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Our latest report highlights six life sciences technology trends that will significantly impact how life sciences companies use technology to drive growth and increase efficiency.
Shifts in the behavioral patterns of patients and healthcare professionals, combined with emerging technologies, have created new opportunities for collaboration and engagement for life sciences companies.
Forward-thinking technology leaders in the life sciences industry will prioritize and take action with these pharma information technology trends in mind while continuing to focus on cutting costs and improving efficiency.
Learn more about these healthcare technology trends or view the infographic to get a quick insight.
Converging data architectures
Industrialized data services
Social-driven information technology (IT)
Platform-as-a-Service (PaaS)-enabled agility
Orchestrated analytical security
Today, life sciences companies face numerous challenges including:
Pricing pressures caused by healthcare reforms and austerity measures
Increased regulatory barriers and decrease in research and development (R&D) productivity
Increased competition, and challenges in bringing new drugs and other products to market
Patent expiries and generics
Companies are responding to these challenges by focusing on operational efficiency and growth opportunities in emerging markets, pursuing innovation through collaboration with industry and academic partners, and revolutionizing the traditional sales and marketing and R&D operating models.
But to stay ahead, life sciences companies must keep up with the emerging life sciences technology trends—that will give them a strategic advantage.
Accenture sees six life sciences technology trends majorly influencing the life sciences industry in the next three to five years. These include:
Context-based services: Driving the next wave of pharma digital services by combining real-time signals from the physical world with location data, online activities, social media and other contextual inputs. For example, Accenture teamed up with the Alzheimer’s Association to build a business model for Comfort Zone, the first comprehensive location management system designed specifically for Alzheimer patients and their caregivers.
Converging data architectures: Rebalancing the data architecture portfolio and developing pharma analytics capabilities required to support new business models such as health outcome analytics and personalized medicine.
Industrialized data services: Sharing data to make it more valuable by managing it differently. For example, in R&D, establishing data services enables the use of clinical-trial data in trial simulations, which can yield findings at lower cost and with lower risk; in sales and marketing, physician data provided as a service may be used to simplify compliance reporting.
Social-driven information technology (IT): Using social media in pharma to foster stronger collaboration and forge links that result in innovation within a life sciences company. According to a recent Accenture study, more than two-thirds of consumers in the United States seek medical advice via the Internet and social media. Marketing teams could share early feedback on products with the R&D team.
Platform-as-a-Service (PaaS)-enabled agility: Shifting the emphasis from cost-cutting to pharma innovation and supporting the rapid evolution of processes that need continuous change. For example, with life sciences companies’ increasing need to focus capital spending in growth areas, the cloud provides an ideal opportunity to switch capital-intensive IT to a more variable operational cost model.
Orchestrated analytical security: Preparing a second line of defense—data platforms—to mitigate the damage caused by attacks that threaten businesses.
Accenture recommends that companies take the following steps to keep up with pharma information technology trends:
Prioritize the life sciences technology trends: Determine which trends will have the biggest impact on your organization, based on your business drivers, current geographic and economic factors, and strategy.
Define an information strategy: Identify the most critical information that drives the highest value across the business. Choose a strategy that helps prioritize the data services important to the business and establish a road map for implementing the data services.
Bolster data services and management capabilities: Identify “data owners” in the business and work with them to understand business processes that can leverage data in the platform. Companies must think in terms of new skills required and build better data architecture skills.
Embed use of social technologies across the organization: Many life sciences companies have invested in technology but adoption is patchy within the enterprise and with partners. IT leaders should have a road map for social collaboration to increase adoption throughout the organization.
Define a cloud strategy: Cloud providers are rapidly developing vertical offerings to meet the specific needs of the life sciences industry. Companies’ IT leaders should enable cloud adoption and position themselves as an enabler or integrator of internally and externally provided services.
Change the security paradigm: Change the security mindset from “perimeter-based security” to “orchestrated security processes.” It calls for a process-specific and data-centric view of enterprise activities—moving from monitoring “who has access to which assets” to monitoring the “trends of unauthorized access attempts to specific assets.”
October 5, 2012
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