Attitudes and Barriers of Physicians toward Palliative Care in Critical Care Setting:Qualitative Content Analysis Using Open-ended Data from Nationwide Self-administrated Questionnaire Survey in Japan

Purpose: Palliative care implementation should take into account the perceptions and acceptability of healthcare providers. This study aimed to identify physicians’ perceptions of palliative care and barriers to palliative care practice in the critical care setting. Methods: A nationwide, self-administered questionnaire was distributed to physicians working in intensive care units, and free-text data were qualitatively analyzed. Results: The questionnaire was sent to 873 respondents, and 436 responded (50% response rate). Of these, 95 (11%) who responded to the open-ended sections were included in the analysis. Conclusion: Japanese physicians working in ICUs recognized that palliative care was their role and practiced it as part of their usual care. They felt, however, that the practice was difficult and not sufficient. Barriers to practice included the lack of human resources and availability of palliative care teams, and the lack of uniformity in the perception of palliative care in the critical care setting.

Similar Drug Proposals Based on Package Inserts Using Latent Semantic Analysis / 医薬品情報学

Objective:The topic model is a well-known method used in the field of natural language processing (NLP)that defines adocument as constructed of topics that combine specific t erms. This method is used to model topic co-occurrencemathematically. In this study,we extracted topics from featu re vectors of explicit documents called medical package insertsby using cluster analysis. Methods:We counted the terms(nouns)recognized by the morphological analysis engine MeCab and created a documentterm matrix. A value of“tf・idf”was calculated in this matrix for term weighting to avoid the effect of term frequency. We reduced the dimensionality of the matrix using singular v alue decomposition,which removed unnecessary data,and weextracted feature vectors attributed to each medical package insert. The distance between feature vectors was calculatedusing cosine distance,and cluster analysis was performed based on the distance between the vectors.Results:Cluster analysis on our document-term matrix show ed that medical package inserts of drugs that have the sameefficacy or active ingredient were included in the same cl uster. Moreover, using term weighting and dimensionalityreduction,we could extract topics from medical package inserts.Conclusion:We obtained a foothold to apply our findings t o the recommendation of similar drugs. Cluster analysis ofmedical package inserts using NLP can contribute to the pro per application of drugs. In addition,our study revealed thesimilarities of drugs and suggested possibilities for new applications from several points of view.