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When repeated harm occurs in healthcare, public debate often centres on identifying an individual responsible. Although accountability is essential, patient safety may be better served by asking another question first: Were there earlier signals that something was going wrong? This blog reflects the perspective of Aditi Desai, a surgeon with nearly three decades of clinical experience and an interest in patient safety systems, surgical quality monitoring and organisational learning. Recent high‑profile cases, such as the case of surgeon Yasser Jabbar at Great Ormond Street Hospital,[1] have prompted difficult reflection across the profession about how systems detect repeated patient harm. These situations understandably lead to questions about individual responsibility, but they also highlight the importance of recognising warning signals earlier. After nearly three decades in surgical practice, I have seen how outcomes can fluctuate. A surgeon may perform many procedures safely, then experience several complications in close succession. Some of this represents natural variation. But sometimes patterns emerge that should prompt earlier concern. Modern healthcare systems collect large amounts of clinical data, yet we rarely use it systematically to detect deteriorating performance early.[2] Risk‑adjusted monitoring of outcomes over time, combined with supportive mentoring and fair accountability, could help organisations intervene sooner, protecting both patients and clinicians. Improving patient safety requires moving beyond a simple choice between blaming individuals or fixing systems. Safer care depends on recognising both the human realities of clinical practice and the need for strong organisational oversight. Recognising the early warning signs of unsafe surgical practice Having practised surgery for more than 28 years, I have learned that clinical outcomes are rarely perfectly predictable. A surgeon may perform a hundred operations without complication. Then, within a short period, several adverse outcomes may occur—like unexpected bleeding, infection or an unintended injury during surgery. When this happens, patients suffer first and most. For clinicians, complications also carry a heavy emotional weight. Many doctors recognise the sleepless nights and intense self‑reflection that follow when a patient is harmed. In recent years, public discussions around cases of repeated patient harm have raised difficult questions about how healthcare systems detect unsafe practice. The case of Yasser Jabbar at Great Ormond Street Hospital, widely reported in the UK, has prompted reflection not only about accountability but also about whether earlier signals of unsafe care might have been detectable. The instinctive response is often to ask: “Who is the rogue clinician?” But from a patient safety perspective, an equally important question may be: “Where was the signal that care was becoming unsafe?” Distinguishing variation from unsafe care All clinical practice carries risk. Even highly skilled surgeons experience complications. Medicine is complex, and outcomes vary according to patient condition, procedural difficulty and chance. The real challenge is distinguishing between: Expected complication rates and natural variation, and Patterns that may indicate deteriorating performance or unsafe practice. This distinction is rarely straightforward. It requires careful interpretation of clinical outcomes and trends over time. The human side of surgical practice Medicine often expects clinicians to perform at a consistently high level throughout long careers. Yet surgeons, like everyone else, experience illness, fatigue, personal stress and periods of reduced resilience. Most clinicians continue working through these pressures because the culture of medicine places great value on strength, reliability and professionalism. Recognising this human reality does not diminish professional responsibility. Instead, it highlights the importance of systems that can identify when a clinician may be struggling and offer support or review before patient harm accumulates. The missing safety infrastructure Healthcare organisations collect vast amounts of data about procedures and outcomes. Yet in many systems, we still lack robust mechanisms that can: Risk‑adjust outcomes for patient complexity. Monitor outcome trends over time. Identify negative outliers early. Trigger timely peer review or mentoring. Such systems are not primarily about punishment. Their purpose is to protect patients while supporting clinicians to maintain safe practice. Moving beyond 'individual versus system' Patient safety discussions often frame harm as either the fault of an individual clinician or the result of system failure. In reality, safety depends on both. Strong systems should be able to detect emerging risks early, while still ensuring fair accountability when unsafe practice becomes clear. This approach aligns with the principles of a just culture, where organisations seek to understand and respond to risks rather than relying solely on retrospective blame.[3] A role for data, mentorship and oversight In other high‑performance fields, such as aviation and elite sport, continuous monitoring and coaching are routine. Medicine has traditionally been slower to adopt this approach. Yet supportive oversight and mentoring could help clinicians identify and address problems earlier in their careers or during periods of difficulty. Clinicians may benefit from ongoing coaching and feedback, not only during training but throughout their professional lives.[4] Surgeon and writer Atul Gawande, the WHO checklist pioneer, highlighted this idea in his TED Talk “Want to get great at something? Get a coach”, where he describes how even experienced surgeons can improve performance and safety through structured coaching and peer observation.[5] Looking forward Cases where repeated harm occurs inevitably raise questions about accountability. Where clear incompetence or unsafe practice exists, fair accountability is essential. But patient safety improves most when healthcare systems are able to recognise warning signs early, before serious harm accumulates. By combining risk‑adjusted data, supportive oversight and a culture of learning, healthcare organisations can better protect patients while supporting clinicians to maintain safe practice. Ultimately, safer care depends not only on responding to failure, but on building systems capable of recognising risk sooner. References Triggle N. Great Ormond Street doctor who botched surgery harmed nearly 100 children. BBC News, 29 January 2026. Royal College of Surgeons of England. Surgical outcomes data and transparency. Outcomes FAQ. NHS England. Being fair tool: supporting staff following a patient safety incident. 9 May 2025. Pradarelli JC, Yule S, Panda N, et al. Optimising the implementation of surgical coaching through feedback from practicing surgeons. JAMA Surgery, 2021; 56;(1): 42-49. doi:10.1001/jamasurg.2020.4581. Gawande A. Want to get great at something? Get a coach. TED Talk, April 2017.

