March 5, 2019/Gil Blustein/Artificial Intelligence

Machine learning is easily one of the most heavily discussed buzzwords within life sciences in the past few years. Defined as a process where large amounts of data and algorithms give a machine the ability to learn how to perform tasks and identify patterns, it often gets a bad rap. Although there are clear benefits to implementing machine learning capabilities in the pharmaceutical industry, there is still apprehension towards this technology.

The biggest misconceptions about machine learning are that it will replace humans and only exists within a black box. In reality, many people may be unfamiliar with what problems machine learning can actually solve for. If you’re interested in implementing a machine learning model within your commercial life sciences strategy, you may be able to do so yourself, without your technical team needing to sift through results and decipher them for you. In fact, you just need to understand the business problem your models are trying to solve and leverage tools to make the model’s output more accessible and business readable.

Here are three key features a good machine learning interface should have in order to make a model’s output more accessible to a wider variety of end users. By ensuring your interface is equipped with these features, you can better understand how the output of these models can assist in achieving your goals.

A pharma machine learning model you can see and understand

Traditional machine learning was typically operated by data scientists or engineers working to serve the business. Business users were often disconnected from using machine learning, relying on technical teams to understand the models and make the necessary changes.

Machine learning technology usage has evolved over the years, and with it, the desire to embed it within business and decision-making processes. Field teams and end users can now rely on existing tools to help them decipher what’s working and what’s not. Delivered in a visually compelling and easily explainable interface, rather than a technical plot or graph that is difficult to decipher, users can easily translate the output of the model to determine what is most impactful. This empowers business users to maintain control over the process while removing the need to have technical teams involved in the business decision.

Outputs you can modify or constrain

A clear-cut model’s output provides insights into what the model is targeted to achieve, allowing transparency for the user and eliminating the black-box experience. In order to allow the business user to gain real control, the interface must provide the ability to simulate the output with user input constraints. This allows the end user to simulate multiple situations and direct the model to consider specific rules. The generated insights are crucial for the user to understand how an event or desired action can impact the outcome, and based on it, make a decision on how to act or what to apply in the business process.

There is a common misconception that implementing machine learning technology or artificial intelligence into your workflows and strategies eliminates the need for human interaction. However, projects that see more success incorporate a blend of both automatic and manual approaches when integrating the output of the machine learning model. Technology is great, but it’s nothing if your team doesn’t trust it. The importance of human input allows the business user to take part in shaping the model’s outcome, allowing them to work alongside the system to enhance confidence in the technology.

Insights you can set in motion automatically

Machine learning curates insights and makes them immediately available, allowing changes to be implemented instantly. The necessary modifications to achieve higher engagement, assist in team collaboration, or maximize brand strategy are possible by implementing a machine learning solution that utilizes the right datasets.

With machine learning, users are enabled to resolve current problems otherwise difficult to solve. Historically, machine learning was treated academically due to difficulties in application and execution. Change management and process management have always been a tediously manual process, which machine learning can now help improve. With the right technology, we can create the model, run it, and gather insights to inform decisions on how to best implement changes. Today’s technological advancements allow us to not just gain insights but make them immediately actionable in the workflow and incorporated it into the decision support process.

Machine learning is still an evolving technology and adopters will need to continue to prove its sophistication and value. However, the future for machine learning in life sciences is bright. Over time, this technology will become more widely adopted, eventually solidifying its place as an integral part of our business lives to improve our outcomes, efficiency, and effectiveness.

Hear more from top life sciences companies who share how machine learning is impacting commercial processes in the industry.