Robust cost data is vital for informing the payment system - the system of financial flows that moves money around the health service. Good cost data can help Healthcare organisations and systems to understand variations in the way that patients are treated and the impact on available resources. When this information is linked to health outcome measures, the healthcare system can make value-based rather than volume-based decisions.
Casemix refers to a system of classifying patients into groups based on their clinical conditions, treatments, and resource use. These groups are designed to be clinically meaningful and resource-homogeneous, meaning patients within the same group are expected to consume similar levels of healthcare resources (e.g., staff time, medications, equipment). Examples of casemix systems include Healthcare Resource Groups (HRGs) in the UK and Diagnostic-Related Groups (DRGs) in other countries like the US.
Price setting is useful as it enables you to quantify each activity and set a price for it that fits within an envelope. For this, you need a model that is reliable, repeatable and automated.
The funding and allocation can be at national level, can be a commissioner level where each commissioner holds a population budget, can be at local level with each healthcare provider. The allocations process can use a statistical formula to make geographic distribution fair and objective or use an activity-based funding.
Monitoring and compliance of prices and payment within healthcare is a critical process to ensure that healthcare providers are reimbursed accurately and fairly, while also maintaining financial integrity and transparency. After calculating prices and allocating funding, you need to monitor how these prices or funding are used and if needed, enforcement.
Health analytics involves analysing and interpreting data related to healthcare to gain insights into patient outcomes, treatment effectiveness, and overall healthcare system performance. The goal is to inform decision-making, improve patient care, and enhance the overall efficiency of healthcare delivery.
Here are some common types of analysis we do within health analytics :
Analysing patient data to understand the characteristics of the population, such as age, gender, ethnicity, and location.
Examining the frequency and distribution of specific diseases or health conditions within a population.
Predicting the likelihood of patients being readmitted to the hospital after discharge by different characteristics such as age, gender, race and region.
Analysing patient feedback and experience data to improve healthcare services.
Evaluating the costs associated with different treatments, healthcare services and patients.
Cost-Benefit Analysis: Assessing the economic impact of health interventions and programs.
Evaluating the extent to which healthcare providers adhere to established clinical guidelines.
Analysing data to identify and reduce incidents of medical errors or adverse events.
Understanding how social and economic factors impact health outcomes.
Analysing data to identify and address disparities in healthcare access and outcomes.
Integrating data from various sources, such as electronic health records, to provide a comprehensive view of patient health.
Ensuring seamless exchange of information between different healthcare systems.
We have individuals with NHS trust and regulatory experience, analysing, producing insights and reports with healthcare data. We are also supported by our IT team who can build interactive tools for our customers to use.