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Editor’s note: This article is from the micro-channel public number “arterial network” (ID: vcbeat), Author: Chen Peng, 36 krypton release authorized.Unlike in the past, China’s science and technology level has now greatly improved, and it even leads the world in certain fields, especially innovative technologies such as cloud services, big data and artificial intelligence technology.In this epidemic prevention and control work, we armed ourselves with high technology to turn these technologies into powerful weapons against the epidemic, and have been widely applied in the areas of epidemic tracking and traceability, path propagation, development model prediction, and resource allocation.In this article, the application of big data in disease control (artery network mapping, for reference only) What contribution has big data made to the prevention and control of this epidemic?What potential role can it play in future disease control?The arterial network combed this.Big data is indispensable for disease control early warning and monitoring, provided that “real” data is reported by the media. On December 26, 2019, Zhang Jixian, director of respiratory medicine at Hubei Province Traditional Chinese and Western Medicine Hospital, stored a case in the electronic medical record system.Surprisingly, three other similar data that morning included three identical keywords for fever, cough, and the South China Seafood Market.The experienced Zhang Jixian felt something abnormal and reported it to the deputy dean, the hospital’s sense and medical affairs on December 27.The hospital decisively reported the data to the Center for Disease Control and Prevention of Jianghan District, Wuhan City.By December 29, similar data had increased to seven, and Zhang Jixian reported to the hospital again.The hospital immediately held a multi-department consultation, and then the Hubei Provincial Hospital of Integrated Traditional Chinese and Western Medicine reported directly to the Hubei Province and the Wuhan Municipal Health and Disease Control Center.Zhang Jixian was considered to be the “first person to report the epidemic”, and Hubei Provincial Hospital of Integrated Traditional Chinese and Western Medicine was the first hospital to officially report the epidemic.The earliest case in the public report dates back to December 8, 2019.During this half month, other hospitals in Wuhan have also received similar cases.According to reports, due to careful consideration, the hospital chose to first perform genetic sequencing to determine the pathogen before reporting.Considering that this is a completely new virus, it takes time for the gene sequence, and this is not to be questioned.However, let’s assume that if there were big data and artificial intelligence at the time to catch these early clues, would the situation develop in another direction?Of course, some people will also question why the national direct reporting system for infectious diseases and public health emergencies (hereinafter referred to as the CDD special network) did not play an early warning when it started operating in 2004.effect?According to the understanding of the arterial network and the public reports of the media, the CDC private network is not ineffective, but its data source depends entirely on the reported data. Due to various factors, the CDC private network cannot directly connect with the hospital’s internal network..No data can be collected, so this system cannot work.On February 3, the Health and Medical Commission issued the “Notice on Strengthening Informationization to Support Pneumonia Epidemic Situation of New Coronavirus Infection”.The health and health commissions in various places have continuously improved the collection method according to the notice. Most hospitals have begun to adopt the method of direct network reporting, and finally they have gradually played the role of a dedicated network.For big data, real data sources are an extremely important step.During the development of this epidemic, we can clearly find that the conclusions drawn from the wrong data are far from the facts.How to get real and reliable data from the front line of medical institutions?The main systems of electronic medical record system (EMR), laboratory information management system (LIS), medical image archiving and communication system (PACS), and hospital information system (HIS) provide us with reliable data sources.EMR is the first link of the data source, and it is also a core system in the promotion of hospital informationization promoted by the state.From 2018 to 2019, a total of 9 policies from the State Council and the National Health and Medical Commission mentioned in detail the hard requirements for electronic medical records.In 2019, a total of more than 7,000 hospitals across the country submitted national electronic medical record ratings.Using big data and artificial intelligence to analyze EMR and apply it to disease control is not impossible, which is a hot topic at present.The disease and surveillance and early warning system that Beijing Dashu Yida built for the Nanjing Centers for Disease Control and Prevention directly opened up the EMR connected to the local hospital.This disease control monitoring and early warning system uses the most advanced big data and artificial intelligence technology of Dashuidaida, builds a model of medical knowledge map, and then directly extracts EMR for semantic structure. The artificial intelligence matches the knowledge base to determine whether the EMR containsKeywords for infectious diseases such as new coronary pneumonia.Once it is judged to be suspected or highly suspected by