Digital health and precision prevention: shifting from disease-centred care to consumer-centred health

Keywords: eHealth, preventive medicine, public health, public health informatics, medical informatics, noncommunicable diseases, childhood obesity, healthcare systems.

Chronic disease is the leading cause of ill health, disability and death in Australia.¹ More than one-third (38%) of this burden is preventable.¹ There are at least two healthcare models for addressing this burden: break–fix (current) and predict–prevent. Predict–prevent is the most efficient; every prevention dollar invested saves approximately US$27 long term.² The break–fix (disease-centred, acute care) system only explains ~20% variance in population health.³ Australia’s healthcare investment logic is paradoxical: 40% (A$74 billion) is necessary to fuel break–fix healthcare but only 9.6% (A$17.9 billion) supports disease prevention.⁴ This inequitable investment model is unsustainable considering our ageing population and growing burden of chronic disease.

Perversely, Australia’s health services are rewarded for failing to keep populations well; every sick patient treated is counted as activity and funded, and there is minimal incentivisation to reduce hospitalisations. It is difficult to incentivise and reward improvements to population health in Australia when it is not measured in a contemporaneous, actionable way to enable funding. Measurement of populations currently comprises retrospective point prevalence surveys, clinical research and disease registries. The maxim ‘what is measured, matters’⁵ is missing in population health but certainly holds true for acute care; this underpins the current paradoxical investment model favouring break–fix.

Digital health enables measurement of health care delivery for every patient in real time. Social, environmental and behavioural health indicators explain ~70% of health variance, yet are not measured meaningfully, and so are poorly funded.⁶ A solution for measuring population health and making disease prevention ‘matter’ is digital health. Digital transformation has resulted in 22.68 million My Health Records¹ and 65% of public hospitals using an electronic medical record (EMR) platform to manage clinical information.⁷ Digital health has yielded significant data availability, decision support, clinical informatics and innovative benefits (e.g. precision medicine, artificial intelligence) to the break–fix system.⁸ These have improved acute health outcomes and system monitoring but at significant cost ($1.26 billion in Queensland)⁹ and with minimal effect on overall population health.

Digital health applied to the prevention sector could leverage real-world evidence (RWE), i.e. health information derived from contemporaneous, dynamic and consumer-centred sources, such as EMRs, electronic health records (EHRs), mobile health (mHealth) applications and digital wearables.¹⁰ Aggregation and meaningful presentation of this preventive data enables: (1) advancement from static and retrospective to accurate and real-time measurement of population health; (2) precision prevention interventions, targeting precise, at-risk groups or communities by tailoring interventions to unique characteristics, modifying care delivery systems or implementing targeted policy or macroenvironmental changes that are customised to each group based on risk and need;¹¹ and (3) monitoring population health intervention and ‘counting’ improvement (via near real-time changes in health determinants, chronic disease risk factors and prevalence, and wellbeing indicators) to create deliverables to justify the necessary funding shift to enable an efficient predict–prevent model.

Demonstrating childhood obesity as a use case, we highlight three barriers to this funding shift and propose three digital horizons¹² to guide health system, organisational and policy decision makers towards precision prevention.

Investment in digital health has exploded. Australia’s digital health market was valued at US$1.599 billion in 2018 and US$1.851 billion in 2020.¹³ Globally, strategic publications in digital health have likewise surged, reflecting years of iterative, multi-national investments and rapidly advancing technologies.^14–16 Despite this growth, the acute sector and disease treatment remain the focus. Prevention is discussed in abstract, conceptual and future-focused terms with little concrete commitment to preventive investment and transformation, likely because there is limited ability to measure prevention delivery and outcomes. This myopic investment strategy that enables break–fix over predict–prevent is explainable from an institutional perspective. Policymakers invest in a system that is easily measurable by counting care delivery in acute health services, a behaviour explained by a complex mix of habits, norms, assumptions and interest.¹⁷ The investment strategy reinforces the status quo rather than transforming it.

Rapid digital health transformation has enabled RWE in acute clinical care. In hospitals, an unwell patient, and treatment of disease, forms the epicentre of data collection. Rich clinical data is necessary for every patient, every time, in real time. This disease-centred data drives contemporaneous, accurate and risk-based decision making to improve patient care. The amount, integrity, granularity and contemporaneity of this data evaporates as the ‘well’ patient is discharged and isolated from the system (and from real-time data collection)^8,18 (see Figure 1).

Fig. 1.  Population health data pyramid from a health system perspective in Australia.

In contrast, data currently used for disease prevention is often years old and captured as point prevalence snapshots. RWE for preventive health (i.e. social, environmental and behavioural) exists in pockets of data excellence; data is rich and plentiful but severely fragmented, aged and static across sectors.¹⁸ To realise precision prevention, consumers (well patients) must be the epicentre of real-time data collection and aggregation for preventing chronic disease.

Privacy concerns around the secondary use of data have underpinned a lack of strategic use of data for population disease prevention. Consumers understand the benefits of secondary use of health data if a balance between individual privacy and public benefit is maintained.¹⁹ We hypothesise that the COVID-19 pandemic may contribute to slowly dissolving public fear around secondary data usage in coming years. Trust, transparency, open public dialogue, consumer feedback loops and robust policies are crucial.

Real-time, consumer-centred aggregation of data on determinants of health is critical for building the digital foundations for precision prevention. New global strategy is aligned with this direction; the World Health Organization Global Strategy on Digital Health outlines a strategic priority to ‘advocate people-centred health systems that are enabled by digital health’¹⁶.

Several state jurisdictions in Australia (Queensland, New South Wales, Western Australia) are pivoting their information systems from disease-specific registries to consumer-centred EMRs. For this perspective, we leveraged the Queensland Digital Health Clinical Charter and its three horizons¹² for digital transformation of acute hospital care to propose three new horizons for precision prevention of chronic disease (Table 1).

Table 1.  Three horizons framework for digital health transformation towards precision prevention of chronic disease in Australia

Figure 2 presents a use case of precision prevention for childhood obesity mapped to our three horizons. The purpose is to provide a pragmatic foundation to guide digital health investment, decision making and research for precision prevention in Australia.

Fig. 2.  Childhood obesity: a use case of the three horizons framework¹² for digital health transformation towards precision prevention of chronic disease.^18,20–25 BMI, body mass index; SES, socioeconomic status.

Digital health transformation is difficult and lengthy. The greatest challenges for precision prevention will be securing societal unity and support, transforming investment behaviours and digitally uniting a fragmented public sector to measure and make population health ‘matter’. Beginning with (a) mapping preventive data points and data assets and (b) engaging with multi-sectoral prevention stakeholders to co-produce a roadmap for advancing the three horizons for precision prevention, Australia can shift from low-value break–fix to high-value predict–prevent for chronic disease to ensure healthcare sustainability.

Data sharing is not applicable as no new data were generated or analysed during this study.

The authors declare that they have no competing interests.

This research was supported by the Digital Health Cooperative Research Centre, Australian Government (DHCRC-0083).