HARNESSING DATA-DRIVEN MEDICINE TO ENHANCE PERSONALIZED CARE

Clinical trials are meticulously designed to gather clinically relevant data while allowing participants to live their daily lives as normally as possible. However, these trials are inherently artificial environments where participants are closely monitored to ensure their safety and assess the efficacy of a developing drug. Participants are expected to remain engaged throughout the trial, providing the data needed by sponsors and the FDA to make informed decisions about drug approval.

Once a drug is commercially launched, the complexity of real-world applications begins to unfold. Unlike the formal agreement a trial participant has with a study site, a patient’s relationship with their healthcare provider is based on an implied contract—a general expectation that they will adhere to clinical guidance. Often, a patient’s adherence to their prescribed dosing schedule serves as a proxy for their level of engagement in their care. In reality, understanding patients' behaviors in real-life settings is far more intricate. By focusing on identifying and applying the right data, healthcare providers can aim to reduce variability in treatments, leading to more precise and personalized care.

Precision in Dosing for Optimal Outcomes

Various factors can influence the effectiveness of a treatment, including when a patient takes their medication (e.g., before or after meals, the time of day) and how their body responds. For example, in Parkinson’s Disease, the timing of medication intake can significantly impact the drug’s efficacy. While this is well-known, determining the ideal time for a patient to take their medication is more complex. By leveraging smartphone applications that use computer vision and artificial intelligence (AI) to remotely assess patient dosing behavior, clinicians can monitor when patients take their medication, evaluate their responses, and help them identify the most effective dosing schedule. This approach facilitates more personalized treatment with minimal disruption to patients' daily lives.

Tailoring Treatment Plans Through Patient Pattern Analysis

Certain individuals and patient populations face significant challenges in adhering to their medication regimens. For instance, clinical trials utilizing computer vision to confirm dosing compliance have shown that patients with schizophrenia tend to fall into three categories:

• Those who consistently adhere to their medication schedule.

• Those who require frequent reminders and outreach to stay on track.

• Those who struggle with adherence despite reminders and assistance.

In real-world settings, a similar distribution may be observed, with varying levels of assistance required for different patients. By implementing monitoring programs during the initial phase of medication prescription, clinicians can deploy support systems to engage patients who may need reminders or frequent check-ins. For those who remain disengaged, clinicians gain greater visibility, enabling timely interventions, such as prescribing alternative administration routes like injections to overcome challenges associated with frequent self-administration. These technologies enhance clinicians' ability to monitor and connect with patients, ultimately enabling more personalized care.

Aspiring to the Quality of In-Person Care in Chronic Condition Management

In inpatient settings, clinicians interact with patients daily, adjusting medications in real-time to optimize treatment. However, in outpatient settings, where visits may occur only every six months, clinicians must make decisions based on limited information, relying on the assumption that patients will adhere to prescribed medications.

To better manage chronic conditions, technology can provide visibility into a patient’s well-being between visits, offering insights into how adherence affects the stability of their condition. For instance, patients with congestive heart failure (CHF), often elderly and managing complex medication regimens, can benefit from tools that allow providers to monitor their overall CHF population. This enables clinicians to identify when a patient’s condition changes and differentiate between a medication issue and other underlying factors. Instead of adjusting dosages based on the assumption that the medication is ineffective, clinicians can make more informed decisions if they know the patient has missed doses due to other issues.

Illuminating Healthcare with Data-Driven Technology

Understanding patient behavior is crucial, and data provides insights that would otherwise be based on assumptions—such as assuming a patient is taking their medication correctly, that it is effective, or that they will report any side effects. These assumptions, whether well-meaning or not, are not data-driven. In our increasingly virtual healthcare ecosystem, patients' health and well-being depend on our ability to understand behavior and deliver precise, personalized care. By applying lessons learned from clinical trials, we can develop better support systems that improve patient outcomes.