Historically, clinicians have primarily used health data as a decision support resource to extract basic information about the number of medication errors that occurred over a given period, for example, and to use this basic information to shape the clinical choices that were made.
Today, the technological capabilities available to healthcare teams have advanced several levels. AI and predictive analytics are no longer just theoretical concepts in healthcare. They are now critically important in addressing some of the industry’s most pressing challenges. From reducing medication errors with advanced decision support systems to predicting antibiotic resistance with sophisticated AI algorithms, these technologies are setting new standards for quality and safety of care.
Moreover, their application to public health surveillance and chronic disease management shows promise for preventing health crises and improving long-term health management.
The benefits of these technologies are increasingly tangible. For example, initiatives that bring together large volumes of patient data from different healthcare providers have demonstrated significant improvements in operational efficiency and patient engagement. They have also proven beneficial in terms of healthcare coordination. Information about an individual patient can be quickly made available to help develop a 360-degree treatment plan for the patient, with social and care workers, dietitians, general practitioners and hospital doctors coming together to discuss how the plan is progressing.
Analytics can also play a key role in patient monitoring, helping to ensure that a patient diagnosed with high blood pressure, for example, is taking their medication as prescribed, and quickly analyzing blood pressure measurements they take at home to ensure no dangerous trends are emerging.
