AI to Enhance Customer Experience in Pharma: Accelerating the Commercial Side of the Industry

AI to create precision medicine

Uber venturing into driverless cars, Amazon’s suggestion engine, or AirBNB’s valuation engine, all of them are illustrations of using AI to improve customer experience. These companies have been able to gauge customer preferences and deliver recommendations with minimal customer information. The pharmaceutical industry can do the same with the help of AI to provide a streamlined, more personalized and targeted experience to its customers. 

“AI also plays a huge role in Amazon’s

recommendation engine, which generates

35% of the company’s revenue.” 

• Forbes, July 16, 2018

Companies such as Amazon or Netflix presents you recommendations based on your previous orders or series selections. Sometimes, these recommendations are spot on, other times they are just not. Pharma industry will adopt an outcome-based approach, drawing on the progress of other industries to deliver the “true customer experience”. With AI’s new insights and research targets incorporated from DNA sequences, proteomics and genomics, the healthcare landscape will be a world where the medicine comes to the patient rather than being the other way around. To quote “The Patient Will See You Now” is going to be the new normal.

The commercial side of the industry has rather been slower in the uptake of AI. There are three major reasons behind this:

1. Pharma’s investment and promotional strategy has been largely focused around the interaction between the sales representative and the customer. This approach has not led the companies to think about applying AI technology to identify, automate and simplify deeper customer insights.
2. There is a lack of true personalization in the multi-channel digital engagement and campaign management. The pharma industry has largely been dependent on prescription data and it tends to judge all data based on that standard. Compared to companies like Amazon and Netflix who have limited, inadequate data for information filtering. AI’s Algorithm fills the gap by doing prognosis on that limited data and providing personalized search results based on each user’s search history and preferences. Pharma companies have been largely spoiled by choice because it has access to massive amounts of prescription data. That data can give clarity on creating personalized treatment and prevention strategies for specific groups of people. Still these companies tend to judge all of its patients' data based on a generalized standard. Because of their routined, conventional approach, the predominant part of their investment has not been dedicated to innovative commercial streams. 

Nearly 80% of respondents to a recent Oracle Health Sciences survey says they expect AI and machine learning to improve treatment recommendations, and in a 2017 paper, Dr. Bertalan Meskó, director of the Medical Futurist Institute, suggested that “there is no precision medicine without AI.” His point, albeit forward-looking, acknowledges that without AI to analyze it, patient data will remain severely untapped.

3. The investments that have been done on sophisticated precision medicines is largely stuck in the experimental or pilot stage. The situation is changing and to expedite it, the retail, profit-making side of the business has to change. This has to be done to actively stimulate innovation and achieve hyper personalized experiences and new levels of growth & efficiency. To achieve it, investments need to be done so that AI algorithms can be incorporated into the operational system of the companies to deliver beneficial solutions to patients. The beneficial and  economically feasible outcomes will lead to high ROIs.

Artificial Intelligence Drives Customer Experience: Zeroing Onto the Commercial Side

  

There are four applications of AI in the commercial space that can significantly stimulate sustainability, profitability and growth within the industry.

1. Intelligent Automation - Automating redundant, time intensive tasks to increase profitability.

2. Enhanced Judgement - Boosting growth and enhancing human decision making abilities through employing quality business intelligence (BI) solutions.

3. Improved Interaction - Serve dynamic & interactive customer experiences based on their personal data such as a patient’s daily routine, their recovery and vital signs.

4. Intelligent Insights - Accelerate growth by using AI and machine-learning based tools to manipulate collected data and recommend valuable insights to clinical staff, improving service effectiveness.

The Market Leaders in the life sciences industry are gradually predicting these possibilities. They are letting the intelligent machines have access to the previously siloed data and embracing latest AI technologies and applications, to help stimulate automation.

Based on a report by Accenture , when their industry executives were asked about the utilization of AI, 74 percent of these life sciences executives believe that the capabilities of AI will significantly impact and completely transform the life sciences.

RPA or Robotic Process Automation is an entry level AI technology that adds immense value to data, report processes or to any traditional marketing activity.  It is a type of business process automation technology that allows organizations to emulate and integrate human interactions within digital systems of pharma companies to generate more efficient business processes.  The immense value are the highly impressive outcomes that can lead to 40% acceleration in accuracy and 20% increase in work efficiency. It can also help accelerate the marketing of pharmaceutical drugs or latest information on drugs. This means patients or providers will have access to the right data or information at the right time. 

In an effort to streamline business processes, more CIOs are turning to robotic process automation, or RPA. KPMG estimates the value of the global IT robotic automation market to reach roughly $5 billion this year.

The technology shows great potential to save companies time and money by performing repetitive, routine tasks at a quicker pace (mentioned earlier in the article).

The Current Use Cases of RPA in the Pharmaceutical Industry

McKinsey predicts that for a majority of occupations, 30 percent of tasks can be automated; RPA is one way of doing that.

RPA robots are capable of mimicking almost any predictable human interaction, allowing them to log in to applications, move files, fill in forms and more. Leveraging this technology in healthcare presents opportunities that can free up clinicians’ time and enhance care delivery. With RPA optimizing and doing general analytics on vast amounts of patient data, it can navigate the clinical staff to do more accurate diagnoses, direct the appointment request of a patient to the correct medical center based on its defining attributes and help streamline the claims process that will save up a lot of time of the clinical staff. This will aid them to dedicate more of them time to enhance clinical workflows and produce tailored treatments for patients.

Some common use cases of RPA include:

• Improving the healthcare cycle: Providers collect vast amounts of data from their patients each day — from personal information to treatment cycle details. With the help of RPA software, healthcare organizations can extract and optimize patient data more effortlessly. In communicating with other digital systems, RPA software can manipulate collected data to generate analytics that offer clinical staff valuable insights to help them make more accurate diagnoses and offer tailored treatments to patients.

• Scheduling new patient appointments: When a patient fills out a new patient appointment request form online, RPA robots can help to scan the incoming data, build out a concise report and direct the appointment request to the correct medical center or health care department based on its defining attributes — including location, diagnosis and insurance carrier. This presents clinics with a cost-effective method of scheduling new patient appointments, leading to increased satisfaction for both patients and providers.

• Simplifying claims processing: Most claims involve processes such as data input, processing and evaluation. Whether those steps are conducted manually or by generic software, the process is often time-intensive and error prone — sometimes, even having a major impact on cash flow. RPA helps streamline the claims process by speeding up data processing and simultaneously reducing the number of errors. It can also help to address recovery of revenue with increased precision that might have been written off.

These are just a few examples of how this technology can improve accuracy across an organization and fuel profitability. Allowing RPA software to handle business processes will not only enhance clinical workflows but will return coveted time to staff and allow tailored treatments for patients.

Key Takeaways:

• Pharma’s investment and promotional strategy has been largely focused around the interaction between the sales representative and the customer. This approach has to be changed to make way for the application of AI technologies that can identify, automate and simplify deeper insights.

• Investments have to be done to automate the retail side of the industry so that organizations can focus on delivering beneficial solutions to patients. This can enhance their health outcomes in economically feasible ways.

• AI and machine-learning based tools can automate the redundant tasks and recommend valuable insights to clinical staff, improving service effectiveness.

• Allowing RPA software to handle business processes to enhance clinical workflows and return coveted time to staff and allow tailored treatments for patients.