WO2022189138A1

WO2022189138A1 - Embedded app

Info

Publication number: WO2022189138A1
Application number: PCT/EP2022/054374
Authority: WO
Inventors: Thomas Doerr; Jens Mueller; Matthias Gratz; R. Hollis Whittington
Original assignee: Biotronik SE and Co KG
Current assignee: Biotronik SE and Co KG
Priority date: 2021-03-12
Filing date: 2022-02-22
Publication date: 2022-09-15
Anticipated expiration: 2023-09-12

Abstract

A patient assessment system is provided for assessing a patient. The system comprises means for providing computer code to a local patient data system, wherein the computer code is adapted to, when executed, process patient data to provide output data, wherein the computer code is adapted to be executed in the local patient data system to process patient data stored in the local patient data system. The system further comprises means for receiving the output data from the local patient data system upon execution of the computer code in the local patient data system.

Description

Embedded App

The present disclosure generally relates to systems and methods for assessing patients, in particular to determining conditions of patients based on patient data.

It has been known to use centralized, server- or cloud-based medical data processing systems that assess patients based on data. For example, data collected by medical devices implanted into patients may be provided to such systems that process this data and determine a condition of the patient. Based on the medical-device-specific data provided from the respective medical devices, the patient can, for example, be continuously monitored or, more generally, a condition, such as a medical condition, of the patient can be determined. In particular, unexpected or otherwise unnoticed events may thus be detected. However, due to strict privacy regulations such systems often are not allowed to access patient data (other than that provided by the medical device itself). Such patient data are typically stored in local data systems that may be installed or operated locally, such that also access may also be provided locally only (such as within a hospital). Therefore, the assessment by the known systems is typically limited to be based on medical-device- specific data, whereas patient data cannot be used. Alternatively, to use patient data, known systems could be operated locally (e.g. within a hospital), which would, however, limit the flexibility and accessibility of their use.

There is hence a need to improve patient assessment by the known systems.

This need is at least in part met by certain aspects of the present disclosure. According to a first aspect of the present disclosure, a patient assessment system is provided for assessing a patient. The patient assessment system comprises means for providing computer code to a local patient data system, wherein the computer code is adapted to, when executed, process patient data to provide output data, wherein the computer code is adapted to be executed in the local patient data system to process patient data stored in the local patient data system. The patient assessment system further comprises means for receiving the output data from the local patient data system upon execution of the computer code in the local patient data system. Hence, the patient data (which may be confidential or otherwise protected patient data) does not need to be exported from the local patient data system or otherwise leave the local patient data system (which may be a confidential or otherwise protected system and/or environment). Nevertheless, it may be used by the patient assessment system for assessing a specific patient. The patient (input) data may be processed by the computer code, e.g. it may be analyzed in a specific manner as needed for assessing the specific patient, and corresponding output data may be provided to the patient assessment system such that the latter may use it for assessing the patient. Hence, a (globally accessible) patient assessment system may harness (only locally accessible) patient data, without the patient data having to leave its protected environment. Patient assessment is thus improved without compromising data privacy.

The patient assessment system (e.g. server-based and/or cloud-based) may hence securely assess a patient and/or in a more holistic manner. It may in particular be enhanced by artificial intelligence based algorithms, for which the provision of patient data may be particularly helpful. It may be provided as a centralized system that may be accessed globally.

For example, the patient assessment system may determine that a certain assessment (e.g. a certain diagnosis, a certain (medical) condition, a recommended action, and/or recommended decision) of a specific patient may be likely, but may still further depend on certain aspects of a patient (e.g. age, certain illnesses of the patient, pre-existing conditions, etc.) or that the likelihood that the assessment is correct may be improved based on certain aspects. The patient assessment system may then provide corresponding computer code to the local patient data system to process the corresponding patient data locally within the local patient data system and to return output data helpful in confirming or correcting the assessment. For example, the output data may comprise a binary result as to whether the assessment is correct or it may comprise a certain likelihood. The computer code may be patient-specific and/or specific to the respective aspects to be inquired/verified by the computer code.

