Diagnosis-related group (DRG) is a systematic program developed in the United States to classify patients according to medical diagnosis for billing rates in hospitals. The program categorizes patients with similar conditions under a particular class to ensure the cost of care and treatment range at the same rate in most healthcare facilities for reimbursement (Dennis, 2019). The program classifies diagnoses into groups with specific conditions requiring similar treatment procedures and care and rates them due to the resources and costs incurred. The medical facilities use (DRG) to estimate the amount to receive for patient treatment based on the condition diagnosed. The program ensures fixed reimbursement to all hospitals for the treatment of diseases.
DRG is important for the medical and billing services in the nation for patients and facilities. Fixed cost for treatment and patient care enables hospitals to utilize resources appropriated since the program focuses on reimbursing a similar amount for a particular diagnosis. DRG implementation has improved the quality of care that involves cost-saving initiatives in healthcare since the fixed amount limits the misuse of essential resources (Chen, 2023). Hospitals exercise the necessary test that leads to certainty in examining and diagnosing the conditions. The program enhances equality and quality patient care since hospitals receive a fixed amount for all patients regardless of economic class. DRG helps the patient to budget for treatment after the diagnosis since the cost is accessible and fixed in the facilities providing the needed services.
Also, the implementation of diagnosis-related groups enables hospitals to compile comprehensive data to align with other facilities since the program relies on the data provided for analysis and development of the fixed cost (Baek, 2018). Therefore, DRG has controlled the health sector in terms of quality and cost of treatment. The treatment cost is determined through analysis of the data that accounts for the requirements of treatment and payment of services that range to accommodate patients depending on their location. The hospitals also provide better care to attract more clients since most facilities have similar costs. However, the program challenges that specialized in offering care since the cost of maintenance relies on patients, which makes them reluctant to offer services to patients.
DRG hospital admission cases depend on various factors based on the hospital and the patient. The hospital’s location and specialization in offering services determine the possibility of admission. The severity of the patient’s condition, age, gender, and proportion of the income of the patient determine the facility for admission (Dennis, 2019). The top hospital admissions under diagnosis-related admission include; heart attack cases that affect the patient in different scenarios, severe sepsis that causes malfunction of body organs due to extremely low blood pressure, and respiratory complications. Psychoses, major joint replacement, esophagitis, and pneumonic conditions also are cases of admission that are covered in the DRG program. Further, the system covers kidney and urinary tract conditions that may result in severe complications.
The average length of stay for identified top 10 hospital admissions by DRG varies depending on the condition and care required, which ranges from less than 30 days. The diagnosis and treatment determine the period of admission in terms of the severity and status of the patient. Patients should be admitted to avoid infections and access hospital care that caters to improvement and recovery processes (Chen, 2023). However, the DRG program makes hospitals hasten the discharge in order to cut costs through the resources required during the admission period. The average length of stay for the admissions by DRG predicts the number of days stays through data analysis that enhances decision-making.
Healthcare managers can reduce the average length of stay for inpatients by forming collaboration teams to assess the data about the patients and coordinate the programs of care. The teams should include medical professionals that help in the development of innovative means of better care that reduce the risk chance of lengthening the stay in the hospital for cases (Baek, 2018). The hospital staff should be educated on better performance to enhance efficiency, facilitate productivity, and minimize hospitalization days. Healthcare managers can introduce scanning programs for early detection of the conditions, which becomes easy to treat and reduces the admission period. Creating awareness of the conditions is another way to help the patients to seek medical care before the severe stages.
Technological advances should also be applied to manage the length of stay of inpatients in hospitals. Telemedicine can be used to monitor patients in various aspects of healthcare, such that they make consultations at their homes, and the doctors can monitor the patient’s progress through the technique (Dennis, 2019). Healthcare managers can partner with caregivers of the patients to reduce their stay in hospitals by giving instructions on the care and management of conditions after discharge of the patients. Healthcare managers reduce the hospitalization period by reviewing cases programmed to longer stay than necessary with DRG. The reviews are validated through analysis of various hospital data on specific data.
References
Baek, H., Cho, M., Kim, S., Hwang, H., Song, M., & Yoo, S. (2018). Analysis of length of hospital stay using electronic health records: A statistical and data mining approach. PloS one, 13(4), e0195901. https://doi.org/10.1371/journal.pone.0195901.
Chen, Y. J., Zhang, X. Y., Yan, J. Q., Xue-Tang, Qian, M. C., & Ying, X. H. (2023). Impact of Diagnosis-Related Groups on Inpatient Quality of Health Care: A Systematic Review and Meta-Analysis. INQUIRY: The Journal of Health Care Organization, Provision, and Financing, 60, 00469580231167011.
Dennis, M., Zmudzki, F., Burns, B., Scott, S., Gattas, D., Reynolds, C., … & Forrest, P. (2019). Cost-effectiveness and quality of life analysis of extracorporeal cardiopulmonary resuscitation (ECPR) for refractory cardiac arrest. Resuscitation, 139, 49-56.
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