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The future of usage reporting in the OR is in machine learning technology.

Although hospitals are spending a lot of money to utilize advanced software solutions to improve processes and optimize resources, they still losing money, a lot of money.

Why? Advanced software might specialize in data management, but they are not suited for collecting data in stressful surgical environments - And that's where the money gets lost. 

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  1. Hospital has up-to-date information only on 60% of the implants used. How? Take sewing thread for example: Though they are present in almost every procedure and can run up costs by $200 in some cases, they are not reported at all. Same situation in sterile orthopedic screws or bulk items that cannot be tagged = loss of money.
  2. Hospitals are wasting time on solving coding errors to submit reimbursement applications for insurance companies or Medicare programs. Or they must hire companies that offer this service at a fee = loss of money.
  3. The process of documenting is carried out by the medical staff, which does not specialize in supply management. Sometimes using notebooks or complicated ERP programs that slow them down, resulting in human and coding errors, missing reporting and less time for patient care = loss of money.
  4. Technology sometimes does not recognize the item in the OR. Items age, new suppliers are added, manufacturer's SKU changes, and as a result, the maintenance of the item master becomes one of the hospital's biggest problems = loss of money.
  5. FDA regulation requires high documentation compliance and consolidation of clinical and logistic information. That in case of a recall, items and patients can be located easily - an impossible task when there is no digital documentation of expiration date, production series, and other essential information in the patient's medical file. Sometimes because the hospital generates an internal catalog because it cannot rely on the different structure and uniformity of the manufacturer's barcodes = loss of money.
  6. Hospitals rely on historical DRG repositories instead of the actual items used. Times have changed, technologies have changed –there is no need to make economic forecasts based on old data = loss of money.

 

The good news is that the solution is already here..

The main goal in machine learning, using technologies like image recognition, OCR, ICR, microphones or even sensors is to handle real-world data for solving a problem when conventional computer software is insufficient. These technologies can collect, process, and manage the information optimally. Imagine if you could "snap" a photo and the system could do the rest.

 

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