In China, the traditional source of medical data is mainly medical institutions such as hospital clinics. Medical informatization has been busy in China for more than ten years. Many medical institutions are equipped with the Hospital Management Information System (HMIS) and the Clinical Information System (CIS). A large amount of medical data has been deposited in each module of HMIS and CIS. Medical data, medication data, medical test results data, and cost data are more common. As technology advances, new data sources continue to emerge, such as genetic testing data.
Although medical data sources are becoming more and more abundant, the current valuable applications are still limited. Perhaps the most important reason for this is that public hospital data is basically not open and incompatible, and most medical service startups can only accumulate some non-core medical data.
For public hospitals, although the medical insurance department and the performance department will have some data analysis, in general, they do not pay much attention to mining medical data. For medical service startups, although almost every family talks about their business model. In fact, the "excavation and application of big data" is indispensable, but in fact, the so-called data mining and application often stay in simple analysis of heart rate / blood pressure / blood sugar and other indicators or statistics on exercise frequency / intensity, from improving medical quality and efficiency This goal is still far away. However, in recent years, capital has entered the medical service industry in a big way, and a number of startups have started to open clinics directly to open hospitals. The SaaS model of the clinic management system has also become popular. It is expected that these changes will greatly promote the in-depth application of medical data.
In stark contrast to Chinese health care providers, many US health care providers have large data departments. The biggest driver behind this is the ongoing reform of payment methods in the United States: from traditional pay-as-you-go to pay-for-value, that is, based on the value that medical services create for patients and society. On the one hand, the value needs to be proved by the detailed data of the people served; on the other hand, the value-based payment model puts higher demands on the fine management level of the medical service organization. For this reason, the mining and analysis of medical data has almost become a compulsory course for US medical services.
There are three types of data analysis methods commonly used by US medical services.
Descriptive Analytics: The most commonly used data analysis method in the medical service industry, mainly used to reflect and analyze the facts that have occurred. For example, analyze the medications used by some patients and the effects of treatment.
Predictive Analytics: Determining patterns based on historical data and predicting future outcomes and trends. For example, how to determine which patients are high-risk patients and prevent and intervene accordingly.
Prescriptive Analytics: Based on existing data, summarize and suggest one or more decisions or actions, and provide possible outcomes for each decision or action plan. In the medical services industry, instructional analysis is often used for clinical decision support.
Through these data analysis methods, healthcare providers can monitor, record, measure, analyze, and manage the processes and outcomes of healthcare services. For example, compare the quality of service of different medical institutions, classify patients, design and adjust the path of diagnosis and treatment.
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