Computer Assisted Evaluation of Clinical Data Quality Nordic

Computer Assisted Evaluation of Clinical Data Quality Nordic Casemix Conference 4. 6. 2010 Olafr Steinum, Sequelae AB Seppo Ranta, Datawell Oy

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

5 Datawell DRG QA Ø DRG QA is a Datawell product Ø Uses three different logics for data quality evaluation 1. 2. 3. Ø The • • • Rules and reference databases (e. g. diagnosis codes) used for Nord. DRG grouping Evaluation of the order of diagnoses (which one is the primary diagnosis, which are secondary diagnoses) inferrred from large statistical database (Normalization) Clinical Validation Rulebase (CVRB) created and maintained by Sequelae AB 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 DRG QA – An Example of Indicator Calculation Logic Input data set Pat. Id Dg-a Dg-d Pr 13213 LOS Age Dischg Sex 3 134 HOME M 43242 H 10. 1 J 80 WX 101 1 54 HOME N 43242 F 0289 E 756 GD 1 BD 6 3 HOSP N 64243 V 02. 0 2 41 HOME N 34212 O 75. 7 4 34 HOME M MAF 00 Validations Missing principal diagnosis Pat. Id Age not within acceptable limits Local procedure code Dg-a Dg-d Pr 13213 Erroneous ICD-10 code LOS Age Dischg Sex 3 134 HOME M 43242 H 10. 1 J 80 WX 101 1 54 HOME N 43242 F 0289 E 756 GD 1 BD 6 3 HOSP N 64243 V 02. 0 2 41 HOME N 34212 O 75. 7 4 34 HOME M External cause code as principal diagnosis MAF 00 Mismatch of diagnosis and gender

7 DRG QA Pilot Benchmark Database Ø Seven • • DRG QA Database contains patient cases from the Ecomed KPP databases from 2008 Data source: Ecomed KPP used in the 7 hospitals Ø Three • • Hospital Districts in Finland County Councils in Sweden DRG QA Database contains all patient visits and stays from 2008 Data source: Patient Administrative Systems in corresponding county councils Ø Number • • of patient cases Finland n = 4. 928. 113 Sweden n = 4. 332. 206

8 DRG QA Database Formation Process Hospital Districts’ Ecomed KPP databases (FI), or similar data retrieval from Patient Administrative Systems (SE) District A District B District C County Council A County Council B County Council C etc. Datawell DRG QA ETL Datawell DRG QA Indicator Calculation Ecomed DRG QA Database Ecomed Analyzer Reporting Analysis of Data Quality • 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 Results from the DRG QA Pilot Benchmark Database were presented in the meeting.

10 The classification of diagnosis (ICD-10) ØA • • 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 Ø Clinical • validation rule base - CVRB A collection of identified rules

Rate of Z 51. 1 Chemotherapy session as Principal or Secondary diagnosis. Swedish county councils 2008 11 Principal dx Secondary dx Data from Swedish National Patient Registry

Some examples of CVRB Rules 2009 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 code 2 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 12

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

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

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

16 Information Process and Benefits of Datawell DRG QA Code systems Entry of data Processing of data Utilization of data and information 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. Reports of data quality incorporated with other reporting

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