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Maternal mental health problem is a major challenge in global public health problems. Women are generally more at risk for depression during pregnancy because of hormonal changes and their role changes in life. Untreated antenatal depression can increase the risk of postpartum depression and other complications for both mother and baby. Some symptoms of antenatal depression are similar to discomfort in pregnancy experienced by pregnant women so that these symptoms are often considered as normal discomfort in pregnancy. The absence of depression screening assessment instruments in pregnancy also makes mental health services for pregnant women not performed by midwives when conducting antenatal care in primary health facilities. This study aims to design an antenatal depression detection system in website-based mental health services for pregnant women. This research was conducted at the UPT Puskesmas Rawat Inap Panjang Bandar Lampung using a prototyping system development method. The results of this study indicate that mental health services for pregnant women have not been carried out in integrated antenatal care services. The final result of this research is a prototype of an information system that is able to detect the risk of depression in pregnant women and assess the risk factors that might be the cause of depression in pregnant women. By knowing these risk factors, midwives can provide appropriate interventions in reducing antenatal depression.
Rabies is an acute infectious disease in the central nervous system causedby the rabies virus. Until now there is no effective treatment to cure rabies, but thedisease can be prevented through the handling of cases of exposure to rabidanimal as early as possible. Rabies is still a public health problem that is seriousenough and has spread to different areas of the original are rabies-free areas. Baliis one free area that is infected with a rabies by the end of 2008.Indonesia has declared rabies-free by 2020 in accordance with theagreement at the ASEAN meeting. In order to achieve rabies-free by 2020, thedisease surveillance is crucial to deliver accurate information for monitoring andevaluation of prevention activities. Supportive management of informationresources can support the success of rabies eradication program.The development of information systems in this study using the SystemDevelopment Life Cycle (SDLC) method and aims to develop RabiesSurveillance Information System Prototype. This prototype is a model ofManagement Information System and a web-based information system that canprocess data automatically and displays graphs and maps that can support policymakers in making decisions.Keywords :Rabies, surveillance, systems development, information system prototype
Latar Belakang Gagal jantung adalah kondisi kronis dan progresif, dengan prevalensi di dunia 1-3% dan di Indonesia 5% (peringkat ke 4 di dunia) dengan kematian 50% dalam 5 tahun. Angka readmisi dalam 90 hari adalah 50%-75% dan dalam 30 hari 2-3%, sedangkan di Indonesia angka readmisi dalam 30 hari adalah 17%. Biaya rawat inap gagal jantung dapat mencapai empat ratus juta rupiah per pasien per tahun. Data BPJS Kesehatan 2018 terdapat 130.275 kejadian rawat inap tingkat lanjut pasien gagal jantung kongestif dan berdasarkan tarif JKN 2023 perkiraan biaya rawat inapnya akan berkisar antara 379 milyar sampai 4,2 triliun rupiah. Dengan memanfaatkan teknologi kekinian dari Artificial Intelligence dan kapabilitas serta kebiasaan masyarakat paska pandemi Covid-19, penelitian ini membuat model prediksi berbasis machine learning dengan menemukan faktor-faktor risiko yang dapat menjadi prediktor rawat inap berulang, yang kemudian diimplementasikan di dalam prototype yang digunakan dalam kolaborasi antara penyedia layanan kesehatan dengan pasien yang turut terlibat melakukan monitoring mandiri sehingga dapat mempertahankan kualitas hidupnya dan mengendalikan biaya perawatan baik yang dibayarkan oleh pasien sendiri, menggunakan asuransi ataupun dengan pendanaan pemerintah. Metode Penelitian ini terdiri atas beberapa tahap, dengan studi kuantitatif dan kualitatif menggunakan data rekam medis pasien gagal jantung di Rumah Sakit Jantung dan Pembuluh Darah Harapan Kita, Jakarta. Dimulai dengan Systematic Literature Review untuk menemukan faktor risiko rawat inap berulang di rumah sakit dan untuk menemukan novelty, pemodelan prediksi dengan studi kohort retrospektif, analisis kebutuhan sistem dengan studi kualitatif, pengembangan prototype, dan uji prototype dengan studi kohort prospektif. Hasil Systematic Literature Review tentang prediktor readmisi gagal jantung dengan machine learning dari PubMed, Science Direct, ProQuest, Scopus, Embase, google scholar menghasilkan 19 artikel terseleksi. 