What are life sciences IT solutions?
Life sciences IT solutions include technology investments – like the Internet of Things (IoT), cloud technology, automation, artificial intelligence, machine learning, analytics, platforms, digital technology, and applications. These components address industry challenges for pharmaceutical, biopharma, biotech, clinical research organizations, and medical device companies. Life science companies that invest in technology solutions are better equipped to address the demand for patient services, medical innovations, drug treatments, disease management, and clinical research. IT solutions help life sciences stay ahead of digital disruption, advance program development, and reduce costs by improving technology architectures and core operations. Life science technology solutions help scale supply chain management, reduce risk, accelerate order flow, manage regulations, and improve operations management.
IT solutions help product innovation, workforce assistance, operational efficiency, research and development, supply chain visibility, customer engagement, and the patient care journey.
For example, intelligent platforms enhance product engineering and create advanced capabilities in hardware and software tool development, helping life science companies maintain competitiveness and market visibility. Modern product design speeds up the development of drug manufacturing, design, and compliance needs. Additionally, automation helps biopharma companies improve compliance workflows and production demands by streamlining tasks, optimizing manufacturing, and reducing changeover times.
What are the IT solutions life science organizations use to accelerate innovation in clinical, product, and patient services?
The IT solutions life science organizations use to accelerate innovation in clinical, product, and patient services:
- Analytics: Life science companies are investing in advanced data analytics to manage patient health outcomes better, increase provider-patient engagement, improve compliance, and keep pace with emerging markets. Analytics can improve the efficiency of drug trials and quality control testing of new products across disciplines in life sciences. Real-time analytics collect and process data at exponential speed and can be used to benefit payer systems, research and development, health initiatives, and medical research. Data fuels AI analytics platforms, building more robust enterprise governance methods that keep data secure while allowing researchers to observe drug and trial results quickly. AI harvests data to produce hidden insights, helping clinicians and researchers understand new medical and drug treatments with improved vital indicators and data anomalies. Analytics can fuel more advanced data visuals that streamline drug discovery, enhancing product development and research funding. Patient data is organized and centralized for easy accessibility, allowing healthcare workers to assess patient medical history and apply preventative treatment methods quickly.
- Cloud solutions: Life sciences can improve drug quality and medical device innovation with cloud technology. Cloud solutions integrate data from various networks and sources, providing a unified platform across domains. Research data and device management can be processed and accessed by multiple stakeholders on a single source, improving the manufacturing process, market access, and operating cost. Cloud technology uses computing power to speed up innovation cycles and standardize processes by enabling analytics. Biotech and pharmaceutical companies can detect molecule patterns, simplify complex medical data, and improve supply chain management. Product innovation is scaled at speed through an open platform, allowing teams to access data cycles and work collaboratively on operating models. Cloud-enabled platforms support business processes by scaling IT infrastructure, allowing life science companies to make better products at a lower cost.
- Artificial intelligence and machine learning (AI/ML): AI/ML solutions are driven by algorithms that produce valuable insights from various data sets, helping life sciences make better decisions and innovate faster. Insights can improve drug development by predicting on drug applicability and safety in the marketplace. AI can help clinical researchers make discoveries faster by examining data from multiple sources like trial information, medical databases, and research abstracts on new therapies. Diagnostic screening for cancer is enhanced with advanced imaging, detecting early signs of disease that cannot be seen with traditional medical evaluation. AI/ML can also help life sciences improve the manufacturing process and supply chain bottlenecks by analyzing data on logistics, production, and disposition cycles.
IT solutions can benefit life science organizations in the following areas:
- Commercialization: Digital technology can improve workflow for commercial launches, cross-channel operations, and omnichannel marketing of new products. Automation provides performance metrics that can assess marketing and commercial strategies.
- Lab informatics: Technology, like AI/ML and IoT, can be used for data collection, analysis, and sharing. Laboratories that use automation tools can enhance business processes, increase workflows, support lab integration, and share information with research institutes like biorepositories.
- Patient engagement: Digital tools such as mobile apps and chatbots can improve patient engagement. IoT powers remote patient monitoring over an integrated network of connected devices, helping report on medication adherence.
- Leveraging real-world data: AI and advanced analytics improve data interoperability and platform scalability by ingesting data from medical reports, Electronic Health Records, claims, and population health. These data sets fuel insights from real-world data that can help support disease monitoring, clinical collaboration, health simulations, and statistical modeling.
- Clinical trials: Advanced analytics innovate the clinical value chain by providing real-time data, team collaboration, virtual trials, and risk management. AI models collect real-time data, helping identify patient cohorts, drug effects, product marketability, and clinical drug progression.