Integrated Intelligence and system modelling in Kent Dr Abraham P George Consultant / Asst Dir in Public Health abraham.george@kent.gov.uk
Today s presentation Context and objectives Public Health Kent work till date Case discussion community bed modelling project in DGS CCG Challenges faced Other projects
Public Health involvement till date Work started in 2012 QIPP LTC programme Whole population profiling using risk stratification Delivery of national YOC programme in Kent - implementation at sub Kent / CCG level Contribution to national guidance and reports eg. designing linked datasets by MONITOR, NHS IQ Contribution to Kent Pioneer and BCF plans Use of linked datasets for other purposes eg. system modelling, service evaluation Currently working with local intelligence in developing whole system intelligence dashboards, mapping and linking other non NHS data
Community beds modelling project for Dartford Gravesham & Swanley CCG Better Care Fund - challenge to reduce non-elective admissions by 10% at the local acute NHS Trust in 2014/15 with a further 5% reduction in 2015/16. Need to understand patient demographic who do not require hospital admission but may require additional / enhanced community support Review the community bed availability and the workforce around primary and social care in order to ensure admissions are sustainably reduced Develop and test a model to evaluate the flow of patients through the existing system and forecast future community bed capacity using integrated discharge teams (IDTs) Still work in progress
Methodology Datasets used SUS A&E & inpatient dataset, Community Beds dataset 3yr period covered 2011/12 2013/14 DGS CCG patients attending Darent Valley de-identified at source, linked thru pseudonymised NHS numbers IThink Software used for modelling referral pathway into community beds Integrated dataset revealed significant proportion of hospital discharges could not be linked to community beds admission (non matching of dates) Datasets were analysed separately to generate assumptions for the model
Baseline analysis DGS only A&E patients 194,801 A&E attendances in the period, an average of 64,934 a year These attendances were from 96,821 patients, a ratio of 2 attendances per patient This computes as a weekly A&E attendance of 1,249, or an average of 178 per day (269 inc Bexley and others) Of these 194,801 attendances, 54,237 resulted in admission to a hospital bed, 18,079 a year 27.8% are admitted to a hospital bed as an emergency 347 a week The remaining 72.2% are discharged elsewhere, either with or without treatment
Baseline analysis - inpatients Patients admitted to hospital beds as emergencies via A&E accounted for 276,124 occupied bed days in the period, an annual average of 92,041 days The average length of stay for these admissions was 4.9 days 54,327 admissions were generated by 33,836 patients
Baseline analysis community beds 2,258 admissions to DGS community beds in the 3 yr period, an average of 753 admissions a year Of these, 84% (1896 632 annually average) via Darent Valley hospital A further 199 admissions (66 per year) via another acute hospital: (10.5% of admissions to community beds during the period) Of the 45,368 bed days in this period, 39,881 (88%) were for admissions via Darent Valley A&E
Age/gender distribution for DGS CCG patients admitted to DGS community beds via Darent Valley hospital A&E, 2011/12 2013/14 (pooled)
Destination on discharge from community bed
Model for patient flows through hospital for DGS patients aged 18+ via Darent Valley hospital A&E department Basic model 919 patients per week attend A&E
Model for patient flows through hospital for DGS patients aged 18+ via Darent Valley hospital A&E department Basic model 68% (625 per wk) are seen, assessed and treated then discharged from A&E
Model for patient flows through hospital for DGS patients aged 18+ via Darent Valley hospital A&E department Basic model 32% (294) are referred to an inpatient bed
Model for patient flows through hospital for DGS patients aged 18+ via Darent Valley hospital A&E department Basic model 284 of these are discharged out of the system
Model for patient flows through hospital for DGS patients aged 18+ via Darent Valley hospital A&E department Basic model This scenario equates to a requirement for 423 inpatient beds for emergencies, assuming 95% bed occupancy 284 of these are discharged out of the system
Model for patient flows through hospital for DGS patients aged 18+ via Darent Valley hospital A&E department Basic model 13 are referred to a community bed
Model for patient flows through hospital for DGS patients aged 18+ via Darent Valley hospital A&E department Basic model An average 1.8 patients per week referred to community bed from other sources
Model for patient flows through hospital for DGS patients aged 18+ via Darent Valley hospital A&E department Basic model 15 patients a week discharged
Model for patient flows through hospital for DGS patients aged 18+ via Darent Valley hospital A&E department Baseline model This scenario equates to a requirement for 45 community beds for referrals from all sources, assuming 95% bed occupancy
Model for patient flows through hospital for DGS patients aged 18+ via Darent Valley hospital A&E department Basic model 68% of A&E attendees sent home or out of system 1.8 patients per week referred from other source 919 patients per week attending A&E 32% of A&E attendees referred to IP bed 13 patients per week transferred to community bed
Model for patient flows through hospital for DGS patients aged 18+ via Darent Valley hospital A&E department Enhanced model 68% discharged out of system 919 patients per week attending A&E Enhanced model sees 10% of A&E attendances diverted to IDT 22% referred to acute bed
Model for patient flows through hospital for DGS patients aged 18+ via Darent Valley hospital A&E department Enhanced model Around 5% of IDT patients referred to community bed 919 patients per week attending A&E
Model for patient flows through hospital for DGS patients aged 18+ via Darent Valley hospital A&E department Enhanced model 919 patients per week attending A&E Remaining IDT patients are discharged out or referred To IP bed
Model for patient flows through hospital for DGS patients aged 18+ via Darent Valley hospital A&E department Enhanced model scenario based on 10% referral rate to IDT 919 patients per week attending A&E ~ 15 patients per week admitted to community bed Initial scenario equates to a requirement for 421 inpatient beds for emergencies, assuming 95% bed occupancy This scenario equates to a requirement for 49 community beds, assuming 95% bed occupancy
Inpatient and community bed requirements for DGS patients attending Darent Valley hospital Percentage discharged home or elsewhere Percentage referred directly to acute bed Percentage referred to IDT Inpatient bed requirement Community bed requirement 68% 22% 10% 421 49 68% 20% 12% 419 52 68% 18% 14% 418 55 68% 16% 16% 417 58 68% 14% 18% 415 60 68% 12% 20% 414 63 68% 7% 25% 410 71 68% 2% 30% 407 78 68% 0% 32% 406 81
Key Challenges Data quality Good support in principle from provider organisations Data sharing arrangements based on previous work However quality / completeness of data variable across different organisations Data linkage between hospital discharge to community bed admission significantly limited NHS numbers not routinely recorded in the IDT dataset Commissioner buy-in Still some way off in application toward CCG plans
Other projects under consideration Estimate capacity for mental health beds for older people across Kent Working with SECSU and KMPT Integrated dataset using mental health service and beds data, hospital data etc. However problems with data linkage being System model currently being designed and tested Modelling demand for CAMHS service Model currently under development
Thank you Acknowledgements Su Xavier Consultant in Public Health DGS CCG Del Herridge Public Health Product & Data Manager, Kent & Medway Public Health Observatory Julian Barlow Senior Public Health Intelligence Analyst, Kent & Medway Public Health Observatory