implementation research December 4, 2017 Maren T. Scheuner, MD, - - PowerPoint PPT Presentation

A logic model for precision medicine implementation research

December 4, 2017

Maren T. Scheuner, MD, MPH, FACMG Chief, Medical Genetics, VA Greater Los Angeles Healthcare System Director, VISN 22 Clinical Genetics Program Professor of Medicine, David Geffen School of Medicine at UCLA

Disclosures

• No conflicts to disclose
• Full-time employee of the Department of Veterans Affairs (VA)
• Views expressed are my own and do not represent the opinions of the VA
• Academic affiliation, David Geffen School of Medicine at UCLA
• Funding from VA HSR&D

Acknowledgements

Administrative support:

• Jamiyla Bolton
• Gregory Brent, MD
• Ismelda Canelo
• Mark Canning
• Britney Chow
• Daniel Foster
• Elise Hulsebos
• Uyi Igodan
• Kristina Oishi
• Demetrius Williams
• Elizabeth M. Yano, PhD, MSPH

Program committee:

• Catherine Chanfreau, PhD
• Barbara Lerner, PhD
• Sara Knight, PhD
• Jane Peredo, MS, CGC
• Dawn Provenzale, MD
• Marcia Russell, MD
• Maren Scheuner, MD, MPH
• Corrine Voils, PhD

VA OR&D Leadership:

• David Atkins, MD
• Amy Kilbourne PhD
• Ron Przygodzki PhD
• Miho Tanaka, PhD

Funding: VA HSRD Field-based Meeting

Goal of the Conference

To foster the creation of partnerships between key stakeholder groups to advance a research agenda focused

• n the outcomes of precision medicine to impact policy,

research, and delivery of precision medicine.

Precision medicine definition: An innovative approach to health care that incorporates genetic information and other biomarkers into personalized clinical decisions that can improve clinical, reproductive, behavioral, and psychosocial

• utcomes for individuals and their family members.

• 80+ participants, about half VA and half non-VA
• Dr. David Shulkin (VA Under Secretary for Health) spoke about the VA’s Precision

Oncology Program

• 46 speakers and 4 moderators described experience with precision medicine,

programmatic goals, research needs, priority areas, opportunities and challenges

• Stakeholder groups:

Clinicians Patients/family members/advocacy organizations Policy/Decision makers Administrators/Managers Ethicists/Lawyers Researchers Information technology experts

Conference Participants

Methods

• Developed and administered a web-based survey that asked participants

to rate the value of and whether a third-party should pay for 44 PM

• utcomes (Poster B94).
• Pre-conference survey results informed the conference discussion
• Audio-recorded and transcribed conference proceedings.
• Identified research topics mentioned during the conference.
• Thematic coding of quotes was performed by two investigators

independently, with a third helping to resolve discrepancies.

• Themes were assigned to a research topic and sub-themes were

characterized as challenges, opportunities, or strategies.

637 total quotes 316 quotes mapped to 10 research topics 44 themes identified  243 sub-themes

Research topic Themes Challenges Opportunities Strategies Information technology 7 7 11 21 Dissemination & Implementation 6 18 6 22 Outcomes research 5 8 10 9 Learning system 5 6 11 6 Organization/Provider behavior 4 8 4 19 Ethics/Equity 4 11 3 9 Veteran engagement 4 1 5 8 Partnered research 3 3 10 8 Economics research 3 5 4 1 Clinical research 3 4 5 TOTAL 44 71 69 103

Classification of Quotes

637 total quotes 316 quotes mapped to 10 research topics 44 themes identified  243 sub-themes

Research topic Themes Challenges Opportunities Strategies Information technology 7 7 11 21 Dissemination & Implementation 6 18 6 22 Outcomes research 5 8 10 9 Learning system 5 6 11 6 Organization/Provider behavior 4 8 4 19 Ethics/Equity 4 11 3 9 Veteran engagement 4 1 5 8 Partnered research 3 3 10 8 Economics research 3 5 4 1 Clinical research 3 4 5 TOTAL 44 71 69 103

Classification of Quotes

Organization/Provider Research, Workforce, Challenge, Genomics expertise is insufficient “…we want the relevant experts. You probably know you can go all over this country and you have people ‘doing what the experts do’ even though they're not expert. And they may also be doing some harm. At the very least, they're doing a lousy job and very often not serving the patient and they're wasting, again, all of our money….” Payer/Policy Maker

637 total quotes 316 quotes mapped to 10 research topics 44 themes identified  243 sub-themes

