Agenda Item 12 Enclosure KPCT/09/82
NHS KIRKLEES Report To:
Trust board
Title:
Predictive Risk
FOI Exemption Category
Open
Lead Director:
Sheila Dilks Director of Patient care and Professions
Author:
Nyasha Mareya – Predictive risk Project Manager (Management Trainee)
Key Points to Note:
Predictive modelling helps to prevent unplanned and avoidable use of emergency as well as secondary care for people with long term conditions. It identifies those patients who are most likely to experience deterioration in their condition and enable proactive interventions to be undertaken to reduce this risk. Predictive modelling identifies key drivers for utilisation, quality, costs and gaps in provision.
Budget Implications:
There is current approval for £205.000 nonrecurrent and all received bids present a significant affordability gap as the model implementation will be at non recurrent costs of £741 507 for a 12 month contract.
Risk Assessment:
Legal Implications:
Predictive modelling will strengthen the PCT’s position in progressing towards becoming a world class commissioner under competencies 4, 5, 6, 7, 8 and 10. It enables focusing care management resources in a way that is proportionate to clinical risk and need. The procurement process has followed the guidance under the FESC framework for tendering, clarification and appointment of preferred bidder and should withstand challenge.
Health Benefits:
Predictive risk modelling is a leading evidencebased analytical approach to gaining insights into current and future health needs as well as trends. This dynamic health data enables the joint strategic needs assessments to incorporate robust and dependable data for current and prospective resource use to be incorporated in developing systematic plans to improve health.
Staffing Implications:
The bidder will undertake training to identified PCT staff to enable the transfer of knowledge and skills. A data quality facilitator and a project manager will be required.
Sub Group/Committee:
The predictive risk subgroup has reported work on this project to the Long Term Conditions Board. The Business and Financial Planning Group received an update on 21st April 2009 and advised that the board’s considerations and recommendations be sought.
Recommendation:
The board is requested to consider benefits of this project, approve the costs and recommend the direction for this work going forward.
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PREDICTIVE RISK 1.0
2.0
Introduction 1.1
There is a large and growing evidence base that predictive modelling is a key way to prevent unplanned and avoidable use of emergency as well as secondary care for people with long term conditions. Predictive modelling identifies those patients who are most likely to experience deterioration in their condition and enable proactive interventions to be undertaken to reduce this risk.
1.2
Predictive modelling provides the most accurate and comprehensive approach that identifies key drivers for utilisation, quality, costs and gaps in provision as well as those currently not receiving services. It enables focusing care management resources in a way that is proportionate to risk and need and for those “at risk� of developing a long term condition in the population.
1.3
This method systematically assesses the entire population, identifying patients along the continuum of risk. The method stratifies current risk and also predicts future risks in all segments of the population. This will enable each clinician, practice or cluster to target interventions based on individual patient’s risk and to tailor care commissioning and provision to deliver the right amount of support at the right time. This will help to ensure that adequate and appropriate care is provided, informing efforts to address health inequalities and will offer a mechanism to improve quality of care across Kirklees.
1.4
Predictive risk modelling will complement all the outcomes of the Long Term Conditions strategy and complete the redesign and overall investment in LTCs.
Background 2.1
Following ongoing discussions within the PCT, PBC consortia and the Long Term Conditions board the Predictive Risk subgroup with representatives from all directorates, the PEC, local authority and practice based commissioners was established. The subgroup has used the FESC procurement process to specify the service required and invite bids with three potential providers having come forward. The bids have been evaluated and a preferred bidder agreed.
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3.0
Discussion 3.1 Budget Implication 3.1.1 Following successful business case proposals, approval was given for ÂŁ205.000 non-recurrent expenditure. The subgroup has since put out a tender for this service and all bids have presented a significant affordability gap. 3.1.2 The model comes at a non recurrent cost of ÂŁ 741 507 for a 12 month contract. Additional resource for data quality input and project management would also be required over the implementation period. 3.1.3 The affordability gap between the business case approved sum and the value of the bids have been understood by the predictive risk subgroup to be due to changes in bidder organisations and also by the rising profile of predictive modelling across commissioning organisations. 3.2 Consultation 3.2.1 Practice based commissioners have previously received information on predictive risk modelling and the expected outputs and their uses. There is ongoing clinical engagement on the matter through the predictive risk subgroup, the Long Term Conditions board and other informal forums. 3.3 Risks and Issues 3.3.1 Predictive modelling will represent a significant culture shift which will require coordinated organisational development to embed predictive modelling into clinical and commissioning practice. The predictive risk subgroup has outlined a work stream looking to address these needs. 3.3.2 If predictive modelling is not undertaken, the PCT risks missing an immense opportunity to achieve world class clinical as well as commissioning practice. 3.4 Legislative Implication 3.4.1 The procurement process has followed the guidance under the FESC framework for tendering, clarification and appointment of preferred bidder and should withstand challenge.
