JP7745378B2 - Medical information processing device - Google Patents

Description

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

Traditionally, various tests are performed on patients. However, if the patient is not treated according to the test results, their condition may worsen. Therefore, patients need to receive medical treatment at the appropriate time.

However, it would be cumbersome to require continuous follow-up surveys for all patients to determine whether they are receiving treatment at the appropriate time.

Japanese Patent Application Laid-Open No. 2020-018705

One of the problems that the embodiments disclosed in this specification and drawings attempt to solve is identifying patients who should be followed. However, the problems that the embodiments disclosed in this specification and drawings attempt to solve are not limited to the above problem. Problems corresponding to the effects of each configuration shown in the embodiments described below can also be positioned as other problems.

A medical information processing apparatus according to an embodiment includes an identification unit, a first detection unit, a second detection unit, a first determination unit, and a setting unit. The identification unit identifies follow-up candidates who are candidates for follow-up target patients. The first detection unit detects abnormalities from first test results for the follow-up candidate patients. The second detection unit detects abnormalities from second test results for the follow-up candidate patients that were generated earlier than the first test results. When an abnormality is detected from the first test results and the second test results, the first determination unit determines whether to exclude the follow-up candidate patient from the follow-up candidate based on the medical records of the follow-up candidate patient. The setting unit sets the patient not excluded from the follow-up candidate by the first determination unit as the follow-up target.

FIG. 1 is a block diagram showing an example of the configuration of a notification system according to this embodiment. FIG. 2 is a block diagram showing an example of the configuration of the notification server according to this embodiment. FIG. 3 is a flowchart showing an example of a test result notification process executed by the notification server according to this embodiment. FIG. 4 is a flowchart showing an example of a reservation notification process executed by the notification server according to this embodiment. FIG. 5 is a flowchart showing an example of a drug notification process executed by the notification server according to this embodiment.

The medical image processing device according to this embodiment will be described below with reference to the drawings. In the following embodiment, parts with the same reference numerals perform similar operations, and duplicate descriptions will be omitted where appropriate.

(Present embodiment)
FIG. 1 is a block diagram showing an example of the configuration of a notification system 1 according to this embodiment. The notification system 1 includes a Hospital Information System (HIS) 10, a Radiology Information System (RIS) 20, a Picture Archiving and Communication System (PACS) 30, a Laboratory Information System (LIS) 40, and a notification server 50. Each system and device is connected to each other via a network 60 so as to be able to communicate with each other. The configuration shown in FIG. 1 is merely an example, and the number of each system and device may be changed as desired. Devices not shown in FIG. 1 may also be connected to the network 60.

The hospital information system 10, radiology information system 20, medical image management system 30, clinical test information system 40, and notification server 50 are realized by computer equipment such as servers and workstations.

The hospital information system 10 stores schedule information 11 and electronic medical record information 12. Schedule information 11 is information that records a patient's medical treatment schedule, such as a history of past visits to the hospital and future planned visits. In other words, schedule information 11 includes information for identifying the patient and the patient's schedule. Electronic medical record information 12 is information that records the progress of a patient's medical treatment. For example, electronic medical record information 12 includes information for identifying the patient, the name of the disease the patient has, the name of the prescribed medication, the treatment period, and other information. Electronic medical record information 12 is an example of a medical record.

The radiology information system 20 stores imaging order information 21 and image interpretation report information 22. Imaging order information 21 is information ordering an examination using imaging. Image interpretation report information 22 is information containing findings by a doctor or other medical professional who interpreted image information 31 captured by a medical image diagnostic device. The image interpretation report information 22 also contains information identifying the doctor or other medical professional who ordered imaging using the medical image diagnostic device.

The medical image management system 30 stores image information 31. The image information 31 includes images captured by a medical image diagnostic device. Examples of medical image diagnostic devices include X-ray CT (Computed Tomography) devices, MRI (Magnetic Resonance Imaging) devices, X-ray diagnostic devices, and ultrasound diagnostic devices. The image information 31 conforms to the DICOM (Digital Imaging and Communications in Medicine) standard.

