What are information quality policies in healthcare?

Information Quality Policies in Healthcare

Information quality policies in healthcare are essential frameworks that establish standards for data collection, management, security, and exchange to ensure high-quality patient care, improve outcomes, and protect privacy. 1 These policies are critical for maintaining the integrity of healthcare information systems and supporting evidence-based decision-making.

Core Components of Information Quality Policies

Data Quality Standards and Assessment

• Regular auditing and transparent reporting of clinical registry data quality should be conducted using approaches advocated by the Agency for Healthcare Research and Quality (AHRQ) 2
• Data quality reports should be made public, including information on systems and processes used to ensure representative sampling and data completeness, accuracy, and reliability 2
• Key data quality dimensions include accuracy, consistency, security, timeliness, completeness, reliability, accessibility, objectivity, relevancy, and understandability 2
• Standardized methodologies for data quality improvement should be implemented, such as plan-do-study-act (PDSA) or define-measure-analyze-improve-control (DMAIC) cycles 2

Data Protection and Security

• A coordinated national approach to protect data is more effective than relying solely on health systems and vendors 2
• Technical safeguards including firewalls, secure transmission modes, and encryption are necessary to protect electronic protected health information (ePHI) 1
• Access controls and authentication mechanisms must be established to verify user identity before granting access to sensitive information 1
• Contractual prohibitions against reidentification of deidentified data and penalties for misuse should be implemented 1

Data Sharing and Exchange

• Patients should "own" their data and be able to grant care team members access to it 2
• Addressing coding and semantic variations is essential to ensure data quality across systems 2
• Policy makers should promote the linkage of clinical registries to supplemental data, with resources provided by federal, state, and private sector sources 2
• Research should be supported to assess alternative approaches to clinical registry linkage and identify the most accurate and efficient methods 2

Implementation Strategies

Governance Framework

• A multidisciplinary approach involving clinical, technical, and administrative stakeholders is recommended for implementing information quality policies 1
• Clear definition of roles for data stewards, managers, and users within the organization is essential 1
• Regular review of compliance and effectiveness of governance measures, with updates based on emerging threats, technologies, and regulations 1
• Establishment of data sharing review committees with appropriate stakeholder representation, similar to Institutional Review Boards 1

Equity and Access Considerations

• Systems must work with diverse audiences, particularly socially and economically marginalized populations 2
• Mobile technology can better reach underserved populations, and community engagement ensures design and implementation helps those most in need 2
• Standard definitions and categories for race, ethnicity, and language should be adopted to ensure comparable disparity information from variable data sources 2
• Registries should generate reports on disparity-related data elements to provide healthcare professionals with information on quality of care compared with other practices/institutions/peers 2

Common Interventions for Improving Data Quality

Most effective data quality improvement initiatives include multiple interventions:

• DQ reporting and personalized feedback (61% of successful interventions) 2
• IT-related solutions (54% of successful interventions) 2
• Training (44% of successful interventions) 2
• Improvements in workflows (13% of successful interventions) 2
• Data cleaning (8% of successful interventions) 2

Challenges and Pitfalls

• Healthcare organizations currently invest less in IT than other information-intensive industries, resulting in relatively primitive systems 3
• Considerable heterogeneity exists in settings and approaches to data quality assessment and improvement 2
• Use of standardized data quality frameworks is limited, with most assessments being largely manual 2
• Patient information is often viewed as a competitive advantage for health systems and vendors, reinforcing information fragmentation over interoperability 2
• Medical information, when taken in isolation without formal medical training, can lead to out-of-context diagnoses and treatment recommendations 2
• Proprietary formats in personal health records (PHR) and electronic health records can result in lack of interoperability 2

To establish effective information quality policies, healthcare organizations must prioritize root cause analysis of data quality issues, adopt standardized frameworks for assessment and improvement, and ensure that policies align with core organizational missions of patient care, education, and research for advancing health 2, 1.