JP7514735B2 - Decision maker support system and decision maker support device - Google Patents

Description

The embodiments disclosed in this specification and the drawings relate to a decision maker support system and a decision maker support device.

Conventionally, there is known technology that predicts a patient's future health condition from various data such as the patient's test results and information on lifestyle habits, and presents the predicted results to the patient. There is also known technology that supplements the explanation given by a medical professional at the time of informed consent based on past information and presents this to the patient who is the recipient of the explanation.

International Publication No. 2017/204233 JP 2015-138457 A

One of the problems that the embodiments disclosed in this specification and the drawings attempt to solve is to reduce the regret that a decision maker currently feels regarding past decisions regarding a disease. However, the problems that the embodiments disclosed in this specification and the drawings attempt to solve are not limited to the above problem. Problems that correspond to the effects of each configuration shown in the embodiments described below can also be positioned as other problems.

A decision maker support system according to an embodiment includes a regret detection unit, a collection unit, and a presentation unit. The regret detection unit detects regret regarding a past medical decision-making for a disease of a subject based on information regarding the subject's regret related to the disease. The collection unit collects the subject's behavioral history or record related to decision-making or the disease. When regret is detected, the presentation unit presents the collected behavioral history or record in association with the decision-making or the disease. The information regarding regret is at least one of a statement, a search history, and a description that expresses the subject's regret related to the disease. The regret detection unit detects regret related to decision-making when at least one of a statement, a search history, and a description that expresses the subject's regret related to the disease is detected after the time of decision-making.

FIG. 1 is a diagram showing an example of the configuration of a decision maker support system according to the first embodiment. FIG. 2 is a diagram illustrating an example of an effort period and a positive event extraction target period according to the first embodiment. FIG. 3 is a diagram illustrating an example of a presentation according to the first embodiment. FIG. 4 is a flowchart showing an example of the flow of a decision maker support process according to the first embodiment. FIG. 5 is a diagram illustrating an example of the configuration of a decision maker support system according to the second embodiment. FIG. 6 is a flowchart showing an example of the flow of a decision maker support process according to the second embodiment. FIG. 7 is a diagram showing an example of a presentation item according to the fourth modification.

Below, we will explain in detail the embodiments of the decision maker support system and decision maker support device with reference to the drawings.

First Embodiment
Fig. 1 is a diagram showing an example of the configuration of a decision maker support system S1 according to the first embodiment. As shown in Fig. 1, the decision maker support system S1 includes a decision maker support device 10a and a user terminal 20a. The decision maker support device 10a and the user terminal 20a are connected via a network such as the Internet.

The user terminal 20a is an information terminal used by a user, such as a tablet terminal, a smartphone, or a PC.

In this embodiment, a case will be described in which the decision maker supported by the decision maker support system S1, the patient suffering from a disease, and the user of the user terminal 20a are the same person. In addition, the decision maker, the patient, and the user are examples of subjects in this embodiment.

The decision maker support device 10a is also connected to various information processing devices outside the decision maker support system S1 via a network such as the Internet. As an example, in this embodiment, the decision maker support device 10a is connected to electronic medical record systems 30a and 30b, and a server device included in a system of a public facility. The electronic medical record system 30a manages the electronic medical records of Hospital A where the user received medical treatment, and the electronic medical record system 30b manages the electronic medical records of Hospital B where the user received a second opinion. As an example of a system of a public facility, FIG. 1 shows a book management system 400 that manages the lending of books in a library. The decision maker support device 10a may also be connected to various other information processing devices.

The decision maker support device 10a is, for example, a computer such as a PC (Personal Computer) or a server device. The decision maker support device 10a may be provided in a cloud environment, or may be installed in a medical institution such as a hospital or in the facilities of a business that provides healthcare services.

The decision maker support device 10a includes a network interface 110, a memory circuit 120, an input interface 130, a display 140, and a processing circuit 150.

The NW interface 110 is connected to the processing circuit 150 and controls the transmission and communication of various data between the decision maker support device 10a and the user terminal 20a, the electronic medical record systems 30a and 30b, and the library management system 400. The NW interface 110 is realized by a network card, a network adapter, a NIC (Network Interface Controller), etc.

The memory circuit 120 is connected to the processing circuit 150 and stores various information and programs used by the processing circuit 150.

The memory circuit 120 is realized, for example, by a semiconductor memory element such as a random access memory (RAM) or a flash memory, a hard disk, an optical disk, etc. The memory circuit 120 is also called a memory unit.

The input interface 130 is realized by a trackball, a switch button, a mouse, a keyboard, a touchpad that performs input operations by touching the operation surface, a touch screen that integrates a display screen and a touchpad, a non-contact input circuit that uses an optical sensor, and a voice input circuit, etc. The input interface 130 is connected to the processing circuit 150, and converts input operations received from the user into electrical signals and outputs them to the processing circuit 150.

The display 140 is a liquid crystal display, an organic electro-luminescence (OEL) display, or the like. The input interface 130 and the display 140 may be integrated. For example, the input interface 130 and the display 140 may be realized by a touch panel. The display 140 is an example of a display unit.

The processing circuit 150 is a processor that reads out programs from the memory circuit 120 and executes them to realize functions corresponding to each program. The processing circuit 150 of this embodiment includes a specification function 151, a regret detection function 152, a period setting function 153, a collection function 154, an effort information extraction function 155, a positive event extraction function 156, a generation function 157, and a transmission function 158. The specification function 151 is an example of a specification unit. The regret detection function 152 is an example of a regret detection unit. The period setting function 153 is an example of a setting unit. The collection function 154 is an example of a collection unit. The effort information extraction function 155 is an example of an effort information extraction unit. The positive event extraction function 156 is an example of a positive event extraction unit. The generation function 157 is an example of a generation unit. The transmission function 158 is an example of a transmission unit.

Here, for example, each of the processing functions of the processing circuit 150, namely, the identification function 151, the regret detection function 152, the period setting function 153, the collection function 154, the effort information extraction function 155, the positive event extraction function 156, the generation function 157, and the transmission function 158, are stored in the memory circuit 120 in the form of a program executable by a computer. The processing circuit 150 is a processor. For example, the processing circuit 150 realizes the function corresponding to each program by reading the program from the memory circuit 120 and executing it. In other words, the processing circuit 150 in a state in which each program has been read has each function shown in the processing circuit 150 of FIG. 1. In FIG. 1, the processing functions performed by the identification function 151, the regret detection function 152, the period setting function 153, the collection function 154, the effort information extraction function 155, the positive event extraction function 156, the generation function 157, and the transmission function 158 are described as being realized by a single processor, but the processing circuit 150 may be configured to be realized by combining multiple independent processors and each processor executing a program. Also, in FIG. 1, the single storage circuit 120 is described as storing a program corresponding to each processing function, but multiple storage circuits may be distributed and the processing circuit 150 may be configured to read out the corresponding program from each storage circuit.

The identification function 151 identifies the disease suffered by the subject, and the content and timing of the subject's decision-making regarding the disease, based on the subject's electronic medical record information acquired from the electronic medical record systems 30a, 30b of Hospital A and Hospital B where the subject was treated. For example, information that can be collected from the electronic medical record systems 30a, 30b and from which the occurrence of decision-making can be estimated includes treatment methods, procedure methods, etc. For example, information regarding treatment methods that are the subject of regret, and if the subject has developed complications after surgery, terms such as "antibiotic administration" can be extracted.

The method of identifying the disease suffered by the subject and the content and timing of the subject's decision-making regarding the disease is not limited to this, and for example, the user himself may register these details in the user terminal 20a. Also, the information may be obtained from either one of the electronic medical record systems 30a, 30b.

Specifically, an example will be described in which a subject is diagnosed with stomach cancer. The subject is first proposed by a doctor to have either "half stomach resection" or "total stomach removal" as a treatment method, and then, as a second opinion, another doctor presents the treatment method "ESD (Endoscopic Submucosal Dissection)". The subject then selects "ESD" from these three options. In this case, the subject's disease is "stomach cancer" and the subject's decision is "selecting ESD". The time of decision-making is, for example, the day the subject informs the doctor of the treatment method to be selected. The identification function 151 identifies this information based on the subject's electronic medical record information acquired from the electronic medical record systems 30a and 30b.

Decision-making is not limited to choosing a treatment method. For example, deciding not to receive treatment is also a decision. In addition, when it comes to decisions about illness, not only the choice of treatment method but also the choice of medical institution and the choice of admission or discharge from hospital are included as decision-making subjects.

The identification function 151 sends the identified subject's disease, the content of the subject's decision, and the time of the subject's decision to the regret detection function 152 and the period setting function 153.

The regret detection function 152 detects regret regarding past medical decision-making for the subject's disease based on information about the subject's disease-related regret.

