Finding space for kindness: public protection and health professional regulation
Bibliographic record
Abstract
The primary aim of health professional regulators is safeguarding public interest, but this cannot be undertaken effectively unless attention is paid to the well-being of all those involved. The unintended consequences of regulatory processes on health professionals include heightened stress, anxiety, shame and, at times, self-harm and even suicide [1–4]. Both professionals and complainants express high levels of dissatisfaction with current regulatory processes that often seem slow, antagonistic and blame focused [5], leading some to question whether legalistic, ‘one-size-fits-all’ regulatory practices are the best way to ensure public safety in all circumstances. Within healthcare, there has been increasing attention paid to incorporating kindness into daily practice [6, 7]. Are there also ways of integrating kindness into regulatory practice while still maintaining a strong focus on patient safety? Kindness is not about sugar-coating or wilfully ignoring risks to protect practitioners. It is instead a recognition that health professionals work in complex systems and teams, often resulting in heightened cognitive and emotional load, sometimes leading to stress and burnout. Kindness in regulation is about developing humane approaches that maintain a strong focus on risk and patient safety while considering practitioner well-being. Within the regulation of health professionals, three actions could embed kindness: Develop a more nuanced approach to regulation, using better risk assessment to differentiate complaints. One example of this would be where complaints about health professionals are based on a minor, one-off error or omission as opposed to serial incompetence or gross misconduct. Regulatory bodies often do not have processes, tools or language to differentiate these. In low-risk matters, this might mean identifying alternative dispute resolution mechanisms to better manage both patients’ needs and expectations and practitioners’ professional self-identity and commitment to improvement. Find ways to identify and address the ‘pain points’ in the regulatory process. Just as patient experience measures now play a critical role in healthcare quality improvement, regulators need better feedback mechanisms. Hearing the voice of practitioners and complainants highlights where to focus improvement efforts. Practitioners and complainants ask for clear, respectful and non-legalistic communication with regular and transparent updating. They also emphasize improved timeliness, as protracted investigations are at the heart of their frustrations. When regulators prioritize these improvements, they are laying the building blocks of kindness in regulation. Collaborate as part of the broader patient safety system and tackle the narrative of blame. Modern healthcare involves complex, interdependent systems and teams, but health professional regulation largely focuses on an individual practitioner. When a concern is raised, it is often about the last person to see the patient in an interprofessional chain of care. Regulators need to consider ways to take better account of the context in which a health professional works, including the place of team-based errors, systems design and clinical governance. Much has been written about the damage caused by a narrative of blame within healthcare organizations which seeks to find fault and punish, driving problems underground and thereby threatening patient safety [8]. An effective patient safety culture recognizes the importance of openness, honesty, disclosure and fairness to learn from mistakes and prevent future problems. While individual accountability is important, shifting from a blame narrative to a learning one will encourage local reporting and resolution of complaints, particularly less serious ones. This outcome is usually better for practitioners and patients and families alike. This highlights the need for professional regulators to work closely with others in the larger patient safety ecosystem to ensure a more holistic view of risk and, ultimately, greater patent safety. The Australian Health Practitioner Regulation Agency (Ahpra) analysed feedback from over 8000 practitioners and complainants to initiate steps towards a more humane and kinder approach to regulation. Ahpra has responded with a move away from a ‘one-size-fits-all’ approach to managing concerns about practitioners. We know that early risk assessment can greatly improve the experience of low-risk practitioners, where no harm has been identified, by managing concerns quickly and appropriately. The organization now places much greater emphasis on hiring and developing staff with stronger communication skills and encourages more personal contact with both practitioners and complainants. Ahpra has changed legalistic language to reduce the stress and shame experienced by practitioners while recognizing the hurt or loss commonly experienced by complainants. Both groups have pressed for more personal interactions. While building kindness into our regulatory systems and processes is consistent with both safe and patient-centred care, the road to real-life implementation may be bumpy. Patients and their families who raise concerns will need confidence that kindness does not equate to lack of appropriate actions or favouring the health professional. It may also be, for example, that existing legislation limits regulators from considering alternative dispute resolution or sharing information openly enough to ease stress for health professionals. And, improvements to ‘pain points’ for practitioners (such as quicker resolution) must never create a worse experience for complainants. Kindness is central to human existence, yet this has been traditionally viewed as incompatible with regulatory culture. Kindness in regulation does not mean diminishing practitioners’ accountability or reducing scrutiny of them. Instead, we believe kindness in regulation means assessing each complaint individually by risk, being quicker and more transparent and ensuring respect for both parties. It should also mean moving away from blame as a default position and towards a culture focused on patient safety which encourages disclosure, cooperation and accountability. Ultimately, we aim for regulatory processes that are evidence informed, effective and proportionate. Let us also work to make them kinder. We thank Kym Ayscough, Executive Director Regulatory Operations, Ahpra, and Dr Eva Saar, Manager, Research and Evaluation, Ahpra. The idea for this paper came from M.F. and S.B. A.v.d.G. and Z.A. contributed regulatory perspectives from the UK and Canada. This is an opinion piece regarding kindness in regulation. Increasing use of feedback from those involved in complaints handling could lead to improvement. No funding was provided for this work
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.043 | 0.101 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.084 |
| Scholarly communication | 0.019 | 0.020 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.036 | 0.019 |
| Insufficient payload (model declined to judge) | 0.016 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".