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Authors . Prof Jonathan Weiner, Johns Hopkins UniversitDr Klaus Lemke, Johns Hopkins UniversityDr Karen Kinder, ACG InternationalStephen Sutch, ACG International. Overview. IntroductionApplying the US ACG system to the English population and dataMethodsData preparation/cross-walksRegression modelsResultsConclusions and Recommendations.
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1. The application of Adjusted Clinical Group Predictive models to the English Population for risk adjustment, funding allocation Steve Sutch, MAppSc, BSc.
Senior Analyst, ACG International
The Johns Hopkins University On JHU faculty and a member of ACG TeamOn JHU faculty and a member of ACG Team
3. Overview Introduction
Applying the US ACG system to the English population and data
Methods
Data preparation/cross-walks
Regression models
Results
Conclusions and Recommendations
4. Introduction The current budgetary allocation formula to primary care trusts is based on capitation adjusted for demographic, socio-economic and population morbidity utilising aggregate and survey statistics.
This study tested the feasibility of applying person based morbidity data as an alternative through the application of the ACG predictive models utilising hospitals secondary care data, primary care data, pharmacy and individual resident population data up to 50 million individuals.
5. The risk measurement pyramid
6. Objective To show the feasibility of adapting a US risk assessment system to UK inpatient and GP EMR data
Morbidity and Pharmacy data
UK uses ICD10, Read/CTV3 codes, BNF/DMD
US uses IC9CM, NDC codes
adaptation of US system via cross-walks “translation” of Read/CTV3 to ICD-10/ATC codes)
7. Overview of Johns Hopkins ACG System The ACG - “Adjusted Clinical Group” system provides conceptually simple, statistically valid, and clinically relevant measures of need/risk for health services.
The grouping process has been computerized. The “grouper” and “predictive modeling” software requires diagnosis information from encounter data, electronic medical records, or insurance records.
ACGs are generally applied using all diagnoses describing the person. They do not focus on individual visits. Ideally they are derived from primary and specialty ambulatory contacts as well as inpatient
There is a comprehensive ACG “suite” of risk/case-mix measures.
8. The Johns Hopkins ACG System: An Expanding Suite of Measures
Diagnosis (ICD) based:
ADGs classify diagnoses into a limited number of clinically meaningful, but not disease-specific, morbidity groups. (For example “chronic unstable”)
EDCs classify diagnoses based on specific diseases. They represent disease markers and can be used to determine disease prevalence (For example, Type I Diabetes, w/o complications).
ACGs (Adjusted Clinical Groups) represent a single, mutually exclusive actuarial cell based on overall disease burden. (For example, 6-9 ADG Combinations, Age >34, 2 major ADGs) Can be combined to any number of Resource Utilization Bands or (RUBs)
Pharmacy based:
Rx-MG pharmacy risk markers, Rx-Morbidity Groups
9. Method (1) The data sources were linked to form a single health record for each individual, comprising of inpatient, outpatient, Accident and Emergency (Emergency Room) and primary care activity data (including pharmacy) and related costs for two years.
Crosswalks were designed to covert the primary care morbidity codes (READ codes) into ICD10 and the pharmacy codes into ATC (WHO Anatomical Therapeutic Chemical classification).
10. Method (2) The data was grouped using the ACG software with the resultant grouping variables used to form a number of predictive models.
Restrictive models, using secondary care data only
Full models, using secondary and primary care morbidity data
Combined morbidity and pharmacy models
Parsimonious models, utilising 10-80 markers (non-parsimonious 300+ markers)
The models were compared using R-squared and Mean Absolute Prediction Error (MAPE) statistics.
11. Data UK inpatient (HES) and PCT Databases
Year 1 inpatient ICD-10 clinical findings for all NHS registrants
Year 2 inpatient costs for all NHS registrants
Year 1 GP EMR data (GP clinical findings and medication prescriptions) for all residents in two selected PCTs
PCT data augmented by HES inpatient data
Year 2 “full” costs (inpatient and pharmacy) for all persons residing within PCT boundaries
12. ACG Data process Import Files
Patient File
Diagnosis data file
Pharmacy data file
Output file (s)
ACG
Analysis tables
13. Cross Walks “Adaptation” of ACG system to UK Read/CTV3 codes
READ2/3 to ICD10 (to ADG, EDC)
READ clinical findings were mapped to ICD-10 codes
BNF/READ Drugs to ATC (to RxMG)
Drug prescriptions were mapped to WHO ATC codes
Subjected to clinical review, unmapped codes
14. ACG Output Fields ACG Code, ADG Codes / Vector
EDC Codes, MEDC Codes
Resource Utilization Band
Pharmacy Cost Band
National Un/rescaled Weight
Local Concurrent weight
Hospital Dominant Count (ADG), Major ADG Count
Frailty flag(s)
Asthma, Congestive Heart Failure, COPD, Chronic Renal Failure, Depression, Diabetes, Hyperlipidemia, Hypertension, Ischemic Heart Disease, Low Back Pain
ACG Predictive Model fields
Total Cost RI, Probability High Cost, Pharmacy Cost RI, Probability High Pharmacy Cost,
15. Data distribution
16. Results
17. Results
18. Results The results for the morbidity models showed that the significant improvement in prediction over age/sex adjusted models, with increased improvement for the combined model (including pharmacy).
The addition of primary care findings did not add to the explanatory power, but no primary care costs (except pharmacy) were applied
The results were also compared to US data and showed higher predictive results for the English data.
19. Conclusion The ACG grouping system can be applied to UK morbidity and pharmacy data from both secondary and primary care activity, with the predictive modelling demonstrating the strength of utilising such data for future budgetary allocation.
A full costed model with primary care costs as well as secondary costs should allow for the greater realisation of benefits and allow the examination of a “total health system” for true World Class Commissioning.
This study provides an initial approach to the introduction of funding allocation for chronic care management programs.
20. ACGs reliably accept UK data inputs Diagnosis based (ICD-9 and ICD-10) has been converted to Read / CTV3 system with high level of completeness.
Has been tested with GPRD (1.2 M) and UK morbidity survey and GP electronic medical records with and data from 5 PCTs (over 600K patients)
Has been tested with secondary data (Over 12 million HES)
We have completed conversion of Rx-MGs (pharmacy risk measures) to WHO ATCs and Read/CTV-3
21. Web Site / Contacts ACG Web Site:
www.acg.jhsph.edu
Contacts:
Dr. Karen Kinder-Siemens, Director International ACG Operations
(kkinder@jhsph.edu)
Steve Sutch (UK ACG Sr. Analyst)
(steve@sutchconsulting.com)
Professor Jonathan Weiner
(jweiner@jhsph.edu)