Patient Demographic Data Quality Framework

Data Quality Planning


Defines an integrated plan to improve and maintain the level of data quality needed to accomplish business goals and objectives.

Introductory Notes

Data quality planning is the process of defining the business goals, objectives, specfic initiatives, and sustained activities to improve data integrity, accuracy, and trustworthiness. The data quality plan, providing an approved organization-wide approach becomes a unifying force to foster shared responsibility for quality.

Organizations typically approach data quality improvement on a project by project basis, data store by data store. However, without an overall organization-wide focus on the business criticality of high quality data, positive results are frequently isolated in disconnected pockets. If processes, methods, and procedures are developed independently for each effort, the organization risks: lack of awareness on the part of business staff about quality needs across the data lifecycle; undue effort and duplicative costs; and uncoordinated inefficient implementations (for example, repeatedly cleansing data in a downstream data store while not improving data quality at the source).

The data quality plan has a simple purpose – support focused, strategic thinking. By engaging in organization-wide collaborative considerations of basic questions, issues and objectives will be surfaced and agreement can be forged. The organization should engage stakeholders from all relevant business areas to pose these questions about patient demographic data:

  • What are our biggest quality issues in each data store containing patient demographic data?
  • What goals and objectives will we achieve if quality is improved?
  • What are the impacts of poor data quality, e.g., cost, risk, compliance, productivity?
  • How will we monitor and sustain our activies to ensure that we meet our goals?
  • How will we prioritize our efforts? And how will we decide where to direct our time and effort, such as cleaning up duplicates, working with vendors to improve capabilities, and training patient registration staff?
  • What is the proposed sequence to accomplish our objectives?
  • How should the organization engage to review objectives, direct resources, and address business process and technical issues?
  • Who should be involved, who will lead and coordinate improvement efforts, and how will we structure their engagement?
  • How will data quality improvements be measured, analyzed, and reported on over time?

In a large organization, this planning effort may extend over a few weeks. In a small organization, it may be completed in a few brief meetings. Once the approach and corresponding specific initiatives are approved (See Data Governance) the data quality plan can be finalized. It should address:

  • Quality goals and objectives (e.g, specific objectives such as minimizing duplicates to under 4% in year one, 3% in year two, etc.);
  • Quality principles (e.g., capture and update patient demographic data in an accurate and timely manner);
  • Major issues noted in the plan that the organization should address (e.g., commitment to reduce patient safety events due to misidentification by 50% in year one);
  • Anticipated benefits (e.g., what improved outcomes are expected);
  • Responsibilities and accountability – (e.g., what roles are needed to encourage coordination across departments and how engagement should be evaluated and modified as needed);
  • Toolsets (e.g., the scope of the issues justifies selecting and implementing an in-house data quality tool, or the organization may choose to request vendor services, etc.);
  • Sequence plan of initiatives – (e.g., the first initiative will be a data profiling pilot project; the next iniative will be training for all staff; and the third will be establishing a data quality assessment process with business representatives);
  • Training – the organization should plan to conduct training for every individual who enters, updates, or modifies data (e.g., during a record merge).

Although technologies, methods, and specific techniques can be required, high quality data is the result of continued attention by all relevant stakeholders, communicated and shared across the organization. Creating and following the data quality plan is a positive cultural shift, demonstrating the organization’s executive commitment, and an intention to educate, engage, and sustain effective and direct attention focused on patient demographic data. It is essential to conduct training for everyone who enters, updates, or modifies data (e.g., during a records merge). Training topics should include: process workflow, current system capabilities, privacy and security/HIPAA, and how feedback should be submitted for suggested improvements.

The data quality plan establishes and is implemented by the key quality processes. (See Data Profiling, Data Quality Assessment, and Data Cleansing and Improvement.) Following the data quality plan will assist the organization to realize the following:

  • Clear, approved path to improved business operations engendering stakeholder involvement and providing confidence that data quality issues will be addressed and prevented in the future;
  • Transformed ad hoc efforts to planned quality progress, saving effort and costs;
  • Organization-wide approach facilitating funding requests for projects and resources;
  • Defined responsibilities parsing out clear roles, from the boardroom to the database administrator;
  • Approved prioritization, consensus, and enhanced awareness among business staff, creating a ‘quality culture’; and
  • Reusability of approaches, methods, toolsets, techniques, and procedures.