artificial intelligence, it should be reported to the disease control department to avoid missing or late reporting by the hospital for some reason.The system is directly connected and connected to the hospital’s EMR as a reporting analysis, data summary and early warning analysis system for provincial and municipal disease control centers.It has a very fine granularity, and in addition to the 40 legal infectious diseases, it also supports regions to supplement their locally identified multiple infectious diseases.Dashu Yida’s regional disease surveillance and early warning system (all displays are for display only and do not represent any practical significance). At the same time, the system learns from historical disease control data, and combines large data such as regional density and population mobility to detect suspected data.The development speed and distribution area of ​​infectious diseases are predicted to provide reference data for disease control decisions.Generally speaking, in order to do a good job in epidemic prevention and control, we need to use AI + big data technology. We need to get the top-down vertical field data of the “Ministry-Province-City-District-County” in the industry and horizontally.A cross-industry “information island.”From the top-level design, real-time structured information interconnection and interoperability and business collaboration and defense are required.The system that Dashuo Yida built for Jiangsu Province was originally prepared for the peak of spring flu, and it did not intend to play a role in this more severe new crown epidemic.According to statistics, if the epicenter of the outbreak is Wuhan, and a circle with a radius of about 2 hours is drawn along the high-speed rail line, it can be clearly seen that the level of disease control in different regions is not the same.Relatively speaking, the new crown epidemic situation in Jiangsu Province is much better than the surrounding area.Changsha Double Number Technology, which specializes in intelligent monitoring of infectious diseases, tries to solve the pain points of the entire infectious disease monitoring by solving the pain points of doctors in the current infectious disease report card.The doctor-in-charge system for first-time physicians requires that the first-patient physicians fill out the infectious disease report card for any patients who are found to be infected and who are suspected to be the source of the disease, and keep records for future reference.The monitoring of infectious diseases places high requirements on the data quality of infectious disease report cards, including the timeliness of infectious disease reports, and the completeness and accuracy of infectious disease report data are important indicators.This is a good thing, but it also objectively makes doctors encounter many problems in the process of reporting the actual infectious diseases.The first is timeliness.The current regulations are that Class A infectious diseases must be reported within 2 hours, and Class B and C infections must be reported within 24 hours.However, it takes a long time for doctors to fill out the information on infectious disease report cards. When encountering patients for a long time, it is easy for omissions to be reported in a timely manner, which will lead to late reporting and underreporting of infectious diseases.The second is the completeness and accuracy of infectious disease report data.At present, most hospitals do not have complete information such as addresses and telephone numbers in the registration system, and they cannot meet the requirements for patient information integrity of infectious disease report cards when filling out reports.Doctors can only ask and enter on the spot. The process takes 3-5 minutes or even longer, which is quite a headache.Doctors’ awareness of infectious diseases also affects the monitoring of infectious diseases.There is an option of “unknown cause of pneumonia” in the infectious disease report card of the CDC Dedicated Network, which can actually correspond to early-onset new coronary pneumonia.The reality is that doctors basically did not choose this selection report, because reporting means that they need to complete a series of registration and filling out and investigation, which is an additional burden for them.Or, even if this option is known, the lack of a clear diagnosis dares not be reported easily or cannot be reported through the rules of existing systems.At present, the infectious disease monitoring and reporting system in hospitals is not intelligent enough, the intervention plan for doctors is not effective enough, and the infectious disease epidemic management department of hospitals is slow and time-consuming to report and report infectious diseases. These are all infectious disease surveillance and cannot be eliminatedThe objective factor of late omission is also the pain point in the surveillance of infectious diseases.Changsha Double Number Technology has been skilled in practice and believes that these pain points can be solved with big data.The operating logic of the intelligent monitoring program for infectious diseases of even number technology In the intelligent monitoring solution for infectious diseases of even number technology, firstly collect the diagnosis and treatment data of the four major systems of EMR, LIS, EMR and PACS, and then use the even number expert knowledge base of infectious diseases,Semantic analysis technology extracts and labels related features of infectious diseases from diagnosis and treatment data.Thereafter, the infectious disease analysis model is used to analyze and compare the characteristics, so as to recognize the infectious