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Record W2946786054 · doi:10.47007/inohim.v5i1.139

Efisiensi Pengelolaan di Bangsal Asoka Berdasarkan Grafik Barber Johnson di Rumah Sakit Sumber Waras Triwulan I-IV Tahun 2016

2017· article· id· W2946786054 on OpenAlexaboutno aff
Intan Novarinda, Deasy Rosmala Dewi

Bibliographic record

VenueIndonesian of Health Information Management Journal (INOHIM) · 2017
Typearticle
Languageid
FieldHealth Professions
TopicHealthcare Quality and Satisfaction
Canadian institutionsnot available
Fundersnot available
KeywordsQuarter (Canadian coin)ChartHumanitiesArtHistoryMathematicsStatistics

Abstract

fetched live from OpenAlex

Abstract The efficiency of hospital management in general can be seen from two aspects, namely the medical aspect that evaluates the efficiency from the point of quality of medical service and economically evaluate efficiency from the point of utilization of existing facilities. To know the efficiency of hospital service management, it is necessary to calculate four parameters of Barber Johnson Chart. Based on the results of initial observations on Asoka ward at Sumber Waras Hospital found a fairly low BOR of 52.26% indicating the occupancy rate in Asoka ward is low. The purpose of this research is to get an overview of management efficiency in Asoka ward based on Barber Johnson Chart in Sumber Waras Hospital of Quarter I-IV 2016. This research uses descriptive method to describe Barber Johnson Graphic analysis results in Asoka ward in Quarter I-IV 2016 supported by the results of observations and interviews to HR reporting and head of unit installation of Medical Record Unit. Based on Barber Johnson Graphic Analysis At Asoka Quarter I-IV meeting four parameters are outside the efficiency area set by Barber Johnson indicating that the system running in Sumber Waras hospital has not been efficient. Suggested to Sumber Waras Hospital should plan marketing strategy so that the utilization of bed at Sumber Waras Hospital can be further enhanced . Keywords : hospital management efficiency, barber johnson chart, health statistics Abstrak Efisiensi pengelolaan rumah sakit secara garis besar dapat dilihat dari  dua  segi  yaitu segi medis  yang meninjau  efisiensi  dari  sudut  mutu pelayanan medis dan dari segi ekonomi yang meninjau efisiensi dari sudut pendayagunaan  sarana  yang  ada. Untuk mengetahui efisiensi pengelolaan pelayanan rumah sakit diperlukan adanya perhitungan empat parameter Grafik Barber Johnson. Berdasarkan hasil observasi awal pada bangsal Asoka di Rumah Sakit Sumber Waras ditemukan BOR yang cukup rendah yaitu 52,26% yang menandakan tingkat hunian di bangsal Asoka rendah. Tujuan penelitian ini adalah untuk mendapatkan gambaran efisiensi pengelolaan di bangsal Asoka berdasarkan Grafik Barber Johnson di rumah sakit Sumber Waras Triwulan I-IV tahun 2016. Penelitian ini menggunakan metode desktiptif menggambarkan hasil analisis Grafik Barber Johnson di bangsal Asoka pada Triwulan I-IV tahun 2016 yang didukung dengan hasil observasi dan wawancara kepada SDM bagian pelaporan dan kepala instalasi Unit Rekam Medis. Berdasarkan hasil analisa Grafik Barber Johnson Pada bangsal Asoka Triwulan I-IV pertemuan empat parameter berada di luar daerah efisiensi yang telah ditetapkan oleh Barber Johnson yang menandakan bahwa sistem yang berjalan di rumah sakit Sumber Waras belum efisien. Disarankan kepada Rumah Sakit Sumber Waras sebaiknya merencanakan strategi pemasaran agar pemanfaatan tempat tidur di Rumah Sakit Sumber Waras dapat lebih di tingkatkan. Kata Kunci: efisiensi pengelolaan rumah sakit, grafik barber johnson, statistik kesehatan

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.028
Threshold uncertainty score0.093

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0280.003

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.066
GPT teacher head0.394
Teacher spread0.328 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations2
Published2017
Admission routes1
Has abstractyes

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