The Factors Associated with Inaccurate Inpatient Diagnosis Code at Health Information System Ibnu Sina Hospital Gresik Regency

Inaccuracies in disease diagnosis codes result in inaccurate reports. The results of the preliminary study showed a high rate of code inaccurate (73%). The research aimed to determine the relationship between ability, leadership, reward, motivation and attitude factors on inaccurate inpatient diagnosis codes at HIS Ibnu Sina Hospital, Gresik Regency. This research method uses a cross-sectional approach with the chi-square test. The population used was nurses who coded diagnoses as many as 42 people with a sample of 39 people. The results of observations of inaccurate inpatient diagnosis codes at HIS Ibnu Sina Hospital, Gresik Regency were 49% inaccurate. The results of the attitude factor analysis have a relationship with the inaccurate of inpatient diagnosis codes with P value 0.00 < α (0.05), while the Ability, Leadership, Reward, and Motivation there is no significant relationship with inaccurate inpatient diagnosis codes with P value > α (0.05) at Hospital Information System Ibnu Sina Regional Hospital, Gresik Regency. The suggestion this research is the Hospital Director needs to create a policy regarding the diagnosis input must be carried out by medical recorders, a clear reward and punishment mechanism, monitoring and evaluation by audit team for the number of inaccurate disease diagnosis codes

Authors

Institutions

Publication Details

Journal
International Journal of Healthcare and Information Technology
Published
2024-08-30
DOI
https://doi.org/10.25047/ijhitech.v2i1.5072
Primary Topic
Healthcare Quality and Satisfaction
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

The Factors Associated with Inaccurate Inpatient Diagnosis Code at Health Information System Ibnu Sina Hospital Gresik Regency

Ervina Rachmawati, Dony Setiawan, Maya Weka Santi, Khumaidah Hasan
International Journal of Healthcare and Information Technology
Healthcare Quality and Satisfaction
article

The Factors Associated with Inaccurate Inpatient Diagnosis Code at Health Information System Ibnu Sina Hospital Gresik Regency

Ervina Rachmawati, Dony Setiawan, Maya Weka Santi, Khumaidah Hasan
article en

Abstract

Inaccuracies in disease diagnosis codes result in inaccurate reports. The results of the preliminary study showed a high rate of code inaccurate (73%). The research aimed to determine the relationship between ability, leadership, reward, motivation and attitude factors on inaccurate inpatient diagnosis codes at HIS Ibnu Sina Hospital, Gresik Regency. This research method uses a cross-sectional approach with the chi-square test. The population used was nurses who coded diagnoses as many as 42 people with a sample of 39 people. The results of observations of inaccurate inpatient diagnosis codes at HIS Ibnu Sina Hospital, Gresik Regency were 49% inaccurate. The results of the attitude factor analysis have a relationship with the inaccurate of inpatient diagnosis codes with P value 0.00 < α (0.05), while the Ability, Leadership, Reward, and Motivation there is no significant relationship with inaccurate inpatient diagnosis codes with P value > α (0.05) at Hospital Information System Ibnu Sina Regional Hospital, Gresik Regency. The suggestion this research is the Hospital Director needs to create a policy regarding the diagnosis input must be carried out by medical recorders, a clear reward and punishment mechanism, monitoring and evaluation by audit team for the number of inaccurate disease diagnosis codes

International Journal of Healthcare and Information TechnologyVol. 2(1)
Universitas Jember (ID), Politeknik Negeri Jember
Openalex Percentile: Top 50%
Healthcare Quality and Satisfaction
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.