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Table 3 Facilitators and barriers to cardiovascular risk scoring in primary care

From: Factors influencing the implementation of cardiovascular risk scoring in primary care: a mixed-method systematic review

CFIR domains involved Factors influencing cardiovascular risk scoring in primary care Facilitators Barriers
Outer setting and inner setting process Healthcare system and clinical setting Resources Adequately resourced healthcare systems with dedicated funding for the prevention of cardiovascular disease Staffing shortages resulting in high workload, inadequate or no budgets for preventive services, lack of health information systems and lack of equipment to measure all the risk factors essential for risk scoring
System and practice-level priorities None reported Prevention not prioritised for practice in health systems
Practice culture and organisation Supportive prevention care programs and pathways, task shifting, reallocation and sharing and having the appropriate individuals involved in prevention activities Lack of interest and motivation to engage in preventive services, the practice of defensive medicine, a lack of collaboration between health workers and other staff and presence of disruptive professional hierarchies
Individual process Users Attributes of the users Patients: knowledge and understanding of cardiovascular risk, disease and management, reinforcing personal circumstances and experiences, support from family, patients’demands and knowledge of their rights
Clinicians: knowledge and understanding of cardiovascular risk, disease and management, knowledge of the benefits of cardiovascular risk scoring regarding patient care and therapeutic decision-making
Patients: complex patients (comorbidities and advanced age), patients who do not want to know their cardiovascular risk, negative perception of risk assessment, lack of knowledge on cardiovascular risk, disease and its management, patients’ health priorities, undermining personal circumstances, experiences and demands, surrounding environment, socioeconomic factors, fears and expectations of risk assessment
Clinicians: negative perception of risk assessment, inadequate knowledge on cardiovascular risk, disease and its management, lack of knowledge on how to use cardiovascular risk scoring tools, lack of motivation to use these tools, difficulty in communicating cardiovascular risk to patients, challenges in communicating prevention and self-management aids to patients
Other stakeholders: perceived interference and misuse of cardiovascular risk scoring as an intervention, lack of consensus on the use of risk scoring tools, general lack of interest, conflicting interests and poor tool reviewing processes
Interaction between the users A supportive and longstanding relationship between the clinician and patient lack of communication and involvement in decision-making between clinicians and other stakeholders
Intervention process Cardiovascular risk scoring tools Characteristics of the tools Easy to use, use of charts or calculators, presenting risk scores as colour codes instead of percentages and incorporating risk tools into clinical systems as programs or software Outdates rapidly, time-consuming leading to prolonged consultations, lowers the quality of the consultation, does not include all the principal risk factors, the risk duration calculated is too long, it is complex to use and explain to patients, has technical problems and does not communicate with other programs
The perceived role of the tools Supportive role to clinical practice, i.e. to help understand risk, motivate patients, improve follow-up, educate patients and serve as a checklist for risk assessment Over and underestimates risk leading to over or under treatment, it interferes with the clinicians’ decision-making process and it is less superior to clinical judgement
Evidence of clinical and cost-effectiveness Providing evidence that these tools were accurate in predicting risk, that they included the main risk factors for cardiovascular disease and that they led to better therapeutic decisions It does not contribute to reducing healthcare costs. Unclear prediction rules were associated with prediction inaccuracies