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3.5 Clinical Governance/Effectiveness Issues 3.5.1 The predictive risk subgroup has established a work stream to ensure that baseline measurements are incorporated as an integral part of the implementation of predictive modelling and will be regularly monitored and audited throughout. A formal evaluation will also be completed at the end of the implementation in partnership with the service provider. 4.0
Benefits of predictive modelling 4.1
Predictive modelling will inform the organisational strategic plan with accurate and reliable future risk and emerging need for the Kirklees population with a strong data base for realising the local interpretation and implementation of the strategic goals for Healthy Ambitions.
4.2
Predictive modelling delivers the strategic plan to identifying and stratifying the current risk of experiencing deterioration and the future risk of developing a long term condition. This will progress the PCT efforts to personalise care and ensure that patients receive appropriate care.
4.3
Predictive risk modelling is a leading evidence-based analytical approach to gaining insights into current and future health needs as well as trends. This dynamic health data enables the joint strategic needs assessments (JSNA) to incorporate robust and dependable data for current and prospective resource use to be incorporated in developing systematic plans to improve health and well being.
4.4
Predictive risk approach will provide practice based commissioners with quality assured information about current as well as predicted level of segmented utilisation as basis for commissioning decision making.
4.5
Using a predictive model will offer evidence based insight into our key priorities and support the skills development required for this. It will offer an opportunity for the PCT to expand internal knowledge and capacity.
4.6
It will provide alternatives to existing currencies of information that can facilitate a move towards quantifiable service specifications that demonstrate the best use of scarce resources, supporting robust and effective business prioritisation and planning.
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5.0
4.7
Predictive modelling offers robust projections and identification of inequalities and gaps in provision. The PCT will therefore be well placed to commission appropriate services and this will deliver improved service and person reported outcomes for health and wellbeing.
4.8
Predictive modelling provides a vehicle through which improved GP practice information and more robust provider specific data can be delivered.
4.9
Using a predictive model integrates primary and secondary care information flows, enabling the PCT to understand and measure care pathway improvement, manage systems effectively and to ensure continuous improvement.
4.10
Predictive modelling provides an evidence base for selecting and addressing health outcomes as directed by measured, known and quantifiable needs of the Kirklees population.
4.11
It strengthens the PCT’s market analyses, management and modelling ability using a rich data source.
4.12
Predictive modelling will help to identify key drivers for utilisation, quality, costs and gaps in provision. It facilitates the focusing care management resources in a way that is proportionate to risk and need, assisting commissioning decision in addressing health inequalities across Kirklees.
4.13
The above benefits of predictive modelling will strengthen the PCT’s position in progressing towards becoming a world class commissioner under competencies 4, 5, 6, 7, 8 and 10.
Conclusion 5.1
Predictive risk modelling represents fundamental transformation in the way we develop robust care data, inform commissioning decisions, deploy current resources, assess population needs as well as prioritise planning and investment.
5.2
We currently use retrospective, historical data to measure utility and activity, to drive commissioning decisions and to inform clinical development. Predictive risk modelling uses primary, secondary and potentially social care data to stratify and segment the Kirklees population according to current and emerging health needs. This enables proactive interventions to reduce health and social care risks as well as future use of health resources.
5.3
The subgroup acknowledges that interdependencies exist between predictive modelling work with other ongoing initiatives and processes across the organisation. These include the information management strategy, data quality improvement, data sharing agreement, needs assessments, public health analyses. 6
6. 0
Recommendation
6.1
As a leading PCT, this provides an opportunity to be among the first to obtain benefits of this way of working.
6.2
The board are requested to consider benefits of this project, approve the costs and recommend the direction for this work going forward.
Name: Nyasha Mareya Title: Project Manager – Predictive risk and LTCs (Management Trainee) Date: May 2009
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