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“Identifying strokes and stroke mimics using mobile technology”

ADHERENCE TO THE PRE-HOSPITAL STROKE PATHWAY IN GREATER MANCHESTER:. Analysis of a large HASU dataset and development of a smartphone app to improve compliance. “Identifying strokes and stroke mimics using mobile technology”. Kate Evans – d2 Digital By Design Ltd

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“Identifying strokes and stroke mimics using mobile technology”

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  1. ADHERENCE TO THE PRE-HOSPITAL STROKE PATHWAY IN GREATER MANCHESTER: Analysis of a large HASU dataset and development of a smartphone app to improve compliance. “Identifying strokes and stroke mimics using mobile technology” Kate Evans – d2 Digital By Design Ltd Chris Ashton – GM Stroke Operational Delivery Network

  2. Identifying strokes and stroke mimics using mobile technology CONSULTATION & PRE-SOLUTION SCOPING

  3. PATHWAY DILEMMAS AND DECISIONS Identifying strokes and stroke mimics using mobile technology • FAST status? • Do I know the area? • Nearest receiving HASU or DSC unit? • Operational hours? • Is it open? • Can I get there before closure? • Any exclusions present? • FAST symptoms resolved/resolving? • Time window? • Medications? • How long on scene? • Pre alert? • Confusion with other pathways! • Clinical Support in uncertainty!

  4. EVALUATION OF QUESTIONS Identifying strokes and stroke mimics using mobile technology Q1. Confidence and knowledge Pre solution scoping – clinicians were asked a number of questions about the existing pathway Q2. Opening/accepting times for GM HASU’s Given 4 options – 44% (14/32) correctly stated 06:45-22:45 Q1. To score their own confidence and knowledge of the GM stroke pathway using a Likert scale (1=poor/10=perfect) Q3. Number of exclusions in the GM Stroke Pathway Q2. What the opening/accepting times for Stepping Hill HASU service and Fairfield General HASU service? 3% (1/32) correctly stated 9 exclusions Q3. How many exclusions does the Greater Manchester Stroke Pathway have? Q4. Correct destination choice for a patient presenting <4 hours onset, with FAST positive symptoms and number of post codes (given 3 HASU options, asked to choose if either confident or a guess) Q4. Which hospital would be the correct destination choice for a patient presenting <4 hours onset and with FAST positive symptoms in Clayton Vale, Droylesden, M43? (Repeated with 2 other postcodes) 19% recorded SRFT confidently, 28% recorded SRFT as a guess and 53% recorded the wrong HASU

  5. Identifying strokes and stroke mimics using mobile technology CHC Dataset: pre-hospital assessment • 6000+ cases included in the data • Salford Royal (SRFT) arrivals only • 18 month period (Sep 2015 to Feb 2017) • Cases brought by ambulance on the stroke pathway • All ambulance patient report form (PRF) observations and relevant assessment notes extracted • Ambulance record cross matched with final hospital diagnosis • Other SRFT dataset measures recorded and cross matched including LOS, imaging used, treatment received etc.…..

  6. Identifying strokes and stroke mimics using mobile technology SRFT attendances only Non SRFT catchment area patients only Airway compromised not measured Pre alert adherence not measured >48 hours since onset not measured 7 patients in Wrong HASU group repeated in FAST Negative group 22 patients in Wrong HASU group repeated in exclusion group CHC Dataset: compliance results

  7. Identifying strokes and stroke mimics using mobile technology DESIGN AND DEVELOPMENT OF PRE HOSPITAL PATHWAY AID (PHPA) APP

  8. Identifying strokes and stroke mimics using mobile technology How did the idea develop? Invited to attend “AHSN: Mobilise the NHS hack day” Partnership included the GMSODN Coordinator, a NWAS Paramedic, a SRFT Neurologist and d2 Digital Selected to receive support from Global Digital Exemplar program Aim was to reduce HASU mimic admittances and aid pathway compliance Designed to support all pre-hospital pathways but initial development/testing focused on the GM Stroke Pathway

  9. Identifying strokes and stroke mimics using mobile technology Aims and Objectives Reproduce the GM pre-hospital stroke pathway in a digital format Direct paramedics to nearest open HASU or ED Prompt to pre-alert (< 4 h) Nearest hospital finder embedded Provide quick and simple decision support tools Make system easy to replicate to provide support tools for other hyper acute pathways Enable NWAS and Trusts to see live use of the app / case data that can help to plan services more effectively across the group and region

  10. Identifying strokes and stroke mimics using mobile technology Initial Pilot Beta version trialled (clinicians volunteered to test the app) Phase 1 – 17th July ’17 to 19th Sept ‘17 (2 months) Phase 2 – 20th Sept ‘17 to 23rd Nov ‘17 (2 months) Email invite sent to clinicians 19 Advanced Paramedics / 55 Paramedics & Technicians agreed to take part (total 74) 44 Registered & actively used the app Live mode used for true incidents

  11. Identifying strokes and stroke mimics using mobile technology Pilot data: Phase 1 Results Green cards = Number diverted to nearest appropriate A&E in relation to incident Total = 32/74 (43%)

  12. Identifying strokes and stroke mimics using mobile technology Pilot results – phase 1 v’s phase 2 Although fewer numbers of clinicians used the app in Phase 2, the percentage outcomes were like for like

  13. Identifying strokes and stroke mimics using mobile technology PHPA APP TO DATE

  14. Identifying strokes and stroke mimics using mobile technology CONTINUED WORK Where are we now & where to go next? Measure the assessment of impact on launch Prospectively evaluate the effect on pathway breaches Assess clinician usage, decisions made and any impact this has on hospital attendances React collaboratively with NWAS and stakeholders when evaluating the impact Future roll out across the North West Expand deployment in other geographical areas Explore development of the app with pathways other than stroke From the data so far it suggested that pathway breaches occur regularly and that the PHPA app may improve pathway compliance The app was launched on 9th April 2019 within NWAS for all GM pre-hospital vehicles & clinicians to have access via an NWAS device Since the 9th April up to 9th May 2019 the app has been visited by clinicians a total of 751 times – once the app has been in deployment for an increased amount of time we will be analysing this data in more depth particularly around pathway decisions

  15. Identifying strokes and stroke mimics using mobile technology Connected Health Cities team, in conjunction with partners, stakeholders and paramedics involved in the pilot, made a short promotional video about the app which can be found here: Is it useful? “The app comes across as a brilliant, simple and effective idea that reduces the need for paramedics to carry around pieces of paper and allows them to instantly decide on the most appropriate place of care….. …having experienced a stroke myself, I vividly remember the paramedic on that day trying to work out and discuss where he should take me. This app would have made that decision clearer and faster ” Ann Bamford, stroke patient and chair of the (GMSODN) patient and carer group https://vimeo.com/267198349

  16. Thank you for listening kate.evans@d2digital.co.uk christopher.ashton@srft.nhs.uk THE POWERPOINT PRESENTATION

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