Additionally or alternatively, the patient assessment system may determine that the quality of a certain assessment may be improved (or an assessment may be enabled in the first place), if algorithms of the patient assessment system (e.g. based on artificial intelligence) are further trained concerning one or more selected aspects (for example, for an algorithm based on an artificial neural network, it may be determined that for some relations within the network, no reasonable or only rough and/or inaccurate weight factors are available). The patient assessment system may then provide corresponding computer code to the local patient data system to process patient data locally within the local patient data system and to return output data providing one or more training results concerning the one or more selected aspects. The training result(s) may include, for example, one or more values for one or more predetermined weight factors to be applied in an artificial neural network.

Generally, the patient assessment system may comprise algorithms including algorithms that operate on patient data. Instead of executing the latter algorithms in the patient assessment system itself, these may be outsourced into the computer code provided to the local patient data system, and only the results of the algorithms may then be returned to the patient assessment system. It is also conceivable that the algorithms include algorithms that operate on patient data, which are executed on data available within the patient assessment system. However, these or other algorithms may alternatively or additionally also be outsourced into computer code provided to local patient data system(s), if it can be expected that further patient data stored there may improve the results of the algorithms. The patient assessment system may of course not only assess a patient at a single point in time but also assess or monitor the patient periodically or otherwise repeatedly (e.g. within every second, every minute, every hour, every day; or according to a certain trigger event) or essentially in real-time. The computer code provided to the local patient data system may vary, for example, depending on the current algorithms implemented in the patient assessment system (these may be adapted to be self-learning and thus change) and/or depending on current data that can directly be accessed by the patient-assessment system (such as medical-device-specific data, as will be outlined further below). The computer code may be (directly) executable, such as in the form of a native code and/or a native application. It may also generally use principles of edge computing and/or edge applications and/or smart applications. The computer code may be provided with access rights to access (confidential) data stored in the local patient data system (or confidential parts thereof), if executed in the local patient data system. The functions of the patient assessment system may be implemented in an embedded application, for example. The embedded application may be based on artificial intelligence algorithms, such as an artificial neural network, for example.

To train the neural network, the deep learning method is used. Preferably, the neural network is a feed forward network with several hidden layers or a recurrent neural network with several hidden layers. The neural network can include input signal conditioning and post processing of the results. Optionally, the neural network has a model governance layer to make the medical application traceable. The type of data to train the neural network essentially depend on the medical domain (Cardiac Rhythm Management, Neuro Spinal Cord Stimulation, etc.) and the desired medical support function.

The patient data may include one or more patient data sets. For example, they may be stored in an electronic health file, e.g. in a hospital system. The patient data may only be locally accessed (e.g., within the local data system, e.g. disparate from the patient assessment system). It is noted that the local patient data system needs not necessarily be a medical data system. Also applying the principles outlined herein to other local patient data systems may be conceivable that store (confidential data associated with the patient to be assessed and/or other patients). Generally, assessing a patient may be based on any data that characterizes the (state of the) patient, e.g. it may include data characterizing the health state of the patient (e.g. blood pressure, heart rate) and/or indirect parameters associated therewith (e.g. humidity or temperature of the patient’s environment), or it may relate to general patient data (e.g. date of birth, blood type). It may generally be based on static and/or dynamic patient data.

According to an example, the patient assessment system may further comprise means for generating the computer code based on device-specific, e.g. medical-device-specific, data associated with the patient. For example, the patient assessment system may (e.g. periodically, repeatedly or essentially in real-time) receive data obtained by a medical device (e.g. a medical device implanted into a patient, such as a cardiologic implant, e.g. a cardiac monitor, a pacemaker, a cardioverter-defibrillator (ICD) or a system for cardiac resynchronization therapy (CRT); a medical device attached to the patient, carried by the patient) or any other device (e.g. a smartwatch or any other wearable), e.g., carried by the patient. Based thereon, it may generate the computer code, e.g. in order to obtain output data that may be helpful in order to further improve the result of the assessment based on the device-specific data. For example, for assessing heartbeat frequency values and/or ranges, it may be helpful to obtain information on the age, weight, and/or general fitness state of the patient. The device-specific data may be associated with an (anonymous) patient-ID, for example.

