|Brand||PRINCE OF WALES HOSPITAL|
|Product / Service||PRINCE OF WALES HOSPITAL - SPINAL UNIT|
|Category||A09. Use of Advanced Learning Technologies|
|Entrant||DELOITTE AUSTRALIA Melbourne, AUSTRALIA|
|Idea Creation||DELOITTE AUSTRALIA Melbourne, AUSTRALIA|
|Media Placement||DELOITTE AUSTRALIA Melbourne, AUSTRALIA|
|PR||DELOITTE AUSTRALIA Melbourne, AUSTRALIA|
|Production||DELOITTE AUSTRALIA Melbourne, AUSTRALIA|
|Additional Company||PRINCE OF WALES HOSPITAL Randwick, AUSTRALIA|
|Charles Baylis||Deloitte Australia||Creative Director|
|Matt Lawson||Deloitte Australia||Chief Creative Officer|
|Rob Spittle||Deloitte Australia||Partner|
|Adrian Mills||Deloitte Australia||Partner|
|Nick White||Deloitte Australia||Principal|
|Deb Lancaster||Deloitte Australia||Director|
Lucy is an AI enabled patient connected mobile solution enabling patients to request assistance without the need to press a button. Simply by speaking their request, nurses are alerted to their need, with AI prioritising and smart-routing requests to the right resource to meet the patient’s needs. Lucy provides immediate response to pa-tients, confirming they have been heard and that a nurse is on their way. Patients also have the ability to access FAQs, without a nurse required. Nurses can spend around a quarter of their time completing tasks that are not critical patient care activities. Lucy changes nursing working processes by using AI to eliminate unnecessary activi-ties. Nurses and patients experience the connected eco-system through a voice-recognition device, AI system, Ser-vice Now integration all connected to mobile devices.
Lucy is now live in the Prince of Wales Spinal Unit for the first 28 patients. In a world first, patients are using the system to request help from nurses – without having to press a button. Instead using only their voice. How it works: Patients speak their request for assistance Lucy. AI interprets the request – enabling patients to operate rooms with connected smart devices, book appointments and request assistance. Where possible, Lucy answers the question immediately from a FAQ database. Patients receive immediate confirmation that their request has been received. Requests are assigned priority and smart-routed to nurses or support teams. Alert messages are sent to directly to right team members' mobile device, requesting them to respond to the patient. Tasks can be moved between teams and assigned by dragging and dropping on their mobile device. Data capture predicts trends in patient needs and workforce management.
Lucy has already met its primary goals, in improving the brand experience of the Prince of Wales Hospital for 100% of patients surveyed. The median response time to a patient request in modern hospitals is 10.4 minutes (The Journal of the American Medical Association). Within the 28 bed initial roll-out of Lucy, the median time is 2 mins and 9 secs. This is a 79.8% improvement versus that benchmark. Importantly, we are witnessing Lucy in the early stages of enhancing patient experience and improved patient satisfaction by providing a patient-centred communication and immediate feedback to the patient and Increased nursing time to care, by reducing the time required for care coordination As we continue to roll-out Lucy to more beds, we hope to see the system lead to improved overall management of clinical teams through enhanced data sets and dashboard that support decision making associated with skills development and rostering.
In medicine the best form of advertising is improving the product. It's an industry that can't be seen to be spend-ing money on marketing when lives are at stake. We needed to improve the experience of the Prince of Wales Private Hospital in order to promote it. Despite best efforts, patients and nurses experience difficulties with the exist-ing patient assistance journey, resulting in inefficient ward operations. External studies put average nurse response times at more than 10 minutes, with response times declining by 15% for every hour a nurse has been on shift. Our hypothesis was that if we could provide contextual information regarding the nature of the patient query, in addition to targeted notification to the right nurses, highlighting the priority of the patient’s needs, then we would see a dramatic improvement on the response time for critical patient requests.