Hereinafter, information related to the subject's regrets that are related to the disease is referred to as regret information. Regret information is a statement, search history, or writing that expresses the subject's regrets related to the disease that the subject suffers from. Writing is, for example, text posted by the subject on a social networking service (SNS) or blog, and text in an email or message application sent by the subject. In addition, if the subject's voice uttered over the phone or orally is recorded, the regret information may include the content of the voice that is related to the regrets related to the disease. In addition, in the following, when the term "text" is used in this embodiment, it may refer to either written text or spoken text.

For example, actions taken by a user who regrets their choice of stomach cancer treatment method include (1) searching the Internet via a smartphone or PC for stomach cancer-related information such as "stomach cancer endoscopic surgery recurrence," "stomach cancer post-treatment progress blog," and "stomach cancer treatment methods." (2) posting a diary entry about their anxiety on a blog or SNS. (3) talking to or consulting with family, relatives, doctors, nurses, etc. (4) tweeting to an AI assistant device with a voice recognition function such as Siri (registered trademark).

Regret detection function 152 extracts clinically relevant information such as medical terms from local data on the smartphone or from a server on the Internet for (1) or (2). For (3), it analyzes voice data from the AI assistant device or user terminal 20a used by the subject to extract similar information.

A sentence that relates to the subject's regret related to a disease means, for example, that the sentence contains words that refer to the subject's past decisions regarding the disease and words that deny those decisions. Note that "words that refer to past decisions" and "words that deny decisions" are not limited to direct references but also include indirect references. For example, sentences that express anxiety or doubts about a decision, and sentences that affirm the option that was not selected, are also considered to indirectly express the subject's regret.

For example, suppose that the subject's disease is "stomach cancer" and the subject's decision is "to choose ESD." In this case, sentences that deny the subject's "choosing ESD" include, for example, words that directly or indirectly mean "ESD" and words that directly or indirectly deny "choosing ESD." For example, sentences such as "I shouldn't have chosen ESD," "I wonder if this treatment was good," or "I wonder why I chose ESD" are regret information. In addition, sentences that affirm the options that were not selected, "half stomach resection" or "total stomach removal," are also regret information because they indirectly deny the decision. In addition, search history that includes the subject's decision and negative words is also included in regret information. For example, if the search history includes search words that combine "ESD" with negative words such as "mortality," "regret," or "recurrence," the search history is regret information.

The regret detection function 152 of this embodiment detects regret regarding a decision-making process when it detects a statement, search history, or entry that indicates the subject's regret related to a disease after the time the subject made the decision.

More specifically, the regret detection function 152 detects regret regarding a decision when stress is detected in the subject after the time of the subject's decision and a statement, search history, or entry that expresses regret related to the subject's illness is detected.

Alternatively, the regret detection function 152 may compare the information acquired from the identification function 151 with medical-related history information to determine whether the user has regret. For example, the regret detection function 152 may determine that the subject has regret regarding the decision to select a treatment method when the amount of association between medical terms searched for multiple times by the user in the search system and medical terms such as disease names or procedure names extracted from the electronic medical record systems 30a and 30b reaches a certain level.

The regret detection function 152 of this embodiment acquires the stress detection result and regret information of the subject from the user terminal 20a via the NW interface 110. Note that the regret detection function 152 may also acquire the stress detection result or regret information of the subject from an information processing device or a wearable terminal other than the user terminal 20a.

When regret is detected, the regret detection function 152 sends a notification of the detection of regret to the period setting function 153.

In addition, the regret detection function 152 determines whether the subject's regret has been reduced when a presentation item is presented to the subject by the presentation function 254 of the user terminal 20a described below. If the regret detection function 152 determines that the subject's regret has not been reduced, it notifies the generation function 157 described below that the regret continues.

The period setting function 153 sets an effort period during which the subject makes an effort regarding a decision regarding the treatment or procedure of the disease, based on the time when the subject was diagnosed with the disease and the time when the subject made a decision regarding the disease. As an example, the effort period is the period from the day when the subject was diagnosed with the disease to the day when the subject made the decision.

The period setting function 153 also sets a target period for extracting positive events based on the time of the subject's decision-making regarding the disease. As an example, the target period for extracting positive events is the period from the day the subject made a decision regarding the disease to the present.

Figure 2 is a diagram showing an example of the effort period and the period for which positive events are extracted according to the first embodiment. In the example shown in Figure 2, the subject was diagnosed with stomach cancer on July 18, 2014, made the decision to select ESD on August 5, 2014, received treatment with ESD, and was discharged from the hospital on September 24, 2014. The current date is May 24, 2015, and the subject is in the follow-up period after discharge.

As shown in Figure 2, if a subject is diagnosed with gastric cancer on July 18, 2014, and makes a decision on August 5, 2014, the effort period will be "July 18, 2014 to August 5, 2014." In addition, if a subject makes a decision to choose ESD on August 5, 2014, and the current date is May 24, 2015, the period for extracting positive events will be "August 5, 2014 to May 24, 2015."

Note that the effort period and the period for which positive events are extracted are not limited to the above examples.

The effort period is, in other words, the period during which the user faced the decision-making process. The period for which positive events are extracted is the period from after the decision-making process until regret occurs. These periods can each be extracted as data representing the results of the user's effort actions or choices. For example, the effort period can be from the date on which the diagnosed disease is entered into the electronic medical record to the date on which the subject informs a doctor or other such person of the choice of treatment method. The period for which positive events are extracted can be from the date on which an article was posted on the subject's blog containing information about the treatment method decision to the date on which an article containing discharge information was posted or to the present time.

The period setting function 153 sends the set effort period and the period for which positive events are to be extracted to the collection function 154.

Returning to FIG. 1, the collection function 154 includes an effort information extraction function 155 and a positive event extraction function 156.

The effort information extraction function 155 and the positive event extraction function 156 collect the subject's behavioral history or records regarding decision-making or disease.

The subject's behavioral history or records include text written by the subject on SNS etc., emails or text in messaging applications sent by the subject, voice uttered by the subject over the phone or orally, the subject's medical records, the subject's location history, and records of use of public facilities such as libraries. The effort information extraction function 155 and the positive event extraction function 156 acquire this information from the user terminal 20a, other server devices, electronic medical record systems 30a, 30b, the AI assistant device used by the subject, or the library management system 400, etc. For example, the effort information extraction function 155 may acquire voice uttered by the subject over the phone or orally from voice data acquired from the AI assistant device used by the subject or the user terminal 20a, etc.

More specifically, the effort information extraction function 155 collects the subject's behavioral history or records as effort information related to the subject's efforts in decision-making. In this embodiment, the behavioral history or records that are the subject of the effort information are behavioral history or records within an effort period set by the period setting function 153.

Effort information is information that directly or indirectly indicates the efforts made by a subject that are related to the subject's disease or decision-making regarding the disease.

For example, the effort information may include information presented to the subject in relation to the decision-making process for a disease related to the subject's past decision-making or during treatment for the disease. The effort information may also include any or all of the frequency, duration, number, and cost of hospital visits, hospitalizations, or medical procedures related to a disease related to the subject's past decision-making.

Specifically, the effort information extraction function 155 extracts from data stored in the user terminal 20a, the electronic medical record system 30a, 30b, the library management system 400, or other information processing device, (5) the number of searches or search time during the effort period when the subject searches for information related to stomach cancer, such as "stomach cancer treatment methods," "post-treatment progress of stomach cancer," "ESD for stomach cancer," "probability of success of ESD for stomach cancer," and "famous doctors for stomach cancer treatment" on a search site; (6) the lending history, number of searches, lending time, and return time when the subject borrowed books related to stomach cancer from a library; (7) the length or number of conversations between the subject and medical professionals such as doctors and nurses; and (8) the length of conversations or calls between the subject and family members, relatives, or friends. (5) to (8) are examples of behaviors that indicate the subject's efforts, but behaviors that indicate efforts are not limited to these.

The positive event extraction function 156 also collects the subject's behavioral history or records, including information about positive events resulting from the subject's decision-making. A positive event resulting from the subject's decision-making is, for example, a positive occurrence brought about by a treatment method selected by the subject from among the treatment options for a disease. Alternatively, a positive event may also include the "non-occurrence" of a negative event that could have occurred if the subject had hypothetically chosen a treatment method that they would not have selected.

For example, the positive event extraction function 156 determines whether the behavioral history or records obtained by the subject's decision-making during the positive event extraction target period are positive events based on the merits of the options selected by the subject and the demerits of the options not selected by the subject.

In this embodiment, among the behavioral history or records of the subject during the period for which positive events are extracted, positive events are events that were made possible by the benefits of the "ESD" selected by the subject, and events that would have been difficult if the subject had chosen the options that were not selected, "half stomach resection" or "total stomach removal."

