Topic Analysis of UK Fitness to Practise Cases: What Lessons Can Be Learnt?
- to demonstrate how topic analysis could be employed for examining published FtP cases;
- to apply the NMF model to enable the identification of topics (themes);
- to determine the extent to which the topics affected the four professions.
2. Materials and Methods
2.1. Data Collection
2.2. Nature of the Data
2.3. Data Pre-Processing
- Removal of duplicate cases. Where the minutes of two or more hearings related to the same case identification number, the file with the largest size was retained on the basis that it provided the greatest descriptive detail
- Removal of ‘boilerplate’ text  that appears as a standard across many cases. For example, the name of the assembled committee, or the address where the meeting took place
- Tokenization (i.e., separating text into the constituent words, or ‘tokens’ that comprise the sentences and paragraphs within it) 
- Removal of all tokens not entirely comprised of alphabetic characters (this removed all numeric tokens)
- Removal of stop words (words that occur with high frequency but add little contextual meaning, for example, ‘the’, ‘and’, ‘but’, ‘in’) 
- Removal of frequently appearing proper nouns including personally identifying names, and place names
- The conversion of text to lower case
2.4. Topic Extraction
2.5. Choosing the Number of Topics
2.6. Data Analysis
2.7. Ethical Approval
- 577 dental (as of July 2019, there were around 40,000 dentists and 60,000 dental care professionals on the register ).
- 481 medical (as of July 2019, there were around 290,000 UK medical practitioners on the register )
- 2199 nursing (as of 31 March 2019, there were 698,237 people on the NMC register )
- 63 pharmacy (as of 31 March 2019, there were 56,288 pharmacists and 23,387 technicians on the GPhC register ).
- Criminal offences
- Drug possession/supply
- English language
- Indemnity insurance
- Patient care
- Personal behavior
- Sexual conduct
- Substance misuse
Conflicts of Interest
- General Pharmaceutical Council (GPhC). Definition of Fitness to Practise. Available online: https://www.pharmacyregulation.org/raising-concerns/registrants/definition-fitness-practise (accessed on 9 July 2019).
- General Dental Council (GDC). Fitness to Practise. Available online: https://www.gdc-uk.org/professionals/ftp-prof (accessed on 9 July 2019).
- General Medical Council (GMC). Professional Behaviour and Fitness to Practise. Available online: https://www.gmc-uk.org/education/standards-guidance-and-curricula/guidance/professional-behaviour-and-fitness-to-practise (accessed on 9 July 2019).
- Nursing and Midwifery Council (NMC). What is Fitness to Practise? Available online: https://www.nmc.org.uk/concerns-nurses-midwives/dealing-concerns/what-is-fitness-to-practise/ (accessed on 9 July 2019).
- Watters, C. Assessing the health consequences of fitness to practise investigations. Br. J. Nurs. 2018, 27, 639–641. [Google Scholar] [CrossRef] [PubMed]
- Casey, D.; Choong, K.A. Suicide whilst under GMC’s fitness to practise investigation: Were those deaths preventable? J. Forensic Leg. Med. 2016, 37, 22–27. [Google Scholar] [CrossRef] [PubMed]
- General Pharmaceutical Council (GPhC). Annual Report, Annual Fitness to Practise Report, Annual Accounts 2018/19. Available online: https://www.pharmacyregulation.org/annualreport/annual-report (accessed on 28 July 2019).
- General Medical Council (GMC). Fitness to Practise Statistics and Reports. Available online: https://www.gmc-uk.org/about/what-we-do-and-why/data-and-research/medical-practice-statistics-and-reports/fitness-to-practise (accessed on 28 July 2019).
- Nursing and midwifery council (NMC). Fitness to Practise Annual Report. Available online: https://www.nmc.org.uk/about-us/reports-and-accounts/fitness-to-practise-annual-report/ (accessed on 28 July 2019).
