**
The focus lies on typical medical complaint codes and the time of the day. Our previous data analysis shows that time is essential for the prediction of waiting times.
**details of how it is innovative**
Patients can better estimate how long they must wait in A&E. Patients can better prepare themselves if they know how long they must wait. This helps to inform family members about the anticipated waiting times. This is also highly beneficial for the planning of local A&Es. In a worst-case situation, one A&E is running at maximum capacity whilst another one is empty. A patient-centred approach would lead to a more balanced demand. This can lead to a behavioural change which is beneficial both for patients and healthcare. A similar approach is performed by google maps in identifying dynamic routes to a destination. In this way, drivers use other routes if there is already a traffic jam in one local area.
The vision of this project is to empower patients to better estimate (1) _how_ long they must wait in A&E and (2) _which_ A&E is currently the best one regarding waiting times. This is also beneficial for the local provision of healthcare as demand is equally shared between hospitals and demands are more balanced.
Most people had to go at least once in their life to A&E either with a family member or as a patient. Often it is unclear, how long they must wait, and which A&E might be the best one. The main assumption is that this is not for life-threatening events which require immediate attention but for patients who use self-presentation to the hospital.
**key objectives**
The key objective is to provide a website or a mobile app which can provide an estimate of waiting time on a self-entered medical complaint code. The user (prospective patient) needs to go through a self-guided triage process. However, our current groundwork shows that we can only do this for a frequent medical complaint code but not for all. It is still possible that the actual waiting time is shorter or longer based on the real triage once they enter the A&E. The final product is a prototype. No real patients will use the service. The legal ownership of the prototype passes to NHS East Kent at the end of the project.
**main areas of focus**
The focus lies on typical medical complaint codes and the time of the day. Our previous data analysis shows that time is essential for the prediction of waiting times.
**details of how it is innovative**
Patients can better estimate how long they must wait in A&E. Patients can better prepare themselves if they know how long they must wait. This helps to inform family members about the anticipated waiting times. This is also highly beneficial for the planning of local A&Es. In a worst-case situation, one A&E is running at maximum capacity whilst another one is empty. A patient-centred approach would lead to a more balanced demand. This can lead to a behavioural change which is beneficial both for patients and healthcare. A similar approach is performed by google maps in identifying dynamic routes to a destination. In this way, drivers use other routes if there is already a traffic jam in one local area.
Lead Participant | Project Cost | Grant Offer |
|---|---|---|
Participant |
||
| HEALTH INFORMATICS LTD |
People |
ORCID iD |
| Holger Kunz (Project Manager) |