A review of the complexity adjustment in the Korean Diagnosis-Related Group (KDRG)

2018 ◽  
Vol 49 (1) ◽  
pp. 62-68 ◽  
Author(s):  
Sujeong Kim ◽  
Chaiyoung Jung ◽  
Junheum Yon ◽  
Hyeonseon Park ◽  
Hunsik Yang ◽  
...  
Keyword(s):  
Diagnosis Related Groups ◽  
Claim Data ◽  
Hospital Charge ◽  
Diagnosis Related Group ◽  
Cost Data ◽  
Hospital Charges ◽  
Average Charge ◽  
Average Hospital ◽  
Adjustment Mechanism ◽  
Related Group

Background: The Korean Diagnosis-Related Groups (KDRG) was revised in 2003, modifying the complexity adjustment mechanism of the Australian Refined Diagnosis-Related Groups (AR-DRGs). In 2014, the Complication and Comorbidity Level (CCL) of the existing AR-DRG system was found to have very little correlation with cost. Objective: Based on the Australian experience, the CCL for KDRG version 3.4 was reviewed. Method: Inpatient claim data for 2011 were used in this study. About 5,731,551 episodes, which had one or no complication and comorbidity (CC) and met the inclusion criteria, were selected. The differences of average hospital charges by the CCL were analysed in each Adjacent Diagnosis-Related Group (ADRG) using analysis of variance followed by Duncan’s test. The patterns of differences were presented with R 2 in three patterns: The CCL reflected the complexity well (VALID); the average charge of CCL 2, 3, 4 was greater than CCL 0 (PARTIALLY VALID); the CCL did not reflect the complexity (NOT VALID). Results: A total of 114 (19.03%), 190 (31.72%) and 295 (49.25%) ADRGs were included in VALID, PARTIALLY VALID and NOT VALID, respectively. The average R 2 for hospital charge of CCL was 4.94%. The average R 2 in VALID, PARTIALLY VALID and NOT VALID was 4.54%, 5.21%, and 4.93%, respectively. Conclusion: The CCL, the first step of complexity adjustment using secondary diagnoses, exhibited low performance. If highly accurate coding data and cost data become available, the performance of secondary diagnosis as a variable to reflect the case complexity should be re-evaluated. Implications: Lack of reviewing the complexity adjustment mechanism of the KDRG since 2003 has resulted in outdated CC lists and levels that no longer reflect the current Korean healthcare system. Reliable cost data (vs. charge) and accurate coding are essential for accuracy of reimbursement.

scholarly journals National treatment trends, complications, and predictors of in-hospital charges for the surgical management of craniopharyngiomas in adults from 2007 to 2011

2014 ◽  
Vol 37 (5) ◽  
pp. E6 ◽  
Author(s):  
Hasan A. Zaidi ◽  
Kristina Chapple ◽  
Andrew S. Little
Keyword(s):  
Regression Model ◽  
Hospital Charge ◽  
Hospital Length Of Stay ◽  
Hospital Charges ◽  
Neurosurgical Procedures ◽  
Health Care Economics ◽  
National Treatment ◽  
Average Hospital ◽  
The Mean ◽  
Treatment Trends