Additionally or alternatively, the patient assessment system may comprise means for generating the computer code adapted to a specific local data system (e.g. adapted to hardware and/or software and/or a data type used by the local system). It is also possible that the computer code is generated based on an access level. For example, the local system(s) may be arranged as (parts of) individual hospitals and/or groups of hospitals. The computer code may then be adapted based on the specific (parts of) individual hospitals and/or groups of hospitals whose data the computer code is supposed to access. Similarly, the computer code may be generated based on a country, in which the local patient data system is to be accessed, e.g. based on data privacy regulations in the specific country.

For example, the device-specific data may be transmitted from the device (possibly via an intermediate local transmitter) to a server of the patient assessment system and/or stored, there. The device-specific data may be globally accessible.

The patient assessment system may further comprise means for assessing the patient, e.g. for determining a medical condition and/or a diagnosis and/or a recommended action and/or recommended medical decision of the patient, at least in part based on the output data and/or based on medical-device-specific data associated with the patient. For example, for a cardiologic device, the patient assessment system may diagnose a certain cardiologic condition and/or recommend a specific action. The means for determining may be based on artificial intelligence. In all these examples, the means for determining may comprise algorithms that operate on medical-device specific data, e.g. stored in the patient assessment system. These may be executed by the patient assessment system itself. The means for determining may further include algorithms that require other patient data (e.g. as stored in the local patient data system), and these may be implemented in the computer code provided to the local patient data system. The means for determining of the patient assessment system may then use the corresponding output data received from the local patient data system to.

In some examples, the means for determining may be adapted to attempt determining the medical condition of the patient (and/or the diagnosis and/or the recommended decision and/or the recommended action for the patient) based on medical-device-specific data associated with the patient. For example, medical-device-specific data available within the patient assessment system may be processed to this end. The means for generating may be adapted to generate, in case the medical condition of the patient (or the diagnosis/decision/action) cannot be determined (with a desired level of accuracy and/or certainty) based on medical-device-specific data associated with the patient, the computer code based on medical-device-specific data associated with the patient, wherein the computer code is adapted for obtaining output data required for determining the medical condition. For example, it may be determined that a condition/decision/action can only be determined with a limited level of accuracy and/or certainty or possibly not at all. In such case, it may be concluded that the determination may be improved by looking at patient data, e.g. of the patient to be assessed, and/or at patient data of other patients.

For example, the patient assessment system may determine, based on processing the device-specific data, that a specific question needs to be answered or a specific information gap needs to be filled in order to determine the condition of the patient (and/or to recommend a decision/action for the patient), e.g. with a pre-determined level of likelihood and/or certainty, and/or that the quality of such determination may be improved. The patient assessment system may then generate computer code adapted to answer the question and/or fill the information gap, based on processing patient data of the same patient and/or other patients available in the local patient data system. As a further example, the patient may determine, based on the device-specific data associated with the patient, that a determination of a medical condition of the patient (or a diagnosis/recommended decision/action for the patient) may be improved or enabled by looking at a certain aspect of patient data specific to the patient. It may then generate computer code to provide output data corresponding to this aspect based on processing patient data specific to the patient. The computer code may for example be adapted to provide, as output data, the assessment of the patient. Alternatively, it may be adapted to provide output data that is then further processed by the patient assessment system.

