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Risa Mallory is a retired psychotherapist from Canada and a hub Topic leader. After a serious cardiovascular event in 2018 she became a patient advocate, collaborating with organisations across the globe.  In this blog, Risa contends that patient-centred care provides a good foundation but should not be the end goal. She calls on healthcare systems to evolve towards patient-led care, suggesting that this is key to ensuring that patients are treated as partners rather than participants.  When you live with heart disease, healthcare stops being abstract very quickly. It becomes personal, constant, and at times overwhelming. Appointments, medications, test results, lifestyle changes—these are not theoretical concepts, they shape how you live each day. Over time, I have learned that how care is delivered matters just as much as what care is delivered. That is where the distinction between patient-centred and patient-led healthcare becomes meaningful. Patient-centred care Patient-centred care is a term I hear often. Clinicians use it to describe care that considers my needs, values, and preferences. On the surface, this sounds exactly right. As a cardiac patient, I want to be treated as a whole person, not just a heart condition. I want my concerns listened to, my fears acknowledged, and my circumstances taken into account. When patient-centred care is done well, it feels respectful. My cardiologist explains options, my nurse checks in on how I’m managing, and decisions are made with me, not just about me. But as someone who lives with this condition every day—not just during clinic visits—I have come to realise that patient-centred care still often keeps control firmly within the healthcare system. The care may be tailored to me, but it is usually still designed, paced, and directed by professionals. I am invited to the table, but I do not always get to set the agenda. That is where patient-led healthcare differs. Patient-led care Patient-led care recognises something fundamental: I am the one living inside this body. I am the one who feels the side effects, manages the fatigue, navigates fear after a hospital admission, and tries to balance medical advice with real life. In a patient-led model, my lived experience is not just considered—it is treated as expertise. As a cardiac patient, being patient-led does not mean I reject clinical knowledge or expect to make decisions alone. I still rely deeply on my healthcare team’s training and experience. What changes is the balance of power. Instead of being asked, “What matters to you?” after decisions are mostly formed, patient-led care asks that question at the beginning—and allows the answer to shape the pathway forward. For example, when discussing treatment options, patient-centred care might present several evidence-based choices and ask which one I prefer. Patient-led care goes further. It asks how those options will affect my daily life, my mental health, my ability to work or care for family, and whether the recommended plan is realistic for me to sustain. It allows me to say, “This may be clinically ideal, but it doesn’t fit my life,” without fear of being labelled non-compliant. From participants to partners The difference becomes especially clear after a cardiac event. In hospital, patient-centred care might ensure good communication, compassionate interactions, and shared decision-making. Once discharged, however, the burden of care shifts heavily onto the patient. Medications, monitoring symptoms, lifestyle changes—suddenly, I am expected to lead my own care without always being given the tools, confidence, or ongoing support to do so. Patient-led healthcare recognises this gap and works to close it. Patient-led care values partnership beyond appointments. It supports education that empowers rather than overwhelms. It acknowledges emotional recovery as part of cardiac recovery. It invites patients into service design, research priorities, and policy decisions—not as a token gesture, but as equal contributors. After all, systems built without patient input often fail to meet patient needs. From my perspective, patient-centred care is an important foundation, but it is not the end goal. It still positions patients as recipients of care, even when that care is compassionate and individualised. Patient-led healthcare moves us from being participants to being partners. It trusts that patients, when supported appropriately, can help guide better, safer, and more humane care. Living with heart disease has taught me that my voice matters—not just in my own treatment, but in shaping the systems meant to support people like me. True progress in healthcare will come when patient-centred care evolves into patient-led care, where lived experience is not an afterthought, but a driving force. More blogs by Risa Compassion is medicine: a patient safety perspective The power of being heard in healthcare When lived experience is embedded at every stage of research Women’s heart health - a patient safety priority Why the patient voice matters when things go wrong

