IDM case study
Gloucestershire colleagues applied the IDM to the Gloucestershire’s Pulmonary Rehabilitation pathway.
The Intervention Decay Model (IDM) tool is ideal for looking at clinical pathways. Public Health colleagues worked with the Respiratory Clinical Programme Group (CPG) to test the tool.
Public Health and CPG colleagues agreed to test the tool in the area of Pulmonary Rehabilitation; an evidence-based intervention for people with Chronic Obstructive Pulmonary Disease (COPD).
Initially, we familiarised members of the Respiratory CPG with the IDM tool itself. We then engaged with the above stakeholders to check that pulmonary rehab was a priority intervention for this exercise, for example, have some inequalities been noted and is this an area in which there is scope to improve equality in outcomes and make a positive difference.
The tool would be used to explore the access, experiences, and outcomes across the pulmonary rehab pathway for different population groups.
The IDM tool can be used to review progress through pathways at whole county level (using countywide data). But it can also help to identify local variation in access, experience, and outcomes, which can then be worked through with place-based partners e.g. Integrated Locality Partnerships (ILPs) to identify locally applicable solutions.
The Intervention Decay Model (IDM) tool is ideal for looking at clinical pathways. Public Health colleagues worked with the Respiratory Clinical Programme Group (CPG) to test the tool.
Public Health and CPG colleagues agreed to test the tool in the area of Pulmonary Rehabilitation; an evidence-based intervention for people with Chronic Obstructive Pulmonary Disease (COPD).
Initially, we familiarised members of the Respiratory CPG with the IDM tool itself. We then engaged with the above stakeholders to check that pulmonary rehab was a priority intervention for this exercise, for example, have some inequalities been noted and is this an area in which there is scope to improve equality in outcomes and make a positive difference.
The tool would be used to explore the access, experiences, and outcomes across the pulmonary rehab pathway for different population groups.
The IDM tool can be used to review progress through pathways at whole county level (using countywide data). But it can also help to identify local variation in access, experience, and outcomes, which can then be worked through with place-based partners e.g. Integrated Locality Partnerships (ILPs) to identify locally applicable solutions.
Members of the Respiratory CPG, a data analyst from the BI Team, public health colleagues, and members of the ICB’s prevention and self-care team.
Other stakeholders will be co-opted into the project group as it develops, for example, the ICB engagement team to support the work at different stages.
Members of the Respiratory CPG, a data analyst from the BI Team, public health colleagues, and members of the ICB’s prevention and self-care team.
Other stakeholders will be co-opted into the project group as it develops, for example, the ICB engagement team to support the work at different stages.
The aim of the IDM tool is to first identify which communities / population groups are underrepresented at different stages of the pathway (we’ll describe this as the ‘what’). The next stage is to effectively and respectfully engage with underrepresented communities to understand why this is the case (the ‘why’), and then to work with them to identify possible adaptations to the pathway or how it is delivered to help deliver a more equitable service (the ‘what next’).
This project formed part of a wider piece of work with the CPG to promote value-based health care, which aims to deliver a maximum return (health outcomes) for investment. It was agreed by the CPG that improving equality of access, experience and outcomes forms an important aspect of value-based care.
A small subgroup (public health, data analyst and ICB lead for pulmonary rehab) was set up to unpick some nuances in the data and how to apply the tool. Considerations with vary from one area to another but for this project they included:
- which data sources will be included and over what time-period
- what the optimum time-period is between reaching the ‘threshold’ for the intervention (pulmonary rehabilitation) and accessing the intervention
- the need to be clear about what the data at each stage in the pathway measures for example, when we talk about the referral stage, are we talking about who has had a referral made for them, or who has taken up a referral?
These issues need thinking through carefully. We learned to take a pragmatic approach to our decisions, accepting that the outputs would not be perfect but would give us an idea of where there were disparities. We were rigorous in recording any assumptions we made so we can refer to these when interpreting our findings.
The data analyst created a model in Excel, which enabled us to see with ease how different cohorts of the overall patient group moved through a pathway. The model brings together the data that is available at each stage of the model, for example, who has the condition, who has been referred, who has engaged in the intervention, etc.
The quality of the data is integral to using the IDM in an optimum way, for example, if a referral was not recorded, is that because a referral was not made, or because it a referral was made but it was not recorded?
It is also difficult to draw firm conclusion when the numbers are small, for example, if you want to explore differences in the progress through the pathway for different ethnic groups.
The model allows us to look at where the drop-offs in numbers are happening; at what stage in the pathway, for which subgroups of the population, and in which locations.
We identified clear inequalities in the pulmonary rehab pathway from using this tool. The biggest drop-off in patients (around 87%) occurred between being identified as eligible for pulmonary rehab and being referred.
