The role of cost‐effectiveness analyses in investment decision making by primary health networks

Scientific rigour and pragmatic implementation are both required, combining research findings with other forms of evidence

Primary health networks (PHNs) have been part of the health landscape in Australia since July 2015. Following the Horvath review of Medicare Locals,1 they were established as locally configured organisations that could support primary health care service providers, design and deliver improved primary health care, and work with hospitals to maximise the efficiency, effectiveness and coordination of care. One key role for PHNs is to commission primary health care services that meet local needs and improve outcomes by procuring services from third party providers, applying market‐making and supply‐shaping principles.2 To do this, PHNs undertake population‐level needs analyses to identify service gaps, reduce hospital burden, and promote value for money. They also help general practices and other primary health care providers deliver community care, optimise quality and safety, and make meaningful use of electronic support systems.

In this issue of the MJA, Patel and colleagues report the results of a simulated cost‐effectiveness analysis of a computerised quality improvement and clinical decision‐support intervention for managing cardiovascular disease in primary care.3 The HealthTracker tool, integrated into the electronic health record systems of providers, estimates absolute cardiovascular risk and provides tailored treatment advice for clinicians and patients. The study modelled outcomes for an average size New South Wales PHN, based on data from the TORPEDO trial.4 This cluster randomised trial of HealthTracker found that it was associated with small reductions over five years in low‐density lipoprotein cholesterol levels, but not of systolic blood pressure, in patients at intervention sites. Patel and colleagues estimated the incremental cost‐effectiveness ratio (ICER) for HealthTracker in terms of cardiovascular disease events avoided. They argue that such modelling studies furnish PHNs with robust cost‐effectiveness data that can inform investment decision making.

The value of health economic analyses, such as cost‐effectiveness analyses, for making decisions about commissioning services, especially when combined with other evidence, is undeniable. Economic analysis supports evidence‐based decision making, quantifies the effectiveness of interventions, enables standardised comparisons, assesses value for money, and contributes to the transparency of decision making. It can underpin legitimacy in contested or difficult decisions, as well as provide information for the decommissioning and disinvestment decisions that are also part of the commissioning cycle.5

While cost‐effectiveness is critical for good investments, the contribution of a single metric — in this case, the ICER — to PHN commissioning decisions may be limited. Data analysis and modelling alone, no matter how elegant, cannot resolve the complex problems faced by PHNs as regional commissioners of primary health care services. An overabundance of data and stand‐alone analytical findings make navigating the process more challenging rather than simplifying it. Health economic data are just one type of evidence in a complex mixture of information that requires synthesis.

So how do PHNs decide where to invest? Commissioning decisions draw on several information sources, including population health data, service mapping, utilisation analyses, and research findings. They emphasise effective service provision relationships, outcomes of importance to patients and communities, value for money, transparent procurement, and achieving optimal results with limited resources. PHNs need comparable information about alternatives and opportunity costs. They need to balance the national priorities to which funding is tied and flexibility in response to regional and local needs. They need to assess and synthesise the outcomes of research studies, including the integrity, completeness and accuracy of underlying assumptions, the suitability of methods, and the appropriate interpretation of data. This might mean grappling with mixed results, such as those reported by Patel and colleagues,3 who found that the cost implications of the HealthTracker intervention for primary prevention (ICER, $7406 per averted cardiovascular event) were substantially different from those for secondary prevention (ICER, $17 988 per event).

As commissioning is cyclical, PHNs need to consider and evaluate decision outcomes and investment performance over time. They may need to review the contextual framing of research inputs to explore specific aspects of implementing an intervention, including behavioural drivers and impacts on participants. For example, other reports from the TORPEDO trial described the support required to implement HealthTracker4 and factors that contributed to substantial variation in its adoption and ongoing challenges with its use.6,7 HealthTracker ultimately improved screening for cardiovascular disease risk factors, but changes in prescribing and patient outcomes were limited. Potential confounders, such as lifestyle interventions and medication adherence, were not assessed.4 Reported barriers to using HealthTracker included the cultural and structural attributes of general practices, limitations related to the leadership and engagement of individual practitioners, and features of the technology itself, including its integration into the clinical workflow.6 These findings, not incorporated by Patel and colleagues into their cost‐effectiveness analysis model for practical reasons, have important implications. Investing in interventions such as HealthTracker may require sustained program support and follow‐up by PHNs. These activities should be included as cost components in models.

In addition to cost‐effectiveness analysis, an integrated array of further tools may be important for service commissioning. Many PHNs consider outcomes on different time scales and levels of detail and employ quality improvement frameworks, such as the quadruple aim of the Institute for Healthcare Improvements,8 or alternative modelling approaches, such as dynamic simulation modelling.9 Other tools include needs‐based resource allocation models that evaluate equity and disadvantage, social return on investment approaches, cost–consequence analyses that blend insights from economics, implementation science and service management,10 and the in‐DEPtH co‐creation framework.11 PHN investment decisions require both scientific rigour and pragmatic implementation, combining research findings with other forms of evidence, including lived experience. They also draw on practitioner and patient preferences and equity considerations, to effectively balance competing demands for limited resources.