For example, disadvantages of gastrectomy or total removal include aftereffects such as "dumping syndrome (early or late)", "loss of appetite", "taste disorder due to zinc deficiency", "anemia", "nutritional disorder", "milk intolerance", "gastroesophageal reflux disease" or "gastroesophageal reflux disease". For this reason, if the subject had chosen gastrectomy or total removal, there is a possibility that the disadvantage of "not being able to eat sweets such as buns or cakes to prevent early dumping syndrome" would have occurred. By converting the positive events for this disadvantage into "eaten" and "ate" events using positive words as opposed to the negative word "could not eat", the positive event extraction function 156 can determine that the subject's actions that occurred after the surgery, such as "ate cake on my birthday" and "went to a hot spring and ate hot spring buns with family and relatives", are positive events. As another example, since the subject may have had a taste disorder due to an aftereffect, resulting in a negative event such as "not being able to taste," the positive event extraction function 156 can determine that an event such as "(eating something delicious) I can properly taste it" is a positive event.

The positive event extraction function 156 uses, for example, language analysis, to determine events in the subject's behavioral history or records that contradict the disadvantages of options not selected or that match the advantages of options selected.

The positive event extraction function 156 may obtain information on the advantages and disadvantages of each option from a server device on the Internet via the NW interface 110, for example. The positive event extraction function 156 may collect information on the advantages and disadvantages of each option from the Internet, such as information on people who have experienced treatment for the disease, and register and update the information in the memory circuit 120 as needed. In addition, information on the advantages and disadvantages of each option may be registered and updated in the memory circuit 120 of the decision maker support device 10a by an administrator.

For example, the effort information extraction function 155 and the positive event extraction function 156 may analyze the features of the subject's behavioral history or records, and extract information corresponding to the above-mentioned effort information and positive events. The analysis of features may include, but is not limited to, structuring unstructured data such as records written in natural language.

Note that positive events may be limited to information related to non-medical daily activities, and effort information may be limited to medical information.

In this embodiment, the effort information extraction function 155 and the positive event extraction function 156 are described as extracting data to be extracted from the connected information processing device, but the effort information extraction function 155 and the positive event extraction function 156 may extract effort information or positive events from the collected information after collecting information for the target period. For example, the positive event extraction function 156 may determine whether the behavioral history or records of the subject during the collected positive event extraction target period are positive events or not, and extract only the behavioral history or records determined to be positive events.

The effort information extraction function 155 and the positive event extraction function 156 send the extracted effort information and information on the positive event to the generation function 157. The effort information and information on the positive event include the content, date and time, and location information of the effort or positive event. The location information is information indicating the location of the subject when the subject makes an effort or a positive event occurs. Specifically, the location information is the GPS location information of the user terminal 20a, or location information related to the system from which the subject's behavioral history or record is obtained. The location information related to the system from which it is obtained is the location of Hospital A, Hospital B, or the library where the electronic medical record systems 30a, 30b, the library management system 400 are used.

The generation function 157 generates a presentation that associates the behavioral history or records collected by the effort information extraction function 155 and the positive event extraction function 156 with decision-making or disease.

A presentation may be, for example, a representation that presents to the subject the subject's past efforts or positive events.

Figure 3 is a diagram showing an example of a presentation 90 according to the first embodiment. Figure 3 illustrates the presentation 90 displayed on the display 240 of the user terminal 20a, which will be described later.

In the example shown in FIG. 3, the generation function 157 generates a presentation 90 in which effort information and positive events are mapped to positions on a map. For example, as shown in FIG. 3, a map is located in the center of the presentation 90, and icons 91a to 91e are associated with the map, indicating points indicating positions where the user performed effort actions and positions where positive events occurred.

The generation function 157 may also display the period during which effort information and positive events were extracted as a struggle period on the presentation 90, for example, at the top of a map. In FIG. 3, the combined period of the effort period and the period during which positive events were extracted is displayed as the struggle period. In FIG. 3, the presentation 90 is titled "Struggle Diary," but this is not limited to this.

The effort information and positive events may be displayed on the map in advance, or details of the efforts or positive events may be displayed when the user clicks on icons 91a to 91e. The details of the efforts or positive events displayed may be photos, videos, parts of text on SNS or blogs, quantitative values that can express the efforts, or comments from non-users. Comments from non-users may be, for example, comments posted on SNS, or utterances of others during a conversation captured as audio data.

Note that the form of presentation 90 shown in FIG. 3 is an example and is not limited to this. For example, other expression formats may be adopted, such as a message format that can be sent using a message application, a slideshow format including photographs or other images, an animation or novel that depicts a story in which the user is the protagonist, etc. Furthermore, presentation 90 is not limited to an expression based on the subject's own perspective, but may also express an impression from the perspective of another person and be presented to the subject.

The generation function 157 may also analyze the features of the effort information and positive events, and determine the presentation method and layout according to the analysis results. The analysis of features may include, but is not limited to, structuring unstructured data such as records written in natural language.

The generation function 157 sends the generated presentation 90 to the transmission function 158.

Returning to FIG. 1, the transmission function 158 transmits the presentation 90 generated by the generation function 157 to the user terminal 20a.

Next, we will explain user terminal 20a.

The user terminal 20a includes a network interface 210, a memory circuit 220, an input interface 230, a display 240, and a processing circuit 250.

The NW interface 210 is connected to the processing circuit 250, and controls the transmission and communication of various data between the user terminal 20a and the decision maker support device 10a. The NW interface 210 also controls the transmission and communication of various data between the user terminal 20a and server devices that provide various SNS, blog, or search services. The NW interface 210 is realized by a network card, network adapter, NIC, etc.

The memory circuit 220 is connected to the processing circuit 250 and stores various information and programs used by the processing circuit 250.

The input interface 230 is realized by a trackball, a switch button, a mouse, a keyboard, a touchpad that performs input operations by touching the operation surface, a touch screen that integrates a display screen and a touchpad, a non-contact input circuit that uses an optical sensor, and a voice input circuit, etc. The input interface 230 is connected to the processing circuit 250, and converts input operations received from the user into electrical signals and outputs them to the processing circuit 250.

In this specification, the input interface 130 of the decision maker support device 10a and the input interface 230 of the user terminal 20a are not limited to those equipped with physical operating parts such as a mouse and a keyboard. For example, examples of the input interfaces 130 and 230 also include electrical signal processing circuits that receive electrical signals corresponding to input operations from an external input device provided separately from the device and output these electrical signals to the processing circuits 150 and 250.

The display 240 is a liquid crystal display, an organic EL display, or the like. The input interface 230 and the display 240 may be integrated. For example, the input interface 230 and the display 240 may be realized by a touch panel. The display 240 is an example of a display unit.

The processing circuitry 250 is a processor that reads out programs from the memory circuitry 220 and executes them to realize functions corresponding to each program. The processing circuitry 250 of this embodiment includes a stress detection function 251, a transmission function 252, a reception function 253, and a presentation function 254. The stress detection function 251 is an example of a stress detection unit. The transmission function 252 is an example of a transmission unit. The reception function 253 is an example of a consultation unit. The presentation function 254 is an example of a presentation unit.

Here, for example, each processing function of the stress detection function 251, the transmission function 252, the reception function 253, and the presentation function 254, which are components of the processing circuit 250, is stored in the memory circuit 220 in the form of a program executable by a computer. The processing circuit 250 is a processor. For example, the processing circuit 250 realizes the function corresponding to each program by reading the program from the memory circuit 220 and executing it. In other words, the processing circuit 250 in the state in which each program is read has each function shown in the processing circuit 250 in FIG. 1. Note that, in FIG. 1, it has been described that the processing functions performed by the stress detection function 251, the transmission function 252, the reception function 253, and the presentation function 254 are realized by a single processor, but the processing circuit 250 may be configured by combining multiple independent processors, and each processor may realize the function by executing a program. In addition, in FIG. 1, a single memory circuit 220 is described as storing a program corresponding to each processing function, but multiple memory circuits may be distributed and the processing circuit 250 may read corresponding programs from individual memory circuits.

In the above explanation of the decision maker support device 10a and the user terminal 20a, an example was described in which the "processor" reads out and executes a program corresponding to each function from a storage circuit, but the embodiment is not limited to this. In this embodiment, the term "processor" refers to a circuit 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)). When the processor is, for example, a CPU, the processor realizes the function by reading out and executing a program stored in a storage circuit. On the other hand, when the processor is an ASIC, instead of storing a program in the storage circuit 120, 220, the function is directly incorporated as a logic circuit in the circuit of the processor. In addition, each processor in this embodiment is not limited to being configured as a single circuit for each processor, but may be configured as a single processor by combining multiple independent circuits to realize its functions. Furthermore, multiple components in FIG. 1 may be integrated into a single processor to realize its functions.

The stress detection function 251 detects the stress of the subject based on the biological information of the subject. For example, the stress detection function 251 acquires physiological indicators obtained from a wearable device (not shown) such as the subject's electrocardiogram, skin temperature, or a respiratory sensor as biological information, and determines whether the subject is stressed based on this information. The method of detecting stress may employ known stress sensing technology.