Additionally or alternatively, the patient assessment system may be adapted to be based on artificial intelligence, and it may determine, based on the device-specific data associated with the patient, that further training, e.g. concerning a specific aspect, may be required. It may then generate the computer code adapted to process patient data to provide output data that provides such further training, e.g. concerning the specific aspect. In an exemplary embodiment, the patient assessment system for assessing a patient comprises a) means for providing computer code to a local patient data system, wherein the computer code is adapted to, when executed, process patient data to provide output data, wherein the computer code is adapted to be executed in the local patient data system to process patient data stored in the local patient data system; b) means for receiving the output data from the local patient data system upon execution of the computer code in the local patient data system; c) means for generating the computer code based on medical- device-specific data associated with the patient and obtained from a medical device; and d)means for determining a medical condition of the patient at least in part based on the output data and/or based on medical-device-specific data associated with the patient, wherein the means for determining is adapted to attempt determining the medical condition of the patient based on medical-device-specific data associated with the patient. Furthermore, the means for generating is adapted to generate, in case the medical condition of the patient cannot be determined based on the medical-device-specific data associated with the patient, the computer code based on medical-device-specific data associated with the patient, wherein the computer code is adapted for obtaining output data required for determining the medical condition. Furthermore the patient assessment system is configured to determine, based on processing the medical-device-specific data, that a specific information gap needs to be filled in order to determine the medical condition of the patient, wherein the patient assessment system then generates computer code adapted to fill the information gap, based on processing patient data of the same patient and/or other patients available in the local patient data system.

The features contained in this embodiment (above) make it possible to improve the determination of the medical condition.

The patient assessment system may comprise means for forwarding the determined medical condition (and/or the diagnosis, and/or the decision and/or the action) to the patient and/or to medical staff. The patient and/or the medical staff may then react accordingly, without the patient having to proactively consult with the medical staff, first. Hence, unexpected and/or unnoticed events may be reacted to more quickly and efficiently. The patient assessment system may comprise an interface to the local patient data system to trigger execution of the computer code provided to the local patient data system. For example, a trigger command may be sent in the same manner as the computer code, e.g., possibly at the same time or later.

The means for providing the computer code may further be adapted to provide the computer code to the local patient data system in a secured and/or confidential manner. For example, a secure connection may be established. Also, the computer code may be secured using a Blockchain. Additionally or alternatively, the means for receiving the output data from the local patient data system may be adapted to the receive the output data in a secure and/or confidential manner. Also here, a secure connection (e.g., the same secure connection) and/or a Blockchain (e.g. the same Blockchain) may be used.

The patient assessment system may be adapted as a cardiologic monitoring and/or decision system. The patient assessment system may for example be adapted to receive data obtained by a cardiologic device, e.g. a cardiologic implant, e.g. as outlined herein.

A further aspect of the present disclosure is a local patient data system for securing patient data. It may comprise means for receiving computer code from a patient assessment system, e.g. as outlined herein. The computer code may be adapted to, when executed, process patient data to provide output data, wherein the computer code is adapted to be executed in the local patient data system to process patient data stored in the local patient data system, e.g. as outlined herein. The local patient data system may further comprise means for executing the computer code to generate the output data. Further, the local patient data system may comprise means for providing the output data to the patient assessment system. Hence, the patient data may effectively be used by the patient assessment system without leaving the local patient data system.

The means (of the local patient data system) for receiving the computer code may be adapted to receive the computer code in a secured manner from the patient assessment system. Additionally or alternatively, the means for providing the output data may be adapted to provide the output data to the patient assessment system in a secure manner. Also here, a secure connection (e.g., the same secure connection) and/or a Blockchain (e.g. the same Blockchain) may be used. For example, the local data system and the patient assessment system may use a single secure connection and/or a single Blockchain.

The local patient data system may be a data system of a hospital, a health insurance company, a clinic network, a doctoral association, a medical IT-service provider, a health care provider, a network for medical services, a national network for health data, and/or an authority.

A further aspect of the present disclosure is a system comprising a patient assessment system for assessing a patient as described herein and a local patient data system as described herein.

A still further aspect of the present disclosure relates to a method for assessing a patient. The method may be adapted to be carried out by a patient assessment system. The method may comprise the step of providing computer code to a local patient data system, wherein the computer code is adapted to, when executed, process patient data to provide output data, wherein the computer code is adapted to be executed in the local patient data system to process patient data stored in the local patient data system. The method may include providing the computer code to the local patient data system by a patient assessment system as described herein. The method may further include the step of receiving the output data from the local patient data system upon execution of the computer code in the local patient data system. The method may include receiving the output data from the local patient data system by a patient assessment system as described herein. The method may comprise one or more, or all steps described herein with respect to functions of the patient assessment system.

