[Applying the clustering technique for characterising maintenance outsourcing].

Area of Science:

• Biomedical Engineering
• Operations Research
• Health Services Management

Context:

• Health institutions rely on external companies for medical equipment maintenance.
• Evaluating the performance of these service providers is crucial for operational efficiency and patient care.
• Traditional evaluation methods may not capture the nuances of service delivery performance.

Purpose:

• To apply clustering techniques for characterizing companies that provide maintenance services to health institutions.
• To identify distinct groups of service providers based on key performance indicators.
• To assess the effectiveness of clustering in identifying competitive maintenance suppliers.

Summary:

• A study analyzed 264 medical devices across seven pilot areas using clustering techniques with 26 variables, including response time (RT), operation duration (OD), and turnaround time (TAT).
• Four clusters of service providers were identified: two high-performing clusters (56% of providers) with average TAT under 2.56 days, one medium-performing cluster (38%) with an average TAT of 9.79 days, and one low-performing cluster (6%) with an average TAT of 101 days.
• The average biomedical equipment obsolescence value was 0.78.

Impact:

• The clustering methodology successfully characterized medical equipment maintenance service providers.
• The technique proved effective in distinguishing between high, medium, and low-performing suppliers based on TAT and other metrics.
• This approach can assist healthcare facilities in making informed decisions when selecting maintenance service partners, potentially improving equipment availability and reducing costs.