NEXT GENERATION TECHNOLOGY PROJECT THE CHALLENGES AND LEARNING SO - - PowerPoint PPT Presentation

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NEXT GENERATION TECHNOLOGY PROJECT THE CHALLENGES AND LEARNING SO - - PowerPoint PPT Presentation

NEXT GENERATION TECHNOLOGY PROJECT THE CHALLENGES AND LEARNING SO FAR JANE CRAWFORD-WHITE AND LUCY FORREST TECHNOLOGY ENABLED CARE SERVICES THE AMBITION Trial of intelligent Lifelines One year project Compare with traditional


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SLIDE 1

NEXT GENERATION TECHNOLOGY PROJECT

THE CHALLENGES AND LEARNING SO FAR

JANE CRAWFORD-WHITE AND LUCY FORREST TECHNOLOGY ENABLED CARE SERVICES

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SLIDE 2

THE AMBITION

  • Trial of “intelligent” Lifelines
  • Compare with traditional Lifelines
  • Are they acceptable to Service

Users and their informal carers

  • Can they demonstrate a return on

investment

  • One year project
  • Project funded by one off grant

from NHS England, via a C&P CCG

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SLIDE 3

CARE@HOME PRO

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SLIDE 4

CARE@HOME PLATFORM

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SLIDE 5

UNUSUAL ACTIVITY DETECTED

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SLIDE 6

WHY DO THE PROJECT?

  • Move telecare from being reactive to preventative
  • Testing the available machine learning systems
  • Making the most of the switch from analogue to digital
  • Enhancing the ASC preventative offering
  • Compatibility with APCP objectives
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SLIDE 7

SUCCESSES

  • Health and social care services well engaged to make referrals
  • Training from Essence delivered for TEC and Call Centre staff for assessments,

equipment installation and for 24/7 monitoring

  • Enhanced response
  • Investigative tool and guidance for ERS staff
  • Leaflet of prevention services for family responders
  • Strengthened 4 referral pathways to CPFT
  • Operational processes agreed for call centre staff for each type of activation
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SLIDE 8

TECHNICAL ACHIEVEMENTS

  • New digital platform delivered and populated with customer data
  • Thorough testing between Care@Home equipment and call centre
  • All activations being received on primary server in call centre
  • Activations received: SOS, Unusual activity, for information eg ‘awake and well’

message, technical messaging eg switching networks, low battery, heartbeat

  • Smoke detectors were developed to instantly open voice communication
  • Secure communications: SCAIP protocol for SOS messaging, TLS2.1 for email

transmission, VPN

  • Rectified loss of voice communication when call closed prematurely
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SLIDE 9

LESSONS FROM PROCUREMENT

  • Competitive tendering process does not necessarily get the right outcomes when

wanting to learn from trials

  • Detailed versus open tender specification
  • Fast changing market – lots of new products coming on stream but also companies

going out of business or being bought out by another company

  • Project reliance on second contracts: Equipment supplier reliant on manufacturer, call

centre reliant on call centre platform supplier. All 5 providers needed on the weekly technical conference calls

  • Very small market of telecare call centre platform suppliers, most not fully digitally

compliant with both open APIs and following recognised protocols

  • Telecare equipment suppliers trying to protect their market share, incompatibility of

hubs with peripherals

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SLIDE 10

LEARNING TO DATE

  • The complexity of the technical integration. Need for involvement of TSA.
  • Time needed to complete the testing: sand pit, live environment test kits, older

person willing to test and feedback

  • Development time was at all partners own expense.
  • The standard Pro pack not fully culturally relevant – houses vs apartments
  • Presentation of activity and activations data can be by SMS, email, app and

user interface login. Flexibility useful for informal carers but not for the call centre staff who need it all presented on their call centre software

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SLIDE 11

LEARNING TO DATE 2

  • Being one of the first local authorities to do end to end digital telecare
  • Maintaining motivation of all providers to deliver outcomes, takes time to

pinpoint faults and rectify.

  • The machine learning may not be suitable for people who self neglect –

learns increasing levels of tolerance before threshold reached

  • Understanding the false positive activations. Machine learning did result in

waking people up at 3.00am in the morning

  • Diversity of people that that have benefitted from Pro
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SLIDE 12

EVALUATION

  • Being completed by independent company
  • Logic model developed from stakeholder interviews to determine the

methodology of the evaluation

  • Undertaking semi structured interviews with Service Users and their informal

carers

  • Comparing utilisation of social care and health care before and after

installation and between intelligent and standard lifeline holders