Who would want to be a national health care administrator? Who would want the responsibility for managing health care and formulating health policy for tens or hundreds of millions of people? It seems obvious that such decisions would rely on quality data. A recent interview impressed upon me how much data managers can learn from a field where data recording millions of separate life and death decisions aggregates to support decisions on the future allocation of health care resources.
Who would want to be a national health care administrator? Who would want the responsibility for managing health care and formulating health policy for tens or hundreds of millions of people? It seems obvious that such decisions would rely on quality data. A recent interview impressed upon me how much data managers can learn from a field where data recording millions of separate life and death decisions aggregates to support decisions on the future allocation of health care resources.
Heather Richards of the Canadian Institute for Health Information (CIHI) was recently interviewed by the Australian magazine Image and Data Manager on CIHI efforts to provide neutral, objective and unbiased information to those making health care allocation policy decisions. Ms. Richards also happens to be Director of Publicity for the International Association for Information and Data Quality (IAIDQ).
In a detailed, concise, and refreshingly buzzword-free conversation, Ms. Richards described CIHI’s approach to improving data quality. To me, that approach boils down to these three themes:
Improve quality at the source with standards and education
Taking a data warehousing point of view, one typical challenge in data management is lack of control of data at the point of entry. Those who enter the data into the originating system usually work in separate divisions, or even separate companies, from those who administer warehouse data quality. Extract, transform, and load routines can typically tell whether a data element on the way to the warehouse is valid, but can’t tell whether or not it is correct. If it is invalid then the ETL process might be able to apply deduction or estimation to “guess” at a reasonable substitute value, but most often the choice is either to accept bad data or reject the transaction and return it for correction.
CIHI also faces this challenge, and “cannot directly affect how [health care] data is captured and collected.” In response, CIHI relies on training, communications, and facilitation: “To counteract this limitation, we establish data quality activities that strengthen our data providers’ awareness and understanding of the importance of data quality. We assist them with implementing practices that promote the most accurate data, such as data submission manuals, vendor specifications for data entry, and educational workshops on how to code and abstract data to meet the national standards.”
Tolerate, manage, and facilitate integration of local differences
Data quality can depend both on the processes and standards at the point of entry and the needs of the data consumer. Ms. Richards makes the point that ensuring data quality is more than simply conforming inputs to destination standards, but also being flexible enough to handle the needs/requirements of data providers.
At the entry point CIHI strongly promotes “consistent data submission”, and supports consistency with standards and communication channels between CIHI and data providers at the provincial and local levels, as well as extensive validation of submitted transactions. However, communication with data providers is two-way: in some cases CIHI will work with local authorities to ensure that, although not strictly compliant, submitted data ”allows comparisons” with other warehoused data.
Support the needs of its Data Consumers
Finally, Ms. Richards described communications, integration, and reporting services for data consumers. CIHI supports a portal that enables “hospitals, regional health authorities and ministries of health to access interactive reports on the delivery of health services at the facility, regional, provincial and national level.” The integrated portal “offers users improved evaluation, stronger decision support and broader knowledge transfer”, as well as presumably making transparent the value of effective data integration through uniform submission standards.
Communication with those using CIHI data is as important as with those providing it. “It’s really a balancing act in terms of allowing some flexibility to address local interests and limitations, while keeping other aspects of data collection standardized across the country to address provincial and national interests. It is important for decision makers to understand this distinction so that they are not misled when developing health policy.”
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When asked how far along CIHI was in its data quality efforts, Ms Richards was guarded: “all I can say is that we are somewhere past the start line.” Time will tell how these efforts turn out, but in the meantime CIHI’s plans are very interesting reading to those seeking to improve data quality business processes and culture.
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