Success story of
a Healthcare client

Revolutionizing healthcare: unleashing the power of AI for enhanced operations and personalized care

About our Customer

Our esteemed customer is a pioneering entity in the healthcare industry, known for its commitment to delivering cutting-edge medical services and personalized care to patients. However, like many healthcare organizations, they faced numerous challenges that impeded their full potential in providing optimized patient care and operational efficiency.


Before implementing the digital solution with integrated AI, the healthcare client faced the following challenges:

  • Manual and time-consuming manual processes
  • Inefficient supply chain management
  • Limited visibility and access to patient records
  • Workforce scheduling and resource allocation issues
  • Reactive maintenance of equipment
These challenges prompted the healthcare client to seek a solution that could automate and optimize processes, improve inventory management, enhance access to patient records, optimize workforce scheduling, and enable proactive maintenance of equipment.


The primary objectives of implementing the digital solution with integrated AI for our healthcare client were:

  1. Enable personalised patient care through data-driven insights and treatment recommendations.
  2. Optimise workforce scheduling to minimise staffing gaps and enhance resource allocation.
  3. Implement predictive maintenance and equipment optimization for enhanced equipment performance and cost savings.
  4. Improve supply chain management by optimizing inventory levels, reducing waste, and minimizing stockouts.

The Solution

KoreCent designed and implemented a comprehensive digital solution with integrated AI technology. The solution comprised the following components:

Personalized Patient care

The integrated AI technology empowered our healthcare client to deliver personalized patient care, tailoring treatment plans and services to individual patients based on their unique needs, preferences, and medical history.

i. Data-driven treatment recommendations

The solution utilized AI algorithms to analyze large volumes of patient data, including medical records, test results, genetic information, and treatment outcomes. By mining this data, the solution identified patterns, correlations, and insights that healthcare professionals could leverage to make more informed treatment decisions. the AI algorithms provided data-driven treatment recommendations and options, taking into account factors such as the patient's medical history, genetic markers, and response to previous treatments.

ii. Precision medicine

The AI technology facilitated precision medicine approaches by identifying patient-specific characteristics that may influence treatment effectiveness. By considering genetic factors, biomarkers, and other personalized data, the solution helped healthcare professionals develop tailored treatment plans and interventions. This personalized approach maximized treatment efficacy while minimizing potential side effects or adverse reactions.

iii. Risk stratification and early intervention

The solution enabled risk stratification by predicting patient-specific risks and complications based on their medical history, lifestyle factors, and other relevant data. By identifying high-risk individuals, healthcare professionals could implement proactive interventions and monitoring strategies to mitigate risks and detect potential health issues at an early stage. This approach supported preventive care and early intervention, leading to improved patient outcomes and reduced healthcare costs.

iv. Patient engagement and empowerment

The AI-powered solution empowered patients to actively participate in their own care through enhanced engagement and education. It provided personalized health information, educational resources, and real-time feedback on treatment progress. Patients could access their medical records, track their health metrics, and communicate with healthcare providers through user-friendly portals or mobile applications. This increased patient engagement, improved adherence to treatment plans, and fostered a collaborative relationship between patients and healthcare professionals.

AI-driven workforce scheduling

The AI-driven workforce scheduling component implemented by Korecent revolutionized the way our healthcare client managed their workforce. By harnessing the power of AI and predictive analytics, the solution provided advanced capabilities for optimizing workforce scheduling and resource allocation.

i. Dynamic scheduling

Unlike traditional manual scheduling methods, the AI-driven solution dynamically adjusted schedules in real-time based on changing conditions. It considered factors such as patient demand, staff availability, skillsets, and workload distribution to optimize shift assignments and ensure appropriate coverage.

ii. Staff preferences and constraints

The AI-driven solution also took into account individual staff preferences and constraints when generating schedules. It considered factors such as preferred working hours, time-off requests, and regulatory requirements, ensuring that the schedules aligned with both staff needs and operational requirements.

iii. Skill-based resource allocation

Recognizing the importance of assigning the right staff with the appropriate skillset, the solution incorporated skill-based resource allocation. It matched staff members' qualifications, certifications, and experience with the specific requirements of each shift or task, optimizing resource utilization and improving the quality of care provided.