MedLed, in partnership with Midlands Air Ambulance Charity, is hosting a Human Factors & Patient Safety for Clinical Leaders course this June and spaces are now open to the wider healthcare sector. Human Factors & Patient Safety for Clinical Leaders is a 2-day face-to-face course built with pre-hospital care as its foundation: the high-stakes, time-pressured, operationally complex environment where Human Factors challenges are most visible. But the principles apply across all of healthcare, and we now have spaces available for clinical and non-clinical professionals beyond the pre-hospital community. What's covered? How human capabilities and limitations influence leadership, management, and the quality of care. Systems thinking and models of safety, moving beyond individual blame and the flawed concept of human error. Why practice doesn't always make perfect and how to recognise error-provoking conditions. The relationship between stress, physiological needs, and performance. Strategies for leading high-performing teams, including ad hoc teams under pressure. How to create an environment of psychological safety for your team. Register

Making mistakes is an inescapable part of being a surgeon, yet research shows that many surgeons feel ill-prepared for this reality and struggle with the deep personal impact that errors can have. Feelings of isolation and lack of support are common, and even conversations with colleagues can sometimes intensify rather than relieve distress. Despite the centrality of this issue to our profession, it is rarely discussed openly. Many surgeons suffer in silence—leading to burnout, dropout, or reliance on unhelpful coping strategies. The True Cut workshop offers a safe and supportive space for honest reflection and practical learning. It explores how we can build better coping strategies in ourselves and our colleagues, how we can respond compassionately to patients and families, and how we can support one another in the aftermath of an error. The workshop is designed to be equally relevant for experienced surgeons and those in training. Target audience: Surgeons at all levels and Trainees Learning style: The day centres on selected excerpts from True Cut, a verbatim play created from interviews with surgeons, their colleagues, and patients. Each scene serves as a starting point for facilitated small-group discussions, held in a safe, supportive, and confidential setting. Scientific evidence is woven together with stimulating perspectives from the arts, encouraging thoughtful engagement and deeper reflection. Aims & objectives: To examine the ever-present possibility of mistakes in surgery, enabling participants to better understand and navigate their impact. Learning outcomes Participants will: better understand the universal nature of mistakes in human activity appreciate the deep and lasting impact of mistakes in surgical practice share and normalise the immediate and late effects on theatre staff - empathise with different perspectives - prepare themselves and others for the aftermath of mistakes support each other to grow and thrive in practice despite and even because of mistakes explore how we should respond to patients and families encourage a more open culture within their own practice, fostering dialogue and candour in their own unit - make links to online and in person resources The course covers the following areas of the Surgical Curriculum: GPC 1 : Values and behaviours GPC 2 : Communication and interpersonal skills. Dealing with complexity and uncertainty GPC 5 : Teamworking GPC 6 : Patient safety Register

The relationship between management and the workforce, in very simplistic terms, can be considered one of reward in return for effort. The contracted effort is communicated through a roster. In organisations that have a continuous operation, blocks of effort are distributed to maintain the flow of output. The organisation of effort, then, is a legitimate function of management.  Norman's previous blog looked at performance variability under normal conditions. In this blog, Norman looks at the impact of physiological states and how management’s organisation of effort degrades decision-making. Fatigue The chart below shows pilot fatigue measured using the Samn-Perelli Scale (S-PS).[1] The S-PS has 7 intervals and a score of 4 indicates the onset of fatigue. The data shows how fatigue increases across the first and second sectors of the day, but, also, that fatigue is significantly higher during night-time operations. A study[2] of urology surgeons using the S-PS, reported that fatigue, as measured pre- and post-operation, increased by 67.95% across the four procedures undertaken in the day. Another study[3] looking at 29 ICU doctors found that the median S-PS score at the start of a day shift was 3 and 4 at the end; however, at the start of a night shift the median was 3 and at the end it was 5. Pilots with less than 6 hours of sleep before a duty started the day with an S-PS score of 4. In a risk assessment of night flights to Queenstown Airport, New Zealand, it was suggested that pilots with an S-PS of 4 or greater should be prohibited from flying.