Abstract: In some examples, data associated one or more medical presentations of a person is received. A mapping that associates the data to one or more candidate diagnoses is determined. The one or more candidate diagnoses correspond to one or more possible medical conditions associated with the person. A validation score for a first candidate diagnosis, of the one or more candidate diagnoses, is generated based on a set of features associated with the person. The validation score indicates an accuracy of the candidate diagnosis. At least partially in response to the generating of the validation score, an indicator is caused to be provided at a user device. The indicator indicates the validation score.

Abstract: Methods, systems, and computer-readable media are disclosed herein for automatically identifying and selecting, based on machine learning model(s), one or more patient educations instructions as suggested smart recommendations for a particular patient, based on parameter matching and filtering with other patient parameters. The parameters of a current patient can be automatically matched to parameters that are associated with other patients, on a one-to-one parameter basis or based on specific combinations parameters. Based on the presence of one or more shared parameters between a current patient and other patients as well as the strength of the parameter match, one or more instructions that were provided to the other patients can be recommended for the current patient automatically. Instruction(s) selected for the current patient can then be stored in association with the parameters of the current patient and used to evaluate and make recommendations for subsequent patients.

Abstract: In some examples, data associated one or more medical presentations of a person is received. A mapping that associates the data to one or more candidate diagnoses is determined. The one or more candidate diagnoses correspond to one or more possible medical conditions associated with the person. A validation score for a first candidate diagnosis, of the one or more candidate diagnoses, is generated based on a set of features associated with the person. The validation score indicates an accuracy of the candidate diagnosis. At least partially in response to the generating of the validation score, an indicator is caused to be provided at a user device. The indicator indicates the validation score.

Abstract: Embodiments of the present disclosure describe methods, systems, and computer storage media for detecting sepsis in a maternal patient and causing for display a visual indicator of a graphical object. In some aspects sepsis may be detected based on a maternal patient's patient information and/or clinical diagnostic. Technologies described herein may be used to determine maternal-fetal sepsis and provide a graphical object of a patient's risk of the maternal-fetal sepsis. The visual indicator and graphical object may be identifiable to a clinician as a warning of a risk for maternal-fetal sepsis. In this way, maternal-fetal sepsis may be identified and a graphical object may be generated, facilitating timely treatment and early diagnosis of maternal-fetal sepsis.

Abstract: Methods, systems, and computer-readable media are provided for computer based healthcare information to automatically provide reminders to a patient to take prescription medications at the appropriate times on the patient's mobile device. The patient enters a medical record number (MRN) into the mobile device and this information is communicated to the patient's electronic medical record. The user device receives the prescription information for the MRN from the patient's electronic medical record. Utilizing the prescription information on the user's device, the user can view and set reminders to take the medication directly from the patient's mobile device.

Abstract: Methods, systems, and computer-readable media are provided for computer based healthcare information to automatically provide reminders to a patient to take prescription medications at the appropriate times on the patient's mobile device. The patient enters a medical record number (MRN) into the mobile device and this information is communicated to the patient's electronic medical record. The user device receives the prescription information for the MRN from the patient's electronic medical record. Utilizing the prescription information on the user's device, the user can view and set reminders to take the medication directly from the patient's mobile device.

Abstract: In some examples, data associated one or more medical presentations of a person is received. A mapping that associates the data to one or more candidate diagnoses is determined. The one or more candidate diagnoses correspond to one or more possible medical conditions associated with the person. A validation score for a first candidate diagnosis, of the one or more candidate diagnoses, is generated based on a set of features associated with the person. The validation score indicates an accuracy of the candidate diagnosis. At least partially in response to the generating of the validation score, an indicator is caused to be provided at a user device. The indicator indicates the validation score.

Abstract: Methods, systems, and computer-readable media are disclosed herein for automatically identifying and selecting, based on machine learning model(s), one or more patient educations instructions as suggested smart recommendations for a particular patient, based on parameter matching and filtering with other patient parameters. The parameters of a current patient can be automatically matched to parameters that are associated with other patients, on a one-to-one parameter basis or based on specific combinations parameters. Based on the presence of one or more shared parameters between a current patient and other patients as well as the strength of the parameter match, one or more instructions that were provided to the other patients can be recommended for the current patient automatically. Instruction(s) selected for the current patient can then be stored in association with the parameters of the current patient and used to evaluate and make recommendations for subsequent patients.

Abstract: Embodiments of the present disclosure describe methods, systems, and computer storage media for detecting sepsis in a maternal patient and causing for display a visual indicator of a graphical object. In some aspects sepsis may be detected based on a maternal patient's patient information and/or clinical diagnostic. Technologies described herein may be used to determine maternal-fetal sepsis and provide a graphical object of a patient's risk of the maternal-fetal sepsis. The visual indicator and graphical object may be identifiable to a clinician as a warning of a risk for maternal-fetal sepsis. In this way, maternal-fetal sepsis may be identified and a graphical object may be generated, facilitating timely treatment and early diagnosis of maternal-fetal sepsis.

Abstract: Methods, systems, and computer-readable media are provided for computer based healthcare information to automatically provide reminders to a patient to take prescription medications at the appropriate times on the patient's mobile device. The patient enters a medical record number (MRN) into the mobile device and this information is communicated to the patient's electronic medical record. The user device receives the prescription information for the MRN from the patient's electronic medical record. Utilizing the prescription information on the user's device, the user can view and set reminders to take the medication directly from the patient's mobile device.

Abstract: Methods, systems, and computer-readable media are provided for computer based healthcare information to automatically provide reminders to a patient to take prescription medications at the appropriate times on the patient's mobile device. The patient enters a medical record number (MRN) into the mobile device and this information is communicated to the patient's electronic medical record. The user device receives the prescription information for the MRN from the patient's electronic medical record. Utilizing the prescription information on the user's device, the user can view and set reminders to take the medication directly from the patient's mobile device.

Abstract: Computerized systems and methods facilitate inputting orders into an electronic order management system based on diagnoses entered into patient care applications. In accordance with the technology described herein, a diagnosis is received via a user interface provided by a patient care application. A type of order for the diagnosis is determined from a data store mapping each of a number of diagnoses to one or more types of orders available for entry to the electronic order management system. An order entry user interface for submitting orders to the electronic order management system is launched, and an order corresponding to the type of order identified for the diagnosis is input into the order entry user interface. The order can be reviewed by a clinician and submitted via the electronic order management system for processing and fulfillment.

Abstract: Methods, systems, and computer-readable media are provided for computer based healthcare information to automatically provide reminders to a patient to take prescription medications at the appropriate times on the patient's mobile device. The patient enters a medical record number (MRN) into the mobile device and this information is communicated to the patient's electronic medical record. The user device receives the prescription information for the MRN from the patient's electronic medical record. Utilizing the prescription information on the user's device, the user can view and set reminders to take the medication directly from the patient's mobile device.