disease.Once an infectious disease case is recognized, the system pushes it to the front end of the specific doctor’s computer instantly or with a delay, and the forced locking system requires the doctor to complete an important infectious disease report.From a practical point of view, doctors do have some nuances when they are unfamiliar with the operation at the initial stage; but in the process of doctors filling in the card, even numbers use big data + artificial intelligence technology to achieve high-precision recognition of infectious diseases and information about the card to be reported.A series of optimization schemes such as automatic prompting, automatic filling of patient information, intelligent parsing of address information to the street, and automatic deduplication of repeated report cards to achieve significant efficiency improvements.In the end, the doctors accepted and approved this.This intelligent monitoring solution for infectious diseases has also achieved the threshold move forward, moving the verification logic for the direct report of the CDC online to the stage where doctors fill out infectious disease reports.However, the verification cannot complete the report, which effectively solves the problem of card integrity and accuracy.After the doctor completed reporting the infectious disease report card, the infectious disease report card with complete and accurate data was reviewed by the hospital’s infectious disease epidemic report staff and then reported to the CDC dedicated network, which also solved the problem of timeliness.Even number infectious disease monitoring platform (all displays are for display only, do not represent any practical significance) On the basis of solving doctors’ infectious disease report cards, even number technology has further realized one-click from the hospital intranet to the disease control direct reporting dedicated networkDirect report function.In May 2017, Xiangya Hospital of Central South University used the system to implement the one-button direct reporting function of the infectious disease reporting card hospital intranet, which took less than 3 seconds.This was also a very rare case of direct reporting on the intranet at the time.At present, in addition to Xiangya Hospital of Central South University in Hunan Province, the Children’s Hospital Affiliated to Chongqing Medical University and the Affiliated Hospital of Zunyi Medical University in Guizhou have adopted the one-key direct report scheme.According to statistics, the use of big data and artificial intelligence to monitor the number of infectious diseases can prevent the late reporting of over 95% of the effective rate; the average infectious disease report time taken by doctors is 5-8 minutes.Significantly reduced to less than 40 seconds; the time taken for the CDC online report to be reduced from 2-3 minutes to a few seconds.In fact, the Zhongnan Hospital of Wuhan University has just adopted the double infectious disease surveillance program last year, and has shown advantages in the efficiency of subsequent epidemic reporting.However, in the early warning of sudden unknown infectious diseases, Shuangliu Technology believes that the analysis of the data of a single hospital is relatively inadequate.Relatively speaking, Double Number Technology believes that the regional integrated infectious disease early warning and monitoring program is the best construction program to respond to major infectious diseases in the region.At present, even number technology has completed the development of regional integrated infectious disease epidemic warning and monitoring programs, and has accelerated deployment plans in other provinces and cities, hoping to explore a new model for the overall infectious disease informatization process in China.Don’t neglect the rapid collection of data in the hospital. Big data operations need to be visualized. With the smoothing of the disease control reporting process, the reporting of disease control information has become a lot simpler, but most hospitals are aware of the number of patients being processed and the distribution of suspected cases, The internal department staffing ratio, scheduling and protective materials and other conditions are difficult to obtain the full picture.The backward way of printing paper forms by Excel to write statistics by hand had to make a comeback.Inefficient data work brings a lot of burden and danger to the work of front-line medical staff, and makes it difficult for managers to obtain a complete picture.The director and dean were unable to perform rapid scheduling and even affected normal scheduling.Zhang Huahong, director of Shanghai Huashan Hospital, “praised party members first” to win praise, but one of the reasons behind it is probably because of the urgent moment that caused the problem of the ideal scheduling process.In fact, most hospitals rely on vendor services and lack the ability to quickly develop data collection and processing data.Once there is no way to deal with unexpected situations, the information system becomes a decoration.Under the circumstances, handwritten forms became understandable.According to the feedback obtained by Fansoft from the hospital, there are many data clogging points in the hospital during the epidemic period, which are mainly divided into three directions: reporting microsystem, report automation and management data application.Reporting the micro-system is one of the hospital’s greatest needs during the epidemic.The hospital’s information system is already quite complicated, but most of the existing information system functions are designed around the hospital’s routine operation, and no additional collection