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Gliklich RE, Bibeau K, Eisenberg F, et al. Outcome Measures Framework: Information Model Report: Registry of Patient Registries [Internet]. Rockville (MD): Agency for Healthcare Research and Quality (US); 2018 Feb.

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Outcome Measures Framework: Information Model Report: Registry of Patient Registries [Internet].

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OMR Governance

Governance Model

The Data Governance Institute (DGI) defines data governance as “a system of decision rights and accountabilities for information-related processes, executed according to agreed-upon models which describe who can take what actions with what information, and when, under what circumstances, using what methods.37 “The DGI identifies ten Rules of Engagement, each of which must be defined, and twelve processes for governing data. Each of these rules is listed below with a description of its application to the OMF Governance.

  1. Mission and Vision
    The OMR will display information on outcome measures currently used in registries, with the short-term goal of reducing variation in outcome measures. Characterizing the outcome measures currently in use will support long-term efforts to develop standard outcome measures by identifying areas of common ground where standards may be developed relatively quickly and areas that will require additional work.
  2. Goals, Governance Metrics and Success Measures, and Funding Strategies
    Goals include content addressing outcomes for a limited set of clinical domains in the first two years of operation with harmonized content.
    Success is defined as clear display and definition of all measure data content without requiring searches for information outside the database with less than 10% overlap in definitions at the end of two years.
  3. Data Rules and Definitions
    Data must be defined using clear algorithms and, where applicable, value sets. Each data element must include information about its clinical focus, what is included, what is excluded, and its scope.
  4. Decision Rights
    Each measure and data element entered must indicate its steward, i.e., the organization responsible for creating and maintaining it. All decisions to update or modify each element are the responsibility of its steward. Stewards agree to routine maintenance and to collaborate and attempt to harmonize their content with other stewards with similar content.
  5. Accountabilities
    The measure and data stewards are accountable to manage their own content.
  6. Controls
    The OMR Staff will assure that tools exist to support the activities of data entry and harmonization.
  7. People and Organizational Bodies
    Data Stakeholders: Data stakeholders include all those with interest in the related clinical condition or procedure.
    A Data Governance Office: A Steering Committee will establish and coordinate the policies and procedures of the OMR.
    Data Stewards: Owners (steward) of data elements and measures are the organizations responsible for their development and maintenance.
  8. Processes: Proactive, Reactive, and Ongoing Data Governance
    Governance is maintained on a community level based on collaboration and harmonization among data and measure stewards. The Scientific Advisory Committee is responsible to adjudicate any disputes or unresolved issues and assure adequate maintenance of all content.

Processes for Governing Data

The remainder of this section will address the processes for governing data (http://www.datagovernance.com/the-dgi-framework/), including:

  1. Aligning Policies, Requirements and Controls
  2. Establishing Decision Rights
  3. Establishing Accountability
  4. Performing Stewardship
  5. Managing Change
  6. Defining Data
  7. Issue Resolution
  8. Specifying Data Quality Requirements
  9. Building Governance into Technology
  10. Stakeholder Care and Support
  11. Stakeholder Communications
  12. Measuring and Reporting Value

Following is a list of existing organizations representing three type of governance models: Fully curated, Community-Sourced, and Hybrid-Community Curated.

Examples of Existing Governance Models

Fully Curated Governance Model

Condition-specific registries managed by clinical specialty societies are often good examples of fully curated governance models. The clinical specialty societies develop content and create definitions and data collection methods based on evidence and/or expert consensus. Similarly, research organizations develop content based on careful study and validation. Examples include the Society for Thoracic Surgery National Database and the Patient Reported Outcome Measurement Information System (PROMIS).

Society of Thoracic Surgeons (STS)

Participation in the Society of Thoracic Surgeons (STS) National Database requires users to sign participation agreements that govern use of the information included in the database. Individual participants own their own patient data and, therefore, may use their own information included in the database as needed. The STS rules of engagement require that an appropriate Institutional Review Board review any particular research hypothesis and study methods for scientific merit and ethical propriety.38 Participants or industry representatives can submit ad hoc queries to STS for data analysis. STS approves such requests based on the merits and intended use of the information (e.g., evaluating quality and patient safety, promoting medical research, or analyzing national trends in practice patterns). The STS Council on Quality, Research and Patient Safety and its Workforce on National Databases is responsible for the development and enhancement of the adult cardiac, general thoracic, and congenital heart surgery databases. Reporting to the Board of Directors, this council is governed by an Operating Board and a Chair and a representative of the Executive Committee. The Workforce members serve for three year terms.39

