Permodelan Dan Analisis Performa Jaringan Antrian Dari Sistem Rujukan Berjenjang BPJS Di Surabaya

Sistem rujukan berjenjang diterapkan oleh BPJS sebagai bagian dari prosedur penjaminan kesehatannya. Pasien yang ingin mendapatkan penjaminan kesehatan pada fasilitas kesehatan (faskes) tertentu harus mendapat rujukan dari faskes tingkat I pasien tersebut terdaftar. Penumpukan terjadi pasien pada faskes lanjutan tertentu disebabkan tidak ada aturan yang membatasi pemberian rujukan [1]. Dalam tugas akhir ini akan dibuat model jaringan antrian dari sistem rujukan BPJS dan dianalisis performa antrian dalam jaringan tersebut. Performa sistem rujukan berjenjang diestimasi menggunakan model sistem antrian hypercube yang dipergunakan dalam masalah antrian perkotaan. Analisis performa jaringan antrian tersebut menunjukkan pengaruh preferensi pasien terhadap penumpukan pasien pada faskes lanjutan. Pemberian rujukan dapat dilakukan dengan cara routing dinamis dan pemberian prioritas pasien. Kedua aturan tersebut disimulasikan untuk menentukan aturan pemberian rujukan yang cocok untuk permasalahan ini. Hasil yang didapatkan adalah preferensi pasien mempengaruhi laju kedatangan pada tiap rumah sakit, penerapan routing dinamis dapat menurunkan utilitas maksimum dan mengurangi rata-rata waktu tunggu, pemberian prioritas pasien memperbaiki performa routing dinamis pada sistem dengan beban kerja tinggi. ======================================================================================================================== Sistem rujukan berjenjang is implemented by BPJS as part of the health insurance policies. Patients who want to get health insurance at a health facility particular should get a referral from the first level healthcare facility in which they are registered. Congestion of patients happens in certain upper level healthcare facility as there are no rules maintaining the referral system [1]. This final project models sistem rujukan berjenjang BPJS as a network of queuing system and analize the performance of queues in the network. Sistem rujukan berjenjang performances is estimated by hypercube queueing system model which used in urban queueing problem. Analysis of queuing network performances shows the influence patient preferences to buildup congestion of patients in upper level healthcare facilities. Referrals can be done by means of dynamic routing and prioritization of patients. Both of these rules are simulated to determine the appropriate rules of referral. The following results are obtained from the project, patient preferences affect the arrival rate at each hospital, the application of dynamic routing can reduce the maximum utility and reduce the average waiting time, prioritization of patients improve dynamic routing performance on systems with a high workload

Analysis and Dynamic Routing Implementation of Hierarcical Healthcare Referral System

Abstract—Hierarchical Healthcare Referral System (HHRS) is implemented by National Insurance Providing Agency (BPJS) as part of the healthcare insurance policies. Patients who want to get health insurance in a hospital should get a referral from the community health center in which they are registered. Congestion of patients happens in certain hospital as there is no policy implemented to govern the referral system. In this paper, HHRS is modeled as a network of queuing system and is analyzed for its queue performances. Analysis of queuing network performances shows the influence patient preferences to buildup congestion of patients in hospitals. Referral is then controlled by means of dynamic routing with considering patient preferences. Estimation of arrival rate is done with hypercube queuing theory which concerns user preference. Simulation shows that patient preferences affect the arrival rate at each hospital, the application of dynamic routing can reduce the maximum utility and reduce the average waiting time, prioritization of patients improve dynamic routing performance on systems with a high workload. Keywords—dynamic routing; hierarchical healthcare referral system; patient priority; queueing networks