Consortium overview, objectives and terms for participation
PartsSource Executive Sponsors:
- Phil Settimi, PartsSource CEO
- Mike Zamis, PartsSource Chief Product Officer
PartsSource Program Leadership/Advisory Team:
- Nelson Bowers Sr. Director Performance Analytics
- David Brennan SVP Product Strategy
Overview:
The Clinical Services Improvement Consortium (CSIC) is a collaborative initiative formed to optimize the management of medical equipment across hospitals and healthcare facilities. By leveraging shared data insights, the consortium seeks to enhance the operational efficiency, safety, and effectiveness of medical equipment, improving both patient care and hospital resource utilization. The consortium will enable the secure exchange of hospital data, particularly related to the usage, maintenance, and performance of medical equipment, to create new insights that support a more streamlined, data-driven approach to equipment management.
Background:
In hospitals, medical equipment plays a critical role in ensuring the delivery of quality healthcare. However, challenges around equipment availability, maintenance, repair, and lifecycle management often result in inefficiencies, equipment downtime, and suboptimal patient outcomes.
Hospital systems generate a wealth of data—ranging from usage statistics to work orders and performance diagnostics—but this data is often siloed, underutilized, or difficult to share between institutions. By pooling hospital data, stakeholders in the healthcare industry can develop innovative solutions to address these challenges, leading to better-managed medical equipment and enhanced patient care.
The CSIC was established to bridge these gaps, fostering a data-sharing framework where leading HTM teams can work together in a secure, compliant, and transparent manner to improve how medical equipment is managed and maintained.
Engagement Summary:
To activate participation, members will be asked to share the following datasets:
- Asset lists – complete inventory of all medical equipment in use at the provider
- Work orders – associated maintenance activity as recorded in provider’s CMMS
- Parts consumed – include listing of parts used associated with each of the completed work orders
- Additional datasets as requested – as the scope of the consortium research agenda expands there may be additional datasets requested.
As data is collected, PartsSource will normalize the data to ensure consistency across customers and perform analyses to derive the desired outcomes and insights. As a result of this work, Consortium members will gain access to the following:
- Problem-Solution Database: Direct access to aggregated, de-identified CMMS data contributed by CSIC participants to empower your own research.
- Asset Performance Benchmarks and Analytics: Evidence-based peer benchmarks and insights to measure your individual performance and drive strategic improvements to power more reliable clinical operations.
- Shared Community Learnings: Discuss best practices for implementing data-driven processes with clinical engineering leaders at other leading providers.
Additional access details will be provided when available.
Objectives:
The main objectives of the Healthcare Data Sharing Consortium for Medical Equipment Management are:
Provide manufacturers with data-driven feedback to inform the design of next-generation medical devices.
1. Improve Equipment Utilization:
- Leverage real-time data to optimize equipment scheduling, usage, and maintenance to reduce downtime and enhance patient care.
- Share best practices and strategies for efficient equipment allocation, particularly in high-demand environments.
2. Enhance Predictive Maintenance:
- Utilize data analytics to predict equipment failures before they occur, enabling proactive maintenance and minimizing unscheduled downtime.
- Establish systems for continuous monitoring of equipment health, identifying issues early to prevent service disruptions.
3. Streamline Lifecycle Management:
- Analyze data to better understand the lifecycle of medical devices, including repair frequency, lifespan, and optimal replacement timelines.
- Develop a framework for the timely and cost-effective decommissioning of outdated equipment.
4. Facilitate Cross-Institutional Learning:
- Promote collaboration between hospitals and healthcare providers to share insights, solutions, and strategies for more effective medical equipment management.
- Establish data-sharing protocols that allow for secure and standardized exchange of information across institutions.
5. Drive Continuous Improvement in Patient Care:
- Ensure that medical equipment management practices support and enhance the overall quality of patient care.
- Monitor and evaluate the impact of improved equipment management on clinical outcomes and patient safety.
6. Support Innovation in Medical Technology:
- Identify trends in medical equipment performance that can guide future innovation and development.
- Provide manufacturers with data-driven feedback to inform the design of next-generation medical devices.
Secure Data Handling:
PartsSource is committed to handling your data in a secure manner to ensure compliance with data protection regulations, mitigates risks associated with Protected Health Information (PHI), and maintains data integrity.
Audit logs are maintained for all data transformations and access events.
1. Data Collection:
- Participant CMMS data is collected through jointly agreed to intake processes
- Specific export requirements are covered in Data Sharing Guidelines & Templates that will be shared accordingly based on Participant’s selected CMMS.
Data is received via secure channels and stored in a protected environment before processing.
2. Data Deidentification
- Prior to loading for analysis and Consortium access, all company identifiable information is removed or replaced with anonymized identifiers.
3. Risk Assessment for PHI:
- Participant will make every effort to ensure PHI/PII has been removed
- Data is assessed using automated and manual checks to determine if any remaining elements could be classified as PHI.
If PHI is detected, associated records will be removed from dataset
4. Data Normalization:
- Data fields are mapped to standard formats to ensure consistency.
- Structured and unstructured data elements are converted into a unified schema.
- Missing or inconsistent data points are addressed using predefined validation and enrichment techniques.
- Normalized data is reviewed to confirm integrity before being loaded for analysis.
5. Data Loading for Analysis
- Deidentified and normalized data is transferred to the analysis environment via secure data pipelines.
- Audit logs are maintained for all data transformations and access events.