The medical industry has long encountered the challenge of massive data and unstructured data. In recent years, many countries are actively promoting the development of medical informatization , which has enabled many medical institutions to have funds for big data analysis. Therefore, the medical industry will first enter the era of big data with the banking, telecommunications, insurance and other industries.
In 1989, Gartner proposed the BI concept. In 2008, Gartner further upgraded the BI concept to Advanced Analytics. In 2011, McKinsey explained the concept of big data. Although the names are different, the problems they have to solve have never changed. However, today's big data analytics technology can handle larger, diverse, real-time (3V) data, or big data, compared to 20 years ago. Compared to BI 20 years ago, big data analytics can generate greater business value, and the development of big data storage and analytics technology is also benefited from the proliferation of data and the variety of data types in business scenarios.
Therefore, before implementing a big data analysis project, companies should not only know which technology to use, but also when and where to use it. In addition to Internet companies that have been using big data earlier, the medical industry may be one of the traditional industries that make big data analytics the first to grow. The medical industry has long encountered the challenge of massive data and unstructured data. In recent years, many countries are actively promoting the development of medical informatization, which has enabled many medical institutions to have funds for big data analysis. Therefore, the medical industry will first enter the era of big data with the banking, telecommunications, insurance and other industries. In his report, McKinsey pointed out that by removing institutional barriers, big data analysis can help the US healthcare industry create $300 billion in added value a year. This article lists 15 applications in the five major areas of the medical services industry (clinical business, payment/pricing, research and development, new business models, public health). In these scenarios, the analysis and application of big data will play a huge role. Improve medical efficiency and medical results.
Clinical operation
In terms of clinical operations, there are five major scenarios for big data applications. McKinsey estimates that if these applications are fully adopted, the United States alone will reduce medical health spending by $16.5 billion a year.
1. Comparative effect research
By comprehensively analyzing patient characterization data and efficacy data, and then comparing the effectiveness of multiple interventions, the best treatment avenues for specific patients can be found.
Efficacy-based studies include Comparative Effectiveness Research (CER). Studies have shown that for the same patient, the medical service providers are different, the medical care methods and effects are different, and the cost is also very different. Accurate analysis of large data sets including patient vital data, cost data, and efficacy data can help doctors determine the most effective and cost-effective treatments in the clinic. Achieving CERs in a medical care system will likely reduce over-treatment (such as avoiding treatments where side effects are more effective than treatment) and under-treatment. In the long run, both over-treatment and under-treatment will have a negative impact on the patient's body and higher medical costs.
Many medical institutions around the world (such as NICE in the UK, IQWIG in Germany, and general drug inspection agencies in Canada) have 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. Under this Act, the Federal Effect Committee established the Comparative Effects Study to coordinate the study of the comparative effects of the entire federal government and allocate funds for $400 million. The investment is going to be successful, and there are a lot of potential problems to be solved, such as the consistency of clinical data and insurance data. Currently, in the absence of EHR (Electronic Health Archives) standards and interoperability, large-scale rush deployment EHR can make it difficult to integrate different data sets. Another example is the patient privacy issue. It is not easy to provide sufficient detailed data to ensure the validity of the analysis results while protecting the patient's privacy. There are also some institutional issues, such as current US law prohibiting health care institutions and Centers for Medicare and Medicaid Services (medical service payers) using cost/benefit ratios to make reimbursement decisions, so even if they pass big data analysis Finding a better way is also difficult to implement.
2. Clinical decision support system
The clinical decision support system can improve work efficiency and quality of diagnosis and treatment. The current clinical decision support system analyzes the entries entered by doctors and compares them to medical guidelines to alert doctors to prevent potential errors, such as adverse drug reactions. By deploying these systems, healthcare providers can reduce the number of medical incidents and claims, especially those caused by clinical errors. In the US Metropolitan Pediatric Critical Care Unit study, the clinical decision support system cut the number of adverse drug reactions by 40% within two months.
Big data analytics technology will make the clinical decision support system smarter, thanks to the growing ability to analyze unstructured data. For example, image analysis and recognition techniques can be used to identify medical images (X-ray, CT, MRI) data, or to mine medical literature data to create a medical expert database (as IBM Watson does) to give doctors advice. In addition, the clinical decision support system can also allow most of the workflow in the medical process to flow to caregivers and assistant doctors, so that doctors can be freed from simple and long-term consultation work, thus improving treatment efficiency.
3. Medical data transparency
Improving the transparency of medical process data can make the performance of medical practitioners and medical institutions more transparent, and indirectly promote the improvement of medical service quality.
Based on the operational and performance data sets set up by the healthcare provider, data analysis and visualization of flowcharts and dashboards can be created to facilitate information transparency. The goal of the flow chart is to identify and analyze the source of clinical variability and medical waste, and then optimize the process. Simply publishing cost, quality, and performance data, even without material rewards, can often improve performance and enable health care providers to provide better services and become more competitive.
Data analytics can streamline business processes, reduce costs through lean manufacturing, and find more efficient employees who meet the needs of the job, thereby improving the quality of care and giving patients a better experience, as well as providing additional performance to healthcare providers. Growth potential. The US Centers for Medicare and Medicaid Services is testing dashboards as part of building a proactive, transparent, open, and collaborative government. In the same spirit, the Centers for Disease Control and Prevention has publicly released medical data, including business data.
Publicly publishing medical quality and performance data can also help patients make more informed health care decisions, which will also help healthcare providers improve overall performance and become more competitive.
4. Remote patient monitoring
Data is collected from a remote monitoring system for chronic patients and the results of the analysis are fed back to the monitoring device (to see if the patient is complying with the order) to determine future medications and treatment options.
In 2010, there were 150 million chronically ill patients in the United States, such as diabetes, congestive heart failure, and high blood pressure, and their medical expenses accounted for 80% of the medical cost of the health care system. The remote patient monitoring system is very useful for treating patients with chronic diseases. The remote patient monitoring system includes a home heart monitoring device, a blood glucose meter, and even a chip tablet. The chip tablet is ingested by the patient and 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 due to the weight gain caused by water retention, which can be prevented by remote monitoring. . The added benefit is that by analyzing the data generated by the remote monitoring system, it is possible to reduce the length of hospital stay, reduce the amount of emergency, and achieve the goal of increasing the proportion of home care and the amount of appointments for outpatient doctors.
5. Advanced analysis of patient files
Applying advanced analysis to patient files can determine who is a susceptible population of a particular type of disease. For example, applying advanced analytics can help identify which patients have a high risk of developing diabetes and get them to receive preventive care as soon as possible. These methods can also help patients find the best treatment options from existing disease management programs.
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