- Tiffin, P.A.; Paton, L.W.; Mwandigha, L.M.; McLachlan, J.C.; Illing, J. Predicting fitness to practise events in international medical graduates who registered as UK doctors via the Professional and Linguistic Assessments Board (PLAB) system: A national cohort study. BMC Med. 2017, 15, 66. [Google Scholar] [CrossRef]
- Humphrey, C.; Hickman, S.; Gulliford, M.C. Place of medical qualification and outcomes of UK General Medical Council “fitness to practise” process: Cohort study. BMJ 2011, 342, d1817. [Google Scholar] [CrossRef] [PubMed]
- Sanders, A.; Taylor, C.A. The effect of medical school on postgraduate fitness to practise decisions: A retrospective cohort study. Br. J. Hosp. Med. (Lond.) 2013, 74, 581–584. [Google Scholar] [CrossRef] [PubMed]
- Wakeford, R.; Ludka, K.; Woolf, K.; McManus, I.C. Fitness to practise sanctions in UK doctors are predicted by poor performance at MRCGP and MRCP(UK) assessments: Data linkage study. BMC Med. 2018, 16, 230. [Google Scholar] [CrossRef]
- Brindley, J. Reflection on fitness to practise. Br. Dent. J. 2016, 221, 495–498. [Google Scholar] [CrossRef]
- Neville, P. Social media and professionalism: A retrospective content analysis of Fitness to Practise cases heard by the GDC concerning social media complaints. Br. Dent. J. 2017, 223, 353–357. [Google Scholar] [CrossRef]
- Taylor, R.; Ali, M.H.; Howe, T.E.; Varley, I. Review of General Dental Council and General Medical Council “fitness to practise” hearings related to maxillofacial surgery. Br. J. Oral Maxillofac. Surg. 2017, 55, 580–583. [Google Scholar] [CrossRef]
- Gallagher, C.T.; Foster, C.L. Impairment and sanction in Medical Practitioners Tribunal Service fitness to practise proceedings. Med. Leg. J. 2015, 83, 15–21. [Google Scholar] [CrossRef]
- Gallagher, C.T.; Greenland, V.A.; Hickman, A.C. Eram, ergo sum? A 1-year retrospective study of General Pharmaceutical Council fitness to practise hearings. Int. J. Pharm. Pract. 2015, 23, 205–211. [Google Scholar] [CrossRef] [PubMed]
- Blei, D.M.; Ng, A.Y.; Jordan, M.I. Latent dirichlet allocation. J. Mach. Learn. Res. 2003, 3, 993–1022. [Google Scholar]
- Deo, R.C. Machine Learning in Medicine. Circulation 2015, 132, 1920–1930. [Google Scholar] [CrossRef][Green Version]
- Han, D.; Wang, S.; Jiang, C.; Jiang, X.; Kim, H.E.; Sun, J.; Ohno-Machado, L. Trends in biomedical informatics: Automated topic analysis of JAMIA articles. J. Am. Med. Inform. Assoc. 2015, 22, 1153–1163. [Google Scholar] [CrossRef] [PubMed]
- Kagashe, I.; Yan, Z.; Suheryani, I. Enhancing Seasonal Influenza Surveillance: Topic Analysis of Widely Used Medicinal Drugs Using Twitter Data. J. Med. Internet Res. 2017, 19, e315. [Google Scholar] [CrossRef] [PubMed][Green Version]
- Casalino, G.; Castiello, C.; Del Buono, N.; Mencar, C. A framework for intelligent Twitter data analysis with non-negative matrix factorization. Int. J. Web. Inform. Syst. 2018, 14, 334–356. [Google Scholar] [CrossRef]
- Introne, J.; Goggins, S. Advice reification, learning, and emergent collective intelligence in online health support communities. Comput. Hum. Behav. 2019, 99, 205–218. [Google Scholar] [CrossRef]
- Nzali, M.D.T.; Bringay, S.; Lavergne, C.; Mollevi, C.; Opitz, T. What Patients Can Tell Us: Topic Analysis for Social Media on Breast Cancer. JMIR Med. Inform. 2017, 5, e23. [Google Scholar] [CrossRef] [PubMed]
- Nguyen, M.T.; Tran, V.C.; Nguyen, X.H.; Nguyen, L.M. Web document summarization by exploiting social context with matrix co-factorization. Inf. Process. Manag. 2019, 56, 495–515. [Google Scholar] [CrossRef]
- Lee, D.D.; Seung, H.S. Learning the parts of objects by non-negative matrix factorization. Nature 1999, 401, 788–791. [Google Scholar] [CrossRef]
- O’Callaghan, D.; Greene, D.; Carthy, J.; Cunningham, P. An analysis of the coherence of descriptors in topic modeling. Expert Syst. Appl. 2015, 42, 5645–5657. [Google Scholar] [CrossRef]
- CRAN.R-Project, Pdftools: Text Extraction, Rendering and Converting of PDF Documents. R package version 1.8. Available online: https://CRAN.R-project.org/package=pdftools (accessed on 9 August 2019).