The clinical test information system 40 stores clinical test order information 41, clinical test result information 42, and clinical test report information 43. Clinical test order information 41 is information ordering a clinical test. Clinical test result information 42 is information indicating the results of a clinical test. Clinical test report information 43 is information containing findings on the clinical test results. In addition, the clinical test order information 41, clinical test result information 42, and clinical test report information 43 contain information to identify the medical professional, such as a doctor, who ordered the clinical test.

The notification server 50 is a server device that notifies the patient of the follow-up target's condition. The notification server 50 is an example of a medical information processing device. More specifically, the notification server 50 identifies candidate patients to follow. A follow-up target is a target that is continuously investigated to determine whether medical treatment is being provided at an appropriate time. For example, the notification server 50 has little need to set a patient with a minor illness such as a cold or a minor injury as a follow-up target. Therefore, the notification server 50 identifies follow-up candidates who are candidates for patients to follow from among patients with injuries or illnesses that have been set in advance.

The notification server 50 also determines whether the test results of the follow-up candidate patient contain any abnormalities. If the test results contain abnormalities, the notification server 50 also determines whether medical records such as the electronic medical record information 12 contain information to cancel the detected abnormality. For example, even if an abnormal shadow is detected by mammography, if it is a dense breast, the patient does not have breast cancer or other conditions. In such cases, the electronic medical record information 12 records information indicating that the abnormal shadow is a dense breast and therefore there is no problem. For example, the notification server 50 detects information indicating a diagnosis that there is no problem as information to cancel the abnormality. The notification server 50 then determines to exclude the patient from the follow-up candidates.

The notification server 50 also sets patients who have not been excluded from the follow-up candidates as follow-up targets. The notification server 50 then follows up with medical professionals such as doctors in charge of the patient to be followed up, the patient themselves, and those related to the patient. For example, the notification server 50 sends an email or the like indicating the content of the follow-up.

Next, we will explain the notification server 50.

Figure 2 is a block diagram showing an example of the configuration of the notification server 50 according to this embodiment. The notification server 50 includes an NW (network) interface 510, an input interface 520, a display 530, a memory circuit 540, and a processing circuit 550.

The NW interface 510 is connected to the processing circuit 550 and controls the transmission and communication of various data between each device connected via the network 60. For example, the NW interface 510 is realized by a network card, network adapter, NIC (Network Interface Controller), etc.

The input interface 520 is connected to the processing circuit 550 and converts input operations received from an operator (medical professional) into electrical signals and outputs them to the processing circuit 550. Specifically, the input interface 520 converts input operations received from the operator into electrical signals and outputs them to the processing circuit 550. For example, the input interface 520 may be implemented by a trackball, switch buttons, a mouse, a keyboard, a touchpad that performs input operations by touching the operation surface, a touchscreen that integrates a display screen and a touchpad, a non-contact input circuit using an optical sensor, and a voice input circuit. Note that in this specification, the input interface 520 is not limited to those equipped with physical operating components such as a mouse and keyboard. For example, an electrical signal processing circuit that receives electrical signals corresponding to input operations from an external input device provided separately from the device and outputs these electrical signals to a control circuit is also included as an example of the input interface 520.

The display 530 is connected to the processing circuit 550 and displays various information and image data output from the processing circuit 550. For example, the display 530 may be implemented as a liquid crystal display, a CRT (Cathode Ray Tube) display, an organic EL display, a plasma display, a touch panel, etc.

The memory circuitry 540 is connected to the processing circuitry 550 and stores various data. The memory circuitry 540 also stores various programs that the processing circuitry 550 reads and executes to realize various functions. For example, the memory circuitry 540 may be implemented using semiconductor memory elements such as RAM (Random Access Memory) or flash memory, or a hard disk, optical disk, etc.

The memory circuitry 540 stores notification destination information 541. The notification destination information 541 includes information indicating the notification destination for each patient. For example, the notification destination information 541 includes the patient's email address and the email address of the doctor in charge of the patient's medical care. Note that the notification destination information 541 is not limited to email addresses, and may also be an SNS (Social Networking Service) account or other information.

