Simulated patients and their reality

An inquiry into theory and method

Datos Bibliográficos

ID4596697
AutoresVeena Das (0000-0001-7397-0815, Johns Hopkins University, autor de correspondencia), Benjamin Daniels (0000-0001-8617-6055, Georgetown University), Alistair Kwan (0000-0003-4889-9433, University of California, San Francisco), Saria (0000-0001-9121-5370, Simon Fraser University), Ranendra Das, Madhukar Pai (0000-0003-3667-4536, McGill University), Jishnu Das (0000-0002-5909-3585, Centre for Policy Research)
Año2022
Volumen300
Páginas114571
Fecha de publicación2022-05-01
Peer ReviewedSí
Open AccessSí
TipoARTICLE
RevistaSocial Science & Medicine (JOURNAL)
Identificadores de la revistaISSN: 0277-9536 • E-ISSN: 1873-5347
EditorialElsevier BV (PUBLISHER)
DOI10.1016/j.socscimed.2021.114571
PMID34865913
OpenAlexW3216166258
IdiomaEN
Citas recibidas12
Referencias citadas29

Simulated standardized patients (SSP) have emerged as close to a 'gold standard' for measuring the quality of clinical care. This method resolves problems of patient mix across healthcare providers and allows care to be benchmarked against preexisting standards. Nevertheless, SSPs are not real patients. How, then, should data from SSPs be considered relative to clinical observations with 'real' patients in a given health system? Here, we reject the proposition that SSPs are direct substitutes for real patients and that the validity of SSP studies therefore relies on their ability to imitate real patients. Instead, we argue that the success of the SSP methodology lies in its counterfactual manipulations of the possibilities available to real careseekers - especially those paths not taken up by them - through which real responses can be elicited from real providers. Using results from a unique pilot study where SSPs returned to providers for follow-ups when asked, we demonstrate that the SSP method works well to elicit responses from the provider through conditional manipulations of SSP behavior. At the same time, observational methods are better suited to understand what choices real people make, and how these can affect the direction of diagnosis and treatment. A combination of SSP and observational methods can thus help parse out how quality of care emerges for the "patient" as a shared history between care-seeking individuals and care providers

Affect (linguistics · Counterfactual thinking · Economics · Epistemology · Gold standard (test · Health care · Observational learning · Observational methods in psychology · Observational study · Proposition · Quality (philosophy · Clinical practice guidelines implementation · Computer Science · Medicine · Nursing · Patient-Provider Communication in Healthcare · Primary Care and Health Outcomes · Psychology · Social Psychology · Artificial Intelligence