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Bupa Health Dialog NHS Wales Predictive Model Review 24 th February 2010.

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Presentation on theme: "Bupa Health Dialog NHS Wales Predictive Model Review 24 th February 2010."— Presentation transcript:

1 Bupa Health Dialog NHS Wales Predictive Model Review 24 th February 2010

2 Bupa Health Dialog Agenda Model Development Methodology Model Performance Potential Impact Discussion/Next Steps 2

3 Bupa Health Dialog Development Methodology Modelling population of 298,077 patients 51 GP practices Split sample methodology Logistic regression to model unique relationship between independent variables in two years of patient history and dependent variable (outcome) in third year Inclusion of lag period Demographics Diagnosis Drugs Utilisation Procedures 25% Random ‘Validation’ Sample 50% Random ‘Development’ Sample TEST Anonymised input data sets (IP, OP, GP Practice) 25% Random ‘Test’ Sample 3

4 Bupa Health Dialog Operational Approach Year Following Prediction Historical Year 2 Historical Year 1 Prediction Predict any emergency admission next 12 months Dependent Variable Identify diagnoses, procedures, drugs IP, OP, and GP data for prior 24 months Independent Variables Lag period 3 month 4

5 Bupa Health Dialog Modelling Steps Quality Check (QC) the IP, OP and GP files Identify patients to include in the model building – ‘membership’ Identify patients with emergency admission outcome – dependent variable Extract variables for inclusion in the model from IP, OP and GP files – ~1300 variables created Randomly partition the data into Development (50%), Validation (25%) and Test (25%) samples Data mining to identify significant variables for entry into the model Logistic regression Variable stabilisation and optimisation of model Reporting of model performance on Test sample – Data presented in the comparative slides following are for the PRISM model vs. the Combined Model run on the same Welsh patient test data set over the same time period to show benchmarked model performance 5

6 Bupa Health Dialog ‘Member’ Selection Creating the Model Development Population Initial cleaning round registration date is prior to date of birth deceased date is prior to registration date removed date is prior to or equal to registration date missing pseudonymised NHS number duplicated records History Lag Outcome 1stMay2004 30thApril2006 1stAug2006 31stJul2007 Continuous Membership Patients were only included if they had continuous membership in any of the 51 practices 8 day gap was allowed for practice-to-practice transfer Final membership total 298,077 from starting population of 534,955 (includes deceased) 6

7 Bupa Health Dialog Defining the Dependent Variable CodeDescription 21 A & E or dental casualty department of the health care provider 22 GP: after a request for immediate admission has been made direct to a hospital provider (i.e. not through a Bed Bureau) by a General Practitioner or deputy 23 Bed bureau 24 Consultant clinic of this or another health care provider 25 Domiciliary visit by Consultant 27 Via NHS Direct Services 28 Other means, including admitted from the A & E department of another provider where they had not been admitted 29 ’29’ is an internally derived code meaning Emergency Transfer from another hospital. Admission Method “81” and Intended Management of “8” Admission Method codes 25, 27 and 29 are specific to Wales (i.e., not used in England) and contribute to the higher overall emergency admission rates than we are used to seeing in England. An admission method with a HES value of (“21”, “22”,”23”,”24”,”25”,”27”,”28” or “29”) and a patient classification of ordinary admission (“1”) 7

8 Bupa Health Dialog Variables in the Model - Demographic Selected from ~1300 variables tested Included Codes Variable Category Variable Name DescriptionBeta Co- efficient RangeLook back Read 2ICD-10OtherClinical Rationale BaselineIntercept -3.1725N/A DemographicDEM_AgeAge-0.0428Capped at 100 yrs Current Age Age is an important indicator of clinical risk DemographicDEM_Age _squ Age squared term0.0005Capped at 100 yrs Current Age Age is an important indicator of clinical risk DemographicDEM_Gen der Gender ‘1’=male ‘0’=female 0.0906N/A Gender is an important indicator of clinical risk 8

9 Bupa Health Dialog Variables in the Model - Diagnoses Selected from ~1300 variables tested Included Codes Variable Category Variable NameDescriptionBeta Co- efficient RangeLook backRead 2ICD-10Variable Category Variable Name DiagnosesGP_Neurosis_di sord Neurotic, personality and other nonpsychotic disorders 0.179724 months Persons with mild to moderate depression and anxiety disorders are significantly higher users of health services, both for specific mental health issues and for issues related to physical health DiagnosesGP_Poisoning_d isord Poisoning0.780624 months Intentional self poisoning is associated with mental health disorders, and thus with service usage. Unintentional poisoning is associated with poor socio-economic circumstance, in turn a predictor of overall clinical risk, as well as of poorer access to routine and preventative services and hence a greater emergency services utilization 9

