Definitions and indexing of ‘simulated patient’ studies in health: a classification system proposal
- At: PPR 2022 (2022)
- Type: Poster
- Poster code: PT-19
- By: STUMPF TONIN, Fernanda (H&trc Estesl-ipl (portugal) And Federal University Of Paraná (brasil))
- Co-author(s): Fernanda S. Tonin, Pharmaceutical Sciences Postgraduate Research Program, Federal University of Paraná, Curitiba, Brazil; H&TRC - Health & Technology Research Center, ESTeSL - Escola Superior de Tecnologia da Saúde, Instituto Politécnico de Lisboa, Portugal
Isabela Pina, Pharmaceutical Sciences Postgraduate Research Program, Federal University of Paraná, Brazil
Roberto Pontarolo, Department of Pharmacy, Federal University Of Paraná, Brazil
Fernando Fernandez-Llimos, CINTESIS - Center for Health Technology and Services Research, Laboratory of Pharmacology, Faculty of Pharmacy, University of Porto, Portugal
Background information: Several inconsistencies on the definition and indexing of the term ‘simulated patient’ have been reported in the health literature, including in pharmacy practice.
Purpose: To propose a classification system for studies on ‘simulated patient’ and to assess the coverage of ‘simulated patient’ in the National Library of Medicine’s (NLM) Medical Subject Headings (MeSH) thesaurus.
Method: This was a cross-sectional study. To identify all the potential terms used to describe simulated patient studies, a systematic search using combinations of synonymous/related words of simulated and patient in MEDLINE until October-2019 was performed. Records presenting at least one MeSH term, with an available abstract and referring to a simulated patient study within the scope of health were included. A flowchart on the different methods and scenarios of patient simulation was developed grounded on scientific literature; five categories were proposed: ‘machine/automation’ (no interaction between humans and the simulated patient); ‘audit’ (aims to inspect the service or the service’ provider behavior where participants are not aware of the simulation); ‘assessment’ (aims to evaluate the clinical skills and competencies of students or health professionals, where participants are aware of the simulation); ‘education’ (aims to educate students or health professionals where participants are aware of the simulation); and ‘others’ (secondary studies, e.g., reviews). The included studies were classified in at least one these categories. Additionally, seven related MeSH terms were identified: ‘Simulation Training’ (affiliated terms: ‘High fidelity simulation training’ and ‘Patient simulation’), ‘Computer simulation’ (affiliated terms: ‘Patient-Specific Modeling’ and ‘Virtual reality’) and ‘Virtual reality exposure therapy’ (no affiliated terms). Exploratory analyses on the included articles allocation considering the seven selected MeSH were performed. Accuracy parameters were calculated to entire sample and for each proposed category.
Results: We retrieved 9,451 registers, of which 2,238 were excluded due the absence of an abstract or MeSH, and other 2,683 were considered irrelevant during screening. The remaining 4,530 studies were classified into: ‘assessment’ (n=1159, 25.6%), ‘education’ (n=491, 10.8%), ‘audit’ (n=441, 9.7%), ‘education and assessment’ (n=364, 8.0%), ‘machine/automation’ (n=316, 7.0%) categories. The ‘machine/automation’ category included studies using an automaton (computer, mathematical model, virtual system) for simulating a human (virtual models of kinetics/dynamics of drugs, computational circuits on therapeutic effect). Most studies on ‘audit’ evaluated the performance of healthcare services (31.1%) or the behavior of physicians/residents (29.3%) or pharmacists (27.7%). Most studies classified as ‘assessment’ were designed to measure the skills or performance (e.g., comprehension, problem resolution) of medical students/residents (75.3%). In the ‘education’ category, studies were mostly represented by humans as simulated patients (49.5%), followed by dummies (23.4%); they targeted medical students/residents (51.1%), nurses (18.5%) and pharmacists (7.1%). The overall accuracy for all MeSH terms was 70% with sensitivity and specificity rates of 55% and 93%, respectively.
Conclusions: The number of publications using ‘simulated patient’ significantly increased in the past years. Yet, around half of studies are not indexed with one of the currently available MeSH terms. The lack of standard definitions for these types of simulations may hinder the retrieval of relevant studies.