When a receptionist in a hospital is asking for the patient ’s date of birth or a medical team pulling records from the Health information exchange, matching of statistical data is performed. Currently, there are two tools in the technology arena being offered to provide a universal patient identifier. Each patient has a unique and private patient identifier that needs to be free of errors, protect patient privacy, and also simplify interoperability.
It is at the federal level that UPIs face most of the barriers, and it was Congress that instituted an essential suspension on studying a UPI. As a result, many IT experts suggest that a hybrid model is the only attainable solution path to UPIs. In a hybrid model, statistical matching is implemented to create a network of UPI-enabled patients, and benefits are seen when the network attains an important mass. Powerful computer power, large sets of data, and inherent network effects are driving the latest trends in machine learning. There is also a distributive model where patients are using the simplicity of smartphones to connect their healthcare records. The abundance of data is helpful to guide the path to many breakthroughs for matching patients.
Major statistical data algorithms are extensively dependent on human intervention for matching records accurately. This task can be easily taken care of by machines as they can see the complete data and process the feedback in real-time. Such an automated process, driven by humans would take a longer time.