10 Bupa Health Dialog Variables in the Model - Diagnoses Selected from ~1300 variables tested Included Codes Variable Category Variable Name DescriptionBeta Co- efficient RangeLook back Read 2ICD-10Variable Category Variable Name DiagnosesGP_GI_Diso rder GI disorders0.146524 months Evidence from our own RCTs show significant (impactable) increase in overall services usage from GORD and IBS. Persons with IBD may also face acute exacerbations DiagnosesGP_Sprain_ disord Sprains and strains of joints and adjacent muscles 0.259024 months Sprains and strains may lead to mobility disorders or problems in self care; particularly among older people DiagnosesGP_Mental_ disord Mental and behavioural disorders 0.228024 months Persons with mild to moderate depression and anxiety disorders are significantly higher users of health services, both for specific mental health issues and for issues related to physical health 10

11 Bupa Health Dialog Variables in the Model - Drug Selected from ~1300 variables tested Included Codes Variable Category Variable NameDescriptionBeta Co- efficient RangeLook back Read 2ICD-10Variable Category Variable Name DrugGP_Cephalospo rins_rx_sqr Cephalosporins & Cephamycins 0.1428Square root 24 months Association with chest and urinary infections which can be associated with debility or ltcs in older people DrugGP_Corticostero id_rx Corticosteroid Clinical Use 0.213224 months Associated with asthma, COPD, Inflammatory bowel diseases etc. diabetogenic DrugGP_Diuretics_rxLoop Diuretics0.208524 months CHF, CKD, Hypertension, diabetogenic DrugGP_Macrolides _rx_sqr Macrolides0.0933Square root 24 months Chest infection, penicillin allergy DrugGP_Analgesics_ rx Narcotic Analgesics 0.169624 months End of life, lower back pain, arthropathies, chronic pain… all strong predictors of service usage 11

12 Bupa Health Dialog Variables in the Model - Drug Selected from ~1300 variables tested Included Codes Variable Category Variable Name DescriptionBeta Co- efficient RangeLook back Read 2ICD-10Variable Categor y Variable Name DrugGP_Antid epressan t_rx Other Antidepressant Drugs 0.182824 months Persons with mild to moderate depression and anxiety disorders are significantly higher users of health services, both for specific mental health issues and for issues related to physical health. Also used for chronic pain and neuropathies/neuralgias DrugGP_Penci llin_rx Penicillinase Res Penicillins 0.056124 months Immunosupression, nosocomial infection, chest infection DrugGP_Sulp honamid es_rx Sulphonamides & Trimethoprim 0.146024 months UTI,HIV, Crohn’s, UC DrugGP_Ulcer _rx Ulcer-Healing Drugs0.104924 months GORD, PUD. Significance independent of GI disease, possibly due to coding issues DrugGP_Vita minB_rx Vitamin B Group0.297624mon ths Alcoholism, Pernicious Anaemia, malabsorption 12

13 Bupa Health Dialog Variables in the Model - Prescribing Selected from ~1300 variables tested Included Codes Variable Category Variable Name DescriptionBeta Co- efficient RangeLook back Read 2ICD-10Variable Category Variable Name PrescribingGP_Polyp harm Polypharmacy0.151812 months Associated with increased adverse reactions and falls and fractures PrescribingGP_Polyp harm_sq u Polypharmacy squared term -0.0062Squared12 months Associated with increased adverse reactions and falls and fractures 13

14 Bupa Health Dialog Variables in the Model – Chronic Condition Selected from ~1300 variables tested Included Codes Variable Category Variable Name DescriptionBeta Co- efficient RangeLook back Read 2ICD-10Variable Category Variable Name Chronic Condition GP_Num _cc Total number of chronic conditions 0.1235All availabl e GP Data Multiple comorbidities greatly increase unscheduled admission risk Chronic Condition GP_Chf_ id CHF (LTC)0.1695All availabl e GP Data Heart failure is a strong predictor of clinical risk Chronic Condition GP_Copd _id COPD (LTC)0.1053All availabl e GP Data COPD is a strong predictor of clinical risk Chronic Condition GP_Epile psy_id Epilepsy (LTC)0.3869All availabl e GP Data Epilepsy is a strong predictor of clinical risk 14

