ABSTRAK Latar Belakang: Implementasi sistem antrean digital merupakan strategi transformasi digital kesehatan untuk meningkatkan efisiensi pelayanan rawat jalan. Charitas Hospital Palembang telah mengimplementasikan sistem antrean multi-kanal sejak 2024, namun pencapaian Standar Pelayanan Minimal (SPM) waktu tunggu ≤60 menit masih belum konsisten, dengan rata-rata kepatuhan 78,64% pada periode Oktober 2025–Januari 2026. Tujuan: Penelitian ini bertujuan untuk mengevaluasi implementasi sistem antrean digital di instalasi rawat jalan Charitas hospital Palembang Metode: Penelitian kualitatif dilaksanakan di Instalasi Rawat Jalan Charitas Hospital Palembang pada periode April–Mei 2026. Pengumpulan data menggunakan observasi partisipatif, Focus Group Discussion, dan wawancara mendalam (n=20) dengan informan dari pasien, petugas front office, perawat, dan manajemen. Teori yang digunakan mengintegrasikan Model Donabedian (input, proses, output) dan Technology Acceptance Model (TAM) untuk menggali persepsi subjektif, pengalaman nyata, serta dinamika operasional pengguna sistem. Analisis data dilakukan secara tematik dengan triangulasi data untuk memastikan validitas temuan. Hasil: Penelitian mengidentifikasi empat komponen utama: (1) Komponen Input menunjukkan infrastruktur teknologi memadai namun ada kesenjangan integrasi dengan SIMRS dan BPJS; (2) Komponen Proses mengungkapkan sistem mengurangi waktu transaksi loket namun terjadi bottleneck shift dan variabilitas durasi konsultasi; (3) Persepsi Pengguna (TAM) menunjukkan Perceived Ease of Use moderat-mudah dengan variasi antar kelompok pengguna, sementara Perceived Usefulness dipengaruhi pengalaman aktual dengan sistem; (4) Output menunjukkan performa fluktuatif, menunjukkan potensi besar sistem dalam meningkatkan efisiensi pelayanan dan kepuasan pasien melalui pengurangan kepadatan fisik di lokasi pendaftaran. Kesimpulan: Keberhasilan sistem antrean digital bergantung pada interaksi kompleks antara penerimaan pengguna, kesiapan infrastruktur, dan optimalisasi proses operasional. Diperlukan strategi komprehensif mencakup peningkatan literasi digital pasien, optimalisasi integrasi sistem, pelatihan petugas, dan manajemen sumber daya manusia yang lebih efektif. Kata Kunci: Sistem Antrean Digital; Penerimaan Pengguna; Efisiensi Pelayanan; Technology Acceptance Model; Rawat Jalan; Evaluasi Kualitatif.

Background: The implementation of digital queuing systems is a health digital transformation strategy to improve outpatient service efficiency. Charitas Hospital Palembang has implemented a multi-channel queuing system since 2024; however, the achievement of the Minimum Service Standard (SPM) for waiting time of ≤60 minutes remains inconsistent, with average compliance of 78.64% during October 2025–January 2026. Objective: This study aims to evaluate the implementation of the digital queuing system in the outpatient installation of Charitas Hospital Palembang. Methods: A qualitative study was conducted in the Outpatient Installation of Charitas Hospital Palembang during April–May 2026. Data collection employed participatory observation, Focus Group Discussions, and in-depth interviews (n=20) with informants from patients, front office staff, nurses, and management. The theoretical framework integrated the Donabedian Model (input, process, output) and Technology Acceptance Model (TAM) to explore subjective perceptions, actual experiences, and operational dynamics of system users. Data analysis was performed thematically with data triangulation to ensure the validity of findings. Results: The study identified four main components: (1) Input Component shows adequate technological infrastructure but gaps in integration with Hospital Management Information System (SIMRS) and BPJS Health; (2) Process Component reveals the system reduces registration counter transaction time but experiences bottleneck shift and consultation duration variability; (3) User Perception (TAM) shows moderate-easy Perceived Ease of Use with variation across user groups, while Perceived Usefulness is influenced by actual system experience; (4) Output shows fluctuating performance, demonstrating the system's significant potential in improving service efficiency and patient satisfaction through reducing physical congestion at the registration location. Conclusion: The success of the digital queuing system depends on complex interactions between user acceptance, infrastructure readiness, and operational process optimization. A comprehensive strategy is needed including improving patient digital literacy, optimizing system integration, staff training, and more effective human resource management.  Keywords: Digital Queuing System; User Acceptance; Service Efficiency; Technology Acceptance Model; Outpatient; Qualitative Evaluation.

