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Abstract
Background: Fitness to practise (FtP) impairment (failure of a healthcare professional to demonstrate skills, knowledge, character and/or health required for their job) can compromise patient safety, the profession’s reputation, and an individual’s career. In the United Kingdom (UK), various healthcare professionals’ FtP cases (documents about the panel hearing(s) and outcome(s) relating to the alleged FtP impairment) are publicly available, yet reviewing these to learn lessons may be time-consuming given the number of cases across the professions and amount of text in each. We aimed to demonstrate how machine learning facilitated the examination of such cases (at uni- and multi-professional level), involving UK dental, medical, nursing and pharmacy professionals. Methods: Cases dating from August 2017 to June 2019 were downloaded (577 dental, 481 medical, 2199 nursing and 63 pharmacy) and converted to text files. A topic analysis method (non-negative matrix factorization; machine learning) was employed for data analysis. Results: Identified topics were criminal offences; dishonesty (fraud and theft); drug possession/supply; English language; indemnity insurance; patient care (including incompetence) and personal behavior (aggression, sexual conduct and substance misuse). The most frequently identified topic for dental, medical and nursing professions was patient care whereas for pharmacy, it was criminal offences. Conclusions: While commonalities exist, each has different priorities which professional and educational organizations should strive to address.
Highlights
The General Pharmaceutical Council (GPhC, the Great British pharmacy regulatory body) considers a pharmacy professional ‘fit to practise’ when they “can demonstrate the skills, knowledge, character and health required to do their job safely and effectively” [1]
This current study examined Fitness to practise (FtP) cases via topic analysis and in a broad sense across various healthcare professions, allowing insight to be gained at a uni- and mutli-professional level
The most frequently identified topic for the dental, medical and nursing professions was in relation to patient care whereas for pharmacy, it was criminal offences
Summary
The General Pharmaceutical Council (GPhC, the Great British pharmacy regulatory body) considers a pharmacy professional ‘fit to practise’ when they “can demonstrate the skills, knowledge, character and health required to do their job safely and effectively” [1]. Similar definitions are provided by other UK healthcare regulators including the General Dental Council (GDC), General. Fitness to practise (FtP) impairment (failure of a healthcare professional to demonstrate skills, knowledge, character and/or health required for their job) can compromise patient safety, the profession’s reputation, and an individual’s career. We aimed to demonstrate how machine learning facilitated the examination of such cases (at uniand multi-professional level), involving UK dental, medical, nursing and pharmacy professionals In the United Kingdom (UK), various healthcare professionals’ FtP cases (documents about the panel hearing(s) and outcome(s) relating to the alleged FtP impairment) are publicly available, yet reviewing these to learn lessons may be time-consuming given the number of cases across the professions and amount of text in each.
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