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The Rivers-1

Amicura implements care planning software across its group of homes

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Features for Falls Prevention

Overview of Falls Prevention

Falls recording and analysis

Recording falls

The Care App allows users to record a fall that has occurred, capturing this at the point of care. This leads to timely reporting of falls and communicates this to the wider team instantaneously.
Good communication promotes awareness of any adverse events occurring and allows care staff to allow for any additional interventions or observations required.

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Falls Matrix

The falls matrix provides insights into trends occurring with falls, such as time of day, impact and location across the care site and/or communities within. This insight provides supporting evidence for any change to care plans or operational procedures and policies, for instance knowledge that there may be higher falls at handover times, or that a resident is rising early and needs support at a particular time.

Risk assessment

Multifactorial falls risk (in line with NICE guidance)

The Digital Care System features a ‘Multifactorial Falls Tool’ that assesses the risk factors that may lead to a risk of falling. The tool provides suggested actions to support residents and manage the risk of falling. Risks identified in the tool feed automatically into the care planning process; meaning important information about risk factors inform the care actions required.

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Post falls process

Post fall observations (in the event of a fall)

Post fall observation planned checks are automatically generated to ensure that best practice processes are being followed.

PCS Dashboard
 

Adverse incidents

The incident/accidents forms in the Digital Care System enable users to capture any contributing factors, lessons learnt and actions required to prevent future falls. This step “completes the circle” and encourages a learning culture about falls and fall prevention.


Prompts for review

The community dashboards shows any resident that has had a fall recorded but not yet had a follow up assessment. This encourages the falls risk and subsequent care plan to be reviewed and updated with any changes in a timely manner. This increases compliance of documentation and can help prevent future falls as the plan of care reflects up-to-date risk factors.
PCS Carer

Moving and handling

Mobility and Functional Assessment

A tool that identifies risk factors that may impact the ability to safely mobilise, such as gait and balance. Special instructions on how to support a person and preferences can be recorded against the different types of transfer (e.g. from one area of the home, getting up or onto a chair, and use of stairs etc.)

eRedBag and Hospital Pack

Hospital pack

The Digital Care System produces a hospital pack in line with the eRedBag that includes the functional abilities and risks. This promotes a continuity of care as the person transitions from one service setting to another, such as hospital. Information can be shared electronically via the National Record Locator (NRL) – giving digital access to ambulance crews, hospital staff and other healthcare professionals. 

PCS eRed bag
PCS automatic updates

Remote monitoring

Person Centred Software partner and integrate with other technology providers to build an ecosystem of solution. This includes providers of monitoring technologies that capture passive data about a resident’s movement. Learning, for example, if a resident has a disturbed night.

Communicating this on the handover in the Digital Care System means that the care team can be alerted to possible changes that could increase the risk of falls, for instance if someone is getting up earlier than usual without the appropriate support available.

Falls prediction

Person Centred Software are committed to building solutions for today as well as the future. Our vision is to promote proactive “Intelligent care” solutions, such as predicting falls or risk of falls that alert users to investigate or intervene early.

Person Centred Software partnered with UCL to look at patterns in our data that could be used to build predictive algorithms, which has shown up to 87% accuracy on predicting a person will fall within the next two weeks.

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Overview of Falls Prevention

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