Research topic Themes Challenges Opportunities Strategies Information technology 7 7 11 21 Dissemination & Implementation 6 18 6 22 Outcomes research 5 8 10 9 Learning system 5 6 11 6 Organization/Provider behavior 4 8 4 19 Ethics/Equity 4 11 3 9 Veteran engagement 4 1 5 8 Partnered research 3 3 10 8 Economics research 3 5 4 1 Clinical research 3 4 5 TOTAL 44 71 69 103

Classification of Quotes

Economics Research, Health care utilization, Opportunity, Ending the diagnostic odyssey “…diagnostic uncertainty has been shown to be a driver

• f utilization of medical services; if you don’t know

what your diagnosis is, then you utilize more services, and where we haven’t done research, at least in genetic diagnosis, even when there’s no treatment, does the establishment of a diagnosis reduce utilization, which in fact would be an economic benefit that could be recognized by healthcare systems and payers, etc.” clinical geneticist

637 total quotes 316 quotes mapped to 10 research topics 44 themes identified  243 sub-themes

Research topic Themes Challenges Opportunities Strategies Information technology 7 7 11 21 Dissemination & Implementation 6 18 6 22 Outcomes research 5 8 10 9 Learning system 5 6 11 6 Organization/Provider behavior 4 8 4 19 Ethics/Equity 4 11 3 9 Veteran engagement 4 1 5 8 Partnered research 3 3 10 8 Economics research 3 5 4 1 Clinical research 3 4 5 TOTAL 44 71 69 103

Classification of Quotes

Dissemination & implementation, Access/Variation in PM Care, Strategy, Centralized technical assistance “So the program has a clinical component… and it tries to do things that individual docs can’t easily do alone, but if there’s a central core doing it, they’re empowered. They’re not worried

• r scared that they’re not delivering the best care. They are

delivering the best care because we have a concerted program that helps them do it, they’re not on their own.” Oncologist

Synthesis of Findings

• Mapped opportunities and challenges to the Consolidated

Framework for Implementation Research (CFIR) constructs to help inform implementation planning.

• Mapped strategies to the Expert Recommendations for

Implementing Change (ERIC) compilation to help prioritize implementation strategies.

• Created a logic model to provide a roadmap for a precision

medicine program.

• The CFIR provides a pragmatic structure for approaching complex, interacting, multi-

level, and transient states of constructs in the real world by embracing, consolidating, and unifying key constructs from published implementation theories.

• It can be used to guide formative evaluations and build the implementation knowledge

base across multiple studies and settings.

• The CFIR comprises five major domains that interact in rich and complex ways to

influence implementation effectiveness.

Damschroder LI, Aron DC, Keith RE, et al. Implent Sci 2009;4:50. doi: 10.1186/1748-5908-4-50.

CFIR Domains and Constructs

Characteristics of the intervention Inner setting Outer setting Individuals involved Implementation process

• Intervention

source

• Evidence strength

and quality

• Relative

advantage

• Adaptability
• Trialability
• Complexity
• Design quality &

packaging

• Cost
• Structural

characteristics

• Networks and

communications

• Culture
• Implementation

climate

• Readiness for

implementation

• Patient needs

and resources

• Cosmopolitanism
• Peer pressure
• External policies

and incentives

• Knowledge and

beliefs about the intervention

• Self-efficacy
• Individual stage
• f change
• Individual

identification with the

• rganization
• Other personal

attributes

• Planning
• Engaging
• Executing
• Reflecting and

evaluating

Characteristics of the intervention Inner setting Outer setting Individuals involved Implementation process

• Intervention

source

• Evidence strength

& quality

• Relative

advantage

• Adaptability
• Trialability
• Complexity
• Design quality &

packaging

• Cost
• Structural

characteristics

• Networks and

communications

• Culture
• Implementation

climate

• Readiness for

implementation

• Patient needs

and resources

• Cosmopolitanism
• Peer pressure
• External policies

and incentives

• Knowledge and

beliefs about the intervention

• Self-efficacy
• Individual stage
• f change
• Individual

identification with the

• rganization
• Other personal

attributes

• Planning
• Engaging
• Executing
• Reflecting and

evaluating

PM Opportunities and Challenges Mapped to CFIR Domains and Constructs

Characteristics of the intervention Inner setting Outer setting Individuals involved Implementation process

