ABSTRACT
INTRODUCTION: This study examined the influence of high value care (HVC)-focused virtual standardized patients (VSPs) on learner attitudes toward cost-conscious care (CCC), performance on subsequent standardized patient (SP) encounters, and the correlation of VSP performance with educational outcomes. METHOD: After didactic sessions on HVC, third-year medical students participated in a randomized crossover design of simulation modalities consisting of 4 VSPs and 3 SPs. Surveys of attitudes toward CCC were administered before didactics and after the first simulation method. Performance markers included automated VSP grading and, for SP cases, faculty-graded observational checklists and patient notes. Performance was compared between modalities using t tests and analysis of variance and then correlated with US Medical Licensing Examination performance. RESULTS: Sixty-six students participated (VSP first: n = 37; SP-first: n = 29). Attitudes toward CCC significantly improved after training (Cohen d = 0.35, P = 0.043), regardless of modality. Simulation order did not impact learner performance for SP encounters. Learners randomized to VSP first performed significantly better within VSP cases for interview (Cohen d = 0.55, P = 0.001) and treatment (Cohen d = 0.50, P = 0.043). The HVC component of learner performance on the SP simulations significantly correlated with US Medical Licensing Examination step 1 ( r = 0.26, P = 0.038) and step 2 clinical knowledge ( r = 0.33, P = 0.031). CONCLUSIONS: High value care didactics combined with either VSPs or SPs positively influenced attitudes toward CCC. The ability to detect an impact of VSPs on learner SP performance was limited by content specificity and sample size.
Subject(s)
Students, Medical , Humans , Computer Simulation , Patient Simulation , Clinical CompetenceABSTRACT
BACKGROUND: Traditional methods are not able to differentiate which feature customers regard as attractive, mandatory, performance, and which feature customers are indifferent about. These categories can only be differentiated based on a specific technique called Kano survey. Specific aim of this study was to categorize the Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) patient satisfaction survey questions into Kano categories. MATERIALS AND METHODS: Design of the study was survey research. It was conducted from 6/2019 to 8/2019 at OSF Saint Francis Medical Centre in Peoria, Illinois, USA. A 34 question Kano survey (17 positive and 17 negative questions) based on HCAHPS patient questionnaire was designed. Surveys were analyzed using Kano analysis template. Comparative analysis of Kano categories based on demographics was also performed. RESULTS: 39 current patients and 25 caregivers completed the survey. All of the 17 HCAHPS questions except "noise level at night" were classified as mandatory requirement with highest number for information on "indications of medicines." There was a minimum variability in the satisfaction coefficients but large variation in the dissatisfaction coefficients. More patients above 50 years consider "help going to bathroom" as mandatory (70.2% vs. 40.7%, P = 0.01). Sixty-four percent of caregivers considered "explain things (nurse)" as mandatory as opposed to 51.2% of patients (P = 0.03). CONCLUSION: Current U. S healthcare consumers have high expectations from healthcare delivery and consider most HCAHPS questions as mandatory requirements. Kano analysis needs to be done on a larger, more diverse hospital setting and potentially the HCAHPS survey needs to be modified to reflect prevailing healthcare customer requirements.
ABSTRACT
INTRODUCTION: High-value care (HVC) suggests that good history taking and physical examination should lead to risk stratification that drives the use or withholding of diagnostic testing. This study describes the development of a series of virtual standardized patient (VSP) cases and provides preliminary evidence that supports their ability to provide experiential learning in HVC. METHODS: This pilot study used VSPs, or natural language processing-based patient avatars, within the USC Standard Patient platform. Faculty consensus was used to develop the cases, including the optimal diagnostic testing strategies, treatment options, and scored content areas. First-year resident physician learners experienced two 90-minute didactic sessions before completing the cases in a computer laboratory, using typed text to interview the avatar for history taking, then completing physical examination, differential diagnosis, diagnostic testing, and treatment modules for each case. Learners chose a primary and 2 alternative "possible" diagnoses from a list of 6 to 7 choices, diagnostic testing options from an extensive list, and treatments from a brief list ranging from 6 to 9 choices. For the history-taking module, both faculty and the platform scored the learners, and faculty assessed the appropriateness of avatar responses. Four randomly selected learner-avatar interview transcripts for each case were double rated by faculty for interrater reliability calculations. Intraclass correlations were calculated for interrater reliability, and Spearman ρ was used to determine the correlation between the platform and faculty ranking of learners' history-taking scores. RESULTS: Eight VSP cases were experienced by 14 learners. Investigators reviewed 112 transcripts (4646 learner query-avatar responses). Interrater reliability means were 0.87 for learner query scoring and 0.83 for avatar response. Mean learner success for history taking was scored by the faculty at 57% and by the platform at 51% (ρ correlation of learner rankings = 0.80, P = 0.02). The mean avatar appropriate response rate was 85.6% for all cases. Learners chose the correct diagnosis within their 3 choices 82% of the time, ordered a median (interquartile range) of 2 (2) unnecessary tests and completed 56% of optimal treatments. CONCLUSIONS: Our avatar appropriate response rate was similar to past work using similar platforms. The simulations give detailed insights into the thoroughness of learner history taking and testing choices and with further refinement should support learning in HVC.
