Hanna Alan, Hanna Lezley-Anne
Queen's Management School, Queen's University Belfast, University Rd, Belfast BT7 1NN, UK.
School of Pharmacy, Queen's University Belfast, University Rd, Belfast BT7 1NN, UK.
Pharmacy (Basel). 2019 Sep 4;7(3):130. doi: 10.3390/pharmacy7030130.
: 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. : 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. : 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. : While commonalities exist, each has different priorities which professional and educational organizations should strive to address.
执业适任性（FtP）受损（医疗保健专业人员未能展现其工作所需的技能、知识、品德和/或健康状况）会危及患者安全、该行业的声誉以及个人的职业生涯。在英国，各类医疗保健专业人员的FtP案例（关于小组听证会以及与所声称的FtP受损相关结果的文件）均可公开获取，然而鉴于各行业案例数量众多且每个案例文本篇幅较长，审查这些案例以吸取经验教训可能颇为耗时。我们旨在展示机器学习如何促进对此类案例（在单一专业和多专业层面）的审查，涉及英国的牙科、医学、护理和药学专业人员。
下载了2017年8月至2019年6月期间的案例（577个牙科案例、481个医学案例、2199个护理案例和63个药学案例）并转换为文本文件。采用主题分析方法（非负矩阵分解；机器学习）进行数据分析。
识别出的主题包括刑事犯罪；不诚实行为（欺诈和盗窃）；持有/供应毒品；英语语言；赔偿保险；患者护理（包括能力不足）以及个人行为（攻击行为、性行为和药物滥用）。牙科、医学和护理专业最常识别出的主题是患者护理，而药学专业则是刑事犯罪。
虽然存在共性，但每个专业都有不同的优先事项，专业组织和教育机构应努力加以解决。
