An example of a system for providing patient care guidance to a caregiver based on ultrasound detection of blood flow includes a defibrillator including an electrode assembly and an output device, a portable computing device communicatively coupled to the defibrillator and including an output device, a Doppler shift waveform evaluation engine disposed at the defibrillator and/or the portable computing device, and a wearable ultrasound blood flow sensor configured to couple to a patient and the defibrillator and/or the portable computing device and to generate data signals representing a Doppler shift waveform. The engine is configured to receive the data signals representing the waveform, generate caregiver instructions according to a cardiac arrest protocol, analyze the waveform based on the received data signals, identify heart-induced blood flow based on the waveform analysis, and generate and provide caregiver instructions according to a non-cardiac arrest protocol based on the identified heart-induced blood flow.

A method for generating and presenting a patient readmission risk using a readmission risk analysis system, comprising: (i) receiving information about the patient, wherein the information comprises a plurality of readmission prediction features; (ii) extracting the plurality of readmission prediction features from the received information; (iii) analyzing the readmission prediction features to determine whether each of a predetermined list of readmission prediction features are present; (iv) replacing one or more identified missing readmission prediction features with a null value to generate a complete set of readmission prediction features for the patient; (v) analyzing the complete set of readmission prediction features for the patient to generate a readmission risk score; (vi) determining, using a populated lookup table of the readmission risk analysis system, an AUC score; and (vii) displaying the generated readmission risk score and the determined AUC score.

A method for generating and presenting a patient readmission risk using a readmission risk analysis system, comprising: (i) receiving information about the patient, wherein the information comprises a plurality of readmission prediction features; (ii) extracting the plurality of readmission prediction features from the received information; (iii) analyzing the readmission prediction features to determine whether each of a predetermined list of readmission prediction features are present; (iv) replacing one or more identified missing readmission prediction features with a null value to generate a complete set of readmission prediction features for the patient; (v) analyzing the complete set of readmission prediction features for the patient to generate a readmission risk score; (vi) determining, using a populated lookup table of the readmission risk analysis system, an AUC score; and (vii) displaying the generated readmission risk score and the determined AUC score.

A medical information processing system of an embodiment includes a processing circuit. The processing circuit acquires examination data showing medical examination results with respect to a medical treatment subject and reply data showing reply results of a medical examination by interview with respect to the medical treatment subject. The processing circuit estimates information on medical treatment of the medical treatment subject by inputting the examination data of the medical treatment subject to a first model and estimates information on medical treatment of the medical treatment subject by inputting the examination data and the reply data of the medical treatment subject to a second model. The processing circuit outputs a first estimation result of the first model and a second estimation result of the second model via an output unit.

Various methods and systems are provided for longitudinal presentation of patient information. In one example, a computing device comprises a display screen, the computing device being configured to display on the screen a timeline of patient medical information including a plurality of symbols representing the patient medical information, wherein a symbol of the plurality of symbols is selectable to launch a details panel and enable a report that references the displayed patient medical information to be seen within the timeline, and wherein the symbol is displayed while the details panel is in an un-launched state.

The present disclosure relates to systems and methods for matching electronic activities with record objects based on entity relationships. The method can include accessing a plurality of electronic activities, identifying an electronic activity, identifying a first participant associated with a first entity and a second participant associated with a second entity, determining whether a record object identifier is included in the electronic activity, identifying a first record object of the system of record that includes an instance of the record object identifier, and storing an association between the electronic activity and the first record object. The method can include determining a second record object corresponding to the second entity, identifying, using a matching policy, a third record object linked to the second record object and identifying a third entity, and storing, by the one or more processors, an association between the electronic activity and the third record object.

This document discusses, among other things, an apparatus comprising a pump configured to deliver insulin, a processor, and a user interface including a bistable display. A display element of the bistable display is placed in one of two stable orientations upon application of a biasing voltage and stays in the stable orientation when the biasing voltage is removed. The processor includes a display module configured to display a non-blank reversion display screen on the bistable display when no input is received at the user interface after a specified time duration, and to recurrently change the reversion display screen until input is received at the user interface.

This document discusses, among other things, an apparatus comprising a pump configured to deliver insulin, a processor, and a user interface including a bistable display. A display element of the bistable display is placed in one of two stable orientations upon application of a biasing voltage and stays in the stable orientation when the biasing voltage is removed. The processor includes a display module configured to display a non-blank reversion display screen on the bistable display when no input is received at the user interface after a specified time duration, and to recurrently change the reversion display screen until input is received at the user interface.

A system for assessing risks of T2DM complications includes: a data acquisition module obtaining and inputting assessment parameters of a patient with T2DM into a risk assessment module; and the risk assessment module inputting the assessment parameters into a number of risk equations and using it to calculate risk values of the complication occurring after a period of time. The risk equation for all diabetic complications (i,j) is:

r.sub.a(t,i,j)=1−exp{[H(t.sub.0)−H(t.sub.1)]C.sub.a(t,i,j)}

r.sub.a(t, i, j) is the risk value for the patient to develop the complication j from the current disease i at age t. t.sub.0 is an age of one patient at a state of the disease i. t.sub.1 is an age of the patient after the period of time. t is an age between t.sub.0 and t.sub.1. H(t.sub.0) and H(t.sub.1) are hazards of the complication occurring at the age t.sub.0 and the age t.sub.1, respectively. C.sub.a(t, i, j) is a Cox proportional hazards regression expression, and is represented by:

C.sub.a(t,i,j)=exp(R.sub.a(t,i,j))

R.sub.a(t, i, j) is an influence degree of risk factors X on the complication j.

A system for assessing risks of T2DM complications includes: a data acquisition module obtaining and inputting assessment parameters of a patient with T2DM into a risk assessment module; and the risk assessment module inputting the assessment parameters into a number of risk equations and using it to calculate risk values of the complication occurring after a period of time. The risk equation for all diabetic complications (i,j) is:

r.sub.a(t,i,j)=1−exp{[H(t.sub.0)−H(t.sub.1)]C.sub.a(t,i,j)}

r.sub.a(t, i, j) is the risk value for the patient to develop the complication j from the current disease i at age t. t.sub.0 is an age of one patient at a state of the disease i. t.sub.1 is an age of the patient after the period of time. t is an age between t.sub.0 and t.sub.1. H(t.sub.0) and H(t.sub.1) are hazards of the complication occurring at the age t.sub.0 and the age t.sub.1, respectively. C.sub.a(t, i, j) is a Cox proportional hazards regression expression, and is represented by:

C.sub.a(t,i,j)=exp(R.sub.a(t,i,j))

R.sub.a(t, i, j) is an influence degree of risk factors X on the complication j.