To evaluate the achievement of health care services in Korea independent of other socioeconomic factors, we observed the time trend of avoidable death between 1983 and 2004. A list of avoidable causes of death was constructed based on the European Community Atlas of "Avoidable Death". We calculated sex- and age-standardized mortality rates of Korean aged 1-64 yr using data of the Korea National Statistical Office. The avoidable mortality rate (per 100,000 persons) decreased from 225 to 84 in men and from 122 to 41 in women. Accordingly, the proportion of avoidable deaths among all classifiable deaths was reduced by 8.1% in men and 6.4% in women. However, mortality rates from some preventable causes such as ischemic heart disease and malignant neoplasms of lung, breast, cervix, and colorectum have been on the rise. Mortality preventable by appropriate medical care showed the greatest reduction (by 77.8%), while the mortality preventable by primary prevention showed the least reduction (by 50.0%). These findings suggest that health care service has significantly contributed to the improvement of health in Korea. However, more effective intervention programs would be needed given the less reduction in mortality avoidable by primary or secondary prevention than expected and unexpectedly increasing mortality from several preventable causes.

This study was conducted to identify the perception regarding palliative care among Korean doctors and referral barriers toward palliative care for terminal cancer patients. Methods: Between May and June 2010, 477 specialists mainly caring cancer patients using a web-based, self-administered questionnaire. Results: A total of 128 doctors (26.8%) responded. All respondents (100%) deemed palliative care a necessary service for terminal cancer patients. More than 80% of the respondents agreed to each of the following statements: all cancer centers should provide palliative care service (80.5%); all terminal cancer patients should receive concurrent palliative care along with anti-cancer therapies (89.1%) and caring for terminal cancer patients requires interdisciplinary approach (96.9). While more than 58% of the respondents were satisfied with their performance of physical and psychological symptoms management and emotional support provided by patient's family members, 64% of the responded answered that their general management of the end-of-life care was less than satisfactory. Doctors without prior experience in referring their patients to palliative care specialists accounted for 26.6% of the respondents. The most common barrier to hospice referral, cited by 47.7% of the respondents, was "refusal of patient or family member", followed by "lack of available palliative care resources" (46.1%). Conclusion: Although most doctors do recognize the importance of palliative care for advanced cancer patients, comprehensive and sufficient palliative medicine, including interdisciplinary cooperation and end-of-life care, has not been put into practice. Thus, more active palliative consultation or referral is needed for effective care of terminal cancer patients.

BackgroundSince medical research based on big data has become more common, the community’s interest and effort to analyze a large amount of semistructured or unstructured text data, such as examination reports, have rapidly increased. However, these large-scale text data are often not readily applicable to analysis owing to typographical errors, inconsistencies, or data entry problems. Therefore, an efficient data cleaning process is required to ensure the veracity of such data.ObjectiveIn this paper, we proposed an efficient data cleaning process for large-scale medical text data, which employs text clustering methods and value-converting technique, and evaluated its performance with medical examination text data.MethodsThe proposed data cleaning process consists of text clustering and value-merging. In the text clustering step, we suggested the use of key collision and nearest neighbor methods in a complementary manner. Words (called values) in the same cluster would be expected as a correct value and its wrong representations. In the value-converting step, wrong values for each identified cluster would be converted into their correct value. We applied these data cleaning process to 574,266 stool examination reports produced for parasite analysis at Samsung Medical Center from 1995 to 2015. The performance of the proposed process was examined and compared with data cleaning processes based on a single clustering method. We used OpenRefine 2.7, an open source application that provides various text clustering methods and an efficient user interface for value-converting with common-value suggestion.ResultsA total of 1,167,104 words in stool examination reports were surveyed. In the data cleaning process, we discovered 30 correct words and 45 patterns of typographical errors and duplicates. We observed high correction rates for words with typographical errors (98.61%) and typographical error patterns (97.78%). The resulting data accuracy was nearly 100% based on the number of total words.ConclusionsOur data cleaning process based on the combinatorial use of key collision and nearest neighbor methods provides an efficient cleaning of large-scale text data and hence improves data accuracy.