[Documentation and computer-assisted evaluation of basic data in a geriatric clinic]

Area of Science:

• Geriatric Medicine
• Healthcare Management
• Clinical Data Analysis

Background:

• Clinic patient data analysis is crucial for understanding healthcare needs and outcomes.
• A custom computer program was utilized for analyzing discharge letters.
• Understanding patient demographics and disease types informs resource allocation and treatment strategies.

Purpose of the Study:

• To analyze patient demographics, disease types, and rehabilitation success rates within a clinic setting.
• To evaluate patient outcomes across different treatment sections (acute care, rehabilitation, long-term treatment).
• To identify factors influencing discharge and homecare needs, especially in elderly patients.

Main Methods:

• Retrospective analysis of clinic discharge letters using a self-developed computer program.
• Categorization of patients by age, sex, disease type, and treatment section.
• Tracking of patient disposition: discharge home, transfer to other sections/facilities, or mortality.

Main Results:

• The average patient age was 80 years, with the 80-85 age group being the largest; females constituted two-thirds of patients.
• Acute care admissions were primarily post-surgery (two-thirds) or from accidents (one-third).
• Rehabilitation patients predominantly had surgical (orthopedic/traumatic) or neurological (stroke) conditions. Discharge rates varied significantly by section, with higher mortality in long-term care (41%).
• Elderly patients (80+) living alone frequently required homecare, unlike younger patients.

Conclusions:

• Clinic patient population is predominantly elderly females with complex medical and surgical needs.
• Discharge outcomes and mortality rates differ substantially across acute, rehabilitation, and long-term care settings.
• Age and living situation significantly impact the need for post-discharge care, highlighting the challenges in geriatric patient management.