Optimizing Lung Cancer Diagnostics: Insights from a Fast-Track Program in a Complex Healthcare Setting

Area of Science:

• Oncology
• Healthcare Management
• Public Health

Background:

• Lung cancer is a leading cause of mortality, with diagnostic delays worsening outcomes.
• Inefficiencies in lung cancer diagnosis persist, especially in resource-limited and geographically complex areas.
• The Valtellina region faced challenges in timely lung cancer diagnosis due to its unique characteristics.

Purpose of the Study:

• To evaluate the effectiveness of the Fast-Track Program in accelerating lung cancer diagnostics.
• To assess the impact of structured workflows on diagnostic timelines and patient experience.
• To determine the feasibility of the program in a resource-constrained setting.

Main Methods:

• A prospective observational study comparing Fast-Track pathway patients (May-August 2024) with standard care controls (January-April 2024).
• Implementation of structured diagnostic slots, a rotating Case Manager, and weekly multidisciplinary team (MDT) meetings.
• Measurement of time to definitive diagnosis, patient adherence, and patient satisfaction scores.

Main Results:

• Mean time to diagnosis reduced significantly from 42.9 days to 25.0 days (p < 0.001).
• Patient adherence improved from 71% to 92% (p < 0.05), and satisfaction scores rose from 64% to 89% (p < 0.05).
• The program showed a favorable trend towards improved efficiency and patient-centered care, though not all predefined criteria were met.

Conclusions:

• The Fast-Track Program demonstrates feasibility and scalability for streamlining lung cancer diagnostics in resource-limited settings.
• Structured diagnostic workflows can enhance efficiency and patient care in complex healthcare environments.
• Findings suggest potential applications for similar structured approaches in broader oncological and chronic disease management.