Patient Reported Outcome Measurement Information System (PROMIS)

The Patient-Reported Outcomes Measurement Information System (PROMIS) is a network of clinicians, clinical researchers, and measurement experts organized around six primary research sites.40 A steering committee (SC), comprised of the seven principal grantees and five NIH scientists governs and assumes ultimate responsibility for the priorities and direction of the network. An independent scientific advisory board (SAB) provides oversight, makes recommendations that support the exchange of research tools and resources, encourages the adoption of common policies on data sharing, leads the creation of item banks and also solicits input and feedback from stakeholders. NIH appoints the SAB and consists of 11 experts from academia, government, and industry.

A statistical coordinating center (SCC) provides a secure, customizable, coordinated data management system for collection, storage, and analysis of data collected by the primary research sites. The SCC also coordinates, facilitates, and maintains information exchange and dissemination, standardizes protocols, study procedures and forms and develops end-user training materials for clinicians. A panel of 22 clinical research and health outcomes experts, the Advisory Panel on Health Outcomes, advises the SCC on relevance and feasibility for clinical research.

Community-Sourced Governance Model

Two examples of community-based governance include (a) management of openEHR content for defining content, or archetypes for use across EHR systems, and (b) the National Library of Medicine (NLM) Value Set Authority Center (VSAC). Each includes some oversight, and the community involvement is briefly described below.


openEHR is a virtual community working on interoperability and computability in e-health. Its main focus is electronic patient records (EHRs) and systems.41 The goal is to standardize health information for computing such that all health data for a person is stored in a lifetime electronic health record that is vendor-independent, enabling analytic functions like clinical decision support to improve health. The Australia National E-Health Transaction Authority applies governance, although development of clinical content descriptions, or archetypes, for openEHR is community-based. Garde, et al., notes that clinical information is constantly evolving with respect to breadth (i.e., new knowledge), depth (i.e., finer-grained detail), and complexity (i.e., new relationships). Therefore, governance comprises “all tasks related to establishing or influencing formal and informal organizational mechanisms and structures in order to systematically influence the building, dissemination, and maintaining of knowledge within and between domains.” A community model addresses concept overlaps (e.g., a cardiovascular assessment for a cardiologist may be applicable to a cardiovascular surgeon) and, therefore, achieves standardization. Definitions must be easily accessible, evidence-based and maintained and systematically updated when knowledge changes. Good governance can remove ambiguity among definitions and encourage understanding of inter-relationships.42

National Library of Medicine Value Set Authority Center

The National Library of Medicine (NLM) Value Set Authority Center (VSAC) established rules for content authors and stewards.43 Authors create, edit and submit value sets to designated stewards. Stewards approve, reject and publish submitted value sets. VSAC governance is more of a community model. The site provides definitions of roles and functions and also tools to enable collaboration and harmonization of concepts. VSAC Administrators arrange VSAC users into two roles, steward groups and author groups. Authors have permissions to create, edit and delete their own draft value sets, as well as to submit value sets to their assigned Stewards for approval, and withdraw value sets from approval. Stewards have permissions to approve, reject, and publish value sets that their assigned author groups create and submit. Stewards provide overall coordination and management of the value sets created by Authors under a specific program or for a specific purpose. Stewards should adhere to the goals of their stewarding organization with regard to the content and maintenance of the value set. VSAC also publishes best practice recommendations regarding development and entry of content into the database. Criteria include clinical validity, complete and correct metadata, non-redundancy, completeness and accuracy of content, alignment with standards, naming conventions.44

New to the VSAC site is the “collaboration management” tool, allowing users to create interactive discussions with stewards of similar or competing value sets to allow harmonization. Harmonization efforts can lead to consolidating one or more value sets. Such efforts can also clarify that the definitional metadata was insufficient to describe the intent of the content; such clarification may result in better description of the value set title and metadata rather than consolidation.

Hybrid Community-Curated Governance Model

Two hybrid governance models address information from a wide-range of stakeholders and incorporate public comment and consensus to publish standards for measurement. Descriptions follow for the National Quality Forum Consensus Development Process (CDP) and the Agency for Healthcare Research and Quality National Quality Measures Clearinghouse (NQMC).

