Background
Development of primary care research
UK primary care as a research setting
Lessons from primary care research
Research readiness
Method
Overview and literature review
Developing a schema
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The results of our literature review
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Lessons from the development of PCRNs to promote research in primary care
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Technical and other advances that facilitated access to primary care data
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Incorporating the learning from a European project which included an assessment of research readiness [28]
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Presentation and workshop at a National Primary Care meeting and the subsequent discussion groups and feedback.
Final model development
Results
English national initiatives
IT developments that facilitate primary care involvement in research
Learning from a European project which included an assessment of research readiness
TIRRE model of research readiness | |
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1
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Data readiness (micro level)
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This will assess the current state of data held within the practice. | |
a | What data |
i. Scope of data recorded | |
ii. How held (distributed or centralised) | |
iii. Single or multiple systems | |
b | Interoperability |
i. Denominator data, - demographics, - unique identifiers | |
ii. Coding system | |
iii. Data quality – metadata Linkages – lab | |
2
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Record system readiness (meso level)
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a | Type of record architecture – encounter based, problem orientated, |
b | Data extraction method (e.g. local or central) |
c | Extract type |
d | Health-system-wide initiatives for data extraction (e.g. CPRD, GPES) |
3
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Organisational readiness (macro level)
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a | Legislative and regulatory compliance readiness |
b | Health system readiness |
i. Organisational structure | |
ii. Local issues or service configuration that might inform data availability | |
iii. Other studies which may involve the target patients/subjects of research | |
c | Socio-cultural readiness |
i. Types of studies that the data provider finds acceptable/is allowed to participate in | |
ii. Other factors that might influence local data | |
iii. Language within records | |
4
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Study readiness
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a | Quality of relevant data |
b | Demographic and other data including access to laboratory and imaging res |
Final research readiness model
Dimension of readiness | Key attribute(s) | Health System & Research network activity to promote research readiness | ||
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Existing | New activity required | |||
1
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Data
| Coded data that identifies: | Pay-for-performance (P4P) has improved (but also distorted) data quality | Active engagement in data quality (of cases & likely controls) |
Denominator | ||||
Cases (& controls) | ||||
Inclusion & exclusion criteria | ||||
2
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Records
| Data are extractable | Networks that extract data (research databases) | Validation of extracts is required: these can have errors and be inconsistent. |
One-off (MIQUEST) extraction | ||||
Practice searches (EPR vendor search tool) | ||||
3
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Organisational
| Health system readiness | Legislation (Health & Social Care Act 2012) | Engagement with local primary care structures (Health service localities; Medical primary care societies etc.) |
Socio-cultural | Government/Health ministry promotion of bioscience research | |||
Incentive schemes for practices | ||||
4
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Governance
| Research governance (RG) | RG emphasis of existing scheme | Educational programme |
Good Clinical Practice (for trials) | ||||
Information governance | ||||
Some confusion about “Opt out” | ||||
Practice has legal responsibility as the Data Controller in the UK (Data Protection Act) | New national guidance about personal data is required. | |||
5
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Study
| Impossible to cover all eventualities | Data quality for the specific study | Responsive support, direct data collection from patients may be possible |
Demographic data | ||||
6
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Business
| Tipped in favour or participation | Mechanism for funding research (e.g. some practices reluctant to carry out studies sponsored by pharmaceutical industry) | Standard payments |
Use quality improvement studies to promote research-relevant activities | ||||
Level of funding and whether provides sufficient incentive to participants | Develop intangible resources | |||
(social/relationship capital) | ||||
Feasibility of study being incorporated into existing workload | ||||
Any risk/perceived risk (e.g. new drug) | ||||
7
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Patient
| Information consent | Individual expectation to participate in research/“pre-consent” models | Learn how to take consent |
Develop intangible resources (relationships with practices) | ||||
Volunteer patient cohorts | ||||
Single disease (e.g. diabetes), where there may be an associated primary care clinic | ||||
Patient-practice culture & ethos about participating in research | ||||
Track record – previous experience of delivering projects - type, clinical domain, number of cases |