The processing circuit 550 controls the overall operation of the notification server 50. The processing circuit 550 has, for example, a candidate identification function 551, a first anomaly detection function 552, a second anomaly detection function 553, an exclusion determination function 554, a schedule acquisition function 555, a follow target setting function 556, and a notification function 557. In this embodiment, the processing functions performed by the components of the candidate identification function 551, the first anomaly detection function 552, the second anomaly detection function 553, the exclusion determination function 554, the schedule acquisition function 555, the follow target setting function 556, and the notification function 557 are stored in the storage circuit 540 in the form of a computer-executable program. The processing circuit 550 is a processor that reads and executes the programs from the storage circuit 540 to realize the functions corresponding to each program. In other words, when each program has been read, the processing circuit 550 has the functions shown in the processing circuit 550 in FIG. 2.

Note that while Figure 2 illustrates a single processor realizing the candidate identification function 551, first anomaly detection function 552, second anomaly detection function 553, exclusion determination function 554, schedule acquisition function 555, follow-up target setting function 556, and notification function 557, it is also possible to combine multiple independent processors to configure the processing circuit 550, and have each processor execute a program to realize the function. Also, while Figure 2 illustrates a single memory circuit such as memory circuit 540 storing programs corresponding to each processing function, it is also possible to have multiple memory circuits distributed and have the processing circuit 550 read corresponding programs from individual memory circuits.

The term "processor" used in the above description refers to circuits such as a CPU (Central Processing Unit), GPU (Graphical Processing Unit), or 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)). The processor achieves its functions by reading and executing a program stored in the memory circuitry 540. Note that instead of storing a program in the memory circuitry 540, the processor may be configured so that the program is directly embedded in its circuitry. In this case, the processor achieves its functions by reading and executing the program embedded in its circuitry.

The candidate identification function 551 identifies follow-up candidates who are candidates for patients to be followed. The candidate identification function 551 is an example of an identification unit. More specifically, the candidate identification function 551 identifies patients in medical records that include information indicating a diagnosis of the set injury or illness, or a possibility of the set injury or illness, as follow-up candidates. For example, if the candidate identification function 551 detects information from the medical records indicating a diagnosis of the set injury or illness or a possibility of the set injury or illness, information indicating that a medication prescribed for the set injury or illness has been prescribed, or information indicating that a test to be performed for the set injury or illness has been ordered, it sets the corresponding patient as a follow-up candidate.

The first abnormality detection function 552 detects abnormalities from the first test results for a follow-up candidate patient. The first abnormality detection function 552 is an example of a first detection unit. Here, the first test is a test performed on the patient. For example, the first test may be a clinical test or an image diagnosis test. The first test result is the result of the first test. That is, the first test result may be clinical test result information 42, which is the test result of the clinical test, clinical test report information 43, which contains findings from the test result of the clinical test, image information 31 captured by a medical image diagnostic device, radiology report information 22, which contains findings for the image information 31, or other information.

That is, the first abnormality detection function 552 detects abnormalities from first test results such as clinical test result information 42, clinical test report information 43, image information 31, and radiology report information 22. An abnormality is an injury or illness, or the possibility of a set injury or illness. For example, the first abnormality detection function 552 detects abnormal shadows, abnormal values in the test results, or text indicating an injury or illness from the first test results. The first abnormality detection function 552 may also detect abnormalities from first test results for follow-up candidate patients that do not have a log indicating they have been viewed. This is because if the results have already been viewed by a medical professional such as a doctor, there is little point in the first abnormality detection function 552 detecting an abnormality again.

The second abnormality detection function 553 detects abnormalities from second test results generated earlier than the first test results for the follow-up candidate patient. The second abnormality detection function 553 is an example of a second detection unit. In other words, the second abnormality detection function 553 detects abnormalities from second test results indicating the test results of a second test performed earlier than the first test.

The second test is an examination performed on the patient. For example, the second test may be a clinical test or an examination using diagnostic imaging. The second test result is the result of the second test. That is, the second test result may be clinical test result information 42, which is the test result of the clinical test, clinical test report information 43, which contains findings from the test result of the clinical test, image information 31 captured by a medical image diagnostic device, radiology report information 22, which contains findings on the image information 31, or other information.

Here, if an abnormality is detected in the results of two tests conducted at different times, the notification server 50 will set it as a follow-up target. Therefore, if the test contents or purposes of the two tests are different, there may be little point in detecting an abnormality in each. For example, even if an abnormality is detected in both the test results of a cancer test and the test results of an allergy test, there may be little correlation between them.