• Intervention

source

• Evidence strength

& quality

• Relative

advantage

• Adaptability
• Trialability
• Complexity
• Design quality &

packaging

• Cost
• Structural

characteristics

• Networks and

communications

• Culture
• Implementation

climate

• Readiness for

implementation

• Patient needs

and resources

• Cosmopolitanism
• Peer pressure
• External policies

and incentives

• Knowledge and

beliefs about the intervention

• Self-efficacy
• Individual stage
• f change
• Individual

identification with the

• rganization
• Other personal

attributes

• Planning
• Engaging
• Executing
• Reflecting and

evaluating

PM Opportunities and Challenges Mapped to CFIR Domains and Constructs

Design quality & packaging, challenges: Limited integration of PM in EHRs; Difficulty extracting high- quality, reliable phenotypic information from admin data or clinical notes; Difficulty getting structured data from genetic test reports; Limited data standards for representing genetic information in the EHR; Format of genetic test reports are difficult to interpret for most clinicians; Difficulty keeping clinical and research data separate.

Characteristics of the intervention Inner setting Outer setting Individuals involved Implementation process

• Intervention

source

• Evidence strength

& quality

• Relative

advantage

• Adaptability
• Trialability
• Complexity
• Design quality &

packaging

• Cost
• Structural

characteristics

• Networks and

communications

• Culture
• Implementation

climate

• Readiness for

implementation

• Patient needs

and resources

• Cosmopolitanism
• Peer pressure
• External policies

and incentives

• Knowledge and

beliefs about the intervention

• Self-efficacy
• Individual stage
• f change
• Individual

identification with the

• rganization
• Other personal

attributes

• Planning
• Engaging
• Executing
• Reflecting and

evaluating

PM Opportunities and Challenges Mapped to CFIR Domains and Constructs

Structural characteristics, opportunities: Electronic health records capture PM outcomes; Integrated health care systems have longitudinal patient data that allows for the study of longer term outcomes

Characteristics of the intervention Inner setting Outer setting Individuals involved Implementation process

• Intervention

source

• Evidence strength

& quality

• Relative

advantage

• Adaptability
• Trialability
• Complexity
• Design quality &

packaging

• Cost
• Structural

characteristics

• Networks and

communications

• Culture
• Implementation

climate

• Readiness for

implementation

• Patient needs

and resources

• Cosmopolitanism
• Peer pressure
• External policies

and incentives

• Knowledge and

beliefs about the intervention

• Self-efficacy
• Individual stage
• f change
• Individual

identification with the

• rganization
• Other personal

attributes

• Planning
• Engaging
• Executing
• Reflecting and

evaluating

PM Opportunities and Challenges Mapped to CFIR Domains and Constructs

Patient needs and resources, challenges: Workforce insufficient/not prepared to meet the demand for PM; Variable access to genetic testing and use among vulnerable populations; Ethical issues/need for informed consent pertaining to PM are complex

Characteristics of the intervention Inner setting Outer setting Individuals involved Implementation process

• Intervention

source

• Evidence strength

& quality

• Relative

advantage

• Adaptability
• Trialability
• Complexity
• Design quality &

packaging

• Cost
• Structural

characteristics

• Networks and

communications

• Culture
• Implementation

climate

• Readiness for

implementation

• Patient needs

and resources

• Cosmopolitanism
• Peer pressure
• External policies

and incentives

• Knowledge and

beliefs about the intervention

• Self-efficacy
• Individual stage
• f change
• Individual

identification with the

• rganization
• Other personal

attributes

• Planning
• Engaging
• Executing
• Reflecting and

evaluating

PM Opportunities and Challenges Mapped to CFIR Domains and Constructs

Reflecting & Evaluating, Opportunities: Registries can monitor PM outcomes; EHR/clinical informatics can evaluate utilization of PM services

• 1. Expert panelists (N = 71) revised an existing compilation of 68

implementation strategies using a modified Delphi process, which resulted in an updated compilation of 73 discrete implementation strategies.

• 63% implementation science expertise; 29% implementation science

and clinical expertise; 8% clinical expertise only

• 69% affiliated with the VA
• 2. Preliminary validation of the compilation of 73 implementation

strategies was achieved by studying the relationships between the strategies and obtaining relative importance and feasibility ratings for each strategy.

