The Data Quality Management Framework is released

posted Oct 14, 2009, 11:16 PM by Post Master   [ updated Apr 7, 2011, 7:55 AM ]

In the present era, data is one of the most important factors for business survival of any industry, and healthcare is no exception. In fact, the drive to reduce cost and the desire to improve patient care makes the healthcare industry one of the most eager customers for clean and integrated organizational data.

 But this data needs to posses a number of characteristics if it is to be used for decision-making and strategic operation. The government of British Columbia defines the state of completeness, validity, consistency, timeliness and accuracy that makes data appropriate for a specific use as data quality

Rounded Rectangle: Data Consumers: Use the data for planning, outcome assessments and decisionsRounded Rectangle: Data Custodians: Code, abstract, verify, validate, aggregate and maintain the data; circulate reports to usersRounded Rectangle: Data Producers: Provide Data from Inside or Outside of the organizationTo build a practical framework for achieving desirable data quality, Strong et al. (2) suggest taking a customer focused view by treating data processing as a data manufacturing system and to produce data that is “fit to use by data customer”. They recognize three fundamental roles: data producers, data custodians and data consumers. Fitness for use would mean that the concept of data quality is relative; therefore dimensions need to be defined to ensure fitness in an integrated health care environment. 

Table 1: Perceived Data Quality Dimensions

Information Quality Category

Information Quality Dimensions

Intrinsic

Accuracy, Objectivity, Believability, Reputation

Accessibility

Access, Security

Contextual

Value-added, Relevancy, Timeliness, Completeness, Amount of Data

Representational

Interpretability, Ease of Understanding, Concise Representation, Consistent Representation

Organizations select from these dimensions based on their own needs and special circumstances. Examples include New Zealand Healthcare data quality framework (6 dimensions), Statistics Canada’s Quality Assurance Framework  (6 dimensions), Ontario Data Quality Management Framework (4 dimensions) , and CIHI Data Quality Framework, which will be presented here in further detail.

CIHI has been publishing a framework for data quality since 2000. the latest revision, 2006, has selected five of these dimensions to implement a data quality assessment tools (10).

CIHI selects Accuracy, Timeliness, Comparability, Usability, and Relevance. They then divide these dimensions into characteristics, and define a set of criteria for each characteristic. Using a ranking system of met, unmet, and unkown or not applicable, the strengths and limitations of data is assessed, and areas of intervention identified. To help prioritize those intervention activities, some of the criteria follow another ranking system of minimal, moderate and significant.

 CIHI defines the roles and responsibilities of data quality framework as follows:

  • Senior Management
    • Provide Resource, Support DQ in all new initiative
  • Product Areas
    • Evaluate data quality and address issues
    • Document quality
    • Conduct studies and identify ways of improvement
  • Data Quality Section
    • Provide guidance, review and report on compliance
    • Assist in studies
    • Conduct R&D on data quality
    • Train and update framework annually

 

 Full text article is available in the Resources section.

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