Improving Patient Data Quality, Part 1: Introduction to the PDDQ Framework

ART02x - edited feature imagePatient demographic data quality issues are pervasive across the healthcare industry. A significant percentage of healthcare professionals report that duplicate patient records negatively impact the quality of care delivery (namely, patient safety). This results in increased risk, higher operational costs, impaired interoperability, and inaccurate analytics for providers, information exchanges, and aggregators in the health care industry.

This is part one of a two-part article from Melanie Mecca and Jim Halcomb of the CMMI Institute about the Patient Demographic Data Quality (PDDQ) Framework.

Part one will introduce the framework and share with you the maturity model from which the framework is based, who it may benefit, and share what is inside the framework.

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According to Health and Human Services, it has been estimated that over 80% of health care providers have incurred errors in diagnosis or treatment due to duplicate or inaccurate patient records.

Many attempts have been made to resolve these issues using algorithms that search for fragments of patient data within or across systems to derive patient identity integrity. Since there is a broad spectrum of diverse standards and vendor products in the health care industry, algorithms alone have failed to provide a sustainable solution.  Patient record matching algorithms are necessary, but they are reactive, and do not address the root cause, which is the lack of industry-wide standards for capturing, storing, maintaining, and transferring patient data.

The problem of duplicate records is a common data quality defect that has dogged organizations across all industries. Billions of dollars have been spent to fix this problem with technology but was met with limited success, since custom software solutions and data stores have been developed without the key element of common data standards and errors may sometimes occur at registration. As a result, data has become increasingly expensive to integrate and maintain.

It is important to recognize that duplicate patient records are a symptom of a deeper and more pervasive issue – the lack of industry-wide adoption of fundamental data management practices.  The new Patient Data Demographic Quality (PDDQ) framework was created to address this purpose. It offers a sustainable solution for building proactive and defined processes that lead to improved and sustained data quality.

The PDDQ Framework features proven practices that support rapid improvement to patient demographic data quality, both within and across healthcare organizations. As sound data management practices are increasingly adopted, the following benefits will be realized:

  • Decreased operational risk through improvements to the quality of patient demographic data. Specifically, patient safety is protected and quality in the delivery of patient care improves.
  • Increased operational efficiency, which requires less manual effort to fix data issues, fewer duplicate test orders for patients, and adoption of standard data representations.
  • Improved interoperability and data integration through adopting data standards and data management practices that are followed by staff across the patient lifecycle.
  • Improved staff productivity by expending fewer hours on detecting and remediating data defects to perform their tasks.
  • Increased staff awareness for contributing to and following processes that improve patient identity integrity.

By employing the PDDQ Framework, health care organizations have guidance.  The PDDQ contains an embedded path for successive improvements that guide practical actions and implementation, along with a concentrated education for everyone dealing with patient demographic data.

The Data Management Maturity (DMM)SM Model –
Foundation for the PDDQ

In 2015, an ONC Community of Practice (CoP) for patient demographic data quality, comprised of government, health care provider, and industry association members, focused on defining the set of data management best practices aimed at improving data quality.  The goals for this effort was to enhance the management of patient demographic data in the health care industry, support more accurate patient record matching, and reduce the number of duplicate records.

The CoP analyzed available frameworks that address processes supporting data quality improvements and selected CMMI® Institute’s Data Management Maturity (DMM) Model as the most comprehensive and accessible framework available.  The DMM’s fact-based approach, enterprise focus, and built-in path for capability growth aligned exactly with the healthcare industry’s need for a comprehensive standard.

The CMMI Institute was commissioned to develop an implementable, practical framework based on the DMM, targeting the specific practices, organizational behaviors, and work products that directly and indirectly affect the quality of patient demographic data.  Working with the ONC through their prime contractor, Audacious Inquiry, a trusted health information technology and policy company, the CMMI Institute’s team set out to develop a derivative product to meet the needs of HHS ONC.

How the PDDQ Was Developed

The first step entailed study of industry research surveys, policy documents, and input from ONC and AI to understand the issues, challenges, and current practices relevant to patient record matching.

To preserve consistency and alignment with the DMM as the CoP mandated, the DMM was analyzed thoroughly to identify the specific functional practices pertaining to the solution scope, including the target data set, demographic data used for record matching, and the set of data management practices that are needed to ensure accurate data across the patient care lifecycle.

This resulted in the elimination of some DMM process areas less directly related to the solution set. Take for example, Data Management Strategy, which is more broadly focused on enterprise data management programs for all industries.  Instead of the 25 process areas addressed in the DMM, the PDDQ contains 19 process areas.