Object Treatment of craniopharyngiomas is one of the most demanding and controversial neurosurgical procedures performed. The authors sought to determine the factors associated with hospital charges and fees for craniopharyngioma treatment to identify possible opportunities for improving the health care economics of inpatient care. Methods The authors analyzed the hospital discharge database of the Nationwide Inpatient Sample (NIS) covering the period from 2007 through 2011 to examine national treatment trends for adults (that is, those older than 18 years) who had undergone surgery for craniopharyngioma. To predict the drivers of in-hospital charges, a multistep regression model was developed that accounted for patient demographics, acuity measures, comorbidities, hospital characteristics, and complications. Results The analysis included 606 patients who underwent resection of craniopharyngioma; 353 resections involved a transsphenoidal approach (58%) and 253 a transfrontal approach (42%). The mean age (± SD) of patients was 47.7 ± 16.3 years. The average hospital length of stay (LOS) was 7.6 ± 9 days. The mean hospital charge (± SD) was $92,300 ± $83,356. In total, 48% of the patients experienced postoperative diabetes insipidus or an electrolyte abnormality. A multivariate regression model demonstrated that LOS, hospital volume for the selected procedure, the surgical approach, postoperative complications, comorbidities, and year of surgery were all significant predictors of in-hospital charges. The statistical model accounted for 54% of the variance in in-hospital charge. Conclusions This analysis of inpatient hospital charges in patients undergoing craniopharyngioma surgery identified key drivers of charges in the perioperative period. Prospective studies designed to evaluate the long-term resource utilization in this complex patient population would be a useful future direction.

Abstract T P302: Analysis of Publicly Available CMS Data on Hospital Charges and Reimbursements for Stroke Care

Stroke ◽  
2014 ◽  
Vol 45 (suppl_1) ◽  
Author(s):  
Roople Unia ◽  
Adam Kelly ◽  
Robert Holloway
Keyword(s):  
Cerebral Infarction ◽  
Intracranial Hemorrhage ◽  
Diagnosis Related Groups ◽  
Stroke Care ◽  
Hospital Charge ◽  
Unintended Consequences ◽  
Hospital Charges ◽  
List Price ◽  
Common Diagnosis

Background/ Purpose: The Centers for Medicare & Medicaid Services recently made 2011 hospital charge (i.e., list price) and total payment data (CMS reimbursement data and co-payments by patient) publicly available for common Diagnosis-Related Groups, including cerebral infarction/intracranial hemorrhage, at www.cms.gov. We provide descriptive statistics of the data and begin to explore the association of these data with the quality of stroke care hospital’s provide. Methods: We report the median, mean and extreme hospital charge and reimbursement data for cerebral infarction or intracranial hemorrhage without complications or comorbidity (CC) or major complications and comorbidities (MCC), with CC and with MCC. We report the correlation between charges and reimbursement as well as charge, reimbursement and hospital stroke volumes using a Spearman correlation coefficient. We also report median charge and reimbursement data by state for pooled stroke DRGs. Results: Data were available for 5735 hospitals. The minimum, median, mean, and maximum charge data were as follows: without CC or MCC: $5392, $19976, $23593, $117831; with CC: $5223, $25151, $29492, $162923; with MCC: $9539, $40953, $48522, $234913. The minimum, median, mean, and maximum reimbursement data were as follows: without CC or MCC: $3916, $5326, $5714, $14744; with CC: $5369, $7280, $7922, $26510; with MCC: $8174, $12084, $13263, $50882. There was modest correlation between hospital charges and reimbursement (without CC or MCC ρ= 0.28; with CC ρ= 0.38, with MCC ρ= 0.46.) There was less correlation between discharge volume and charges or reimbursements (ρ =0.15 and 0.12 respectively). By state, pooled median charges ranged from $10,150 (Maryland) to $58,032 (New Jersey). Pooled median reimbursements ranged from $6,306 (Alabama) to $11,529 (Alaska). Conclusions: The variability in the amount hospitals charge for stroke admissions is enormous and currently inexplicable. Much more research is needed to understand the reason for this variation and if there is any association to the quality of care provided; otherwise, this information may have unintended consequences for patients and providers.

scholarly journals Challenges and Adverse Outcomes of Implementing Reimbursement Mechanisms Based on the Diagnosis-Related Group Classification System: A systematic review

