What are the domestic medical big data companies? It is best to combine cases.

The application of big data in the medical industry can play an active role in the following aspects:

(1) serving residents. The health guidance service system for residents provides accurate medical care and personalized health care guidance, which enables residents to maintain continuity in hospitals, communities and online services. For example, provide intervention, management, health early warning and health education for chronic diseases such as cardiovascular disease, cancer, hypertension and diabetes (subscribe to and promote health care programs); At the same time, it reduces the hospitalization time of patients, reduces the amount of emergency treatment, and improves the proportion of home care and the number of appointments made by outpatient doctors.

(2) serving doctors. Clinical decision support, such as drug use analysis, adverse drug reactions, disease complications, correlation analysis of treatment effect, antibiotic application analysis, etc. Or make a personalized treatment plan.

(3) Serving scientific research. Including disease diagnosis and prediction, statistical tools and algorithms for improving clinical trial design, and analysis and processing of clinical trial data, such as identifying disease susceptibility genes and extreme expression populations of major diseases; Provide the best treatment.

The Internet is a magical big network, and medical big data and software customization are also a model. Here's a quote. The starting number of this skill is a pure one, with three children's zeros in the middle and a four two five zero at last. You can find it by combining them in order. What I want to say is, unless you want to do or understand this, if you just join in the fun, don't come.

(4) Service management organization. Standardize drug evaluation and management performance analysis; Assess preventive interventions and measures for epidemics and acute diseases; Public health monitoring, payment (or pricing), clinical pathway optimization, etc.

(5) Public health services. Including monitoring and early warning of health threat factors, network platform, community service, etc.

In addition to Internet companies that started to use big data earlier, the medical industry may be one of the traditional industries that first developed big data analysis. The medical industry has been facing the challenge of massive data and unstructured data for a long time. In recent years, many countries are actively promoting the development of medical informatization, which makes many medical institutions have the funds to do big data analysis. Therefore, the medical industry will first enter the era of big data with banking, telecommunications, insurance and other industries. The following is a list of 15 applications in five major areas of the medical service industry (clinical business, payment/pricing, research and development, new business model and public health). In these scenarios, the analysis and application of big data will play a huge role in improving medical efficiency and medical effect.

Clinical operation

In terms of clinical operation, there are five scenarios for big data applications. Mckinsey estimates that if these applications are fully adopted, the national health expenditure of the United States alone will be reduced by $654.38+$06.5 billion every year.

1, comparative effect study

By comprehensively analyzing patient characteristic data and curative effect data, and then comparing the effectiveness of various intervention measures, we can find the best treatment plan for specific patients.

Efficacy-based research includes comparative effect research. Research shows that for the same patient, different medical service providers have different medical care methods and effects, and there are also great differences in costs. Accurate analysis of large data sets, including patient signs data, cost data and curative effect data, can help doctors determine the most effective and cost-effective treatment methods in clinic. The implementation of CER in the health care system will probably reduce overtreatment (such as avoiding those whose side effects are obviously greater than the curative effect) and insufficient treatment. In the long run, both over-treatment and under-treatment will bring negative effects on patients' health and lead to higher medical expenses.

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. In order to succeed in this investment, there are still a lot of potential problems to be solved, such as the consistency between clinical data and insurance data. At present, 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 Medicaid service centers (medical service payers) from using the cost/benefit ratio to make reimbursement decisions, so even if a better method is found through big data analysis, it is difficult to implement it.

2. Clinical decision support system

Clinical decision support system can improve work efficiency and quality of diagnosis and treatment. At present, the clinical decision support system analyzes the items input by doctors and compares them with medical guidelines, thus reminding doctors to prevent potential mistakes, such as adverse drug reactions. By deploying these systems, medical service providers can reduce the rate of medical accidents and the number of claims, especially those caused by clinical errors. In the study of pediatric intensive care unit in metropolitan America, within two months, the clinical decision support system reduced the adverse drug reactions by 40%.

Big data analysis technology will make clinical decision support system more intelligent, which benefits from the increasing ability to analyze unstructured data. For example, we can use image analysis and recognition technology to identify medical image (X-ray, CT, MRI) data, or mine medical literature data to establish a database of medical experts (as IBMWatson did), so as to provide medical advice to doctors. In addition, the clinical decision support system can also make most of the workflow in the medical process flow to nurses and assistant doctors, so that doctors can be freed from the simple consultation work that takes too long, thus improving the treatment efficiency.

3. Transparency of medical data

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.

According to the operation and performance data set set set by the medical service provider, data can be analyzed and visual flow charts and dashboards can be created to promote information transparency. The goal of the flow chart is to identify and analyze the sources of clinical variation and medical waste, and then optimize the flow chart. Only by publishing the data of cost, quality and performance, even if there is no corresponding material reward, it can often promote the improvement of performance and make medical service institutions provide better services and be more competitive.

