Inventory data has become a vital element of efficient healthcare supply chain management.
This blog highlights the implications of poor data quality in healthcare and looks at how to improve the analytics that drive decision making.
- Getting the basics right in medical inventory tracking
- The importance of raw data in healthcare inventory management
- Data quality goals for inventory management in healthcare
- The cost of poor data quality in healthcare
- Accurate data collection tools for the healthcare setting, including data capture in OR.
Getting the basics right
We sometimes get carried away with all the clever stuff that data can do, but when we review an inventory management system, there’s one vital principal that should never be forgotten – the value of robust raw data.
Raw data is the data that is collected from the source. In hospital supply chain management this may be during enrollment, storage or at the point of care.
Healthcare providers need a reliable system in place to track medical inventory in order to understand the items that are in stock, those that have been consumed, and those that have been lost or wasted.
Providers require real-time visibility to make sound decisions around expiry management, procurement, billing and more. In addition, supply chain data is used to support forecasting for budgets and care delivery.
Without full and accurate data, providers will have impaired vision which will lead to ill-informed decisions.
It all comes down to this:
The importance of raw data in inventory management
There’s one very clear message:
If there are data gaps at the point of collection then the integrity of everything that follows is compromised, risking the validity of all subsequent analysis.
We keep celebrating processed data, but the real hero is raw data.
Raw data. is also known as primary data and is any data that has not yet been processed.
➡️ It feeds information systems.
➡️ It spots trends.
➡️ It informs forecasts.
➡️ It generates insights.
➡️ It supports decision making.
Raw data drives everything, so ensuring complete and correct data collection from the clinical setting is vital for accurate supply chain management.
Data quality goals for inventory management in healthcare
Since data is the ears and eyes of the hospital, it needs to meet key data quality goals by being:
ACCURATE: Data collection results in the full and accurate capture of all vital item information.
COMPLETE: Full data is collected, without any omissions.
VALIDATED: All data is validated before being saved in the system.
SHARABLE: Interoperability is vital to prevent error-prone entries into duplicate systems.
TIMELY: A real-time picture should be achieved.
All the data collection systems in the hospital need to meet these basic data quality requirements in order to provide the high level of data required for optimized decision-making.
The cost of poor data quality in healthcare
So, how prevalent is the issue of poor data quality in healthcare?
Research by Gartner provides figures across all industry types, but we can get a feel for the scale of the issue from the figures.
Gartner’s research found that poor data quality can cost businesses around $9.7-14.2 million each year and that nearly 60% of organizations don’t measure the annual financial cost of poor-quality data. They comment, “Failing to measure this impact results in reactive responses to data quality issues, missed business growth opportunities, increased risks and lower ROI.”
According to a 2019 study by the American Hospital Association (AHA), 66% of hospitals suffer from data quality issues. They conclude that hospitals need to take steps to address data quality issues.
While high quality data can have a really positive impact on healthcare management, it is also true to say that poor quality data can have negative implications for healthcare decision-making.
Healthcare management don’t need data, they need quality data.
Accurate data collection tools for the healthcare setting
It’s clear that many healthcare leaders are using incomplete supply chain data when making serious decisions with significant cost implications.
The problem is that inventory tracking systems in storerooms, core areas and at the point of care, are often outdated and inefficient, failing to achieve full data integrity. Operating rooms and procedural areas often struggle with achieving data integrity due to inefficient systems that can’t cope with the complexity of documenting supply utilization at the point of care.
At IDENTI, our core strength is the collection of full and accurate raw data, as we know that this lies at the heart of smart decision making that’s based on the real picture.
Our data collection tools are custom designed to meet the challenges of the clinical setting and achieve 100% data capture.
The full data captured is then processed using artificial intelligence (AI), machine learning and predictive analytics to convert supply chain data into vital business intelligence.
Healthcare organizations planning to adopt AI technology need to ensure they start by planting strong foundations in the form of robust data capture tools that generate full and precise data.
Contact us to find out how custom data collection tools for the healthcare setting can ensure your management see the full picture.