In addition, the biological information used for stress detection is not limited to the above examples. For example, if the user terminal 20a is a smartphone, the stress detection function 251 may perform stress detection using a screen time function, the frequency with which the subject browses the smartphone, analysis of radio wave fluctuations and sighing breaths, analysis of physical features of voice, facial expression analysis using a camera, the number of steps counted by a pedometer built into the smartphone, etc.

When the stress detection function 251 detects stress in the subject, it sends a notification of the detection of stress to the transmission function 252.

When stress is detected by the stress detection function 251, the transmission function 252 transmits to the decision maker support device 10a that the subject is feeling stressed.

The transmission function 252 also transmits the subject's behavioral history or records during the effort period and the period for which positive events are extracted to the decision maker support device 10a. For example, the subject's behavioral history or records during the effort period and the period for which positive events are extracted may be searched for by the effort information extraction function 155 and the positive event extraction function 156 of the decision maker support device 10a, and information that meets the search criteria may be transmitted to the decision maker support device 10a by the transmission function 252.

The receiving function 253 receives the presentation 90 from the decision maker support device 10a via the NW interface 210. The receiving function 253 sends the received presentation 90 to the presentation function 254.

When regret of the subject is detected, the presentation function 254 presents the collected behavioral history or record of the subject in association with the decision or disease. Specifically, the presentation function 254 presents a presentation 90 generated by the generation function 157 of the decision maker support device 10a. As an example of a presentation method, the presentation 90 may be displayed on the display 240 of the user terminal 20a, as shown in FIG. 3.

The timing for presenting the presentation object 90 is preferably when the decision maker support device 10a detects the subject's regret. The presentation function 254 may present the presentation object 90 in stages over multiple times. For example, a format may be adopted in which multiple presentation objects 90 are presented in a message format accompanied by an exchange with the subject. In this case, the content of the presentation object 90 may change depending on the subject's reaction. The timing for presenting the presentation object 90 may also be determined arbitrarily by the subject. For example, the presentation function 254 may present the presentation object 90 when the subject performs an operation requesting the presentation of the presentation object 90.

In addition, if the regret detection function 152 of the decision maker support device 10a determines that the subject's regret has not decreased after the presentation of the presentation object 90, the presentation function 254 changes the presentation format and presents it again.

Next, we will explain the flow of decision maker support processing by the decision maker support system S1 configured as described above.

Figure 4 is a flowchart showing an example of the flow of the decision maker support process according to the first embodiment. This flowchart shows the process executed by the decision maker support device 10a and the user terminal 20a.

First, the stress detection function 251 of the user terminal 20a acquires and monitors the subject's biological information from a wearable device (not shown) or the like (S1).

Then, the stress detection function 251 detects the stress of the subject from the monitoring results of the acquired biological information (S2). If the stress detection function 251 detects stress, the transmission function 252 of the user terminal 20a transmits to the decision maker support device 10a that the subject is feeling stressed.

The identification function 151 of the decision maker support device 10a also collects decision-related information regarding the subject's disease (S3). For example, the identification function 151 identifies the disease from which the subject suffers and the content and timing of the subject's decision-making regarding the disease based on the subject's electronic medical record information acquired from the electronic medical record systems 30a, 30b of Hospital A and Hospital B where the subject was treated. Note that the timing of identifying the content and timing of the decision-making is not limited and may be specified in advance.

Then, the regret detection function 152 of the decision maker support device 10a analyzes the occurrence of regret in the subject based on the remarks, search history, or writings that express the subject's regret related to the subject's disease (S4). For example, the regret detection function 152 detects regret related to decision-making when stress in the subject is detected after the time of decision-making and a remark, search history, or writing that expresses the subject's regret related to the disease is detected.

Then, the regret detection function 152 determines whether or not the subject has experienced regret based on the regret detection result (S5). If the regret detection function 152 does not determine that regret has occurred (S5 "No"), the processing in the decision maker support device 10a ends. In this case, the processing returns to S1, and the stress detection function 251 of the user terminal 20a continues monitoring the subject's biological information and stress detection processing.

If the regret detection function 152 determines that regret has occurred (S5 "Yes"), it sends a notification to the period setting function 153 that regret has been detected.

The period setting function 153 identifies a decision-making event that is the basis for the effort period and the period for which positive events are extracted (S6). A decision-making event is an occurrence related to decision-making, and in this embodiment includes making a decision about a disease, being diagnosed with the disease for which a decision is being made, being presented with a treatment option to choose from, and so on. The period setting function 153 identifies these decision-making events based on the subject's medical records registered in the electronic medical record systems 30a and 30b, or on the subject's SNS and other entries stored in the user terminal 20a and various server devices.

The period setting function 153 sets the effort period of the subject and the period for which positive events are extracted based on the identified decision-making event (S7). Specifically, the period setting function 153 sets the period from the day the subject is diagnosed with the disease to the day the subject makes a medical decision as the effort period. In addition, the period setting function 153 sets the period from the day the subject makes a decision regarding the disease to the present as the period for which positive events are extracted.

The effort information extraction function 155 extracts the user's effort information (S8). In this embodiment, the effort information extraction function 155 acquires the subject's behavioral history or records representing the subject's efforts during the effort period from the user terminal 20a, other server devices, electronic medical record systems 30a and 30b, an AI assistant device used by the subject, or the library management system 400, etc.

In addition, the positive event extraction function 156 extracts positive events during the period for which positive event extraction is targeted (S9).

Next, the generation function 157 analyzes the features of the effort information and positive events extracted by the effort information extraction function 155 and the positive event extraction function 156 (S10).

Then, the generation function 157 selects a presentation method based on the analysis results (S11). For example, the generation function 157 selects one that matches the extracted effort information and positive events from a number of predefined presentation methods, such as mapping the effort information and positive events onto a map image, animation, novels, or slideshows. As an example, when the extracted effort information and positive events include location information, the generation function 157 selects mapping the effort information and positive events onto a map image. Note that the method of selecting the presentation method is not limited to this.

Then, the generating function 157 generates the presentation 90 based on the selected presentation method (S12). The transmitting function 158 of the decision maker support device 10a transmits the presentation 90 generated by the generating function 157 to the user terminal 20a. In addition, the receiving function 253 of the user terminal 20a receives the presentation 90 from the decision maker support device 10a.

The presentation function 254 of the user terminal 20a presents the presentation 90 generated by the decision maker support device 10a to the user (S13).

Here, the stress detection function 251 of the user terminal 20a acquires and monitors the subject's biological information from a wearable device (not shown) or the like (S14). The stress detection function 251 also detects the subject's stress from the monitoring results of the acquired biological information.

Then, the regret detection function 152 of the decision maker support device 10a analyzes the effect of the presentation 90 based on the detection result of the subject's stress (S15). For example, the regret detection function 152 compares the intensity of the subject's stress before and after the presentation of the presentation 90. Note that the regret detection function 152 may evaluate the effect of the presentation 90 from the subject's remarks or actions in addition to stress.

Then, the regret detection function 152 determines whether the presentation object 90 has had the effect of reducing the subject's regret (S16).

For example, if the subject's stress after presentation of the presentation object 90 remains the same as before presentation of the presentation object 90 or has increased, the regret detection function 152 determines that the presentation object 90 has not been effective in reducing the subject's regret (S16 "No"). In this case, the regret detection function 152 notifies the generation function 157 that the regret continues. Then, the process returns to the selection of the presentation method in S11. If the presentation object 90 has not been effective in reducing the subject's regret, the generation function 157 changes the presentation method or the contents of the presentation object 90 to generate a presentation object 90 that is different from the previous one. In this way, the presentation function 254 presents the presentation object 90 in a different form from the previous one.

Furthermore, if the subject's stress level is reduced after the presentation of the presentation object 90 compared to before the presentation of the presentation object 90, the regret detection function 152 determines that the presentation object 90 has the effect of reducing the subject's regret (S16 "Yes"). In this case, the processing of this flowchart ends.

In this way, when the decision maker support system S1 of this embodiment detects regret regarding a past medical decision-making for the subject's disease, it presents the subject's behavioral history or record regarding the decision-making or disease in association with the decision-making or disease. Therefore, the decision maker support system S1 of this embodiment can reduce the regret currently felt by the decision maker regarding a past decision. For example, if the subject feels regret some time after making a decision, the presentation 90 can remind the subject of the past behavioral history or record, allowing the subject to re-recognize past efforts and positive events, and helping the subject to regain a positive attitude toward his or her own decision-making. Furthermore, reducing the regret currently felt by the subject can help the subject to make future decisions and treatments with a positive attitude.

In addition, the decision maker support system S1 of this embodiment collects the subject's behavioral history or records as effort information regarding the subject's efforts related to decision-making. According to the decision maker support system S1 of this embodiment, by presenting such effort information to the subject, it is possible to remind the subject of his or her own efforts in the past and to support the subject in viewing the process of decision-making and his or her own past in a positive light.

In addition, the decision maker support system S1 of this embodiment collects a behavioral history or record including information about positive events that occurred as a result of the subject's decision-making. According to the decision maker support system S1 of this embodiment, by presenting the subject with the collected information about positive events, the subject can be reminded of the positive events that occurred as a result of his or her own decision-making, and the subject can be supported in positively viewing the process of decision-making and his or her own past.

