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Optimizing Technician-Client Matching for Mental Health Services
Introduction: Enhancing Care Coordination for Autism Services
A leading mental health provider specializing in autism patient care sought to address serious inefficiencies in their technician-client assignment workflows. Manual mapping processes, reliant on human judgment and static scheduling tools, resulted in suboptimal technician utilization, long wait times, and reduced client satisfaction. Recognizing these operational hurdles, the provider partnered with JBS to implement a data-driven, AI-powered solution that optimized field service management and elevated the quality of care delivered.
Problem Statement: Manual Mapping Bottlenecks
⚠️ Complex Matching Criteria
Assigning technicians based on skills, availability, preferences, and client compatibility was an inefficient manual process.
Assigning technicians based on skills, availability, preferences, and client compatibility was an inefficient manual process.
📍 Geographic Inefficiencies
Drive times and technician proximity to clients were not considered systematically, increasing travel time and dormancy.
Drive times and technician proximity to clients were not considered systematically, increasing travel time and dormancy.
📅 Scheduling Blind Spots
There was no mechanism to recommend the best-fit client for an available technician, often leaving skilled staff idle while clients waited.
There was no mechanism to recommend the best-fit client for an available technician, often leaving skilled staff idle while clients waited.
These inefficiencies hindered care delivery, increased costs, and limited scalability.
Impact: Lower Utilization & Reduced Satisfaction
The challenges manifested in:
15%+ Technician Dormancy
Valuable clinician hours were underutilized due to scheduling mismatches.
Valuable clinician hours were underutilized due to scheduling mismatches.
Lower Customer Satisfaction
Clients experienced delayed service, impacting trust and retention.
Clients experienced delayed service, impacting trust and retention.
Administrative Burden
High manual overhead in scheduling consumed back-office resources.
High manual overhead in scheduling consumed back-office resources.
Solution: Predictive, Location-Aware Assignment Engine
JBS developed a predictive analytics-driven system leveraging location-based services and real-time data for technician assignment:
Haversine Distance for Proximity:
Used the Haversine formula to calculate technician-client distances and prioritize nearby resources.
Used the Haversine formula to calculate technician-client distances and prioritize nearby resources.
Google Maps API for Dynamic Scheduling:
Incorporated live travel estimates and historical schedule data for dynamic, geography-aware scheduling.
Incorporated live travel estimates and historical schedule data for dynamic, geography-aware scheduling.
AI-Driven Recommendations:
Delivered real-time, optimal technician-client match suggestions to Field Service Managers (FSMs).
Delivered real-time, optimal technician-client match suggestions to Field Service Managers (FSMs).
Business Benefits: Operational Transformation
25% Boost in Technician Utilization:
Improved alignment between demand and technician availability.
Improved alignment between demand and technician availability.
20% Increase in Customer Satisfaction:
Reduced wait times and better-matched technician visits.
Reduced wait times and better-matched technician visits.
Lower Operational Overhead:
Automation decreased time spent on scheduling.
Automation decreased time spent on scheduling.
Scalable Framework:
The AI-powered model supports growth without increasing staffing complexity.
The AI-powered model supports growth without increasing staffing complexity.
Technology Stack
Python
SQL
Azure
Power BI
Google Maps API
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