Therefore, the second test results may be limited to test results of the same type of test as the first test. For example, if the first test result is clinical test report information 43, the second abnormality detection function 553 may limit it to clinical test report information 43 for the second test results of a second test that has the same test content as the first test. Alternatively, the second test results may be limited to test results of a test that has the same purpose as the first test. For example, if the first test is a test for a specific disease, the second abnormality detection function 553 may limit the second test results to test results for the same disease as the first test. In other words, when the test content and test purpose of the second test are set, the second abnormality detection function 553 detects abnormalities from the second test results for the test content and test purpose identified by the first test results.

If an abnormality is detected in a patient from the second test results, the exclusion determination function 554 determines whether to exclude the patient from the follow-up candidate list based on the patient's medical record. The exclusion determination function 554 is an example of a first determination unit. More specifically, the exclusion determination function 554 determines to exclude from the follow-up candidate list a patient whose medical record, such as electronic medical record information 12, contains abnormality cancellation information indicating that the abnormality should be canceled. The abnormality cancellation information is information that cancels the abnormality detected by the second abnormality detection function 553. For example, the abnormality cancellation information is text or numerical values that indicate a diagnostic result that determines that the abnormality is not a problem.

This will be explained using a specific example. For example, abnormality cancellation information is a diagnosis of a sequela with little chance of improvement. If the sequela with little chance of improvement continues for a certain period of time, the condition becomes normal and no longer corresponds to an abnormality. Also, if the sequela has little chance of improvement, the patient may not need to return to the hospital. In such cases, the exclusion determination function 554 determines that the patient in question should be excluded from follow-up candidates, as there is no need to continually check whether treatment is being provided.

Alternatively, the abnormality cancellation information is a false positive diagnosis. For example, abnormal shadows are observed in cases of dense breast tissue, but they are not breast cancer. In such cases, the patient does not need treatment and does not need to visit the hospital. Therefore, the exclusion determination function 554 determines to exclude from follow-up candidates patients whose medical records, such as the electronic medical record information 12, contain abnormality cancellation information indicating a false positive.

Alternatively, abnormal cancellation information is information indicating that treatment has ended. For example, information indicating that treatment has ended includes a sentence indicating that treatment has ended, information indicating that a certain period of time has passed since the last medical record, information indicating that no medication was prescribed in the last medical consultation, or information indicating that the last medical record only contained instructions to improve lifestyle habits. If treatment has ended, the patient does not need to visit the hospital. Therefore, the exclusion determination function 554 determines to exclude from follow-up candidates patients whose medical records contain abnormal cancellation information indicating that treatment has ended.

The follow-up target setting function 556 sets patients who have not been excluded from follow-up candidates by the exclusion determination function 554 as follow-up targets. The follow-up target setting function 556 is an example of a setting unit. In other words, the follow-up target setting function 556 sets patients who are follow-up candidates in whom abnormalities have been detected by the first abnormality detection function 552 and the second abnormality detection function 553 as follow-up targets.

The schedule acquisition function 555 acquires the medical treatment schedule of the patient being followed. The schedule acquisition function 555 is an example of an acquisition unit. More specifically, the schedule acquisition function 555 acquires, for example, the medical treatment schedule contained in the schedule information 11. For example, the medical treatment schedule is information indicating the scheduled date of the next hospital visit of the patient being followed. Or, the medical treatment schedule is information indicating the scheduled date when the medication prescribed to the patient being followed will run out.

Here, the patient being followed up is required to visit the hospital regularly, but if they do not do so, there is a high possibility that their condition will worsen. Furthermore, if the patient being followed up runs out of their prescribed medication and stops taking it without receiving a diagnosis, there is a high possibility that their condition will worsen. Therefore, the patient being followed up will schedule a diagnosis when the date on which their prescribed medication is due to run out is approaching. Therefore, the schedule acquisition function 555 acquires the medical treatment schedule of the patient being followed up.

The notification function 557 executes notifications according to the patient's condition. More specifically, the notification function 557 notifies the patient who is the follow-up target set by the follow-up target setting function 556 that an appointment for medical treatment is to be made. The notification function 557 is an example of a notification unit.