• 9 clusters identified with practical heuristic value

40 PM Implementation Strategies Mapped to ERIC Compilation

Evaluative and iterative strategies (7)

• Assess for readiness; identify barriers and

facilitators (1)

• Purposefully reexamine the

implementation (2)

• Develop and implement tools for quality

monitoring (1)

• Develop and organize quality monitoring

systems (2)

• Develop a formal implementation

blueprint (1)

Provide interactive assistance (13)

• Facilitation (1)
• Provide local technical assistance (1)
• Provide clinical supervision (4)
• Centralize technical assistance (7)

Adapt and tailor to context (15)

• Promote adaptability (3)
• Use data experts (4)
• Use data warehousing techniques (8)

Train and educate stakeholders (3)

• Provide ongoing consultation (1)
• Conduct education outreach visits (1)
• Create a learning collaborative (1)

Develop stakeholder relationships (27)

• Identify and prepare champions (1)
• Inform local opinion leaders (2)
• Build a coalition (1)
• Conduct local consensus discussions (3)
• Capture and share local knowledge (6)
• Use advisory boards and workgroups (1)
• Model and simulate change (1)
• Develop academic partnerships (4)
• Recruit, designate, and train for leadership (1)
• Promote network weaving (7)

Support clinicians (8)

• Facilitate relay of clinical data to providers (6)
• Revise professional roles (1)
• Create new clinical teams (1)

Engage consumers (12)

• Involve patients/consumers and

family (8)

• Intervene with patients/consumers to

enhance uptake and adherence (2)

• Prepare patients/consumers to be

active participants (2)

Use financial strategies (6)

• Fund and contract for the clinical

innovation (1)

• Alternative incentive/allowance

structures (4)

• Use other payment schemes (1)

Change infrastructure (39)

• Change record systems (9)
• Change physical structures and

equipment (9)

• Change service sites (9)
• Mandate change (8)
• Start a dissemination organization (4)

Waltz TJ, et al. Implementation Science (2015) 10:109

PM Implementation Strategies Mapped to ERIC Compilation

Evaluative and iterative strategies (7)

• Assess for readiness; identify barriers and

facilitators (1)

• Purposefully reexamine the

implementation (2)

• Develop and implement tools for quality

monitoring (1)

• Develop and organize quality monitoring

systems (2)

• Develop a formal implementation

blueprint (1)

Provide interactive assistance (13)

• Facilitation (1)
• Provide local technical assistance (1)
• Provide clinical supervision (4)
• Centralize technical assistance (7)

Adapt and tailor to context (15)

• Promote adaptability (3)
• Use data experts (4)
• Use data warehousing techniques (8)

Train and educate stakeholders (3)

• Provide ongoing consultation (1)
• Conduct education outreach visits (1)
• Create a learning collaborative (1)

Develop stakeholder relationships (27)

• Identify and prepare champions (1)
• Inform local opinion leaders (2)
• Build a coalition (1)
• Conduct local consensus discussions (3)
• Capture and share local knowledge (6)
• Use advisory boards and workgroups (1)
• Model and simulate change (1)
• Develop academic partnerships (4)
• Recruit, designate, and train for leadership (1)
• Promote network weaving (7)

Support clinicians (8)

• Facilitate relay of clinical data to providers (6)
• Revise professional roles (1)
• Create new clinical teams (1)

Engage consumers (12)

• Involve patients/consumers and

family (8)

• Intervene with patients/consumers to

enhance uptake and adherence (2)

• Prepare patients/consumers to be

active participants (2)

Use financial strategies (6)

• Fund and contract for the clinical

innovation (1)

• Alternative incentive/allowance

structures (4)

• Use other payment schemes (1)

Change infrastructure (39)

• Change record systems (9)
• Change physical structures and

equipment (9)

• Change service sites (9)
• Mandate change (8)
• Start a dissemination organization (4)

Waltz TJ, et al. Implementation Science (2015) 10:109

Provide technical supervision: Prior authorization to ensure appropriate genetic testing; Gatekeeper for genetic testing (e.g., only

• rderable by genetics professional)

PM Implementation Strategies Mapped to ERIC Compilation

Evaluative and iterative strategies (7)

• Assess for readiness; identify barriers and

facilitators (1)

• Purposefully reexamine the

implementation (2)

• Develop and implement tools for quality

monitoring (1)

• Develop and organize quality monitoring

systems (2)

• Develop a formal implementation

blueprint (1)

Provide interactive assistance (13)

• Facilitation (1)
• Provide local technical assistance (1)
• Provide clinical supervision (4)
• Centralize technical assistance (7)

Adapt and tailor to context (15)

• Promote adaptability (3)
• Use data experts (4)
• Use data warehousing techniques (8)

Train and educate stakeholders (3)