is made for many operational data.As a result, many temporary demand data cannot be collected. Once the manufacturer’s engineers cannot respond to the site in time (such as an epidemic situation), the hospital can only catch blind.The filing of the micro-system is mainly to collect some data that the hospital may collect daily through Excel, or to collect sudden and temporary data in the database for emergency use, such as the temporary management of hospital protective materials in epidemics.The second requirement is the automated reporting business, which aims to free the hospital from complex and tedious reporting tasks.At the same time, automated reports can also be adjusted and modified quickly based on the hospital’s own situation.During the epidemic, automated reporting also played a significant role in reducing hospital workload.The third is managed data applications.By collecting the data of the hospital’s EMR, HIS and LIS systems, and combining with the data collected by the reporting micro-system, it is very convenient for the hospital administrator to provide the current operating status and command force of the hospital.In this regard, after summing up long-term practical experience, Fansoft provided hospitals with a variety of templates for filling out micro-systems and automated reports, including a variety of hospital operation indicator templates such as material handling, health reports, and remote office data statistics.Filling in dozens of indicators in three directions, including the workload of the hospital, the overview of protective supplies, and the development of the epidemic situation, solved the hospital’s urgent needs.When it comes to the application of big data, the promotion of most applications cannot be separated from data visualization.In addition to the regular bar, line, and pie charts, the epidemic map is always the most interesting part.This is not a recent phenomenon. As early as 1854, the British anesthesiologist and epidemiologist John Snow’s study of the cholera outbreak in Soho, Westminster, was considered an epidemiological study.pioneer.At that time, little was known about cholera, and it was even believed to be transmitted by air.Snow counted the number of deaths per household and marked them on the map.The analysis found that most of the cases lived near the Broad Street pump. Combined with other evidence, Snow believed that the case was related to the water contamination of the pump, so the pump was turned off and cholera was brought under control.It has since been recognized that cholera is transmitted by water.Although the medical level at that time was still difficult to cope with, cholera could be effectively controlled using data visualization.Snow correctly selected the map over the other charts to make statistical analysis at a glance.The role of data visualization is obvious.Fansoft’s outpatient monitoring kanban (picture provided by Fansoft, the display is for display only, does not represent any practical significance) Fansoft’s expertise in data visualization is also used in epidemic prevention and control.Its data report visualization tool can combine the data obtained by the hospital information system with the data captured by the reporting micro-system to generate a visual large-screen Kanban, which is convenient for hospital managers to make timely and accurate decisions and judgments in the face of complex data.Wanghai Kangxin’s emergency material management system, which was launched on February 4th, helped 140 hospitals to complete the shortage of materials in less than 20 days, and also analyzed the overall material situation of hospitals across the country through big data and data visualization.The registration status of Wanghai Kangxin Emergency Material Management System (as of February 23, 2020, the picture comes from Wanghai Kangxin HIA data platform) The visualization chart in the report released by Wanghai Kangxin HIA data service platform can easily find that medical teams have been sent toSupported by Hubei Province, the shortage of hospital supplies is not limited to central China, but spreads throughout the country.During the epidemic, the situation of medical personnel in Wuhan assisted by provinces and cities (as of February 15, 2020, the picture comes from Wanghai Kangxin HIA data platform). Through the staffing map, you can also see the support of medical systems across the country to Hubei Province.Taking the data as of February 15 as an example, 3,182 medical personnel have been dispatched in Jiangsu Province, which was the largest at that time, but its proportion to the total number of medical personnel in the province is about 4%, which shows that Jiangsu Province has strong medical resources.Relatively speaking, Ningxia Autonomous Region sent a total of 536 medical staff, but its proportion in the total number of local medical staff has reached 8.62%, which was the highest proportion at that time.Such visual data has a decisive role for managers to coordinate resources to make decisions.Big data restores the overall situation and public opinion of the urban epidemic, and improves the epidemic prevention and control process. Combining medical small data with a variety of big data can create a lot of practical anti-epidemic data applications: peer flight query, peer trip query, peripheralConfirmation of community diagnosis, national distribution of epidemic situation, etc.This is also the most intuitive and most concerned data in this epidemic, and many medical big data companies have played a significant role in it.During the epidemic period, new crown pneumonia epidemic monitoring big data platforms, fever diagnosis and monitoring screens, and new