15 Bupa Health Dialog Variables in the Model – Clinical findings Selected from ~1300 variables tested Included Codes Variable Category Variable NameDescriptionBeta Co- efficient RangeLook backRead 2ICD-10Variable Category Variable Name Clinical findings GP_Current_ smoker Patient has stated that they are a current smoker 0.3043All available GP Data Smoking is an important risk predictor Clinical findings IP_CerebralP _dx Inpatient admission with diagnosis Cerebral Palsy and other paralytic syndromes 0.671124 months Persons with CP have complex needs and may have challenges with regard to self care Clinical findings IP_Circulator y_dx Inpatient admission with diagnosis Symptoms and signs involving the circulatory and respiratory systems 0.295924 months This includes acute MI, pneumonia, acute severe asthma, acute heart failure and many high care need conditions Clinical findings IP_Digestive _dx Inpatient admission with diagnosis Symptoms and signs involving the digestive system and abdomen 0.297824 months Evidence from our own RCTs show significant (impactable) increase in overall services usage from GORD and IBS. IBD and PUD patients are also high users of unscheduled care Clinical findings IP_Urine_dxInpatient admission with diagnosis Abnormal findings on examination of urine, without diagnosis 1.132724 months UTI, haematuria Clinical findings IP_Alcohol_d x Inpatient admission with Alcohol related diagnosis 0.869124 months Alcohol related head injury 15

16 Bupa Health Dialog Variables in the Model - Inpatient Selected from ~1300 variables tested Included Codes Variable Category Variable Name DescriptionBeta Co- efficient RangeLook back Read 2ICD-10Variable Category Variable Name Inpatient Utilisation IP_Emer g_admit 12 Emergency admissions in last 12 months of history period 0.444712 month s Previous emergency admission is a strong predictor of future emergency admission Inpatient Utilisation IP_Non _emerg _admit Non-emergency admission 0.098912 month s Elective admissions are also a predictor of emergency admissions Inpatient Utilisation IP_Dayn ight Inpatient day & night cases 0.723812 month s Ambulatory admissions are also predictive of emergency admissions Inpatient Utilisation IP_Emer g_admit 12 Emergency admissions in last 12 months of history period 0.444712 month s Previous emergency admission is a strong predictor of future emergency admission 16

17 Bupa Health Dialog Variables in the Model – OP and Deprivation Selected from ~1300 variables tested Included Codes Variable Category Variable Name DescriptionBeta Co- efficient RangeLook backRead 2ICD-10Variable Category Variable Name Outpatient Utilisation OP_Referr al_Emerge ncy OP visit following an emergency admission 0.195924 months Follow-up activity is an additional indicator of emergency admission risk Outpatient Utilisation OP_Referr al_GP OP visit with referral from a GP 0.104224 months Persons with illnesses which require referral are at higher risk of urgent care need Outpatient Utilisation OP_Appoin tment OP visit with outcome 'Another appointment given' 0.176524 months Follow-up activity is an additional indicator of emergency admission risk Deprivation Index DEM_Depr ivation Deprivation0.0055Curren t Dep Index There is strong evidence for an association between deprivation, health need and urgent service usage 17

18 Bupa Health Dialog Agenda Model Development Methodology Model Performance Potential Impact Discussion/Next Steps 18

19 Bupa Health Dialog Model Performance Comparison with the Combined Model (England) on Test sample Test Sample N = 74,114 The PRISM model consistently outperforms the Combined Model (CM) when looking at specific cutpoints by ‘numbers’ of patients 19

20 Bupa Health Dialog Model Performance Comparison with Combined Model on Test sample Test Sample N = 74,114 20

21 Bupa Health Dialog PRISM Model vs. Combined Model 0.5% segment – 370 patients Test Sample N = 74,114 The PRISM model identifies a slightly younger Very High Risk population than the CM; LTC prevalences are generally higher except hypertension 21

22 Bupa Health Dialog Agenda Model Development Methodology Model Performance Potential Impact Discussion/Next Steps 22

23 Bupa Health Dialog Using segmentation and evidence-based clinical quality indicators to target impactable patients Risk Segment Very High (Top 0.5%) High (1%-5%) Moderate (6%-20%) Low (Bottom 80%) Number of Patients 470 4,230 14,100 75,273 Total = 4,230 LTC = 2,582 CHF = 439 Gap = 290 High Risk CHF Beta Blocker Gap ‘Campaign’ 23

24 Bupa Health Dialog Opportunity to Impact Admissions A segmentation approach, using multiple commissioning and intervention strategies aligned to risk, can significantly impact emergency admissions © 2008 Health Dialog UK Ltd – Commercial in Confidence 24

25 Bupa Health Dialog Agenda Model Development Methodology Model Performance Potential Impact Discussion/Next Steps 25


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