How predictive data models can vitalise Ireland’s health system planning

A dynamic, iterative approach to demand and capacity modelling has the power to deliver transformative change in Ireland’s healthcare service.

Ireland’s population has grown by over 10% in the last 10 years and to compound this, we have one of the fastest ageing populations in Europe. With people living longer, often with multiple chronic diseases, and the health system already suffering from creaking waiting lists, overcrowded emergency departments and staff shortages in key areas, new thinking is required to address these old problems. The pace of change with regard to both the demographic as well as rapidly evolving modernised care pathways guarantee that the challenges of the past with regard to service delivery will increase exponentially in the future if we don’t find a new way.

Amongst the key difficulties faced by those charged with running our health service is the enormous and increasing complexity of the system. The number of separate and interconnected moving parts to be dealt with is vast whilst demand, by its nature, is uncertain and can change rapidly.

Health service planning, whether it is at national, local, clinical service, or clinician level therefore requires a deep understanding and interrogation of many variables, including how they interact with each other and how they will evolve in the short, medium, and long term across a range of plausible scenarios.

Looking at long-term strategic goals

Traditional budgeting and planning processes have tended to be reactive and short term. They have relied on subjective factors and opinions. Of course, these opinions are based on critical experience of the system. However, hard experience has shown that this in isolation is an unreliable means of forecasting system needs. It inevitably incorporates unintended bias and is frequently not aligned with long-term strategic objectives.

Additionally, the service planning process has tended to be a secondary function of health leadership and suffered the pressures of being squeezed into timeframes and constraints that are externally dictated. This can be at the expense of due consideration of and investment in long-term strategic priorities such as preventative medicine and other wellbeing initiatives which have the potential to deliver significant population health benefits and cost savings in the longer term.

The planning processes and environment are therefore vulnerable to planning decisions that have not been sufficiently data driven and evidence based. These decisions can involve very significant expenditure and more importantly have the potential to affect the health outcomes of large cohorts of the most vulnerable in our population. Additionally, the nature of the process can lead to significant challenges with respect to both implementation and impact measurement.

The ability to evaluate implementation success and impact compared with strategic objectives should be a core part of the service planning process and cycle. However, a further consequence of sub-optimal planning processes can be an undermined capacity to measure the success or failure of implementation in the first instance and impact in the second.

Bridging demand and capacity gaps

The application of data and analytics to health planning functions to deliver robust modelling tools and methodologies are core components of a data-driven service planning function. They should be integral to planning and fully aligned with health strategy and policymakers, the lived reality of service provision and, most importantly, the healthcare needs of the population.

These leading-edge tools can help address the fundamental issue of the all too frequent mismatch between demand and capacity in key areas of the healthcare system.