The notification function 557 determines whether the patient being followed has made an appointment for medical treatment based on the medical treatment schedule acquired by the schedule acquisition function 555. The notification function 557 is an example of a second determination unit. The notification function 557 then notifies the patient to make an appointment for medical treatment based on the determination result of whether a medical treatment schedule has been made. For example, the notification function 557 notifies the patient being followed that an appointment for medical treatment will be made based on the determination result of whether a medical treatment schedule has been made if the patient being followed has not made an appointment for medical treatment. In other words, the notification function 557 notifies the patient to make an appointment for medical treatment if there are no appointments for medical treatment within a certain period of time in the medical treatment schedule of the patient being followed acquired by the schedule acquisition function 555. Here, the certain period may be a period set commonly for multiple people, a period set for each patient, or a period set according to the patient's condition.

Furthermore, based on the determination result of whether a medical appointment has been made, the notification function 557 notifies a patient being followed up who is estimated to have a remaining amount of prescribed medication below a threshold, to make an appointment for medical treatment if the patient has not made an appointment for medical treatment. Here, a doctor or other medical professional prescribes medication along with the period for which the medication will be taken. Therefore, the notification function 557 can estimate the remaining amount of prescribed medication based on the period for which the medication will be taken and the current date and time. Therefore, the notification function 557 notifies a patient to make an appointment for medical treatment if the remaining amount of prescribed medication is estimated to be below a threshold.

Specifically, when issuing a notification to a patient, the notification function 557 extracts the notification destination of the patient being followed from the notification destination information 541. The notification function 557 then notifies the extracted notification destination to make an appointment for medical treatment.

In addition, the notification function 557 notifies the patient of the appointment date and time when the patient being followed has an appointment. This allows the notification function 557 to remind the patient of the appointment date and time. In other words, it is possible to prevent the patient from forgetting to make an appointment.

In addition, the notification function 557 may notify content appropriate to the situation, not just of the patient being followed, when no behavioral history indicating that behavior appropriate to the situation has been performed is stored.

For example, if an abnormality is detected in the first test results and the second test results, the notification function 557 notifies the doctor who ordered the first test for the patient set as a follow-up target by the follow-up target setting function 556 that an abnormality was detected in the first test results and the second test results. Specifically, the notification function 557 extracts information that can identify the doctor who ordered the first test from the imaging order information 21 or clinical test order information 41 that ordered the first test. The notification function 557 also extracts the notification destination of the doctor identified by the extracted information from the notification destination information 541. The notification function 557 then notifies the extracted notification destination that an abnormality was detected in the first test results and the second test results.

The notification function 557 notifies the user to check the first test results if no abnormality is detected in the first test results or if an abnormality detected in the first test results is canceled. Specifically, the notification function 557 extracts information that can identify the doctor who ordered the first test from the imaging order information 21 or clinical test order information 41 that ordered the first test. The notification function 557 also extracts the notification destination of the doctor identified by the extracted information from the notification destination information 541. The notification function 557 then notifies the extracted notification destination to check the first test results.

When an abnormality is detected in the test results, the notification function 557 checks whether an abnormality has also been detected in the test results of another test to confirm whether the test result is valid. When an abnormality is detected in the first test results, if there are no second test results and no order has been placed, the notification function 557 notifies the user that a test should be ordered. Specifically, the notification function 557 extracts information that can identify the physician who ordered the first test from the imaging order information 21 or clinical test order information 41 that ordered the first test. The notification function 557 also extracts the notification destination of the physician identified by the extracted information from the notification destination information 541. The notification function 557 then notifies the extracted notification destination that an order should be placed.

If an abnormality is detected in the first test results, there are no second test results, but an order for a test has been issued, the notification function 557 notifies the patient to explain the test results. Specifically, the notification function 557 determines whether the test results of the issued order have been explained to the patient. That is, the notification function 557 determines whether an action history indicating that the patient has been explained is recorded. If the test results have not been explained to the patient, the notification function 557 extracts information that can identify the physician who ordered the test from the imaging order information 21 or clinical test order information 41. The notification function 557 also extracts the notification destination of the physician identified by the extracted information from the notification destination information 541. The notification function 557 then notifies the extracted notification destination to explain the test results to the patient. This prevents the notification server 50 from leaving the patient without taking any action, such as explaining the results, after the test. Therefore, the notification server 50 can prevent the patient's condition from worsening due to the patient not taking any action, such as explaining the results, after the test.

