As per a systemic review1 published in Annals of Internal Medicine, assessing the impact of interventions to improve adherence, it was found that about half the medications prescribed for chronic diseases are not consumed as directed by the physician, and around 20-30% of prescriptions are never fulfilled. This non-adherence results in over 125,000 deaths every year as well as increased emergency department visits and hospitalizations, which costs the American healthcare system to the tune of $100 to $300 billion1.
The reasons for lack of compliance to medication prescribed by their health care practitioners (HCPs), amongst others, are:
Pharma companies have, in the past, used human intervention (including nurses, case managers, pharmacists, etc.) to improve adherence, which although effective, can be very expensive, thus limiting the reach. Phone reminders have also been successful to some extent but can be too generic, offering no personalization and an inability to ensure that the reminder resulted in adherence, leading to patients losing interest.
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To address compliance concerns, organizations are looking into creative solutions utilizing artificial intelligence (AI) and machine learning (ML) to increase patient adherence. Some of the solutions that have shown results in this field, include:
Examples of such tools that are available for patients today include Roborto2 and Maxwell3.
AI and ML can also be used to predict patients at high risk of non-adherence. Certain factors are known to affect adherence such as zip code, diagnosis, the complexity of dosing regimen, ethnicity, employment status, patient age, gender, payer formulary status, insurance status, and medication cost. Data scientists can run analysis on available patient data utilizing this knowledge to classify patients into risk groups. Life sciences companies can then proactively intervene in cases of high-risk patients to ensure that they stay adherent. One can go a step further and integrate pharmacy retailers' point-of-sale (POS) data at the customer level from pharmacy retailers to create alerts for both patients and retailers, before an impending drug refill or even initiation of therapy, in certain cases. This will help bridge the last-mile gap from prescription to fulfillment.
Of course, a major technology disclaimer is that for any measure aiming to improve adherence to be successful, the patient must be willing and motivated. Until that can be achieved, no matter how smart, the technology will not be able to force the patient to take the medication. The support network plays a vital role and must be tapped, especially in the case of chronic diseases, to ensure that the patient remains compliant. For younger patients, social media analysis can provide important insights on patient behavior. All stakeholders and touchpoints during a patient’s journey could play an important role in engaging the patient and ensuring compliance.
The use of AI and ML-powered digital tools is the next logical step in ensuring connected healthcare. Leveraging AI and ML to boost adherence can reduce the unnecessary burden on the healthcare system, improve patient outcomes, make clinical trial data more reliable, and impact pharma revenues.