JP2026002180A - Medical information processing device, medical information processing system, and medical information processing method - Google Patents

Abstract

The present invention provides support for efficiently determining the treatment context for an individual subject.
[Solution] A medical image processing apparatus according to an embodiment includes a path identification unit and a presentation unit. The path identification unit identifies paths that connect nodes representing the first state and nodes representing the second state and that define the relationships between the nodes, based on multiple graphs in which the relationships between a first state of a subject, clinical tasks representing medical procedures performed for the first state, and second states of the subject that change as a result of performing the clinical tasks, and a time series of the clinical tasks. The presentation unit presents context information that indicates the relationships between the nodes connected by the paths.
[Selected Figure] Figure 1

Description

The embodiments disclosed in this specification and drawings relate to a medical information processing device, a medical information processing system, and a medical information processing method.

Traditionally, in intensive care units (ICUs) at hospitals and other medical facilities, a single patient is cared for 24 hours a day by a team of multidisciplinary staff. When providing such care, the person in charge of the patient's care collects information about the patient's condition before they took over their care, as well as information about the patient during their absence, and determines policies for treatment and procedures.

However, the above information is generally scattered across various departmental systems. It is also difficult to know who recorded what information and when, and collecting the information is time-consuming. For example, if an order is noticed to have been changed, the person in charge cannot easily determine who made the change and why, which could result in a lengthy decision-making process.

For these reasons, there is a need to efficiently determine the treatment context for each patient.

Patent No. 6780520

One of the problems that the embodiments disclosed in this specification and drawings attempt to solve is to provide support for efficiently determining the treatment context for an individual subject. However, the problems that the embodiments disclosed in this specification and drawings attempt to solve are not limited to the above problem. Problems corresponding to the effects of each configuration shown in the embodiments described below can also be positioned as other problems.

A medical image processing device according to an embodiment includes a path identification unit and a presentation unit. The path identification unit identifies paths that connect nodes representing the first state and nodes representing the second state and that define the relationships between the nodes, based on multiple graphs in which the relationships between a first state of a subject, clinical tasks representing medical procedures performed for the first state, and a second state of the subject that changes as a result of performing the clinical tasks, and a time series of the clinical tasks. The presentation unit presents context information that indicates the relationships between the nodes connected by the paths.

FIG. 1 is a diagram illustrating an example of the configuration of a medical information processing system according to an embodiment. FIG. 2 is a diagram illustrating an example of a clinical card according to the embodiment. FIG. 3 is a diagram illustrating an example of a path identification process according to the embodiment. FIG. 4 is a diagram illustrating an example of a path identification process according to the embodiment. FIG. 5 is a diagram illustrating an example of the operation of each function of the medical image processing apparatus according to the embodiment. FIG. 6 is a diagram illustrating an example of utilization of context information according to the embodiment. FIG. 7 is a diagram illustrating an example of utilization of context information according to the embodiment. FIG. 8 is a flowchart showing an example of processing executed by the medical image processing apparatus according to the embodiment.

Embodiments of a medical information processing device, a medical information processing system, and a medical information processing method will be described in detail below with reference to the drawings.

Figure 1 is a diagram showing an example configuration of a medical information processing system according to this embodiment.

The medical information processing system according to this embodiment is installed in a medical facility such as a hospital, and supports the efficient determination of the treatment context for each subject. Specifically, the medical information processing system according to this embodiment creates multiple general-purpose graphs that define the relationships between nodes, with nodes representing the condition of the subject and the medical procedures performed on the subject. Furthermore, by identifying the paths connecting the nodes based on the condition of the subject and the results of the medical procedures performed on the subject, the system presents subject-specific context information that represents the relationships between the nodes.

For example, as shown in FIG. 1, the medical information processing system 100 according to this embodiment includes an information integration system 110 and a medical information processing device 120. Here, the information integration system 110 and the medical information processing device 120 are connected to each other so that they can communicate with each other via a network 130 such as a LAN (Local Area Network) or a WAN (Wide Area Network).

The information integration system 110 includes a patient information database (DB) 111. For example, the information integration system 110 is realized by computer equipment such as a server or workstation that implements an electronic medical record system, a hospital information system (HIS), a radiology information system (RIS), etc.

Patient information DB111 stores various types of medical information related to patients. A patient is an example of a subject. For example, patient information DB111 stores, as medical information for multiple patients, information such as each patient's illness (pre-existing conditions, complications, side effects, etc.), the results of various measurements and tests for each patient, symptoms (pathological conditions) in response to treatments (including administered medications), connected devices, etc.

The medical information processing device 120 is a device used by users who are medical professionals involved in multidisciplinary rounds (e.g., attending physicians, ICU doctors, ICU nurses, pharmacists, physical therapists, clinical engineers, etc.), and has functions to support decision-making regarding treatment for patients admitted to the ICU. For example, the medical information processing device 120 is realized by a personal computer, etc.

Specifically, the medical information processing device 120 has a network (NW) interface 121, a memory circuitry 122, an input interface 123, a display 124, a camera 125, a microphone 126, and a processing circuitry 127.

The NW interface 121 controls the transmission and communication of various data sent and received between the medical information processing device 120 and other devices connected via the network 130. Specifically, the NW interface 121 is connected to the processing circuitry 127 and outputs patient information received from the information integration system 110 to the processing circuitry 127. For example, the NW interface 121 is realized by a network card, network adapter, NIC (Network Interface Controller), etc.

The memory circuitry 122 stores various data, programs, etc. For example, the memory circuitry 122 may be implemented using semiconductor memory elements such as RAM (Random Access Memory) or flash memory, or a hard disk, optical disk, etc.

Specifically, the memory circuit 122 includes a clinical card DB 122a and a context DB 122b.

Clinical card DB 122a stores multiple clinical cards. For example, a clinical card is a graph in which nodes represent the current state (symptoms, etc.) of a generalized patient, rather than an individual patient, and clinical tasks (examinations, treatments, etc.) that represent medical procedures for the patient, and multiple nodes are arranged based on the relationships between the nodes. Clinical card DB 122a stores information representing the patient's state or medical procedures in association with the clinical card.

Clinical cards are constructed by arranging related nodes, for example, for specific medical procedures or specific diseases. The scope of what a single medical card can represent using nodes is limited, and it is not possible to represent the entire condition of a specific subject. However, through the processing described below, clinical cards can be linked together to form larger network nodes based on the condition of individual patients, or the medical procedures to be performed on those patients, or the medical procedure plan.

This makes it possible to construct a large-scale node network that can efficiently and accurately predict the condition of individual patients and their future conditions.

Context DB 122b stores context information for each patient. For example, the context information is a graph that represents the relationship between the patient's condition and the clinical tasks performed for that condition using paths connecting nodes. Context DB 122b stores a patient ID that identifies the patient and the context information in association with each other.

The input interface 123 accepts input operations of various instructions and information from the user. Specifically, the input interface 123 is connected to the processing circuitry 127, converts the input operations received from the operator into electrical signals, and outputs them to the processing circuitry 127.

For example, the input interface 123 may be realized by a trackball, switch buttons, mouse, keyboard, or other device for performing various inputs, a touchpad for performing input operations by touching the operation surface, a touchscreen that integrates a display screen and a touchpad, a non-contact input circuit using an optical sensor, and an audio input circuit.

In this specification, the input interface 123 is not limited to an interface equipped with physical operating components such as a mouse and keyboard. For example, an example of the input interface 123 also includes an electrical signal processing circuit that receives an electrical signal corresponding to an input operation from an external input device provided separately from the device and outputs this electrical signal to a control circuit.

The display 124 displays various types of information and data. Specifically, the display 124 is connected to the processing circuit 127 and displays various types of information and data output from the processing circuit 127. For example, the display 124 may be implemented as an LCD monitor, a CRT (Cathode Ray Tube) monitor, a touch panel, etc.