Next, we will explain the various processes performed by the notification server 50.

Figure 3 is a flowchart showing an example of the test result notification process executed by the notification server 50 according to this embodiment. The test result notification process notifies the patient of the test results and sets the patient to be followed.

The candidate identification function 551 determines whether a certain period of time has passed since the previous processing (step S11). If the certain period of time has not passed (step S11; No), the candidate identification function 551 waits.

If a certain period of time has passed (Step S11: Yes), the candidate identification function 551 identifies follow-up candidates who are potential patients to be followed (Step S12). In other words, the candidate identification function 551 identifies patients with a specific injury or illness, or patients at risk of developing a specific injury or illness, as follow-up candidates.

The first abnormality detection function 552 determines whether the first test results for the follow-up candidate patient contain an abnormality (step S13). If the first test results do not contain an abnormality (step S13; Yes), the notification function 557 notifies the medical professional, such as the doctor who ordered the first test, that they should check the first test results (step S14). That is, based on the notification destination information 541, the notification function 557 notifies the medical professional's email address, SNS account, etc., that they should check the first test results.

If the first test results contain an abnormality (step S13; No), the second abnormality detection function 553 determines whether second test results for the same patient as the first test results are stored (step S15).

If the second test results are stored (step S15; Yes), the second abnormality detection function 553 determines whether the second test results contain an abnormality (step S16).

If the second test results do not contain any abnormalities (step S16; Yes), the notification function 557 proceeds to step S14. For example, the notification function 557 may detect an abnormality in the first test results but not in the second test results, and therefore notify the user that the first test results should be checked.

If the second test results contain an abnormality (step S16; No), the exclusion determination function 554 determines whether the medical records, such as the electronic medical record information 12, of the follow-up candidate patient in whom the abnormality was detected contain abnormality cancellation information that cancels the patient (step S17).

If abnormality cancellation information is included (step S17; Yes), the notification function 557 proceeds to step S14. For example, the notification function 557 detects an abnormality in the first test result and the second test result, but notifies the user that the first test result should be checked because abnormality cancellation information is included.

If abnormality cancellation information is not included (Step S17; No), the notification function 557 notifies the medical professional that an abnormality has been detected from the first test result and the second test result. In other words, the notification function 557 notifies the medical professional's email address, SNS account, etc. that an abnormality has been detected (Step S18).

The follow-up target setting function 556 sets the follow-up candidate patient in whom abnormalities were detected from the first and second test results as the follow-up target (step S19).

If the second test results are not stored in step S15 (step S15; No), the notification function 557 determines whether an order for a test has been placed (step S20). That is, the notification function 557 determines whether clinical test order information 41 or imaging order information 21 is stored.

If an order for a test has not been issued (step S20; No), the notification function 557 notifies the medical professional, such as the doctor who ordered the first test, that an order for a test will be placed (step S23).

If an order for a test has been issued (Step S20; Yes), the notification function 557 determines whether the test results of the test performed in response to the issued order have been explained to the patient (Step S21). That is, the notification function 557 determines whether an action history indicating that the test results have been explained to the patient is stored. If the test results have been explained to the patient (Step S21; Yes), the notification server 50 terminates processing.

If the test results have not been explained to the patient (step S21; No), the notification function 557 notifies the medical professional, such as the doctor who issued the test order, that the test results should be explained to the patient (step S22).

The notification server 50 then completes the test result notification process.

Figure 4 is a flowchart showing an example of appointment notification processing executed by the notification server 50 according to this embodiment. The appointment notification processing is processing for notifying a patient who is being followed if they have not yet made an appointment for medical treatment.

The schedule acquisition function 555 acquires the medical treatment schedule from the electronic medical record information 12 of the patient being followed (step S31).

The notification function 557 determines whether there are any scheduled medical appointments (step S32). That is, the notification function 557 determines whether the patient being followed is currently visiting the hospital. For example, the notification function 557 determines whether there are any scheduled medical appointments within a certain period of time since the last medical appointment.

If there is no medical appointment scheduled (Step S32; Yes), the notification function 557 notifies the patient being followed to obtain a medical appointment schedule (Step S33). That is, the notification function 557 notifies the patient's email address, SNS account, etc., based on the notification destination information 541, to obtain a medical appointment schedule.

