1. Computer Assisted Evaluation of Clinical Data Quality Nordic Casemix Conference 4.6.2010 Olafr Steinum, Sequelae AB Seppo Ranta, Datawell Oy

2. 2 Introduction  Reported health care information is widely used by authorities For health care planning For quality analyses For reimbursement For science and research  It is of paramount importance that the reported health data are correct and valid  Quality assurance is needed Coding audits Automatized controls

3. 3 Datawell DRG QA for data quality assurance  DRG QA is a Datawell product  Uses three different logics for data quality evaluation 1. Rules and reference databases (e.g. diagnosis codes) used for NordDRG grouping 2. Evaluation of the order of diagnoses (which one is the primary diagnosis, which are secondary diagnoses) inferrred from large statistical database (Normalization) 3. Clinical Validation Rulebase (CVRB) created and maintained by Sequelae AB  The software can be used as part of encoder system for immediate feedback of coding quality standalone system for evaluation of organization data quality benchmarking system comparing several peer organizations

4. 4 Datawell DRG QA  DRG QA is a Datawell product ****  Uses three different logics for data quality evaluation 1. Rules and reference databases (e.g. diagnosis codes) used for NordDRG grouping 2. Evaluation of the order of diagnoses (which one is the primary diagnosis, which are secondary diagnoses) inferrred from large statistical database (Normalization) 3. Clinical Validation Rulebase (CVRB) created and maintained by Sequelae AB  The software can be used as part of encoder system for immediate feedback of coding quality standalone system for evaluation of organization data quality benchmarking system comparing several peer organizations Sequelae AB Is a joint colloboration between Emendor Consulting AB, (Staffan Bryngelsson) and Olafr Steinum (diaQualos AB) Gunnar Henriksson (DRG Henriksson AB) Sequelae AB Is a joint colloboration between Emendor Consulting AB, (Staffan Bryngelsson) and Olafr Steinum (diaQualos AB) Gunnar Henriksson (DRG Henriksson AB)

5. 5 Datawell DRG QA  DRG QA is a Datawell product  Uses three different logics for data quality evaluation 1. Rules and reference databases (e.g. diagnosis codes) used for NordDRG grouping 2. Evaluation of the order of diagnoses (which one is the primary diagnosis, which are secondary diagnoses) inferrred from large statistical database (Normalization) 3. Clinical Validation Rulebase (CVRB) created and maintained by Sequelae AB  The software can be used as part of encoder system for immediate feedback of coding quality standalone system for evaluation of organization data quality benchmarking system comparing several peer organizations

6. 6 PatIdDg-aDg-dPrLOSAgeDischgSex 132133134HOMEM 43242H10.1J80WX101154HOMEN 43242 F0289 E756 GD1BD63HOSPN 64243V02.0241HOMEN 34212O75.7MAF00434HOMEM DRG QA – An Example of Indicator Calculation Logic Age not within acceptable limits External cause code as principal diagnosis Input data set Erroneous ICD-10 code Missing principal diagnosis PatIdDg-aDg-dPrLOSAgeDischgSex 132133134HOMEM 43242H10.1J80WX101154HOMEN 43242 F0289 E756 GD1BD63HOSPN 64243V02.0241HOMEN 34212O75.7MAF00434HOMEM Local procedure code Mismatch of diagnosis and gender Validations

7. 7 DRG QA Pilot Benchmark Database  Seven Hospital Districts in Finland DRG QA Database contains patient cases from the Ecomed KPP databases from 2008 Data source: Ecomed KPP used in the 7 hospitals  Three County Councils in Sweden DRG QA Database contains all patient visits and stays from 2008 Data source: Patient Administrative Systems in corresponding county councils  Number of patient cases Finland n = 4.928.113 Sweden n = 4.332.206

8. 8 Hospital Districts’ Ecomed KPP databases (FI), or similar data retrieval from Patient Administrative Systems (SE) etc. County Council C DRG QA Database Formation Process Datawell DRG QA ETL Ecomed DRG QA Database Ecomed Analyzer Analysis of Data Quality Reporting County Council B County Council A District C District B District A Datawell DRG QA Indicator Calculation Data format transformations: hospital code  common code mappings Calculation of DRG grouping indicators DRG normalization Calculation of CVRB matching Includes refence population data (1-year intervals) for standardization

9. 9 Results from the DRG QA Pilot Benchmark Database were presented in the meeting.

10. 10 The classification of diagnosis (ICD-10)  A complex system for collecting data for statistics Many axes Many rules Explicit rules Rules expressed in the Tabular volume in connection to code categories Rules assumed, but not explicitely expressed  Clinical validation rule base - CVRB A collection of identified rules

11. 11 Rate of Z51.1 Chemotherapy session as Principal or Secondary diagnosis. Swedish county councils 2008 Data from Swedish National Patient Registry Principal dx Secondary dx

12. 12 Code not to be used Not to be used for children < 15 years Ought not to be used for children < 15 yrs Not to be used in inpatient care Ought not to be used in inpatient care Rare code inpatient care Must be combined with code2 Ought not to be combined with code2 Ought not to be used as principal dx Not to be used as secondary dx Ought not to be used as secondary dx etc. © Sequelae AB Some examples of CVRB Rules 2009

13. 13 Distribution of CVRB violation in test database (10 provinces) CVRB violation rule

14. 14 CVRB Violation rules Ought not to be used as principal dx Not to be used as secondary dx

15. 15 Information Process and the Identified Sources of Quality Failure Human-Computer interface Feeding of structured information into the PAS Processing rules and logics of the information systems Usability and maintenance of national code systems (ICD, NCSP, DRG etc.) Current transversal study of the information process Entry of data Processing of data Utilization of data and information Code systems

16. 16 Information Process and Benefits of Datawell DRG QA Immediate feedback of coding results to coding personnel Information on organization data quality for focusing education and other corrective actions. Benchmarking data quality with peer organizations. Entry of data Processing of data Utilization of data and information Code systems Reports of data quality incorporated with other reporting

17. 17 Data which nobody is using has a quality that nobody wants Thank you!