Camera 125 captures images of medical staff or patients. Note that multiple cameras 125 may be installed within a medical facility.

Microphone 126 records the voices of medical staff or patients. Note that multiple microphones 126 may be installed within a medical facility.

The processing circuitry 127 controls the components of the medical information processing device 120 in response to input operations received from the operator via the input interface 123.

Specifically, the processing circuit 127 has an acquisition function 127a, a card identification function 127b, a collection function 127c, a path identification function 127d, and a presentation function 127e. Here, the acquisition function 127a is an example of an acquisition unit. The card identification function 127b is an example of a graph identification unit. The path identification function 127d is an example of a path identification unit and a node identification unit. The presentation function 127e is an example of a presentation unit.

The acquisition function 127a acquires medical data indicating the current condition of the target patient to be processed or the medical procedures planned to be performed on the target patient. For example, the acquisition function 127a accepts input from the user via the input interface 123 of information indicating the current condition of the target patient or the medical procedures planned to be performed on the target patient. The patient's current condition may be information expressing the patient's symptoms or diagnosis, or information indicating the test results of various tests.

Alternatively, information obtained by processing the test results of various tests using a judgment algorithm or the like may be stored. For example, whether or not a numerical value obtained from a blood test is abnormal can be determined by applying threshold processing to the numerical value. Instead of accepting the blood test numerical value itself, information on whether or not a specific blood test item is an abnormal value obtained as a result of threshold processing may be received. Alternatively, these pieces of information may be combined.

Specifically, when a patient transported to the ICU is found to be exhibiting symptoms of respiratory failure, the attending physician, who is a user, inputs information indicating "respiratory failure" as the patient's condition. The acquisition function 127a acquires this information as medical data representing the patient's condition. Furthermore, an ICU doctor, who is another user, inputs information indicating "artificial respiration" as the planned medical treatment for "respiratory failure." The acquisition function 127a acquires this information as medical data representing the medical treatment for "respiratory failure."

Similarly, if symptoms of renal failure are observed, the attending physician inputs information indicating "renal failure" as the patient's condition. The acquisition function 127a acquires this information as medical data representing the patient's condition. The physician providing treatment to the patient inputs information indicating "dialysis" as the planned medical procedure for "renal failure." The acquisition function 127a acquires this information as medical data representing the medical procedure for "renal failure."

In the following example, a target patient is described as being admitted to an ICU to receive treatment, but the admission location is not limited to this and may be a department or specialized medical institution where treatment is received. In this embodiment, when a target patient is admitted to the ICU, the acquisition function 127a acquires information indicating the target patient's condition at the time of admission to the ICU as medical data. In addition, the acquisition function 127a acquires medical data each time a medical procedure is performed by a medical professional.

The card identification function 127b identifies clinical cards that have nodes related to clinical tasks that represent the patient's condition or medical procedures to be performed on the patient indicated in the medical data acquired by the acquisition function 127a. A clinical card is an example of a graph.

For example, the card identification function 127b refers to the clinical card DB 122a in the memory circuit 122 and identifies the clinical card corresponding to the input content received by the acquisition function 127a.

Specifically, if the input content received by the acquisition function 127a is information representing the patient's condition of "respiratory failure," the card identification function 127b identifies "respiratory failure," which is the clinical card corresponding to "respiratory failure." Furthermore, if the input content received by the acquisition function 127a is information representing the clinical task of "artificial respiration," the card identification function 127b identifies "artificial ventilator," which is the clinical card corresponding to "artificial respiration."

Similarly, if the input content received by the acquisition function 127a is information representing the patient's condition of "renal failure," the card identification function 127b identifies "renal failure," which is the clinical card corresponding to "renal failure." Furthermore, if the input content received by the acquisition function 127a is information representing the clinical task of "dialysis," the card identification function 127b identifies "dialysis," which is the clinical card corresponding to "dialysis."

Here, Figure 2 is a diagram illustrating an example of a clinical card. Figure 2 shows an example of a clinical card CC1 for "dialysis" and a clinical card CC2 for "artificial ventilator."

Clinical card CC1 has node n11 for "Normal fluid volume," node n12 for "Decreased blood pressure," node n13 for "Fluid removal discontinued," node n14 for "Dyspnea," and node n15 for "Increased fluid volume." Note that nodes of the same type but with different time series, such as node n11 and node n15 (which in this example have in common the fact that their content is information related to fluid volume), may also be arranged.

Specifically, the test values at the time of transport are input into node n11 for "normal fluid content," and another node may be set up to input the normal value for "fluid content" and the target value after treatment. The information input into each node on the clinical card described above may simply be the type of planned medical procedure, or it may also include the input of details of that medical procedure.

For example, when entering a medical procedure such as "artificial respiration," parameters and additional information detailing the nature of the medical procedure, such as the type of artificial respiration equipment to be used, the planned length of the procedure, or the amount of oxygen to be administered, can also be entered into each node.

In addition, clinical cards specify the chronological order relationships between nodes that can be connected. In clinical card CC1 in Figure 2, the chronological order relationships between nodes are predefined according to the patient's condition, medical intervention, and their causal relationships, such as "node n11 → node n13 → node n15" or "node n12 → node n13 → node n14."

Also, the nodes in Figure 2 are displayed as unidentified nodes that have not been adopted as context information (described below), but nodes that have been adopted as context information are displayed as identified nodes.

In addition, nodes are connected to each other by paths. All paths in Figure 2 are displayed as unspecified paths, but paths identified by the path identification function 127d (described below) are displayed as specified paths.

In addition, clinical card CC1 contains related information r12 representing "vital signs" related to node n12, related information r13 representing "dialysis setting values" related to node n13, related information r14 representing "observation items" related to node n14, and related information r15 representing "vital signs" related to node n15. The path identification function 127d identifies a path representing the context of the target patient based on the content of the related information and the relationships between the nodes defined in each path. The process of identifying a path will be described later.

Note that information within a node may be updated depending on the content of the medical data and path identification, and nodes that prompt the user for input may be added. As an example, suppose that "decreased blood pressure" is recorded as the patient's blood pressure value in the "vital signs" item collected as related information r12 in node n12 in Figure 2.

If more detailed information such as "systolic blood pressure drop of 30 mmHg or more" is obtained from measuring the patient's blood pressure, the additional information "systolic blood pressure drop of 30 mmHg or more" can be recorded in addition to the "blood pressure drop" entry recorded in node n12.

Alternatively, a separate node for inputting blood pressure details such as "systolic blood pressure drop" can be provided near node n12, and blood pressure details can be recorded in that node when that information is obtained from the patient through additional testing. It is also possible to switch the connected node in response to changing patient conditions or newly obtained medical data.

For example, if a patient's blood pressure measurement shows that the blood pressure, which initially dropped, has now stabilized at a normal value, the "dropped blood pressure" entry in the "Vital Signs" section can be changed to "normal blood pressure." In this case, the connections between nodes can also be changed from a node connection relationship suitable for a patient with "dropped blood pressure" to a node connection relationship suitable for a patient with "normal blood pressure."

In addition, clinical card CC2 has node n21 for "dyspnea," node n22 for "SPO2 decrease," node n23 for "oxygen concentration change," node n24 for "stable breathing," and node n25 for "stable SPO2."

Furthermore, in clinical card CC2 in Figure 2, the chronological order relationship between nodes is specified, such as "node n21 → node n23 → node n24" or "node n22 → node n23 → node n25."

In addition, clinical card CC2 contains related information r12 representing "vital signs" associated with node n12, related information r13 representing "dialysis setting values" associated with node n13, related information r14 representing "observation items" associated with node n14, and related information r15 representing "vital signs" associated with node n15.