Data analysis can simplify business processes, reduce costs through lean production, and find more efficient employees who meet the needs, thus improving the quality of care, bringing better experience to patients, and bringing additional performance growth potential to medical service institutions. The Center for Medicare and Medicaid Services in the United States is testing the dashboard as part of building an active, transparent, open and collaborative government. In the same spirit, the centers for disease control and prevention.

Publicly publishing medical quality and performance data can also help patients make more informed medical care decisions, which will also help medical service providers improve their overall performance and become more competitive.

4. 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 in the United States, such as diabetes, congestive heart failure and hypertension, and their medical expenses accounted for 80% of the medical expenses in 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 swallows 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 emergency volume 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.

5. Advanced analysis of patient files

Applying advanced analysis in patient files can determine who is susceptible to a certain disease. For example, the application of advanced analysis can help identify high-risk patients with diabetes, so that they can receive preventive health care plans as soon as possible. These methods can also help patients find the best treatment scheme from the existing disease management schemes.

Payment/pricing

For medical payers, big data analysis can better price medical services. In the United States, for example, this will have the potential to create an annual value of $50 billion, half of which will come from the reduction of national medical expenditure.

1, automation system

Automated systems (such as machine learning technology) detect fraud. Industry insiders estimate that 2%~4% of medical claims are fraudulent or unreasonable every year, so it is of great economic significance to detect claims fraud. Through a comprehensive and consistent claims database and corresponding algorithm, the accuracy of claims can be detected and fraud can be found. This fraud detection can be retrospective or real-time. In real-time detection, the automatic system can identify fraud before payment occurs and avoid heavy losses.

2. Pricing scheme based on health economics and curative effect research.

In terms of drug pricing, pharmaceutical companies can participate in sharing treatment risks, such as formulating pricing strategies according to treatment effects. This has obvious benefits for medical payers and is conducive to controlling medical expenses. For patients, the benefits are more direct. They can get innovative drugs at reasonable prices, and these drugs have been studied according to their efficacy. For pharmaceutical companies, a better pricing strategy is also beneficial. They can get higher market access possibilities, and they can also get higher income through innovative pricing schemes and the introduction of more targeted therapeutic drugs.

In Europe, there are some pilot projects of drug pricing based on health economics and efficacy.

Some medical payers are using data analysis to measure the services of medical service providers and price them according to the service level. Medical service payers can pay according to the medical effect, and they can negotiate with medical service providers to see whether the services provided by medical service providers meet specific benchmarks.

R&D

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.

1, predictive modeling

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.

2. Improve statistical tools and algorithms for clinical trial design.

Using statistical tools and algorithms can improve the design level of clinical trials and make it easier to recruit patients in clinical trials. By mining patient data, we can evaluate whether the recruited patients meet the test conditions, thus speeding up the clinical trial process, putting forward more effective clinical trial design suggestions and finding out the most suitable clinical trial base. For example, those trial bases with a large number of potentially qualified clinical trial patients may be more ideal, or they may find a balance between the size and characteristics of the trial patient population.

3. Clinical experimental data analysis

By analyzing clinical trial data and patient records, more indications can be determined and side effects of drugs can be found. After analyzing clinical trial data and patient records, drugs can be repositioned or marketed for other indications. Collecting ADR reports in real time or near real time can promote pharmacovigilance (pharmacovigilance is a safety system for listed drugs, monitoring, evaluating and preventing ADR). Or in some cases, clinical trials have hinted at some situations but there is not enough statistical data to prove it. Now the analysis based on big data of clinical trials can give evidence.

These analysis items are very important. It can be seen that the number of drugs withdrawn from the market has hit record highs in recent years, which may bring a devastating blow to pharmaceutical companies. Vioxx, a painkiller that came off the market in 2004, caused a loss of $7 billion to Merck, and caused a loss of 33% shareholder value in just a few days.

4. Personalized treatment

Another promising big data innovation in R&D is the development of personalized therapy through the analysis of large data sets (such as genome data). This application investigates the relationship among genetic variation, susceptibility to specific diseases and response to specific drugs, and then considers individual genetic variation factors in the process of drug development and medication.

Personalized medical care can improve the effect of medical care, for example, providing early detection and diagnosis before patients show symptoms of diseases. In many cases, patients use the same treatment plan but have different curative effects, partly because of genetic variation. The side effects can be reduced by adopting different diagnosis and treatment schemes for different patients or adjusting the drug dosage according to the actual situation of patients.

Personalized medical care is still in its infancy. McKinsey estimates that in some cases, by reducing the number of prescription drugs, medical costs can be reduced by 30% to 70%. For example, early detection and treatment can significantly reduce the burden of lung cancer on the health system, because the cost of early surgery is half that of late treatment.

5. Disease pattern analysis

By analyzing the patterns and trends of diseases, it can help medical product enterprises make strategic R&D investment decisions, and help them optimize R&D priorities and allocate resources.

New business model

Big data analysis can bring new business models to the medical service industry.

Summarize patients' clinical records and medical insurance data sets.

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.

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 will be greatly improved and the risk of infectious diseases will be reduced. All these will help people create a better life.