Keys to Optimizing Asset Utilization for Medical Imaging Operations
Strategically managing connected equipment in the smart-hospital era
Modern healthcare relies heavily on advanced medical equipment, particularly with the increasing prevalence of chronic illness. The global market for medical devices, which includes high-tech imaging tools such as MRIs, CTs, and PETs, is rapidly expanding. A report from Fortune Business Insights projects that this market, valued at $495.46 billion in 2022, will reach a staggering $718.92 billion by 2029.
Smart hospitals demand smart asset management
As smart hospitals emerge and medical equipment costs rise, effective management and optimization of clinical assets are becoming high operational priorities. Clinical asset utilization rates are reportedly around 40 percent, indicating room for improvement. Contributors to low utilization rates include poor machine maintenance, excessive machine downtime and unpredictability, and inefficient patient scheduling.
Hospitals, health systems, imaging centers, and other healthcare organizations can maximize clinical asset utilization by managing entire fleets of connected machines more strategically throughout their lifecycles. To do so requires going beyond basic asset management strategies—organizations must embrace cutting-edge technologies such as artificial intelligence (AI) and machine learning, including predictive and prescriptive analytics, to track and improve equipment performance and wear and tear while providing top-notch quality patient care.
Organizations can pinpoint trouble spots and plan for future maintenance by identifying key utilization patterns among individual and multiple machines. This can increase machine uptime, ensure timely patient exams, and boost revenue simultaneously while controlling costs.
- What is asset utilization?
- Why is asset utilization important in healthcare?
- How to calculate asset utilization
- Key metrics in the calculation of asset utilization in healthcare
- The most effective ways to improve asset utilization for healthcare providers
- The high cost of low medical equipment utilization
- The value of advanced analytics from multiple data sources
- The challenges that AI and machine learning solve for connected medical equipment
- Getting a 360-degree view of your facility’s asset utilization patterns
What is asset utilization?
Within the healthcare provider ecosystem, “asset utilization” generally refers to how hospitals, imaging centers, and other facilities use their medical devices and equipment. Medical devices commonly monitored for utilization include:
- Patient imaging equipment
- Laboratory and testing equipment
- Surgery equipment
- Patient monitoring equipmen
Why is asset utilization important in healthcare?
The importance of asset utilization, especially in the U.S., cannot be overstated due to the high usage, high cost of medical equipment, and consumers’ increasing demand for advanced technology. However, healthcare organizations often encounter obstacles such as insufficient capital funding and low budgets owing to increasing labor, supply, and drug costs. Consequently, it is a challenge to replace outdated medical equipment and cover maintenance expenses, which can be as high as $10,000 per month for MRI maintenance agreements.
Furthermore, hospital administrators and radiology directors are under pressure to produce a rapid return on investment (ROI) when investing in new equipment. Therefore, strategic management and proper usage of clinical assets are essential to control costs, maintain accurate inventory counts, and ensure optimal performance and maintenance throughout the equipment’s lifespan.
Asset utilization and productivity
A productive organization, department, and workforce are crucial for proper asset utilization. Take, for instance, a public hospital with a limited budget and capital spending that can only afford two MRIs (a single MRI can cost between $1 million and $3 million). The hospital must ensure the maximum output of these machines, which requires a high-functioning radiology department.
Any scheduling snafus or extended maintenance periods can result in idle machines, negatively affecting patient care and the hospital’s profitability. Hence, effective maintenance, clinical workflows, and patient scheduling are all essential to optimizing machine performance, patient throughput, and market share. Additionally, a well-functioning operation allows clinicians, radiology techs, and other staff to work more efficiently, leading to greater job satisfaction.
How to calculate asset utilization
Calculating asset utilization is straightforward: It involves determining the number of exams or procedures performed per device over a specified period. For Example, an ultrasonic imaging device may perform 17 sonograms within a 10-hour workday. An organization can optimize its capital budget expense and reduce the need to purchase additional machines when it actively calculates and manages asset utilization.
Key metrics in the calculation of asset utilization in healthcare
To truly maximize the value of your clinical assets, it’s critical to watch a few key performance indicators (KPIs). These metrics serve as vital signposts to determine whether a machine is functioning optimally and generating a healthy rate of return. Here are some of the primary KPIs to track:
First, the Utilization Rate is a critical metric that measures the number of exams conducted within a given timeframe. A major indicator of a machine’s performance, this KPI is tied closely to revenue projections.
