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Singapore’s rapid aging population coupled with a shrinking healthcare workforce is simply not sustainable in the longer term if we want to ensure that we continue to deliver a high quality of care.  Today, nurses spend a significant amount of their time performing labour-intensive and repetitive non-clinical tasks such as picking and bringing of care consumables from ward stores to patients’ bedside, or responding to ad-hoc requests from patients for items like food and beverages or additional blankets. Consequently, such administrative activities can significantly reduce the amount of time nurses have to perform direct patient care.

Inefficiencies are also seen in other hospital areas, for example in the outpatient pharmacy where long waiting times in a crowded environment may compromise a good patient experience. To overcome this, healthcare institutions today are increasingly adopting self-service lockers as an alternative for patients, where possible. The benefits of doing so can include providing a better experience for patients and their caregivers as they can pick up their medication at their own convenience, as well as relieving the crowding situation and shortening unnecessary waiting time in the pharmacy. For pharmacy staff, they are now able to focus more on patients with more complex needs. However, we recognise that the work of assigning and loading medications into the self-service lockers can also create additional tasks for staff.

In order to allow our clinical staff to practice at “top-of-license”, we want to tap on technology to automate these administrative tasks. In this project, we aim to develop a smart co-bot (collaborative robot) that is able to perform autonomous delivery to patient bedside, as well as autonomous locker loading. This project entails research on end-effectors (a.k.a. “grippers”) that can function in an unstructured environment, a robotic arm that can operate safely in a human-rich environment with infection control considerations, as well as an algorithm that can predict patients’ consumable and linen needs upon admission. These components come together to achieve what we term as the “last-last mile” autonomous delivery solution.


Principal Investigator: Ms Yan Yan

Host Institution: Woodlands Health Campus

SHARP Grant ref: 192 22 00002

 

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