The approach and method that CMMI Institute uses to navigate the DMM is based on the engagement and consensus of an organization’s key stakeholders.  Without cross-organizational participation, a comprehensive view of the data cannot be easily achieved.  Therefore, the PDDQ’s capabilities are represented in the form of questions, intended to be posed to a group of individuals representing the patient care lifecycle – e.g., registration, care delivery, laboratory, pharmacy, diagnostic imaging, facility assignment, billing, claims, etc.  – that is, every care function in which patient demographic data may be created or updated.

The 414 practice statements in the DMM were transformed into 76 questions arrayed across the 19 process areas in the PDDQ. Once the questions were formulated, the team developed supporting information and relevant examples for each question to assist organizations in interpreting the questions in the context of their organization.

The five capability ‘Levels’ in the DMM, representing an abstracted typical growth path within organizations, were modified into three capability ‘Tiers’ for the PDDQ to simplify the organization’s application of the framework.  The capability questions within a process area are intended to provide a practical and reasonable path for enhancing data management capabilities.

Finally, the DMM’s 596 example work products were analyzed and a core set of the most important and policies, processes, and standards that demonstrate capability improvements were selected for the PDDQ.

Who Can Benefit from the PDDQ?

The intended audience for this framework ranges from small health care practices (which currently have no data management resources) to very large health care providers.  The capability practices and work products contained in the PDDQ are equally applicable to the entire spectrum of organizations for which patient demographic data quality is a major consideration.

The PDDQ introduces a new concept that HHS ONC is supporting across the health care industry – the role of the Data Quality Coordinator.  The coordinator is essentially the lead data steward for patient demographic data.  The role may be filled part-time in a smaller practice, or may expand to an entire organizational unit in large health care organizations.  This individual or group takes responsibility for advocating, facilitating, documenting, and gaining approval from representatives across the patient life cycle with the aim of establishing data management processes, implementing both procedural and technical improvements, and continuous monitoring of quality and record matching metrics.

Large organizations, such as health care systems, health insurers, and health information exchanges focused on patient data quality, are advised to begin their internal capability evaluation with the PDDQ to implement needed improvements, and then utilize the full scope of the DMM to address interoperability, data design, analytics, repositories, measures and metrics, etc.

PDDQ Walkthrough – What’s Inside

The PDDQ Framework advocates organization-wide alignment on the following key factors:

  • Implementing governance functions
  • Planning and prioritizing data quality improvements
  • Implementing data quality improvements and providing assurance
  • Managing operational components
  • Defining and mapping data dependencies
  • Supporting access to shared data interoperability
  • Ensuring that data is understood and trusted across the organization

The PDDQ is intended as an encouraging and helpful mechanism for discovery.  While the Framework addresses and advocates requirements and activities for effective data management, it does not prescribe how an organization should achieve these capabilities. It should be used by organizations both to assess their current state of capabilities and as input to a customized roadmap for data management implementation.

The PDDQ is organized into 5 categories with 3-5 Process Areas in each, representing the broad topics that need interrogation by the health care organization to understand current practices and determine what activities need to be established, enhanced, and followed.

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Process Areas serve as the principal mechanisms to communicate the themes, context, benefits, and example work products of the model, focused around the key evaluation questions contained within each section. Fulfilling practices assist an organization to chart its path and progress in building capabilities.

The PDDQ Framework is structured so that an organization can implement any combination of categories or process areas, and obtain baseline conclusions about their capabilities. The organization can focus on a single process area, a set of process areas, a category, a set of categories, or any combination up to and including the entire PDDQ Framework. This allows flexible application to meet specific organizational needs and address for resource and time constraints.  The Process Areas within categories are listed below:

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In part two we will cover what is evaluated as part of the framework, how the framework is intended to be used and how you can access the framework.

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About Jim Halcomb

Jim Halcomb, CMMI Institute’s DMM Practice Lead, has deep and diverse global data management experience in financial institutions, exchanges, and financial regulators. He led the development of data management best practices for the EDM Council, and was a primary author of the CMMI’s Data Management Maturity Model.

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About Melanie Mecca

Melanie Mecca, Director of Data Management Products and Services, CMMI Institute, led development of the Data Management Maturity (DMM) SM Model. Her team created a highly interactive method for assessing an organization’s capabilities against the DMM, and she has led numerous assessments for organizations in the financial, Federal, and technology industries. She directed creation of the Building EDM Capabilities, Mastering EDM Capabilities, and Enterprise Data Management Expert (EDME) courses leading to DMM certification. In 30+ years solving enterprise data challenges, Ms. Mecca has architected and implemented data management programs and projects, data strategies and architectures, and designed enterprise data services. She is an active presenter of classes, seminars, webinars and case studies, and is a strong advocate for data management education, with a passion for assisting organizations to realize business value from their data management programs.

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