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Defining dimensions of research readiness: a conceptual model for primary care research networks.

Carr H, de Lusignan S, Liyanage H, Liaw ST, Terry A, Rafi I - BMC Fam Pract (2014)

Bottom Line: Recruitment to research studies in primary care is challenging despite widespread implementation of electronic patient record (EPR) systems which potentially make it easier to identify eligible cases.Seven dimensions of research readiness were identified: (1) Data readiness: Is there good data quality in EPR systems; (2) Record readiness: Are EPR data able to identify eligible cases and other study data; (3) Organisational readiness: Are the health system and socio-cultural environment supportive; (4) Governance readiness: Does the study meet legal and local health system regulatory compliance; (5) Study-specific readiness; (6) Business process readiness: Are business processes tilted in favour of participation: including capacity and capability to take on extra work, financial incentives as well as intangibles such as social and intellectual capital; (7) Patient readiness: Are systems in place to recruit patients and obtain informed consent?The model might enable the development of interventions to increase participation in primary care-based research and become a tool to measure the progress of practice networks towards the most advanced state of readiness.

View Article: PubMed Central - PubMed

Affiliation: Department of Health Care Management and Policy, Clinical Informatics and Health Outcomes Research Group, University of Surrey, Guildford, UK. hfcarr@doctors.org.uk.

ABSTRACT

Background: Recruitment to research studies in primary care is challenging despite widespread implementation of electronic patient record (EPR) systems which potentially make it easier to identify eligible cases.

Methods: Literature review and applying the learning from a European research readiness assessment tool, the TRANSFoRm International Research Readiness instrument (TIRRE), to the context of the English NHS in order to develop a model to assess a practice's research readiness.

Results: Seven dimensions of research readiness were identified: (1) Data readiness: Is there good data quality in EPR systems; (2) Record readiness: Are EPR data able to identify eligible cases and other study data; (3) Organisational readiness: Are the health system and socio-cultural environment supportive; (4) Governance readiness: Does the study meet legal and local health system regulatory compliance; (5) Study-specific readiness; (6) Business process readiness: Are business processes tilted in favour of participation: including capacity and capability to take on extra work, financial incentives as well as intangibles such as social and intellectual capital; (7) Patient readiness: Are systems in place to recruit patients and obtain informed consent?

Conclusions: The model might enable the development of interventions to increase participation in primary care-based research and become a tool to measure the progress of practice networks towards the most advanced state of readiness.

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Dimensions of research readiness. Bold arrow – TIRRE model, shaded arrow extended model.
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Fig2: Dimensions of research readiness. Bold arrow – TIRRE model, shaded arrow extended model.

Mentions: The “dimensions of research readiness” model specifies seven perspectives from which a practice might be assessed in order to determine its readiness to participate in research (Figure 2, Table 2). Including a “study specific” requirement enables researchers to set out any research requirements not covered elsewhere in the readiness model. The business process, including workload, has to work if practices are to participate in studies; and whilst this aspect of readiness is modulated by the type of study, in the end most practices require reimbursement for the time taken to participate in research. Patients also need to be “ready” to participate in research; the authors’ experiential learning is that it is more difficult to recruit in research-naïve practices than in those experienced in research. The other new dimension is that of “governance readiness”. This has been moved out of its previous position within organisational readiness to be a dimension in its own right, acknowledging the enormous increase in its prominence since development of the original TIRRE model.Figure 2


Defining dimensions of research readiness: a conceptual model for primary care research networks.

Carr H, de Lusignan S, Liyanage H, Liaw ST, Terry A, Rafi I - BMC Fam Pract (2014)

Dimensions of research readiness. Bold arrow – TIRRE model, shaded arrow extended model.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4260213&req=5

Fig2: Dimensions of research readiness. Bold arrow – TIRRE model, shaded arrow extended model.
Mentions: The “dimensions of research readiness” model specifies seven perspectives from which a practice might be assessed in order to determine its readiness to participate in research (Figure 2, Table 2). Including a “study specific” requirement enables researchers to set out any research requirements not covered elsewhere in the readiness model. The business process, including workload, has to work if practices are to participate in studies; and whilst this aspect of readiness is modulated by the type of study, in the end most practices require reimbursement for the time taken to participate in research. Patients also need to be “ready” to participate in research; the authors’ experiential learning is that it is more difficult to recruit in research-naïve practices than in those experienced in research. The other new dimension is that of “governance readiness”. This has been moved out of its previous position within organisational readiness to be a dimension in its own right, acknowledging the enormous increase in its prominence since development of the original TIRRE model.Figure 2

Bottom Line: Recruitment to research studies in primary care is challenging despite widespread implementation of electronic patient record (EPR) systems which potentially make it easier to identify eligible cases.Seven dimensions of research readiness were identified: (1) Data readiness: Is there good data quality in EPR systems; (2) Record readiness: Are EPR data able to identify eligible cases and other study data; (3) Organisational readiness: Are the health system and socio-cultural environment supportive; (4) Governance readiness: Does the study meet legal and local health system regulatory compliance; (5) Study-specific readiness; (6) Business process readiness: Are business processes tilted in favour of participation: including capacity and capability to take on extra work, financial incentives as well as intangibles such as social and intellectual capital; (7) Patient readiness: Are systems in place to recruit patients and obtain informed consent?The model might enable the development of interventions to increase participation in primary care-based research and become a tool to measure the progress of practice networks towards the most advanced state of readiness.

View Article: PubMed Central - PubMed

Affiliation: Department of Health Care Management and Policy, Clinical Informatics and Health Outcomes Research Group, University of Surrey, Guildford, UK. hfcarr@doctors.org.uk.

ABSTRACT

Background: Recruitment to research studies in primary care is challenging despite widespread implementation of electronic patient record (EPR) systems which potentially make it easier to identify eligible cases.

Methods: Literature review and applying the learning from a European research readiness assessment tool, the TRANSFoRm International Research Readiness instrument (TIRRE), to the context of the English NHS in order to develop a model to assess a practice's research readiness.

Results: Seven dimensions of research readiness were identified: (1) Data readiness: Is there good data quality in EPR systems; (2) Record readiness: Are EPR data able to identify eligible cases and other study data; (3) Organisational readiness: Are the health system and socio-cultural environment supportive; (4) Governance readiness: Does the study meet legal and local health system regulatory compliance; (5) Study-specific readiness; (6) Business process readiness: Are business processes tilted in favour of participation: including capacity and capability to take on extra work, financial incentives as well as intangibles such as social and intellectual capital; (7) Patient readiness: Are systems in place to recruit patients and obtain informed consent?

Conclusions: The model might enable the development of interventions to increase participation in primary care-based research and become a tool to measure the progress of practice networks towards the most advanced state of readiness.

Show MeSH