National Quality Forum Consensus Development Process

The National Quality Forum (NQF) defined policies and processes for evaluating clinical quality measures as part of the Consensus Development Process (CDP).45 The CDP includes a nomination process for clinically relevant Steering Committee membership, a call for submission of measures (standards), Steering Committee review, public and member comment, voting, and approval by a subcommittee of the Board of Directors, the Consensus Standards Approval Committee (CSAC) which make final endorsement decisions. The CSAC enforces harmonization of measures or measure content for those that address identical clinical concerns. A separate Appeals Board evaluates disputes. The CSAC, all Steering Committees and all other activities performed by NQF include representation from each of the eight member councils: Consumers, Health Plans, Health Professionals, Provider Organizations, Public/Community Health Agencies, Purchasers, Quality Measurement, Research and Improvement, and Suppliers and Industry.

In addition to the CDP process, NQF coordinates three other measurement-related organizations, each with its own governance structure:

  1. The “Measure Incubator” facilitates development of outcome measures, especially taking advantage of data collected through EHRs.46 An Incubator Advisory Council (IAC) governs the Measure Incubator process, addressing conflicts of interest and advising on funding, project selection and consistency. The IAC includes seven industry leaders in measure development and quality management, as well as business leadership.
  2. The National Priorities Partnership (NPP), encompassing 52 major national organizations, identifies areas important to improve health in a safe, equitable and value-driven health care system based on the National Quality Strategy (NQS). Managed by its own governance structure, the NPP advises HHS on the NQS and identifies areas with measurement gaps.47
  3. The Measure Applications Partnership (MAP) is a public-private partnership created to provide input to the Department of Health and Human Services (HHS) on the selection of performance measures for public reporting and performance-based payment programs.48 Annually, the MAP solicits public comment on measures under consideration for implementation by HHS programs in the subsequent year.49
AHRQ National Quality Measures Clearinghouse (NQMC)

AHRQ manages the National Quality Measures Clearinghouse (NQMC).50 The NQMC provides search capabilities for users to find healthcare quality measures. NQMC provides structured, standardized summaries containing information about measures and their development, using the NQMC Template of Measure Attributes. The NQMC/NGC (National Guideline Clearinghouse) Editorial Board is composed of health care professionals with collective expertise in evidence-based quality measures and clinical practice guidelines. The Editorial Board works with AHRQ to govern content and as a resource for feedback and guidance on developments in health care, providing expert commentaries on topics germane to the quality measures and guidelines. An NQMC/NGC Expert Panel is composed of health care professionals with collective expertise in all aspects of evidence-based health, clinical practice guidelines, quality measurement and reporting, health care policy and administration, and health informatics. The Expert Panel provides feedback and guidance to NQMC and NGC on broad project areas.51 NQMC provides: a Domain Framework and Inclusion Criteria; a Template of Measure Attributes; a Glossary clarifying definitions and examples of terms used to describe common properties of health care measures used in the NQMC structured summaries; and a Classification Scheme to facilitate searching and information retrieval as well as advising on content development and naming conventions.

Promotion of Collaboration Within OMR

To support its mission, the OMR must be a dynamic resource for clinicians, healthcare organizations, researchers, purchasers of healthcare services, payer organizations, and all persons seeking information to evaluate care they receive. The resource must also provide information that is clear, unambiguous, and with sufficient detail for users to differentiate one measure from another. The goal is to allow users to determine which measure(s) might be appropriate for their individual needs. To meet this goal, measures must include very discrete information about the definition of each component (e.g., numerator, denominator, etc.), and further, the definition of the elements in those components. As an example, a measure about acute myocardial infarction (AMI) may, on the surface, seem comparable to other measures of AMI. However, if one measure defines AMI as one of a list of diagnoses entered on a problem list by a physician, and another defines an AMI based on achievement of a threshold of electrocardiograph changes, troponin and myocardial-specific creatinine kinase (CK) levels, the two measures may not be defining the same patients in the denominator. Further, using the same AMI definition example, if one measure uses different thresholds of test results for troponin and CK levels, the populations may also vary. Therefore, to allow users to clearly differentiate measures and to avoid confusion, the OMR must contain sufficient information about the measures at the atomic level (I.e., referencing specific codes, or value sets used for each data element, and where applicable, the specific thresholds, units of measure, and calculation logic employed). While such detail may seem overly complex to include in a single database, existing infrastructure in electronic measurement landscape provides a model for moving forward. Moreover, clear description of the data element detail allows measure developers to collaborate and, where possible, agree on standard definitions; where consensus is not possible, the collaboration enables measure developers to more clearly describe the differences in the definition and naming of their data elements. Existing models for such collaboration also exist today.

