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Digital innovation, driven by artificial intelligence (AI), will undoubtedly bring the next wave of significant disruption in the healthcare industry, impacting organizations in multiple ways. AI technologies such as machine learning, virtual reality and robotics have started to find their way into several industries. Use cases from early adopters demonstrate the potential for significant improvement in profitability as well as competitiveness. Businesses need to prepare for change now or risk losing relevance in the future.
Investments in AI are exploding globally, driven by digital giants like Google, Microsoft and Apple. Seed funding and PE / VC funding have also grown rapidly, creating an exciting start-up space and a robust M&A environment.
Despite significant investments in AI, industry adoption is still significantly limited. Business leaders still don’t know how AI can help their organizations, as clear business cases have yet to be developed to justify the membership of larger groups. A McKinsey Institute study published in 2017 showed that adoption of AI generally follows the maturity of an industry’s digitization.1 The technology, telecommunications and financial services sectors are leading the adoption curve, while the healthcare sector has a low rate of adoption, due to a lack of digital maturity.
Successful adoption of AI requires a solid digital foundation. The key to success is ensuring that an appropriate data ecosystem is built and that digital transformation is successfully implemented with the required investments in people, processes and systems.
AI in healthcare
AI and other related technologies have the potential to have a significant impact on business processes, efficiency and profitability in various sectors of the healthcare industry. Although the current adoption rate is low, an analysis by the McKinsey Global Institute indicates that AI can unlock more than $ 100 billion in the pharmaceutical and medical industry, and up to $ 300 billion in health systems and services.2CB Insights reported that healthcare AI start-ups have raised US $ 4.3 billion in 576 deals since 2013, more than any other active industry.3
AI has potential uses and applications in healthcare:
Application in healthcare delivery / services
Clinical imaging and diagnostics
AI-enabled systems / applications are being developed to provide more efficient and accurate diagnostics. Systems that read scans, ECGs, etc. with higher speed and precision to enable earlier detection are already being tested and deployed. The US FDA has already approved AI-based software that can diagnose diabetic retinopathy. Similar positive results have been seen in the use of AI to spot tumors, identify early osteoporosis, and other diagnoses.
Choice of treatment
AI will help to more objectively examine treatment protocol data and associated outcomes. Large-volume analyzes of this data can help develop optimal protocols that would improve outcomes, reduce complications, minimize waste, and optimize costs. Such use of AI will also help physicians deliver more personalized care plans for individual patients.
Advanced analytics can be used to streamline functions such as admissions, equipment / asset usage, billing and collection systems, patient readmission costs, etc.
Applications for pharmaceutical and medical products
Discovery and development
There are several areas where AI has demonstrated the potential to have a significant impact on innovation. For example, AI helps evaluate and analyze volumes of published scientific papers to help bring out obscure ideas and ideas. AI and advanced analysis can be used in the drug development process by helping to assess potential active compounds in greater numbers and facilitating the selection of the most promising drug candidates for the study. Rapid analysis of large drug libraries can help eliminate weak candidates and select the best, saving considerable time and money.
Management of clinical trials
Several interesting uses are being developed in the management of clinical trials. Such applications include – matching patients to clinical trials; analyzes large data sets from clinical trials to find new and valuable information.
Manufacturing / Production operations
Manufacturing operations in the pharmaceutical industry are becoming increasingly complex, especially with increasing regulatory compliance requirements. New generation biologics, gene therapies, etc. are likely to make them more difficult. Organizations would not assimilate large amounts of data from connected IoT devices in manufacturing plants. They could then use advanced analytics and machine learning to establish performance and efficiency metrics, such as targeted batch yields or critical quality monitoring systems.
Supply chain efficiency
Advanced analytics can bring great improvements in this area. Data-driven applications can help optimize inventory management by increasing the accuracy of forecasting and planning. Analytics can help develop collaborative supplier networks, thereby improving supplier delivery performance and risk management. Visual pattern recognition technologies are helping to revolutionize the warehousing and management of physical assets. Optimization of the distribution network, product traceability and dynamic responsiveness to consumer demands are other assets to be exploited.
Sales and Marketing
With the changing technological environment and emerging competitive scenarios, pharmaceutical and medical device companies are struggling to engage meaningfully with their customers. Competitive congestion and overlapping offerings make it difficult to deliver a differentiated customer experience. Predictive analytics can help optimize the synergy between a company’s sales and marketing efforts, leading to better customer engagement and increased revenue. Using data from multiple sources could help identify patterns and generate insights for a better customer experience, including methods to prioritize and personalize engagement efforts. Salesforce deployment can be optimized for efficiency and impact.
As the healthcare industry is still in its infancy in the adoption curve of artificial intelligence and advanced analytics, it is crucial for companies to use these innovative solutions to gain an advantage. competitive. Businesses can engage early, work to organize data, and drive such initiatives that facilitate continued long-term success.
1. McKinsey Global Institute Discussion Paper June 2017 – Artificial Intelligence: The Next Digital Frontier
2. McKinsey Global Institute Discussion Paper April 2018 – Notes from the AI ââFrontier: Insights from Hundreds of Use Cases
3. CB Insights, AI Industry Series: Top AI Healthcare Trends to Watch
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