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Heather Richards
 

Heather Richards

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    Heather Richards Heather Richards Presentation Transcript

    • Ensuring Qualityof Health Care Data:A Canadian PerspectiveData Quality Asia Pacific Congress 2011Heather RichardsConsultantCanadian Institute for Health Information (CIHI)Tel:+1 250 220 2206Email: hrichards@cihi.ca 1
    • Agenda> The Canadian Institute for Health Information> Data Quality Challenges in Canada> Strategies to Ensure Data Quality: – CIHI’s Data Quality Framework – Data Quality Reporting Tools and Studies – Techniques for Communicating Data Quality 2
    • The Canadian Institute forHealth Information 3
    • Canadian Institute for Health Information> National, independent, not-for-profit agency, established in 1994> One of Canada’s leading sources of high-quality, reliable and timely health information> 27 health databases and registries> 7 offices 4
    • CIHI’s Mandate> Coordinate, develop, maintain and disseminate health information on Canada’s health system and the health of Canadians 5
    • CIHIs Mandate Cont> Provide accurate and timely information required for: – Sound health policy – Effective management of the health system – Public awareness about factors affecting good health 6
    • Data Quality Challengesin Canada 7
    • Data Challenge: Variety of Partners> Accommodating different coding standards at provincial/territorial level versus national level;> Recognizing different uses of the data and different focus on data quality;> Adjusting for differing data collection methods 8
    • CIHI Partners Health Regional facilities health authorities Statistics Health Canada Canada Ministries CIHI Professional of health associations Non-governmental Private sector organizations organizations Researchers 9
    • Data Challenge: Secondary Data Collector> CIHI does not collect data directly> Our data comes from: – provincial governments; – hospitals; and – professional associations … this means that we cannot affect first hand how that data is captured and collected. 10
    • Data Challenge: Secondary Data Collector> CIHI relies on data providers (some are voluntary data providers) to report accurate information> Poor quality data often result from difficulties in collection standards, coding standards and chart documentation – and lack of training 11
    • Data Challenges: Other> Variety of databases and usability> Data flow and timeliness> Coding and comparability> Hospital practices and data completeness 12
    • End-stage renal failure www.vancouversun.com 13
    • Question: Are Risk Factors CompletelyCaptured at all Facilities? Prevalence of Pulmonary Edema70%60%50%40% Inter-Quartile Range: 13-27%30%20%10%0% 14
    • Questionnaire Reveals a Correlation ofData Completeness to Hospital Practices Prevalence of Pulmonary Edema70%60%50%40% IQR: 8-21% IQR: 16-29%30%20%10%0% Reviews select Reviews all documentation documentation 15
    • Chart Review Confirms Under-Reporting Prevalence (%) Data Captured Data Captured by CIHI coder by Dialysis during Chart Clinic Staff ReviewPulmonary edema 22 27 Sensitivity=62% Specificity=93% Epidemiologists and clinical researchers prefer seeing PPV=77% these statistics… NPV=87% 16
    • Strategies to Ensure Data Quality • CIHI’s Data Quality Framework • Data quality reports and studies • Techniques for communicating DQ 17
    • Strategies to Ensure Data Quality CIHI’s Data Quality Framework 18
    • CIHI’s Data Quality Framework> Objective approach to assessing data quality and producing standard documentation> Three parts 1. Work Cycle 2. Assessment Tool 3. Documentation 19
    • 1. Data Quality Work Cycle Plan Assess Implement 20
    • 2. Data Quality Assessment Tool> Provides a consistent approach for defining data quality 5> Five dimensions Dimensions – Accuracy – Comparability 19 Characteristics – Timeliness – Usability 61 – Relevance Criteria 21
    • 2. Data Quality Assessment ToolAccuracyComparability CoverageTimeliness Capture and collectionUsability Unit non-response Item (partial) non-responseRelevance Measurement error Edit and imputation Processing and estimation Population of reference explicitly stated Coverage issues are documented Frame validated Under or over-coverage rate 22
    • Assessment Tool: Educational Component 23
    • 3. Data Quality Documentation> Details the data quality documentation required for each data holding 24
    • Metadata DocumentationRetainknowledgeabout themanagementof adatabasewith thedatabase. 25
    • Strategies to Ensure Data Quality Data Quality Reporting Tools and Studies 26
    • Deputy Minister Data Quality Reports> Bird’s eye view> Broad DQ scope: assess accuracy, timeliness, comparability and usability> Specific audience: Deputy Ministers of Health 27
    • Features of the Deputy MinisterData Quality Reports> Each indicator is important to the success of a database and has a defined action to improve performance – Snapshot of results across all jurisdictions – Trending over time> 11 databases – 8 from CIHI – 3 from Statistics Canada 28
    • Components of the Deputy MinisterData Quality Reports P/T indicator tables Trending Database- results specific reports TechnicalFlags table documents Each DM package 29
    • Trending: Discharge Abstract Database Indicator 1: Total Outstanding Hard Error Rate, per 1,000 Abstracts2.52.0 2003-041.5 2007-08 2008-091.0 2009-100.5 2004-05 2005-06 2006-070.0 Optimal Value = 0 30
    • Response to ReportsPositive:> Highlights to DM the value of a database; increases coverage of data holdings> Reveals systemic problems causing DQ issues; helps Deputy Ministers prioritize and reallocate resources> Congratulates on past DQ improvements; facilitates creation of DQ improvement action plans 31
    • Reabstraction Studies> Detailed review> Narrow DQ scope: assess coding consistency, correctness, completeness> Wide audience 32
    • Study Methods> A chart review to recapture the data and compare Reabstractor assigns Application reasons for compares differences data Application reveals original data Reabstractor recodes from chart 33
    • Overview Determine Share study results method Study Objectives Process Develop and data analyze collection data tool Train coders, collect data 34
    • Reabstraction Study ExampleDAD: Discharge Abstract Database> Data on acute-care hospital activity> Data supports: – funding and system planning decisions at government level – management decisions at the facility level – clinical research at the academic level 35
    • Strategies to Ensure Data Quality Techniques for Communicating Data Quality 36
    • Communicating Data Quality UsingDifferent LensesStatistics for OECDinternational Isolating determinantscomparisons of good healthHealth Assessing quality of careindicators ClinicalCategorizing researchhospitalizations purposesfor hospital such asmanagement purposes survival analysis 37
    • Health > Assess population health and health system performanceIndicators > Will look at one indicator: ACSC hospitalizations 38
    • Health Indicator: ACSC Hospitalizations Age-Standardized Rate of ACSC Hospitalizations per 100,000 Population600500 459400 2001-02 2002-03 2003-04 326 2004-05 2005-06300 2006-07 2007-08200100 0 39
    • 2007-08 DAD Study: ACSC Hospitalizations> Question: Is the decrease in ACSC hospitalizations real or is it due to changes in coding quality?> Answer: The observed decrease is real – National rates are indeed decreasing – Reabstraction studies found that certain patient populations had lower quality data 40
    • 2007-08 DAD Study: ACSC Hospitalizations SensitivityGrand mal status, epileptic convulsions 81%Chronic obstructive pulmonary diseases 91%Asthma 90%Diabetes 95%Heart failure and pulmonary edema 84%Hypertension 100%Angina 94%Any ACSC hospitalization 90% 41
    • Data Quality Challenges that Lie Ahead> The health sector is a changing landscape – Electronic health record – Health care funding – New technologies – New modes of delivering health care> New data will bring new quality challenges 42
    • “Taking health information further” 43