TY - JOUR
T1 - Simulated patients and their reality
T2 - An inquiry into theory and method
AU - Das, Veena
AU - Daniels, Benjamin
AU - Kwan, Ada
AU - Saria, Vaibhav
AU - Das, Ranendra
AU - Pai, Madhukar
AU - Das, Jishnu
N1 - Funding Information:
The study reported in this article was funded by Grand Challenges Canada, the Bill & Melinda Gates Foundation (OPP1091843), and the Knowledge for Change Program at the World Bank. MP is a recipient of a Tier 1 Canada Research Chair from Canadian Institutes of Health Research. The funders had no role in the study design; data collection, analysis, or interpretation; the writing of the article; or the decision to submit it for publication. We thank Puneet Dewan, Sameer Kumta, Shibu Vijayan, Sirisha Papineni, Nita Jha, and Srinath Satyanarayana for useful inputs throughout SP implementation; Rajan Singh, Purshottam, Chinar Singh, Geeta, Devender, Varun Kumar, Anand Kumar, Babloo, and Charu Nanda, who supervised and implemented the ISERDD field work; all the standardized patients for their dedication and hard work; and Caroline Vadnais and Whitney Tate for excellent administrative support.
Funding Information:
The study reported in this article was funded by Grand Challenges Canada , the Bill & Melinda Gates Foundation ( OPP1091843 ), and the Knowledge for Change Program at the World Bank . MP is a recipient of a Tier 1 Canada Research Chair from Canadian Institutes of Health Research. The funders had no role in the study design; data collection, analysis, or interpretation; the writing of the article; or the decision to submit it for publication.
Publisher Copyright:
© 2021 The Authors
PY - 2022/5
Y1 - 2022/5
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85120923665&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85120923665&partnerID=8YFLogxK
U2 - 10.1016/j.socscimed.2021.114571
DO - 10.1016/j.socscimed.2021.114571
M3 - Article
C2 - 34865913
AN - SCOPUS:85120923665
SN - 0277-9536
VL - 300
JO - Ethics in Science and Medicine
JF - Ethics in Science and Medicine
M1 - 114571
ER -