If a medical appointment is scheduled (Step S32; No), the notification function 557 determines whether the current date and time is before the scheduled medical appointment date (Step S34). If it is before the scheduled medical appointment date (Step S34; Yes), the notification function 557 notifies the patient being followed of the scheduled medical appointment date (Step S35).

If the date is after the scheduled medical appointment date (step S34; No), the notification function 557 notifies the patient being followed to obtain their medical appointment schedule (step S36).

The notification server 50 then completes the reservation notification process.

Figure 5 is a flowchart showing an example of the drug notification process executed by the notification server 50 according to this embodiment. The drug notification process is a process for notifying a patient when the remaining amount of drug prescribed to the patient being followed is estimated to be below a threshold.

The notification function 557 determines whether the remaining amount of prescribed medication is estimated to be below a threshold based on the electronic medical record information 12 of the patient being followed (step S41). In other words, the notification function 557 determines whether the remaining time until the end of the period for which the medication was prescribed is below a threshold. If the remaining amount of medication is estimated to be greater than the threshold (step S41; No), the notification function 557 waits.

If the remaining amount of medication is estimated to be less than the threshold (Step S41; Yes), the schedule acquisition function 555 acquires the medical treatment schedule from the electronic medical record information 12 of the patient being followed (Step S42).

The notification function 557 determines whether or not there are any scheduled medical appointments (step S43). In other words, the notification function 557 determines whether or not there are any scheduled medical appointments until the last day of the period for which the medication was prescribed.

If there is no scheduled medical appointment (Step S43: Yes), the notification function 557 notifies the patient being followed to obtain a medical appointment schedule in order to receive a medication prescription (Step S44). That is, the notification function 557 notifies the patient's email address, SNS account, etc., based on the notification destination information 541, to obtain a medical appointment schedule.

If a medical appointment is scheduled (Step S43; No), the notification function 557 determines whether the current date and time is before the scheduled medical appointment date (Step S45). If it is before the scheduled medical appointment date (Step S45; Yes), the notification function 557 notifies the patient being followed of the scheduled medical appointment date (Step S46).

If the date falls after the scheduled medical appointment (step S45; No), the notification function 557 notifies the patient to obtain a medical appointment to receive a medication prescription (step S47).

The notification server 50 then completes the medication notification process.

As described above, the notification server 50 according to this embodiment identifies patients who are candidates for follow-up. The notification server 50 detects abnormalities from the first and second test results of the patient who is a candidate for follow-up. The notification server 50 also determines whether to exclude a patient in whom an abnormality has been detected from the list of candidates for follow-up, based on whether information to cancel the abnormality is included in the patient's medical records, such as the electronic medical record information 12. The notification server 50 then sets patients who have not been excluded from the list of candidates for follow-up as patients to be followed. In this way, the notification server 50 excludes patients whose medical records, such as the electronic medical record information 12, contain abnormality cancellation information from the list of patients to be followed, thereby enabling the notification server 50 to identify patients to be followed.

In this way, if the patient being followed does not have an appointment, the notification server 50 notifies them to obtain a medical appointment schedule. This allows the patient to receive medical treatment as appropriate. Therefore, doctors and other medical professionals can use the time of the consultation to explain test results, schedule subsequent tests, and prescribe medications. As a result, patients are less likely to be left untreated. Therefore, the notification server 50 can prevent the patient's condition from worsening.

(Modification)
In the present embodiment, the exclusion determination function 554 has been described as excluding patients whose medical records, such as the electronic medical record information 12, contain abnormality cancellation information from the follow-up candidates. However, when the medical records contain abnormality cancellation information, the exclusion determination function 554 may determine that a patient who was excluded from the follow-up candidates is a follow-up candidate if a set condition is met. For example, the set condition may be that a set period has elapsed since the abnormality cancellation information was recorded in the medical record, or that an abnormality has been detected in the test results.

For example, if a set period of time has passed since the abnormality cancellation information was recorded in the medical record, the exclusion determination function 554 determines that the abnormality cancellation information is invalid. In other words, the exclusion determination function 554 determines that the patient is a follow-up candidate. If the set period of time has passed, there is a possibility that the patient has developed a new illness. Therefore, the exclusion determination function 554 determines that the abnormality cancellation information is invalid.

