Care teams misunderstand what most upsets patients about their care

Healthcare(2022)

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摘要
Background: Negative healthcare delivery experiences can cause lasting patient distress and medical service misuse and disuse. Yet no multi-site study has examined whether care-team members understand what most upsets patients about their care.Methods: We interviewed 373 patients and 360 care-team members in the medical oncology and ambulatory surgery clinics of 11 major healthcare organizations across six U.S. census regions. Patients deeply upset by a service-related experience (n = 99, 27%) answered questions about that experience, while care-team members (n = 360) answered questions about their beliefs regarding what most upsets patients. We performed content analysis to identify memorably upsetting care (MUC) themes; a generalized estimating equation to explore whether MUC theme mention frequencies varied by participant role (care-team member vs. patient), specialty (oncology vs. surgery), facility (academic vs. community), and gender; and logistic regressions to investigate the effects of participant characteristics on individual themes.Results: MUC themes included three systems issues (inefficiencies, access barriers, and facilities problems) and four care-team issues (miscommunication, neglect, coldness, and incompetence). MUC theme frequencies differed by role (all Ps < 0.001), with more patients mentioning care-team coldness (OR = 0.37; 95% CI, 0.23-0.60) and incompetence (OR = 0.17; 95% CI, 0.09-0.31); but more care-team members mentioning system inefficiencies (OR = 7.01; 95% CI, 4.31-11.40) and access barriers (OR, 5.48; 95% CI, 2.81-10.69). Conclusions: When considering which service experiences most upset patients, care-team members underestimate the impact of their own behaviors and overestimate the impact of systems issues. Implications: Healthcare systems should reconsider how they collect, interpret, disseminate, and respond to pa-tient service reports. Level of evidence: Level III.
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关键词
Patient experience,Systems issues,Content analysis,Mixed methods
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