crown virus medical observer management platforms were established in various places to provide the public and the government with the epidemic analysis report.The epidemic monitoring platform uses big data technology to construct urban and regional epidemic dynamic heat maps, which helps to restore the entire process of urban epidemics and improve the process of epidemic prevention and control.Use intelligent analysis and forecasting to provide intuitive support for government decision-making, emergency management, resource scheduling, major event research and prediction, and help decision makers understand the development and situation of emergency events in the region based on key data and dynamic changes, so that they can do it quicklyMake decisions.The kanbans of most monitoring platforms can dynamically display the latest information on confirmed case changes, different types of cumulative cases, forecast of confirmed cases, comparative analysis of epidemic development trends, RT index, RT change trends, and cure rate.Areas with relatively complete regional medical platform construction can also conduct multidimensional dynamic supervision of local medical institutions.The hot-spot diagnosis monitoring big screen can be connected with the hot-spot diagnosis in the area, and the hot-spot diagnosis of the hospitals in the area can be monitored in real time through the big-screen method.It can also realize risk early warning, which can directly pop up reminders to ensure the safety of medical staff.Of course, the epidemic prevention and control command platform should not only focus on the direct information related to the epidemic, but public opinion should also become an important object of concern.In the early days of this epidemic, various information was mixed and transmitted, making it difficult to distinguish the authenticity of the situation.Some of them are unintentional misinformation, while others are rumors with ulterior motives.These messages crowd up a large number of channels, so that the help information that really needs help is buried in spam.In order to find and clarify rumors in time, promote the spread of truth and positive energy.The Qingbo Big Data platform launched an “epidemic rumor smasher” during the epidemic period, which can comprehensively understand the relevant information that the media, professional institutions and professionals have removed rumors.Help to provide reliable rumor information for the people and reduce their anxiety and panic.Qingbo assisted the “Fire Research” volunteers in completing more than 50 public opinion materials.Since January 30, the compilation of the “Population Book of Thunder and Fire” has been compiled daily. The content is selected from the latest news related to the epidemic on that day.Among them, the basic version has publicly released 32 issues as of March 2, and is intended to provide reference for government and corporate decision makers, media and Internet reporters, and researchers at home and abroad.In addition, big data technology is more important to find and mine help information from clues.Qingbo Big Data used big data to mine and summarize public opinion during the epidemic, and used the new coronavirus pneumonia network for help behavior and transmission methods launched on social tools including Weibo, WeChat, Douyin, Quickhand, Forum, and Post Barresearch.Through data cleaning, semantic analysis and feature extraction of online help information, structured information extraction of unstructured text information; and use of index model design and strategy optimization to automatically classify online help information, and divide the help information intoSummarize and transfer to the relevant departments for the three major categories of emergency help (involving life and death), heavy help (more significant help), and routine help.The staff will conduct the next step of information verification, rescue implementation and return visit after the rescue.As of the afternoon of March 2nd, Qingbo assisted the “Thunderfire Rescue” volunteers to collect a total of 3157 pieces of help information, and returned to 2,572 people through various means, assisting in treating 1693 people in total.According to statistics from Qingbo Big Data, the overall public sentiment trend during the epidemic accompanied the epidemic’s prevention and control, and it peaked on January 28. With the effective prevention and control of the epidemic, the public sentiment gradually decreased.It is worth pondering that in the era when the Internet and mobile Internet are so developed, the executive departments of some districts and counties have insufficient Internet awareness, and they have not paid enough attention to online help information on various network platforms, and have a lot of room for improvement.Therefore, Qingbo Big Data plans to continue to update the online help discovery and mining system developed for the epidemic in the future to make it more versatile and open to the public.Provide technical support to the government and society in response to various disasters.In addition to facilitating resource scheduling for decision makers, it is also convenient for help seekers to find available resources nearby to help themselves.Big data + artificial intelligence help initial screening before diagnosis. Patients’ medical care is assured that the outbreak occurred during the epidemic of winter infectious diseases, especially similar to the initial symptoms of influenza.This puts a much larger number of ordinary flu patients in a dilemma-in the past, they only needed to go to the hospital for a simple examination, but now they dare not go to the hospital, letting go and