In the United States, major changes affect how medical information is stored, shared, and produced. New regulations outline how data is managed and the type of information systems used in the medical industry. The analytics adoption model defines more requirements for medical professionals and how they serve patients.
Personalized Medicine and Prescriptive Analytics
The level provides an analytic approach to wellness management and assessments for physical or behavioral functional health. The findings provide more mass customization of patient care. It provides doctors with an option to improve patient outcomes. An assessment of population outcomes defines more personalized treatment strategies. The analytics include biometric, familial, and genomic data.
Clinical Risks Intervention and Predictive Analytics
The level defines forecasting, predictive modeling, and risk stratification to manage patient outcome and streamline treatment. It also reviews possible financial rewards for patients that follow a healthy diet, don’t smoke, and mitigate health risks with exercise. The system flags patients who don’t follow their care plans and predict future health issues.
Population Health Management and Suggestive Analytics
The level defines accountable care organizations that share rewards and risks based on patient outcomes. The analytics define data related to pharmacies, individual patient care, bedside devices, and home monitoring for patients. It also reviews compensation plans for doctors and healthcare professionals who manage patient data properly.
Waste and Care Variability Reduction
The level defines analytics focused on reviewing medical professionals that adhere to standards and lower waste and stabilize variability. It also defines support care management teams that focus on improving the health of patients. Multidisciplinary teams are defined to determine if medical professionals are providing high-quality care for chronic conditions and keeping patients safer. The analytic model also reviews patient registries, insurance claims, and evidence-based data.
Automated External Reporting
The level defines consistency of report production for patients and regulatory requirements. It defines requirements for clinical text data and how it is located in information systems. It applies centralized data governances outlining requirements for sharing information externally.
In the U.S., federal regulations apply to how medical and patient data is assessed and analyzed. The changes identify areas in which the medical industry should improve to provide higher quality care for patients in the future. Medical professionals who want to learn more about the model read more information about healthcare analytics today.