Background: The Patient Enablement Instrument (PEI) was designed to encapsulate consultation outcome from the perspective that increasing their understanding and coping ability would underpin a positive consultation outcome for patients. The objective of the study was the validation of the PEI in Lithuanian general practice and comparison of Lithuanian patients' enablement with previous studies in Europe to see if factors associated with patient enablement in Lithuania were reflective of those in the previous studies.
Methods: The Patient Enablement Instrument was translated into Lithuanian and included in the questionnaire along with the questions about a person's health, reasons for visiting the doctor and feeling about the consultation. Practices from 4 different municipalities that are situated in different geographical regions which have both town and rural areas were sampled randomly. Patients scheduled consecutively aged 18 years or more were the subjects of the study. The data analyses focused on internal reliability and concept validity.
Results: The overall mean patient enablement score was 6.43. Enablement scores declined with increasing patient age, and female patients were more enabled. Patients with biomedical problems had the highest enablement results, while patients with complex problems had the lower results. Enablement was positively related to receiving a prescription and knowing a doctor, and negatively related to wish having consultation with another doctor.
Conclusions: This study substantiates the rationality of using PEI in assessing primary care consultations in Lithuania. The correlations of enablement largely reflect the situation in Western and Central Europe: longer consultation and access to the same physician increases patient enablement.
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http://dx.doi.org/10.1186/s12875-019-1061-1 | DOI Listing |
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View Article and Find Full Text PDFJ Imaging Inform Med
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College of Computer, Chongqing University, No. 55 Daxuecheng South Rd, Shapingba, 401331, Chongqing, China.
Convolutional neural networks (CNNs) have become indispensable to medical image diagnosis research, enabling the automated differentiation of diseased images from extensive medical image datasets. Due to their efficacy, these methods raise significant privacy concerns regarding patient images and diagnostic models. To address these issues, some researchers have explored privacy-preserving medical image diagnosis schemes using fully homomorphic encryption (FHE).
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