187 results about "Hospital admission" patented technology

A self-service system and user interface supports a patient in initiating, monitoring, and managing administration of admission, discharge and scheduling activity and in accessing medical information. A system supports patient access to medical information of the patient. The system includes an interface processor for receiving patient identification information and a database linking a patient identifier derived using the patient identification information, with prescription information identifying a medication for the patient. A data processor accesses the database to retrieve the prescription information. A display processor initiates generation of data representing an image including the prescription information, in response to the received patient identification information and a request by the patient to access the prescription.

The invention discloses an inpatient medical management quality assessment method which screens historical data and builds models, including authenticating and clearing data, DRG(Diagnosis Related Group)and model classifying, classification and collection of ICD complication and other variables during hospital admission, the statistic tests and screening of hospital admission and complication variables, the building of statistic models and quality assessment. The method also screens current data and calculates a predicated value, including predicated value of risk when a patient is sent in hospital, to realize risk predictions on the mortality rate of each patient, the days spent in hospital and the medical cost in hospital as well. According to the invention, the method effectively converts medical data to find solutions so as to achieve the values of data based on the analysis of big data, the mathematic statistical method and machine learning method. The method also makes medical data comparable. The method not only performs assessment to medical quality among diseases but also realizes proper assessment to managements of treatment in a hospital between doctors, between different departments and between different patients.

The invention discloses a chronic disease condition change event prediction device based on a recurrent neural network, and the device comprises a memory, a processor, and a computer program, a preprocessing module and a chronic disease condition change event prediction model are stored in the memory, and the prediction model comprises a preprocessing module, a condition feature extraction module,and a classification module. When the processor executes a computer program, the following steps are realized: receiving long-term longitudinal data generated by multiple hospitalization of a patient, performing data preprocessing on the number by the preprocessing module, and reconstructing the data of each hospitalization into a feature vector as a to-be-tested data set; Taking the to-be-detected data set as input, extracting disease characteristics by a disease characteristic extraction module, and inputting the disease characteristics into a classification module; And enabling the classification module to output the prediction probability of various events indicating that the illness state changes. The prediction device can predict the event that the chronic disease patient has markeddisease condition change in the target time window, thereby assisting the doctor to formulate reasonable diagnosis and treatment measures and reducing the medical expenditure.

A method and an apparatus for carrying medical and/or emergency information of a person so as to be readily available to emergency response personnel is disclosed. The apparatus has a memory device and a connector. The memory device is a portable storage device and the connector may be a USB interface or connector. More particularly, the apparatus is a keychain size USB flash memory device. The memory device is loaded with an appropriate software application or template and user specific emergency information is installed using that software application or template. When the memory device is plugged into a host computer, typically a hospital admission room computer or a mobile computer in an emergency vehicle, the software application is launched or the template is displayed. Consequently, emergency personnel are able to readily access an individual's emergency information.

The invention discloses a virtual simulation hospital nursing teaching and training system, and a method of constructing the virtual simulation hospital nursing teaching and training system by using a computer multimedia technology and a 3D graphics algorithm of VRT and a virtual reality technology of WEB VRML. The virtual simulation hospital nursing teaching and training system comprises a hospital admission nursing module, a hospitalization nursing module, a patient treatment module, and a hospital discharge nursing module. Each module is composed of an interaction layer, a function layer, a driver layer, an integration layer, and a resource layer. The virtual simulation hospital nursing teaching and training system is constructed using the current advanced virtual reality technology, and thus, a wider and convenient simulation teaching and research platform is provided for all levels of hospitals and medical colleges and universities. The virtual simulation hospital nursing teaching and training system is mainly used for teaching and training students in medical colleges and universities and newly-employed medical staff in hospitals in order to reduce teaching cost, reduce medical risk, save training resources and improve the quality of teaching and training.

The invention relates to an intelligent cerebral apoplexy risk monitoring system, aims at monitoring cerebral apoplexy risk by utilizing personal information (comprising age, gender, blood pressure, stature, weight, constitutional index and the like) of health examination, physical examination and hospital admission, and blood routine examination and blood biochemistry data, belongs to the application of artificial intelligence and big data in the field of health care, and belongs to the cross technical field of artificial intelligence, big data and health care. The invention mainly aims at providing a simple, practicable and high-correctness cerebral apoplexy risk monitoring system. The intelligent cerebral apoplexy risk monitoring system is established through carrying out over ten thousands of predictions, simulations and analysis assessments on the data of more than five hundred and ninety thousands of normal persons and more than twenty thousands of cerebral apoplexy patients by means of prediction simulation and big data value extraction technologies of artificial intelligence, and is capable of helping users and doctors to carry out cerebral apoplexy risk monitoring so as to provide hope for the users to monitor the cerebral apoplexy risks and finally keep away from cerebral apoplexy.

The invention discloses an electronic medical record information correction method and system. The method comprises the steps of establishing a medical knowledge graph; receiving first disease courserecord information input based on the admission record, the latest examination result and doctor's advice information of a patient; receiving first ward round disease course record information input based on the first disease course record, the latest examination result and the doctor's advice information; receiving subsequent disease course record information input based on the previous two disease course records, the latest examination result and the doctor's advice information of the patient; receiving and displaying discharge record information input based on the admission record, all disease course records, the latest examination result and doctor's advice of the patient. In the process, the admission record information, the first disease course record information, the first ward round disease course record information, the subsequent disease course record information and the discharge record information of the secondary patient are subjected to information correction by using themedical knowledge graph, and the correction result is displayed, so that the efficiency of writing medical records by doctors is effectively improved.

The invention discloses a prediction model for organ failure induced by acute pancreatitis, and the model comprises the following steps: S100, preprocessing patient information, and recording events and time nodes by means of {Variables, Time}; s200, sorting the events according to a time sequence, and filling missing values by adopting a Decay mechanism; and S300, carrying out one-hot coding on the data by using an Embedded mechanism, mapping the data to a real vector space, normalizing the data and then inputting the normalized data into a Phased LSTM model. Time gate output is calculated according to the interval time of a patient from admission to the time node of the event, the model training process is accelerated by utilizing the output result of the time gate, the neuron of the output layer is 2, and a softmax function is adopted as an activation function. According to the method, heterogeneous multi-dimensional data can be processed, time information can be flexibly used, andmeanwhile, the judgment of the model is closer to a description of a disease natural process in the real world.

Systems, method, and computer program products are provided for a patient care application. The patient care application provides for a personal reference list for patients, and healthcare providers alike. According to specific embodiments, the patient care application aids in facilitating communication between ambulatory and inpatient care settings such that a patient can track their physician and/or healthcare specialist visits and hospital admissions/discharge history via a mobile device. Likewise, a physician or healthcare professional may access patient demographics, insurance information, and healthcare admission/discharge history via the mobile device. Furthermore, the patient care application allows a physician or healthcare professional to receive or submit information pertaining to the admission of a patient via the mobile device.

The invention discloses a cloud department based on inpatient management and department member collaboration. The cloud department comprises at least one doctor terminal device, at least one nurse and/or nurse practitioner terminal device, at least one patient terminal device, a cloud platform for connecting the doctor terminal device, the nurse and/or nurse practitioner terminal device and the patient terminal device, and a server for connecting the cloud platform, wherein the server is connected with a doctor work station; the server comprises an information publish module, an information push module, an information recovery module, an information reading module and an information statistic module. The cloud department disclosed by the invention is provided with a patient management platform based on a hospital administrative department, and can form a complete set of flow management of hospital admission, hospitalization, hospital discharge and follow-up visit beginning from the hospital admission of the patient, intelligently sends a follow-up visit notification or questionnaire, improves the follow-up visit efficiency and accuracy, and avoids the omission; the follow-up visit data can be processed, calculated or exported at any time.

The invention discloses an operation opportunity prediction model for a patient with acute necrotizing pancreatitis, and the model comprises the following steps: S100, collecting and cleaning case data which comprises admission data and hospitalization data of the patient; S200, sorting the case data to obtain preprocessed data, wherein the sequence form of the preprocessed data is {Variables, Time}; S300, carrying out one-hot coding on the category type variables in the preprocessed data by using an Embedding mechanism, and then mapping the category type variables into a real vector space; S400, normalizing the data obtained in the step S300, introducing an LSTM model which is a multi-task model, wherein the output layer of the LSTM model is provided with four outcome indexes: 48-hour death after the patient is hospitalized, the number of remaining hospitalized days, whether organ failure occurs or not and whether an operation is performed or not. The optimal operation opportunity ofeach patient with acute pancreatitis necrosis can be determined according to the characteristics of the patient, so the prognosis condition of the patient is improved.

The invention provides an intelligent 5G emergency ambulance cloud emergency system and method. The intelligent 5G emergency ambulance cloud emergency system comprises an emergency ambulance (1), a mobile communication base station (2) and a hospital (3). The emergency ambulance (1), the mobile communication base station (2) and the hospital (3) are connected through a 5G communication technology.The emergency ambulance (1) sends vital sign data of a patient and high-definition audio and video data in the emergency ambulance to the hospital (3). The hospital (3) generates a rescue and treatment scheme before the emergency ambulance (1) arrives at the hospital (3). The system can selectively transmit high-definition images and vital sign data of a patient before admission through a 5G transmission technology. The invention further discloses an intelligent 5G emergency ambulance cloud emergency method, a doctor can remotely guide the emergency process in real time before a patient enters a hospital, the pre-hospital rescue treatment efficiency and quality are improved, and a reception doctor can know and diagnose the illness state of the patient and the equipment condition as soon as possible.

The invention discloses an early prediction method and system for prognosis risk degree of a patient infected with COVID-19. According to the method and the system, an early prediction model of the prognosis risk degree of the patient infected with COVID-19 is established based on the admission information and the dynamic change of the COVID-19 infected patient, and the prediction model can provide important risk indexes for early identification of important critical patients, so that in the prevalence period of report case increase and medical resource shortage, severe COVID-19 cases can be recognized more accurately and quickly, and the treatment effect of the patient is improved.

The invention discloses a hospital self-service terminal system and an intelligent triage management system thereof. The hospital self-service terminal system comprises a self-service terminal, a hospital server and a hospital computer. The hospital server stores inpatient information, receives a signal of the self-service terminal, and responds to the self-service terminal. The hospital computeris connected to the hospital server through an optical fiber and receives data from the hospital server. The self-service terminal scans a hospitalization certificate issued by a doctor and communicates with the hospital server for verification, and a patient handles a hospital admission procedure and selects a ward bed through the self-service terminal. The hospital server is connected to the self-service terminal through the optical fiber, receives the data of the self-service terminal, and manages a personal account of the patient. The self-service terminal reads the information of the personal account of the patient and provides patient information inquiry and printing services. The self-service terminal has a built-in card warehouse and provides distribution, binding and recharge services of inpatient department magnetic cards. According to the hospital self-service terminal system and the intelligent triage management system, the time and energy of the patient are effectively saved, a hospital admission preparation process is simplified, the quick hospital admission of the patient is facilitated, and a complete purchase service of daily necessities is provided for the patient.