Asset-To-Patient Ratio is another necessary metric to monitor. This KPI calculates the number of patients using a particular device, which can provide insight into a device’s overall efficiency.
Planned Downtime refers to scheduled maintenance. Advanced service analytics can significantly impact this KPI. Organizations can increase utilization rates and minimize unplanned downtime by proactively planning for required maintenance.
Unplanned Downtime is a KPI that measures the time a machine is out of service due to unexpected maintenance or scheduling issues, which can significantly impact patient care and revenue. Proactive, predictive analytics and service response times are important factors to this KPI.
Maintenance Spend covers service and maintenance costs per clinical asset. Lifecycle maintenance costs can often exceed the original capital purchase price.
Finally, the Lifecycle Cost of Equipment calculates the overall expense for a machine, including purchase costs and planned and unplanned maintenance spend. This KPI is important to track as it can help organizations determine the total cost of ownership and make informed purchasing decisions in their capital planning.
By tracking and analyzing these metrics, organizations can boost the performance of their staff and machines, improve patient care, and increase revenue.
The most effective ways to improve asset utilization for healthcare providers
In today’s healthcare landscape, where financial resiliency and high-quality patient care are paramount, equipment must operate at peak efficiency. The more efficient the equipment, the stronger the financial investment, and the less likely an organization must purchase additional clinical assets to meet patient demand.
Unlocking medical equipment ROI
Here are four strategies for enhancing asset utilization:
- Prioritize machine maintenance and functionality. Medical equipment, such as ultrasounds and CTs, must be well-maintained to produce accurate, high-quality images. Regular testing and inspections should make certain that equipment works properly and adheres to industry standards. A preventative maintenance schedule and service analytics, which alerts organizations to impending part failures, will reduce unplanned downtime and service costs. Many hospitals use a combination of in-house repairs and vendor servicing contracts to maintain equipment. At the same time, biomedical departments with specialized staff may also provide additional support.
- Monitor clinical asset performance. Refining clinical asset performance requires a clear view of how assets regularly operate across your entire fleet, regardless of manufacturer or modality. Healthcare organizations, including imaging department leaders, must be able to identify if they are meeting asset utilization goals and, if not, determine the root causes. AI, analytics, and automation can provide real-time data on everything from single-machine failures to utilization rates across entire fleets. With precise data at hand, healthcare providers can proactively manage and monitor their equipment, ensuring it operates at peak efficiency.
- Track lifecycle costs of machines. Another way to ensure equipment is producing at a top rate is by tracking lifecycle costs. By using data analytics to monitor medical equipment maintenance, repairs, and expenses at different life stages, organizations can determine if it is more cost-effective to continue investing in an aging machine or to retire it.
Keep up with technology and software requirements. It is also crucial to continually assess whether medical equipment complies with modern technology standards to maximize utilization and performance. Have a plan and set of parameters that help your organization determine when to upgrade equipment or invest in new software or hardware for better functionality.
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The high cost of low medical equipment utilization
Healthcare organizations can pay a steep price when their medical equipment fails to operate at peak performance, both in terms of their reputation and bottom line. A well-functioning radiology department is a top requisite for assuring that expensive equipment, such as the latest 3D mammography machine, runs at its best. Appropriate staffing, streamlined patient scheduling, and careful management and maintenance of clinical assets are crucial for achieving this goal. When any of these areas fail, it can trigger a domino effect of problems, including:
- Low equipment usage
- Lost machines/unnecessary purchases
- Inaccurate inventory counts
- Loss of patient referrals and volumes
- Low staff productivity
- Declining reputation and income
Unfortunately, asset utilization is not given sufficient attention for a variety of reasons:
- Lack of information: Many healthcare organizations need more tools, people resources, and analytics to track trending data on equipment capacity and utilization rates, which must be monitored daily, weekly, and monthly for optimal performance.
- Competing priorities: Patient care and other priorities, such as filling in due to staff shortages, often preoccupy hospital leaders and radiology directors, leaving them limited time to monitor equipment performance and ensure that patient appointments are on time.
