Winter planning. Seasonal cycles.

Area of Science:

• Healthcare Management
• Predictive Analytics
• Public Health

Background:

• Effective hospital resource management is crucial for patient care and operational efficiency.
• Accurate forecasting of patient flow, including emergency admissions, is a persistent challenge in healthcare systems.
• Understanding factors influencing hospital bed demand is essential for strategic planning.

Purpose of the Study:

• To develop and validate a predictive model for forecasting emergency hospital admissions and bed requirements.
• To analyze the impact of environmental factors (weather), epidemiological events (flu epidemics), and demand fluctuations on hospital capacity.
• To investigate hospital discharge patterns, particularly weekend discharge trends, and their implications for patient care quality.

Main Methods:

• Utilizing historical admission data and external variables such as weather patterns and flu surveillance data.
• Developing a time-series forecasting model incorporating regression analysis and machine learning algorithms.
• Analyzing hospital discharge data to identify trends and correlations with day of the week and patient acuity.

Main Results:

• The developed model demonstrates considerable accuracy in forecasting emergency admissions and bed needs up to one year in advance.
• Preliminary analysis indicates that weather conditions and flu epidemics significantly influence demand surges.
• A notable pattern of increased hospital discharges observed towards the weekend warrants further investigation regarding care continuity.

Conclusions:

• Predictive modeling offers a viable solution for accurate long-term forecasting of hospital resource requirements.
• Integrating diverse data sources, including weather and public health information, enhances forecasting precision.
• Weekend discharge practices require careful consideration to ensure appropriate patient care and prevent adverse outcomes.