When a single condition isn’t the whole story

Moving away from a single-disease focus

While people working in health policy have been advocating for more person-centred, integrated care for years, implementation often falls short of intent. There is the question of who is best placed to lead a person-centred approach. Strengthening primary care to take on this role seems obvious, but resource constraints – particularly in terms of the availability of general practitioners – demand innovative solutions. Expanding the role of pharmacists, nurse practitioners, care navigators and community health workers has shown promise in pilot schemes in many countries: Italy has designated community health centres as integrated care hubs with multidisciplinary teams, and England has established integrated care boards.

Changes are most needed in secondary care. Hospital departments and medical specialties have conventionally been designed around a single disease or organ, with accountability and funding systems following a similarly siloed pattern. The result for patients is fragmented care, with inefficiencies in terms of multiple appointments, polypharmacy, information loss, duplicated tests – and no central point of contact who can address their needs holistically.

Formal cross-training programmes among medical specialties that are known to be closely correlated may be a useful start so that physicians and nurses are trained to recognise and manage common multimorbidities. Multimorbidity-specific clinics – such as the CKD-HF clinic at St George’s Hospital in London that manages chronic kidney disease and heart failure – are also a potential solution.

The right evidence to guide clinical practice

The rise of multimorbidity has significant consequences for the evidence we use to inform best practice. Clinical research standards require trials to be designed to isolate treatment effects; therefore, people with multiple conditions are often excluded. This limits the applicability of trial results for people with comorbidities, potentially leading to ineffective or harmful practices. For example, many people with heart failure also have diabetes, yet few trials include them, making it unclear if treatments work for this group.

Expanding criteria to include people with known comorbidities will be important if clinical research is to deliver clinically relevant and implementable evidence. However, this is feasible only if we include individuals with conditions known to frequently co-occur within the target indication – and that would need to be guided by epidemiological research to identify so-called ‘condition clusters’.

This evidence needs to be fed into collaborative guidelines between different specialties. For example, the American Heart Association coined the term ‘cardiovascular-kidney-metabolic syndrome’ in 2023 and developed joint guidelines to address coexisting chronic kidney disease and cardiometabolic conditions. The concept has been expanded to include certain liver conditions, including metabolic dysfunction associated steotohepatitis (MASH). These joint guidelines can play an important role in fostering collaboration between specialties to design care pathways that allow for intersections and overlap.

Data and AI to the rescue?

Data are a key enabler of adapting health planning and health systems to multimorbidity.  Predictive analytics of large, real-world data sets, powered by AI-based machine learning, have transformed our ability to understand where disease and risk-factor clusters lie.

The outputs of these analyses are only as good as the data fed into them and the algorithms used to drive analysis. Data from comprehensive electronic health records (EHRs) that cover the entirety of people’s encounters with the health system have been essential to this transformation, enabling data to follow the patient in a secure, centralised virtual platform.

However, having an EHR system is not enough. Too often, researchers cannot harness their full potential, as EHR data are reserved for administrative purposes. Also, EHRs may not be usable across the health system. For example, although 99% of primary care settings in Spain use EHRs, only 8 of its 17 autonomous regions can effectively share data due to persistent interoperability issues. By contrast, England has expansive EHR data that have helped identify multimorbidity trajectory patterns in both a large London borough and across England, and uncover cross-sectional disease clusters via machine learning.

EHRs also have important implications for clinical decisions, as they allow physicians to interrogate past events that may affect a person’s risk of other conditions later on. For example, pre-eclampsia during pregnancy doubles a person’s risk of future coronary heart disease, stroke and death from a cardiovascular cause. If coupled with training and tools such as red-flag alerts, interrogation of EHRs can signal to a physician to verify their patients’ history of pre-eclampsia when prescribing routine medications that increase cardiovascular risks, to avoid putting them in danger.

Multimorbidity and a shifting perspective on healthcare

Multimorbidity is our new reality, and we need to rethink our health systems to ensure they are equipped to shift from a single-disease framework to one that is more suited for people presenting with multiple conditions. Some avenues include:

• Further epidemiological research to better understand patterns of multimorbidity. For example, in higher-income countries, it is often NCDs that coexist, whereas in low- and middle-income countries there is a double burden of persistent infectious diseases and rising NCDs.
• Exploiting comprehensive health checks to screen for multiple conditions, enabling early detection. Integrated screening approaches for cardiovascular disease, kidney disease and metabolic conditions are already emerging, as they share common risk factors and pathophysiology.
• Exploring opportunities for new interventions to act on multiple conditions. GLP-1 agonists target the GLP-1 receptor, which is involved in multiple disease pathways. As a result, in addition to treating obesity, they are being investigated for potential use in musculoskeletal inflammation, cardiovascular diseases, metabolic conditions, neurodegenerative diseases and various cancers.

Adopting a data-driven, integrated and person-centred approach to care is essential to effectively address the complexities of multimorbidity. But new models of care are not enough; they must be accompanied by a fundamental cultural shift among health professionals, service planners and patients themselves, recognising that a person’s health should be looked at through a multi-condition perspective.

The opinions expressed in this blog are those of the authors and do not necessarily represent the views of The Health Policy Partnership.