OBJECTIVES: This study examined the validity of the principal diagnoses on discharge summaries and coding assessments. METHODS: Data were collected from the National Health Security Office (NHSO) of Thailand in 2015. In total, 118,971 medical records were audited. The sample was drawn from government hospitals and private hospitals covered by the Universal Coverage Scheme in Thailand. Hospitals and cases were selected using NHSO criteria. The validity of the principal diagnoses listed in the “Summary and Coding Assessment” forms was established by comparing data from the discharge summaries with data obtained from medical record reviews, and additionally, by comparing data from the coding assessments with data in the computerized ICD (the data base used for reimbursement-purposes). RESULTS: The summary assessments had low sensitivities (7.3%–37.9%), high specificities (97.2%–99.8%), low positive predictive values (9.2%–60.7%), and high negative predictive values (95.9%–99.3%). The coding assessments had low sensitivities (31.1%–69.4%), high specificities (99.0%–99.9%), moderate positive predictive values (43.8%–89.0%), and high negative predictive values (97.3%–99.5%). The discharge summaries and codings often contained mistakes, particularly the categories “Endocrine, nutritional, and metabolic diseases”, “Symptoms, signs, and abnormal clinical and laboratory findings not elsewhere classified”, “Factors influencing health status and contact with health services”, and “Injury, poisoning, and certain other consequences of external causes”. CONCLUSIONS: The validity of the principal diagnoses on the summary and coding assessment forms was found to be low. The training of physicians and coders must be strengthened to improve the validity of discharge summaries and codings.
Clinical Coding* ; Diagnosis* ; Hospitals, Private ; International Classification of Diseases* ; Medical Records ; Poisoning ; Sensitivity and Specificity ; Thailand* ; Universal Coverage

Objectives: This study examined demographic factors hampering access to healthcare at hospitals and suggests policy approaches to improve healthcare management in Thailand. Methods: The data for the study were drawn from a health and welfare survey conducted by the National Statistical Office of Thailand in 2017. The population-based health and welfare survey was systematically carried out by skilled interviewers, who polled 21 519 384 individuals. The independent variables related to demographic data (age, sex, religion, marital status, education, occupation, and area of residence), chronic diseases, and health insurance coverage. The dependent variable was the degree of access to healthcare. Multiple logistic regression analysis was subsequently performed on the variables found to be significant in the univariate analysis. Results: Only 2.5% of the population did not visit a hospital when necessary for outpatient-department treatment, hospitalization, or the provision of oral care. The primary reasons people gave for not availing themselves of the services offered by government hospitals when they were ill were—in descending order of frequency—insufficient time to seek care, long hospital queues, travel inconvenience, a lack of hospital beds, unavailability of a dentist, not having someone to accompany them, and being unable to pay for the transportation costs. Multiple logistic regression analysis showed that failure to access the health services provided at hospitals was associated with demographic, educational, occupational, health welfare, and geographic factors. Conclusions Accessibility depends not only on health and welfare benefit coverage, but also on socioeconomic factors and the degree of convenience associated with visiting a hospital.

Objectives: This study examined demographic factors hampering access to healthcare at hospitals and suggests policy approaches to improve healthcare management in Thailand. Methods: The data for the study were drawn from a health and welfare survey conducted by the National Statistical Office of Thailand in 2017. The population-based health and welfare survey was systematically carried out by skilled interviewers, who polled 21 519 384 individuals. The independent variables related to demographic data (age, sex, religion, marital status, education, occupation, and area of residence), chronic diseases, and health insurance coverage. The dependent variable was the degree of access to healthcare. Multiple logistic regression analysis was subsequently performed on the variables found to be significant in the univariate analysis. Results: Only 2.5% of the population did not visit a hospital when necessary for outpatient-department treatment, hospitalization, or the provision of oral care. The primary reasons people gave for not availing themselves of the services offered by government hospitals when they were ill were—in descending order of frequency—insufficient time to seek care, long hospital queues, travel inconvenience, a lack of hospital beds, unavailability of a dentist, not having someone to accompany them, and being unable to pay for the transportation costs. Multiple logistic regression analysis showed that failure to access the health services provided at hospitals was associated with demographic, educational, occupational, health welfare, and geographic factors. Conclusions Accessibility depends not only on health and welfare benefit coverage, but also on socioeconomic factors and the degree of convenience associated with visiting a hospital.