2020 ◽  
Vol 20 (3) ◽  
pp. 260
Author(s):  
Mohsen Barouni ◽  
Leila Ahmadian ◽  
Hossein Saberi Anari ◽  
Elham Mohsenbeigi
Keyword(s):  
Systematic Review ◽  
Health Insurance ◽  
Classification System ◽  
Diagnosis Related Groups ◽  
Patient Outcome Assessment ◽  
Adverse Outcomes ◽  
Diagnosis Related Group ◽  
Third Party ◽  
Reimbursement Mechanisms ◽  
Related Group

In health insurance, a reimbursement mechanism refers to a method of third-party repayment to offset the use of medical services and equipment. This systematic review aimed to identify challenges and adverse outcomes generated by the implementation of reimbursement mechanisms based on the diagnosis-related group (DRG) classification system. All articles published between 1983 and 2017 and indexed in various databases were reviewed. Of the 1,475 articles identified, 36 were relevant and were included in the analysis. Overall, the most frequent challenges were increased costs (especially for severe diseases and specialised services), a lack of adequate supervision and technical infrastructure and the complexity of the method. Adverse outcomes included reduced length of patient stay, early patient discharge, decreased admissions, increased re-admissions and reduced services. Moreover, DRG-based reimbursement mechanisms often resulted in the referral of patients to other institutions, thus transferring costs to other sectors.Keywords: Health Insurance; Third-Party Payments; Reimbursement Mechanisms; Diagnosis-Related Groups; Quality of Health Care; Patient Outcome Assessment; Systematic Review.

scholarly journals Constructing a Novel Early Warning Algorithm for Global Budget Payments

Mathematics ◽  
2020 ◽  
Vol 8 (11) ◽  
pp. 2006
Author(s):  
Che-Wei Chang
Keyword(s):  
Health Insurance ◽  
Early Warning ◽  
Control Charts ◽  
Diagnosis Related Groups ◽  
Financial Burden ◽  
Payment System ◽  
Diagnosis Related Group ◽  
Medical Expenditure ◽  
Global Budget ◽  
Related Group

The National Health Insurance Administration of Taiwan has implemented global budget payments, the Diagnosis-Related Group (DRG) inpatient diagnosis-related group payment system, and the same-disease payment system, in order to decrease the financial burden of medical expenditure. However, the benefit system reduces the income of doctors and hospitals. This study proposed an early warning payment algorithm that applies data analytics technology to diabetes hospitalization- and treatment-related fees. A model was constructed based on the characteristics of the Exponentially Weighted Moving Average (EWMA) algorithm to develop control charts, which were first employed using the 2001–2017 health insurance statistical database released by the Department of Health Insurance (DHI). This model was used to simulate data from inpatients with diabetes, to create an early warning algorithm for diagnosis-related groups’ (DRGs’) medical payments as well as to measure its accuracy. This study will provide a reference for the formulation of payment policies by the DHI.

Frequency and Cost of Diagnosis-Related Group Outliers Among Newborns

PEDIATRICS ◽  
1987 ◽  
Vol 79 (6) ◽  
pp. 874-881
Author(s):  
S. E. Berki ◽  
Nancy B. Schneier
Keyword(s):  
Intensive Care ◽  
Length Of Stay ◽  
Neonatal Intensive Care ◽  
Diagnosis Related Groups ◽  
Diagnosis Related Group ◽  
Teaching Hospitals ◽  
Short Term ◽  
Related Group ◽  
The Mean ◽  
Mean Charge

Analysis of outliers, as defined by the Health Core Financing Administration, among 47,776 newborns discharged from 33 short-term hospitals in Maryland in 1981 shows that the three prematurity diagnosis-related groups (DRGs) (386 to 388) represented only 5.3% of all discharges of newborns, but more than one fifth of all outliers and more than three fifths of outlier days of care for newborns. The disparity in charges for outliers and inliers (not exceeding the "trim point") is even more dramatic. Newborns with "extreme immaturity" (DRG 386) and "prematurity with major problems" (DRG 387) together accounted for less than 3% of all newborn discharges but for nearly one fourth of all outlier discharges. The mean length of stay in hospitals for outliers in those two DRGs was more than 2 months. The mean charge per outlier discharge in DRG 386 was $27,061 in 1981. Nearly one third of the discharges and more than two thirds of the days of care in this DRG were for outliers. Outliers occurred up to five times more often among premature neonates than among normal newborns and occurred preponderantly in teaching hospitals, especially those with more than 400 beds. This finding may require a reevaluation of the outlier trim points and the reimbursement method for newborn DRGs to assure adequate payment to the providers of neonatal intensive care, mainly large teaching hospitals.

