TABLE 2.
Facilitators and Barriers From Literature and Interview Classified by CFIR Domain
| CFIR Domain16,17 | Factor | Facilitator/Barrier | Literature 29 Papers (reference number)a | Interviews (n = 23)a | Total | CFIR Implementation Strategies to Consider (ERIC strategies)19 |
|---|---|---|---|---|---|---|
| Innovation | Black box, explainability | B | 915,23-30 | 7 | 16 | Promote adaptability Identify and prepare champions Conduct educational meetings Inform local opinion leaders Conduct local consensus discussions Capture and share local knowledge Develop educational materials Conduct educational outreach visits Identify early adopters |
| Privacy and security | B | 119,15,24,26,28,29,31-35 | 2 | 13 | ||
| Data availability and quality | B | 121,9,10,12,15,23,26-29,33,35 | 2 | 14 | ||
| Validation models, validityb | B | 327,36,37 | 3 | 6 | ||
| Interoperability, standardization | B | 21,12 | 3 | 5 | ||
| QA, updating modelsb | B | 134 | 4 | 5 | ||
| Complexity | B | 238,39 | 2 | 4 | ||
| Legal liability | 329,40,41 | 3 | ||||
| Transparency, usability, and liability | B | 326,27,41 | 3 | |||
| Technical design | F | 112 | 1 | 2 | ||
| Good feasibility and desirability | F | 238,39 | 2 | |||
| Generalizability | B | 226,27 | 2 | |||
| Quality and safety | B | 140 | 1 | |||
| Reliability, accuracy | B | 137 | 1 | |||
| Scalability | B | 115 | 1 | |||
| Reproducibility | B | 136 | 1 | |||
| Expected added benefit | F | 140 | 1 | |||
| Minimize workflow changes | F | 140 | 1 | |||
| Systemic bias in the data | B | 137 | 1 | |||
| Outer setting | Laws and legislation, policy (MDR, GDPR, CE marking) | B | 91,12,13,27,28,31,34,40,41 | 5 | 14 | |
| Meeting standards and quality requirements | B | 113 | 1 | |||
| Lack of political commitment | B | 137 | 1 | |||
| Analysis of multicenter data is limited because of differences in database structures across systems (eg, electronic medical records database of different service providers) | B | 137 | 1 | |||
| AI models are not reimbursed by insurance | B | 1 | 1 | |||
| Inner setting | Finance and resources | B | 312,40,42 | 3 | 6 | Assess for readiness, and identify barriers and facilitators Identify and prepare champions Conduct local consensus discussions Conduct educational meetings Build a coalition Create a learning collaborative Conduct local needs assessment Capture and share local knowledge Alter incentive/allowance structure Facilitation Promote adaptability Inform local opinion leaders Involve executive boards Tailor strategies Recruit, designate, and train for leadership Organize clinician implementation team meetings Identify early adopters Promote network weaving Use advisory boards and workgroups Access new funding Develop a formal implementation blueprint Use an implementation adviser Distribute educational materials Fund and contract for clinical innovation Conduct cyclical small tests of change Involve patients/consumers and family members Visit other sites |
| Communication | B | 223,40 | 4 | 6 | ||
| Transformation of health care professions and care processes | B | 212,13 | 3 | 5 | ||
| Good management/leadership | F | 412,23,30,42 | 4 | |||
| Resistance to change | B | 142 | 2 | 3 | ||
| Gap research—clinic | B | 3 | 3 | |||
| Innovation strategy | F | 140 | 1 | |||
| Innovation manager | F | 140 | 1 | |||
| Local champions | F | 140 | 1 | |||
| Timing: clinical need v data availability | B | 1 | 1 | |||
| Culture | B | 1 | 1 | |||
| Clinicians with too little time and/or interest in AI | B | 1 | 1 | |||
| Lack of resources to build and maintain IT infrastructure to support AI process | B | 137 | 1 | |||
| Regulatory compliance issues in the process of managing a high volume of sensitive information | B | 137 | 1 | |||
| Raw fragmented or unstructured data (eg, electronic medical records, imaging reports), which are difficult to aggregate and analyze | B | 137 | 1 | |||
| Lack of well-described patient-level health databases | B | 137 | 1 | |||
| Support | F | 1 | 1 | |||
| Good = good enough | B | 1 | 1 | |||
| Individuals | Knowledge and understanding of AIb | F | 81,8,12,23,28,32,40,42 | 3 | 11 | Identify and prepare champions Conduct educational meetings Develop educational materials Inform local opinion leaders Conduct educational outreach visits |
| Trust in AI | F | 810-13,28,40,42,43 | 2 | 10 | ||
| Confidence in clinical data from which an AI/ML model learns | B | 110 | 3 | 4 | ||
| Autonomy loss physician | B | 124 | 2 | 3 | ||
| Ownership | B | 1 | 1 | |||
| Lack of appropriate skills for applying AI methods | B | 137 | 1 | |||
| Implementation process | Lack of stakeholder involvement/engagement/consensus | B | 611-13,29,30,40 | 6 | 12 | Identify and prepare champions Conduct local consensus discussions Inform local opinion leaders Assess for readiness, and identify barriers and facilitators Build a coalition Identify early adopters Conduct local needs assessment Develop a formal implementation blueprint Involve patients/consumers and family members Obtain and use patients/consumers and family feedback Conduct educational meetings Recruit, designate, and train for leadership Develop and implement tools for quality monitoring Facilitation Audit and provide feedback Use advisory boards and workgroups Capture and share local knowledge Create a learning collaborative Develop and organize quality monitoring systems Prepare patients/consumers to be active participants Organize clinician implementation team meetings |
| Internal and external multidisciplinary collaborationb | F | 48,13,23,30 | 7 | 11 | ||
| Educationb | F | 78,9,13,23,37,42,43 | 1 | 8 | ||
| Lack of effect measurement | B | 131 | 3 | 4 | ||
| Implementation strategy/guidelines | F | 29,13 | 2 | |||
| Clear goals and process | F | 238,39 | 2 | |||
| Risk analysisb | F | 28,9 | 2 | |||
| Evaluation and testingb | F | 28,12 | 2 | |||
| Frequent project/team meetingsb | F | 28,12 | 2 |
Abbreviations: AI, artificial intelligence; CE, Conformité Européenne; CFIR, Consolidated Framework for Implementation Research; ERIC, Expert Recommendations for Implementing Change; GDPR, General Data Protection Regulation; IT, information technology; MDR, Medical Device Regulation; ML, machine learning; QA, quality assurance.
a
Numbers in this column refer to the number of times mentioned in literature/interviews.
b
Factors included in current recommendations that can be interpreted as a consensus of radiotherapy centers.2