Preventive care is a pillar of values-based care and a critical element of patient-centered virtual care solutions. Preventive medicine helps patients avoid the onset of illness, slows disease progression, and reduces the chance of developing severe complications.
Preventive interventions reduce unnecessary testing, treatments, and procedures. Interventions involving collaboration between patients, providers, and care management teams can reduce the risk of hospital readmission. Such interventions offer measurable cost savings for hospitals by eliminating readmission fines for Medicare patients.
Advances in augmented intelligence continually provide new solutions for more effective prevention, improved primary care quality, and lower medical costs. AI-driven preventive care can extend life expectancy and improve quality of life in patients living with chronic conditions, including diabetes, asthma, and chronic obstructive pulmonary disease (COPD).
Preventive Analytics Models
Augmented intelligence uses AI algorithms to enhance clinician understanding and decision-making regarding an individual patient. Predictive and prescriptive analytics are designed to enhance prevention and support positive outcomes.
Predictive Analytics
Predictive models use past patient data to identify trends that suggest future outcomes. Machine learning continuously incorporates ongoing patient data to make individual predictions more accurate. Predictive models help clinicians understand risks and potential paths of disease progression and healthcare requirements so they can design more effective care plans.
Prescriptive Analytics
Prescriptive models look beyond medical history, incorporating cultural, economic, and environmental factors associated with specific health outcomes. These models support provider decision-making with patient-specific recommendations that make interventions more effective. They also offer guidance designed to empower patients in their health management choices.
Medication and Prevention
In chronic conditions, medication adherence directly impacts outcomes, longevity, quality of life, and healthcare costs. Adherence to at-home medication administration is essential in managing diabetes and asthma. Research published in the journal Nature Medicine in 2021 describes a wireless sensor system that uses AI to detect when patients use their insulin pens and inhalers. The system also identifies errors in steps followed for proper administration and flags them so patients can improve their technique.
The study confirmed that the system accurately identified when patients used an insulin pen (99%) or inhaler (97%). The results also show accuracy in detecting missing steps and inaccurate duration of administration.
Care Management
Virtual care innovations have the potential to elevate patient engagement and care management. Augmented intelligence provides solutions that increase participation in disease management programs and customize care management options.
A 2019 study conducted by McKinsey & Company revealed that in-depth analytics tools can help care managers recognize patterns associated with negative outcomes and higher healthcare costs.
The McKinsey model identified a connection between poor medication adherence and increased ER visits in chronic obstructive pulmonary disease (COPD) patients. Machine learning algorithms then identified patterns associated with willingness to change behavior to improve health.
Those insights help care managers implement targeted interventions to increase medication adherence. Patient-specific guidance for intervention delivery, such as optimal times, frequency, and communication methods (phone calls, email, text), also boost patient compliance.
Preventive Diagnostics
Diagnostic AI models analyze symptoms to assist providers in accurately diagnosing health conditions. Diagnostic analytics are most commonly used in diagnosing patients who already present with concerning symptoms. However, AI enables diagnostics to play a larger role in preventive medication.
Innovations in preventive diagnostic models can detect serious conditions in their earliest stages, in some cases earlier and more accurately than standard health screenings.
Medial EarlySign, an Israeli company focused on AI healthcare solutions, developed a machine-learning model to diagnose non-small cell lung cancer (NSCLC) earlier than exiting methods. According to research published in 2021 in the American Journal of Respiratory and Critical Care Medicine, this model outperformed standard NSCLC screening protocols.
Early confirmation of an NSCLC diagnosis provides the highest chance of effective treatment and reduces the risk of dying from lung cancer. Considering that NSCLS currently has a life expectancy of just five years past diagnosis, Medial EarlySign could dramatically change what this diagnosis means for patients and families.
Patient monitoring
New advances in AI have placed image-signal processing techniques in the foreground of remote vital signs monitoring. Simple cameras can provide enough digital data for leading-edge machine learning to accurately monitor heart and respiratory rates, blood oxygen levels, and blood pressure.
Like wearable sensors, image-based AI can identify risk factors in real-time, enabling early interventions that help prevent declining health and adverse patient events. However, smartphone solutions, such as this software development kit currently pursuing FDA approval, have advantages over wearables, including lower costs and simplified user interfaces.
Care facilities can utilize in-room cameras to monitor patient vital signs. A 2021 paper exploring contactless, image-based monitoring of hospitalized COVID-19 patients describes AI and machine learning applied to images captured by ordinary cameras. Such platforms detect barely perceptible changes in skin color and subtle body movements to measure vital signs and identify abnormal patterns associated with negative health trajectories.
Conclusion
Preventive analytics have far-reaching potential for value-based healthcare. However, like all technology, they do carry some risk. Ongoing development must continue to address issues arising from inadequate or corrupted datasets, technical and user errors, and over-reliance by providers. Raw data and human bias can compromise prescriptive models, undermining the goal of health equity.
Proactive developers and other stakeholders are working to correct these issues. Nevertheless, providers must remember that AI tools support, but do not replace, clinical experience, knowledge, and critical reasoning skills.
Combining precision AI with provider expertise, however, has already caused a shift in the healthcare industry. As the technology continues to evolve, patients and providers will continue to experience the transformative benefits of augmented intelligence.
Caregility uses data analytics to provide robust decision support for providers in acute settings across the care continuum. Our award-winning, HIPAA-certified, interoperable platform connects all stakeholders, optimizing patient engagement, care delivery options, and clinician workflows. To learn more about how our virtual care platform enhances preventive care, contact us today.