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The average length of stay in inpatient facilities of developed countries remains static or shows a moderate decline over a 20-year period. This statistic has become a critical indicator of efficiency for hospitals and the overall health system. It reduces costs per patient as the expensive inpatient costs can be shifted to cheaper alternatives such as post-acute care. Medical progress causes longer average lifespans, which results in more people seeking health care services. Health care costs accrue exponentially and put financial pressure on individuals, health systems, insurance companies, and government budgets.
The system aims to redistribute and lower the costs, particularly on a case-by-case basis, through a variety of methods such as decreasing the average length of stay. However, it must maintain a balance between lowering costs and reducing efficiency. Medical care continues to follow a trend of quality patient care as a profit-boosting approach (OECD, 2017). Reduced length of stay helps to address issues such as hospital-acquired diseases and safety risks (such as patient falls) which prolong inpatient care. Length of stay is a complex statistic that is influenced by and has an effect on many factors of hospital operation and management.
Attention is given to developing models to monitor the most viable length of stay required without decreasing quality of inpatient care. Duration of stay for patients differs based on the medical diagnosis as well as demographics. This is analyzed and compared to the possible costs that the hospital may incur. It is an attempt to standardize hospital resource distribution based on the data. In turn, it provides a fair and clear statistic on cost allocation. Many facilities have limited spots for inpatient care, especially if accommodations are required for treatment. Current data only encompasses occupancy rates and average rates. However, models able to accurately predict the length of stay of a patient will sufficiently improve hospital administration and increase patient care efficiency (Tsai et al., 2016).
References
OECD. (2017). Length of hospital stay. Web.
Tsai, P., Chen, P., Chen, Y., Song, H., Lin, H., Lin, F., & Huang, Q. (2016). Length of hospital stay prediction at the admission stage for cardiology patients using artificial neural network. Journal of Healthcare Engineering, 1-11. Web.
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