What is a Master Patient Index (MPI) and what are the data quality standards in a Data Quality Management (DQM) model?

Master Patient Index (MPI) and Data Quality Standards in Data Quality Management Model

A Master Patient Index (MPI) is a database that maintains consistent, accurate, and unique patient identification information across healthcare systems, while data quality standards in a Data Quality Management model include six core dimensions: completeness, uniqueness, timeliness, consistency, validity, and accuracy. 1

Master Patient Index (MPI)

Definition and Purpose

• An MPI is a database that serves as the cornerstone for proper implementation of electronic health records by ensuring correct patient identification 2
• It creates a unique identifier (UID) for each patient to ensure data interoperability across all points of patient care within a health system 3
• MPIs link crucial patient information across different healthcare facilities and systems 4

Importance of MPI

• Up to 20% of registered patients are duplicated in most healthcare systems 2
• Duplicate patient files pose significant risks by reducing information available for clinical decision-making 5
• MPIs protect medical record integrity and improve patient service 4
• They enable the establishment of an evidence-based, constantly improving "learning health system" with feedback loops 3

Implementation Challenges

• Many healthcare facilities have limited scope or effectiveness in their data quality and linkage activities 5
• Strong identification policies and robust systems are needed to minimize identification errors 2
• Implementation requires both online and offline modes of operation to accommodate different healthcare settings 3

Data Quality Standards in Data Quality Management Model

Core Data Quality Dimensions

The Data Management Association (DAMA) defines six fundamental data quality dimensions 1:

1. Completeness: The presence of expected data

   • Most commonly assessed dimension (79% of studies) 1
   • Measured by counting records with blank, unknown, empty, "NULL," or "NaN" values 1
   • Poor completeness can lower statistical power and lead to biased assumptions 1

2. Uniqueness: Uniqueness of records where duplication is not expected

   • Addresses the 60-90% of clinicians who routinely copy and paste data between systems 1
   • Helps identify and resolve redundant workflows and processes 1
   • Prevents duplication of patient records when disparate data flows are combined 1

3. Timeliness: A measure of data freshness

   • Assessed as the difference in time between point of data capture versus actual timing of events 1
   • Critical for clinical decision-making that relies on current information 1

4. Consistency: A check of consistency between multiple sources of the same data elements

   • Helps identify potential duplication and redundancy between different EHR sources 1
   • Addresses inconsistencies caused by individual documentation habits 1

5. Validity: The validity of data against data standards or plausible values, ranges, or patterns

   • Often uses data standards such as ICD, SNOMED, HL7, or RxNorm 1
   • Can be assessed using business rules incorporating expected values, formats, or ranges 1

6. Accuracy: A check of consistency of source data against a reference gold standard

   • Measures the extent to which data reflect the truth of events 1
   • Often involves comparison to a gold standard (paper records, manual reviews, etc.) 1

Data Quality Improvement Frameworks

Several structured frameworks exist for data quality improvement 1, 6:

1. Plan-Do-Study-Act (PDSA):

   • Plan: Identify a change hypothesis and plan a small test
   • Do: Conduct a study plan with data collection
   • Study: Analyze and interpret results
   • Act: Adapt the change based on feedback and plan the next iteration

2. Total Data Quality Management (TDQM):

   • Adapts traditional quality management principles specifically to data management 1
   • Focuses on data as a key asset or product 1

3. Define-Measure-Analyze-Improve-Control (DMAIC):

   • Provides a structured, data-driven quality improvement methodology 1
   • Emphasizes quantifiable metrics and statistical analysis 1

Effective Data Quality Improvement Interventions

Most effective data quality improvement initiatives include multiple interventions 6:

1. DQ reporting and personalized feedback (61% of successful interventions)

   • Sharing curated results with specific stakeholders 1
   • Encouraging improved data capture behavior 1

2. IT-related solutions (54% of successful interventions)

   • Implementation of technical safeguards 6
   • Automated data quality assessment tools 1

3. Training (44% of successful interventions)

   • Staff education on proper data entry 1
   • Training on data standards and quality importance 1

4. Workflow improvements (13% of successful interventions)

   • Process mapping techniques 1
   • Addressing clinical workflow inefficiencies 1

5. Data cleaning (8% of successful interventions)

   • Correcting existing data issues 1

Common Pitfalls and Challenges

1. Inconsistent Terminology and Definitions

   • Studies report inconsistent terminology and definitions for data quality dimensions 1
   • Variations in how dimensions like completeness are assessed 1

2. Lack of Standardized Frameworks

   • Limited use of standardized data quality frameworks 6
   • Most assessments being largely manual, negatively impacting data quality 6

3. Varying Gold Standards

   • Different definitions for what constitutes a "gold standard" for accuracy 1
   • Includes paper charts, national data, manually curated data sets, or expert validation 1

4. Implementation Challenges

   • Less than 20% of PDSA implementations comply with core features 1
   • Considerable heterogeneity in settings and approaches to data quality assessment 6

5. Overlapping Dimensions

   • Data quality dimensions sometimes overlap or are subsumed by others 1
   • For example, data could only be deemed accurate when both complete and correct 1

By addressing these challenges and implementing robust data quality management practices, healthcare organizations can improve patient identification, enhance clinical decision-making, and ultimately provide better patient care.