PARR-30: a predictive model for readmission within 30 days

1. PARR-30: a predictive model for readmission within 30 days Presenter: Ian Blunt

2. Development of a predictive model for readmission within 30 days of discharge (PARR-30) • Model developed by Billings, Blunt, Steventon, Georghiou, Lewis and Bardsley • Motivation • Development • Model performance • Testing in hospitals • Conclusions

3. Why predict readmissions within 30 days? • Readmissions are costly, suboptimal health care - costs to the NHS estimated at £1.6 billion each year • DH guidance for the NHS proposes commissioners do not pay provider hospitals for emergency readmission within 30 days of a selected index elective admission • Rate of readmissions will also play an important part in monitoring health system performance, as one of the new English Public Health “outcome indicators”

4. Not first to try this, but… • Number of international 30 day models • Predictive tools built in one setting may not necessarily be accurate when used in other health care settings • Used hospital episode statistics (HES) data to develop model for NHS in England • Make PARR-30 freely available for use across the NHS in England (possibly tablet/smartphone app) See Kansagara et al JAMA 2011

5. How is PARR30 different from PARR++? • Readmission in next 30 days vs next 365 days • Tools operate in different ways, trigger different responses • Next year – longer for clinicians and care managers/coordinators to contact and engage with high-risk patients, effect behavioural change • 30 days – highest likelihood of an unplanned admission, focussing their discharge planning efforts and post-discharge support for high-risk patient • Aim for speed of low-variable models with accuracy of PARR

6. How is PARR30 different from PARR++? PARR30 Hospital provides SUS Patient nears discharge PCT runs PARR++ Risk score calculated on ward Patients selected for intervention (via GP) Any extra intervention put in discharge plan Predicts readmission in next year – PPV 65% Predicts readmission in 30 days – PPV ???%

7. Model development • Developed using 10% sample HES from April 2006 to May 2009 • Index discharges in FY 2008/09 • Readmissions within 30 days reflected 2011-12 operating framework • Logistic regressions identify variables that contributed most to predictions • Validated with split sample

8. Model development Hospital of current admission Patient age Deprivation (via post code) History of emergency admissions: Current? Last 30 days? Past year? History in the prior two years of eleven major health conditions drawn from the Charlson co-morbidity index

9. Results • The performance of the model was respectable, with a positive predictive value (PPV) of 59.2% and area under the ROC curve (“c-statistic”) of 0.70. • For the higher-risk patients (risk score > 50%), readmission rates ranged from 47.7% up to 88.7%. However, these patients only represented a small share (1.1%) of all patients analysed. Receiver Operating Characteristic Curve (ROC) for the bootstrapped central estimate (red line) and 95% confidence Intervals (shaded area)

10. Results • Predictive modelling only as effective as the intervention it is used to trigger. Providers need to know potential costs of readmission to build business case for intervention • For patients risk score > 50%, mean readmission cost per patient was £1,088. Assuming that an intervention can reduce the number of readmissions by 10% for this group, £109 per patient could be spent at breakeven

11. Testing PARR-30 in hospitals • Testing: • Is the tool easy to use? • Bedside info vs admin systems? • Does ward PPV reflect national? • Chelsea & Westminster Hospital running tool direct from their data warehouse: • Proved it can be done easily • Looking into PPV and clinical engagement • Royal Berkshire Hospital using spreadsheet version of tool on wards: • Completed by junior doctors • Test tool stored its output • Later reconciled with admin systems for analysis • Applied on four care of the elderly wards in Feb/March 2012

12. Testing PARR-30 in hospitals • Tool was used 88 times • Median time to complete 1m 41s • Median patient age was 86, mostly emergency admissions • Average 1.3 co-morbidities, max 4 • 10 patients had emergency readmission within 30 days • Low risk scores – max 39% • Em admit in last 30 days diff 10% • Em admits last year diff 20%, ±1,2 • Even split whether tool or system has more • 14% where system has diagnosis not ticked as co-morbidity • Results from using spreadsheet on the wards:

13. Conclusions • Built a predictive model using a limited set of variables that were generated from hospital episode statistics • Variables easily available from patients’ notes or from the patients themselves – can calculate from spreadsheet or in PAS • The performance of the model was respectable - highest risk patients had a 88.7% chance of hospital readmission within 30 days – but high risk patients relatively rare • Cost data suggests interventions need to be lower-cost to break even • Easily used on wards in trials - less than 2 minutes per application • Some differences in data on ward, but not huge