Returning to Figure 1, we will continue the explanation. The collection function 127c collects related information set on clinical cards. For example, the collection function 127c collects related information set on each node of the clinical card identified by the card identification function 127b.

As an example, if the card identification function 127b identifies the clinical card CC1 in Figure 2, the collection function 127c collects information on "vital signs," "dialysis settings," and "observation items" set as related information r12 to r15.

In addition, the related information collected by the collection function 127c includes the time representing the related information (for example, if the related information is "vital signs," the time the vital signs were measured). This allows the collection function 127c to collect appropriate related information based on the time representing the related information and the chronological order relationship between the nodes defined on the clinical card, even when the content is the same "vital signs," as in the case of related information r12 and related information r15.

In addition, if there is related information that cannot be collected at the time the clinical card is identified due to reasons such as non-measurement, the collection function 127c may monitor the patient information DB 111 of the information integration system 110 via the NW interface 121 and the network 130. In this case, when information corresponding to the related information is added to the patient information DB 111, the collection function 127c collects that information as related information.

The collection function 127c may also collect related information based on images of medical personnel captured by the camera 125 and audio of the medical personnel captured by the microphone 126. The collection function 127c may also monitor storage devices provided in testing equipment (not shown) connected via the network 130. In this case, when information corresponding to related information is stored in the storage device of the testing equipment, the collection function 127c collects that information as related information.

The path identification function 127d identifies paths that connect nodes and define the relationships between them based on multiple clinical cards and the timeline of clinical tasks performed. This process connects multiple clinical cards in a manner appropriate to the condition of each individual patient, allowing for the construction of dedicated network nodes that are useful for assessing the condition of each individual patient and predicting medical procedures.

For example, the path identification function 127d identifies the condition of the target patient from the related information collected by the collection function 127c. The node corresponding to the identified patient's condition changes from an unidentified node to a specific node. Also, for example, the path identification function 127d identifies the clinical task performed on the target patient from the related information collected by the collection function 127c. The node corresponding to the identified clinical task changes from an unidentified node to a specific node.

Furthermore, for example, the path identification function 127d identifies a path representing the context of the target patient based on multiple nodes corresponding to the identified patient's condition or clinical task, and the causal relationships between nodes, the chronological order relationships between nodes, and clinical relationships such as IF-then between nodes, etc., specified in each path on the clinical card.

In addition to the degree of association such as causality, paths corresponding to the patient's condition, diagnosis, medical action, and planned medical action may also be identified based on SOAP syntax. SOAP syntax is a recording method that organizes patient conditions and medical actions according to "Subject (subjective information)," "Object (objective information)," "Assessment," and "Plan."

In this case, for example, the node related to the patient's vital signs represents objective information, while the node related to the diagnosis or assessment of the patient's condition inferred or determined from the vital signs represents an evaluation, and both nodes can be connected as a path linking objective information and evaluation.

Whether it's a causal relationship, a chronological relationship, or some other path, the paths between nodes are predefined based on the type of item the node represents and are designed within the clinical card. Of the multiple nodes corresponding to the target patient's condition or clinical task, nodes into which information has been entered are identified as paths representing the target patient's context, and change from unidentified paths to identified paths.

The path identification function 127d may identify paths in chronological order, or in reverse order. For example, if a node representing the target patient's past condition and a node representing the target patient's current condition are identified, the path identification function 127d may identify the path leading to the node representing the target patient's current condition in chronological order by identifying the condition of the target patient that follows the node representing the target patient's past condition or the medical procedure performed on the target patient after that node.

Furthermore, for example, when a node representing the current condition of a target patient is identified, the path identification function 127d may identify the path in reverse order by identifying the condition of the target patient before that node or the medical procedure performed on the target patient before that node.

Here, Figure 3 is a diagram explaining an example of the path identification process. It is assumed that the card identification function 127b has identified the clinical card CC1 for "dialysis" and the clinical card CC2 for "artificial ventilator" shown in Figure 2.

Also, assume that related information r12 to r15 and r22 to r25 have been collected by the collection function 127c.

In the example of Figure 3, the path identification function 127d determines that the target patient's blood pressure has decreased based on the blood pressure measurement value (hereinafter also referred to as blood pressure value) of the target patient included in the "vitals" of the collected related information r12. As a result, the path identification function 127d can determine that the target patient's condition is "decreased blood pressure" represented by node n12.

Furthermore, the path identification function 127d determines from the information contained in the "dialysis setting value" of the collected related information r13 that the medical professional has stopped the water removal that was being performed during dialysis. This allows the path identification function 127d to determine that "water removal stop" represented by node n13 has been performed on the target patient.

Generally, if a patient undergoing fluid removal during dialysis experiences a drop in blood pressure, the fluid removal is discontinued. For this reason, in the example of Figure 3, the path connecting node n12 and node n13 defines a causal relationship between "drop in blood pressure" and "cessation of fluid removal."

In the example of Figure 3, the path identification function 127d identifies from the collected related information that node n12 represents "blood pressure drop" and node n13 represents "water removal discontinued." From this, the path identification function 127d determines that a causal relationship exists between node n12 and node n13, as defined by the path connecting the two. Therefore, the path identification function 127d identifies the path connecting node n12 and node n13 as a path representing the context of the target patient.

The path connecting node n12 and node n13 may also have a chronological order defined such that "water removal is discontinued" after "blood pressure drop" is confirmed. In this example, if the path identification function 127d determines from the collected related information that a chronological order relationship exists between node n12 and node n13, it identifies that path as a path representing the context of the target patient.

Furthermore, the path connecting node n12 and node n13 may have a predetermined IF-then rule that states, "If a drop in blood pressure is detected, discontinue water removal."

In this example, if the path identification function 127d determines from the collected related information that a relationship expressed by an IF-then rule exists between node n12 and node n13, it identifies the path connecting node n12 and node n13 as a path representing the context of the target patient. In this case, the path identification between nodes acts as a connection from the node representing the evaluation to the node representing the plan.

The path identification function 127d may also connect paths between nodes by identifying that the target patient is experiencing symptoms of respiratory distress from the contents of the "observation items" in the collected related information r14. In this case, the path identification between nodes acts as a connection from a node representing objective information to a node representing evaluation. This allows the path identification function 127d to identify that the target patient's condition is "respiratory distress," as represented by node n14.

Generally, when fluid accumulates in the lungs or chest, it can cause shortness of breath and difficulty breathing. Therefore, if the fluid removal performed during dialysis is stopped, the patient may experience symptoms of respiratory distress. For this reason, the path connecting node n13 and node n14 defines the causal relationship between "water removal stopped" and "dyspnea."

In the example of Figure 3, the path identification function 127d identifies from the collected related information that node n13 represents "water removal discontinued" and node n14 represents "dyspnea." From this, the path identification function 127d determines that a causal relationship exists between node n13 and node n14, as defined by the path connecting the two. Therefore, the path identification function 127d identifies the path connecting node n13 and node n14 as a path representing the context of the target patient.

Furthermore, the path identification function 127d determines that node n14 of clinical card CC1 and node n21 of clinical card CC2 both represent "dyspnea," and that "dyspnea" is a node common to clinical card CC1 and clinical card CC2 (hereinafter also referred to as a common node). From this, the path identification function 127d determines that the path connecting node n13 and node n14 is also a path connecting node n13 and node n21.

For example, even if "dyspnea" cannot be identified from the related information r21 set on clinical card CC2, the path identification function 127d may identify node n21 as representing "dyspnea" because it has identified that node n14 represents "dyspnea" and because clinical card CC2 for "artificial ventilator" has been identified.

By linking peripheral information in this way and connecting it to nodes that evaluate the patient's condition, it is possible to inform the user of an evaluation of the patient's condition that may be difficult for the user to notice on their own.