The National Library of Medicine (NLM) Value Set Authority Center (VSAC) currently provides such collaboration tools for authoring and maintaining value sets used in electronic clinical quality measures (eCQMs) developed for U.S. government programs. The tools enable value set stewards to navigate code systems (e.g., LOINC and SNOMED-CT) and tools also alert the value set stewards about updates to the code systems used (i.e., on publication of a new version), highlighting the content in each value set impacted by the update. The tool thus gives value set stewards the ability to manage and version their content. The NLM VSAC also provides a collaboration space allowing measure developers with questions or suggestions about existing value sets to query the respective value set steward. Such queries may result in (a) addition or removal of concepts from existing value sets, or (b) greater documentation of the purpose, inclusion and exclusion criteria for the value set to reduce potential ambiguity. Cases of continued conflict in definition could require escalation to other forums to review.

Rather than duplicating existing infrastructure, the OMR should use and extend the NLM-VSAC collaboration space. Ideally, such community-driven collaboration will extend to include review at the value set, the clause, or phrase level (e.g., the AMI definition described above that incorporates observation values), and the measure component level (e.g., a denominator that includes the AMI definition during a specified time frame and other population restrictions) (see Figure 4). Also, to enable harmonization and collaboration at each level of abstraction, the OMR tools will need to provide reference libraries of existing and harmonized value sets, clauses and measure components. Using the AMI example, the same, harmonized value sets may describe the referenced laboratory tests with LOINC codes, there may be several harmonized AMI “clauses” (e.g., posterior AMI, anterior AMI, etc.) and several harmonized AMI “populations” available in a library of reusable measure components at each level of abstraction.

Shareable components of measures start at the basic, or atomic level, the codes, or value set used to express a concept. Multiple value sets to express the same concept should be discouraged; rather reuse should be encouraged and concerns about missing or extraneous content should be managed by collaboration, or harmonization. Similarly, use of the value set in different context may require collaboration (e.g., use of a medication value set to express medications administered and also medications to which a patient may exhibit adverse reactions). Higher level expressions using value sets, phrases or clauses, may relate the values by timing relationships (e.g., medication administered within 1 hour prior to a surgical incision), and combine phrases into sentences to describe a broader population (e.g., medication administered within 1 hour of a surgical incision for all patients with mitral valve replacement surgery), and further into measure components (e.g., the denominator indicating all patients with medication administered within 1 hour of mitral valve replacement surgery during the calendar year). Each of these components from the value set up through the measure component, may be reusable in other measures; thus, each is potentially open for collaboration and harmonization as other measure stewards consider reuse.

Figure 4

Use of shareable components in across measures.

Each level of measure abstraction will evolve over time and each requires updating and potential harmonization with corresponding versions of the underlying code systems and/or clinical evidence. Comments and requests in a community collaboration environment must be continuous, and the infrastructure to perform such collaboration requires tools to track the frequency of requests, the time from request to resolution, and the successful outcome of resolution (i.e., consensus has or has not been reached). The tools must be able to escalate to OMR staff all cases in which resolution has not occurred in acceptable time frames, or when consensus has not been reached (i.e., 80% of collaborating parties have not approved the resolution). The OMR staff may then decide to work with the parties involved to understand the issues and encourage a resolution, or, where resolution is not forthcoming, refer the issue to the Clinical Advisory Committee for review and recommendation (see Figure 5).

The ultimate goal of the OMR is to generate consensus and harmonization of all data included in outcome measures. The figure depicts a progression of curation and collaboration at the community level, allowing harmonization to occur among measure stewards (Step 1). The online tools provide sufficient monitoring to allow OMR staff to escalate unresolved issues to the Clinical Advisory Committee (Step 2) and thus reach a negotiated resolution with agreement regarding a standard solution, or more distinct (i.e., less ambiguous) definitions and metadata so users can clearly distinguish among the different measures or measure elements (Step 3).

Figure 5

Hybrid curation model.