[4] Fatigue affects error rates. The Line Operations Safety Audit (LOSA)[5] shows that crew that slept for 6 hours or less before a duty committed more errors. In a study[6] of crew flying night cargo operations, crew acclimatised to the local day but flying during their local night had an error rate of 13.18/sector. However, crews who were flying at night in a different time zone but operating on their home daytime body clock had an error rate of 5.4 errors/sector. It is well-understood that performance is degraded during the 'window of circadian low' – that phase of the circadian cycle when humans are supposed to be sleeping – but in my previous blog, I made the point that raw error rates are not necessarily the issue, rather it was how errors shape the operation. Fatigue and decision-making The table below shows error outcomes across consecutive flights. An ‘additional risk’ is where, in dealing with the initial error, the crew either committed a subsequent error or the consequence was a ‘Undesired Aircraft State’ (UAS). It is common to see improved performance on the second sector as crew build familiarity but there is a sharp fall-off in performance on the third sector, including a significant increase in the number of mistakes made by crew. Mistakes in this context are errors of decision-making. In short, fatigue affects judgement. We see the same in other domains: in finance, traders make riskier trades when fatigued.[7] This data on fatigue and error points to job design and staff deployment as risk factors. Organisational responses to self-management of fatigue Workers absent themselves from the workplace for a variety of reasons. It could be for genuine ill-health, no-notice personal needs and disaffection (morale). Or it could be personal fatigue management. Again, the control of unplanned absence is a legitimate management activity. Workforce absenteeism places an increased burden on the attending workforce and adds to fatigue. The graph below shows the absence rate for a group of pilots and the percentage of pilots who did not take a single day of unplanned absence in a year. The absence management rules were changed to address the problem. The next graph shows how the duration of absences changed in response to the new policy: Pilot absence episode duration (days) The data suggests that management and workforce exist in a dynamic relationship and management’s attempt to exert control results in a corresponding response. The deployment of the workforce is a legitimate management function, but the way contracted effort is utilised shapes safety. Shift duration and timing induce fatigue and, importantly, fatigue can result in riskier decisions. In the previous blog, decision-making in normal operations was also seen to affect risk. Conclusion In this series of blogs, I have suggested that to understand safety we need to look at the factors that increase risk. Risk is a function of the tension between organisational controls and the need for flexibility that flows from variability in the workplace. Three areas of interest have been suggested: the preparation of staff for work, their control and, finally, their deployment. To understand ‘what goes on here’ we need to better understand the dynamics of these three domains. References Samn SW, Perelli LP. Estimating aircrew fatigue: A technique with application to airlift operations. Brooks Air Force Base. San Antonio, TX. Report No: SAM-TR-82-221, 1982. Petrut B, et al. Mental fatigue evaluation of surgical teams during a regular workday in a high-volume tertiary healthcare center. Urol Int 2020; 104(3-4): 301–308. Bihari S, et al. ICU shift related effects on sleep, fatigue and alertness levels. Occup Med (Lond) 2020; 70(2):107-112. Navigatus Consulting (2017). Queenstown Airport Night Operations Foundation Safety Case. Klinect JR. Line Operations Safety Audit: A Cockpit Observation Methodology for Monitoring Commercial Airline Safety Performance. Unpublished PhD thesis, 2005. University of Texas. Unpublished PhD thesis. University of Texas. MacLeod N. Crew Resource Management Training: A Competence-based Approach for Airline Pilots. CRC Press, 2021. Dickinson DL, Chaudhuri A, Greenaway-McGrevy R. Trading while sleepy? Circadian mismatch and mispricing in a global experimental asset market. Exp Econ 2019; 23:526–553. Further reading from Norman Can you measure safety? Part 1 Errors as clues in the search for safety measures: Measuring safety part 2

In a three-part series of blogs for the hub, Norman Macleod explores how systems behave and how the actions of humans and organisations increase risk.  In part 1 of this blog series, Norman suggested that measuring safety is problematic because the inherent variability in any system is largely invisible. Unfortunately, what we call safety is largely a function of the risks arising from that variability. In this blog, Norman explores how error might offer a pointer to where we might look.  Safety as risk propagation It is common in safety management to talk in terms of hazards. We can identify three classes of hazards: substances or objects that could cause loss or harm; engineered situations where humans engage in activity involving known hazards but under controlled conditions; acts by individuals that inadvertently expose the operation to a hazard (we might call these ‘errors’). Controls are put in place to contain hazards but controls are designed by humans and are fallible. Healthcare is an example of a hazardous condition: things are done to patients that would be illegal if inflicted upon a healthy person. Procedures act as controls in these situations but there is always a tension between work-as-imagined (WAI) and work-as-done (WAD). WAI describes the least-risky solution to a problem that will work in most circumstances (or, at least, those envisaged by the procedure designers), whereas WAD reflects the inherent flexibility needed in the real world. In a study of maritime accidents,[1] it was found that collisions have occurred between ships actively trying to follow the ‘rules of the road.’ Procedures contain affordance spaces, or lacunae, that must be filled by actors applying expertise. Procedures, or rules, form a hierarchy. At the top there are rules about goals: ‘first, do no harm.’ Then there are IF-THEN rules that aid decision-making: IF <symptom> THEN <condition>. The lowest order of rules are task prescriptions: step 1, step 2, step n. As we ascend the hierarchy, actors need more extensive training to cope with the lacunae that invariably exist. Many airlines use a process called the Line Operations Safety Audit (LOSA).[2] Trained observers monitor flight crew under normal flight conditions and log departures from procedures, crew responses and subsequent outcomes. In most cases, 95% of errors are inconsequential: error is very much noise in the system. LOSA can let us see what happens when crew attempt to fill in the gaps in procedures. The observer can tag an error as 'intentional’ (an INC) if certain criteria are met and figures of between 8.8% and 26.4% of INC errors have been seen. However, ‘Intentional’ errors are usually attempts to adapt to local circumstances or to solve problems. These departures from prescribed activity reflect system buffering. The outcome of an error can be categorised in LOSA as ‘inconsequential’, can trigger an additional error or results in an ‘Undesired Aircraft State’ (UAS) if the observer feels that safety has been jeopardised. In one study I looked at UASs arising from INCs versus non-intentional errors. INCs were twice as likely to result in a UAS. I then looked at who committed the error. For INCs, captains accounted for 91.66% of UASs compared with 40.6% when the error was non-intentional. The data suggests that agents actively choose courses of action that contravene procedures to maintain the flow of work but those decisions increase risk. Captains are over-represented in the data because they are the primary decision-makers in the team. Ironically, compliance with procedures is often the starting point for any safety investigation. However, rather than police ‘compliance’, organisations should probably find ways to capture variability and render it as knowledge. What error does To view error simply as failure, however, is to miss the fact that they change the work process in a way that needs to be addressed if safety is to be maintained. This can happen in one of three ways. First, they reduce performance margins. Even slight departures from the optimum aircraft configuration mean that, should a subsequent event occur, the crew have less flexibility to respond. In the flight data shown in the previous blog, an aircraft operating in the outer bands of the distribution is migrating towards the margins of the safe space. Something as commonplace as a change in windspeed or direction could result in a critical outcome. Second, error transfers risk when my action affects others. For example, passengers have been killed when aircraft have flown into turbulence. If a pilot delays or fails to turn on the seat belt sign in time the cabin crew and passengers are exposed to risk because they will not have taken steps to protect themselves (such as sitting down or fastening seat belts). Sometimes, and in contravention of procedures, pilots start the ‘after landing’ checklist early to save time. This usually results in pausing the checklist while air traffic control issues directions to the terminal building. LOSA shows that crew then often forget to finish the checklist and aircraft park with the weather radar still turned on, exposing the ground handlers to a radiation hazard. Finally, separation reduction describes the condition where aircraft are placed in closer proximity to hazardous objects (other aircraft, the ground) than was intended. Again, should something happen, the crew will have less time to react. Error, then, can reveal how the risk profile is shaped by the deliberate actions of crew. What goes on here? This examination of normal work suggests two candidate domains for measures of safety. First, what is the organisation’s understanding of the utility of its control structures (policies and procedures, codes of conduct)? How well-written and comprehensive are the structures? Where are the contradictions and ambiguities that flow from multiple stakeholders in the process of oversight? Second, what is the skills mix of those required to work within the system, recognising the need to cope with the variability inherent in the real world. Does the organisation have a competence model for the different functions in the system? What are the risks associated with substituting staff (bank staff, staff on loan)? Conclusion In this post I have looked how workplace variability shapes risk. I have suggested two key aspects of the structure of an organisation – control and competence – that could be candidates for measuring ‘safety’. In my final blog I want to explore how organisations actively design unsafety into their operations. References Belcher P. ‘A Sociological Interpretation of the COLREGS”. Journal of Navigation, 2002; 55(02): 213-224. Klinect JR, 1st Klinect JR. Line Operations Safety Audit: A Cockpit Observation Methodology for Monitoring Commercial Airline Safety Performance. Unpublished PhD thesis, 2005. University of Texas. Unpublished PhD thesis. University of Texas. Read part one and part three of Norman's blog series.