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Article

Topic Analysis of UK Fitness to Practise Cases: What Lessons Can Be Learnt?

1
Queen’s Management School, Queen’s University Belfast, University Rd, Belfast BT7 1NN, UK
2
School of Pharmacy, Queen’s University Belfast, University Rd, Belfast BT7 1NN, UK
*
Author to whom correspondence should be addressed.
Pharmacy 2019, 7(3), 130; https://doi.org/10.3390/pharmacy7030130
Received: 16 August 2019 / Revised: 31 August 2019 / Accepted: 2 September 2019 / Published: 4 September 2019
(This article belongs to the Special Issue Continuing Professional Development in Pharmacy)
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. View Full-Text
Keywords: dental; fitness to practice; machine learning; medical; non-negative matrix factorization; nursing; pharmacy; text mining; topic analysis dental; fitness to practice; machine learning; medical; non-negative matrix factorization; nursing; pharmacy; text mining; topic analysis
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MDPI and ACS Style

Hanna, A.; Hanna, L.-A. Topic Analysis of UK Fitness to Practise Cases: What Lessons Can Be Learnt? Pharmacy 2019, 7, 130. https://doi.org/10.3390/pharmacy7030130

AMA Style

Hanna A, Hanna L-A. Topic Analysis of UK Fitness to Practise Cases: What Lessons Can Be Learnt? Pharmacy. 2019; 7(3):130. https://doi.org/10.3390/pharmacy7030130

Chicago/Turabian Style

Hanna, Alan, and Lezley-Anne Hanna. 2019. "Topic Analysis of UK Fitness to Practise Cases: What Lessons Can Be Learnt?" Pharmacy 7, no. 3: 130. https://doi.org/10.3390/pharmacy7030130

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