• Provide ongoing consultation (1)
• Conduct education outreach visits (1)
• Create a learning collaborative (1)

Develop stakeholder relationships (27)

• Identify and prepare champions (1)
• Inform local opinion leaders (2)
• Build a coalition (1)
• Conduct local consensus discussions (3)
• Capture and share local knowledge (6)
• Use advisory boards and workgroups (1)
• Model and simulate change (1)
• Develop academic partnerships (4)
• Recruit, designate, and train for leadership (1)
• Promote network weaving (7)

Support clinicians (8)

• Facilitate relay of clinical data to providers (6)
• Revise professional roles (1)
• Create new clinical teams (1)

Engage consumers (12)

• Involve patients/consumers and

family (8)

• Intervene with patients/consumers to

enhance uptake and adherence (2)

• Prepare patients/consumers to be

active participants (2)

Use financial strategies (6)

• Fund and contract for the clinical

innovation (1)

• Alternative incentive/allowance

structures (4)

• Use other payment schemes (1)

Change infrastructure (39)

• Change record systems (9)
• Change physical structures and

equipment (9)

• Change service sites (9)
• Mandate change (8)
• Start a dissemination organization (4)

Waltz TJ, et al. Implementation Science (2015) 10:109

Facilitate relay of clinical data to providers: Extract discrete data from genetic test reports to improve documentation and communication in the EHR (including personal health record); Integrate decision support tools in the EHR (e.g., to increase guideline-concordant genetic test ordering, reduce variations/disparities in genetic testing, recognize genotype-drug interactions); Create a genetic test result section in the EHR; Include educational material for providers and patients in genetic test reports

PM Implementation Strategies Mapped to ERIC Compilation

Evaluative and iterative strategies (7)

• Assess for readiness; identify barriers and

facilitators (1)

• Purposefully reexamine the

implementation (2)

• Develop and implement tools for quality

monitoring (1)

• Develop and organize quality monitoring

systems (2)

• Develop a formal implementation

blueprint (1)

Provide interactive assistance (13)

• Facilitation (1)
• Provide local technical assistance (1)
• Provide clinical supervision (4)
• Centralize technical assistance (7)

Adapt and tailor to context (15)

• Promote adaptability (3)
• Use data experts (4)
• Use data warehousing techniques (8)

Train and educate stakeholders (3)

• Provide ongoing consultation (1)
• Conduct education outreach visits (1)
• Create a learning collaborative (1)

Develop stakeholder relationships (27)

• Identify and prepare champions (1)
• Inform local opinion leaders (2)
• Build a coalition (1)
• Conduct local consensus discussions (3)
• Capture and share local knowledge (6)
• Use advisory boards and workgroups (1)
• Model and simulate change (1)
• Develop academic partnerships (4)
• Recruit, designate, and train for leadership (1)
• Promote network weaving (7)

Support clinicians (8)

• Facilitate relay of clinical data to providers (6)
• Revise professional roles (1)
• Create new clinical teams (1)

Engage consumers (12)

• Involve patients/consumers and

family (8)

• Intervene with patients/consumers to

enhance uptake and adherence (2)

• Prepare patients/consumers to be

active participants (2)

Use financial strategies (6)

• Fund and contract for the clinical

innovation (1)

• Alternative incentive/allowance

structures (4)

• Use other payment schemes (1)

Change infrastructure (39)

• Change record systems (9)
• Change physical structures and

equipment (9)

• Change service sites (9)
• Mandate change (8)
• Start a dissemination organization (4)

Waltz TJ, et al. Implementation Science (2015) 10:109

Change physical Structures and equipment: Computing environment that facilitates data sharing and collaboration; Leverage the centralized nature of VA to deploy tele-genetic services; Build IT infrastructure to support bio-banking (e.g., MVP); Provide an infrastructure that integrates/updates data rapidly; Provide analysis tools to researchers remotely to enable use of EHR data; Develop an interface (e.g., mobile apps) to deliver genetic test reports to the medical record/data warehouse.

Waltz TJ, et al. Implementation Science (2015) 10:109

1 2 3 4 5 6 7 8 9

Waltz TJ, et al. Implementation Science (2015) 10:109

Ranking by #

• f PM themes

• Starts with assumptions underlying the program.
• Planned work:

Inputs: human, financial, organizational, and community resources available Activities: the processes, tools, events, technology, and actions that bring about the intended changes or results.

