Jacob Vazquez | June 11, 2020Read full Article
4 Ways Artificial Intelligence Enhances Patient Journey Management
Kimberly Gregorio | December 9, 2021
Artificial intelligence solutions can benefit pharma companies in many areas:
- Clinical Trial Management and Monitoring
- Drug Discovery and Development
- Supply Chain Monitoring
- Sales Territory Mapping
- Sales and Marketing Analytics
All of these areas have a range of AI use-cases which can be powerful.
However, enhancing the patient experience is one of the most transformative AI applications in pharma. Whether it’s through communications, health monitoring, or any number of other applications, AI is already enhancing the way pharma companies connect with patients, even while old methods of engagement and monitoring are fading away.
What Does AI Actually Do?
There’s no shortage of hype for artificial intelligence – almost every day, there’s a new opinion piece discussing the possibility of widespread automation taking all of our jobs. What AI tools like machine learning are actually good at right now is processing large amounts of data and using it to generate actionable insights. If there’s one thing healthcare industries have in abundance, it’s data.
Everything anyone in the pharma system does - from providers to patients to sales reps – generates information. But unlike in other industries, where data is relatively “clean” or normalized, as much as 80% of healthcare info is stored in widely varying formats and often hidden within regulatory or organizational silos. Having so much hard-to-access information can weigh healthcare companies down instead of driving innovation and smart decision-making: this is where machine learning comes in.
AI is particularly good at performing narrowly defined pattern recognition tasks. Given enough historical information to “learn” from, AI can quickly turn otherwise inaccessible data into evidence-based knowledge, and then keep generating decisions in the future based on that data. Other AI technologies, like natural language processing and robotics, make it possible to build stronger, more efficient connections with patients, improving their experience.
At a basic level, this makes for more businesses that are more responsive to consumer needs and can adjust quickly based on the way the market changes.
Artificial Intelligence and the Patient Journey
Pharma has systemic issues with patient adherence and brand reputation:
Up to half of those taking medications1 don't take them as directed, and that number doesn't even take into account those who prescribed medications, but never take them. Up to half of all treatment, failures are caused by poor adherence.2
Measures of public trust have been consistently decreasing each year despite the effort being put in to improve the industry's reputation.
As of 2018, less than 40% of consumers 3 said they'd trust pharmaceutical companies to do the right thing.
Both of these issues are complicated and thorny to untangle, but improving the experience patients have with pharmaceutical brands may be the key to both: happier patients are more likely to adhere to treatment plans and trust pharma companies. Patient outcomes are strongly connected to adherence; improving experiences should be a clear win-win for patients and life science companies alike, but doing so takes significant technological improvement.
In many cases, AI may be the answer: it can provide a faster, easier method of meeting patients where they are, tracking their journey through the pharma life cycle, and increasing communication between companies and stakeholders.
To name just a few examples:
4 Ways AI Can Enhance Patient Journey Management
1. AI-Augmented Patient Communication
Pharma patients are dispersed – much of their treatment takes place inside their own homes, far away from providers and pharmaceutical information. Increasing contact is one of the most powerful ways of boosting adherence and improving outcomes, but it’s also expensive and often impractical to implement.
Patient experience analytics allow you to create personalized messages precisely targeted to specific audiences.
AI allows much of this process to be automated, which reduces the cost of outreach without losing all of the human touch.
Natural language processing can support tools which answer patient questions, provide appointment or refill reminders, and other outreach in a natural format.
2. Digital Health and Virtual Care
Prescription apps and other emerging technologies have made the news recently – companies are increasingly creating technologies which allow patients to receive care in their own homes.
Even in a traditional medical setting, virtual health care is taking off. From robotic customer support services through more comprehensive online platforms, companies are rapidly innovating to help better serve patient needs remotely.
Health monitoring tools such as health devices and mobile apps can track health data, record prescriptions and refills, increase compliance, and send patient reminders.
They can also often communicate directly with providers, increasing the total communication between patients and their doctors.
Ultimately, machine learning will play a big role in improving patient experiences and safety by making sure that at-risk patients are identified and information is sent to the right people at the right time.
3. Personalized Approach
AI can crunch numbers faster than any statistician, which ultimately allows both providers and patients to get more personalized attention.
Answering questions like:
- Which providers have patients who need this medication?
- What kind of patient is likely to ask their providers for medication?
- How many undiagnosed patients with this condition are there?
And finding other patterns in prescribing and patient behavior is difficult to do manually—especially if you don’t already know what you’re looking for. AI allows you to track physicians’ prescribing records, as well as patient journeys and can identify new patterns much more efficiently.
It’s also possible to promote specific treatments or run advertising campaigns based on patient-journey data, adding to the visibility of your treatment among undiagnosed patients, and increase awareness among those who are diagnosed.
This doesn’t just help open new markets and increase sales, but can help identify people who may be at high risk and get them to life-saving treatment faster.
Similarly, patient management tools allow you to manage each patient with personalized care across each stage of therapy, from initial welcoming and onboarding through financial assistance, clinical trial steps, or adherence for chronic conditions down the road.
4. Designing More Effective Treatment Plans
The process of tracking how a patient suffering from a disease is responding to medication can be used to proactively predict outcomes and adjust treatment plans. AI can help provide patients with information in near real-time. If new symptoms emerge, a digitized app can respond proactively and help patients play an active role in adjusting their treatment regimens 4.
Sensorized monitoring and IoT tools can also automate risk-based monitoring approaches to non-invasively detect changes in patient vital signs or other factors that can represent a potential adverse outcome.
The key factor here is time – the faster a bad reaction or side effect is identified, the more likely the treatment can be changed or adjusted to patient comfort.
AI can also help empower patients to play more active roles as agents in determining their own health, a key element of patient satisfaction.
AI is a Powerful Tool for Patient Experience Management
As of this year, all of the largest 10 pharma companies5.
Developments in AI application in the industry are occurring across the spectrum of pharma business. A lot of the focus has previously been on drug discovery, but post-FDA-approval AI applications have the potential to be just as impactful.
This is particularly true as the industry shifts to prioritize patients as customers who are active players in their healthcare decision-making.
Building AI capability in-house is still challenging and expensive for many life science companies. However, as more software and pre-built applications are built, the barrier to entry will continue to lower, increasing the speed with which companies begin to take advantage of AI capabilities.