Manufacturing safer medics

Reducing the risk of NHS disasters

How could we better use public inquiries to stem the recurrence of healthcare failures? The question seems ever relevant, prompted this time by the inquiry into how former nurse Letby was able to murder newborns under National Health Service care. While criminality, like Letby's, can be readily condemned, other factors like poor leadership and culture seem more often regretted than reformed. I would argue this is where inquiries struggle, in the space between ethics and law-with what is awful but lawful. In response, we should learn from progress with informed consent. Inquiries and civil litigation have seen uninformed 'consent' shift from being undesirable to unlawful. If better leadership and culture were sole drivers here, we would likely be doing far better in many other areas of healthcare too. Instead, one could argue that progress on consent has been made by reducing epistemic injustice-by naming and addressing epistemic issues in ways that enhance social power for patients. If this is an ingredient that transforms clinician-patient working, might it also shift conduct within other key relationships, by showing up what else should become unlawful and why? Naming medical paternalism may have helped with consent reform, so I continue this approach, first naming two areas of epistemic injustice: management feudalism and legal chokeholds Remedies are then considered, including the democratisation of management and reforms to legal ethics, legislation and litigation. In brief, public inquiries may improve if they also target epistemic injustices that should become unlawful. Focus on informed consent and epistemic relationships has improved the medical profession. Likewise, it could help healthcare leaders shift from fiat towards consent, and their lawyers from a stifling professional secrecy towards the kind of candour a prudent public expects.

Surgomics: personalized prediction of morbidity, mortality and long-term outcome in surgery using machine learning on multimodal data

BACKGROUND

Personalized medicine requires the integration and analysis of vast amounts of patient data to realize individualized care. With Surgomics, we aim to facilitate personalized therapy recommendations in surgery by integration of intraoperative surgical data and their analysis with machine learning methods to leverage the potential of this data in analogy to Radiomics and Genomics.

METHODS

We defined Surgomics as the entirety of surgomic features that are process characteristics of a surgical procedure automatically derived from multimodal intraoperative data to quantify processes in the operating room. In a multidisciplinary team we discussed potential data sources like endoscopic videos, vital sign monitoring, medical devices and instruments and respective surgomic features. Subsequently, an online questionnaire was sent to experts from surgery and (computer) science at multiple centers for rating the features' clinical relevance and technical feasibility.

RESULTS

In total, 52 surgomic features were identified and assigned to eight feature categories. Based on the expert survey (n = 66 participants) the feature category with the highest clinical relevance as rated by surgeons was "surgical skill and quality of performance" for morbidity and mortality (9.0 ± 1.3 on a numerical rating scale from 1 to 10) as well as for long-term (oncological) outcome (8.2 ± 1.8). The feature category with the highest feasibility to be automatically extracted as rated by (computer) scientists was "Instrument" (8.5 ± 1.7). Among the surgomic features ranked as most relevant in their respective category were "intraoperative adverse events", "action performed with instruments", "vital sign monitoring", and "difficulty of surgery".

CONCLUSION

Surgomics is a promising concept for the analysis of intraoperative data. Surgomics may be used together with preoperative features from clinical data and Radiomics to predict postoperative morbidity, mortality and long-term outcome, as well as to provide tailored feedback for surgeons.