13 studi berasal dari USA, tidak ditemukan studi serupa di Indonesia, dengan algoritma terbaik adalah Neural Network. Pada tahap pemodelan prediksi diperoleh 2738 data pasien gagal jantung paska rawat inap di RS Jantung dan Pembuluh Darah Harapan Kita Jakarta, dengan ketersediaan 64 variabel. Dengan Orange Data Mining, terseleksi sebanyak 31 features. Model terbaik menggunakan Random Forest, dengan AUC 0,976, CA 0,912, F1 0,912, Precision 0,916 dan Recall 0,912, diimplementasikan dalam prototype aplikasi Fineheart dengan fitur aplikasi profil pasien, dashboard, catatan harian jantungku, penilaian kualitas hidup, rencana kontrol, instruksi medis dan obat, catatan asupan makanan dan cairan, edukasi, konsultasi. Uji efikasi prototype menunjukkan angka readmisi pada kelompok intervensi (20%), lebih rendah daripada kelompok kontrol (43,3%). Perubahan signifikan terjadi pada 2 parameter KCCQ yaitu Quality of Life (p=0,029) dan Overall Summary Score (p=0,001). Tingkat kepatuhan menggunakan prototype aplikasi juga berpengaruh signifikan terhadap kedua parameter tersebut dan mencegah readmisi. Kesimpulan Model prediksi readmisi pasien gagal jantung dengan machine learning yang diimplementasikan ke prototype aplikasi dapat digunakan untuk monitoring di rumah untuk mencegah readmisi dan mempertahankan kualitas hidup.
Background Heart failure is a chronic and progressive condition, with a prevalence in the world of 1-3% and in Indonesia 5% (ranked 4th in the world) with a mortality of 50% within 5 years. The readmission rate in 90 days is 50%-75% and in 30 days it is 2-3%, while in Indonesia the readmission rate in 30 days is 17%. The cost of hospitalization for heart failure can reach four hundred million rupiah per patient per year. The government health insurance of Indonesia (BPJS Kesehatan) data for 2018 shows 130,275 advanced hospitalizations for congestive heart failure patients and based on the 2023 tariff, the estimated cost of hospitalization will range from 379 billion to 4.2 trillion rupiah. By utilizing the latest technology from Artificial Intelligence and the capabilities and habits of society after the Covid-19 pandemic, this research creates a machine learning-based predictive model by finding risk factors that can lead to hospital readmission, which are then implemented in the prototype that is used in collaboration between health care providers with patients who are also involved in conducting self-monitoring so that they can maintain their quality of life and control the costs of care whether paid by the patient himself, using insurance or with government funding. Method This research consisted of several stages, with quantitative and qualitative studies using medical records of heart failure patients at the Harapan Kita Cardiovascular Center. Starting with a Systematic Literature Review to find risk factors of readmission and to find novelties, predictive modeling with retrospective cohort study, system requirements analysis with qualitative studies, prototype development, and prototype testing with prospective cohort study. Results A systematic literature review on predictors of heart failure readmission using machine learning from PubMed, Science Direct, ProQuest, Scopus, Embase, Google Scholar resulted in 19 selected articles. 13 studies came from the USA, no similar studies were found in Indonesia, with the best algorithm being Neural Network. At the prediction modeling stage, data was obtained on 2738 post-hospitalization heart failure patients at Harapan Kita Cardiovascular Hospital, Jakarta, with the availability of 64 variables. With Orange Data Mining, 31 features are selected. The best model uses Random Forest, with AUC 0,976, CA 0,912, F1 0,912, Precision 0,916 and Recall 0,912, implemented in the Fineheart application prototype with patient profile application features, dashboard, my heart diary, quality of life assessment, control plan, medical instructions and medication, food and fluid intake records, education, consultation. The prototype efficacy test showed that the readmission rate in the intervention group (20%), was lower than the control group (43.3%). Significant changes occurred in 2 KCCQ parameters, Quality of Life (p=0.029) and Overall Summary Score (p=0.001). The level of presence of application prototypes also has a significant effect on these two parameters and prevents readmissions. Conclusion The readmission prediction model for heart failure patients with machine learning implemented in the application prototype can be used for home monitoring to prevent readmissions and maintain quality of life.
This study discusses the design of prototype for an employee performance monitoring system at BBPK Ciloto. This research is a qualitative research with Iterative and Incremental system development methods until prototyping interfaces. Based on the results of data collection conducted by the interview method to respondents at BBPK Ciloto, problems were found where the monitoring system of employee performance that was still manual resulted in the process of evaluating employee performance not based on measurable data. This is an opportunity for researchers to design a prototype web- based employee performance monitoring system in the hope that the monitoring process of employee work results can be carried out in real-time by superiors. Based on this, a web-based employee performance monitoring system prototype was designed.