The path identification function 127d also determines that the oxygen concentration of the ventilator has been changed from the oxygen concentration setting value included in the "ventilator setting value" in the collected related information r23. This allows the path identification function 127d to determine that the clinical task "oxygen concentration change" has been performed by a medical professional.

Generally, breathing difficulties are caused by a lack of oxygen, so in the case of patients using ventilators, measures are taken to increase the oxygen concentration setting on the ventilator. For this reason, the path connecting node n21 and node n22 defines the causal relationship between "dyspnea" and "change in oxygen concentration."

In the example of Figure 3, the path identification function 127d identifies from the collected related information that node n21 represents "dyspnea" and node n22 represents "oxygen concentration change." From this, the path identification function 127d determines that a causal relationship exists between node n21 and node n22, as defined by the path connecting the two. Therefore, the path identification function 127d identifies the path connecting node n21 and node n22 as a path representing the context of the target patient.

By identifying paths connecting nodes as described above, the path identification function 127d generates context information specific to the target patient, in which nodes representing the target patient's condition or clinical tasks performed on the target patient are connected by paths. The generated context information is stored in the context DB 122b in association with the target patient's patient ID.

The path identification function 127d may identify a path representing the context of the target patient by taking into account the temporal relationship. For example, even if a correlation is recognized between nodes, the path identification function 127d can determine whether to identify a path by taking into account the temporal relationship between the nodes. For example, if the events represented by two nodes are distant in time, the path connecting the nodes may not be identified as a path representing the context of the target patient.

This is because even if a causal relationship between the nodes of an event itself is recognized, if the time interval between the nodes is quite far and the temporal relationship is distant, the connection between the two is not considered to be very important. If even such things that are considered to be of low importance were identified as paths representing the context of the target patient, the number of paths would increase, which could make it difficult for users to visually grasp the context of the target patient.

For example, when defining paths between clinical cards, the degree of relationship between nodes can be defined numerically, and the degree of relationship can be multiplied by a coefficient corresponding to the time interval between the nodes, so that the longer the time interval, the lower the relationship value multiplied by the coefficient. In this case, paths with relationship values above a predetermined threshold are identified as valid paths.

Furthermore, paths may not only connect nodes within the same clinical card, but may also connect a node within one clinical card to a node within another clinical card. In this case, the connected clinical cards may be considered as a single larger node that encompasses multiple nodes.

Here, Figure 4 is a diagram illustrating another example of the path identification process. In Figure 4, in addition to the example in Figure 3, the card identification function 127b has identified clinical card CC3 for "antipyretic," clinical card CC4 for "sleeping pill," clinical card CC5 for "delirium," and clinical card CC6 for "sepsis."

Clinical card CC3 has a node n31 for "Antipyretic medication" and a node n32 for "No fever." Clinical card CC4 has a node n41 for "Sleeping medication" and a node n42 for "Sleep state assessment." Clinical card CC5 has a node n51 for "Delirium assessment" and a node n52 for "Awake monitoring."

In addition, clinical card CC6 has a node n61 for "Decreased fluid removal," a node n62 for "Infectious disease B," a node n63 for "Fever," a node n64 for "Infectious disease A," and a node n65 for "No fever."

In the example of Figure 4, the path identification function 127d determines that a relationship defined in the path is recognized between node n42 ("Sleep State Assessment") of clinical card CC4 for "Sleeping Pills" and node n51 ("Delirium Assessment") of clinical card CC5 for "Delirium." It is also assumed that a predetermined period of time has passed between the time the "Sleep State Assessment" was performed on the target patient and the time the "Delirium Assessment" was performed on the target patient.

Furthermore, in the example of Figure 4, even if a path-defined relationship is recognized between nodes, if the temporal relationship between the nodes is distant, the path identification function 127d will not identify the path connecting the nodes as a path representing the context of the target patient.

In the example of Figure 4, as described above, a predetermined period of time has passed between the time the "sleep state assessment" was conducted on the target patient and the time the "delirium assessment" was conducted on the target patient. Therefore, the path identification function 127d does not identify the path connecting node n42 and node n51 (the path indicated by the dashed line in Figure 4) as a path representing the context of the target patient.

In the example of Figure 4, node n11a represents "edema," and a path is defined between node n11a and clinical card CC6 for "sepsis." In the example of Figure 4, the path identification function 127d determines that a relationship defined in the path is recognized between node n11a and clinical card CC6, and identifies the path connecting node n11a and clinical card CC6 as a path representing the context of the target patient.

In this case, the user can understand that the patient may have developed sepsis due to "edema."

In the example of Figure 4, node n61, "Decreased fluid removal," indicates that the amount of fluid removed from the target patient has decreased, and a path is defined between node n61 and node n62, "Infectious disease B." "Infectious disease B" indicates that the target patient is infected with infectious disease B.

Furthermore, in the example of Figure 4, the path identification function 127d determines that a path-defined relationship is recognized between node n61 and node n62, and identifies the path connecting node n61 and node n62 as a path representing the context of the target patient.

In this case, the user can understand from the chronological order of the nodes "Decreased Water Removal" → "Infectious Disease B" that the patient may have contracted Infectious Disease B due to a decrease in the amount of water removed.

In the example of Figure 4, "Fever" in node n63 indicates that the target patient has a fever, and a path is defined between node n62 and node n63. In the example of Figure 4, the path identification function 127d determines that a relationship defined in the path is recognized between node n62 and node n63, and identifies the path connecting node n62 and node n63 as a path representing the target patient's context.

In this case, the user can understand from the chronological order of the nodes "Infectious Disease B" → "Fever" that the patient developed a fever as a result of contracting Infectious Disease B.

In the example of Figure 4, "Antipyretic Administration" in node n31 indicates that an antipyretic has been administered to the target patient, and a path is defined between node n63 and node n31. In the example of Figure 4, the path identification function 127d determines that a relationship defined in the path is recognized between node n63 and node n31, and identifies the path connecting node n63 and node n31 as a path representing the target patient's context.

In this case, the user can understand from the chronological order of the nodes "Fever" → "Administered Antipyretic" that the patient was administered an antipyretic as a treatment for the fever caused by infectious disease B.

In the example of Figure 4, "No fever" in node n32 indicates that the target patient does not have a fever, and a path is defined between node n31 and node n32. In the example of Figure 4, the path identification function 127d determines that a relationship defined in the path is recognized between "Antipyretic medication" and "No fever," and identifies the path connecting node n31 and node n32 as a path representing the target patient's context.

In this case, the user can understand from the chronological order of the nodes "Antipyretic Medications Administered" → "No Fever" that the fever subsided when the patient was administered an antipyretic. Furthermore, from this series of events, the user can understand the patient's context: the patient had a fever due to infection B, but the fever subsided after being administered an antipyretic.

Furthermore, in the example of Figure 4, a path is defined between clinical card CC2 for "Ventilator" and node n52 for "Awake Monitoring." In the example of Figure 4, the path identification function 127d does not identify this path as a path representing the context of the target patient.

However, for example, if the collection function 127c collects related information that can derive the relationship between clinical card CC2 and node n52, the path identification function 127d will identify that path as a path representing the context of the target patient.

The path identification function 127d may use known machine learning techniques (including deep learning techniques) to identify a path representing the context of the target patient using a trained model that has learned the connection relationships between nodes. For example, the trained model is a trained model that infers, from information representing a first node, information representing a second node that is connected to the first node.

As an example, the trained model is a trained model that learns the relationship between the first node and related information of the first node using a training dataset in which information representing the first node and related information of the first node are used as input training data, and information representing the second node is used as output training data.

The path identification function 127d inputs information representing the first node and related information for the first node into the trained model, and identifies a path representing the context of the target patient based on the output results of the trained model.

