How Labs Can Use AI and ML to Improve Operational and Clinical Data Quality

As vendors race to roll out new technologies powered by artificial intelligence (AI) and machine learning (ML), professionals across industries must work to determine what opportunities these technologies pose for their businesses. 

Many clinical laboratories face this challenge after investing in business intelligence solutions that provide great insight into daily lab operations and the business as a whole. With a well-tuned analytics program in place, laboratory professionals should look for opportunities to leverage AI and ML to address needs that cannot be met by traditional analytics alone. It’s important to understand that these technologies can be applied to a diverse set of practical use cases. 

For example, ML can be particularly helpful for overcoming challenges of scale by automating manual, labor-intensive tasks, such as data cleaning and normalization. ML creates opportunities to pursue more advanced use cases, such as predictive analytics and risk modeling, which can extract new insight that may have been inaccessible through conventional data analysis. These capabilities help clinical labs further improve their operations while unlocking greater value for the providers they serve.

Enhance data management systems

Efficient data management is essential for handling the large volume of information generated. Lab data is an essential asset and an often-untapped resource that presents a holistic and objective picture of a patient’s health. However, the data within a laboratory information system (LIS) is not natively organized in a patient-centric way. 

AI/ML can help overcome this challenge by normalizing and curating customized datasets related to patient care and public health. Now, instead of simply focusing on delivering individual test results, labs can maintain a patient-centric perspective to infer therapeutic insights of greater value.

Address resource limitations

Amid staff shortages and budget cuts, lab leaders find it challenging to maintain high performance and testing accuracy. Identifying resource gaps and advocating for appropriate investments can help overcome such roadblocks and ensure smooth operations.

Using AI to predict demand for lab services solves crucial operational problems, like staffing, for lab leaders. Machine learning models that connect timekeeping with lab testing data can help project future trends. For example, if data indicates test volumes are typically higher one day of the week, but staffing levels are typically lower that day, the lab team is empowered with the insight to incentivize their team to work on that particular day to ensure turnaround times do not suffer. 

Leveraging historical data, real-time trends, and AI to forecast sample volumes and provide staffing recommendations ensures lab departments and teams are prepped to handle any kind of workload, while maintaining testing accuracy and reliability.

Foster innovation

Encouraging innovation within laboratories drives advancements in diagnostic techniques, technologies, and treatment methods. Lab data is an important input into clinically oriented AI/ML solutions, but the LIS does not generate or structure this data in a way supports these use cases. Labs must consider what they'll need to do to be efficient partners to the health systems as they pursue innovation. 

One way is to find technology partners that can help labs manage and curate the lab data to better support clients’ needs. Expertise combined with AI/ML can help health systems be proactive in solving challenges and delivering top-class patient care.