Skip to main content

Transforming Clinical Uncertainty into a Strategy for Safe Care

Managing uncertainty is a core skill in primary care and other frontline clinical specialties such as emergency medicine where patients frequently present with early, evolving or non-specific symptoms that do not fit clear diagnostic categories. Rather than eliminating uncertainty, safe practice depends on recognising it, communicating transparently with patients and colleagues, and managing risk over time. 

This is especially important for serious illnesses that may initially present with vague or common symptoms where premature or inappropriate reassurance can delay diagnosis. An example is ovarian cancer which can present with symptoms such as abdominal pain and bloating; and where there is overlap with other, less serious illnesses such as irritable bowel syndrome/

Safe management in patients with such presentations begins with a structured clinical assessment that considers red flags, comorbidities and the patient’s clinical and social contexts; alongside an explicit acknowledgement of diagnostic uncertainty. Shared decision-making is crucial: clinicians should explain what is known, what is uncertain, and why a “watchful waiting” approach may be appropriate. Clear safety netting then becomes a key risk-management tool.

Effective safety netting includes: specifying which symptoms should prompt concern; giving clear advice to patients on when and how to seek further help; setting an expected time course for symptom resolution; and arranging planned follow-up where appropriate. Documenting this advice and ensuring it is understood -  particularly in patients with language barriers, health literacy challenges or high levels of anxiety - is essential. Used well, safety netting transforms uncertainty from a source of risk into an active, patient-centred strategy for safe care.

Safety netting is the primary risk-management tool that transforms passive waiting into an active, patient-centred strategy.

Stage

 

Action

Symptom Specificity

Define exactly which "red flag" symptoms (e.g., weight loss, night sweats) should prompt immediate concern

Clear Pathways

Provide explicit instructions on how and when to seek further help (e.g., "Call the urgent line, don't wait for a routine appt").

Timeboxing

Set a definitive time course for symptom resolution (e.g., "If this is a virus, you should feel better in 5 days").

Planned Follow Up

Arrange a "fail-safe" review to ensure the patient does not get lost in the system.


Comments

Popular posts from this blog

What is the difference between primordial prevention and primary prevention?

Primordial prevention and primary prevention are both crucial strategies for promoting health, but they operate at different levels. Primordial prevention aims to address the root causes of health problems and improve the wider determinants of health. It focuses on preventing the emergence of risk factors in the first place by tackling the underlying social, economic, and environmental determinants of health. This involves broad, population-wide interventions such as: Policies that promote healthy food choices: Think about initiatives like taxing sugary drinks to discourage unhealthy consumption, or providing subsidies for fruits and vegetables to make them more accessible. Urban planning that prioritises well-being: This could include creating walkable neighborhoods with safe cycling routes, ensuring access to green spaces for recreation and relaxation, and designing communities that foster social connections. Social programs that address inequality: Initiatives aimed at reducing pov...

What makes a good doctor – and who gets to decide?

What Makes a Good Doctor? This is the question that Waseem Jerjes and I explore in the Journal of the Royal Society of Medicine . It is a key question that underpins the architecture of medical education, clinical practice, regulation, and professional identity. It cannot be answered by regulators, educators, or employers in isolation. It must be answered together – by doctors and patients – revisited throughout a career, and adapted as society and the profession change. Without that shared reflection, the danger is not simply disillusionment, but the erosion of the moral foundations of clinical work. As we enter an era when diagnosis will increasingly involve artificial intelligence and when performance metrics reward volume over value, reclaiming this question as a professional one is imperative. The integrity of our institutions – and of the practitioners within them – depends on reimagining excellence in inclusive, relational terms. A good doctor is not a flawless technician or a f...

Relevance Over Recall: Rethinking How AI Uses Clinical Data

Our article in the Journal of the Royal Society of Medicine argues that safe and effective AI in healthcare must incorporate mechanisms that emulate human judgement - down-weighting old, inaccurate or superseded information and prioritising what is recent, clinically relevant and reaffirmed - so that AI supports, rather than disrupts, high-quality patient care.  Clinicians constantly revise, reinterpret and filter past information so that only what is relevant, accurate and timely shapes present-day management decisions; medical records function as dynamic “working tools” rather than fixed archives. By contrast, many AI systems lack this capacity for selective forgetting and often treat all historical data as equally meaningful.  This can lead to outdated or low-confidence diagnoses being repeatedly resurfaced, persistent labels influencing clinical expectations, and irrelevant, long-resolved events cluttering summaries and decision-support outputs. Such indiscriminate recall...