The decision maker support system S1 of this embodiment also determines whether the behavioral history or record obtained as a result of the subject's decision-making is a positive event. Therefore, according to the decision maker support system S1 of this embodiment, it is possible to select, as the presentation target, from among the behavioral histories or records, those that are likely to make the subject feel positive.

The decision maker support system S1 of this embodiment also collects, as effort information, information presented to the subject in relation to past decision-making regarding a disease related to that decision or during treatment of that disease. Therefore, according to the decision maker support system S1 of this embodiment, by presenting again to the subject the information that was the basis for the subject's consideration when making a decision, it is possible to help the subject recall the consideration process at the time of the decision and feel a sense of satisfaction with their own decision.

The decision maker support system S1 of this embodiment also collects, as effort information, information including any or all of the frequency, time, number and cost of hospital visits, hospitalizations or medical procedures related to illnesses related to past decision making. According to the decision maker support system S1 of this embodiment, by presenting such effort information to the subject, the subject can be reminded of the efforts made for treatment and can be supported in a positive light about his or her past.

In addition, the information regarding regret in this embodiment is a statement, search history, or description that expresses the subject's regret related to a disease, and the decision maker support system S1 of this embodiment detects regret related to decision-making when it detects a statement, search history, or description that expresses the subject's regret related to a disease after the time of decision-making. Therefore, the decision maker support system S1 of this embodiment can detect regret related to a subject's decision-making, regardless of whether the subject is aware of it or not.

Furthermore, the decision maker support system S1 of this embodiment detects the subject's regret regarding decision-making if stress is detected in the subject after the time of decision-making and if a statement, search history, or description expressing the subject's disease-related regret is detected. That is, the decision maker support system S1 of this embodiment collects effort information and positive events and presents the presentation 90 when the subject is in a stressed state and has negative feelings about his or her decision. Therefore, according to the decision maker support system S1 of this embodiment, the presentation 90 can be presented to the subject at an effective timing.

The decision maker support system S1 of this embodiment also sets an effort period during which the subject made efforts to treat the disease based on the time when the subject was diagnosed with the disease and the time when the subject made a decision regarding the disease, and collects the subject's behavioral history or records during this effort period as effort information. Therefore, according to the decision maker support system S1 of this embodiment, the subject's behavior during the period spent making a decision regarding the disease can be presented in a positive manner as effort.

In addition, after presenting the behavioral history or record, the decision maker support system S1 of this embodiment determines whether the subject's regret has been reduced, and if the regret has not been reduced, changes the presentation format or content and presents it again. Therefore, according to the decision maker support system S1 of this embodiment, it is possible to provide the subject with a presentation 90 with a format or content that effectively affects the subject.

In this embodiment, the case where the subject makes a single decision has been described as an example, but the decision maker support system S1 of this embodiment can also be applied to cases where the subject makes multiple decisions. For example, after receiving a diagnosis, the subject may make a decision to start a certain treatment, and then decide to discontinue the treatment. In such a case, the period from when the subject receives a diagnosis to when he or she makes a decision to start the treatment may be defined as the first effort period, and the period from when the subject makes a decision to start the treatment to when he or she makes a decision to discontinue the treatment may be defined as the second effort period.

Second Embodiment
In the first embodiment described above, support for the subject is provided by utilizing the subject's private information such as a private terminal and SNS, etc. In contrast, in the second embodiment, a case will be described in which support for the subject is provided in a medical institution such as a hospital.

Figure 5 is a diagram showing an example of the configuration of a decision maker support system S2 according to the second embodiment. The decision maker support system S2 of this embodiment includes a decision maker support device 10b and a medical staff terminal 20b. The decision maker support device 10b is provided in, for example, a cloud environment.

The medical staff terminal 20b is, for example, a PC or tablet terminal used by a doctor or the like. In this embodiment, the user of the medical staff terminal 20b is a medical staff member such as a doctor, and is assumed to be a different person from the decision maker and the subject. In this embodiment, the patient who is actually afflicted, the decision maker, and the subject are assumed to be the same person. The medical staff terminal 20b may also be referred to as a user terminal.

The medical staff terminal 20b is also connected to the electronic medical record system 30a via a network such as an in-hospital LAN (Local Area Network). The medical staff terminal 20b and the electronic medical record system 30a are included in the information system 500 of Hospital A. The medical staff terminal 20b may also be connected to an information processing device inside or outside the hospital (not shown).

The configuration of the decision maker support system S2 is not limited to the example shown in FIG. 5. For example, the decision maker support device 10b may also be included in the information system 500 of Hospital A. Alternatively, the medical staff terminal 20b may only have the function of a display device including the display 240, and all other functions may be configured as functions of the decision maker support device 10b on the cloud. Furthermore, the decision maker support system S2 may include the entire information system 500.

The decision maker support device 10b includes a network interface 110, a memory circuit 120, an input interface 130, a display 140, and a processing circuit 150.

The processing circuit 150 of this embodiment includes a determination function 151, a regret detection function 152, a period setting function 153, a collection function 159, a generation function 157, and a transmission function 158.

The identification function 151 of this embodiment identifies the disease suffered by the subject, and the content and timing of the subject's decision-making regarding the disease, based on the subject's electronic medical record information obtained from the electronic medical record system 30a or other medical information.

The regret detection function 152 of this embodiment detects regret regarding past medical decision-making regarding the subject's disease based on information regarding the subject's regret related to the disease. Specifically, the regret detection function 152 of this embodiment analyzes the situation when the stress detection function 251 of the medical professional terminal 20b detected stress and information related to past medical procedures, and analyzes and determines whether the stress is due to regret.

Specifically, when stress is detected, the regret detection function 152 analyzes the voice picked up by the microphone to see if the patient has been thinking back to the past, and obtains information from the electronic medical record system 30a or other medical information database as to whether the patient's current treatment or disease status could be a cause of regret. In this embodiment, the medical information database includes a database in the electronic medical record system 30a, as well as databases of prescription data, ordering data, test data, etc. stored in information processing devices inside and outside the hospital (not shown).

If the regret detection function 152 determines that the detected stress is due to regret over a medical procedure involving a patient's decision-making, it identifies the medical procedure in question and determines that regret has occurred. Here, when identifying the medical procedure in question, patterns of diseases, medical procedures, and prognosis that are generally the subject of regret may be stored by medical department, and the medical procedure may be identified based on this. Note that the presence or absence of regret may be determined after the content of the decision-making process has been identified first.

The period setting function 153 of this embodiment, like the first embodiment, sets a period for extracting the patient's effort behavior, etc. However, in this embodiment, since the effort behavior, etc. is extracted from the information system 500 of Hospital A, there are cases where the correspondence between the target medical procedure and the data to be extracted is clear. In such cases, since the data can be extracted directly, it is not necessary to set a period. Also, in the first embodiment, the effort period and the period for extracting positive events were set separately, but in this embodiment, since medical information is the main target for collection, the period setting function 153 sets only the effort period.

The collection function 159 of this embodiment extracts (collects) the patient's effort behavior, etc. Specifically, the number of hospital visits, length of stay at the hospital, number of medical procedures, total treatment costs, etc. related to the target medical procedure are extracted from the electronic medical record system 30a or other medical information database. The information extracted may be that during the effort period, or that linked to the disease that is the subject of decision-making.

The collection function 159 may also extract information from the electronic medical record system 30a or other medical information database regarding counseling, informed consent, or other explanations given by a doctor or counselor to help the subject make a decision when undergoing a medical procedure. Alternatively, this information may be stored in advance in the memory circuit 120 of the decision maker support device 10b. Specifically, when standardized materials are prepared for each medical procedure that requires the patient to make a decision and are used during explanation or counseling, the standardized materials are stored in the memory circuit 120 by the doctor or the information system 500. Then, when regret occurs later, the collection function 159 may extract information about the standardized materials from the memory circuit 120.

In addition, a patient decision support system may be used that is not limited to the above-mentioned standardized materials, but that can display the state of the disease specific to the patient along with various test data of the patient, or that can select the content of the explanation based on the attributes, preferences, values, and reaction of the patient to the explanation. In this case, the patient decision support system and the decision maker support system S2 may be linked, so that the explanation content selected by the patient can be saved so that it can be extracted in the event of later regret. In this decision support system, different patient explanation information is presented depending on the degree of progression of the disease and the presence or absence of complications. In addition, even if the disease is the same, different patient explanation information is presented depending on the attribute information of the patient. For example, it is preferable to present different patient explanation information to a man in his 80s who lives alone and a woman in her 40s who has several minor children. Furthermore, it is possible that the explanation information changes depending on the personality of the patient, such as risk preference tendency and procrastination tendency, and not limited to such clear attribute differences. Furthermore, for diseases in which one must be selected from multiple treatment methods, explanation information that compares and displays the advantages and disadvantages of each treatment method is also useful. Such a patient decision support system is useful when patients make decisions, but it is also useful in the event of later regret, to reaffirm how best the decision was made at the time. The patient decision support system may be included as one function of the decision maker support system S2.