- Vogels, T.; Ganea, O.E.; Eickhoff, C. Web2text: Deep structured boilerplate removal. In Proceedings of the 40th European Conference on Information Retrieval Research, ECIR 2018, Grenoble, France, 26–28 March 2018; Pasi, G., Piwowarski, B., Azzopardi, L., Hanbury, A., Eds.; Springer: Heidelberg, Germany, 2018; Available online: https://link.springer.com/book/10.1007/978-3-319-76941-7 (accessed on 12 August 2019).
- Bird, S.; Klein, E.; Loper, E. Natural Language Processing with Python: Analyzing Text with the Natural Language Toolkit; O’Reilly Media, Inc.: Sebastopol, CA, USA, 2009. [Google Scholar]
- Ranks NL, Stopwords. Available online: http://www.ranks.nl/stopwords (accessed on 12 August 2019).
- Loughran, T.; McDonald, B. When is a liability not a liability? Textual analysis, dictionaries, and 10-ks. J. Financ. 2011, 66, 35–65. [Google Scholar]
- Pedregosa, F.; Varoquaux, G.; Gramfort, A.; Michel, V.; Thirion, B.; Grisel, O.; Blondel, M.; Prettenhofer, P.; Weiss, R.; Dubourg, V.; et al. Scikit-learn: Machine learning in python. J. Mach. Learn. Res. 2011, 12, 2825–2830. [Google Scholar]
- Koltcov, S.; Ignatenko, V.; Koltsova, O. Estimating Topic Modeling Performance with Sharma–Mittal Entropy. Entropy 2019, 21, 660. [Google Scholar]
- Mimno, D.; Wallach, H.M.; Talley, E.; Leenders, M.; McCallum, A. Optimizing semantic coherence in topic models. In Proceedings of the Empirical Methods in Natural Language Processing Conference 2011, Edinburgh, Scotland, UK, 27–31 July 2011; Barzilay, R., Johnson, M., Eds.; Association for Computational Linguistics: Stroudsburg, PA, USA, 2011; Available online: https://www.aclweb.org/anthology/volumes/D11-1/ (accessed on 13 August 2019).
- General Dental Council (GDC). What We Do. Available online: https://www.gdc-uk.org/about/what-we-do (accessed on 28 July 2019).
- General Medical Council (GMC). The Medical Register. Available online: https://www.gmc-uk.org/registration-and-licensing/the-medical-register (accessed on 28 July 2019).
- Donaldson, L.J.; Panesar, S.S.; McAvoy, P.A.; Scarrott, D.M. Identification of poor performance in a national medical workforce over 11 years: An observational study. BMJ Qual. Saf. 2014, 23, 147–152. [Google Scholar] [CrossRef] [PubMed]
- Lillis, S.; Takai, N.; Francis, S. Long-term outcomes of a remedial education program for doctors with clinical performance deficits. J. Contin. Educ. Health Prof. 2014, 34, 96–101. [Google Scholar] [CrossRef] [PubMed]
- Myers, H.; Taylor, J.; Finn, R.S.; Beckert, L. Doctors learn new tricks, but do they remember them? Lack of effect of an educational intervention in improving Oxygen prescribing. Respirology 2015, 20, 1229–1232. [Google Scholar] [CrossRef] [PubMed]
- Jayaweera, H.K.; Potts, H.W.W.; Keshwani, K.; Valerio, C.; Baker, M.; Mehdizadeh, L.; Sturrock, A. The GP tests of competence assessment: Which part best predicts fitness to practise decisions? BMC Med. Educ. 2018, 18, 2. [Google Scholar] [CrossRef]
- Pharmacists’ Defence Association (PDA). Regulatory Spotlight Turned on General Practice Pharmacist Competency. Available online: https://www.the-pda.org/regulatory-spotlight-turned-on-general-practice-pharmacist-competency/ (accessed on 31 August 2019).
