A powerful duo: Converging AI with patient data to predict outcomes
October 30, 2023
Dr. Brian Covino
By Brian Covino
We all know that the US healthcare system tends to focus more on treatment than prevention. Even hospital systems and medical facilities naturally tend to focus less on proactive care than reactive care models. However, the emergence of artificial intelligence technology (AI) will enable a new era of preventive, patient-centric care. Fueled by vast troves of patient data, AI is transforming early diagnosis and treatment plans by predicting patient outcomes and overhauling outdated prior authorization processes, enabling physicians to further help their patients achieve faster, better outcomes.
Untangling the web of patient data
Patient data, encompassing electronic medical records (EMRs), medical imaging, and genetic information, holds untapped valuable information that can be better used to get patients the right care faster. With the ability to process and analyze extensive data at unprecedented scale and speed, AI delves deeply into population health data, allowing for precise, patient cohort-specific predictions. This empowers physicians to anticipate potential health issues and intervene proactively for optimal patient outcomes.
AI’s individual-level analysis of patient data enables personalized treatment plans, replacing a one-size-fits-all approach to care. Healthcare providers can tailor interventions to each patient’s unique needs, considering factors such as genetics, lifestyle, and environmental influences to optimize care. These plans can improve care quality and reduce adverse reactions, hospital readmissions, and treatment-related complications. This synergy is giving rise to an era wherein predictive analytics and decision support systems will become indispensable tools.
Responsible AI: The key to clinical decision-making
Responsible AI refers to the ethical and accountable development of AI systems before deployment. It also involves considering the social, ethical, and legal implications of AI technologies, and ensures that AI systems are designed to be used in ways that align with human values while protecting patient data and privacy. Predicting patient outcomes is one of the most promising applications of responsible AI in healthcare.
Predictive AI models can detect areas of concern by analyzing laboratory test results, potentially uncovering risks before symptoms become apparent. The healthcare industry is increasingly utilizing responsible AI for predictive analytics, leveraging patient datasets to forecast the probability of specific diseases or disorders. This may include early diagnosis and treatment for cancer patients by assessing a patient’s risk of developing cancer or chronic illnesses like heart disease, thereby enabling proactive interventions such as lifestyle modifications or medication adjustments. AI also demonstrates remarkable potential for identifying and diagnosing traditionally challenging conditions, including rare hereditary and neurodegenerative diseases. This predictive capability has the potential to save lives via faster diagnoses and effective treatments, thus reducing the burden on health systems and decreasing costly hospital bills.
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