The generating function 157 of this embodiment creates a presentation 90 based on the information extracted by the collecting function 159. More specifically, the generating function 157 creates as presentations at least any of the following: the number of visits made by the patient to the hospital, the length of stay at the hospital, the number of medical procedures, the total cost of treatment, and information given during explanations, such as the display contents of software used when making a decision with the patient. Here, in addition to information about the patient, related statistical information may be added. For example, adding information such as the average or median of other patients with the same disease for the number of visits to the hospital, the length of stay at the hospital, the number of medical procedures, and the total cost of treatment can help recognize the level of effort.

For example, the generation function 157 may generate a presentation 90 that includes, as effort information, information that compares the subject's information with statistical information of others regarding any or all of the frequency, time, number, and/or cost of hospital visits, hospitalizations, or medical procedures related to a disease related to past decision-making.

In addition, the generating function 157 of this embodiment may determine the degree to which each piece of presented information indicates the patient's effort behavior, and may make a decision to delete it from the presented information depending on the degree. For example, the generating function 157 may delete information if the number of visits to the hospital or the number of medical procedures is less than that of patients with the same disease. Also, if it is estimated that the patient did not fully understand the explanation at the time of decision-making, information related to the explanation may be deleted. The generating function 157 may determine the deletion based on the display time during the explanation or information related to the patient's cognitive ability recorded in the electronic medical record information, etc. For example, when the patient is young at the time of decision-making or in the highly acute phase, the patient does not have the ability to make decisions and the family member or the like receives an explanation and makes the decision, so it cannot be said that the patient is making an effort behavior. On the other hand, since the effort behavior including the family member is presented, it is possible that the information is not deleted from the presented information even in such cases. If the presented information is lost as a result of such a determination, a medical professional such as a doctor may be notified of this.

In this embodiment, the transmission function 158 transmits the presentation object 90 generated by the generation function 157 to the medical staff terminal 20b.

Next, we will explain the medical staff terminal 20b.

Like the user terminal 20a, the medical staff terminal 20b includes a network interface 210, a memory circuit 220, an input interface 230, a display 240, and a processing circuit 250.

The processing circuit 250 has a stress detection function 251, a transmission function 252, a reception function 253, and a presentation function 254.

The stress detection function 251 acquires the patient's biometric information from, for example, an information input/output terminal in the hospital (not shown), and detects stress based on the acquired biometric information. Specifically, the stress detection function 251 detects the patient's stress by analyzing the patient's facial expression and breathing movement acquired by an optical camera or the like, and the skin temperature acquired by a temperature sensor, or by analyzing the content of a conversation recognized from a voice acquired by a microphone. The stress detection function 251 may perform these analyses and detections by acquiring information from a terminal installed in an examination room in association with an examination by a doctor or an opportunity for a conversation between a patient and a nurse, or by importing data acquired from various places in the hospital or from outside the hospital into the information system 500.

The stress detection function 251 may be configured as an information input/output terminal provided outside the medical staff terminal 20b.

Furthermore, if a doctor or nurse, etc., observes a patient and determines that the patient is feeling stress due to regret, the doctor or nurse may directly input this fact. For example, this may be achieved by providing an input button on part of the input screen of the electronic medical record. In this case, the stress detection function 251 detects the patient's stress by accepting input from the doctor or nurse, etc. As an alternative means, the stress detection function 251 may detect stress using various information such as input information in the electronic medical record and nursing records.

When the stress detection function 251 detects stress in the patient, the transmission function 252 notifies the decision maker support device 10b that the patient is feeling stressed.

The receiving function 253 also receives the presentation 90 from the decision maker support device 10b. Note that in this embodiment, the presentation 90 does not need to be configured as a display screen like the presentation 90 of the first embodiment described in FIG. 3, and may simply be a data group including presentation information.

The presentation function 254, the effort behavior information presentation function, displays the presentation information on the display 240. Alternatively, the presentation function 254 may display the presentation information on a display device (not shown) outside the medical staff terminal 20b.

When the presentation function 254 displays the information, if the determination of regret is not made by manual input by the doctor or the like, it displays that the patient has detected a state of regret. Furthermore, when presentation information regarding effort behavior is created, the presentation function 254 displays a window on the screen that allows the user to select whether or not to present the information. Here, if the doctor or the like allows the display, a presentation object 90 indicating the presentation information is displayed on the screen and can be presented to the patient. Note that the notification of regret detection and the selection screen for whether or not to present the presentation information until the presentation information is finally presented to the patient should not be visible to the patient, and the monitor and the display screen that are arranged so that only the doctor or the like can see them may be different devices. For example, the presentation function 254 may display the notification to the doctor on the display 240, and when the presentation information is finally presented to the patient, it may be displayed on the display 240 and an external display device.

In addition, if the presentation 90 includes, as effort information, information that compares the subject's information with statistical information of others regarding any or all of the frequency, time, number, and cost of hospital visits, hospitalizations, or medical procedures related to a disease related to past decision-making, the presentation function 254 presents this information to the subject by displaying the presentation 90 on the display 240, etc.

Figure 6 is a flowchart showing an example of the flow of decision maker support processing according to the second embodiment. This flowchart shows processing executed by the decision maker support device 10b and the medical staff terminal 20b.

First, the stress detection function 251 of the medical staff terminal 20b acquires and monitors the subject's biological information (S21).

Then, the stress detection function 251 detects the stress of the subject from the monitoring results of the acquired biological information (S22).

In addition, the regret detection function 152 of the decision maker support device 10b collects regret-related information regarding the subject's regret related to the disease from the electronic medical record system 30a or other medical information database (S23).

The identification function 151 of the decision maker support device 10b also collects information about the patient's clinical decision-making from the electronic medical record system 30a or other medical information database (S24). Based on the collected information, the identification function 151 identifies the disease suffered by the subject and the content and timing of the subject's decision-making regarding the disease. Note that the order of execution of the processes of S23 and S24 may be reversed.

Then, the regret detection function 152 analyzes the occurrence of the patient's regret based on the collected information and information regarding the patient's decision-making (S25).

Then, the regret detection function 152 determines whether or not the patient has experienced regret (S26). If the regret detection function 152 determines that the patient has not experienced regret (S26 "No"), the process returns to S21.

In addition, if the regret detection function 152 determines that the patient has regret (S26 "Yes"), the period setting function 153 sets a period for extracting the patient's effort behavior, etc., as in the first embodiment (S27).

Then, the collection function 154 extracts the patient's effort information for the set period from the electronic medical record system 30a or other medical information database (S28).

The generation function 157 analyzes the features of the effort information extracted by the effort information extraction function 155 (S29). For example, the generation function 157 may structure the information extracted from the electronic medical record system 30a or other medical information database.

Then, the generating function 157 generates the presentation 90 based on the extracted effort information, etc. (S30). The transmitting function 158 of the decision maker support device 10b transmits the presentation 90 generated by the generating function 157 to the medical worker terminal 20b. In addition, the receiving function 253 of the medical worker terminal 20b receives the presentation 90 from the decision maker support device 10b.

Then, the presentation function 254 of the medical staff terminal 20b presents the effort information by means of displaying the presentation object 90 on the display 240, etc. (S31).

In this way, the decision maker support system S2 of this embodiment has the same functions as the first embodiment, and can also support patients who are decision makers in medical institutions.

In addition, the decision maker support system S2 of this embodiment presents the subject with information comparing the subject's information with statistical information of others regarding any or all of the frequency, time, number and cost of hospital visits, hospitalizations or medical procedures related to illnesses related to past decision-making, as effort information. Therefore, according to the decision maker support system S2 of this embodiment, by comparing the subject's efforts with others, it is possible to support the subject in objectively recognizing his/her own efforts. For example, if the subject is making a higher level of effort than a typical patient, even if the subject is not aware of it, having the subject understand the level of his/her effort can encourage him/her to view his/her past decision-making in a positive light.

In the decision maker support system S2 of this embodiment, an example is given in which stress detection is used as a trigger to present a presentation item 90 including effort information in a facility such as a hospital, but as in the first embodiment, personal information of the subject may also be utilized.

For example, stress detection and regret analysis may use information from the patient's smartphone, wearable device, SNS, etc. The decision maker support system S2 may acquire this information and use it to determine regret occurrence and present effort information at the patient's next visit to the hospital. In this case, the method of the first embodiment may also be applied to the display format of the presentation object 90.

Below, specific examples of application of the decision maker support systems S1 and S2 of the first and second embodiments will be described for each disease.