Yet another aspect of the present disclosure relates to a method for securing patient data. The method may be adapted to be implemented by a local patient data system. The method may comprise the step of receiving computer code from a patient assessment system. The computer code may be adapted to, when executed, process patient data to provide output data, wherein the computer code is adapted to be executed in a local patient data system to process patient data stored in the local patient data system. The method may further include receiving the computer code from a patient assessment system by a local patient data system. The method may further comprise the step of executing, e.g. by the local patient data system, the computer code to generate the output data. Further, the method may include the step of providing the output data to the patient assessment system, e.g. by the local patient data system. The method may further comprise one or more, or all steps described herein with respect to functions of the local patient data system.

Yet another aspect of the present disclosure relates to a method for assessing a patient while securing patient data. The method may include the step of providing computer code to a local patient data system, wherein the computer code is adapted to, when executed, process patient data to provide output data, wherein the computer code is adapted to be executed in the local patient data system to process patient data stored in the local patient data system. The method may include providing the computer code to the local patient data system by a patient assessment system as described herein. The method may comprise the further step of receiving the computer code from the patient assessment system, e.g. by the local patient data system. The method may further comprise the step of executing, e.g. by the local patient data system, the computer code to generate the output data. Further, the method may include the step of providing the output data to the patient assessment system, e.g. by the local patient data system. The method may further include the step of receiving the output data from the local patient data system upon execution of the computer code in the local patient data system. The method may include receiving the output data from the local patient data system by a patient assessment system as described herein. The method may comprise one or more, or all steps described herein with respect to functions of the patient assessment system and/or the local patient data system.

A further aspect relates to a computer program. The computer program may comprise instructions which, when executed, cause a computer (and/or the means generally described herein) to perform the steps according to any of the methods as described herein.

Whether described as method steps, computer program and/or means, the functions described herein may be implemented in hardware, software, firmware, and/or combinations thereof. If implemented in software/firmware, the functions may be stored on or transmitted as one or more instructions or code on a computer-readable medium. Computer-readable media include both computer storage media and communication media including any medium that facilitates transfer of a computer program from one place to another. A storage medium may be any available media that can be accessed by a general purpose or special purpose computer. By way of example, and not limitation, such computer-readable storage media can comprise RAM, ROM, EEPROM, FPGA, CD/DVD or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to carry or store desired program code means in the form of instructions or data structures and that can be accessed by a general-purpose or special-purpose computer, or a general-purpose or special-purpose processor.

Fig. 1 shows an example of a system including an exemplary patient assessment system and an exemplary local patient data system according to the present disclosure.

Fig. 1 shows a possible example for a patient assessment system 110. It may be realized as a decision system. It may be implemented as a central, server- and/or cloud-based system. Alternatively or additionally it may be implemented as a decentralized, server- and/or cloud-based system due to regional regulatory requirements. Therefore, it should be possible that in one or more countries a local patient data system 160 communicates with a locally operated patient assessment system 110.

In an example, patient assessment system 110 may be implemented with the Home Monitoring Service Center (HMSC) of the applicant.

The patient assessment system 110 may comprise one or more embedded applications implementing the functions described herein. The patient assessment system 110 may be accessible from all over the world, e.g. via the internet. For example, one or more medical devices 120, such as medical implants, implanted or otherwise associated with a patient, may transmit data to the patient assessment system 110. In some examples, an intermediate unit (not shown) may receive data (locally, wirelessly, e.g. via WiFi, Bluetooth, 3G, 4G or 5G, etc.) transmitted by the one or more medical devices 120, and the intermediate unit may then transmit the data to the patient assessment system 110, e.g. over the internet. The patient assessment system 110 may thus receive data, in particular medical-device-specific data. The data may be associated with a patient, into which the medical device is implanted and/or which carries, wears, and/or is otherwise connected to the medical device 120. A secure connection may be used for transmission, and the data may be encrypted. Similarly, the patient assessment system 110 may transmit data to medical staff and/or the patient 180. Specifically the data may be transmitted to the intermediate unit at the patient and/or to a processing device accessible by the medical staff and/or to a smartphone of the patient (e.g., via the internet and/or using any of the communication technologies described herein). The data received by the patient assessment system 110 from the medical devices 120 generally only includes data that can be acquired by the medical devices 120 themselves and does not include further patient data 170, such as current information on the patient’s illness history or the patient’s medication. The latter patient data 170 is typically only available in a local patient data system 160, such as a network of a hospital.

