This is the first blog in a four-part series that explores the value of artificial intelligence (AI) and how it can accelerate mission-critical initiatives for patient organizations.

AI brings opportunities for game-changing transformation in the way patients are diagnosed and managed. The value in AI is not in replacing the role clinicians play, rather, to inform their decision-making processes at the right time, for the right patient and thus, improve patient outcomes. By sifting through vast amounts of clinical data, AI provides actionable, data-driven insights that might otherwise be overlooked. AI can enhance decision-making and ensure that care is both personalized and effective.

The excitement of AI isn't only for the care setting, it's something that patient organizations can be involved with to accelerate their mission-centric initiatives. Whether by speeding up research into innovative treatments or making clinical care more accessible, AI provides new avenues for patient organizations to address the unmet needs of their communities.

The promise of AI offers a holistic approach to patient care. It can create comprehensive healthcare profiles by analyzing medical histories, biomarkers, genetics, and lifestyle choices. This multifaceted perspective is crucial for early disease detection and risk assessment. For those potentially undiagnosed, this proactive approach is invaluable for the care setting. With these insights at their disposal, patient organizations, in tandem with healthcare providers (HCPs), can initiate early interventions, potentially preventing diseases from progressing to critical stages.

The benefits of AI

Integrating AI empowers patient organizations in several ways to support their communities, address strategic goals, address patient needs and treatment or care gaps, and position themselves as valuable partners to HCPs, life science organizations, and healthcare technology institutions. Let’s outline a few examples of areas where AI can support disease and disorder communities to reduce the burden on the healthcare system that has hindered advancements in clinical care.

Disease detection

For many rare disease patients, the journey to a diagnosis can take years. Patients may be bounced from specialist to specialist around the healthcare system, leading to worsened prognosis and wasted time and resources, among other challenges. These delays can be extremely distressing for patients, who may face skepticism from medical professionals and family members about the legitimacy of their symptoms. In the meantime, patients are also not afforded the opportunities to start therapies that could slow or halt the progression of their diseases.

AI can play a vital role in disease detection by identifying patients with elevated risk for undiagnosed, early stage or under-reported disease based on the patients “digital fingerprint.” Once these patients are identified, they can then be referred to the appropriate specialist to confirm the diagnosis, and start the patient on treatments, if any are available.

Patient adherence

When patients do not adhere to their prescribed therapy, they can experience significant consequences in terms of the efficacy of the therapy leading to poor outcomes. With AI, you can obtain valuable insights into understanding the drivers of patient adherence to medicine in addition to flagging patients at risk of not adhering. This understanding is deeply connected to social determinants of health, which can shed light on core drivers behind patient motivation and behaviors including financial constraints, lack of understanding about their medical condition, or educational barriers.

For example, AI might identify patients with schizophrenia as an elevated risk for non-adherence to self-administered oral medications. Such insights can guide clinicians in choosing the most effective treatment delivery mechanisms. Opting for in-clinic administered injections can mitigate adherence challenges often seen with oral medications that patients take at home. This AI-backed proactive approach ensures patients receive treatments that not only are effective but also align with their likelihood to adhere.

Furthermore, predictive algorithms can more intensively examine the nuances of patient non-adherence. Extracting these details offers a chance to weave insights into clinical workflows. As a result, care teams are better equipped to pinpoint and address the root causes of non-adherence and make data-driven decisions to prevent disease progression and mitigate adverse events through timely and appropriate interventions.

Disease progression

HCPs are increasingly pressed for time and focus while juggling the demands of acute and chronic disease management within the context of an office visit. AI, deployed responsibly within clinical workflow, can inform underlying risk for disease progression that might otherwise be overlooked.

For example, patients with clinically defined chronic kidney disease in stages 3A and 3B can remain unaware of their condition and carry elevated risk for disease progression and increased impact on quality of life. However, AI-driven diagnostic evaluations can raise clinician awareness to the absence of a clinical diagnosis and identify those at heightened risk of progressing to more advanced kidney disease, necessitating interventions like dialysis or transplantations. By identifying these high-risk cohorts, care teams can intervene early, assess the patient, and explore opportunities to prevent further progression.

Adverse event management and hospital readmission

AI can predict adverse events arising from available treatments and identify patients likely to seek emergency care due to underlying conditions. A potential case example is the identification of high-need, high-cost patients with multiple chronic conditions at risk for emergency treatment or hospitalization, especially those with common readmission diagnoses like heart failure or COPD exacerbation. Using historical patient data, AI can identify patient cohorts with similar characteristics of those who have had the medical event in question, in this case readmission, and narrow focus on the patient population to those who have elevated risk based on similar features and predictors to those readmitted in the past. Identifying this high-risk population before the predicted event allows for intervention by care teams to address identifiable risk predictors and prevent the disease exacerbation through direct care with the patient.

To learn more about how IQVIA can help you with AI, contact us at pr-contact@iqvia.com.