worrying about new crown pneumonia.Based on this demand, many Internet medical companies have opened remote clinics, pre-questions or online self-diagnosis.Before the 19th year of the outbreak, Ta Shue Medical had built a health communication app for the Jiangsu Health and Health Committee. In addition to the conventional online remote consultation and triage consultation, it also launched an artificial intelligence-based self-test during the epidemic.During the outbreak, the symptoms of new coronary pneumonia were upgraded.This function also played a significant role in Hubei Province, which is in the middle of the epidemic.As part of a national plan to support Hubei Province, Jiangsu Province sent 732 doctors to support Huangshi City, Hubei Province.The remote online system is also used to support online consultation for residents of Huangshi City, which includes a common disease self-diagnosis module based on big data and artificial intelligence.The online self-diagnosis and triage module provides some questions for the initial screening of symptoms and converts them into micro-cases for each patient, thereby helping remote doctors to pre-diagnose online doctors.Before the doctors who subscribed to the corresponding department online opened the patient’s graphic consultation or video consultation, artificial intelligence has structured the dialogue, including the information previously entered by the individual, such as the patient’s age, height, weight, history of hypertension,Whether pregnant or not, etc., and summarize the entire information directly.Doctors can directly see the micro medical records when receiving patients, and directly ask some more in-depth questions according to judgment, which greatly improves the efficiency of consultation.At the same time, as various data platforms have been opened up, once residents are defined as suspected or highly suspected by the preliminary screening artificial intelligence, the disease control department will also capture these data and analyze and sample them.By observing whether highly suspected cases are scattered in various places or regions, and making analysis and early warning and judgment, in fact, it has also played a role in strengthening prevention and control in the affected areas.The Internet hospital platform developed by Dashuida for the Jiangsu Health Commission has successfully connected 68 Internet hospitals in Jiangsu Province and nearly 20 health medical platforms.As of February 12, hundreds of thousands of residents and suspected patients had clicked the self-test.There are about hundreds to thousands of calls per day, with the most being called more than 7,000 times a day.At the same time, Tasut Elekta has also developed a 5G and artificial intelligence-based robot with partners, which can be used to screen patients with fever, and perform actions such as measuring body temperature, measuring blood pressure, and blood oxygen.Triage the patient.If there is a high degree of suspected new coronary pneumonia, notify the hospital for treatment.This intelligent robot has been in trial operation at two hospitals in Wuhan and Shanghai for nearly one month. At the peak of the peak, more than 360 preliminary screenings were completed in one day, which greatly eased the pressure on the hospital when the epidemic broke out and reduced the number of medical staff.Risk of infection.Use big data thinking to help the grassroots level to safely and efficiently implement personnel management. In addition to data from the medical system, data from outside the medical system, especially data from the grassroots community, has also played a huge role in this epidemic prevention and control investigation.However, the efficiency of grassroots investigations in the early stages was not high. In many areas, paper-based reports were used to enter the system, which consumed a lot of time and effort, and the results were not good.This “table anti-epidemic” indirectly reflects the weaknesses in the prevention and control of the epidemic, low efficiency, and the lack of corresponding technical means. The phenomenon of multiple data sources fighting each other was quite serious.Over time, as well as central guidance, big data thinking and technology have begun to be applied to the grassroots.Siming District of Xiamen City began to carry out epidemic prevention and control as early as late January. Considering that the local administrative area is small and the foreign population is large, the tide of rework after the holiday will cause greater pressure for prevention and control. Because of the public’s return journey, the epidemic prevention and prevention jointThere is an urgent need for control, and the district government needs to be able to develop it in a short period of time and quickly deploy an online health management platform for key populations to further improve the efficiency of grassroots community epidemic prevention and control.Zhiye Internet (Xiamen) Health Technology Co., Ltd. (hereinafter referred to as Zhiye Health) leverages the Internet and cloud computing technology to develop and complete a burning cloud health management platform in a short period of time and quickly go online.This is also Fujian’s first key population health management platform.Using this platform, residents and returnees in the jurisdiction can make personal health declarations, register online information and register temperature themselves, and improve the efficiency of information collection for key control groups.The enterprises in the jurisdiction can register the enterprise information and employee health data online and track them, so that the enterprise can understand the status of the employees