107 results about "Clinical decision support system" patented technology

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.

The invention discloses a clinical medication decision support system which relates to the field of medical systems. The system comprises a standard treatment medication module, a medication safety reviewing module and a prescription monitoring and warning module, wherein the standard treatment medication module is used for providing diagnosis and treatment reference information and a medication reference scheme according to pathology and physiology information of a patient, so that a doctor can make a prescription; the medication safety reviewing module is used for reviewing the medication safety condition of the patient according to the prescription and generating a reviewing log; and the prescription monitoring and warning module is used for estimating the prescription and generating warning information according to the reviewing log. The system can automatically provide the medication scheme according to the pathology and physiology information of the patient, assist in the doctor to make the prescription, review the safety of the prescription, estimate the prescription and conduct warning on the reviewing result, thus remarkably improving the security, normalization and standardization of clinical medication; and furthermore, by virtue of an antibacterial agent management module, the effective management and monitoring on antibacterial agents are realized, and thus the safety of the clinical medication is further improved.

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 clinical decision support system enabling sharing of medical knowledge in a machine executable format, implementable as a network-based system in which individual executable knowledge modules (EKMs) define the data requirements for assessing a patient, the conclusions that can be drawn using that data, and instructions on how to generate those conclusions. Using standards-based messages transmitted over a network, client decision support applications provide patient data to the system and receive patient-specific assessments and recommendations. The system permits re-use of executable medical knowledge across diverse applications and care settings, easy authoring of knowledge modules, and use of the system framework to implement decision support applications having significant clinical utility.

A clinical decision support system for patient treatment having a monitoring device operably coupled to a patient. The monitoring device outputs a monitoring signal in response to a measured parameter of the patient. A controller receives the monitoring signal and outputs a display signal. The controller further compiles clinical documentation relating to the patient based on the monitoring signal and outputs a documentation signal related thereto. A display device then receives the display signal and the documentation signal and displays the same in connection with various indicia thereby displaying both the monitored signal and associated clinical documentation.

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.

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.

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.

User interfaces for proactive and interactive clinical decision support events are provided. When a clinical decision support event is initiated for a patient, relevant stored clinical information associated with the patient is accessed. A user interface is generated using the stored clinical information. A clinician may interact with the user interface by providing user-provided clinical information that may add to and/or modify the stored clinical information in the user interface. Clinical advice is provided via the user interface based on the stored clinical information and the user-provided clinical information.

Computer-implemented methods, systems and computer-storage media that provide a link to a clinical decision support event interface are described. Clinical information for a patient is received and compared against one or more rules associated with a clinical decision support even. It is determined whether at least one of the rules associated with the clinical decision support event have been satisfied based on the clinical information. A clinical decision support event user interface is generated for presentation utilizing the clinical information for the patient. The user interface includes at least a portion of the clinical information for the patient and clinical advice based on the clinical information for the patient. A link to the user interface is displayed to a user.

The invention discloses a clinical decision support system and a decision-making method and belongs to the field of medical artificial intelligence. The clinical decision support system and the decision-making method are provided to solve the problems that in the prior art, existing decisions are made for a special discipline, the correct rate of data diagnosis is still not high, and the resource sharing degree is low. The system comprises a medical knowledge base, an inference machine, an interpreter and a human-computer interaction module providing an interaction platform for patients and doctors. According to the method, after a doctor selects symptom characteristics, the symptom characteristics are input into the internal inference machine, information of a possible disease of a patient and the probability of the disease are calculated, then a doctor client refreshes a new to-be-inquired symptom problem and displays the probability of the disease to the doctor through a chart, and an interpretation module on a page can display interpretation of the possible disease. Through the clinical decision support system and the decision-making method, clinical decision support for primary medical symptoms can be realized, the decision level is increased, and the correct rate of data diagnosis is high.

A clinical decision support system for diagnosis and monitoring of a disease of at least one patient includes a computing system, a storage media, in communication with the computer system, configured to store medical data sets from two or more different medical training data sources, where each of the two or more different medical training data sources have uniquely defined objective findings influencing the disease. The severity of the objective findings within each data source is indicated by independent weight factors. A computer program operating on the computing system is configured to perform a knowledge mapping between the data in the two or more different medical training data sources for obtaining plurality of clinical weight factors that define a unique classification rules. An input module is provided for receiving medical data about a patient, and a processor processes the received medical data from the input module.

