What’s inside:

If your organization is embracing AI to optimize the healthcare supply chain, then don’t forget to check the supporting systems are in place to make AI an effective management tool.

This blog looks at:

  • IT priorities for AI success
  • The foundations needed to support accurate AI data insights
  • Data capture at the point of care
  • Return on investment for fortifying the foundations
  • Image recognition – the gamechanger for POU data integrity

Everyone’s talking about AI and analytics – but you need to get the basics right first, and that means full and accurate data collection.

A recent TechTarget article highlighted the vital role of data quality when incorporating AI into work processes. For healthcare leadership looking to implement AI into their supply chain workflows, there are some crucial points to consider.

IT priorities for AI success

TechTarget undertook research of IT decision-makers in May 2023, looking at the path organizations are taking to achieve data-driven empowerment.

The survey found that a major priority for many is the implementation of data quality tools that can optimize the value of insights.

Data quality directly correlates to actionable, trusted data from analytics and AI processes.”

The survey stresses the importance of data collection and processing, if meaningful analytics are to be achieved:

At the heart of every analytical endeavor and AI-driven application lies data. The quality of this data profoundly impacts the outcomes of the processes it fuels. Data quality encompasses a range of factors, including accuracy, completeness, consistency, reliability and timeliness. When data quality is compromised, the accuracy of insights and predictions derived from it is at risk, leading to erroneous conclusions and misguided decisions.

Getting the foundations right for AI in healthcare

Data builds a picture for healthcare supply chain management. It gives visibility of the here and now, as well as provides enhanced predictions.

But if there are data integrity issues that compromise the completeness or accuracy of data, then the foundations of the entire process will be weak. If this happens then the data produced will be distorted and management will be misinformed. When distorted data ‘insights’ mis-steer management, their decisions will be flawed, and the implications can be costly.

While so much attention is focused on AI, and the insights and analytics that can be produced, smart leaders are fortifying their foundations, by strengthening their data collection capabilities. Informed managers understand that in order to grow, they first need to bolster the tools used to capture data, so that they feed through every vital drop of data.

Data capture tools at the point of care

For the healthcare supply chain to flow efficiency there needs to be end-to-end tracking in place. Medical devices and implants, in particular, require accurate tracking. These are high value items, and implant tracking makes good business sense, but is also a regulatory and audit requirement.

The point of care is commonly the weakest link in the chain when it comes to implant tracking, and data capture challenges are common:


Point of Care data capture challenge Examples of point-of-care system issues
Non-stock items: The wide variety of products used in surgery. Regular reliance on just-in-time deliveries and bill-only items – that are not preloaded in the system.
Stock items: May not be routinely recorded – many require additional admin. Reasons include barcode readability issues and updated barcodes on implant packaging that are not reflected in the Item Master.
System limitations Barcode scanners and data-entry are labor-intensive, time-consuming and often error-prone.
Data silos Some providers have to undertake duplicate documentation as their systems are not interoperable.


With OR and procedure rooms being the largest revenue point for healthcare organizations, and with medical supplies being the second largest expense – it pays dividends to get point of care systems for data capture working, so that data quality is high.

Return on investment for POU data capture

Point of use surgical documentation has got a bad reputation!

Traditional methods make it time consuming, frustrating and inefficient, often relying on post-surgery checks and audits to enhance completeness and accuracy.

That’s a lot of time and effort by management, back-office staff, nurses and physicians, just to get clinical documentation reflecting reality.

Aside from the costs relating to compensating for inefficient point of care data capture, the impact goes far beyond surgery.

Point-of-care data flows through the organization and supports:

  • Optimized RCM and medical billing
  • Streamlined, reduced-cost, accurate inventory management
  • Enhanced patient safety, improved compliance, reduced litigation
  • Increase in ability for nurses to focus on patients, not products.
  • Improved OR performance in terms of patient outcomes, cost-savings and revenue.


AI is the future of clinical documentation – supported by data quality

So, we know that AI is extremely beneficial for hospital supply chain management.

We agree that having strong foundations in place supports accurate data insights.

We understand that point of care data collection has many limitations that require workarounds to fix.

We know that data integrity at the point of care is crucial for healthcare management and improved margins.

So, the only thing left to do is to identify a robust data collection tool that is suited to the complexities of the surgical setting.


Snap&Go point of care data capture tool using image recognition and AI technology.
Snap&Go point of care data capture tool using image recognition and AI technology.


Image recognition – the POU gamechanger

Manual data entry has failed.

Barcode scanning is inefficient and inadequate.

But image recognition, like a knight in shining armor, is here!

Image recognition sensors in the OR and procedure room setting are simplifying and speeding up product usage documentation.

The perioperative nurse now just needs to hold the product package under the scanner and the packet is digitally recorded, with all relevant data captured in the system.

And for the nurse, that’s it. Job done.

Snap&Go is a groundbreaking image recognition tool used to capture every item used in surgery in just 3 seconds.

Once digital data has been captured the system identifies the item using a global product database – breaking the reliance on the hospital’s Item Master, which continuously struggles to remain up to date.

Once identified all item data including UDI, expiry date and batch details are stored in the EHR, ERP and MMIS. Charge capture is also automated and accurate.

Accurate, timely metrics, reports and data insights are produced based on full and complete data, for optimized supply chain management.

New technology is driving through data quality at the point of care – ensuring that healthcare management get the full picture and make informed decisions based on full and accurate data.


To find out how to fortify your OR and procedure room data collection, let’s talk!

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About the author

Alon is IDENTI’s VP Research and Development Director. Alon has 25 years’ experience leading software teams to develop complex technology products to support healthcare organizations to make better decisions. Hundreds of hospitals, medical device manufacturers and international logistics companies across the globe are using IDENTI’s technology to make better enterprise decisions.
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