scholarly journals Using Electronic Medical Records and Health Claim Data to Develop a Patient Engagement Score for Patients With Multiple Chronic Conditions: An Exploratory Study

2021 ◽  
Vol 8 ◽  
pp. 237437352098148
Author(s):  
Surachat Ngorsuraches ◽  
Semhar Michael ◽  
Nabin Poudel ◽  
Gemechis Djira ◽  
Emily Griese ◽  
...  
Keyword(s):  
Electronic Medical Records ◽  
Patient Engagement ◽  
Medical Records ◽  
Chronic Conditions ◽  
Claim Data ◽  
Hospital Charges ◽  
Multiple Chronic Conditions ◽  
Health Claim ◽  
Study Objective ◽  
Health Related

The study objective was to (1) develop a statistical model that creates a novel patient engagement score (PES) from electronic medical records (EMR) and health claim data, and (2) validate this developed score using health-related outcomes and charges of patients with multiple chronic conditions (MCCs). This study used 2014-16 EMR and health claim data of patients with MCCs from Sanford Health. Patient engagement score was created based on selected patients’ engagement behaviors using Gaussian finite mixture model. The PES was validated using multiple logistic and linear regression analyses to examine the associations between the PES and health-related outcomes, and hospital charges, respectively. Patient engagement score was generated from 5095 patient records and included low, medium, and high levels of patient engagement. The PES was a significant predictor for low-density lipoprotein, emergency department visit, hemoglobin A1c, estimated glomerular filtration rate, hospitalization, and hospital charge. The PES derived from patient behaviors recorded in EMR and health claim data can potentially serve as a patient engagement measure. Further study is needed to refine and validate the newly developed score.

Analysis of Profit and Loss by Injury Severity over 20 Years at a University Level I Trauma Center

2016 ◽  
Vol 82 (7) ◽  
pp. 644-648
Author(s):  
Zachary Dietch ◽  
Jeffrey S. Young ◽  
Steven D. Young
Keyword(s):  
Trauma Center ◽  
Injury Severity ◽  
Positive Margin ◽  
Cost Data ◽  
Hospital Charges ◽  
Data Set ◽  
Average Loss ◽  
University Level ◽  
Level I ◽  
Injured Patients

We examined financial data from a University Level I Trauma Center from 1994 to 2014. We sought to investigate the hypothesis that lower injury severity correlates with increased profitability. We examined data from July 1994 to December 2014. This included hospital charges, Medicare cost data, final reimbursement, and payor source. Patients were separated into Injury Severity Score (ISS) groupings: 0 to 9, 10 to 14, 15 to 24, >24, and >14. Mean and standard deviation of mean are reported. We had complete data on 27,582 patients. Overall profit per case when subtracting costs from reimbursements was $1,932/case (total profit in unadjusted dollars = $53,475,828 or $2,673,791/year). When examined by ISS, profitability was significantly different between ISS 0 to 14 and 15 to 24, and > 24. When charge data were examined, the average loss per case was -$31,313 for the 27,582 patient data set. When using cost, and not charge data, overall trauma care had a positive margin. Severely injured patients (ISS > 14) were the most profitable, with a significantly higher profit per case than all other groupings. Only through examination of cost data can realistic determinations of trauma center profitability be made. If only charge data had been examined in this study, the overall loss from the 20-year period would have been $863,675,166 and not a profit of $53,475,828.

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