The invention discloses a multi-source data assisted clinical decision support system and method. The system comprises a medical/pharmaceutical data storage unit, a clinical pathway/evidence storage unit, an information input unit, a comparison processor unit, a diagnosis and treatment result output unit and an early warning and operation recording unit. According to the multi-source data assisted clinical decision support system and method of the invention, feasible clinical diagnosis and treatment scheme suggestions and corresponding clinical research and real world research evidence support are provided for medical workers, the whole disease cycle of diagnosis, treatment, prognosis, out-of-hospital management, disease burden and the like is covered, and doctors are assisted to quickly improve the clinical diagnosis and treatment level; the contradiction that time and energy of medical workers are difficult to keep up with clinical research progress is solved, and medical resources are balanced. After the system is deployed to a hospital end, when a doctor makes a prescription, the system can carry out reasonable early warning on treatment and medication schemes specifically involved in a clinical path; based on individual differences of patients, the reasonability and safety of the medication schemes are ensured, and the overall medical service quality is improved.

Certain embodiments of the present invention provide a system for clinical decision support including a rules engine and a notification component. The rules engine is capable of processing message data based at least in part on a rule to determine an action. The rule is user-configurable. The notification component is capable of initiating a notification based at least in part on the determined action.

The present invention relates to computer-based clinical decision support tools including, computer-implemented methods, computer systems, and computer program products for clinical decision support. These tools assist the clinician in identifying epilepsy patients who are candidates for surgery and utilize a combination of natural language processing, corpus linguistics, and machine learning techniques.

A clinical decision support system comprises a clinical guideline (1) extended with expert recommendations. At least one of the nodes (3) is associated with a pair (4) of a clinical question (5) and a corresponding clinical answer (6), the pair forming an extension to the clinical guideline (1) for the purpose of the clinical decision support. A node unit (7) is arranged for determining a node (3) of the plurality of nodes, based on a condition of a specific patient and the set of clinical preconditions of the node. A presenting unit (8) is arranged for presenting at least a part of the pair (4) of the question (5) and/or the corresponding clinical answer (6) associated with the relevant node (3). A matching unit (10) is arranged for matching a question against a collection of existing questions previously answered, in dependence on the relevant node (3).

The invention discloses a clinical decision supporting system of tumor diagnosis and treatment. The clinical decision supporting system comprises a data layer, an application layer and a user layer, wherein the application layer comprises a cache; the user layer comprises an input module and a client side, wherein the client side comprises a comparison module; the comparison module is connected with a tumor specialized knowledge base and a clinic path module; the comparison module outputs tumor staging information after the comparison module is compared with the tumor specialized knowledge base according to diagnosis information input by the input module; the clinic path module calls a diagnosis and treatment measure according to the output tumor staging information; and the comparison module compares the diagnosis information of a patient with the tumor diagnosis information in the tumor specialized knowledge base, and carries out continuous output on a comparison result by the output of a diagnosis byte to form tumor staging information. The clinical decision supporting system greatly improves the diagnosis efficiency of a tumor doctor and the stability of a diagnosis result, liberates more time and vigor of a tumor specialist to focus on the fine differentiation of a tumor case, and provides directional reference and guidance for forming a final tumor treatment scheme.

The invention provides an innovative structured form labelling method using support documents, so that the documents can achieve the processible effect through medical research and development software. The structured labelling method makes up the shortage that the resource integration of a clinical decision support system is not timely and the sorting of information is not smooth; the data information is labelled and then quickly classified to facilitate query and use, and help a doctor to diagnose the condition of a patient in detail and accurately; the system implements whole-process monitoring from receiving an electronic medical record to diagnosing a patient finally, and respectively does the prompt record, alert analysis and missed and delayed diagnosis analysis before, during and after the diagnosis, so as to achieve the true control in the whole medical process. The structured form enables the doctor to have a basis in the diagnosis, controls the wrong action in the diagnosis process fundamentally, and effectively prevents the occurrence of the adverse events such as the frequent medical accidents, continuous medical disputes and tense doctor-patient relationship.

Systems and methods for providing clinical decision support are provided. It is determined whether a health attribute qualifies for a condition for which a care provider is to be notified. When the health attributes qualifies for such a condition, a patient indication is provided. A first care protocol that includes a plurality of rules and that corresponds to the patient indication is selected. From the plurality of rules, a first plurality of treatment-observation options is generated. Each of the first plurality of treatment-observation options is displayed. A first user-generated response that corresponds to at least one of the plurality of treatment-observation options is received. A treatment recommendation is determined based at least in part on the first user-generated response. The treatment recommendation is displayed. Feedback is accepted with respect to the treatment recommendation.

The present invention relates to an apparatus and method for clinical decision support to identify patients at high risk of thrombosis based on a combination of clinical risk factors and molecular markers, e.g., protein concentrations. These clinical risk factors and molecular markers are combined in a machine learning based algorithm which returns an output value, relating to an estimated risk of a thrombosis event in the future.