1. Dr Foster High-Impact Users Analysis October 05: Paper supplied by Mansfield & Ashfield PCTs Note Dr Foster use “High Impact” as synonomous with “Frequent Flyer” (FF) Definitions –High impact patients: basically  3 emergency admissions / year (or 3+ spells in 12 months) –Very high impact patients:  9 emergency admissions between Apr 1 st 2001 and Mar 31 st 2003 –ACS (Ambulatory Care Sensitive) high-impact users: as per high impact but with a PDX belonging to an ICD classification (don’t know if this is a Dr Foster classification ??)

2. ACS classification Other group names include: Asthma, Congestive Heart Failure, Diabetes Complications, COPD, Angina, Iron Deficiency Anaemia, Hypertension, Nutritional Deficiencies, Dehydration and Gastroenteritis, Pyelonephritis, Peforated/Bleeding Ulcer, Cellulitis, Pelvic Inflammatory Disease, Ear Nose and Throat Infections, Dental Conditions, Convulsions and Epilepsy, Gangrene

3. Other Aspects of Analysis 01/02 to 03/04 use HES; unique ID is HESID 04/05: use NWCS – unique id  dob,sex,pcode Use routinely available data off HES like –Age, Sex, Source of Admssn, Ethnicity Append other info from external datasets –Mosaic, Deprivation Quintile (based on IMD 04), Charlson Index of Comorbidity etc, HRG plus v3.5 tariffs (for costing purposes) Analysis focuses on –Patients, spells, superspells (join up spells when there is a transfer to another provider), beddays, cost based on HRG tariffs, breakdown by practice

4. Modelling Steps Built a logistic regression model for 1 st admissions in 03/04 Outcome variable = FF patient (Yes/No; 1/0) Predictor Variables These factors came out as significant predictors of FF patients

5. All these predictors significant Most significant: previous emergency admission (not including any spell included in the 3 spells in 12 months of FF patients) Next most: Charlson index of comorbidity Least important: source of admission, sex

6. Modelling Steps predictive model built and validated Using information on the predictor variables for each patient the probability of becoming a FF can be calculated Then applied to a dataset where FF status not known Choose arbitrary thresholds –eg if probability (FF)  0.3  class as FF –eg if probability (FF) < 0.3  class as not a FF

7. Results for England

8. sensitivity means: if you are a FF what is the chance the model predicts you are a FF specificity means: if you are not a FF what is the chance the model predicts you are not a FF positive predictive value means: if the model predicts you are a FF; what is the chance you really are a FF