In addition, the exclusion determination function 554 determines that the abnormality cancellation information is invalid if the number of times an abnormality is detected in the test results is equal to or exceeds a threshold number. In other words, the exclusion determination function 554 determines that the patient is a follow-up candidate. If the number of times an abnormality is detected is equal to or exceeds a threshold number, there is a high possibility that the patient is suffering from some kind of disease. Therefore, the exclusion determination function 554 determines that the abnormality cancellation information is invalid.

In this way, the exclusion determination function 554 determines that the abnormality cancellation information is invalid when the set conditions are met, and sets the patient as a follow-up target. In other words, a patient is set as a follow-up target when there is any doubt about the abnormality cancellation information. Patients set as follow-up targets are then notified to obtain their medical treatment schedule. Therefore, since the patient receives medical treatment, the exclusion determination function 554 can prevent the patient's injury or illness from being overlooked.

At least one of the embodiments described above makes it possible to identify patients who should be followed.

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

1 Notification System 10 Hospital Information System (HIS)
11 Schedule information 12 Electronic medical record information 20 Radiology Information Systems (RIS)
21 Imaging order information 22 Image interpretation report information 30 Medical image management system (PACS: Picture Archiving and Communication Systems)
31 Image information 40 Laboratory Information System (LIS)
41 Clinical test order information 42 Clinical test result information 43 Clinical test report information 50 Notification server 541 Notification destination information 550 Processing circuit 551 Candidate identification function 552 First abnormality detection function 553 Second abnormality detection function 554 Exclusion determination function 555 Schedule acquisition function 556 Follow-up target setting function 557 Notification function

Claims (10)

an identification unit that identifies follow-up candidates who are candidates for patients to be followed;
a first detection unit that detects abnormalities from a first test result of the follow-up candidate patient;
a second detection unit that detects abnormalities from second test results generated earlier than the first test results for the follow-up candidate patient;
a first determination unit that, when an abnormality is detected from the first test result and the second test result, determines whether or not to exclude the patient from the follow-up candidate based on a medical record of the patient of the follow-up candidate;
a setting unit that sets the patient who has not been excluded from the follow-up candidates by the first determination unit as a follow-up target;
A medical information processing device comprising: The first determination unit determines to exclude the patient whose medical record includes abnormality cancellation information indicating that the abnormality is canceled from the follow-up candidate.
The medical information processing device according to claim 1 . The first determination unit determines to exclude the patient whose medical record includes the abnormality cancellation information indicating a false positive from the follow-up candidate.
The medical information processing device according to claim 2 . The first determination unit determines to exclude the patient whose medical record includes the abnormality cancellation information indicating that treatment has ended from the follow-up candidate.
The medical information processing device according to claim 2 . When the abnormality cancellation information is included in the medical record, the first determination unit determines that the patient who has been excluded from the follow-up candidates is the follow-up candidate on condition that a set condition is satisfied.
The medical information processing device according to claim 2 . the identification unit identifies, as the follow-up candidate, the patient in the medical record that includes information indicating a diagnosis result that the patient has the set injury or illness or is at risk of the set injury or illness;
The medical information processing device according to any one of claims 1 to 5. The system further includes a notification unit that notifies the patient to be followed that an appointment for medical treatment has been made, the notification unit being configured to notify the patient to be followed by the system.
The medical information processing device according to any one of claims 1 to 6. an acquisition unit that acquires a medical treatment schedule of the patient to be followed;
a second determination unit that determines whether the patient to be followed up has made an appointment for medical treatment based on the medical treatment schedule,
the notification unit notifies the user to make an appointment for medical treatment based on a determination result as to whether or not the user has made an appointment for medical treatment.
The medical information processing device according to claim 7 . the notification unit notifies the patient to make an appointment for medical treatment if the patient to be followed up has not made an appointment for medical treatment based on a determination result as to whether or not the patient has made an appointment for medical treatment;
The medical information processing device according to claim 8 . the notification unit notifies the patient to make an appointment for medical treatment when the patient to be followed up, whose remaining amount of prescribed medicine is estimated to be equal to or less than a threshold, has not made an appointment for medical treatment based on a determination result as to whether or not an appointment for medical treatment has been made;
The medical information processing device according to claim 8 .