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Pendahuluan Kementrian Kesehatan sedang berkomitmen untuk melakukan transformasi system Kesehatan guna meningkatkan layanan kesehatan yang lebih baik, merata, dan berkualitas bagi Masyarakat. Terdapat 6 pilar utama untuk menopang SKN. Melalui Keputusan Kemenkes RI No HK.0107/Menkes/11983/2022 ditaur mengenai penerapan sistem pemerintahan berbasis elektronik bidang kesehatan dan strategi transformasi digital kesehatan. Tetapi Kemenkes telah mempunyai banyak aplikasi pada setiap program. Pada Pada program KIA, ada 6 aplikasi yang terkait yaitu: e-Kohort, Komdat, EPPGBM, RME, ASIK dan SIP. E-Kohort dan EPPBGM merupakan aplikasi KIA yang mempunyai sasaran sama dan isian data yang sama. Sehingga perlu analisis untuk mengetahui gap pada kedua aplikasi tersebut. Tujuan Penelitian ini bertujuan untuk mengetahui Melakukan analisis secara komprehensif terhadap sistem pencatatan dan pelaporan KIA di E-Kohort dan EPPGBM di Jakarta Pusat. Metode Penelitian ini merupakan penelitian kualitatif menggunakan pendekatan Performance of Routine Information System Management (PRISM) Framework, dengan melihat pada aplikasi E-Kohort dan EPPBGM di Puskesmas didaerah Jakarta Pusat. Hasil dan Pembahasan Terjadinya perbedaan sasaran pada E-Kohort dan EPPBGM, yang mengakibatkan penjaringan permasalahan gizi di Ibu dan Anak juga tidak berjalan dengan baik. E-Kohort dan EPPBGM mempunyai isian data yang sama, meskipun E-Kohort lebih lengkap dibandingkan EPPBGM. Sehingga lebih efisien untuk dilakukan peleburan pada kedua aplikasi tersebut.
Introduction
The Ministry of Health is committed to transforming the national health system in order to provide better, more equitable, and higher-quality healthcare services for the population. There are six main pillars that support the National Health System (SKN). Through the Decree of the Minister of Health of the Republic of Indonesia No. HK.0107/Menkes/11983/2022, the implementation of an electronic-based government system in the health sector and a digital health transformation strategy has been regulated. However, the Ministry of Health currently operates numerous applications for each health program. In the Maternal and Child Health (MCH) program, there are six related applications: e-Kohort, Komdat, EPPGBM, RME, ASIK, and SIP. Among them, e-Kohort and EPPGBM are MCH applications that target the same population and collect similar data. This overlap necessitates an analysis to identify the gaps between the two systems.
Objective
This study aims to conduct a comprehensive analysis of the MCH recording and reporting systems in e-Kohort and EPPGBM in Central Jakarta.
Methods
This is a qualitative study using the Performance of Routine Information System Management (PRISM) framework, focusing on the use of e-Kohort and EPPGBM applications in community health centers (Puskesmas) located in Central Jakarta.
Results and Discussion
The study found inconsistencies in target populations between e-Kohort and EPPGBM, which have led to ineffective identification and management of maternal and child nutrition issues. Although both applications require similar data inputs, e-Kohort provides a more comprehensive dataset than EPPGBM. Therefore, integrating or merging the two systems would be a more efficient solution.
Background: Cardiovascular Disease (CVD) is one of Non communicable Diseaseswhich cause the biggest number of death all over the world. The prevalence ofcardiovascular disease in Indonesia is high, but the screening or earlier detection of riskfactor of CVD is still low. In this context, this research is aimed to build online applicationdevelopment of monitoring and evaluating system of the CVD risk factor which isintegrated with the surveillance and NCD information system to facilitate Indonesian tohave earlier detection of CVD risk factor. Method: System development in research usingSystem Development Life Cycle (SDLC) method of Rapid Application Development withchoice of iterative system model. Results: the prototype of online application developmentof monitoring and evaluating system of the CVD risk factor is named SEHATJANTUNGKU. This prototype is online responsive web based which facilitate the usersto access this system anywhere and by using many platforms. Conclusions: the onlineapplication development of monitoring and evaluating system of the CVD risk factor willhelp the NCD program manager and stakeholders to monitor, evaluate, and createrelated policies.Keywords: NCD, CVD risk factor, early detection, RAD, monitoring and evaluating.
Global TB Report 2016 states only about 35,3% of people with TB who successfullyfound/has been reported in Indonesia of about 1.020.000 estimation of incident in theyear 2016. This is certainly making the risk of people with TB who still has not beenfound to transmit the disease will increase. From around the districts in Indonesia noteverything has a coverage of the discovery of TB cases. Many of the factors that lead toit, so the discrepancy in the discovery and reporting TB cases. The characteristics of thedistricts with TB households diagnosed it is important to note that when there are othercounties that have similar characteristics so it can be suspected the possibility ofdiagnosed TB households in the district Although no case of TB was found. This thesisexamines the characteristics of districts with TB households diagnosed in Indonesia.Research with secondary data analysis using Data Riskesdas 2013 and 2014 PODESData. The analysis conducted to see the difference in the proportion of each of thevariables and assess the influences between variables independent of the dependentvariable. Fractional regression test used to measure the value of risk variables areindependent of the dependent variable. The results showed the influence ofcharacteristics of household environment for the district comprising the counties withthe proportion of slum households (1%), with the proportion of the village have slums(0.3%), and district with the proportion the village does not exist health care facility(1%). Influence of the characteristics of district to household conditions physically seenfrom districts with solid household proportion (1%), with the proportion of householdsthere are no window (3%), and district with the proportion of the village that has a homethe staircase there are indoor pollution (1%), while the influence of the districts with theproportion of households with less lighting and a proportion of the village householdswithout electricity against the characteristics of districts with TB households is difficultto explained. Districts with low proportion of household economy (0.6%) influence onthe characteristics of districts with TB households diagnosed. This research suggestedthat the strengthening of programs related to TB prevention and control efforts on at-risk households and as a basis for the intervention priorities based on refinementsepidemic levels of TB at the district/city.Key words:TB, Influence, District.