The aim of the IDM tool is to first identify which communities / population groups are underrepresented at different stages of the pathway (we’ll describe this as the ‘what’). The next stage is to effectively and respectfully engage with underrepresented communities to understand why this is the case (the ‘why’), and then to work with them to identify possible adaptations to the pathway or how it is delivered to help deliver a more equitable service (the ‘what next’).
This project formed part of a wider piece of work with the CPG to promote value-based health care, which aims to deliver a maximum return (health outcomes) for investment. It was agreed by the CPG that improving equality of access, experience and outcomes forms an important aspect of value-based care.
A small subgroup (public health, data analyst and ICB lead for pulmonary rehab) was set up to unpick some nuances in the data and how to apply the tool. Considerations with vary from one area to another but for this project they included:
- which data sources will be included and over what time-period
- what the optimum time-period is between reaching the ‘threshold’ for the intervention (pulmonary rehabilitation) and accessing the intervention
- the need to be clear about what the data at each stage in the pathway measures for example, when we talk about the referral stage, are we talking about who has had a referral made for them, or who has taken up a referral?
These issues need thinking through carefully. We learned to take a pragmatic approach to our decisions, accepting that the outputs would not be perfect but would give us an idea of where there were disparities. We were rigorous in recording any assumptions we made so we can refer to these when interpreting our findings.
The data analyst created a model in Excel, which enabled us to see with ease how different cohorts of the overall patient group moved through a pathway. The model brings together the data that is available at each stage of the model, for example, who has the condition, who has been referred, who has engaged in the intervention, etc.
The quality of the data is integral to using the IDM in an optimum way, for example, if a referral was not recorded, is that because a referral was not made, or because it a referral was made but it was not recorded?
It is also difficult to draw firm conclusion when the numbers are small, for example, if you want to explore differences in the progress through the pathway for different ethnic groups.
The model allows us to look at where the drop-offs in numbers are happening; at what stage in the pathway, for which subgroups of the population, and in which locations.
We identified clear inequalities in the pulmonary rehab pathway from using this tool. The biggest drop-off in patients (around 87%) occurred between being identified as eligible for pulmonary rehab and being referred.
The IDM helped us to look at routine health data systematically to identify disparities in progress through the pathway by protected characteristic, or other inequality group. The Excel model created is an accessible, visual, and flexible tool for making such comparisons at the touch of a button.
The IDM helped us to look at routine health data systematically to identify disparities in progress through the pathway by protected characteristic, or other inequality group. The Excel model created is an accessible, visual, and flexible tool for making such comparisons at the touch of a button.
We have gained an understanding of the IDM and have developed an in-house Excel model to support use of the tool.
Key learning so far:
- Importance of securing analyst capacity from the start
- Crucial to think critically through the data issues and record any assumptions made – checking interpretation with clinicians as relevant
- Advantageous to have access to public health expertise if you are unfamiliar with the tool.
- Members if the subgroup are happy to share their learning with others.
We have gained an understanding of the IDM and have developed an in-house Excel model to support use of the tool.
Key learning so far:
- Importance of securing analyst capacity from the start
- Crucial to think critically through the data issues and record any assumptions made – checking interpretation with clinicians as relevant
- Advantageous to have access to public health expertise if you are unfamiliar with the tool.
- Members if the subgroup are happy to share their learning with others.
This case study is ongoing.
Next steps are to engage with relevant stakeholders to unpick the reasons for the disparities we’ve seen along the pathway. For example, for the big drop of we have noted at referral stage, we will engage with Primary Care Networks to better understand the issues and codevelop possible solutions.
This stage will include engaging with representatives of those groups that are under-represented in the service or who have poorer outcomes, to better understand what would help them to progress successfully through the pathway.
This case study is ongoing.
Next steps are to engage with relevant stakeholders to unpick the reasons for the disparities we’ve seen along the pathway. For example, for the big drop of we have noted at referral stage, we will engage with Primary Care Networks to better understand the issues and codevelop possible solutions.
This stage will include engaging with representatives of those groups that are under-represented in the service or who have poorer outcomes, to better understand what would help them to progress successfully through the pathway.
Training and the sharing of learning on how to use the tool and apply it to your work area is recommended.
Training and the sharing of learning on how to use the tool and apply it to your work area is recommended.
Sue Weaver, Head of Commissioning for Health Improvement, Gloucestershire County Council - Sue.Weaver@gloucestershire.gov.uk
Sue Weaver, Head of Commissioning for Health Improvement, Gloucestershire County Council - Sue.Weaver@gloucestershire.gov.uk
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