Data analytics is critical to supply chain management. 60% of hospital supply chain leaders prioritize implementing data analytics. In healthcare, data analytics fall into three categories:
- Descriptive analytics
- Predictive analytics
- Prescriptive analytics
Analysis improves decision-making by recognizing patterns in data. Descriptive analytics simply describes patterns. Predictive analytics predicts future outcomes from those patterns.
Prescriptive analytics recommends a course of action.
Hospital supply chains are working to improve the accuracy and utility of these analytics. This improvement work accelerates supply chain optimization in three key modes.
1. Next-Gen Hospital Supply Chain Analytics Systems, Software Tools
As supply chain management moves forward, accurate analytics becomes more vital than ever. Accuracy improves with increased investment in new inventory management software, systems, and tools.
Some of these tools fall under the umbrella of AI-enhanced inventory control. Others are streamlined systems for reporting or aggregating data. And a few fall into a third category.
These are inventory management tools integrated with broader enterprise resource planning systems.
AI-enhanced inventory control is expansive enough to require its segment. To read about it, scroll down to "Improved Prediction With Automation, Machine Learning."
Big Data, Made Useful
Big data is a collection of extremely large data sets, typically updated in real-time. Hospital supply chains benefit from data aggregation systems. The best of these systems make many facets of inventory data readily visible, including:
- Supply utilization
- Price
- Physician preference
- Product lifecycle
Hospital supply chain leaders are increasingly adopting data aggregation models like CISOM. CISOM integrates inventory data via AIDC capture. And, it uses information from electronic health records (EHRs).
As a result, CISOM offers two benefits:
- Increased inventory management efficiency
- A sharp reduction in medical errors
Both of these benefits lead to considerable financial gains for hospitals. CISOM meets the new GS1 Standard in Healthcare.
GS1 Standard, AIDC
GS1 is a global standard that ensures medical product traceability. The standard encourages medical product management through Automatic Identification and Data Capture.
AIDC streamlines inventory management and improves the accuracy of the data set. At the same time, GS1 standardizes naming conventions. This improves communication among different hospitals and manufacturers.
The nomenclature is both "nurse-friendly," in that it's straightforward and internationally consistent.
AIDC typically uses RFID-tag technology to automate data capture. Hospitals can choose from a range of RFID-oriented inventory systems. Some of the most useful are highly visual, like the 2-Bin Kanban system.
ERP Interoperability
Supply-chain management is increasingly focused on integrating systems with different uses.
Next-gen inventory management systems are compatible with enterprise resource planning (ERP) software. ERP software gives leaders a birdseye view into all core management system processes, including:
- Cash flow
- Production
- Human resources
- Manufacturing
The Kanban system is interoperable with both ERP software and CISOM.
CISOM is one system that manages medical supply chain data widely. Its scope runs from manufacturing to the ultra-granular level of point-of-care.
Another supply chain management tactic is to use big data to validate mergers and acquisitions. Vertical and horizontal integration likewise improves hospital supply chain analytics.
2. Hospital Supply Chains: Improved Collaboration, Resilience With Integration
The GS1 standards drive improvement in communication and collaboration. They encourage the adoption of AIDC systems. And, they also drive improvements in communication and collaboration.
Horizontal and vertical integration are additional methods to improve hospital supply chain analytics.
Vertical integration is a function of mergers and acquisitions. One 2017 study demonstrated vertical integrations' utility in healthcare. A healthcare group increases its control over manufacturing and distribution. Thus, it's empowered to develop a useful, continuous flow of data.
The more integrated the chain becomes, the more intuitively useful the data is. Useful data enables better decisions. This, in turn, increases supply chain resilience.
Horizontal integration likewise improves efficiency and cuts costs.73% of hospitals see reductions in treatment costs, per patient, per day. That statistic reflects the value of horizontal integration solely among healthcare providers.
This integration is easier with advanced, automated reporting and communication systems. Integrated systems offer hospitals and suppliers:
- Coordinated visibility of product availability
- Product location tracking
- Stocking profile information
- Product lifecycle data and near-expiration warnings
- Streamlined facilitation of timely supplies transfers
Collaborative analytics is improved further with data cleansing. Data cleansing removes inaccurate or non-standard data from datasets. In supply chains, routine data cleaning must be supported by systems architecture. Ideal architecture is typically cloud-based.
Data cleansing increases query and retrieval speeds from the database. It's flexible and cultivates a visible classification process. And, cleansing also improves the accuracy of Machine Learning predictive models.
Clean, coordinated data bridges the discrepancies between vendors and hospitals' value analyses.
3. Improved Prediction With Automation, Machine Learning
Finally, hospital supply chains are improving analytics with automation and machine learning. AI-driven solutions can predict medical demand (patient volume) more accurately.
These systems can infer the demand for distinct hospital supplies. This, in turn, completes the predictive picture throughout the supply chain.
One factor improving prediction is the advances in supply chain IT systems, as they're integrated with EHR. This integration can empower novel treatment risk assessments. New systems compatible with the CISOM model can factor in:
- Physician preferences
- Estimated lead times
- Preferential pricing
This factoring increases the accuracy of predictive algorithms. Machine-learning systems can also evaluate the accuracy of their predictions after the fact. This evaluation subsequently improves the machines' analytics methodologies.
Machine learning systems can work with continually updated data sets. A hospital can automate manual work with asset-scanning tools. This ensures that the system works with the most accurate data available.
AI-enhanced inventory control also enables superior contingency planning. Hospital supply chains develop contingency plans per CDC recommendations. Yet, a plan is only as functional as the data is accurate.
Recently, innovators have adapted machine learning systems to model labor shortage risks. These models predict which hospital staff are most at risk for burnout. This can inform interventions that improve the entire supply chain.
Eliminate Shortages, Elevate Data Accuracy
At BlueBin, we know how critical it is to optimize hospital supply chains. With BlueQ Analytics and Inventory Intelligence, you can get the supplies you need, when you need them.
BlueQ orders see a 98% fill rate, on average. And, BlueQ Analytics and SmartScan have 100% ERP compatibility—guaranteed.
Are you ready for the next generation of supply chain technology? Contact us, and discover the right supply chain solutions for you.
