[This guest column was written by Beyond Clean Advisory Group member Brandon Todd, MDiv, ThM, CRCST, CHL, CIS, CER, Sterile Processing Manager at Norton Healthcare in Louisville, KY]
“Data is the new oil!”¹ In 2006 when English mathematician Clive Humbry declared that data had usurped oil as the world's most valuable commodity, the hot pink version of the Motorola RAZR was fresh on the market and Facebook had just granted access to users outside of the Harvard bubble. Mark Zuckerburg, like Humbry, saw the value of data and was revolutionary in how he monetized it. But what is data? Data is simply a collection of facts. This collection can include numbers, pictures, videos, words, measurements, observations, and more. It is estimated that the totality of this data will be valued at $745 Billion by 2030.² But like oil, data is useless in its raw form. It must be refined, processed, and interpreted to be turned into something useful; its value lies only in its potential.³
Data analytics is the process of turning that raw data into real value. It is the process of examining, transforming, and interpreting data to uncover meaningful insights, patterns, and trends. This process will provide accurate information to improve decision-making, problem-solving, and optimizing processes.
Data analytics typically involves five stages:
Data Collection: Gathering relevant data from various sources.
Data Cleaning and Preparation: Cleaning and organizing the collected data to remove inconsistencies, errors, and missing values.
Data Analysis: Exploring the data to gain an initial understanding of its characteristics, identifying patterns, correlations, and potential outliers. Applying statistical techniques and other analytical methods to extract insights.
Data Visualization: Presenting the analyzed data in the form of charts, graphs, dashboards, or other visual representations to facilitate understanding and effectively communicate findings.
Data-Driven Decision: Application of the analysis, providing recommendations, incorporate feedback, and implement process improvements
Applying Data Analytics to Sterile Processing
Data analytics is commonly associated with commercial enterprises. However, in sterile processing, where meticulous attention to detail and adherence to rigorous standards are crucial, the strategic use of data analytics can be transformative. By applying the principles of data analytics, sterile processing professionals can uncover valuable insights, identify areas for process improvement, and enhance patient safety. In the remainder of this article, we will explore a few areas where data analysis can benefit sterile processing experts. I will also introduce you to some tools that will aid your analysis.
1. Process Efficiency:
Data analytics can help identify bottlenecks, inefficiencies, and areas of improvement within the sterile processing workflow. By analyzing data related to instrument turnaround times, equipment utilization rates, and resource allocation, SPD leaders can pinpoint areas where process streamlining can lead to significant time and cost savings. For example, through data analysis, it may be revealed that certain instruments require more frequent maintenance, prompting proactive adjustments to minimize downtime and optimize resource allocation.
2. Quality Assurance:
Data analysis plays a critical role in monitoring the quality of inspection when assembling trays. By systematically analyzing OR complaints and QA audits, SPD leaders can identify trends and uncover gaps in their process. Through data analysis, patterns may emerge that highlight areas additional education may be needed for staff members. By utilizing data analysis techniques, sterile processing leaders can also enhance accountability, ensuring adherence to best practices, and ultimately optimize the quality of patient outcomes.
3. Equipment Performance:
Data analytics allows sterile processing professionals to monitor and assess the performance of sterilization equipment. By analyzing data on cycle completion rates, maintenance records, and equipment downtime, leaders can identify patterns of equipment malfunction or deterioration. This information empowers proactive maintenance planning, minimizing equipment downtime, and reducing the risk of instrument contamination.
4. Compliance and Documentation:
Data analytics can aid in maintaining regulatory compliance and robust documentation practices. By analyzing data related to instrument tracking, decontamination records, and sterilization validation, SPD practitioners can ensure comprehensive and accurate documentation. This assists in meeting regulatory requirements, facilitating audits, and providing a traceable history of instrument processing, thereby ensuring patient safety and mitigating potential legal risks.
Tools of Data Analysis
Data analytics is a sprawling and complex field with a lot of specialization. While most SPD leaders will not have the interest or knowledge to do advanced data analytics, below are a few recommended tools to become familiar with if you want to take your data analysis to the next level.
Excel:
Excel will be your best friend to collect and analyze your data. Excel is more than just a spreadsheet to organize data. It has some powerful features that can revolutionize how you analyze your data. I recommend everyone become familiar with some of the advanced features. It will make your life easier and your data more compelling.
Tracking Software:
If you are lucky enough to have a tracking software you will have a plethora of resources at your fingertips. There are many wonderful softwares on the market that have integrated sophisticated data analytics into a user-friendly tool. I promise you, these tools are far more capable than you realize. Take the time to understand how the system works in order to get the most out of the product. Set up a meeting with your software account manager to go over all the bells and whistles of the program. Share information that you want and they will be able to teach you how to better utilize the platform.
Programing Languages:
This will be far more than most SPD leaders care to learn but having an introductory knowledge of programming logic can be useful to get the most out of your data analysis. Every application or software you use is built with code. To get started I recommend looking into Structured Query Language (SQL), Python, Javascript, and R. Having a cursory knowledge of these building blocks will allow you to see past the user interface and better understand how your programs work and how to improve them.
Data Visualization Tools:
Excel, Word, and Powerpoint offer very useful data visualization tools that will help you make your data more compelling to your stakeholder. Creating attractive charts can be a game changer when getting approval for your department's needs. Through data visualization you paint a picture that persuades C-suites to invest in your needs. Tableau and Google Data Studio are more advanced tools to get the most out of your data visualization.
Conclusion:
Data analytics has the potential to revolutionize your sterile processing department by providing valuable insights into process efficiency, quality assurance, equipment performance, compliance, and so much more. By harnessing the power of data, sterile processing professionals can drive continuous improvement, enhance patient safety, and optimize resource utilization. Embracing data analytics as an integral part of sterile processing practices enables leaders to make informed decisions, streamline processes, and ensure the highest standards of instrument decontamination and sterilization.
Comment below some data that you have used to impact positive change in your departments? What tools are you using to succeed?
¹Miller, P. (2013, August 23). Tech giants are sitting on a goldmine of data. The Guardian. Retrieved from https://www.theguardian.com/technology/2013/aug/23/tech-giants-data
²Fortune Business Insights, "Big Data Analytics Market Size, Share | Growth Statistics [2030]" (March 8, 2023). Retrieved from https://www.fortunebusinessinsights.com/big-data-analytics-market-106179
³Griffiths, P. (2022, December 12). Data Isn't 'the New Oil' - It's Way More Valuable Than That. The Drum. Retrieved from https://www.thedrum.com/opinion/2022/12/12/data-isn-t-the-new-oil-it-s-way-more-valuable
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