Returning to Figure 1, the explanation continues. The presentation function 127e presents context information that indicates the relationship between nodes connected by paths. For example, the presentation function 127e presents context information to the user by displaying the nodes represented by solid lines in Figures 3 and 4 and the paths connecting those nodes represented by thick lines on the display 124 in a manner that makes the chronological order relationship between the nodes clear.

Furthermore, for example, when presenting context information, if a common node exists, the presentation function 127e overlaps the common nodes and displays them as a single node on the display 124. This makes it possible to present the context information to the user as a single graph, even if the context information involves multiple clinical cards.

The presentation function 127e may also extract and present a portion of the context information in accordance with user instructions. In this case, the presentation function 127e accepts a selection input from the user of one of the nodes presented as context information. The presentation function 127e then extracts and presents the nodes and paths located within a specified range of the selected node (for example, within a range of two nodes before and after the selected node).

By extracting and presenting a portion of the context information in accordance with the user's instructions in this way, users can more easily understand the patient conditions and medical procedures that are closely related to the patient conditions and medical procedures they wish to focus on.

The presentation function 127e may also automatically extract nodes of high importance or nodes surrounding clinical cards.

In this case, the presentation function 127e determines the level of importance using information such as the number of related information collected as related information for the node, the number of medical professionals who have recorded information about the node in electronic medical records, etc., the number of times information about the node has been viewed, whether the node or clinical card is related to the main illness of the target patient, and the number of nodes in the clinical card that represent highly urgent treatments as indicators.

By extracting and presenting the most important parts of the context information in this way, users can more easily understand the patient's condition and medical procedures that are most closely related to the patient's condition and medical procedures that are of high importance.

When extracting and presenting a portion of the context information, the presentation function 127e may display a reduced image of the entire context information together with information indicating which portion of the context information has been extracted.

This makes it easier for users to understand which part of the overall context information the extracted portion corresponds to.

In addition, the presentation function 127e presents related information along with the context information. For example, the presentation function 127e accepts input from the user selecting one of the nodes presented as context information. The presentation function 127e then displays the context information on the left side of the display 124 and the related information specified for the selected node on the right side.

The presentation function 127e may display not only the related information of the selected node, but also the related information of nodes located within a specified range of the selected node. In this case, the presentation function 127e may display the related information in accordance with the chronological order relationship between the nodes.

By presenting related information as described above, users can more easily understand the basis on which nodes are connected.

The operation of each function will be explained below using Figures 5 to 7. Figure 5 is a diagram illustrating an example of the operation of each function of the medical information processing device 120. In the example of Figure 5, it is assumed that attending physician HW1, ICU doctor HW2, ICU nurse HW3, and ICU doctor HW4 are responsible for caring for the target patient.

For example, when it is decided that a target patient will be admitted to the ICU, the attending physician HW1 inputs information representing the patient's current condition into the medical information processing device 120 ((1)-1). Specifically, the attending physician HW1 uses the input interface 123 to input information representing "respiratory failure" and "renal failure" as the patient's current condition.

Next, the acquisition function 127a accepts input of information representing "respiratory failure" and "renal failure" via the input interface 123. The card identification function 127b references the clinical card DB 122a and identifies the clinical card CC7 corresponding to "respiratory failure" and the clinical card CC8 corresponding to "renal failure."

The collection function 127c then automatically collects related information ((1)-2).

Specifically, the collection function 127c collects "information regarding respiratory condition (dyspnea)" and "information regarding transport condition (emergency transport)" specified as related information r7 on the clinical card CC7 from the patient information DB 111 of the information integration system 110 via the NW interface 121 and the network 130. Similarly, the collection function 127c collects "information regarding the frequency of dialysis (dialysis twice a week)" specified as related information r8 on the clinical card CC7.

Next, ICU doctor HW2, who is in charge of the day shift, inputs a clinical task into the medical information processing device 120 ((2)-1). Specifically, ICU doctor HW2 inputs information representing "artificial respiration" as the clinical task corresponding to "respiratory failure." Similarly, ICU doctor HW2 inputs information representing "dialysis" as the clinical task corresponding to "renal failure."

Next, the card identification function 127b references the clinical card DB 122a and identifies the clinical card CC2 corresponding to "artificial ventilation" and the clinical card CC1 corresponding to "artificial dialysis."

The path identification function 127d then automatically identifies the path ((2)-2).

Specifically, the path identification function 127d determines that a path-defined relationship exists between clinical card CC7 and clinical card CC2 based on the related information r7 (dyspnea, emergency transport) of the clinical card collected by the collection function 127c, as well as "respiratory failure" and "artificial ventilator." Therefore, the path identification function 127d identifies the path between clinical card CC7 and clinical card CC2 as a path representing the context of the target patient.

Similarly, the path identification function 127d determines that a path-defined relationship exists between clinical card CC7 and clinical card CC2 based on the clinical card related information r8 (dialysis twice a week) collected by the collection function 127c, as well as "renal failure" and "dialysis." Therefore, the path identification function 127d identifies the path between clinical card CC7 and clinical card CC2 as a path representing the context of the target patient.

The collection function 127c then automatically collects related information ((2)-3).

Specifically, the collection function 127c collects collectable information from among "blood gas values," "ventilator settings," and "observation items" defined as related information r22 to r25 on the clinical card CC2. Similarly, the collection function 127c collects collectable information from among "vital values," "dialysis settings," and "observation items" defined as related information r12 to r15 on the clinical card CC1.

Next, the path identification function 127d automatically identifies the clinical task performed on the target patient ((2)-4). Specifically, the path identification function 127d identifies that fluid removal is being performed during dialysis based on the related information r13 (dialysis setting values) of the clinical card collected by the collection function 127c. In other words, the path identification function 127d identifies the node n13a representing "water removal" as the clinical task performed on the target patient.

Next, ICU nurse HW3, who is in charge of the day shift, performs clinical tasks ((3)-1). Specifically, ICU nurse HW3 measures vital signs, blood gases, and observes the patient's respiratory condition, etc.

The collection function 127c automatically collects related information ((3)-2). Specifically, the collection function 127c monitors the patient information DB 111, and when information corresponding to the related information specified in clinical card CC1 and clinical card CC2 is added, the collection function 127c collects that information as related information. In the example of Figure 5, the collection function 127c collects information corresponding to "vital signs," "blood gas values," and "observation items" as related information.

Next, the path identification function 127d identifies the patient's condition ((4)-1). Specifically, the path identification function 127d identifies that the target patient's blood pressure has decreased from the blood pressure measurement results included in the collected "vital" data. In other words, the path identification function 127d identifies node n12, which represents "decreased blood pressure," as the patient's condition.

Next, ICU doctor HW4, who is on the night shift, performs a clinical task ((4)-2). Specifically, ICU doctor HW4 stops water removal during dialysis. After this, the collection function 127c collects information that water removal during dialysis has been stopped as information included in the "dialysis setting value" of related information r13.

Next, the path identification function 127d identifies the clinical task ((4)-3). Specifically, based on the collected information that water removal during dialysis has been discontinued, the path identification function 127d identifies "water removal discontinued" as the clinical task performed on the target patient.

Next, ICU doctor HW4 performs clinical tasks ((4)-4). Specifically, ICU doctor HW4 observes the patient's respiratory condition and changes the oxygen concentration settings on the ventilator.

After this, the collection function 127c collects information that the target patient is experiencing respiratory distress, as information included in the "Observation Items" of related information r14 and r24. The collection function 127c also collects information that the oxygen concentration setting has been changed, as included in the "Ventilator Setting Value" of related information r13.

Next, the path identification function 127d identifies the patient's condition and clinical task ((4)-5). Specifically, based on the collected information that the target patient is experiencing respiratory distress, the path identification function 127d identifies "respiratory distress" as the target patient's condition. Also, based on the collected information that the oxygen concentration setting for the ventilator has been changed, the path identification function 127d identifies "oxygen concentration change" as the clinical task performed on the target patient.

