scholarly journals Factors influencing patient decision-making between simple mastectomy and surgical alternatives

BJS Open ◽  
2018 ◽  
Vol 3 (1) ◽  
pp. 31-37 ◽  
Author(s):  
W. Q. Lee ◽  
V. K. M. Tan ◽  
H. M. C. Choo ◽  
J. Ong ◽  
R. Krishnapriya ◽  
...  
Keyword(s):  
Decision Making ◽  
Patient Decision Making ◽  
Factors Influencing ◽  
Patient Decision ◽  
Simple Mastectomy

An economic theory of patient decision-making

2005 ◽  
Vol 2 (3) ◽  
pp. 153-164 ◽  
Author(s):  
Douglas O. Stewart ◽  
Joseph P. DeMarco
Keyword(s):  
Decision Making ◽  
Economic Theory ◽  
Patient Decision Making ◽  
Patient Decision

scholarly journals 181. I FOUND IT MOST CONFUSING, I DIDN’T KNOW WHAT TO BELIEVE: EXPLORING PATIENT DECISION MAKING AROUND GOUT AND DIET

Rheumatology ◽  
2017 ◽  
Vol 56 (suppl_2) ◽  
Author(s):  
Jennifer Liddle ◽  
Jane C. Richardson ◽  
Christian D. Mallen ◽  
Samantha L. Hider ◽  
Priyanka Chandratre ◽  
...  
Keyword(s):  
Decision Making ◽  
Patient Decision Making ◽  
Patient Decision

scholarly journals An Epistemic Network Analysis of Patient Decision-Making Regarding Choice of Therapy

2021 ◽  
Author(s):  
Szilvia Zörgő ◽  
Gjalt - Jorn Ygram Peters ◽  
Csajbók-Veres Krisztina ◽  
Anna Jeney ◽  
Andrew Ruis
Keyword(s):  
Decision Making ◽  
Network Analysis ◽  
Methodological Approach ◽  
Critical Factor ◽  
Structured Interviews ◽  
Pull Factors ◽  
Push Factors ◽  
Patient Decision Making ◽  
Patient Decision ◽  
Choice Of Therapy

Background: Patient decision-making concerning therapy choice has been thoroughly investigated in the Push/Pull framework: factors pushing the patient away from biomedicine and those pulling them towards Complementary and Alternative Medicine (CAM). Others have examined lay etiology as a potential factor in CAM use.Methods: We conducted semi-structured interviews with patients employing only biomedicine and those using CAM. The coded and segmented data was quantified and modelled using epistemic network analysis (ENA) to explore what effects push/pull factors and etiology had on the decision-making processes.Results: There was a marked difference between our two subsamples concerning push factors: although both groups exhibited similar scaled relative code frequencies, the CAM network models were more interconnected, indicating that CAM users expressed dissatisfaction with a wider array of phenomena. Among pull factors, a preference for natural therapies accounted for differences between groups but did not retain a strong connection to rejecting conventional treatments. Etiology, particularly adherence to vitalism, was also a critical factor in both choice of therapy and rejection of biomedical treatments.Conclusions: Push factors had a crucial influence on decision-making, not as individual entities, but as a constellation of experienced phenomena. Belief in vitalism affects the patient’s explanatory model of illness, changing the interpretation of other etiological factors and illness itself. Scrutinizing individual push/pull factors or etiology does not explain therapeutic choices; it is from their interplay that decisions arise. Our unified, qualitative-and-quantitative methodological approach offers novel insight into decision-making by displaying connections among codes within patient narratives.

scholarly journals A Typology of Patients Based on Decision-Making Styles: Cross-Sectional Survey Study (Preprint)

2019 ◽  
Author(s):  
Mary Anne FitzPatrick ◽  
Alexandra Claudia Hess ◽  
Lynn Sudbury-Riley ◽  
Peter Johannes Schulz
Keyword(s):  
Health Care ◽  
Decision Making ◽  
Health Information ◽  
Health Professionals ◽  
Online Health Information ◽  
Cross Sectional ◽  
Patient Decision Making ◽  
Health Care Practitioners ◽  
Patient Decision ◽  
Decision Making Styles

BACKGROUND Although previous research shows broad differences in the impact of online health information on patient-practitioner decision making, specific research is required to identify and conceptualize patient decision-making styles related to the use of online health information and to differentiate segments according to the influence of online information on patient decision making and interactions with health professionals. OBJECTIVE This study aimed to investigate patients’ decision making in relation to online health information and interactions with health care practitioners. We also aimed to present a typology of patients based on significant differences in their decision making. METHODS We applied a large-scale cross-sectional research design using a survey. Data, generated using a questionnaire that was administered by companies specializing in providing online panels, were collected from random samples of baby boomers in the United Kingdom, the United States, and New Zealand. The total sample comprised 996 baby boomers born between 1946 and 1964, who had used the internet in the previous 6 months to search for and share health-related information. Data were analyzed using hierarchical cluster analysis and confirmatory factor analysis, as well as one-way analysis of variance, chi-square tests, and paired sample <italic>t</italic> tests. RESULTS Analyses identified 3 key decision-making styles that served as the base for 4 unique and stable segments of patients with distinctive decision-making styles: the Collaborators (229/996, 23.0%), the Autonomous-Collaborators (385/996, 38.7%), the Assertive-Collaborators (111/996, 11.1%), and the Passives (271/996, 27.2%). Profiles were further developed for these segments according to key differences in the online health information behavior, demographics, and interactional behaviors of patients. The typology demonstrates that collaborative decision making is dominant among patients either in its pure form or in combination with autonomous or assertive decision making. In other words, most patients (725/996, 72.8%) show significant collaboration in their decision making with health care professionals. However, at times, patients in the combination Autonomous-Collaborative segment prefer to exercise individual autonomy in their decision making, and those in the combination Assertive-Collaborative segment prefer to be assertive with health professionals. Finally, this study shows that a substantial number of patients adopt a distinctly passive decision-making style (271/996, 27.2%). CONCLUSIONS The patient typology provides a framework for distinguishing practice-relevant and addressable segments with important implications for health care practitioners, including better-targeted communication programs for patients and more successful outcomes for health care services in the long term.

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