Diagnosa Preeklampsia pada Ibu Hamil Menggunakan Sistem Informasi Berbasis Web

  • Fajaria Nur Aini Program Studi D3 Kebidanan, Poltekkes Kemenkes Semarang
  • Melyana Nurul Widyawati Program Pascasarjana, Poltekkes Kemenkes Semarang
  • Bedjo Santoso Program Pascasarjana, Poltekkes Kemenkes Semarang

Abstract

This study aims to produce a preeclampsia diagnostic system online and to determine the difference in the speed of time required to diagnose preeclampsia using a web-based information system with a manual system. This research method is to arrange a framework with a quasi experimental research design with a post test only with control group. This research was conducted at the Community Health Center. The results showed that the majority of respondents were healthy pregnant women namely in the intervention group of 20 pregnant women (30.30%) and in the control group of 15 pregnant women (22.73%). The smallest number of diagnoses is in the case of superimposed preeclampsia. Web-based information systems are also able to increase diagnostic speeds to 7.21%. Statistical test results using the Independent T-Test obtained a P-value of 0.041 <0.05 (α). Conclusion, there is a difference in the time of diagnosis of preeclampsia using a web-based information system with a manual system.

 

Keywords: Diagnosis, Preeclampsia, Information Systems, Web

References

Abu, A. D. K. H., Kusumawati, Y. & Werdani, K. E. (2017). Hubungan Karakteristik Bidan dengan Mutu Pelayanan Antenatal Care Berdasarkan Standar Operasional. Jurnal Kesehatan Masyarakat Andalas, 10, 94-100

Allen, R., Rogozinska, E., Sivarajasingam, P., Khan, K. S. & Thangaratinam, S (2014). Effect of Diet‐And Lifestyle‐Based Metabolic Risk‐Modifying Interventions on Preeclampsia: A Meta‐Analysis. Acta Obstetricia Et Gynecologica Scandinavica, 93, 973-985

Bakibinga, P., Kamande, E., Omuya, M., Ziraba, A. K. & Kyobutungi, C. (2017). The Role of A Decision-Support Smartphone Application in Enhancing Community Health Volunteers’ Effectiveness to Improve Maternal and Newborn Outcomes in Nairobi, Kenya: Quasi-Experimental Research Protocol. Bmj Open, 7, E014896

English, F. A., Kenny, L. C. & Mccarthy, F. P (2015). Risk Factors and Effective Management Of Preeclampsia. Integrated Blood Pressure Control, 8, 7

Fatkhiyah, N. (2015). Motivasi, Kualitas Supervisi dan Kepatuhan Bidan dalam Mendeteksi Preeklampsia. Jurnal Kesehatan Masyarakat, 10, 195-202

Graham, W., Woodd, S., Byass, P., Filippi, V., Gon, G., Virgo, S., Chou, D., Hounton, S., Lozano, R., Pattinson, R. & Singh, S. (2016). Diversity and Divergence: The Dynamic Burden of Poor Maternal Health. The Lancet, 388, 2164-2175

Padila, P. (2016). Asuhan Keperawatan Maternity II. Yogyakarta: Nuha Medika

Padila, P., Lina, L., Febriawati, H., Agustina, B., & Yanuarti, R. (2018). Home Visit Berbasis Sistem Informasi Manajemen Telenursing. Jurnal Keperawatan Silampari, 2(1), 217-235. https://doi.org/https://doi.org/10.31539/jks.v2i1.305

Padila, P., Amin, M., & Rizki, R. (2018). Pengalaman Ibu dalam Merawat Bayi Preterm yang Pernah dirawat di Ruang Neonatus Intensive Care Unit Kota Bengkulu. Jurnal Keperawatan Silampari, 1(2), 1-16. https ://doi.org /https: //doi.org /10.31539 /jks. v1i2.82

Park, H. J., Kim, S. H., Jung, Y. W., Shim, S. S., Kim, J. Y., Cho, Y. K., Farina, A., Zanello, M., Lee, K. J. & Cha, D. H. (2014). Screening Models Using Multiple Markers for Early Detection of Late-Onset Preeclampsia in Low-Risk Pregnancy. Bmc Pregnancy and Childbirth, 14(35)

Park, H. J., Shim, S. S. & Cha, D. H. (2015) Combined Screening for Early Detection of Pre-Eclampsia. International Journal of Molecular Sciences, 16, 17952-17974

Rana, S., Karumanchi, S. A. & Lindheimer, M. D. (2014). Angiogenic Factors in Diagnosis, Management, and Research in Preeclampsia. Hypertension, 63, 198-202

Shiferaw, S., Spigt, M., Tekie, M., Abdullah, M., Fantahun, M. & Dinant, G.-J. (2016). The Effects Of A Locally Developed Mhealth Intervention on Delivery and Postnatal Care Utilization; A Prospective Controlled Evaluation Among Health Centres in Ethiopia. Plos One, 11, E0158600

Tagare, H. D., Rood, K. & Buhimschi, I. (2014). A. An Algorithm to Screen for Preeclampsia Using A Smart Phone. Healthcare Innovation Conference (Hic). IEEE, 52-55

Utami, D. H. N. & Nuryati, S. (2015). Evaluasi Ketepatan Reseleksi Diagnosis Utama Sebelum dan Setelah Verifikasi pada Kasus Pasien BPJS di Rumah Sakit Hidayah Boyolali. Universitas Gadjah Mada

Warren, C. E., Abuya, T., Kanya, L., Obare, F., Njuki, R., Temmerman, M. & Bellows, B. (2015). A Cross Sectional Comparison Of Postnatal Care Quality in Facilities Participating in A Maternal Health Voucher Program Versus Non-Voucher Facilities In Kenya. Bmc Pregnancy And Childbirth, 15, 153

Wibowo, Irwinda, Frisdiantiny, Karkata, Mose, Chalid. (2016). Pedoman Nasional Pelayanan Kedokteran Diagnosis dan Tatalaksana Pre-Eklampsia, Jakarta, Perkumpulan Obstetri dan Ginekologi Indonesia Himpunan Kedokteran Feto Maternal

Wójtowicz, A., Żywica, P., Stachowiak, A. & Dyczkowski, K. (2016). Solving the Problem of Incomplete Data in Medical Diagnosis Via Interval Modeling. Applied Soft Computing, 47, 424-437

Wright, D., Syngelaki, A., Akolekar, R., Poon, L. C. & Nicolaides, K. H. (2015). Competing Risks Model in Screening for Preeclampsia by Maternal Characteristics and Medical History. American Journal of Obstetrics & Gynecology, 213, 62. E1-62. E10
Published
2019-03-30
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