- Performance challenges: Clinicians primarily focus on providing high-quality patient care and may need more training and resources to improve performance. They often don’t have access to the proper framework and data to develop a clear path to improvement.
By addressing these reasons and prioritizing asset utilization, healthcare organizations can avoid negative consequences and position themselves for success in their local markets.
The value of advanced analytics from multiple data sources
Comprehensive analytics tools that use data from multiple sources are critical to healthcare organizations looking to boost medical equipment utilization. Healthcare leaders face significant pressure from the pandemic and other events to manage their medical equipment more efficiently while operating on tight budgets. Consequently, hospital administrators, including CFOs and COOs, pay close attention to equipment data for deeper insights into machine performance to make informed investment decisions for their organizations.
Until recently, it has been challenging to integrate data from different systems because they speak other languages and use different terminology to label standard terms such as imaging exams or radiology exams. Fortunately, AI and machine learning solve this problem by pulling together structured and unstructured data from disparate sources, organizing it into a cloud-based system, and presenting it in a more straightforward, more understandable format.
Advanced service and utilization analytics can provide valuable insights by drawing on multiple data sources, including:
- Machine logs
- Scheduling systems
- Billing systems
The result is richer, more precise data providing better visibility into patient throughput, medical equipment use, and performance. For Example, analytics can reveal patient exam turnaround times for a piece of equipment or the number of exams performed during a specific time. Data analytics can also paint a powerful picture of how different care sites are performing compared to one another. Experienced operators with access to advanced utilization analytics solutions can drive a 10%+ increase in clinical asset utilization.
Having connected machine data enables hospitals to achieve the following benefits:
- Optimize machine uptime
- Effectively manage rigorous equipment maintenance guidelines
- Provide rapid technical staff training
- Effectively monitor equipment usage
- Identify potential future capital purchases
Unleashing the power of next-generation tools
Overall, machine analytics takes a massive weight off administrators’ shoulders, helping them quickly understand operational performance and make critical decisions to improve underperforming areas. By drilling down to compare throughput across different medical equipment and techs, advanced analytics can highlight discrepancies to help determine the root causes of low utilization. Organizations utilizing this approach are better positioned to identify maintenance needs, scheduling issues, patient mix, and other factors impacting performance.
The challenges that AI and machine learning solve for connected medical equipment
AI and machine learning are transforming the management of connected medical equipment. These innovative technologies now provide hospitals, imaging centers, and other facilities with real-time machine data, enabling them to make important decisions, including when to maximize available capital and operational funds to refresh devices, improve staff efficiency, and streamline patient flow.
AI analytics solutions can predict potential part failures, enabling organizations to schedule proactive maintenance for connected medical equipment. AI and machine learning are disrupting “how healthcare organizations use machine data by combining unstructured data with other data sources” to address several common challenges:
- Lack of insight into machine use and performance. AI and machine learning offer a complete view of clinical assets by enabling organizations to extract critical data from connected machines.
- Low machine utilization. Advanced analytics can monitor machine performance in real time, highlighting maintenance, staffing, patient scheduling, physician referral, and other factors contributing to low usage.
- Unplanned downtime. Predictive analytics allows radiology directors and others to proactively analyze when a machine will be down rather than only following a pre-determined maintenance schedule. For Example, if a CT tube is about to go out, the director can schedule overnight maintenance with a vendor to minimize unplanned downtime and patient impact. Predictive analysis avoids prematurely replacing a part or waiting three days for one to be delivered.
Poor productivity. Real-time analysis spotlights productivity issues before they become critical, ensuring machines are well-maintained and running maximally.
Getting a 360-degree view of your facility’s asset utilization patterns
The most successful provider organizations don’t just understand asset utilization – they live and breathe it, knowing how it impacts every aspect of their operations, from patient outcomes to revenue. They constantly analyze data points like machine uptime and error codes, exam volume, usage hours, open work orders, and customer escalations to stay on top of their game.
Technology is changing faster than ever before. And as it evolves, it’s transforming how we approach healthcare, including clinical asset utilization. Outdated or disconnected technology systems may cause you to miss key insights into equipment utilization patterns. Luckily, advancements in connected devices, machine learning, and analytics allow healthcare facilities to view their clinical assets holistically. With the correct information, healthcare providers can make strategic, data-driven decisions that take their operations to the next level.
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