The initial objective of the OMR is to collect sufficient information to characterize the types of outcome measures that are currently used in patient registries. The long-term objective of the OMR is to support efforts to standardize outcome measures and to facilitate access to that information. The OMR will display information on the outcome measures currently used in registries, with the short-term goal of reducing variation in outcome measures. Characterizing the outcome measures currently in use will support long-term efforts to develop standard outcome measures by identifying areas of common ground where standards may be developed relatively quickly and areas that will require additional work.

More importantly, the OMF conceptual model can serve as a content model for developing standard outcome measures in specific disease areas. While existing outcome measures may fit into the conceptual model, a long-term goal of the OMR is to encourage groups that are developing outcome measures to use the conceptual framework to define new measures. The increasing recognition of the value of outcome measures has led to a need for more outcome measures across a broad range of conditions. By promoting the use of the OMF conceptual framework, it will be possible to simplify the task of aggregating measures across multiple conditions while encouraging researchers and others to think of outcome measures in a standardized way, both of which will support the long-term goals of the OMR.

Composition of the OMR Governance

A hybrid community-curated model for managing OMR governance is proposed here. Combining the NLM-VSAC community-based efforts and the NQF CDP and MAP process, input from the community is important to identify when harmonization is necessary, and further, tools to enable harmonization among the various measure stewards will enhance collaboration and improve clarification of the content. Similar to these existing organizational processes, administration and maintenance of the OMR will require a Steering Committee to provide high-level policy and structure and a broad-based Clinical Advisory Committee with clinical expertise to meets its goals and objectives, and dedicated management staff. The responsibilities of these groups are described in Figure 6 further below. The organizational structure may be modified to incorporate a role for the funding source(s), once a funding plan has been identified for the OMR.

Scope of Responsibilities

The OMR Governance Structure includes (1) a Steering Committee to establish rules, oversee tool requirements and development and perform outreach, (2) a Clinical Advisory Committee to manage consensus for harmonization of measures and data elements, and to recommend common solutions to operational challenges, and (3) OMR Operational Staff to develop and manage the tools, help desk, perform initial data loading to reach a critical mass of information in the database, and also to monitor community-based harmonization and escalate unresolved issues to the Clinical Advisory Committee as needed.

Figure 6OMR governance structure

Steering Committee

The OMR governance structure should include a broad set of stakeholders, similar to the either NQF categories to encompass consumers, health plans, health professionals, provider organizations, public and community health agencies, purchasers, quality measurement, research and improvement experts, and suppliers and industry (including software vendors and pharmaceutical companies). By including stakeholders in the governance structure, the OMR will be able to ensure that it meets the needs of multiple potential user groups especially in addressing the outcome categories. Potential users include those who provide the measure information (e.g., registry owners) and registry seekers, who may search the OMR to identify outcomes or outcome measures of interest for new research, for managing a group of patients based on their preferences, or consumers seeking information about their conditions or procedures. Groups seeking to develop data standards for specific disease areas may also use the OMR to understand how data are defined as collected in existing measures and registries.

A Steering Committee that includes a broad set of stakeholder representatives will allow the OMR to be responsive to user needs while still achieving its primary goals and objectives. The Steering Committee will be responsible for making strategic and executive decisions for the OMR, covering three major areas:

  1. Establishing rules to ensure that the content of the OMR remains relevant and useful to registry holders and registry seekers, and maintaining the balance between the need for detailed information and the burden on users. This primary focus will address the needs and ease of use for registry users. The Steering Committee will assure content is clearly defined and also promote the objectives of the OMR and disseminate information about its purpose and use, in order to encourage submission of patient and population outcome measures.
  2. Providing oversight for tools that enable community collaboration and curation and that provide sufficient measures to evaluate the success of the process. The Steering Committee will decide on timing of update releases for the OMR and should give priority to users’ interests when contemplating changes or revisions.
  3. Performing outreach to assure coverage of appropriate clinical domains in the database.

The Steering Committee should include stakeholders with clinical expertise, experience in registry design and conduct, and information technology system design. Members should be selected from community nominations and appointed for a fixed renewable term of three years, consistent with common practice. A staggered term is desirable so that not all committee members rotate off at the same time. The Steering Committee should develop bylaws to govern its activities and a regular schedule for meetings (e.g., quarterly). The Steering Committee will work closely with the Clinical Advisory Committee and the OMR staff.

