Strategic advice to leverage new technologies
Technology is at the heart of nearly every enterprise, enabling new business models and strategies, and serving as the catalyst to industry convergence. Leveraging the right technology can improve business outcomes, providing intelligence and insights that help you make more informed and accurate decisions. From finding patterns in data through data science, to curating relevant insights with data analytics, to the predictive abilities and innumerable applications of AI, to solving challenging business problems with ML, NLP, and knowledge graphs, technology has brought decision-making to a more intelligent level. Keep pace with the technology trends, opportunities, applications, and real-world use cases that will move your organization closer to its transformation and business goals.
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This issue explores how to make needed changes happen by examining three systems change topics. First, how can we use the linkages between environmental and economic systems to change market structure and, consequently, how actors compete? Second, how do we redefine waste to alter how market actors impact the natural environment through producing and disposing of materials? Third, what system changes can we accomplish through innovation or by using technologies like IT, artificial intelligence (AI), and blockchain to address unsustainable practices?
Northwestern Medicine has developed a very innovative NLP application — integrated within the hospital EHR system — designed to solve a practical and pressing healthcare problem: missed follow-ups associated with incidental findings from diagnostic imaging. This Advisor explores the significance of this innovative artificial intelligence application.
In this Advisor, we talk about two of the near-term challenges in the pharma data processing space: data standards and data quality management.
Healthcare and life sciences companies are applying machine learning, Internet of Things, big data analytics, wearables, blockchain, and other advanced technologies across various processes and operations. Simply put, the degree of innovation taking place within these industries utilizing these technologies is stunning — even to the point where we are seeing new business models that could potentially threaten existing industries.
Curt Hall focuses on the benefits of integrating unstructured data into electronic health records. He describes how biometric data, lifestyle data, and general healthcare information can come together to help clinicians, researchers, and health/wellness companies better understand the effect of patient health behaviors and lifestyles on potential approaches and treatments. More personalized medical treatments, improved health trend identification, and lower healthcare costs are all possible outcomes.
Five Arthur D. Little Partners and Principals predict that big data will move the healthcare industry’s digital transformation forward, providing better admission rate estimation, more effective chronic-care treatments, and a reduction in medication-error rates. Their article includes detailed descriptions of eight drivers of data-driven healthcare: technology trends, data quality and availability, data security, an enabling ecosystem, public-private partnerships, patient participation, the need for better change management, and the development of employees with data analysis skills.
Cutter Expert San Murugesan looks at why health data is so valuable to cybercriminals, why criminals are often successful in their attacks, and the cost of these breaches. He outlines seven technologies/approaches that can help: authentication and access control, encryption, data anonymization, mobile device security, monitoring and auditing, artificial intelligence, and zero trust. Murugesan concludes with a list of processes that should always be in place to secure health data.
Jacek Chmiel examines current challenges in the data processing space. He outlines the issues stemming from multiple health data standards, the need for more developed data quality processes, and the industry’s perhaps unnecessary aversion to data streaming. Chmiel offers hope in the form of federated analytics and federated learning to allow more collaborative data processing between countries and proposes increased use of automation. He also advocates for employing publicly and commercially available data sets and looks at how natural language processing, machine learning, and quantum computing are the future of data-driven pharma.