- General Pharmaceutical Council (GPhC). Regulate: New Requirements for Pharmacy Professionals on English Language Skills and Indemnity Arrangements. Available online: https://www.pharmacyregulation.org/regulate/article/new-requirements-pharmacy-professionals-english-language-skills-and-indemnity (accessed on 13 August 2019).
- Phipps, D.L.; Walshe, K.; Parker, D.; Noyce, P.R.; Ashcroft, D.M. Job characteristics, well-being and risky behaviour amongst pharmacists. Psychol. Health Med. 2016, 21, 932–944. [Google Scholar] [CrossRef]
- Paton, L.W.; Tiffin, P.A.; Smith, D.; Dowell, J.S.; Mwandigha, L.M. Predictors of fitness to practise declarations in UK medical undergraduates. BMC Med. Educ. 2018, 18, 68. [Google Scholar] [CrossRef]
|Groups of Words (10 Highest Scoring Words)||Allocated Topic Title|
|sentence, sentencing, conviction, crown, imprisonment, judge, sentenced, convicted, court, remarks||Criminal offences|
|assault, conviction, violence, beating, magistrates, police, criminal, court, guilty, assaulting||Criminal offences (assault)|
|sexual, child, images, sex, offences, offenders, photograph, prevention, convictions, photographs||Criminal offences (sexual conduct)|
|jury, terrorism, conviction, Islamic, murder, trial, prison, judge, sentencing, defendant||Criminal offences (terrorism-related)|
|speeding, traffic, offences, driver, vehicle, drivers, liable, declarations, magistrates, sentences||Criminal offences (traffic)|
|dishonesty, dishonest, dishonestly, honesty, integrity, knew, conceal, false, difficult, honest||Dishonesty|
|falsified, forged, signatures, signature, false, submitting, purported, verification, dishonest, stamp||Dishonesty (fraud)|
|cash, thefts, money, till, theft, planned, additionally, caution, repay, repaid||Dishonesty (theft)|
|drugs, controlled, drug, misuse, possession, supply, class, book, theft, quantity||Drug possession/supply|
|English, language, registrar, knowledge, kingdom, united, qualification, speaking, score, skills||English language|
|indemnity, insurance, cover, compensation, indemnified, hold, arrangements, claim, membership, policy||Indemnity insurance|
|gloves, control, instruments, infection, decontamination, cross, nurses, items, cleaned, inspection||Patient care|
|administered, chart, administer, mar, medication, prescribed, dose, errors, medications, incorrectly||Patient care (competency)|
|factors, attitudinal, behavior, harm, deep, seated, mark, harmful, personality, actions||Personal behavior|
|room, words, link, video, conversation, nurse, aggressive, staff, call, rude||Personal behavior (aggression)|
|sexual, touching, breasts, boundaries, sexually, touched, harassment, thigh, leg, knee||Personal behavior (sexual conduct)|
|cannabis, cocaine, consumed, abstinence, coping, mid, relapse, redacted, results, hair||Personal behavior (substance misuse)|
|Criminal offences (sexual conduct)||1.4%||3.5%||1.5%||3.2%|
|Criminal offences (substance misuse)||3.1%||3.3%||1.0%||4.8%|
|Patient Care (competency)||4.0%||38.9%||17.9%||17.5%|
|Personal behavior (aggression)||0.3%||1.5%||3.3%||0.0%|
|Personal behavior (sexual conduct)||0.9%||5.6%||0.6%||3.2%|
© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
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
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/pharmacy7030130Chicago/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