Next, the path identification function 127d identifies a path ((4)-6). Specifically, the path identification function 127d determines that a path-defined relationship exists between node n12 and node n13 based on "blood pressure drop" and "water removal discontinued." Therefore, the path identification function 127d identifies the path between node n12 and node n13 as a path representing the context of the target patient.

Similarly, the path identification function 127d determines that a path-defined relationship exists between nodes n13 and n14 based on "water removal discontinued" and "dyspnea." Therefore, the path identification function 127d identifies the path between nodes n13 and n14 as a path representing the context of the target patient.

The path identification function 127d also identifies that nodes n14 and n21 are common nodes representing "dyspnea." The path identification function 127d also identifies that the path connecting nodes n13 and n14 is also the path connecting nodes n13 and n21.

Furthermore, the path identification function 127d determines that a path-defined relationship exists between node n21 and node n23 based on "dyspnea" and "oxygen concentration change." Therefore, the path identification function 127d identifies the path between node n21 and node n23 as a path representing the context of the target patient.

In this way, each function of the medical information processing device 120 generates context information for the target patient that represents the chronological order of each node: "low blood pressure" → "discontinue fluid removal" → "dyspnea" → "change in oxygen concentration."

Next, we will explain the use of context information. Figures 6 and 7 are diagrams that explain an example of the use of context information. In Figure 6, on the second day since the target patient in the example of Figure 5 was admitted to the ICU, ICU doctor HW2, who is on the day shift, is considering weaning the target patient from the ventilator.

If context information had not been generated, ICU doctor HW2 would have had to check the current ventilator settings and investigate why the oxygen concentration settings had been changed by a medical professional other than himself.

When multiple medical professionals are responsible for caring for a single patient, each medical professional will be responsible for recording information about medical procedures, and the location where the information is recorded may differ depending on the medical professional. In this case, ICU physician HW2 will have to search through various recording locations, which is likely to take a long time to collect the necessary information.

Furthermore, even if information is recorded in the same place, the amount of information recorded can be large because many medical professionals are involved in care, and it can take a long time to find the information you need. For this reason, if context information were not generated, ICU physician HW2 could have had to perform a cumbersome task to understand who performed what medical procedure and why.

In contrast, in this embodiment, by focusing on portion AR1 of the context information shown in Figure 6, ICU doctor HW2 can easily understand that "the oxygen concentration setting was changed by a medical professional other than himself because the target patient was experiencing respiratory distress."

In Figure 7, it is assumed that ICU doctor HW2 wants to investigate the cause of the patient's worsening respiratory condition. In this case, by focusing on part AR2 of the context information shown in Figure 7, ICU doctor HW2 can easily understand that "water removal was originally performed during dialysis, but a drop in blood pressure was observed, so water removal was stopped" and "the respiratory condition worsened as a result of water removal being stopped."

Furthermore, by understanding the above, ICU doctor HW2 can decide on a policy of "first addressing the issue of fluid removal and hypotension." In this way, the medical information processing device 120 according to this embodiment can support efficient care policy decisions by presenting the user with context information specific to the patient.

Next, we will explain the processing performed by the medical information processing device 120. Figure 8 is a flowchart showing an example of the processing performed by the medical information processing device 120.

First, the acquisition function 127a accepts input of information representing the patient's condition (step S101). For example, the acquisition function 127a accepts input of information representing the patient's current condition from the user via the input interface 123.

Next, the card identification function 127b identifies a clinical card corresponding to the patient's condition (step S102). For example, the card identification function 127b refers to the clinical card DB 122a in the memory circuit 122 and identifies a clinical card corresponding to the patient's current condition accepted in step S101.

Next, the acquisition function 127a accepts input of clinical tasks (step S103). For example, the acquisition function 127a accepts input of information representing clinical tasks for the patient's current condition accepted in step S101 from the user via the input interface 123.

Next, the card identification function 127b identifies the clinical card corresponding to the clinical task (step S104). For example, the card identification function 127b refers to the clinical card DB 122a in the memory circuitry 122 and identifies the clinical card corresponding to the information representing the clinical task received in step S103.

Next, the collection function 127c collects related information (step S105). For example, the collection function 127c references the patient information DB 111 of the information integration system 110 and collects related information specified in the clinical card corresponding to the current condition of the patient identified in step S102. Similarly, the collection function 127c also collects related information specified in the clinical card corresponding to the clinical task identified in step S104.

Note that the process of collecting relevant information specified on the clinical card corresponding to the patient's current condition may be performed after step S102 and before step S103.

The path identification function 127d then identifies the patient's condition represented by the node on the clinical card based on the related information (step S106). For example, the path identification function 127d derives the patient's condition from the related information collected in step S105 and identifies the patient's condition represented by the node on the clinical card identified in steps S102 and S104.

The path identification function 127d then identifies the clinical tasks represented by the nodes on the clinical card based on the related information (step S107). For example, the path identification function 127d derives clinical tasks from the related information collected in step S105 and identifies the clinical tasks represented by the nodes on the clinical card identified in steps S102 and S104.

Note that the processing of step S107 may be performed in parallel with the processing of step S106, or may be performed after step S105 and before step S106.

Next, the path identification function 127d identifies paths between nodes (step S108). For example, the path identification function 127d identifies paths between nodes to be adopted as paths representing the context of the target patient based on the related information collected in step S105 and the relationships between nodes defined in the paths. This generates context information for the target patient.

Then, the presentation function 127e presents the generated context information of the target patient (step S109). For example, the presentation function 127e presents the context information of the target patient to the user by displaying on the display 124 a predetermined range of nodes and paths arranged around a node selected by the user, along with related information for that node.

Next, the processing circuitry 127 stores the generated context information of the target patient in the context DB 122b of the storage circuitry 122 (step S110), and terminates this process. For example, the processing circuitry 127 associates the generated context information of the target patient with the patient ID of the target patient and stores them in the context DB 122b. Note that the processing of step S110 may be performed after step S108 and before step S109.

As described above, the medical information processing device 120 according to this embodiment identifies paths connecting nodes having relationships defined on each of the clinical cards based on multiple clinical cards that define, with nodes, the relationship between the condition of the target patient, clinical tasks representing medical procedures performed for that condition, and changes in the patient's condition as a result of performing those clinical tasks, as well as the timeline of the clinical tasks performed on the patient, and presents context information that represents the relationship between the nodes connected by the paths.

This allows the user to, for example, understand the condition of the target patient, the treatment performed for that condition, and how the treatment changed the target patient's condition in chronological order. Therefore, even if a medical professional other than the user performed treatment on the target patient, the user can easily understand why the treatment was performed and how the target patient's condition changed as a result, without having to collect various data about the target patient that is stored in a scattered manner or organize the collected data. In other words, the medical information processing device 120 according to this embodiment can efficiently assist in determining the treatment context for each subject.

The above-described embodiments can be modified as needed by changing some of the configurations or functions of each device. Therefore, below, several modifications of the above-described embodiments will be described as other embodiments. Below, differences from the above-described embodiments will be mainly described, and detailed descriptions of commonalities with the contents already described will be omitted. The modifications described below may be implemented individually or in appropriate combinations.

(Variation 1)
In the above embodiment, a form was described in which a path representing the context of a target patient is identified from the related information defined in a clinical card and the relationship between nodes. In this modified example, a form will be described in which a path representing the context of a target patient is identified by further taking into account the attributes defined in the nodes.

In this variation, attributes are defined for clinical cards and nodes placed on those clinical cards. Attributes are defined, for example, using a SOAP method classification. Generally, SOAP methods are classified as follows:

S (subjective): Subjective information (information obtained from what the patient said, etc.)
O (objective): Objective information (objective information obtained from examinations, tests, etc.)
A (assessment): Evaluation (comprehensive evaluation based on the doctor's diagnosis and analysis and interpretation of the contents of O and S)
P (plan): Treatment plan (treatment policy and content decided based on A, lifestyle guidance, etc.)

