The implementation of an integrated e-leprosy framework in a leprosy control program at primary health care centers in Indonesia.

BACKGROUND AND OBJECTIVE: The implementation of health information systems (HIS) could overcome obstacles in human resources and infrastructure at primary health care centers (PHCs). This study involved an e-Leprosy framework being integrated into the real setting of a leprosy control program in Indonesia. The objectives of this implementation study were to integrate e-Leprosy into a leprosy control program at 27 PHCs in Pekalongan District. Central Java Province, Indonesia to explore factors related the success or failure of such an implementation regarding the usability, involvement, and acceptance of e-Leprosy by PHC staff and to evaluate the effect of the implementation on leprosy patient attendance at PHCs. This paper is based on the Standards for Reporting Implementation Studies (StaRI) statement. METHOD: This study used mixed methods implementation research with longitudinal analysis and involved two groups of participants: Leprosy Surveillance Officers (LSOs), patients, and the relatives of patients. This study involved four phases consisting of preparation, baseline assessment, intervention, and evaluation. The qualitative study conducted focus group discussions and in-depth interviews. The e-Leprosy program automatically sent SMS reminders regarding leprosy treatment to the LSOs, patients, and patients' relatives every month. FINDINGS: This study determined that LSO had difficulties related to their workloads in PHCs while managing information and monitoring treatment and contact after release from treatment. The baseline assessment phase found that LSOs in Pekalongan District were unfamiliar with email but familiar using the internet. Overall, LSOs had a positive perception of the e-Leprosy program. The usability of this e-Leprosy program tended to increase over time, while acceptance of the e-Leprosy exhibited a significant relationship with computer and internet fluency (r = 0.48, p < 0.05) and age (r = 0.621, p < 0.01). The responsible patients correlated (r = 0.67, p < 0.01) with involvement in the e-Leprosy program. This study revealed that patient reminders increased on-time attendance by 13.9 % (p < 0.01 with OR = 2.41). CONCLUSION: Factors that should be considered during implementation HIS included the digital gap, PHC's staff workload, as well as the level of commitment and leadership in the health office.

Developing an Indonesia's health literacy short-form survey questionnaire (HLS-EU-SQ10-IDN) using the feature selection and genetic algorithm.

BACKGROUND AND OBJECTIVE: Measuring health literacy becomes more important because its association with health status and healthcare outcomes. Studies have developed at least 133 measurement tools for health literacy. HLS-EU-Q47 is a questionnaire consisting of 12 sub-dimensions and 47 questions developed by the Europe Health Literacy Consortium. Many countries in Europe and Asia have used HLS-EU-Q47 as a tool for measuring health literacy in the general public. Indonesia has conducted general health literacy survey using HLS-EU-Q47 but finding the difficulties because of the time-consuming interview. A shorter version of HLS-EU-Q47 is needed to apply in health literacy researches in Indonesia. This paper reports the results of feature reduction to develop a short Indonesian version HLS-EU questionnaire and measures the accuracy of the model compared with other short form like HLS-EU-SQ16 or HLS-SF12. METHOD: The analysis was performed on a population-based dataset from Indonesia-Semarang Health Literacy Survey for which there were specific target variables as the classification of health literacy level. All attributes were assessed as potential targets in the models derived from the full dataset and its subsets. The feature selection methods with genetic algorithm were used as the filter as well as validation (cross validation) and classification (k-NN:k-nearest neighbor). The predictive accuracy of health literacy level and the complexity of models based on the reduced datasets were compared among the methods and other short versions such as HLS-EU-SQ16, HLS-SF12. RESULT: The accuracy of the existing short form models were 90.64% with the HLS-EU-SQ16 and 88.67% with the HLS-SF12. This study proposed a model with 10 features as the construct of a short Indonesian-version (proposed as the HLS-EU-SQ10-IDN) since the model was with higher accuracy than the HLS-SF12, but fewer features for measuring general health literacy index. Moreover, the short version only completed part of 12 dimensions of the full questionnare. CONCLUSION: A data mining technique using feature selection with combination of genetic algorithm and k-NN algorithm was applied to develop a short version questionnaire and proved to have better accuracy, as compared with the short version developed by traditional statistical technique.