Transforming Performance Reporting Through the Power of AI
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New possibilities in performance reporting are emerging due to artificial intelligence (AI), and among its compelling applications are predictive analytics and natural language processing (NLP).
The education technology company DeepLearning.AI describes NLP as a discipline that enables machines to understand, interpret, and generate human language. NLP can also analyze unstructured data from diverse sources. On the other hand, predictive analytics, according to IBM, uses historical data, statistical modeling, data mining techniques, and machine learning to make predictions about future outcomes.
Case Study: AI-Powered Performance Reporting in Healthcare
United Healthcare Services’ (UHS) utilization of Nuance’s medical dictation solution Dragon Medical One and Cerner Document Quality Review (DQR) with Nuance’s Computer-Assisted Physician Documentation™(CAPD) serves as an example of how predictive analytics and NLP can be used to advance performance reporting.
In its case study about UHS, Nuance stated that Dragon Medical One allows doctors to verbally record patient updates, current illness history, and treatment plans directly into their electronic health record (EHR) from almost any location. Using software like Microsoft Power BI, Dragon Medical One can also provide detailed insights into performance metrics at various levels. The reports include productivity forecasts, dictation quality data, and industry-wide comparisons.
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Nuance pointed out the growing need for efficient documentation in healthcare as doctors are now being evaluated on quality metrics, which are publicly reported to the government. The software company also explained that the scores of physicians and hospitals are determined based on the accuracy of the documentation of patient conditions and the care provided. “Physicians, however, don’t always give themselves credit for the real medical complexity of the patient because of the extra time it takes to fully document it,” the report states.
In light of this, Nuance emphasized that CAPD aims to strengthen human intelligence by providing automatic suggestions to the doctor during patient care, but only when there is an alternate diagnosis or additional medical data that needs to be taken into account. This fully integrated system also allows UHS to analyze patient interactions in real time through the use of NLP.
Results of UHS’ initiatives show that there was a 12% increase in the case mix index (CMI) when physicians agreed with the CAPD clarifications and updated patient documentation. The healthcare AI system also improved the identification of “extreme” cases of disease severity by 36% and mortality risk by 24%. In addition, they recorded a 69% reduction in transcription costs year over year, resulting in $3 million in savings.
Next Steps
Companies planning to use AI in performance reporting can start by identifying areas in their operations where unstructured data is prevalent and manual processes are time-consuming. Next, it is important to develop an AI strategy that aligns with the company’s objectives.
Moreover, organizations should consider forming partnerships with AI solution providers due to their specialized expertise, experience, and ability to provide customized solutions more quickly and cost-effectively than developing in-house capabilities from scratch. Lastly, companies should invest in training their staff to work alongside AI technologies to cultivate a culture of innovation and continuous improvement.
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While integrating AI into performance reporting is promising, it requires alignment with organizational objectives and flexibility from stakeholders. Click here if you’re interested in more practical applications of AI in strategy and performance management.
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Editor’s Note: This article was written with the help of Francesco Colamarino, a former Management Consultant at The KPI Institute.
Tags: AI, artificial intelligence, Healthcare performance, Natural Language Processing, NLP, Predictive Analytics, United Healthcare Services