As an example, attributes are defined as follows: "dyspnea" is "S," "decreased SPO2" is "O," "hypoxemia" is "A," and "change in oxygen concentration" is "P." In this modified example, the path identification function 127d identifies a path representing the context of the target patient so that the node attributes are linked in the following manner: "S" → "O" → "A" → "P."

For example, when the path identification function 127d identifies the "S" or "O" node, it identifies the path representing the target patient's context by inferring the "A" → "P" node that follows from "S" → "O" from the related information and causal relationships specified in the clinical card.

Furthermore, for example, if the path identification function 127d is unable to identify the "A" node, it identifies the path representing the context of the target patient by inferring the "A" node leading to the "P" node from the related information and causal relationships specified in the clinical card.

The path identification function 127d may also use known machine learning techniques to identify a path representing the target patient's context using a trained model that has learned the relationships between "S" → "O" and "A" → "P." For example, the trained model is a trained model that infers "A" → "P" from "S" → "O."

As an example, the trained model is a trained model that learns the relationship between "S" → "O" and the related information of each node representing "S" → "O" as input training data, and "A" → "P" as output training data.

The path identification function 127d inputs "S" → "O" and the related information of each node representing that "S" → "O" into the trained model, and identifies a path representing the context of the target patient based on "A" → "P" output by the trained model.

According to this modified example, even if the SOAP is not organized and recorded, it is possible to present the user with context information for the target patient in which the SOAP is organized based on the connection relationships between nodes. Also, even if the SOAP is organized and recorded, the user may be able to identify missing elements (e.g., A, which was not considered when planning P) from the connection relationships between nodes.

(Variation 2)
In the above-described embodiment, a form has been described in which context information is presented that represents the relationship between nodes representing the past states of a target patient or clinical tasks performed in the past up to a node representing the present (a node representing the current state of the target patient or a node representing the clinical task most recently performed). In this modified example, a form will be described in which nodes representing the future state of a target patient or clinical tasks to be performed in the future are predicted and presented.

In this variant, the path identification function 127d predicts the next node connected to a node identified as the patient's condition or a clinical task performed on the patient by a path.

For example, the path identification function 127d identifies, as a similar case, the node that is most likely to be connected by a path next, based on statistical values, using context information generated using the same type of nodes or clinical cards as the clinical cards or nodes used to generate the context information for the target patient. The path identification function 127d predicts the node that will be connected to the node by a path next.

The path identification function 127d may also predict the next node that will be connected by a path based on the predicted node.

Note that the path identification function 127d may predict the next node connected by a path using a trained model that has learned the relationship between a certain node and the node next connected by a path to that node using known machine learning techniques. For example, the trained model is a trained model that infers information representing the next node connected to that node by a path from information representing a certain node.

As an example, a trained model is a model that learns the relationship between a node and related information about that node using a training dataset in which input training data is information representing the node and related information about that node, and output training data is information representing the node and the next node connected by a path.

The path identification function 127d inputs information representing the current node and related information about the current node into the trained model, and predicts the next node to be connected by a path based on the output results of the trained model.

In addition, in this modified example, if the nodes of the currently identified clinical card do not contain a node that can be connected next by a path, the card identification function 127b uses the same method as described above to identify a clinical card that has a node that can be connected next by a path.

In addition, in this modified example, the presentation function 127e presents the generated context information and the predicted result of the next node to be connected by the path. For example, the presentation function 127e displays the predicted result on the display 124 in a manner that allows the two to be distinguished from each other, such as by displaying the context information representing the path up to the node representing the current time in a different color.

Furthermore, for example, the presentation function 127e may also present information representing related information of the node representing the current state and related information defined for the predicted node in addition to the above. This makes it easier for the user to understand on what basis the next node to be connected by the path was predicted.

According to this modified example, for example, the user can easily understand not only the progression of the patient's condition to its current state, but also the predicted progression of the patient's condition in the future.

(Variation 3)
In the above-described embodiment, the context information is generated by combining clinical cards stored in advance in the clinical card DB 122 a. In this modified example, a clinical card added by the user or a clinical card with changed content can be used.

In this modified example, the Unified Medical Language System (UMLS) concept of "inheritance" is applied to clinical cards, allowing for efficient addition and modification of clinical cards.

For example, when registering a new clinical card in the clinical card DB 122a, in this modified example, the user selects a card to serve as a parent card from the clinical cards stored in the clinical card DB 122a. The processing circuit 127 identifies a card similar to the parent card from among the clinical cards stored in the clinical card DB 122a.

For example, the processing circuitry 127 determines whether each clinical card is similar to the selected parent card based on the similarity of the check flow related to the clinical card. As an example, since the double-check or single-check flow at the time of administration is common regardless of the drug, if a clinical card related to a certain drug is designated as the parent card, the processing circuitry 127 determines that clinical cards related to other drugs are similar to the parent card.

As another example, if the parent card is a clinical card related to a general decision-making flow such as that described in the guidelines, the processing circuit 127 determines that other clinical cards related to that general decision-making flow are similar to the parent card.

As another example, if the parent card is a clinical card related to a standardized flow such as a clinical pathway, the processing circuit 127 determines that other clinical cards related to that standardized flow are similar to the parent card.

In addition, the processing circuit 127 may set a clinical card that is determined to be similar to a parent card as a child card of that parent card.

The processing circuitry 127 then defines the elements (nodes, related information defined in the nodes, etc.) common to the parent card and all of the clinical cards identified as clinical cards similar to the parent card as common parts. The processing circuitry 127 sets the defined common parts as common parts of the newly added child card that inherit the contents of the parent card.

If the user wants to change an element among the elements defined as a common part, the user may input an instruction to exclude that element from the common part. In this case, the processing circuitry 127 will exclude that element from the common part.

In addition, the processing circuit 127 sets elements that are not defined as common parts of the parent card as unique parts, which are changeable elements of the child card.

The user creates a child card by changing the content of the set unique part (e.g., the patient's condition that is a condition for performing a clinical task, the content of the clinical task, etc.) to unique content. As an example, in the case of a clinical card related to a clinical pathway, the user changes the content of the observation items, variances, etc. that are defined as related information for the nodes that the clinical card has.

The processing circuit 127 stores the created child card in the clinical card DB 122a, correlating it with information representing the corresponding patient's condition and clinical task.

Since child cards inherit the parent card, if the content of the parent card changes, the content of the child card will also change accordingly. This means that if a user wants to change the content of the common parts between a parent card and a child card, they can change the content of the child card simply by changing the content of the parent card.

According to this modified example, if a medical facility reviews the operation of clinical tasks and it becomes necessary to change the contents of clinical cards or add new clinical cards, the user can perform such tasks efficiently.

(Variation 4)
In the above-described embodiment, a configuration has been described in which the medical information processing device 120 executes the above-described processing via its own input interface 123, display 124, camera 125, and microphone 126. In this modified example, a configuration will be described in which the above-described processing is executed by a terminal device used by a medical professional and a medical information processing device communicably connected to the terminal device.

In this modified example, the terminal device has a configuration similar to the input interface 123, display 124, camera 125, and microphone 126 described as components of the medical information processing device 120 in the above-mentioned embodiment. For example, the terminal device may be realized by a smartphone, tablet terminal, personal computer, small dedicated device similar to a smartphone, etc.

In addition, the medical information processing device executes the processing via a terminal device, rather than via the input interface 123, display 124, camera 125, and microphone 126 in the above-described embodiment. For example, the medical information processing device is realized by computer equipment such as a server or workstation.