• Intended results:

Outputs: the direct product of program activities. Outcomes: specific changes in program participants’ behavior, knowledge, skills, status, and functioning. Impacts: intended or unintended change in organizations, communities or systems as a result of program activities.

Logic Model

• W. K. Kellogg Logic Model, https://www.wkkf.org/resource-directory/resource/2006/02/wk-kellogg-foundation-logic-model-development-guide

Data sources, Structured data Data standards, Data mining, Data extraction, Data analytics

Activities Inputs

Infrastructure Big Data Resources

Outputs Outcomes Impacts

Logic Model for Precision Medicine Implementation

Workforce, Funding, Leadership, Incentives for collaboration Research/IRB, PM practice, Integrated HCS, IT/EHR

Planned Work Intended Results Assumptions

• PM will improve health
• PM has value beyond clinical utility
• Lack of evidence re: value of PM
• PM is complex: rare diseases; context is key
• VA is ideal setting to evaluate PM outcomes

(stable population with longitudinal f/u)

• Confluence of clinical practice and research is

critical for PM success

• Need IT to facilitate PM practice and research
• Genetics literacy is barrier
• Workforce is not prepared
• Not enough genetics practitioners

Evidence-based; Identify care models (PBM); Care processes/ coordination: Improve access, reduce variation

PM Practice Research Education

Translational, clinical trials, CER, PRO research, HSR to bridge gap, Collaboration/ Data sharing, Veteran engagement

Genetic evaluation Learning System

Confluence of PM and research; Data integration; Report formats End diagnostic

• dyssey;

inform treatment; predisposition, management; Identify environmental triggers; eligibility for clinical trials; implications for family Providers; Org leadership; Public

Utility

Clinical utility/ actionability; Personal utility/ Veteran-centric Health Care Utilization If diagnosed; if predisposed; resulting from population screening Define value

• f PM for

Veterans; for populations; Coverage & reimbursement Diversity; vulnerable populations; potential harms; communica- tion; ELSI ROI; cost- effectiveness

Value Equity & Access Economic Indicators

Assumptions

• PM will improve health
• PM has value beyond clinical utility
• Lack of evidence re: value of PM
• PM is complex: rare diseases; context is key
• VA is ideal setting to evaluate PM outcomes (stable population with longitudinal f/u)
• Confluence of clinical practice and research is critical for PM success
• Need IT to facilitate PM practice and research
• Genetics literacy is barrier
• Workforce is not prepared
• Not enough genetics practitioners

Logic Model for a Precision Medicine Program

Planned Work

Logic Model for a Precision Medicine Program

Inputs

Infrastructure Research/IRB; PM Practice; VA and other integrated HCS; Information technology/EHRs Big Data Data sources; Structured data; Data standards; Data mining; Data extraction; Data analytics Resources Workforce; Funding; Leadership; Incentives for collaboration

Activities

Research Translational, clinical trials, CER, PRO research, Health services research to bridge gap; Collaboration/Data sharing; Veteran engagement PM Practice Evidence-based; Identify care models (e.g., PBM); Care processes/coordination of care; Improve access, reduce variation Education Of providers, organizational leadership, the public

Intended Results

Logic Model for a Precision Medicine Program

Outputs

Genetic evaluation End diagnostic odyssey; Inform treatment, disease predisposition, management; Identify environmental triggers, eligibility for clinical trials; Implications for family Learning System Confluence of PM and research; Data integration; Genetic test report formats

Outcomes

Utility Actionability of test results; Clinical utility; Personal utility; Veteran-centric outcomes Health Care Utilization If diagnosed with a genetic condition; if predisposed to a genetic condition; resulting from population screening

Impacts

Value For Veterans, for specific populations; Coverage & reimbursement issues Equity & Access Diversity; vulnerable populations; potential harms; communication; Ethical, legal and social issues Economic Indicators Return on investment; Cost-effectiveness analysis

Summary

• Logic model developed with input from key stakeholder groups that can be

used to help plan for precision medicine implementation in the VA and

• ther health care organizations.
• Existing implementation frameworks (CFIR & ERIC) appear suitable for PM.

CFIR domains and constructs were identified that could help to inform implementation planning. Themes characterized as opportunities and challenges suggest we are early in the adoption of PM. Implementation strategies mapped well to the ERIC compilation, which could help prioritize strategies when planning a PM implementation initiative; however, importance of strategies may differ from those according to Waltz et al. 2015, possibly due to the range of participating stakeholders and characteristics of PM.

Thank You

maren.scheuner@va.gov