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Accurately and consistently matching patient data records is critical to ensuring safe and effective care to patients, preventing incorrect treatment and diagnosis decisions. The Patient Demographic Data Quality (PDDQ) Framework was derived from the Data Management Maturity Model by CMMI Institute, commissioned by Health and Human Services, Office of the National Controller for Health IT. The PDDQ helps organizations develop and implement sound data management practices, establish governance, and create data quality standards and processes, contributing to minimization of the number of duplicate records across the industry, improving patient safety across the care lifecycle.
View the presentation, Improving Patient Safety: Introduction to the Patient Demographic Data Quality Framework
Our industry has a wealth of approaches, methods, and tool sets to improve data management. However, despite knowledge, capabilities, and technologies, many organizations fail to gain traction, resulting in isolated pockets of excellence. The lack of cohesive enterprise data management programs hampers realization of sharper analytics, improved data quality, efficiency gains, and cost savings. The DMM quickly answers the questions: “How are we doing in data management?” and “What should we do next?” It offers a unifying measurement instrument of best practices, enabling a rapid and detailed evaluation of current capabilities, and fosters the behavioural consistency needed to:
- Create a shared, approved organization-wide vision, program, and data strategy
- Implement a collaborative, business-driven approach to determining priorities and responsibilities
- Increase governance awareness and participation
- Develop repeatable processes and rational, achievement-based metrics
Applying the process areas of the DMM allows organizations to quickly discover strengths and gaps, realize increased focus, and guide their journey to improvement. For a services organization, an Assessment is a powerful tour de force of a clientӳ priorities and challenges, and what they are trying to achieve with their data assets; recommendations produced yield actionable initiatives pre-socialized with stakeholders.
View the presentation, Evaluating Data Management Capabilities
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