A Framework for Cross-Organizational Patient Identity Matching Available Now for Public Comment

The healthcare industry is making significant progress on technical interoperability, but we continue to fall short of the promise of true health information exchange. Until we can consistently send and receive accurate and useful patient data nationwide, we will fail to realize the documented benefits of well-executed health information exchanges: improvements in clinical decision making and patient safety, business process improvement, and support for value based payment.  Among the remaining challenges to successful nationwide exchange is patient matching across organizational boundaries.

A Framework for Cross-Organizational Patient Identity ManagementThe inability to consistently and accurately match patient data creates a number of problems for physicians and other health care providers.  Providers may have an incomplete view of a patient’s medical history, care may not be well coordinated, patient records may be overlaid, unnecessary testing or improper treatment may be ordered, and patient confidence may be damaged or diminished.

In addition, providers may experience a number of clinical workflow inefficiencies that are costly. Those include prolonged troubleshooting to find the correct patient record, a reversion to manual telephone and fax information exchange workflows, waiting for a duplicate lab test order, or a manual effort to fix a patient record match.

To address these critical patient matching and identity management issues, The Sequoia Project, in collaboration with the Care Connectivity Consortium (CCC), has developed a framework for future growth and improvement that will shed light on several key topics:

  • A case study illustrating one organization’s inter-organizational patient matching journey from a 10% success rate to a greater than 95% success rate and what we can learn from their experiences;
  • A patient matching maturity model designed to help organizations assess their current state and provide a roadmap towards methodically improving; and
  • A list of minimally acceptable patient matching practices for CIOs, CTOs and other technology leaders to adopt, if they haven’t already, and implement. This list establishes a “floor” in terms of matching patients across organizational boundaries.
The intent of this first framework draft is to solicit feedback. That feedback will be publicly dispositioned, resulting in a freely-reusable resource for the industry.  Over time, it is the intent of The Sequoia Project that this document is periodically updated to capture and advance cross-organizational patient matching innovations.
A Patient Identity Management Work Group of nine to 12 member representatives from across the healthcare industry with an interest in patient identity management policy or the technical needs for successful patient matching across organizations is forming now. Email admin@sequoiaproject.org to receive an application. 

 

Download the Framework for Cross-Organizational Patient Identity Matching paper now:

To download the framework draft, please complete the form below. 







 


Below is a more detailed explanation of the topics explored within The Sequoia Project Framework for Patient Identity Management paper:

     Case Study

Intermountain Health Care, a CCC member, is a sophisticated healthcare provider and payer, and has invested heavily in healthcare information technology.  So when they started exchanging data with other organizations, they expected a reasonably high degree of success.  They surprisingly only achieved a 10% success rate in accurately matching patient records across organizational boundaries.  Through algorithmic performance and refinement, and data quality analysis, Intermountain and its partners gained significant initial improvement from 10%.  Human workflow and data entry issues were next inspected, with an expected improvement to 90%, but actually only resulted in an improvement to 62%.

Patient Match Rate Analysis

Patient Match Rate Analysis

The remaining 38% error rate was analyzed in detail and found to break down into algorithmic issues, authorization/consent issues, network and IT issues, and other errors such as inconsistent encoding.  These improvements resulted in a match rate of 85%.  A number of best practices were then identified and implemented resulting in successful patient match rates of over 95%.  These practices include systematically working with partners, addressing patient consent issues, proactively improving “fragile” identities, and addressing human workflow issues.  An analysis of common patient matching traits is presented, along with a list of rules developed during the study. These rules are further analyzed in terms of completeness and uniqueness.        The result was significantly improved match rates for Intermountain and its partners, and patient matching improvement strategies that can be replicated across the industry.

At the conclusion of the case study a number of lesson’s learned and best practices are shared.  There are also suggestions to improve cross-organization patient matching success to greater than 99%, which include the use of a supplemental identifier, involving the patient in the identity management process, and more.  We also provide a self-diagnostic tool, in the form of a questionnaire, to jump start a self-assessment of your own patient matching strategies.
             Patient Matching Maturity Model
We believe that more precise definitions of the maturity model will give organizations the ability to adopt more advanced patient identity management in a methodical manner.  The levels currently being contemplated in our proposed maturity model, now open to public comment, include:

  • Level 0: Indicating ad hoc processes and outcomes, little to no management oversight or recognition;
  • Level 1: Indicating adoption of basic defined processes with associated repeatable outcomes, and limited management involvement;
  • Level 2: Indicating increasing maturation of processes, definitions of most key processes, data governance, algorithm use, active management involvement, accumulation of quality metrics;
  • Level 3: Indicating advanced use of existing technologies with associated management controls and senior management awareness, use of quality metrics; and
  • Level 4: Indicating innovation, on-going optimization, and senior management active involvement.       

Assessments are based on the International Organization for Standardization (ISO) framework, which includes people, process, and technology, with the added dimension of governance.  Current traits associated with the various levels include patient identity management validation plans, community collaboration, use of standards, quality metrics, knowledge sharing, partner onboarding maturity, and more.  Each of these traits is defined and mapped to appropriate maturity levels.

      Patient Matching Minimal Acceptable Principles
This proposed framework illustrates a very low-level, and concrete, list of cross-organizational patient matching practices.  The target audience for this, the last section in this paper, are senior technical staff responsible for implementing clinical document care summary exchange. A number of specific rules are presented, such as the prohibition of using exact character-by-character matching, the corresponding responsibilities on both partners to an exchange of patient data, and similar practices.  Other principles include not relying on any specific identifier (such as a social security number), not making any assumptions about the life cycle of a patient identifier, using normalized traits, and more.

 

Once comments have been received by the community, The Sequoia Project will organize the feedback, and will then hold a series of one or more comment disposition working sessions to publicly review and resolve each comment.  The result will be a “living document” reflecting the cutting-edge in patient matching that will be updated on a periodic basis and shared for reuse.