Stefan Peil summarises a pilot study he has done to see whether a structured systems model can support the preparation of a morbidity and mortality (M&M) conference discussion. The example used is a coronary angiography risk scenario to explore whether a model-based representation of patient safety knowledge could serve as a reliable basis for an artificial intelligence (AI)-assisted decision template. The work was produced to address a practical problem in patient safety: relevant information for M&M preparation is often spread across diagrams, reports and team knowledge, which can slow and make shared understanding less consistent. The pilot study, therefore, examined whether systems modelling could help organise, make transparent and reuse safety relevant information in a more structured way. The full study is attached at the end of this page. The challenge The identified challenge was the lack of a structured, reusable approach to preparing patient safety discussions for M&M conferences. The aim was not to automate clinical judgement, but to test whether a model-based risk analysis derived from team knowledge could serve as a structured input for drafting an M&M decision template. M&M preparation often relies on fragmented information and informal interpretation. In complex clinical environments, such as coronary angiography, risks do not arise from a single isolated factor. They emerge from the interaction between tasks, people, technology, information flow and organisational conditions. In this specific pilot example, the safety concern was a risk scenario in coronary angiography in which cognitive overload during real-time decision-making and escalation could contribute to complications not being detected in time. This formed the basis for testing whether a structured model could provide a clearer and more traceable starting point for discussion. Method and measures To explore this, a systems model based on Systems Engineering Initiative for Patient Safety (SEIPS) 2.0 was created in Systems Modeling Language (SysML) using SPARX Enterprise Architect. The objective was to represent the work system, the contributory task factor, the resulting risk and the proposed measures in a traceable form. The model focused on one coronary angiography scenario. The critical task factor was described as cognitive density in real-time decision-making and potential escalation. In the model, this contributed to the risk that complications would not be detected in time. The text states an impact on quality of care, an occurrence rating described as relevant and an overall risk class of moderate. The proposed measures were: pre-procedure briefing risk-adapted staffing standardised laboratory layout regular simulation drills. The intended achievement was a more structured, transparent and reusable basis for M&M preparation and discussion. Outcomes and lessons learned The pilot showed that a structured model can be a useful way to organise safety-relevant knowledge. Because the model linked work system elements, risks and measures in a traceable way, it provided a clearer starting point for discussion than unstructured text alone. The practical process tested in this pilot was: defining a relevant patient safety scenario in coronary angiography modelling the work system and the contributory task factor linking this to a patient safety risk documenting possible mitigating measures using the model as the basis for an AI-assisted one-page decision template. One important observation was that the AI-generated output reflected the underlying model's content. This suggests that a structured model can support more consistent synthesis than relying only on memory or informal interpretation. The text does not describe multiple alternative technical approaches in detail, so it cannot be stated from the source whether other options were formally compared or ruled out. It also does not state direct patient involvement. Staff involvement is referenced indirectly by using team knowledge as an input to the model. The text does not report formal measurement tools, outcome metrics, time savings, patient safety indicators or model costs. Therefore, no validated impact measurement can be claimed from the source. A key lesson learnt was that AI can assist with drafting and synthesis, but cannot replace clinical judgement, governance or safety review. Any output generated from the model still needs to be checked against the source material and reviewed by responsible clinical and patient safety leads. Impact This work is only a prototype, not as a formal effectiveness study. As a result, the impact that can be claimed is limited. The main result was that the structured model appeared to support: clearer organisation of safety-relevant knowledge better traceability between work system factors, risks and proposed measures a more consistent starting point for multidisciplinary discussion reuse of modelled information for drafting a one-page M&M decision template. At the same time, the the study is explicit about what was not demonstrated. The pilot did not test whether the approach: improved patient outcomes reduced harm shortened preparation time in routine practice improved care delivery in a measurable way. A further limitation was that only a single, limited example was used, and some information was withheld for data protection reasons. This means the results were narrower than would be needed for broader implementation decisions. What worked was the structured linkage between the work system, contributory factors, risks and measures. What remains uncertain is whether this translates into measurable operational benefit in routine clinical governance. A likely barrier to improvement is the need for continued expert review, because AI-generated output cannot be used without clinical validation and governance oversight. If repeated, the next stage would need a clearer evaluation design, including defined measures of clarity, consistency, usability and possibly preparation time. Next steps The next step is a practical pilot in real clinical governance settings. A suitable next-stage comparison would be conventional M&M preparation versus model-supported preparation in a small, clearly defined pilot. The proposed questions for the next phase are: Does the approach improve clarity and shared understanding? Does it help teams identify contributory factors more systematically? Does it support consistency and traceability of measures related to patient safety? The study does not provide evidence of long-term organisational change, staff reaction, patient impact statistics or system-wide implementation results. Therefore, those elements cannot yet be stated as outcomes. However, based on insights from the pilot study, the anticipated longer-term value would be to make patient safety knowledge: more structured more reusable easier to discuss across professional groups more clearly linked to the wider work system rather than to isolated errors. A sensible next step would, therefore, be a controlled local test with defined governance, clinical review and evaluation criteria before any broader adoption.

Pregnancy in women with aortic disease can be high risk. However, the risk can be reduced with the right care and planning. This leaflet developed by Aortic Dissection Awareness UK & Ireland, provides advice and guidance for women with aortic disease who are planning on having a baby.

Cardiovascular disease (CVD) is one of the leading causes of morbidity, disability and mortality in England and a significant driver of health inequalities. It disproportionately affects people in deprived and ethnic minority communities and accounts for one-fifth of the gap in life expectancy between most and least deprived areas. The King’s Fund report, Cardiovascular disease in England, highlights the need to prevent and manage CVD. CVD accounts for one in four of all deaths in England. The yearly health care costs related to CVD are estimated at £7.4 billion with an annual cost to the wider economy of £15.8 billion. At a time when the NHS and social care workforce and finances are facing unprecedented and rising pressures, urgent comprehensive action across the public health, health and care sectors is needed to significantly reduce the adverse health impacts of CVD and associated workloads and costs. Leaders and experts from across the NHS and its partners will gather to discuss how best to prioritise and deliver services to reduce the prevalence of CVD and its risk factors across the population, and to improve early detection, management and treatment of CVD and its risk factors. Register