Utilizing AI to Mitigate Patient Risk

Predicting Patient Post Treatment Issues

AI is utilized to identify potential post-treatment complications early to help keep patients healthy. By analyzing a wide range of healthcare data sets, such as patient records, treatment outcomes, and historical trends, AI identifies patterns to predict post-treatment issues based upon similar cases that may not be immediately apparent to clinicians. Clinicians tailor their approach to patient treatment leveraging the patient’s unique characteristics and potential risk factors that can trigger post-treatment complications or require patient readmission.

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AI is used to predict patients with the highest likelihood of post-treatment issues and allow proactive remediation. 

• AI is be used to a wide range of datasets containing patient demographics, clinical indicators, medical history, treatment plans, and outcomes to identify patterns and correlations associated with post-treatment complications. These algorithms are trained on historical data to predict the likelihood of specific complications occurring based on individual patient characteristics.
• Machine learning (Natural Language Processing (NLP)) techniques are also leveraged to analyze unstructured data sources such as electronic health records (EHRs), clinical notes, medical literature, societal data, and historical data to extract valuable insights related to possible and probable post-treatment complications. By processing textual data, NLP algorithms identify additional risk factors, symptoms, and other relevant information that may contribute to predicting adverse outcomes.

AI analyzes streaming data to provide indicators and early warning signals for proactive intervention during post-treatment recovery.

• Going beyond traditional healthcare tools, AI is used in connection with wearable devices to detect early signs of post-treatment health risks by analyzing streaming data that monitor patients' vital signs, biomarkers, and other physiological parameters in real-time. Patients are incentivized to use the wearable devices to minimize their health and financial risks associated with post-treatment complications. For example, patients with open wounds can benefit from wearing AI-powered bandages that deliver electrotherapy to the wound site and then dissolve. The bandage is equipped with sensors that allow for physicians to monitor the healing process and intervene if needed. This minimizes the need for the patient to come in for additional medical appointments, while still benefiting from care. Real-time feedback from healthcare providers improves patient experience in the moment and longer term, as their data can be integrated with electronic health records. If risks do present themselves, AI algorithms can alert healthcare providers to potential issues before they escalate and suggest mitigation strategies.
• AI-powered monitoring systems can continuously track patient vital signs, biomarkers, and other relevant data in real-time. By comparing this data against established thresholds and predictive models, AI can raise alerts or notifications when there are indications of potential post-treatment issues, allowing healthcare providers to intervene promptly.

AI is used for patient risk rating to define tailored treatment, check-ins, and allow the clinician to create a more precise post-treatment recovery plan.

• These same techniques is applied to patient risk stratification. AI stratifies patients into different risk categories based upon their health conditions, treatment plans, and other relevant factors. By assigning risk scores, healthcare providers prioritize resources and interventions for patients who are at higher risk of experiencing post-treatment issues.