The Steering committee will be comprised by seven members. The contracts/project officer for the OMF project shall serve as the Chair of the Steering Committee. Representatives from the following disciplines shall comprise the membership of the Steering Committee: one (1) from a member representing a group involved in harmonization efforts, one (1) registry owner focusing on quality improvement program, one (1) registry owner representing a registry operated within an academic setting, one (1) potential user of the OMF [a research naïve setting new to setting up a registry who has not yet been involved in harmonization efforts], one (1) technical expert, and one (1) member representing technical solutions and hosting efforts.

Clinical Advisory Committee

The OMR will contain clinical content for a wide range of disease areas. Clinical expertise will be necessary to determine clinical equivalency for similar definitions and to assess whether some submitted items are appropriate for inclusion in the OMR. The OMR Staff will solicit nominations of clinical experts in various clinical domains and maintain a list of advisors including physicians, nurses and other clinicians involved in each relevant domain to provide guidance on many topics; however, additional guidance may be needed for particularly complex questions. The Clinical Advisory Committee will include experts from a broad range of clinical areas who will provide guidance to the OMR Staff and the Steering Committee on clinical issues and decisions that affect the clinical content of the OMF. The OMR Staff will consult with Committee members individually or in small groups on a regular basis to discuss specific clinical questions related to those members’ areas of expertise. Discussions with the full committee will occur less frequently.

It is envisioned that the Clinical Advisory Committee will be comprised of approximately twelve members. These members will represent relevant aspects of clinical practice including not only physicians, but representatives of nursing, payers, consumers and patients. The goal of this team will be to manage the curation activity. Each member will be specifically responsible for a division of labor to make the resourcing on the OMR curation activities manageable. One member of the OMR Staff will likely be responsible for the content entry for each clinical domain as it comes into the OMR database. From there, 2–3 members of the OMR Staff will lead activities on clinical equivalency projections, consulting members of the Clinical Advisory Committee as needed. A member of the Clinical Advisory Committee will serve as technical advisor, with expertise in database structure and informatics. The group will be led by a scientific advisor who serves as a direct counterpart to the Steering Committee. All other members will share responsibilities on data curation activities.

OMR Staff

The OMR Staff will be responsible for the day-to-day operations of the OMR. The staff will require technical resources to maintain the OMR database, clinical resources to review submitted content, project management staff to manage relationships with third-party systems (e.g., the RoPR), and support staff to interact with users. The operations will also coordinate the activities of the Steering Committee and Clinical Advisory Committee and consult with these groups as needed. Tasks include:

  1. Provide tools to the measure development community to develop and collaborate on value sets, measure clauses and measure components. Such tools may include infrastructure developed external to the OMR tools, and yet available for use as reference by OMR.
  2. Evaluate new submissions to the OMR and approve inclusion of all submissions that meet basic criteria (i.e., sufficient metadata, mapping to OMF outcome criteria, steward agreement to participate in collaboration and to maintain currency of measure clinical content).
  3. Monitor the output of the community collaboration tools and report to the Steering Committee and the Clinical Advisory Committee monthly regarding active collaboration, resolved issues (and resolution type – i.e., those items modified or those with more clearly defined metadata to reduce potential ambiguity), unresolved issues including the time unresolved and the number referred for resolution.
  4. Advance harmonization of outcome measures and measure components by investigating harmonization issues not resolved by community curation and escalate issues that cannot be resolved to the Clinical Advisory Committee, inviting members with specific expertise to review the relevant clinical domain.
  5. Maintain a list of members of the Clinical Advisory Committee assuring coverage of common, high-volume clinical domains in cooperation with the Steering Committee and AHRQ. To maintain membership with 3-year terms and maintain some consistency, the first set of members will be assigned 1, 2 or 3 year terms, each subsequent term to be awarded for 3 years. The OMR staff will submit a public call for membership in the Clinical Advisory Committee for relevant domains and submit a recommendation for appointment to the Steering Committee for appointment.

Interactions Between the Steering Committee and Clinical Advisory Committee

The interaction between the steering committee and operations team should be iterative and cyclical. That is, members of the OMF Staff will be expected to provide regular updates to the Steering Committee on lessons learned from the OMR on a quarterly basis.

In turn, the Steering Committee may use this information to make or endorse recommended modifications as necessary to the OMR. These interactions will be most successful if both parties offer an exchange of information suited to the improvement of the overall OMR, either from a clinical or operational perspective.


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