Assessment of the Financial Implications of The Ghana-Diagnosis Related Grouping (G-DRG) Payment for Inpatient Service Delivery at Greater Accra Regional Hospital, Ghana

Abstract

Introduction: Ghana introduced National Health Insurance in 2004 as means of achieving Universal Health Coverage. The Ghana-Diagnosis Related Groupings (G-DRG) has been one of the payment mechanisms used to reimburse providers for services and procedures. However, anecdotal evidence suggests that the amount reimbursed for services or G-DRG tariffs may be lower than the market prices for those same, creating a financial gap for services rendered by providers. Objective: This study assessed the financial implications of the Ghana-Diagnosis Related Grouping (G-DRG) payment system on service delivery at Greater Accra Regional Hospital. Methods: The design was a longitudinal and descriptive cross-sectional study from the provider perspective using mixed (Quantitative and Qualitative) approach. The 2021 annual performance Results: The study established a statistically significant linear relationship between G-DRG price differentials and a reduced average length of hospital stay for medical conditions (p-value, <.01). There was a cumulative financial gain of 23% on the utilization cost of the top ten inpatient medical conditions over a five year period. Providers also perceived G-DRG tariff causes non-satisfaction of clients and encourages co-payment practices. Conclusion:The National Health Insurance Authority’s G-DRG payment system has varying financial implications such as net financial gain on services rendered, an associated reduction in average length of hospital stay, perceived non-satisfaction of clients and co-payment practices for healthcare provision at Greater Accra Regional Hospital. report of the Greater Accra Regional Hospital was used to purposefully identify the top hundred (100) frequently reported inpatient admissions. Institutional data on G-DRG tariffs issued by the National Health Insurance Authority and the approved hospital charges for managing the sampled medical conditions were extracted and analysed for difference in cost. The top ten conditions from the sample were used to determine the burden of the cost difference on the facility in a five-year trend analysis by comparing price differences of respective years. The qualitative approach utilized purposive sampling method to identify fifteen (15) Key Informants and interviewed on the G-DRG implementation challenges at the facility. Data was analysed using Microsoft Excel 365 and Stata software version 16. Linear regression was used to establish a relationship between G-DRG price difference and average length of stay for medical conditions. In-depth interview data was analysed using NviVo software after categorization of recorded responses under themes.