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Every enterprise is challenged to use organizational data effectively and apply intelligent analytics to execute their business processes optimally. This U.S.-based healthcare company provides revenue cycle management solutions designed and developed to engage patients, as well as assist physicians and hospitals and improve the latter’s financial results.
Over a period of time, the organization had generated thousands of reports, aggregates and metrics compiled in multiple formats and stored in diversified locations with varied entitlements. It needed an automated way to discover insights, search relevant reports and visually represent trends so team leaders could take the next best action. The company also wanted a way to field requests for all relevant metrics, measures and dimensions, and it wanted those details in real time.
We developed an intelligent system capable of “conversing” with users and providing not only specific information but also relevant synopses from underlying analytics in the moment. Using Microsoft’s Azure AI platform, Cognizant built a smart business operational assistant called RESOLV that answers business analytical queries in real time using natural language processing.
With it, stakeholders gain a better understanding of business processes using the most relevant metrics, generated by the latest insights from on-premises or cloud-based enterprise data—including information from CRM software, precompiled reports and statements. RESOLV learns and understands acronyms, and it provides natural language narratives of visual charts. The tool also supports smart integration with multiple communication channels in a variety of languages.
The virtual assistant we implemented enables 24/7 interaction in multiple languages through enterprise instant messaging applications such as Skype for Business and Microsoft Teams. It auto-generates reports, saving 88% of manual efforts.
The tool quickly responds to queries on patient responsibility, coding and billing. Artificial intelligence analyzes claim rejection reasons, physician productivity, collection performance, modality analysis, coding profiles and back-office operations, as well as payer and payee transactions for deeper insights.
in manual effort spent searching for reports and creating narratives
decision-making based on real-time insights
in operational processing with sustained information accuracy
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