Background: Hospital has an important referral system role in the implementation on the National Health Insurance (NHI) Scheme. BPJS Kesehatan (NHI Implementing Agency) pays hospitals by Indonesian Case Based Groups (INA-CBGs) method. This payment method may potentially cause loss or profit to the hospital, when there is discrepancy between hospital inpatient cost and INA-CBGs tariff of inpatient care. This study aimed at investigating the discrepancy between hospital inpatient cost and INA-CBGs tariff of inpatient care and the determinants of hospital inpatient cost. Subjectsand Method: This was an analytic and observational study cross sectional approach. This study was conducted in 2 publichospitals and 2 private hospitals, from October to December 2016. A total sample of 100 inpatients was selected at random for this study. The dependent variables were hospital inpatient cost and INA-CBGs tariff. The independent variables included hospital type, inpatients class, disease severity, use of ICU, and length of stay. The data were analyzed by a multiple linear regression model. Results: Average hospital inpatient cost (mean= Rp. 2,280,000; SD=1,690,000) was lower than average INA-CBGs (mean=Rp. 3,060,000). There were negative relationships between hospital type, inpatient class, disease severity, and hospital inpatient cost. Private hospital inpatient cost (b=-5.66; 95% CI= -1.20 to 0.06; p= 0.078) was lower than public hospital inpatient cost. Class 2 inpatient care (b= -0.34; 95% CI=-1.09 to 0.41, p =0.371), class 3 inpatient care (b =-0.50; 95% CI= -1.23 to 0.23, p=0.177), had lower hospital inpatient cost than class 1 inpatient care. Severe disease (b= -0.12; 95% CI= -1.95 to 1.71; p= 0.894) had lower hospital inpatient cost than mild disease, although it was not statistically significant. There were positive relationships between use of ICU, disease severity, length of stay, and hospital inpatient cost. Using ICU (b= 1.58; 95% CI= 0.76 to 2.4; p= <0.001) had higher hospital inpatient cost than not using ICU. Moderate disease severity (b= 0.55; 95% CI = -0.20 to 1.30; p= 0.150) had higher hospital inpatient cost than mild disease. Longer stay (b= 0.27; 95% CI= 0.08 to 0.45; p= 0.005) had higher hospital inpatient cost than shorter stay. Conclusion: Average hospital inpatient cost was lower than average INA-CBGs tariff. Hospital type, use of ICU, and length of stay, are important determinants of hospital inpatient cost.

<span>The accuracy of the diagnosis code has implications for future patient care planning, provision of health services and patient care costs. Therefore, <br /> this study has analyzed the influence of the quality of medical information on the quality of the diagnosis code which includes the accuracy, consistency, completeness and timeliness in coding the diagnosis of inpatients at <br /> Dr. Moewardi hospital.</span><span>This was an observational analytic study with <br /> a sample of 250 medical records taken using stratified random sampling. Data were analyzed by chi square test</span><span lang="IN">. </span><span>High quality of medical information has a better diagnosis code quality (73.80%) compared to poorly quality of medical information (36.00%). High quality of medical information has a log odds of 1.54 better in the quality of diagnosis code than poorly quality of medical information (b=1.54; 95% CI=0.81-2.27, p&lt;0.001).</span>

Dr. Moewardi General Hospital is a fully accredited hospital. Based on the results of a preliminary survey of 10 medical record documents in the Melati ward, 40% are inaccurate documents. This study aims to determine the relationship of coder workload with the accuracy of the diagnosis code in inpatients of the jasmine ward at Dr. Moewardi in 2018.This research is an analytic study with cross sectional approach. The sample in this study were 99 medical records of inpatient Melati ward with proportionate stratified random sampling technique. Data management by collecting, editing, coding, classification, tabulating, analysis and presenting data.The percentage of the workload is low at 60% (59 documents) while the workload is high at 40% (40 documents). Percentage of accuracy of Melati ward code 59% (58 documents) while inaccuracy is 41% (41 documents). The data is processed using the chi square and show that p = 0.01 so that p <0.01.The conclusion drawn is that Ho is rejected and Ha is accepted, meaning that there is a relationship between the workload of the coder and the accuracy of the diagnosis code. The suggestion for hospitals should plan and analyze the workload of the coder in each ward.

