3 ways AI and patient-centred support can improve medication adherence

Healthcare is transitioning to a patient-centric approach and healthcare consumerism. Patients have fast become consumers first, with the freedom to make decisions about their health journeys, and they’re looking for choices that put them at the centre.

Over half of patients surveyed by Deloitte, for instance, say they would be very/ extremely likely to tell their doctor if they don’t agree with them, and 63% would change doctors if they do not like how they communicate. Evidence has also shown that adopting an approach focused on the patient leads to better health outcomes.

More than 70% of consumers have grown to expect companies to deliver personalised interactions, and healthcare may be no different. Many consumers report feeling frustrated when outreach from companies and organisations is not personalised to them, according to McKinsey.

The impact of patient centricity

Patient centricity at its core is about keeping the patient’s circumstances, behaviours, and medication barriers in mind. Engaging patients and caregivers in healthcare decisions can positively impact medication initiation, adherence, and long term persistence.

Engaged patients are more likely to adhere to prescriptions, medication schedules, and care plans, make healthier choices, and experience better health outcomes. In fact, according to a study from Health Affairs, patients who are engaged in their own medical care are three times less likely to have unmet medical needs, and are twice as likely to seek care in a timely manner when compared to unengaged patients.

There is also a growing body of evidence suggesting that giving patients the skills, confidence, and tools to become more engaged in their healthcare leads to greater health outcomes, lower costs, and better overall medication use and adherence.

The role of AI and patient centricity

As patients become more active healthcare consumers, they are seeking options that empower them to make better decisions about their treatments. Yet, despite pharma’s significant investment in patient support programmes, only one in five patients say they were aware of the types of services offered to help them.

Pharmaceutical companies can help by becoming more familiar with a patient’s background, behaviours, and medication adherence barriers preventing them from starting and taking medication as directed. This can be applied across therapeutic areas, from mass market, to specialty, and rare disease therapies, through tools like predictive artificial intelligence (AI).

Predictive AI can help pharmaceutical companies, healthcare organisations, and patients in a number of ways. In particular, it can help to:

1.Proactively identify barriers to medication adherence, including identifying and targeting patients who may be experiencing gaps in medication usage or those who may need more support. AI is able to analyse vast amounts of data to identify disparities and patterns that clinicians and others may not immediately notice.

AI can be integrated within existing CRM and hub platforms through secure APIs to ensure smooth data flows and orchestration while safeguarding sensitive information. Pharmaceutical professionals need to ensure that the AI technologies that they are using are fully HIPAA-compliant, HITRUST-certified, and meet privacy and security requirements.

When patients get exactly what they need through predictive analytics and personalisation, programmes are also more efficient. Redundant, ineffective communications are removed. As an example of greater efficiency, we have found that a patient support programme for an immunology biologic therapy saw a 25% reduction in spending on operations when AI was used to tailor phone calls. Informed outreach specialists call only patients who truly need support, instead of their previous workflow where they called everyone.

2.Personalise engagements based on patient needs, determining the optimal outreach channel, content, timing, and frequency to make the greatest impact for each person.

AI can help to better target patients with content that directly resonates with them on the channels they’re on, with the messages they care about, at the times that it will have the most receptivity. AI-enabled personalisation does not replace the content that’s been well-researched and approved by legal and the manufacturer’s process, instead it takes what’s already created and tailors it toward the patients who will most likely benefit and engage.

For instance, some patients may be more responsive to higher-touch communication, such as speaking with a nurse or outreach specialist on the phone. Others may be more engaged and supported by text message reminders, or benefit from offers or educational resources via email.

AI can help to optimise patient marketing and communications at the individual level by determining:

○ Channel of outreach (be it phone call, SMS, email, in-app, or something else)
○ Messages based on likelihood of missing the next prescription refill
○ The best content among different types of messages
○ Frequency and timing of messages

3.Evolve programmes over time to ensure lasting behavioural changes - AI can continuously gather and learn from new data about a patient, including their intent and behaviours. It can synthesise information to optimise communications and target patients who will benefit from specific touchpoints, predicting the next-best-action for the individual that will result in filling or refilling a prescription. This allows for a more proactive approach, leading to better medication behaviours, adherence, and health outcomes. For instance, in a case study, AI-enabled personalisation was used to support patients on an anticoagulant medication, which led to an 18.8% increase in days on therapy.

Commitment to continuous learning and optimisation demonstrates to both healthcare providers and consumers the quality of services being offered, boosting patient engagement, medication initiation and adherence, and brand loyalty. Improving patient engagement paves the way for delivering greater long-term value for patients.

No two patients are exactly alike. A patient support programme that works for one person may not work for another. Tailored outreach at the individual level, with predictive AI, can play an important role in delivering effective, personalised, patient-centric strategies. When patients have resources that empower them, they are more likely to ask questions, actively participate in their health journey, and be adherent to medications, as well as experience better health outcomes.