in time, and assist the jurisdiction government to do a good job of epidemic prevention and control.In the filling process, the platform supports single account registration information for family members, and can choose attribution registration in the area under its jurisdiction to facilitate follow-up visits by community members.At the same time, after the people understand the clues surrounding the epidemic situation, they can contact offline community health centers and community streets to report possible hidden dangers or illegal behaviors (such as the movement of suspicious people, crowd gathering activities, and making fake sales).Promptly investigate the hidden dangers of the epidemic situation, curb illegal behaviors such as raising prices, etc., to facilitate community collective management.In order to prevent concealment of false negatives and omissions of grass-roots staff, health platforms need to obtain the geographic information of mobile phones when filling in the reports, and the grass-roots staff will take pictures and keep them.At the same time, the platform adopts big data thinking at the information collection end, and uses the provincial unified form to fill in the information strictly to improve the standardization of the data in order to support the regional big data platform.The platform is currently accessing local online hospitals for hot consultation online consultation services to reduce cross-infection during offline consultations.When doctors at the grassroots level and community workers conduct follow-up visits offline, they can use the platform to complete tracking object information collection and temperature registration at one time, and provide online physical data tracking at the same time, thereby freeing grassroots staff from filling in forms and effectively reducing grassroots work.Pressure on personnel to improve the efficiency of epidemic prevention and control.At the same time, the platform also provides a background management function, which is convenient for managers at all levels of government and enterprises to implement personnel registration information and data query statistics in the background based on their respective permissions.Zhiye Health Burn Cloud Health Management Platform (All displays are for demonstration only and do not represent any practical significance) At present, the health management platform in Siming District has entered a total of 220,000 people, including 60,000 employees belonging to 4,500 enterprises.The platform includes 6,000 of them in key management, which effectively reduces the work pressure of grassroots personnel, improves the efficiency of prevention and control, and reduces the local risk of prevention and control.Next, the platform can also conduct separate health management for students in the education system to cope with the peak of resumption.At the same time, the platform is also updating functions in order to achieve subsequent iterations of functions such as scanning ID cards for automatic reporting and face recognition.There is a similar close management platform in the epidemic center Wuhan.Due to the epidemic center, the local disease control work has been under tremendous pressure, and scientific and technological means are urgently needed to improve work efficiency.As in most other places, the investigation of close contact groups (hereinafter referred to as close contact groups) with confirmed patients in Wuhan was initially conducted through an original paper questionnaire and then entered into a computer.This mode is not only inefficient and error-prone, but also has a long data turnaround period.In Wuhan, every second of waste could mean a potential agglomeration.In order to solve this difficult problem, Yi Yi’s scientific research team with expertise in the field of flow regulation customized the EDC flow regulation system for the Wuhan Center for Disease Control and Control and went online within a week.This system can manage the local close contact population in Wuhan. After obtaining the close contact list provided by the big data platform, the system will import the list into the close contact management system, store it by district, and allocate relevant information according to the community grid.At the grassroots level, the mobile personnel in the close contact group can use the system to make inquiries directly during the on-site investigation.After filling in the data, it can be uploaded to the CDC in a timely manner, and a graphical interface and reports are generated. The CDC personnel can see the updated data in time, and can analyze the data more intuitively and conveniently.This system has greatly improved the efficiency of the local CDC’s transmission of confidential information, and greatly reduced the reporting work of grassroots community workers.The actual process of close reporting is the community grid staff- street health service center- district disease control- city disease control- provincial disease control. The reports and functions required by personnel at different levels are different.In order to meet this demand, the Yi Yi team quickly developed customized reports and data export functions on the basis of the previous system, and accurately performed reports according to data permissions at all levels, maximizing the reporting efficiency of units at all levels.Based on big data thinking, the close contact system includes three layers of data filtering functions.The first is the front-end entry standardization, filtering unreasonable data from the beginning.Secondly, the Yiyi team applied the existing Yiyi data management artificial intelligence to automatically remind errata of suspicious data