The patient assessment system 110 includes one or more algorithms 130 that may at least in part be artificial intelligence (AI) based. For example, algorithm(s) 130 may implement an artificial neural network. Algorithms 130 may be implemented by one or more embedded applications. Algorithms 130 may be adapted to assess a patient, e.g., provide a diagnosis, a recommended decision and/or a recommended action (to the patient and/or medical staff 180), based on medical-device-specific data 120 and further patient data 170, e.g. in an automated manner. Those parts of algorithms 130 that operate on medical- device-specific data may be adapted to operate on this data directly within the patient assessment system 110. However, to increase data privacy, those parts of algorithms 130 that operate on further patient data may be adapted to provide computer code 140 to local patient data system 160 (e.g., those parts may be outsourced). The computer code 140 may be implemented as and/or outsourced to one or more native applications (and/or smart applications and/or edge applications). The computer code 140 and/or the native application(s) may be generated (e.g. automatically) by patient assessment system 110 based on current medical-device-specific data. In other words, computer code 140 may be patient-specific and repeatedly generated based on the current data available concerning the patient, e.g. it may be generated based on device-specific data that may be associated with the patient, e.g. using an (anonymous but otherwise unique) patient ID. Also, computer code 140 may be generated (e.g. automatically) in a customized manner, adapted to the current question(s) to be answered, e.g. as determined by the patient assessment system 110, e.g. based on current medical-device-specific data.

Patient assessment system 110 may comprise one or more interfaces to export computer code 140 to one or more local patient data systems 160. An interface may generally refer to a hardware and/or a software interface. A hardware interface may be understood as e.g. a USB connection, an ethernet connection or any other suitable hardware interface which allows a transfer of computer code 140. The hardware interface may be based on a wireless (e.g. Wi-Fi, 5G, LTE, Bluetooth or any other suitable standard and/or air interface) and/or a wired connection (e.g. a copper wired connection), e.g. via the internet. Additionally or alternatively, the interface may also be a software interface, e.g. that allows exporting the computer code 140.

Computer code 140 is then executed on local patient data system 160 and operates on the local patient data 170 within the bounds of the local patient data system 160. Only within local patient data system 160, access may be allowed to computer code 140 to access the local patient data 170 stored in local patient data system 160. Computer code 140 may be representative of a question that may be answered based on the local patient data 170. Computer code 140 may be generated by patient assessment system 110, e.g., after algorithm 130 has operated on the medical-device-specific data within the patient assessment system. Hence, computer code 140 may be specific to individual patients and/or to the specific question that currently needs to be answered for patient assessment. Consequently, also the answer to the question or, more generally, the result of the processing of the local patient data 170 by the computer code 140 in the local patient data system 160, that may then be returned to the patient assessment system 110 may be patient-specific and/or optimized based on currently available medical-device-specific data.

Patient assessment system 110 may thus harness the (confidential) local patient data 170 for its assessment in an optimum manner, but the local patient data 170 does not need to leave that local patient data system 160. For example, if medical-device-specific data implies that a certain atypical condition seems to be present with the patient, it may be consulted whether the patient has a certain pre-determined illness condition that may explain the atypical condition. Similarly, for example if a certain diagnosis implied by the medical-device-specific data seems likely only under certain special circumstances, e.g. within a certain age group, it may be consulted, whether the patient falls within these special circumstances. In other examples, the “answer” provided by the output data may directly be a medical diagnosis, decision, action and/or treatment. It may also be a partial result of the diagnosis, and/or concerning the decision, action, and/or treatment.