Background: Hospital has an important referral system role in the implementation on the National Health Insurance (NHI) Scheme. BPJS Kesehatan (NHI Implementing Agency) pays hospitals by Indonesian Case Based Groups (INA-CBGs) method. This payment method may potentially cause loss or profit to the hospital, when there is discrepancy between hospital inpatient cost and INA-CBGs tariff of inpatient care. This study aimed at investigating the discrepancy between hospital inpatient cost and INA-CBGs tarif of inpatient care and the determinants of hospital inpatient cost. Subjects and Method: This was an analytic and observational study cross sectional approach. This study was conducted in 2 publichospitals and 2 private hospitals, from October to December 2016. A total sample of 100 inpatients was selected at random for this study. The dependent variables were hospital inpatient cost and INA-CBGs tariff. The independent variables included hospital type, inpatients class, disease severity, use of ICU, and length of stay. The data were analyzed by a multiple linear regression model. Results: Averagehospital inpatient cost (mean= Rp. 2,280,000; SD= 1,690,000) was lower than average INA-CBGs (mean= Rp. 3,060,000). There were negative relationships between hospital type, inpatient class, disease severity, and hospital inpatient cost. Private hospital inpatient cost (b= -5.66; 95% CI= -1.20 to 0.06; p= 0.078) was lower than public hospital inpatient cost. Class 2 inpatient care (b= -0.34; 95% CI= -1.09 to 0.41, p= 0.371), class 3 inpatient care (b= -0.50; 95% CI= -1.23 to 0.23, p= 0.177), had lower hospital inpatient cost than class 1 inpatient care.Severe disease (b= -0.12; 95% CI= -1.95 to 1.71; p= 0.894) had lower hospital inpatient cost than mild disease, although it was not statistically significant. There were positive relationships between use of ICU, disease severity, length of stay, and hospital inpatient cost. Using ICU (b= 1.58; 95% CI= 0.76 to 2.4; p= <0.001) had higher hospital inpatient cost than not using ICU. Moderate disease severity (b= 0.55; 95% CI= -0.20 to 1.30; p= 0.150) had higher hospital inpatient cost than mild disease. Longer stay (b= 0.27; 95% CI= 0.08 to 0.45; p= 0.005) had higher hospital inpatient cost than shorter stay. Conclusion: Average hospital inpatient cost was lower than average INA-CBGs tariff. Hospital type, use of ICU, and length of stay, are important determinants of hospital inpatient cost.

No abstract

An accurate diagnosis code is crucial to support the smooth submission of health service fee claims. In Indonesia, kidney disease is ranked as the second largest financing from BPJS. The preliminary study results show that of the 30% of claims submitted by patients with chronic kidney disease (CKD) that were not approved, 10% of them were due to inaccurate diagnosis codes. This study aimed to prove the relationship between the accuracy of the CKD diagnosis code and the approval of BPJS claims. This research is a quantitative study with a cross-sectional study design. A sample of 97 CKD patient claim files was taken at a hospital in Surakarta. There are two variables: the accuracy of the diagnosis code and the approval of BPJS claims. Researchers used observation guidelines and ICD-10 to analyze the accuracy of the diagnosis code and observe the reasons for returning BPJS claims. Analysis of the relationship between the two variables using the Fisher Exact test. The results showed 93 (95.9%) valid CKD diagnosis codes and 4 (4.1%) inaccurate codes. Claim files were approved by 79 (81.5%) and not approved by 18 (18.5%). The analysis showed that the accuracy of the CKD diagnosis code had a significant relationship with the approval of BPJS claims (b=6.643; 95% CI=4.099-10.765; p=0.001). An Accurate CKD diagnosis code that is accurate has a 6.643 times greater chance of increasing claim approval than one that is inaccurate. Hospitals should try to improve the accuracy of the diagnosis code through regular training, monitoring and evaluation to minimize the occurrence of claims return.Keyword: code, diagnosis, claim, accuracy, CKD AbstrakKode diagnosis yang akurat sangat penting untuk mendukung kelancaran pengajuan klaim biaya pelayanan kesehatan. Di Indonesia, penyakit ginjal menduduki ranking kedua pembiayaan terbesar dari BPJS. Hasil studi pendahuluan menunjukkan bahwa dari 30% pengajuan klaim pasien dengan chronic kidney disesase (CKD) yang tidak disetujui, 10% diantaranya disebabkan karena ketidakakuratan kode diagnosis. Tujuan penelitian ini untuk membuktikan hubungan antara keakuratan kode diagnosis CKD dengan persetujuan klaim BPJS. Penelitian ini menggunakan desain studi cross sectional dengan sampel sebanyak 97 dokumen klaim pasien CKD. Terdapat dua variabel yaitu keakruratan kode diagnosis dan persetujuan kliam BPJS. Peneliti menggunakan pedoman observasi dan ICD-10 untuk menganalisis keakuratan kode diagnosis serta mengamati penyebab pengembalian klaim BPJS. Analisis hubungan antara variabel bebas dengan variabel terikat dengan menggunakan uji Fisher Exact. Hasil penelitian didapatkan kode diagnosis CKD yang akurat sebanyak 93 (95,9%) dan tidak akurat sebanyak 4 (4,1%). Berkas klaim yang disetujui sebanyak 79 (81,5%) dan tidak disetujui sebanyak 18 (18,5%). Hasil analisis menunjukkan bahwa keakuratan kode diagnosis CKD memiliki hubungan yang signifikan dengan persetujuan klaim BPJS (b=6,643; 95% CI=4,099-10,765; p=0,001). Setiap kode diagnosis CKD yang akurat memiliki peluang sebesar 6,643 kali lebih besar dalam meningkatkan persetujuan klaim dibandingkan yang tidak akurat. Rumah sakit sebaiknya melakukan upaya peningkatan keakuratan kode diagnosis melalui pelatihan, pengawasan dan evaluasi secara berkala sehingga meminimalisir terjadinya pengembalian klaim.Kata Kunci: kode, diagnosis, klaim, kekauratan, CKD