Additional Information

The data quality plan should align with organizational goals (e.g., improve patient safety). Consequently, the plan’s objectives should support organizational goals (e.g., patient identity integrity) and the scope should address the entire lifecycle for patient demographic data. Relevant stakeholders should select the data attributes critical to important processes performed along the lifecycle.

It is essential that all relevant stakeholders define and approve the quality objectives and criteria that will ensure that the data is ultimately fit for purpose. This provides guidance on the definition of thresholds and targets to be developed through data quality assessments (See Data Quality Assessment). In addition, it is important to understand that data quality is a learning process. Accordingly, objectives and success criteria should be updated periodically to reflect refined data quality requirements resulting from successive data cleansing, feedback, and improvement efforts.

Additional Information

Sustainable improvements to the quality of data require the implementation of quality rules, because data defects often result from a lack of thorough requirements analysis. Data quality rules can be implemented in the form of procedures to be followed for data entry personnel or as enhancements to data capture systems. It is best to strengthen data capture with automated quality rules whenever possible.

Automation of data quality rules can range from partial to nearly complete automation. For example, full automation can include exception reports that still must be manually reviewed. Nearly fully automated quality checks are typically applied to data stores, which integrate bulk data from multiple sources. Alternatively, partial automation of data quality rules is often implemented at the point of data capture, which is usually accomplished through manual entry. Here quality rules enforce the use of standards by restricting data entry to a predefined range of inputs that are stored in a consistent format (e.g., state code, phone number, ZIP code, etc.), and accommodate a diverse patient population with unique identifiers.

Example Work Products

  • Data quality plans, criteria, and rules
  • Status updates against plans
  • Data quality rules documented as requirements

Additional Information

Data quality policies, plans, and processes define standards and guidelines for performing the activities that will fulfill the organization’s quality objectives. In particular, the policies and processes should provide specificity for implementing activities across the patient demographic data lifecycle as well as satisfy the goals of the data quality plan.

Adoption of data quality principles, policies, and processes is key to the success of the data quality plan. Correspondingly, it is important for data governance to be comprised of stakeholders who represent the critical steps along the entire lifecycle of patient demographic data.

Additional Information

Data profiling, assessment, and cleansing activities are often performed sequentially and iteratively. The entire process is often repeated, because anomalies may surface at any point and may require analysis. Once defects have been determined, all affected steps must be refined to ensure defect rates adhere to the tolerances specified in the data quality plan. Therefore, it is important to regularly monitor the implementation of the data quality plan and make adjustments when needed.

Example Work Products

  • Data quality sequence plan with key milestones identified
  • Policies, processes, and guidelines

Additional Information

Sustaining data quality over time depends on aligning the efforts of stakeholders who impact shared data at different points along the lifecycle of patient demographic data. Turnover of personnel is one of many risks to data quality that can be mitigated through effective governance. The organization should assign clearly defined roles, responsibilities, and accountabilities that are aimed to ensure a consistent stewardship of shared data assets from their creation to destruction.

Assigning accountability is necessary for ensuring that the organization maintains sustainable data quality policies, repeatable processes, and effective methods. Therefore, it is recommended that the data management function carry out those responsibilities for the data quality program and serve as the assigned steward for overall data quality metrics.

Example Work Products

  • Data management standards providing quality criteria and guidelines
  • Standard data quality processes, reporting, and dashboards
  • Data quality roles and responsibilities

Practice Evaluation Questions

Tier 1: Foundational

1.1 Are all relevant stakeholders involved in determining data quality objectives and success criteria for patient demographic data?

1.2 Does the organization have plans to improve data quality through the application of quality rules?

Tier 2: Building

2.1 Has the organization established policies, processes, and guidelines to implement the data quality plan?

2.2 Is the data quality plan regularly monitored to evaluate progress?

Tier 3: Advanced

3.1 Has the organization defined roles, responsibilities, and accountability for data quality governance and management of data quality processes?