through dynamic learning of the existing data range; finally, it can cooperate with manual review to sample and approve the data.In order to ensure the quality of the extracted data, it has helped the application of data on the big data platform.As of now, the EDA EDC close contact management platform covers all administrative areas and centralized isolation points in Wuhan. It is used by more than 700 CDC and community service personnel and has accumulated more than 80,000 close contact data.At the same time, the local disease control department is also applying to connect with the resident identity system, complete the connection of all data at one time from the entry source, connect all the data such as close contact, flow adjustment, residence information and social relations, and bring the data value to a greater extent.The construction of big data should not change the original intention of opening up the “information island” into more “information islands”. The fact is that in the informatization of health informatization in recent years, the progress of the construction of public health informatization has lagged significantly.The CDC dedicated network mainly stays in the reporting of various infectious disease information, and the use rights are basically at the national level, and the provinces, cities, and counties cannot use the data and cannot form timely and effective analysis conclusions.At the same time, the work of the disease control centers at all levels, including disease surveillance, vaccination, and health emergency management, has failed to establish an information system that connects China and Shanghai.Although various regions are investing in the establishment of big data platforms, the national universal health basic information system promoted by various regions is currently far from the information system of disease control agencies. Both basic information collection, entry, standard use and management departments are separated and promoted, and they have not yet been completed.Unified and efficient public health information platform.Much data cannot be used effectively.Frankly, every department and region is fighting.Without the top-level overall design and promotion, the original intention of eliminating “information islands” might have created more “information islands” in the future.For this potential problem, industry experts believe that it is necessary to carry out unified planning and standardization for the construction of big data, so that different platforms can be effectively connected, and do not create more “information islands”.At the same time, we should pay particular attention to the effective operation of data, and the application must be implemented in specific business scenarios.Otherwise, the big data collected through painstaking efforts will be too large and useless, which will offset its due effect.At the end of the writing, we must soberly realize that, although there are a lot of great points in this anti-epidemic campaign, Internet + and big data applications are in full swing, but it also reveals that the informationization level of China’s disease control system needs to be improved.In the article “Thinking and Suggestions on the Modernization of Disease Prevention and Control System”, several experts from the Chinese Association of Preventive Medicine Association New Coronavirus Pneumonia Expert Group pointed out that public health big data and information systems are an important component of modernization of disease control systemIn part, it is also an important means and support for improving the capacity of public health services.In the future, we should build on the national informatization of national health basics, rely on the reform and improvement of the public health service system, deeply integrate medical services and basic public health information, and use blockchain, big data, artificial intelligence, cloud computing, the Internet of Things, etc.Technology, closely centered on “accurate full-dimensional real-time big data collection system”, “disease surveillance and epidemic law artificial intelligence deep learning system”, “big data cloud computing intelligent early warning and prediction system” and “uniform emergency management unified resource management and deployment system”,It plays an important supporting role in normalized monitoring, early warning and treatment of epidemic conditions, research and judgment of trend prediction, traceability of source of infection, resource allocation and prevention and treatment.Relying on the national health information platform, based on electronic medical records, health records and the entire population database, and under the support of information security, standards, and operation and maintenance guarantee systems, the nationwide epidemic report monitoring and early warning and its public health emergencyIncident information network system.Establish a public health cloud platform and disease control business application system, and implement a series of platform-based platforms such as dynamic disease surveillance and early warning and treatment, full-process management of child vaccination, monitoring and evaluation of health hazard factors, occupational health, maternal and child health, and comprehensive supervision servicesBusiness Applications.Through the public health cloud platform, establish public-oriented public health information services so that ordinary people can truly appreciate the convenience brought by informatization, thereby improving the timeliness, convenience, and fairness of public health services, and increasing public satisfaction..

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