Abstract
Introduction
The Continuous Improvement in Care – Cancer (CIC Cancer) Project is a multi-institutional program of research that seeks to bring value-based healthcare (VBHC) – with evolution to patient centred value-based healthcare (PCVBHC) – to public and private healthcare settings in Western Australia (WA). The project aims to establish if PCVBHC can become part of routine cancer care in WA – improving both the experience and outcomes of care for those affected by cancer and providing a rich databank for ongoing research into improving cancer services and outcomes.
Methods
The implementation of the multi-faceted and multi-dimensional CIC Cancer Project is underpinned by a data capture and analysis cycle with five overlapping stages to drive improvements in care and patient outcomes. Integral to the success of this model was effective, wide-ranging engagement and development of a bespoke informatics system utilising the ICHOM standard dataset for lung, colorectal, and breast cancer.
Evaluation of project activities identified important lessons for others seeking to introduce the capture of clinical and patient-reported outcomes into standard care within health settings.
Results
Four key lessons learnt:
• Engagement of clinicians and health services is essential to the success of this work, both for the research project and long-term integration into routine clinical practice. Whilst tailoring to local needs is important for clinical team engagement, the possibility for duplication of data – with associated costs – should be carefully considered. It is also vital to have policy and operational champions within the governing health authorities. These champions should be engaged and have sufficient seniority, authority, resources, and influence to drive implementation across sites and to other disease types and facilitate effective co-ordination and collaboration. They must also authorise any startup and ongoing costs.
• Standardised datasets are an important resource to initially engage with clinicians, patients, and healthcare services; however, adaption is required to meet local conditions and enable practical implementation. Inclusion of customised data variables also allows clinicians to take the lead in identifying areas of difference between procedures, processes, and sites. This has the potential, however, to increase costs and cause delays in implementation. Datasets can also become cumbersome when attempting to be ‘all things to all people’. It is important to gain a balance between the minimum number of variables required to adequately measure clinical and patient outcomes and keep the burden low for both patient completion of the PROMs and clinical team review of responses.
• When seeking to introduce and integrate IT systems across health sectors – public hospital, private hospital, and clinician’s private rooms – the differing environments and governance requirements can result in significant duplication of effort. Substantial resources are required to ensure that IT systems a) meet stringent security requirements, b) provide security of confidential patient information, and c) are compatible with existing systems across multiple IT architecture platforms. Whilst expensive, purchase of an off the shelf ‘software as a service’ (SaaS) – a software licensing and delivery model in which software is licensed on a subscription basis and centrally hosted – minimises risk and removes reliance on any one developer or team. However, they can be hard to customise to local needs.
• Visualisation is vital for effective use of data in clinical settings, but consideration needs to be given to optimising ease of use against capability for complex analysis of PROMs against clinical variables.
Conclusions
The move to a PCVBHC model for the delivery of cancer services across the public and private sector was greatly facilitated by the availability of standardised ICHOM datasets. This 6-year study, however, identified several key lessons that need to be addressed to ensure long term integration of PROMs in standard clinical care.
The Continuous Improvement in Care – Cancer (CIC Cancer) Project is a multi-institutional program of research that seeks to bring value-based healthcare (VBHC) – with evolution to patient centred value-based healthcare (PCVBHC) – to public and private healthcare settings in Western Australia (WA). The project aims to establish if PCVBHC can become part of routine cancer care in WA – improving both the experience and outcomes of care for those affected by cancer and providing a rich databank for ongoing research into improving cancer services and outcomes.
Methods
The implementation of the multi-faceted and multi-dimensional CIC Cancer Project is underpinned by a data capture and analysis cycle with five overlapping stages to drive improvements in care and patient outcomes. Integral to the success of this model was effective, wide-ranging engagement and development of a bespoke informatics system utilising the ICHOM standard dataset for lung, colorectal, and breast cancer.
Evaluation of project activities identified important lessons for others seeking to introduce the capture of clinical and patient-reported outcomes into standard care within health settings.
Results
Four key lessons learnt:
• Engagement of clinicians and health services is essential to the success of this work, both for the research project and long-term integration into routine clinical practice. Whilst tailoring to local needs is important for clinical team engagement, the possibility for duplication of data – with associated costs – should be carefully considered. It is also vital to have policy and operational champions within the governing health authorities. These champions should be engaged and have sufficient seniority, authority, resources, and influence to drive implementation across sites and to other disease types and facilitate effective co-ordination and collaboration. They must also authorise any startup and ongoing costs.
• Standardised datasets are an important resource to initially engage with clinicians, patients, and healthcare services; however, adaption is required to meet local conditions and enable practical implementation. Inclusion of customised data variables also allows clinicians to take the lead in identifying areas of difference between procedures, processes, and sites. This has the potential, however, to increase costs and cause delays in implementation. Datasets can also become cumbersome when attempting to be ‘all things to all people’. It is important to gain a balance between the minimum number of variables required to adequately measure clinical and patient outcomes and keep the burden low for both patient completion of the PROMs and clinical team review of responses.
• When seeking to introduce and integrate IT systems across health sectors – public hospital, private hospital, and clinician’s private rooms – the differing environments and governance requirements can result in significant duplication of effort. Substantial resources are required to ensure that IT systems a) meet stringent security requirements, b) provide security of confidential patient information, and c) are compatible with existing systems across multiple IT architecture platforms. Whilst expensive, purchase of an off the shelf ‘software as a service’ (SaaS) – a software licensing and delivery model in which software is licensed on a subscription basis and centrally hosted – minimises risk and removes reliance on any one developer or team. However, they can be hard to customise to local needs.
• Visualisation is vital for effective use of data in clinical settings, but consideration needs to be given to optimising ease of use against capability for complex analysis of PROMs against clinical variables.
Conclusions
The move to a PCVBHC model for the delivery of cancer services across the public and private sector was greatly facilitated by the availability of standardised ICHOM datasets. This 6-year study, however, identified several key lessons that need to be addressed to ensure long term integration of PROMs in standard clinical care.
| Original language | English |
|---|---|
| Publication status | Published - 21 Oct 2024 |
