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There will be greater investment in supply chain resilience in the future, as well as the use of real-time analytics, big data, and linked technologies to make better decisions and improve supply networks.
Fremont, CA: The Covid-19 outbreak a year ago transformed the world and pushed many organizations to show resilience and agility. Collaboration accelerated across the global health and life sciences ecosystem, resulting in novel medicines and medical breakthroughs. Analytics became increasingly crucial at every level – from patients to cost to quality to outcomes – to create insights, make educated decisions, and realize the value of digital transformation. Here are three analytics trends in healthcare:
Health and life science companies will invest more in digital research and engagement platforms.
While this business has been slower to adopt digital technology than retail or banking, many health and life sciences companies have been forced into digital-first scenarios due to the pandemic's threats. As a result, health systems are now utilizing digital transformation to increase efficiency across clinical and operational decisions, ranging from earlier detection of infectious disease to claim processing automation.
Life sciences organizations are also using data to improve interaction strategies with health care providers and keep clinical trials decentralized. Throughout the rest of 2021, this change is expected to accelerate.
Supply chain management will be scrutinized as the industry balances readiness and cost.
We will see greater investment in supply chain resilience in the future
and the use of real-time analytics, big data, and linked technologies to make better decisions and improve supply networks. These "intelligent supply networks" will be able to predict not only routine changes but also large shocks such as a pandemic or a geopolitical shift.
Manufacturing capacity must be visible and horizontally connected with supply networks. To manage inventory, generate signals across end-to-end supply chains, provide real-time location analysis, and automate conventional activities, artificial intelligence (AI) will be deployed.
Digital transformation and AI will allow patient-centricity at all touchpoints.
For clinicians and health plans alike, becoming more member-centric, personalizing the patient journey, and using precision medicine has been a burning platform. Patients must be at the core of digital transformation activities, covering all locations where a patient interacts with a health system.
Data management built on interoperable platforms with explainable algorithms and models, including open source, is also critical for digital transformation. Before the next crisis strikes, businesses must have a well-established analytics infrastructure in place to operationalize AI and use predictive models in clinical applications. To know where to go for answers, you'll need established forecasting and predictive modeling skills. This approach is aided by advanced analytic solutions, which make models more repeatable and regulated.
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