The terminal device and medical information processing device are then connected so that they can communicate with each other, for example, via the Internet. In this case, the medical information processing system may be constructed in the form of cloud computing, in which the medical information processing device provides services in response to requests from the terminal device.

Furthermore, the above-described embodiment describes an example in which the acquisition unit, graph identification unit, node identification unit, path identification unit, and presentation unit in this specification are realized by the acquisition function 127a, card identification function 127b, path identification function 127d, and presentation function 127e of the processing circuit 127, respectively, but the embodiment is not limited to this.

For example, the acquisition unit, graph identification unit, node identification unit, path identification unit, and presentation unit in this specification may be realized by the acquisition function 127a, card identification function 127b, path identification function 127d, and presentation function 127e of the processing circuit 127, respectively. Alternatively, the same functions may be realized by hardware only, software only, or a combination of hardware and software.

Furthermore, in the above-described embodiment, an example was described in which the processing circuitry 127 was realized by a single processor, but the embodiment is not limited to this. For example, the processing circuitry 127 may be configured by combining multiple independent processors, with each processor executing a program to realize each processing function. Furthermore, each processing function possessed by the processing circuitry 127 may be realized by being distributed or integrated as appropriate across a single or multiple processing circuits.

Furthermore, each processing function possessed by the processing circuitry 127 may be realized by a combination of hardware and software, such as circuits. While an example in which the programs corresponding to each processing function are stored in a single storage circuitry 122 has been described here, the embodiment is not limited to this. For example, the programs corresponding to each processing function may be stored in multiple storage circuits, and the processing circuitry 127 may read and execute each program from each storage circuit.

Furthermore, the term "processor" used in the description of the above-mentioned embodiments refers to circuits such as a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), an Application Specific Integrated Circuit (ASIC), a programmable logic device (e.g., a Simple Programmable Logic Device (SPLD), a Complex Programmable Logic Device (CPLD), and a Field Programmable Gate Array (FPGA)).

Instead of storing the program in a memory circuit, the program may be directly embedded in the processor circuit. In this case, the processor realizes its function by reading and executing the program embedded in the circuit. Furthermore, each processor in this embodiment is not limited to being configured as a single circuit, but may also be configured as a single processor by combining multiple independent circuits to realize its function.

The program executed by the processor is provided in advance in a ROM (Read Only Memory), storage circuit, etc. The program may also be provided in a format that can be installed on these devices or in a file format that can be executed, recorded on a computer-readable, non-transitory storage medium such as a CD (Compact Disk)-ROM, FD (Flexible Disk), CD-R (Recordable), or DVD (Digital Versatile Disk).

This program may also be provided or distributed by being stored on a computer connected to a network such as the Internet and downloaded via the network. For example, this program is composed of modules that include each of the processing functions described above. In terms of actual hardware, the CPU reads and executes the program from a storage medium such as ROM, loading each module into the main memory and generating it on the main memory.

Furthermore, in the above-described embodiments and variations, the components of each device shown in the figures are functional concepts and do not necessarily have to be physically configured as shown. In other words, the specific form of distribution or integration of each device is not limited to that shown, and all or part of them can be functionally or physically distributed or integrated in any unit depending on various loads, usage conditions, etc.

Furthermore, all or any part of the processing functions performed by each device may be realized by a CPU and a program analyzed and executed by the CPU, or may be realized as hardware using wired logic.

Furthermore, among the processes described in the above embodiments and variations, all or part of the processes described as being performed automatically can be performed manually, or all or part of the processes described as being performed manually can be performed automatically using known methods. In addition, the information, including the processing procedures, control procedures, specific names, various data, and parameters shown in the above documents and drawings, can be changed as desired unless otherwise specified.

Note that the various data discussed in this specification are typically digital data.

At least one of the embodiments described above can efficiently assist in determining the treatment context for an individual subject.

While several embodiments have been described, these embodiments are presented as examples and are not intended to limit the scope of the invention. These embodiments may be embodied in a variety of other forms, and various omissions, substitutions, modifications, and combinations of embodiments may be made without departing from the spirit of the invention. These embodiments and their variations are included within the scope and spirit of the invention, as well as within the scope of the invention and its equivalents as set forth in the claims.

100 Medical information processing system 120 Medical information processing device 122 Memory circuit 122a Clinical card DB
127 Processing circuit 127a Acquisition function 127b Card identification function 127c Collection function 127d Path identification function 127e Presentation function

Claims (11)

a path specifying unit that specifies a path that connects the node that represents the first state and the node that represents the second state and specifies a relationship between the nodes, based on a plurality of graphs that define, with nodes, a relationship between a first state of a subject, a clinical task that represents a medical procedure that is performed for the first state, and a second state of the subject that changes as a result of performing the clinical task, and a time series of the clinical task;
a presentation unit that presents context information that indicates a relationship between the nodes connected by the path;
A medical information processing device comprising: an acquisition unit that acquires medical data indicating a current condition of the subject or a medical procedure to be performed on the subject;
a graph identification unit that identifies the graph having the node corresponding to the condition or the clinical task based on the condition or the medical action indicated in the medical data;
Further provided with
the path identification unit identifies the node representing the first state among the nodes included in the graph identified by the graph identification unit, and identifies the path connecting the identified node and the node representing the second state in chronological order or in reverse chronological order, with the identified node as a base point;
The medical information processing device according to claim 1 . a node specifying unit that specifies a common node indicating a node included in a second graph, which is a graph other than the first graph and which is common to the node included in a first graph among the graphs specified by the graph specifying unit, based on association information for deriving the association, which is defined for each node;
the presentation unit presents the context information connecting the first graph and the second graph by overlapping the identified common nodes.
The medical information processing device according to claim 2 . the presentation unit presents the related information of the node designated by the user together with the context information.
The medical information processing device according to claim 3 . the presentation unit presents, together with the context information, the related information of the node designated by the user and the nodes located within a predetermined range of the node;
The medical information processing device according to claim 4 . the presentation unit presents the context information configured from the nodes arranged within a predetermined range starting from the node designated by the user.
The medical information processing device according to any one of claims 1 to 5. the first state represents a current state of the subject;
the second state represents a state of the subject at a time prior to the present time;
the path identification unit identifies the path by identifying the node that represents the second state from the node that represents the first state and the related information of the node;
The medical information processing device according to any one of claims 3 to 5. the first state represents a current state of the subject;
the second state represents a state of the subject in the future from the present time;
the path identification unit identifies the path by predicting the node that represents the second state from the node that represents the first state and the related information of the node;
The medical information processing device according to any one of claims 3 to 5. The node has one of the following attributes defined: S (subjective information), O (objective information), A (evaluation), and P (plan);
the path identification unit, when the node of any of the attributes is not identified among the plurality of nodes linked in the order of S, O, A, and P, identifies the path of the subject by inferring the node of the attribute that is not identified and is connected to the identified node based on the content of the identified node, the attribute of the identified node, and the related information;
The medical information processing device according to any one of claims 3 to 5. a path specifying unit that specifies a path that connects the node that represents the first state and the node that represents the second state and specifies a relationship between the nodes, based on a plurality of graphs that define, with nodes, a relationship between a first state of a subject, a clinical task that represents a medical procedure that is performed for the first state, and a second state of the subject that changes as a result of performing the clinical task, and a time series of the clinical task;
a presentation unit that presents context information that indicates a relationship between the nodes connected by the path;
A medical information processing system comprising: A medical information processing method by a medical information processing device, comprising:
a path identifying step of identifying paths that connect the nodes representing the first state and the nodes representing the second state and that define the relationship between the nodes, based on a plurality of graphs in which the relationship between a first state of a subject, a clinical task representing a medical procedure performed for the first state, and a second state of the subject that changes as the clinical task is performed, and a time series of the clinical task;
a presentation step of presenting context information representing a relationship between the nodes connected by the path;
A medical information processing method including: