227 results about "Clinical decision" patented technology

The information management system disclosed enables caregivers to make better decisions, faster, using aggregated genetic and phenotypic data. The system enables the integration, validation and analysis of genetic, phenotypic and clinical data from multiple subjects who may be at distributed facilities. A standardized data model stores a range of patient data in standardized data classes that encompass patient profile information, patient symptomatic information, patient treatment information, and patient diagnostic information including genetic information. Data from other systems is converted into the format of the standardized data classes using a data parser, or cartridge, specifically tailored to the source system. Relationships exist between standardized data classes that are based on expert rules and statistical models. The relationships are used both to validate new data, and to predict phenotypic outcomes based on available data. The prediction may relate to a clinical outcome in response to a proposed intervention by a caregiver. The statistical models may be inhaled into the system from electronic publications that define statistical models and methods for training those models, according to a standardized template. Methods are described for selecting, creating and training the statistical models to operate on genetic, phenotypic and clinical data, in particular for underdetermined data sets that are typical of genetic information. The disclosure also describes how security of the data is maintained by means of a robust security architecture, and robust user authentication such as biometric authentication, combined with application-level and data-level access privileges.

An integrated medical software system with embedded transcription functionality is disclosed. The system comprises a clinical module for capturing clinical data for a patient in a first electronic document and a communication component that communicates the clinical data to a rule-based clinical decision support (CDS) system and receives at least one of an alert, warning, reminder, and recommendation back from the CDS system based on the clinical data. The CDS system is configured to compare the clinical data against a knowledge base to identify the at least one of an alert, warning, reminder, and recommendation; the clinical data is serialized into a standardized database language and placed into a first electronic clinical document defined by a clinical document exchange standard before being communicated to the CDS system; and the at least one of an alert, warning, reminder, and recommendation is provided in a second electronic clinical document defined by the clinical document exchange standard when received back from the CDS system.

A method of presenting glucose data to a person with diabetes from a blood glucose meter is provided in which an effective meal average (EMA) value is presented, followed by two or more of the individual values that make up the EMA, to provide improved feedback data for clinical decisions by patients who need to alter their dose of insulin. The EMA can also comprise a measure of the variability of its constituent values. The EMA encompasses those values that occur at specified times such as 1 hour before and 1 hour after a specified meal time. The EMA is calculated over a limited number of days previous to the calculation (e.g., 3 days) and has a minimum number of values that must be obtained within the time and date ranges. An algorithm allows for exclusion of any given reading from the average (e.g., post-prandial or control solution readings). Patients can use 1 to 8 EMA on any given date range (e.g., preferably 4, that is, breakfast, lunch, supper and bedtime snack).

The use of a medical software platform to collect, normalize, and aggregate clinical data. Supports clinical care and provides research and clinical tools for institutions, healthcare providers, researchers, and patients. The invention provides a graphical interface to customize searches in the database for specified subsets of conditions, treatments or outcomes. The invention also provides a system and method for searching through clinical databases for desired terms which may provide additional information to a physician regarding patient care. Furthermore, this invention facilitates personalized medicine-based practices as relationships between genetics, personal heath data from multiple sources, disease risk and drug response can be more easily visualized and utilized for patient care and research.

The present invention involves a system and method of providing decision support for assisting medical treatment decision-making. A patient agent software module processes information about a particular patient. A doctor agent software module processes information about a health status of a particular patient, beliefs relating to patient treatments, and the actual effects of treatment decisions. By filtering information over time from the patient agent into the doctor agent, a plurality of decision-outcome nodes are created and formed into a patient-specific outcome tree with the plurality of decision-outcome nodes. An optimal treatment is determined by evaluating the plurality of decision-outcome nodes with a cost per unit change function to output the optimal treatment. When additional information is available from at least one of the patient agent and the doctor agent, the filtering, creating, and determining steps are repeated thus allowing for the system to “reason over time”, continuously updating and learning as new information is received.

Embodiments of clinical support systems and methods are disclosed. In one embodiment, methods for clinical performance measurement are disclosed comprising defining a range of acceptable treatment variable values for a treatment variable of a subject, performing a selected intervention, determining an affected treatment variable value and determining a selection performance measure by comparing the affected treatment variable value to the acceptable treatment variable values. In one embodiment, methods for clinical decision training are disclosed comprising defining a treatment variable of a simulated subject, selecting an intervention on the treatment variable, modeling the selected intervention and presenting a visual representation of the selected intervention. In one embodiment, methods for clinical decision support are disclosed comprising defining a treatment variable of a subject, presenting a decision support variable as clinical decision support, performing a selected intervention and determining an affected treatment variable value.

Improved risk categorization is provided for clinical decision support and forecasting future health care spend. A risk index is provided that improves on other risk stratification models by synthesizing electronic medical records and health questionnaires with an individual patient's claim histories. Machine learning algorithms catalogue patients into distinct group clusters, based on risk which may be associated with annual health care spending, thereby enabling administrators to forecast future health care spending on the individual and population level.

A method and system for providing information for facilitating health care and clinical decisions. The method includes receiving input data specifying patient information, comparing that data with one or more predetermined sets of reference data, and determining and displaying the output data. The output data can be displayed graphically, such as using bubbles of different color and size, to indicate risk factors and their contribution to potential health problems. After displaying the data, a second set of input data can be entered, and this data is compared to the first set of data and to one or more sets of reference data. A second set of output data is generatable and displayable alongside the first set of output data,

The invention relates to a medical big-data acquisition and analysis system and method. The medical big-data acquisition and analysis method includes: installing a data acquisition unit at the place where medical data are acquired, and uploading the acquired data to an online medical acquisition platform; subjecting to the stored data to data processing; finding out a rule in the data; applying the data subjected to analysis to medical fields to carry out clinical decision and disease warning and analyze behaviors of patients. The medical big-data acquisition and analysis system comprises the online acquisition platform, an HDFS (hadoop distributed file system), an Hbase and a Hadoop frame, and is divided into four layers, namely a foundation layer, a platform layer, a function layer and a service layer. Staff health conditions can be acquired in real time, massive medical data are stored in a distributed manner, the massive data are analyzed rapidly by batch processing of big data, the clinical decision and disease warning are carried out, the behaviors of the patients are analyzed, multiple treatment schemes are put forward clinically, and early warnings are made for outbreaks of epidemic diseases.

A system and method is disclosed to reduce variation of the clinical decision making process when an image reporting and data system (IRADS) for medical diagnosis is used. Image reporting and data systems provide guidelines for an operator to identify images as belonging to one of a number of categories and specific clinical actions are then recommended based upon such categories. Some clinical actions such as biopsies may be recommended by IRADS even when they are not necessary. The present inventive concept is configured to utilize a Computer-Assisted Diagnosis (CAD) system that is specifically programmed to minimize discrepancies between the recommended clinical actions of an individual or specific group of experts using the standard IRADS process and the optimum clinical actions based on correlation with biopsy proven data. The resulting CAD system reduces the number of unnecessary clinical actions such as biopsies based on the operator's error profile.

A computer-implemented method for transmitting a scheduled appointment to a patient. Input is received, wherein the input comprises at least one of current data provided by the patient, prior data provided by the patient prior to the current data, and known medical history of the patient. A possible condition of the patient is diagnosed based on the input. The appointment is scheduled for the patient based on the diagnosis. The scheduled appointment is transmitted to the patient.

A method and system for providing medical decision support is disclosed herein. The method comprises: accessing a record pertaining to a patient stored with different patient parameters, including patient status and linking various patient parameters based on their relation. A change in at least one patient parameter is detected and the corresponding related parameters are identified. The related patient parameter may be displayed to a clinician for assisting him in providing clinical decisions or for seeking his feedback on updating the related patient parameter in the record. The record is further updated with the detected change in patient parameter and corresponding changes in the related patient parameter. In an embodiment, the record is an electronic medical record.

A handheld device, such as a personal digital assistant ("PDA"), can be used at the point-of-care to find an appropriate pair of diagnosis code and procedure code for use in writing an order for further medical procedures for a particular patient. The choice of diagnosis code and procedure code can be checked for conformance with the requirements set forth in a particular set of medical necessity policy rules. In a preferred embodiment, the codes and rules are aggregated by medical specialty so that a specialist can work with solely those codes and rules that are relevant to that particular medical specialty. This abstract is provided as a tool for those searching for patents, and not as a limitation on the scope of the claims.

Systems, methods, and user interfaces for providing dynamic risk stratification for clinical decision support are provided. Selections of patient types are received for patients. Assessments to utilize are determined in accordance with the patient types. The assessments are displayed to facilitate first clinicians assessing risk factors and contraindications for the patients. Information associated with the patients is received. Second clinicians may be alerted if risk factors for the patients are identified and the assessments have not been completed. Pharmacologic and/or mechanical prophylaxis is recommended and overall risk levels associated with the patients are indicated.

The invention provides a construction method and device for a clinic decision auxiliary system and the clinic decision auxiliary system and relates to the clinic medical technology field. The construction method comprises steps that the clinic reference information is acquired, and the clinic reference information comprises case data and/or medical literatures; the entity information is extractedfrom the clinic reference information, and types of the entity information comprise disease names, symptoms and treatment means; data structure pairs are constructed through at least two types of entity information so as to construct the clinic decision auxiliary system based on the data structure pairs. The construction method is advantaged in that a technical problem of inconvenient use existingin a traditional clinic decision auxiliary system is alleviated.

The present invention, defined as MEGICS (Medical+Logics), has been developed in order to improve quality of care and enhance the efficiency of operation of healthcare facilities and providers. When front-line healthcare doctors and nurses make various clinical decisions, MEGICS management system can provide them with relevant clinical knowledge in a timely manner. MEGICS management system can, therefore, increase the level of user satisfaction and provide patients with better quality of healthcare services. The Inventor believes that the present invention, MEGICS, can penetrate a new market and be easily adopted by healthcare facilities, due to its flexible system structure, experience-based approach, and user-friendly features.

The invention discloses an electronic medical record phenotype extraction and phenotype name normalization method. The method comprises the following steps: phenotypic extraction: taking natural statements of a medical record text as original data, and adopting Bi-; Named entity recognition is carried out on the LSTM model and the CRF model, and a phenotypic entity class is extracted; And phenotype standardization is carried out, an LSTM encoder is adopted to encode each phenotype, cosine similarity between non-standard phenotype codes and standard phenotype codes in the medical records is calculated, and the non-standard phenotype codes are mapped to the phenotype with the highest cosine similarity. The invention further discloses an electronic medical record phenotype extraction and phenotype name normalization system. According to the method, the named entity identification accuracy, the recall accuracy and the phenotypic mapping accuracy in the electronic medical record are improved; The labor consumption in the medical record structuring process is avoided, and the medical record structuring efficiency is improved; The method can more efficiently and accurately serve medical data mining, clinical decision support, clinical risk assessment and the like.

A method, device, and computer program product for monitoring the physiological state of a subject is disclosed. A first plurality of physiological signals are acquired from the subject and a second plurality of signal parameters are determined based on the first plurality of physiological signals. Relevant signal information is selected from among the signal information produced and at least one screen page is displayed at a time, thereby to make at least part of the signal information visible to a user. The signal information visible to the user on the at least one screen page may then be revised based on the selection and without user interaction, thereby to accelerate clinical decision-making and to diminish the possibility of undetected changes in the state of the subject.

An informatics platform comprising method, system, and computer program is provided for interacting with clinical decision support systems such as the IBM Watson for Oncology, for the purpose of utilizing cognitive processing, machine learning and natural language processing to facilitate the manual to semi-automatic to automatic acquisition of a patient's electronic medical record data points, into a CDS system for populating the attributes questionnaire, for generating a patient diagnostic report of treatment recommendations. The platform supports deployment on several informatics architectures including client-server, cloud-based and blockchain.

In a typical embodiment, the system is deployed as a Software-as-a-Service (SAAS) application; it implements a cloud-based, real-time architecture comprising: (a) an adaptive user interface providing responsive dashboards for real-time data presentation and user interaction, (b) a hub controller for real-time data flow transformation, (c) a data validation engine which incorporates cognitive natural language processing (NLP) to extract structured and unstructured patient record data from the EHR and employs methods that provide an optimally automated input to the CDS, (d) cloud storage of aggregated CDS data and other clinical datasets, and (e) plug-in support for additional cognitive capabilities such as predictive analytics and data mining.

Embodiments of the present invention may integrate and analyze the output from various medical devices and clinical information systems in order to assist the clinical staff in making the more informed clinical decisions, output intelligent alarms, predict and prevent adverse events, and in some circumstances enable automated patient care.

The invention discloses a sepsis early prediction method based on machine learning. The method comprises the following steps: firstly, extracting clinical data, including demographic statistics (suchas age and gender), vital sign variables (such as heart rate and systolic pressure) and laboratory measurement indexes (such as creatinine and platelet count), of a patient within 24 hours after a patient enters an ICU by utilizing an electronic medical record, preprocessing the data, inputting the preprocessed data into an improved deep forest algorithm model for training, and outputting the disease probability of the patient after training optimization. And meanwhile, the algorithm model can also sort characteristic variables and output an early warning factor which has great influence on early prediction of sepsis. Finally, corresponding variables of the patient needing to be predicted are input into the trained model, so that early prediction of sepsis can be carried out on the patient. According to the invention, early prediction is carried out on sepsis based on a machine learning method, doctors can be assisted in making clinical decisions, and the prediction accuracy is improved.

Certain embodiments of the present invention provide a clinical decision support system comprising a clinical decision support processing component adapted to determine the occurrence of an event based at least in part on at least one message received by the clinical decision support processing component and an alert feed server adapted to publish a clinical alert to an alert feed based at least in part on the event. The alert feed is in a standard syndication format.