iv. Real-time updates and notifications

To keep all stakeholders informed and ensure smooth communication, the AI-driven solution provided real-time updates and notifications. Staff members received automated notifications regarding their assigned shifts, any schedule changes, or requests for shift coverage. This streamlined communication minimized confusion and improved overall coordination.

v. Performance monitoring and optimization

The AI-driven workforce scheduling component also facilitated performance monitoring and optimization. It provided analytics and reports on staff performance metrics, such as productivity, attendance, and adherence to schedule. This data-driven insight enabled managers to identify areas for improvement and make data-informed decisions to enhance workforce productivity and efficiency.

Predictive maintenance and equipment optimization

The integrated AI technology also facilitated predictive maintenance and equipment optimization, ensuring the healthcare organization's equipment and medical devices were operating at peak efficiency.

i. Real-time data monitoring

The solution continuously monitored real-time data from medical equipment and devices, collecting information on performance metrics such as temperature, pressure, and usage patterns. This data was analyzed by AI algorithms to identify anomalies, detect potential issues, and predict maintenance requirements before equipment failures occurred.

ii. Proactive maintenance scheduling

Based on the insights gathered from the data analysis, the solution generated proactive maintenance schedules. It predicted when specific equipment or devices would require maintenance, enabling healthcare staff to schedule repairs or servicing during planned downtime, minimizing disruptions to patient care.

iii. Optimal resource utilization

The AI algorithms analyzed equipment utilization patterns to identify underutilized or overutilized assets. This information allowed the healthcare organization to optimize resource allocation, ensuring that equipment was efficiently distributed across different departments and facilities. By identifying redundant or underutilized assets, the organization could make informed decisions about equipment replacement or redistribution.

iv. Equipment performance analytics

The solution provided comprehensive analytics on equipment performance, including metrics such as uptime, downtime, and mean time between failures (MTBF). These insights allowed healthcare administrators and technicians to monitor the performance of equipment, identify trends, and make data-driven decisions on equipment maintenance, upgrades, or replacements.

Smart inventory control

The integrated AI technology revolutionized inventory management for our healthcare client, enabling them to optimize inventory levels, improve accuracy, and streamline inventory control processes.

i. Real-time inventory monitoring

The solution provided real-time monitoring of inventory levels and stock movements. By integrating with various data sources such as point-of-sale systems, electronic health records, and supply chain databases, the AI algorithms continuously tracked inventory quantities and locations, ensuring up-to-date visibility and accuracy.

ii. Demand forecasting and inventory optimization

utilizing advanced machine learning algorithms, the solution analyzed historical data, including patient flow, seasonal trends, and supply chain information, to accurately forecast future demand. By leveraging these insights, our healthcare client could optimize inventory levels, reducing excess stock and minimizing the risk of stockouts. The AI algorithms automatically adjusted inventory levels based on demand fluctuations, ensuring optimal stock levels at all times.

iii. Automated reordering and Supply Chain Integration

The smart inventory management solution automated the reordering process by integrating with suppliers and supply chain systems. when inventory levels reached predetermined thresholds, the system generated automated purchase orders or replenishment requests. this streamlined the procurement process, reducing manual efforts and minimizing the risk of human error in inventory replenishment.

The Achievements

i. Enabled personalized patient care through data-driven insights and treatment recommendations, leading to improved patient outcomes and increased satisfaction.

ii. Optimized workforce scheduling, minimizing staffing gaps and enhancing resource allocation, resulting in improved operational efficiency and reduced costs.

iii. Implemented predictive maintenance and equipment optimization, enhancing equipment performance, reducing downtime by 20%, and generating cost savings.

iv. Improved supply chain management by optimizing inventory levels, reducing waste by 40%, and minimizing stockouts by 15%, resulting in streamlined operations and cost savings.

v. Solution implemented minimised time spent on routine, administrative tasks, which was taking 70 % of a healthcare practitioner’s time

The healthcare client experienced tangible benefits in terms of cost savings, operational efficiency, and improved patient outcomes, solidifying their position as a leader in providing advanced and personalized healthcare services.

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