Transitioning from volume-based to value-based healthcare
Healthcare — a new paradigm
The current challenges facing the NHS are known and well documented. We live at a time of increased healthcare costs driven by an ageing population and a growing burden of chronic conditions, a key feature of which is multi-morbidity. Multi-morbidity is the new normal.
Integrated Care Systems meet these challenges head on. In this model, networks of providers — collaborating with commissioners — establish a new operating model for the delivery of healthcare designed to improve health outcomes for populations and individuals, provide a better patient experience and reduce costs. Integrated Care Systems are a key part of the Sustainability and Transformation Partnerships (STPs) being developed to meet the financial targets set by NHS England.
New health systems will be:
- Prevention based
- Partnership driven
- Interdisciplinary and integrated
- Outcomes focused
Integrated care models seek to improve the health of entire patient populations and population health management strategies are key to the delivery of new care models.
Population health management is a coordinated effort to care for at-risk patient populations across the care continuum in order to improve their outcomes and reduce costs.
Population health management strategies are data driven
Information, data sharing and business analytics are required to support a successful population health management strategy. For population health management to work, healthcare professionals must have the ability to identify the target populations for provision of services. This requires the ability to link patient data from disparate data sources such as prescribing, primary care, community and mental health, as well as hospital activity.
Providers and commissioners alike need an understanding of activity, outcomes and costs across the continuum of care. Through robust data analysis, gaps in care can be identified, preventive measures taken and risks mitigated.
In this model, longitudinal data is used to track disease progression, while the outcomes of different interventions are continuously gauged and the population re-assessed accordingly.
Integrated care and population health management are all part of a story in which healthcare transitions from volume-based to value-based.
None of this is easy and none of it can be achieved without data and robust data analytics.
Are you looking to implement a population health management strategy?
Read our Five Steps to Population Health Management article.
Analytics drives insight
Integrated care models need to target populations and understand their needs, and they must set, measure and monitor outcomes on a routine and forensic basis. Sophisticated and flexible tools are required to analyse target populations and define interventions. Once appropriate interventions are deployed there is a need to track results in order to understand what does and doesn’t work. Analytics is the catalyst for turning data into action.
Population profiling and risk stratification
In an integrated care model a key component will be the identification of one or more specified populations for which providers are jointly accountable.
Population health management is a model for helping providers and commissioners assess the populations they serve across the continuum of care. It involves the stratification of patients into well-defined risk groups and the creation of differential care strategies based on each group’s needs.
When it comes to targeting patient groups then it is not as simple as the number of patients with chronic conditions. It is important to understand and target patients with multiple co-morbidities. Successful integration programmes demand the stratification of patients based on their health and social care needs. Only then can a tailored package of interventions be implemented.
The need for information which describes populations has never been greater.
The starting point of the population health management journey is an understanding of population health needs. Population-level data is needed to understand health need across populations. Population segmentation and risk stratification tools and techniques are fundamental to the identification of the needs of different groups within the population.
Risk adjustment and case-mix analysis
Risk adjustment and case-mix are processes by which the current and/or future health status of an individual or population is taken into consideration when setting budgets and capitation rates. Furthermore they are processes which can assist in the evaluation of provider performance and the assessment of outcomes of care, helping to deliver insight to those charged with the allocation of resources based on equity and need.
Sollis has delivered data and business analytics to the NHS since 1994. We have an extensive knowledge of the complexities involved in the management, linkage and presentation of health and care data. We are experts in population segmentation and risk stratification strategies. Our collaboration with the world renowned academic and research institution, The Johns Hopkins University, means that we can deliver unique insights into population profiling and risk adjustment.
Sollis Clarity and the Johns Hopkins Adjusted Clinical Groups® (ACG®) System provide a comprehensive family of measurement tools designed to help explain and predict how healthcare resources are delivered and consumed. With Sollis Clarity we deliver actionable data to support population profiling, case management, performance analysis and finance and budgeting.
In summary we deliver data and business analytics that enable healthcare commissioners and providers to understand the needs of local populations. We deliver a body of intelligence that enables the key stakeholders involved in accountable, integrated care — commissioners and providers — to better understand the factors that drive cost and create risk.
Sollis. Better Insights for Better Outcomes