The medical industry is one of the traditional industries that used big data analysis earlier. Among them, the five major medical services include clinical services, network platforms, public health management, remote patient monitoring, and new drug research and development. The depth and breadth of big data applications are ahead. Big data analysis has greatly improved the medical effect and user satisfaction.
Clinical records and medical insurance big data
Summarizing patients' clinical records and medical insurance data sets for advanced analysis will improve the decision-making ability of medical payers, medical service providers and pharmaceutical enterprises. For example, for pharmaceutical companies, it can not only produce drugs with better efficacy, but also ensure that drugs are marketable. The market for clinical records and medical insurance data sets has just begun to develop, and the speed of expansion will depend on the speed at which the medical care industry completes the development of electronic medical records and evidence-based medicine.
Many medical institutions around the world (such as NICE in Britain, IQWIG in Germany, General Administration of Drug Inspection in Canada, etc. ) has started the CER project and achieved initial success. In 2009, the Recovery and Reinvestment Act passed by the United States was the first step in this direction. According to this act, the Federal Coordinating Committee for Comparative Effect Research was established to coordinate the comparative effect research of the entire federal government and allocate 400 million US dollars for investment. There are still many potential problems to be solved if this investment is to succeed. For example, the consistency of clinical data and insurance data, in the absence of EHR (Electronic Health Record) standards and interoperability, large-scale hasty deployment of EHR may make it difficult to integrate different data sets. Another example is patient privacy. Under the premise of protecting patients' privacy, it is not easy to provide enough detailed data to ensure the validity of the analysis results. There are still some institutional problems. For example, at present, American law prohibits medical insurance institutions and medical insurance and Medicaid service centers (medical service payers) from using the cost/benefit ratio to make reimbursement decisions. Therefore, even if they find a better method through big data analysis, it is difficult to implement it.
Network platform and community
Another potential big data business model is network platform and big data, which generate a lot of valuable data. For example, PatientsLikeMe.com website, where patients can share their treatment experience; Sermo.com website, where doctors can share their medical opinions; The website "Participatorymedicine.org Website" operated by this non-profit organization encourages patients to actively treat. These platforms may be valuable data sources. For example, Sermo.com charges pharmaceutical companies to allow them to obtain membership information and online interactive information.
public health
Using big data can improve public health monitoring. Public health departments can quickly detect infectious diseases, conduct comprehensive epidemic monitoring and respond quickly through the nationwide patient electronic medical record database, integrating disease monitoring and response procedures. This will bring many benefits, including the reduction of medical claims, the reduction of infectious disease infection rate, and the health department can find new infectious diseases and epidemics faster. By providing accurate and timely public health consultation, public health risk awareness can be greatly improved and the risk of infectious diseases can be reduced. All these will help people create a better life.
Remote patient monitoring
Collect data from the remote monitoring system for patients with chronic diseases, and feed back the analysis results to the monitoring equipment (check whether the patients follow the doctor's advice) to determine the future medication and treatment plan.
In 20 10, there were1500,000 patients with chronic diseases such as diabetes, congestive heart failure and hypertension in the United States, and their medical expenses accounted for 80% of the medical expenses of the medical and health system. Remote patient monitoring system is very useful for treating patients with chronic diseases. Remote patient monitoring system includes home cardiac monitoring equipment, blood glucose meter and even chip tablet computer. After the patient ingests the chip tablets, the data is transmitted to the electronic medical record database in real time. For example, remote monitoring can remind doctors to take timely treatment measures for patients with congestive heart failure to prevent emergencies, because one of the signs of congestive heart failure is weight gain caused by water retention, which can be prevented by remote monitoring. More benefits are that by analyzing the data generated by the remote monitoring system, the hospitalization time of patients can be reduced, the number of outpatient and emergency departments can be reduced, and the purpose of improving the proportion of home care and the number of appointments made by outpatient doctors can be achieved.
New drug development
Medical product companies can use big data to improve research and development efficiency. Take the United States as an example, this will create more than $654.38+000 billion in value every year.
In the stage of new drug research and development, pharmaceutical companies can determine the most efficient input-output ratio through data modeling and analysis, so as to equip the best resource combination. The model is based on the data set before the drug clinical trial stage and the data set in the early clinical stage, and can predict the clinical results as soon as possible. Evaluation factors include product safety, effectiveness, potential side effects and overall test results. Predictive modeling can reduce the research and development costs of pharmaceutical products companies. After predicting the clinical results of drugs through data modeling and analysis, the research on suboptimal drugs can be suspended or the expensive clinical trials on suboptimal drugs can be stopped.
In addition to research and development costs, pharmaceutical companies can get returns faster. Through data modeling and analysis, pharmaceutical companies can bring drugs to market faster, produce more targeted drugs, and have higher potential market returns and treatment success rate. It turns out that the time from research and development to market of general new drugs is about 13 years. Using the forecasting model can help pharmaceutical enterprises to advance the time to market of new drugs by 3 ~ 5 years.