Gone are the days when people used to walk into hospitals only when they fell ill. Today's 'smart' customers have health information available at their fingertips. They are looking at expert advice and proactive care from the health industry.
Life sciences companies are trying to keep pace with the customer. They are moving from treatment to preventive scenarios. But are they doing enough? How can they use the vital patient information available to them to the best of their advantage to manage patients' health outcomes? Just like the other industries, it is time that the life sciences companies adopt data analytics and leverage the insights strategically to their advantage.
Early detection of patterns and the strategic intent to get to real-world results is the key for effective business strategies. Most decision-makers have felt the need to respond to the unprecedented change in the life sciences world. This change has been compounded by developments like transitioning sales and marketing models, greater collaboration among regulators across the globe, evolving physician-patient dynamics and the all-important growth avenue of emerging markets. Life sciences companies are moving from treatment to preventive scenarios and management of the patient's health outcomes.
The benefits of analytics in life sciences are manifested in significant areas such as early detection of prescription and treatment patterns, strategizing the intent of the patient to real world results and most importantly achieving the operational excellence to drive through the intellectual journey of patient centricity.
The pharma world needs to transform today's health system to reduce healthcare costs, improve patient outcomes and enable access to health information. This requires that organizations transform from being traditional 'pharma players' to 'health players'. The smallest change in one area has a cascading effect through the entire health system. Therefore organizations must embrace the potential of signal, detection and prediction enabled by technology.
Though there has been some adoption of analytics in the life sciences sector, there still are a lot of gaps to the plugged. There is definitely a need for better tools and processes to get the industry players closer to understanding their customer needs. Losing out on analytics driven insights is affecting the real world effectiveness of their business strategies and translating in to heavy losses in certain cases. It is time that the life sciences sector internalizes the insights generated from analytics into its basic operating model and track execution against strategy.