Latar Belakang: Early Warning System (EWS) merupakan sistem deteksi dini yang dirancang untuk mengenali perburukan klinis sebelum pasien jatuh pada kondisi kegawatan. Namun, di Charitas Hospital Palembang masih ditemukan kejadian Code blue pada pasien dengan kategori EWS hijau/kuning, yaitu kelompok risiko rendah hingga sedang yang seharusnya masih berada dalam fase preventif. Tujuan: Penelitian ini bertujuan mengevaluasi implementasi EWS dalam deteksi dini perburukan klinis pada kejadian Code blue kategori hijau/kuning di instalasi rawat inap dewasa Charitas Hospital Palembang tahun 2025 dengan pendekatan Swiss Cheese Model. Metode: Penelitian ini menggunakan desain kualitatif evaluatif dengan audit rekam medis sebagai tahap awal. Audit dilakukan terhadap 68 kasus Code blue kategori EWS hijau/kuning dengan 204 window   audit, yaitu W0, W1, dan W2 sebelum aktivasi Code blue. Data diperoleh melalui audit rekam medis, telaah dokumen, wawancara mendalam, dan Focus Group Discussion. Analisis dilakukan secara deskriptif dan tematik dengan memetakan temuan ke dalam lapisan Swiss Cheese Model, meliputi organisasi, supervisi, prekondisi kerja, teknologi, komunikasi, dan perilaku individu. Hasil: Implementasi EWS telah tersedia melalui kebijakan, SPO, dokumentasi EMR, alur eskalasi, dan dashboard skor otomatis. Namun, EWS belum sepenuhnya berfungsi sebagai sistem deteksi dini aktif. Dari 68 kasus, sebanyak 45 kasus atau 66,2% memiliki monitoring tanda vital yang tidak sesuai SPO. Dari 204 window   audit, 181 window   atau 88,7% memiliki dokumentasi lengkap, tetapi 45 window   menunjukkan skor EWS tidak akurat. Dari 102 window   yang memiliki indikasi eskalasi, hanya 40 window   atau 39,2% yang dilakukan sesuai skor, sedangkan 62 window   atau 60,8% tidak dilakukan atau tidak terdokumentasi. Outcome pasca Code blue terdiri atas 31 pasien masuk ICU/perawatan intensif dan 37 pasien meninggal. Wawancara dan FGD menunjukkan celah implementasi berupa sosialisasi yang belum merata, supervisi yang masih berfokus pada kelengkapan dokumentasi, beban kerja, persepsi aman terhadap kategori hijau/kuning, ketidakakuratan input parameter, komunikasi SBAR-EWS yang belum konsisten, serta belum adanya reminder monitoring ulang dan alert eskalasi aktif. Kesimpulan: Kejadian Code blue pada pasien EWS hijau/kuning merupakan hasil pertemuan celah pada berbagai lapisan sistem, bukan akibat satu faktor tunggal. Perbaikan perlu difokuskan pada audit pra-Code blue 24 jam, reminder monitoring ulang, watch list pasien berisiko, alert eskalasi, validasi skor, penguatan SBAR-EWS, pelatihan lintas profesi, dan umpan balik kasus secara rutin agar EWS berfungsi sebagai mekanisme prediktif dan preventif dalam meningkatkan keselamatan pasien. Kata Kunci: Code blue, Early Warning System, EWS hijau/kuning, keselamatan pasien, Swiss Cheese Model.

Background: The Early Warning System (EWS) is designed to detect early signs of clinical deterioration before patients progress to critical conditions. However, Code blue events still occurred at Charitas Hospital Palembang among patients categorized as green or yellow EWS, a low-to-moderate risk group that should conceptually remain within the preventive phase. Objective: This study aimed to evaluate the implementation of EWS in detecting clinical deterioration among green/yellow EWS Code blue cases in adult inpatient units at Charitas Hospital Palembang in 2025 using the Swiss Cheese Model. Methods: This study used an evaluative qualitative design, beginning with a medical record audit. The audit involved 68 green/yellow EWS Code blue cases with 204 audit window  s, consisting of W0, W1, and W2 before Code blue activation. Data were obtained through medical record audits, document reviews, in-depth interviews, and Focus Group Discussion. Data were analyzed descriptively and thematically by mapping the findings into the Swiss Cheese Model layers, including organization, supervision, work preconditions, technology, communication, and individual behavior. Results: EWS had been implemented through policies, standard operating procedures, EMR documentation, escalation pathways, and an automated scoring dashboard. However, EWS had not yet fully functioned as an active early detection system. Among 68 cases, 45 cases or 66.2% did not comply with vital sign monitoring requirements according to the standard operating procedure. Among 204 audit window  s, 181 window  s or 88.7% were completely documented, but 45 window  s showed inaccurate EWS scores. Among 102 window  s requiring escalation, only 40 window  s or 39.2% were escalated according to the score, while 62 window  s or 60.8% were not escalated or not properly documented. Post-Code blue outcomes consisted of 31 patients admitted to intensive care and 37 deaths. Interviews and Focus Group Discussion revealed implementation gaps, including uneven socialization, supervision that remained focused on documentation completeness, workload, false reassurance toward green/yellow categories, inaccurate parameter input, inconsistent SBAR-EWS communication, and the absence of monitoring reminders and active escalation alerts. Conclusion: Code blue events among green/yellow EWS patients resulted from aligned gaps across multiple system layers rather than a single causal factor. Improvement efforts should focus on routine 24-hour pre-Code blue audits, monitoring reminders, risk-based patient watch lists, escalation alerts, score validation, strengthening SBAR-EWS communication, interprofessional training, and regular case-based feedback to ensure that EWS functions as a predictive and preventive mechanism to improve patient safety. Keywords: Code blue, Early Warning System, green/yellow EWS, patient safety, Swiss Cheese Model.