Computer code 140 may be transmitted to the local patient data system 160 via a secure connection 150 between the patient assessment system 110 and the local patient data system 160 (e.g. providing an encryption). The output data which may be a medical diagnosis, for example, may then similarly be returned to the patient assessment system 110 via the secure connection 150 without patient data 170 being transmitted. Additionally or alternatively, the provision of computer code 140 and/or output data may be protected (e.g. from falsification, abuse, hampering, and/or ambiguity) using blockchain techniques. For example, the computer code 140 and/or the output data may be stored in and retrieved from a blockchain.

The output data may then be further processed at the patient assessment system 110 to provide an assessment of the patient, e.g., a diagnosis, a recommended decision and/or a recommended action. In some examples, the output data may already include the assessment of the patient, e.g., the diagnosis, the recommended decision and/or the recommended action. The assessment may then be transmitted to the patient and/or medical staff 180.

Claims

1. A patient assessment system (110) for assessing a patient comprising: means for providing computer code (140) to a local patient data system (160), wherein the computer code is adapted to, when executed, process patient data to provide output data, wherein the computer code is adapted to be executed in the local patient data system to process patient data (170) stored in the local patient data system; means for receiving the output data from the local patient data system upon execution of the computer code in the local patient data system.

2. The system of claim 1, further comprising means for generating the computer code based on medical-device-specific data associated with the patient.

3. The system of claim 1 or 2, further comprising means (130) for determining a medical condition of the patient at least in part based on the output data and/or based on medical-device-specific data associated with the patient.

4. The system of claim 3, wherein the means for determining is adapted to attempt determining the medical condition of the patient based on medical-device-specific data associated with the patient; and the means for generating is adapted generate, in case the medical condition of the patient cannot be determined based on the medical-device-specific data associated with the patient, the computer code based on medical-device-specific data associated with the patient, the computer code adapted for obtaining output data required for determining the medical condition.

5. The system of claim 3 or 4, further comprising means for forwarding the determined medical condition to the patient and/or to medical staff (180).

6. The system of any of claims 1 to 5, wherein the means for providing the computer code is adapted to provide the computer code to the local patient data system in a secured manner (150) and/or the means for receiving the output data from the local patient data system is adapted to receive the output data in a secured manner (150).

7. The system of any of claims 1 to 6, wherein the system is a cardiologic or neurologic monitoring system.

8. A local patient data system (160) for securing patient data (170) comprising: means for receiving computer code (140) from a patient assessment system (110), wherein the computer code is adapted to, when executed, process patient data to provide output data, wherein the computer code is adapted to be executed in the local patient data system to process patient data stored in the local patient data system; means for executing the computer code to generate the output data; means for providing the output data to the patient assessment system.

9. The system of claim 8, wherein the means for receiving the computer code is adapted to receive the computer code in a secured manner (150) from the patient assessment system and/or the means for providing the output data is adapted to provide the output data to the patient assessment system in a secure manner (150).

10. The system of any of claims 1 to 9, wherein the local patient data system (160) is at least one of a data system of: a hospital, a health insurance company, a clinic network, a doctoral association, a medical IT-service provider, a health care provider, a network for medical services, national network for health data, an authority.

11. System comprising a patient assessment system (110) for assessing a patient according to any of claims 1 to 7 and a local patient data system (160) according to any of claims 8 to 10.

12. A method for assessing a patient comprising: providing computer code to a local patient data system, wherein the computer code is adapted to, when executed, process patient data to provide output data, wherein the computer code is adapted to be executed in the local patient data system to process patient data stored in the local patient data system; receiving the output data from the local patient data system upon execution of the computer code in the local patient data system.

13. A method for securing patient data comprising: receiving computer code from a patient assessment system, wherein the computer code is adapted to, when executed, process patient data to provide output data, wherein the computer code is adapted to be executed in a local patient data system to process patient data stored in the local patient data system; executing the computer code to generate the output data; providing the output data to the patient assessment system.

14. A method for assessing a patient comprising the steps of claims 12 and 13.

15. A computer program comprising instructions which, when executed, cause a computer to perform the steps according to the method of any of claims 12 to 14.