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Dr Foster High-Impact Users Analysis

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)

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Dr Foster High-Impact Users Analysis

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  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 1st 2001 and Mar 31st 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 1st 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

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