MASTER PATIENT INDEX (MPI)

Problem:

Brief description:

The Single Source of Truth for Patient Identity and Demographics

- One Patient One Unified Care Record -

Product Goal:

Building a patient-centered digital health ecosystem; Accurate patient identification is a key to achieving the Triple Aim and enables the success of all strategic initiatives. Patient-centric care, population health, accountable care, patient engagement, and value-based reimbursement are just buzzwords without effective patient identity management

An Accurate MPI may be considered the most important resource in a healthcare facility because it’s the link that tracks patient, person, or member activity within an organization (or enterprise) and across patient care settings.

USP: (unique selling proposition)

During the initial assessment, we will load and score your data using the Kodjin Big Data MPI™, which analyzes data quality to identify your real duplication rate.

Then, as part of the overall assessment, you’ll receive a report that outlines a phased-in approach to clean all data in your MPI, and ongoing processes to maintain a 1% (or less) duplication rate.

This report is yours to keep, whether or not you decide to have our team to assist in cleansing your data to remove duplicates, or any of our ongoing MPI maintenance services.

Master Patient Index Software Market:

Introduction

• A master patient index software (MPI) refers to a patient master index, patient registry, and client registry. It is an electronic database system that holds demographic patient information. The benefits of MPI are the elimination of duplicate patient registration entries and the maintenance of a central registry of all patients.

Global Master Patient Index Software Market: Key Trends

• The global master patient index software market is driven by a shift toward paperless data management systems and cost-effective cloud-based technologies for patient data management. Moreover, the adoption of health care data management software has increased for the benefit of health care organizations, medical practitioners, and patient welling and health.
• An increase in adoption of patient record management software and solution for improving patient compliance and effective healthcare management are projected to drive the global master patient index software market during the forecast period
• A master patient index software ensures data integrity with minimum duplicate records. This is projected to drive the global master patient index software market in the next few years.

Global Master Patient Index Software Market Segmentation

• In terms of type, the global master patient index software market can be bifurcated into software and services
• Based on the deployment model, the global master patient index software market can be classified into cloud-based and on-premises. The cloud-based segment is projected to grow at a rapid pace during the forecast period. The adoption of cloud-based technologies will help to simplify the patient data management process.
• In terms of end-user, the global master patient index software market can be divided into healthcare organizations, hospitals, clinics, and others. The healthcare organizations segment accounted for a major share of the global master patient index software market in 2020. This is attributed to increasing in database management software and services for large organizations.

North America accounted for a Major Share of Global Market

• North America is projected to dominate the global master patient index software market during the forecast period due to the presence of advanced healthcare systems and high adoption of advanced technology to maintain patient master index & patient registry
• The majority of market players, research institutes, and academic centers in the region focus on adopting patient data for clinical studies. This factor is anticipated to drive the master patient index software market in North America during the forecast period.

Key Players Operating in Global Master Patient Index Software Market

Major players operating in the global master patient index software market are:

• McKesson Corporation
• Oracle Corporation
• Wipro Limited
• Allscripts Healthcare Solutions, Inc.
• NextGate
• Just Associates, Inc.
• Verato
• QuadraMed Affinity Corporation
• MEDITECH
• ARGO Data Resource Corporation
• Intersystems Corporation
• Epic
• Others

Best practices:

- Just Associates, Inc.:

- NextGate

- QuadraMed Affinity Corporation

- Verato

Global Master Patient Index Software Market: Research Scope

Global Master Patient Index Software Market, by Type

• Software
• Services
• Managed Services

Global Master Patient Index Software Market, by Deployment Mode

• Cloud-based
• On-premises

Global Master Patient Index Software Market, by End-user

• Healthcare Organization
• Hospitals
• Clinics
• Others

Global Master Patient Index Software Market, by Region

• North America
• The U.S.
• Canada
• Europe
• Germany
• France
• The U.K.
• Italy
• Spain
• Rest of Europe
• Asia Pacific
• China
• Japan
• India
• Australia & New Zealand
• Rest of Asia Pacific
• Latin America
• Brazil
• Mexico
• Rest of Latin America
• Middle East & Africa
• GCC Countries
• South Africa
• Rest of the Middle East & Africa

How Does an eMPI Work?

An eMPI constantly looks for potential matches across all data sources that are loaded into the eMPI. The matching is handled by a matching algorithm that locates and assesses the quality of matches even when demographic information is missing or wrong. The algorithm follows the same sort of logic that a well-trained data integrity specialist would when comparing two records to see if they belong to the same person. The algorithm should account for:

• common data entry errors like a transposed month and day in a date of birth misspelled names and inconsistent addresses.
• common false positives like twins or junior/senior
• corrupt records where one person’s information is overlaid with another person’s information.
• default values such as all ones or nines for social security or phone number or ‘baby girl’, or ‘John Doe’ for the name.

When looking for potential matches, an eMPI will start by looking for records that have similar values in key demographic fields. Then it narrows those results by looking at the overall quality of the matches and assigns a score and classification to each potential match.

How Are Matches Classified?

Potential matches are given either a ‘Yes’ or ‘Maybe’ classification. Records that are given a ‘Yes’ classification, meaning that there is no doubt these records belong to the same person, will be automatically linked together by assigning them the same enterprise identifier. No human interaction is required. When two records look like they could be the same person but there are enough differences that it is not safe to auto-link them, they will be given a ‘Maybe’ classification.

Many eMPIs have the ability to use semi-public information from third-party data aggregators like Lexis Nexis to help identify people. You may hear this functionality referred to as Research Automation or Referential Matching. The result is that many of the maybe matches will be turned into yes matches, shrinking the amount of manual work required. Those may be matches that are left become tasks that will need to be reviewed by a data integrity person.

Benefits of MPI:

• Up to date information anytime and anywhere for access to these services
• Ensures medical staff have the correct and most up to date health record for the right patient
• Accurate identification of an individual and their health record quickly when using a health service
• Improves patient safety by reducing the risk of error due to misdirection of clinical information
• Support the provision of cost-effective and timely health care services

General guidelines:

• Medical records in each facility should be uniform and organized systematically
• Authors should be clearly identifiable
• All entries should be permanent
• Abbreviations should not be used
• Errors should include a strikeout with a single line and initiate with a date

There are four objectives that a modern eMPI should support. They are:

1. Identifying that records from different source systems belong to the same patient and linking them together even when key information such as social security number is missing or wrong. To link the records together, an eMPI will assign the same enterprise identifier to each record belonging to the same patient in each source system.
2. Making use of outside semi-public data to reduce or eliminate the manual effort involved in patient matching.
3. Generating a golden record that contains the best-known information for a person whose information is spread across many different sources.
4. “De-duping” a transactional system by identifying cases where there’s more than one record for the same patient and providing workflow for resolving them.