Problems being faced by naval medical facilities are the lack of infrastructure andhuman resources as well as non-availibility of database which can be usedsimultaneously in carrying out monitoring of health facility. This research isintended to develop a monitoring system for health facilities in the Department ofthe Naval Health. The development of systems is using the System DevelopmentLife Cycle (SDLC) with a prototype approach. Primary data collection is carriedout by the method of in-depth interviews. Secondary data research is done bypaper based document recording and reporting. Rating of monitoring andevaluation systems in naval health facilities is subjected to the Ministry of Healthregulations. The system being developed is having a capacity of showing theaccurate information on the ability of health facilities (hospitals and BK / BP) inarea of health care, infrastructures and human resources. Merging of system iscarried out by way of full migration as to enable the direct data sharing. Follow-up suggestion is to revise the recording and reporting implementation guidelinesin accordance with Ministry of Health regulations.Keywords:System Monitoring and Evaluation, Health Care Facilities, Prototype, MergerSystems
ABSTRAK Nama : Nasrudin Program Studi : Ilmu Kesehatan Masyarakat Judul Tesisi : Pengembangan Sistem Registrasi Bidan Online Berbasis Web Guna Penguatan Profesi Bidan di Pengurus Ikatan Bidan Indonesia Provinsi Sulawesi Selatan. Pendahuluan: Perkembangan teknologi informasi yang begitu pesat di berbagai bidang menuntut setiap personal/individu untuk selalu update dan mau tidak mau untuk mengikutinya tak terkecuali Bidan. Seperti halnya dalam mengedukasi masyarakat dapat menggunakan Website sebagai media Informasi. Penelitian dilakukan pada Mei – Agustus 2017 di Organisasi Profesi Ikatan Bidan Indonesia (IBI) Provinsi Sulawesi Selatan. Penelitian ini bertujuan untuk Merancang sistem informasi registrasi bidan berbasis web yang mampu meningkatkan kinerja system, integgrasi data antar pengurus yang terbebas dari kesalahan dan redudansi dan dapat diakses kapan saja dimana saja dalam melakukan registrasi. Pengembangan system informasi dengan menggunakan pendekatan SDLC waterfall dengan teknik prototyping. Pengumpulan dilakukan dengan metode wawancara dan observasi. Hasil penelitian ini telah membangun prototipe system registrasi bidan online berbasis web tetapi prototipe yang dihasilkan tidak sepenuhnya sesuai dengan rancangan pengembangan system yang telah direkomendasikan kepada Pengurus Daerah IBI. Selain itu kemampuan sistem registrasi bidan online berbasis web mampu menyediakan informasi database yang terintegrasi yang bebas dari kesalahan dan redudansi data. Dibutuhkan komitmen berupa kebijakan dan kesiapan untuk melakukan pemeliharaan sistem informasi serta sosialisasi dan pelatihan web registrasi online bidan agar system informasi ini dapat dimanfaatkan secara maksimal. Kata Kunci: Sistem registrasi, Bidan, Prototipe, Website, Kata Kunci: Sistem informasi, Website, Bidan
ABSTRACT Name : Nasrudin Study Program : Public Health Sciences Judul Tesisi : Development of Web-Based Bidan Online Registration System for the Strengthening of Midwives Profession in Regional Management of Indonesian Midwives Association of South Sulawesi Province Introduction: The development of information technology is so rapidly in various fields requires every person / individual to always update and inevitably to follow it is no exception Midwife. As well as in educating the public can use the Website as a medium of information. The study was conducted in May - August 2017 at the Organization of Indonesian Midwives Association (IBI) South Sulawesi Province. This study aims to Design a web-based midwife registration information system capable of improving system performance, integrating data between administrators free from error and redundancy and can be accessed anytime anywhere in the registration. Development of information system using SDLC waterfall approach with prototyping technique. The collection is done by interview and observation method. The results of this research have been built prototype web-based online registration information system but the resulting prototype is not fully in accordance with the design of system development that has been recommended to the Regional Board of IBI. In addition, the ability of web-based online midwife registration system is able to provide integrated database information that is free from error and redundancy of data. It takes commitment in the form of policy and readiness to perform information system maintenance and socialization and online registration web training midwives for this information system can be utilized optimally. Keyword: Registration system, Midwife, Prototype, Website,
OHS, information system, prototype
