Cognitive AI: Empowering Clinical Teams with Data-Driven Insights

The following is a guest article by Steve Lazer, Global Healthcare & Life Sciences CTO at Dell Technologies.

‍The landscape of healthcare and biotechnology is changing at a rapid pace, with data taking center stage. As our ability to utilize data to demonstrate positive clinical outcomes continues to be enhanced through technology, healthcare and biotech companies are under increasing pressure to demonstrate positive clinical outcomes and patient benefits from these investments. With all the data available to us, clinicians need tools to help them wade through the river of inflowing information. This is driving demand for artificial intelligence (AI)-based solutions that can help teams drive faster, more accurate decisions, and avoid costly ramifications of missing red flags.

The primary use cases for cognitive AI in biotech include accelerating data analysis, automating document management, improving collaboration, and reducing risk of human error. Cognitive AI, along with Large Language Models (LLMs) like Chat GPT, Bard, GitHub, and CoPilot, are becoming popular in the creation of documentation; providing inferencing and clinician-like communication to help clinicians become more efficient. Even though LLMs have garnered headlines recently around their ability and potential inability to be applied in an equitable and secure fashion, it can be applied to repetitive tasks helping to minimize clinician administrative overhead time.  As a result, manufacturers of clinical and life science solutions are engaging these capabilities to improve quality. 

Traditionally, clinical laboratories were designed to treat specimens, perform tests, and generate reports. Today, these organizations are expected to perform accurate tests, generate reports quickly, and deliver reliable data to inform medical decisions. To achieve these goals, clinical teams must sift through thousands of data points. They must make accurate assessments of trends and understand the implications of their results on patient care.  

Although existing data analysis tools offer increased speed and automation, they do not offer the full benefits of cognitive AI. In a cognitive AI-enabled environment, data is ingested in near real-time and analyzed in a distributed architecture. This allows computers to make sense of data and identify patterns, while also generating insights that are presented in an easy-to-understand format. Cognitive AI tools enable users to easily query and explore data, without the need for programming. They also offer the ability to tie together heterogeneous data sources and generate automated insights.

One example of such a tool underpinning the latest in machine learning and AI is a graph database platform. While traditional relational databases have their place, their rigid structures fail to adapt easily to the complexity of unstructured data, its context and its interconnections. Graph database technology is specifically designed and optimized for discovery of interrelationships between siloed data sets – allowing for the identification of patterns and hidden connections between data points to create unique opportunities for discovery. This ability to store and analyze disparate data sets in a highly connected and structured way allows healthcare organizations to better understand patient relationships, disease patterns, and treatment outcomes. By integrating data from multiple sources and applying advanced analytics, graph data platforms can provide valuable insights that inform clinical decision-making, population health management, and healthcare research. Additionally, these platforms can help healthcare organizations comply with regulatory requirements and ensure the privacy and security of patient data.

While most of the data points created by life sciences organizations are useful for operational and strategic decision-making, most of these documents also contain data that can be used for clinical analytics. This opens up a new frontier for cognitive AI tools to play an expanded role in biotechnology and care delivery. In a cognitive AI-enabled environment, both structured and unstructured data can be ingested into the system. These tools can be used to ingest data from lab instruments, as well as from documents (such as clinical trial reports, regulatory filings, and lab test results) – even incorporating voice and vision capabilities. They can also ingest data from other data sources, such as patient medical records and Electronic Health Records (EHRs), and Imaging archives. Cognitive AI tools can perform data normalization and validation to ensure that data is consistent and accurate, limiting the impact of potential human error. This data is then analyzed to identify trends, patterns, and other insights that can be used for clinical decision-making. 

Modern clinical teams are required to collaborate more frequently, and in a more geographically dispersed fashion than in the past. To ensure that teams remain productive, it’s important to have the right tools and infrastructure in place. Cognitive AI solutions can be used to create a single source of truth that is accessible to all stakeholders. These “north stars” can be used to create real-time dashboards to monitor performance and generate alerts when thresholds are exceeded. Cognitive AI tools can be used to create a virtual knowledge map based on team member expertise and existing information. This allows teams to quickly collaborate and share information on a joint virtual canvas.

Biotech companies are under increasing pressure to demonstrate positive clinical outcomes and patient benefits. Cognitive AI is becoming an essential tool for diagnostics manufacturers and life sciences research firms to meet these new challenges. Applying AI to the mountains of data in life sciences can help companies reshape business models, streamline biopharma manufacturing, and enhance everything from cognitive molecule research and clinical trial data flow to self-healing supply chain applications and product intelligence. Ultimately, it can enable life sciences companies to be more personalized and authentic in how they engage with healthcare professionals, patients, and other stakeholders. As we continue to enhance our ability to utilize data to demonstrate positive clinical outcomes, cognitive AI is poised to play an increasingly important role in empowering clinical teams.

About Steve Lazer

Steve is the Global Healthcare and Life Sciences CTO for Dell Technologies. He brings robust Health IT competencies and management strategies for healthcare organizations ensuring successful Healthcare IT solution delivery. He drives technical strategy and solutions development for Healthcare and ISV technical relationships including joint solutions R&D, technical advisory, and technical escalation processes. Steve is part of one of the strongest healthcare practices in the technology industry with a heritage of more than 30 years building solutions around the globe with clinical ISV partners and providing essential technology infrastructure to hospitals of all sizes.

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