(Regarding cancer cases)
There are many types and locations of cancer, but the typical treatment methods are surgical removal, radiation therapy, chemical therapy such as anticancer drugs, and even genetic therapy and immunotherapy, and there are many options for treatment that cannot be simply compared, so it can be said that it is a disease that is prone to regret. In selecting a treatment method, it is useful to use a patient decision support system such as the one described above. In addition, regardless of which treatment is selected and the prognosis, in many cases long-term hospitalization or outpatient treatment is required after treatment, so there is also an opportunity to detect stress and determine the occurrence of regret in medical institutions.

More typically, if a recurrence occurs after treatment or if metastasis is discovered, this can be an opportunity to regret one's previous choice, so if such information is recorded in an electronic medical record, the regret occurrence analysis function can use this information as an element to determine the occurrence of regret. In addition, this information alone can be used to detect stress in the stress detection function.

In addition, if a patient is suffering from side effects from anti-cancer drug treatment, they may regret their choice of treatment. In this case, since patients have many opportunities to interact with nurses while hospitalized, it is thought that stress detection and regret occurrence analysis based on information such as nursing records would be effective. Furthermore, detection using microphones and cameras in the hospital room is also possible.

In all of the above cases, if the patient subsequently feels regret, it is useful to reduce the patient's regret if they objectively review the information that was presented to them when a decision was made about a treatment in the past. In addition, since multiple treatments are often performed during cancer treatment, reviewing efforts such as the number of hospital visits, procedures, and treatment history is also expected to have the effect of reducing regret. Reducing regret in this way may lead to a more proactive approach to future treatment, and in some cases may support a timely change in policy from active treatment to a system centered on palliative care.

As a specific example, let us consider the choice of surgical method for breast cancer. In breast cancer surgery, there are breast-conserving therapy, in which only the cancer is removed and the breast is preserved, and total mastectomy, in which the entire breast is removed. In certain circumstances, there is no difference in survival rate between the two methods, and the choice is based on the patient's wishes. Note that reconstruction with an artificial breast is only possible in the case of total mastectomy.

For example, consider the case where a patient chooses breast-conserving therapy and then experiences a recurrence in the breast (occurrence of regret). Let's say that the patient decided on breast-conserving therapy after taking into consideration that "her own nipple would remain" and "reconstruction with an artificial breast after total mastectomy would not be performed." In this case, in order to understand the process that led to the decision and to understand her choice, it would be sufficient to extract and present her behavioral history, such as the search history on the Internet or at a library that compared breast-conserving therapy with total mastectomy, and records of consultations with counseling centers.

In addition, by presenting economic and time predictions for the method not selected, patients can be reminded of how not selecting the method affected their decision. Economic predictions can be calculated as individualized values based on the patient's health insurance (national, welfare, 10% copayment, 30% copayment, etc.), household income, the terms of the private insurance they are subscribed to, and the insurance points for the procedure. In addition, costs such as transportation to the facility, costs of leaving children or those in need of care, and the burden associated with taking time off work can also be included. Time predictions can be calculated using standard methods based on guidelines.

(About cases of heart disease)
Even in heart disease, there are often options such as medical therapy, catheterization, and open-heart surgery, so the use of a patient decision-making system like the one described above is useful. Furthermore, hospitalization and outpatient visits are often necessary even after treatment, so there are likely to be opportunities for medical institutions to detect stress and determine the occurrence of regret, just as in the case of cancer.

There are various types of heart disease, but they all share a common tendency for severe cases to progress to heart failure, leading to repeated re-hospitalization due to acute exacerbations and a worsening condition. For example, if information about an acute exacerbation is recorded in an electronic medical record, the regret occurrence analysis function can use this information as an element to determine the occurrence of regret, and this information alone can be used to detect stress in the stress detection function. However, since there may not be time to reflect on past actions in the midst of a highly acute phase, the presentation of effort behavior information may be made after a certain degree of recovery.

Even patients who are living relatively trouble-free lives at home after treatment still have certain restrictions on their lifestyle, such as restrictions on exercise, and often feel dissatisfied. In addition, many patients also visit hospitals regularly, and these visits provide an opportunity for the hospital to detect stress and determine the occurrence of regret.

In either of the above cases, if the patient later feels regret, it is useful to reduce the patient's regret if the patient objectively reviews the information that was presented to them when they made a decision about a treatment in the past. This reduction in regret is thought to have the effect of making the patient more proactive about future treatment.

(For cases of gynecological, obstetric or pediatric related illnesses)
For example, in infertility treatment, the treatment starts with timing therapy, then progresses to artificial insemination and in vitro fertilization, but the patient may continue to visit the hospital for a long time without becoming pregnant or giving birth. During such visits, if stress detection and regret occurrence judgment are performed, and the number of visits to the hospital, the time spent at the hospital, the number of medical procedures (artificial insemination, in vitro fertilization, etc.), the total amount of treatment costs, etc. are displayed, the patient can objectively confirm the efforts made so far, which has the effect of assisting in positive judgment regardless of whether the treatment is continued or not. Furthermore, if such long-term infertility treatment fails, the patient may continue to feel mental stress, such as not being able to give up on giving birth even after the treatment is stopped. In such cases, in counseling facilities such as psychiatry and psychosomatic medicine, stress detection and regret occurrence judgment are performed, and the patient can access information on such infertility treatment, objectively confirm the past efforts, and reduce the mental stress.

In obstetrics, congenital diseases in fetuses may be discovered before birth. For example, certain chromosomal abnormalities can be discovered relatively early through new prenatal diagnosis. When new prenatal diagnosis is carried out, it is recommended that counseling be provided by a professional counselor, and medical institutions that carry out the diagnosis have a variety of materials available for presentation for counseling. Congenital heart disease may also be diagnosed before birth, and explanations according to the type of anomaly are provided using materials. Which of these materials has been presented can be stored and used as the information to present when regret is detected later.

More specifically, among chromosomal abnormalities, those with trisomy 21 (Down syndrome) have a long life expectancy after birth, and those with congenital heart disease often have a good prognosis, although surgery is necessary, as treatment techniques have been established. However, in both cases, some disease almost always remains, and compared to healthy children, visits to the pediatrician's office and hospitalization are more common, meaning that both parents and the child have many opportunities to feel stressed. Detecting stress and assessing the occurrence of regret during such visits to the pediatrician's office or hospitalization, and objectively reviewing the materials that were previously explained to the child by the obstetrician, can be useful in reducing parental regret. In addition, it is thought that it would be useful to present such information about parental efforts to the child after he or she has grown up, and not just to the parents.

In contrast, in the case of congenital heart disease, diagnosis is made late and the prognosis is generally good once treatment is administered, so birth and treatment are often the norm. However, chromosomal abnormalities can be diagnosed relatively early using new prenatal testing, and in over 90% of cases, when a chromosomal abnormality is diagnosed, abortion is the choice. Even if such a choice is made, the mental burden on the parents may continue, so in psychiatric, psychosomatic, and other counseling facilities, stress detection and regret determination can be performed, and information from the time of counseling can be accessed, allowing the parents to objectively confirm the information from the time of previous decision-making and help them sort out their thoughts psychologically.

(Variation 1)
In the above-mentioned embodiments, the case where the support target and the patient suffering from the disease are the same person has been described. However, the support target may be a person different from the patient suffering from the disease. For example, the subject of the detected stress, the determined regret, the effort and the positive events is not limited to the patient himself, but may be a parent, a family member, a surviving family member after the patient's death, or a medical professional such as a doctor or a nurse.

More specifically, bereaved families who have lost family members to cancer or other causes may feel regret and self-blame regarding the treatment and care they provided to the patient, wondering if it was the right choice. This can be an obstacle to recovering from the grief of losing a family member, and may even lead to the development of mental illnesses such as depression.

For example, the decision maker support systems S1 and S2 can also be applied when surviving family members feel regret about their own decisions regarding the disease suffered by the deceased.

For example, in this modified example, the decision maker support system will have the functionality to collect information showing that the surviving family members "did everything they could" or "made an effort" from the deceased's medical records and information recorded on the mobile devices of the deceased and related parties, and present this information to the family members.

Specifically, it is possible to search for words of gratitude associated with acknowledging efforts, such as informed consent for treatment, consent forms used in shared decision-making, or "thank you" from the deceased while they were alive. Words associated with acknowledging efforts may be registered in advance, for example, in a word database (not shown) in the memory circuit 120 of the decision maker support device 10a, 10b. In this case, the collection function 154, 159 may extract words that match words registered in the word database as effort information from the mobile terminal, SNS, email, etc. of the deceased or surviving family members. Note that in this modified example, the mobile terminal of the deceased may be an example of a user terminal 20a, and the mobile terminal of the surviving family members may be an example of a user terminal 20a.

In addition, since it has been reported that words of appreciation from medical or care workers who accompany the bereaved family members to the final resting place can also bring comfort to the bereaved family members, the collection functions 154, 159 may collect records of situations in which the relevant medical or care workers helped the bereaved family members fight the illness, based on information such as time in the nursing records and care records. Furthermore, the presentation function 254 presents these records to the bereaved family members.

(Variation 2)
In addition, in the above-mentioned embodiments and variant 1, the regrets of patients, their families, or their surviving relatives have been described as targets, but the decision maker support systems S1 and S2 may be applied to resolve the regrets of medical professionals such as doctors and nurses. Even doctors and nurses may feel regret about their own choices or actions when the progress of the patient they are in charge of is poor. In such cases, if the information referred to at the time of decision-making is presented as effort behavior information, this leads to a reduction in mental burden and has the effect of making them more positive about their next activity.

(Variation 3)
In addition, in each of the above-described embodiments, the detection of the subject's stress is set as a condition for detecting regret, but the detection of stress does not have to be a necessary condition for detecting regret. Furthermore, the decision maker support systems S1 and S2 may perform the process in the order of detecting the subject's regret and then determining whether or not the subject is stressed.

In addition, in each of the above-described embodiments, a decision was identified in advance, and then the presence or absence of the subject's regret was determined based on that decision. However, the decision maker support systems S1 and S2 may detect some regret in the subject and then execute a process to estimate a medical decision that may be the cause of the regret. In this case, if no medical decision that may be the cause of the regret is found, the decision maker support systems S1 and S2 may determine that the subject's regret is unrelated to medical care. In this case, the decision maker support systems S1 and S2 may not need to generate or present a presentation 90 in particular.

In addition, a configuration may be adopted in which the subject himself/herself inputs into the user terminal 20a or the decision maker support device 10a, 10b that he/she is feeling stressed or regrets about a decision.

In addition, in each of the above-described embodiments, detection of regret based on the subject's writing or statement has been exemplified, but the regret detection function 152 may also detect regret based on the analysis results of the subject's facial expression. For example, the regret detection function 152 may use a known facial expression analysis method to determine that the subject has regret when it detects a negative emotion of the subject from a captured image of the subject.

(Variation 4)
In addition, the presentation is not limited to the aspects described in the above-mentioned embodiments. Fig. 7 is a diagram showing an example of a presentation 92 according to Modification 4. For example, the generating function 157 may extract a history and a display time when information related to a disease was displayed from the history of a web browser of the user terminal 20a held by the subject, and generate a presentation 92 capable of visualizing the history and the time.

For example, the generation function 157 may not only analyze the time required for the search, but may also obtain information on the location of the subject at the time of the search from the GPS history of the user terminal 20a, etc., and include it in the presentation 92. In the example shown in FIG. 7, the generation function 157 includes in the presentation 92 the time required to search for diseases while the subject was using the web browser, and the location of the subject at the time of each search.

(Variation 5)
Some or all of the functions described as the functions of the user terminal 20a in the first embodiment may be functions of the decision maker support device 10a.

For example, the stress detection function 251 may be a function of the decision maker support device 10a. Also, some or all of the functions described as the functions of the decision maker support device 10a may be provided in the user terminal 20a. In this case, the user terminal 20a is an example of a decision maker support device.

In the second embodiment, some or all of the functions described as functions of the medical professional terminal 20b may be functions of the decision maker support device 10b. Also, some or all of the functions described as functions of the decision maker support device 10b may be provided in the medical professional terminal 20b. In this case, the medical professional terminal 20b is an example of a decision maker support device.

In addition, in each of the above-described embodiments, the presentation function 254 of the user terminal 20a or the medical staff terminal 20b is an example of a presentation unit, but the transmission function 158 of the decision maker support device 10a, 10b may also be an example of a presentation unit.

(Variation 6)
Furthermore, the information included in the presentations 90 and 92 is not limited to efforts or positive information. For example, the subject's behavior during the period set by the period setting function 153 may be presented even if it is not a particularly positive event. Furthermore, the presentations 90 and 92 may include the behavioral history or records of the subject's family or friends, rather than the subject himself/herself.

(Variation 7)
In addition, in each of the above-described embodiments, the decision maker support devices 10a, 10b transmit the presentation items 90, 92 to the user terminal 20a or the medical staff terminal 20b, but may transmit the presentation items 90, 92 to a server that provides an application service such as SNS. In other words, the decision maker support devices 10a, 10b may not transmit the presentation items 90, 92 directly to the user terminal 20a, but may cooperate with an application service such as SNS and present the presentation items 90, 92 to the subject as a function within the application.

(Variation 8)
In the above embodiments, detection of regret is used as a trigger for collecting effort information and positive events and generating the presentation 90, but other triggers may be adopted. For example, the subject himself/herself may use an operation on the user terminal 20a or the decision maker support device 10a to request the start of collecting effort information and positive events and generating the presentation 90 as a trigger for these processes.

At least one of the embodiments described above can reduce the regret currently felt by a decision maker regarding past decisions regarding a disease.

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

10a, 10b Decision maker support device 20a User terminal 20b Medical staff terminal 30a, 30b Electronic medical record system 90, 92 Presentation 91a to 91e Icon 110, 210 NW interface 120, 220 Memory circuit 130, 230 Input interface 140, 240 Display 150, 250 Processing circuit 151 Identification function 152 Regret detection function 153 Period setting function 154, 159 Collection function 155 Effort information extraction function 156 Positive event extraction function 157 Generation function 158 Transmission function 251 Stress detection function 252 Transmission function 253 Reception function 254 Presentation function 500 Information system S1, S2 Decision maker support system

Claims (13)

a regret detector configured to detect regret of the subject with respect to a past medical decision-making regarding the disease based on information regarding the subject's regret related to the disease;
A collection unit that collects a behavioral history or record of the subject regarding the decision-making or the disease;
A presentation unit that presents the collected behavioral history or the record in association with the decision-making or the disease when the regret is detected,
The information regarding regret is at least one of a statement, a search history, and a description that expresses the subject's regret related to the disease;
The regret detection unit detects regret regarding the decision-making when at least one of a statement, a search history, and a description that indicates regret of the subject related to the disease is detected after the time of the decision-making.
Decision maker support system. The collection unit collects the behavioral history or the record as effort information regarding the subject's effort related to the decision-making.
2. The decision maker support system of claim 1. The behavioral history or the record collected by the collection unit includes information about positive events resulting from the subject's decision-making.
3. A decision maker support system according to claim 1 or 2. The collection unit determines whether the behavioral history or the record obtained based on the result of the decision-making of the subject during a positive event extraction target period is a positive event based on the merits of the options selected by the subject and the demerits of the options not selected by the subject .
The positive event extraction target period is the period from the date on which the subject made a decision regarding the disease to the present ;
4. The decision maker support system of claim 3. The collection unit collects, as the effort information, information presented to the subject in relation to the disease related to the past decision-making or in relation to the decision-making during treatment of the disease.
3. The decision maker support system of claim 2. The collection unit collects, as the effort information, information including any one or all of frequency, time, number, and cost of hospital visits, hospitalizations, or medical procedures related to the disease related to the past decision-making.
3. The decision maker support system of claim 2. The presentation unit presents, as the effort information, information comparing the subject's information with statistical information of others regarding any one or all of the frequency, time, number, and cost of hospital visits, hospitalizations, or medical procedures related to the disease related to the past decision-making.
3. The decision maker support system of claim 2. A stress detection unit that detects stress of the subject based on biological information of the subject,
The regret detection unit detects regret regarding the decision-making when stress of the subject is detected after the time of the decision-making and a statement, search history, or description expressing regret of the subject related to the disease is detected.
A decision maker support system according to any one of claims 1 to 7 . A setting unit that sets a period of effort made by the subject regarding the treatment of the disease based on the time when the subject was diagnosed with the disease and the time when the subject made a decision regarding the disease,
The collection unit collects the behavior history or the record within the effort period as the effort information.
3. The decision maker support system of claim 2. the regret detection unit determines that the presentation has an effect of reducing the subject's regret if the subject's stress level is reduced compared to before the presentation of the presentation after the presentation unit presents the behavioral history or the record as a presentation , and determines that the presentation has not an effect of reducing the subject's regret if the subject's stress level is the same as before the presentation of the presentation or has increased;
When the presentation unit does not have an effect of reducing the regret of the subject, the presentation unit changes the mode or content of the presentation and presents it again.
A decision maker support system according to any one of claims 1 to 9 . The subject is a patient suffering from the disease.
A decision maker support system according to any one of claims 1 to 10 . The subject is different from a patient suffering from the disease.
A decision maker support system according to any one of claims 1 to 10 . a regret detector configured to detect regret of the subject with respect to a past medical decision-making regarding the disease based on information regarding the subject's regret related to the disease;
A collection unit that collects a behavioral history or record of the subject regarding the decision-making or the disease;
A presentation unit that presents the collected behavioral history or the record in association with the decision-making or the disease when the regret is detected,
The information regarding regret is at least one of a statement, a search history, and a description that expresses the subject's regret related to the disease;
The regret detection unit detects regret regarding the decision-making when at least one of a statement, a search history, and a description that indicates regret of the subject related to the disease is detected after the time of the decision-making.
Decision maker support device.

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