GENERATING EXPLANATIVE CONTENT FOR PROVIDING INFORMATION ON MEDICAL SUBJECT MATTER

FIELD OF THE INVENTION

The present invention relates to the generation of explanative content for a clinical setting.

BACKGROUND OF THE INVENTION

Due to the inherently complex nature of the medical field, caregivers can often find it difficult to explain medical subject matter to a patient or their relatives/friends. Moreover, it is possible for caregivers to not fully understand certain medical topics or content themselves, e.g. if it lies outside their area of expertise.

Failure of a caregiver, a patient or relative/friend to understand some medical information can have significant consequences. For example, failure of a patient to understand the prognosis of different treatment strategies could lead to an inappropriate treatment strategy being selected by the patient, leading to a reduced quality of life for the patient. Failure of a relative/friend to understand the condition of the patient could lead to potentially unnecessary stress or their underappreciating a potential serious condition.

There is therefore a need to provide information for an audience member (e.g. caregiver, patient or relative/friend) that is designed to aid and improve the audience member’s understanding of relevant medical subject matter associated with a patient. For example, there is a need to provide information that can aid an audience member to understand a condition, diagnosis, treatment strategy or environment (examples of “medical subject-matter”) associated with the patient.

It is somewhat common for a clinical setting, such as a hospital or clinic, to provide textual information that provides generic explanative content of aspects of medical content associated with a patient. For example, a hospital may provide a brochure providing explanative content of a certain disease or condition or explanative content of the clinical setting (e.g. an overview of the responsibilities of a hospital or department). There is a desire to improve the information provided to the audience member, to help improve their understanding of relevant medical content.

SUMMARY OF THE INVENTION

The invention is defined by the claims.

According to examples in accordance with an aspect of the invention, there is provided a method of generating explanative content for providing a patient-specific explanation of medical subject-matter associated with a patient.

The method comprises: obtaining generic explanative content providing generic descriptions and/or illustrations of different possible aspects of medical subject- matter; obtaining patient-specific data associated with a patient; and processing the generic explanative content, using one or more computer-based rules or algorithmic processes, based on the patient-specific data to generate patient-specific explanative content of medical subject-matter associated with the patient.

The invention thereby provides a method of generating patient-specific explanative content. The explanative content is thereby adapted to a specific patient, to enable improved understanding of various medical topics that may be of concern to the patient, such as their condition (e.g. current status or prognosis), a treatment strategy, their diagnosis and/or their environment (e.g. information about a care unit or care team for the patient).

By processing generic explanative content using patient-specific data, information can be tailored to the actual situation/condition of the patient.

Embodiments can thereby provide more understandable or relevant information, which can save time when explaining potentially complex medical content to a non-medical person (e.g. the patient or their family members). Adapting explanative content based on patient-specific data can also avoid miscommunication of the medical content, as the information will be more specific to the patient’s situation.

Moreover, providing patient-specific explanative content can make the audience (of the patient-specific explanative content) more aware of future situations and events, thereby avoiding the need for further explanation down the line, further saving time. In the context of clinical decision making, patient-specific explanative content can help contextualize the explanative content to the real-time condition of the patient, providing a more accurate overview to facilitate dialogue between members of the audience (e.g. between a caregiver and a patient or their family).

Improving the understanding of an audience member can also lead to improved outcomes for the patient, e.g. as a more understanding family could be taught which medical data to provide or a caregiver can have a better understanding of patient- specific approaches or requirements.

Thus, patient-specific explanative content helps to increase understanding and contextualize medical content with respect to the patient. This means that a more patient-specific understanding can be achieved.

The generic explanative content may comprise one or more textual data files, each textual data file providing a generic explanation of a different possible patient condition, treatment strategy, diagnosis or environment.

The method may further comprise a step of obtaining information about an intended audience for the explanative content, wherein the step of processing the generic explanative content is further based upon the information about the intended audience for the explanative content.

The intended audience may include, for example, members of clinical staff for presenting the patient-specific explanative content to the patient or the patient’s family, or information about the patient or patient family themselves.

The information about the intended audience may include, for example, a job title or description of clinical staff who intend to present the patient-specific information. The information about the intended audience may include demographic, biological, emotional information about the audience, such as the patient or their family. By way of example, the information about the intended audience may include information about a relationship between an audience member (in the intended audience) and the patient (associated with the patient-specific data), e.g. “the patient themselves” or “parent” or “child”, a phenotype of an audience member, an emotional state of an audience member, a (health) literacy of an audience member, a religion or culture of an audience member, a language of an audience member and so on. The method may comprise a step of obtaining information about an intended purpose for the patient-specific explanative content, wherein the step of processing the generic explanative content is further based upon the information about the intended purpose for the patient-specific explanative content.

The purpose of the patient-specific explanative content may thereby influence how the patient-specific explanative content is generated. This helps to increase an understanding of an audience member viewing the patient-specific explanative content.

For example, if the purpose of the patient-specific explanative content is to improve a family member’s understanding, then potentially complex medical jargon can be omitted from the explanative content (as this will not be understood by the family member).

As another example, if the purpose of the patient-specific explanative content is to enable a patient to make a clinical decision, then the patient-specific explanative content may be directed towards outcomes and prognoses of different treatment strategies, e.g. rather than detailed explanations of a cause of their condition.

Thus, it will be clear that processing the generic explanative content based on an intended purpose of the patient-specific explanative content enables more relevant and useful information to be presented to an audience member to improve their understanding with respect to the intended purpose.

In embodiments, the patient-specific data comprises real-time patient monitoring data and/or an electronic medical record of the patient.

The step of processing the generic explanative content may comprise filtering the generic explanative content using the one or more computer-based rules or algorithmic processes.

Thus, generic explanative content may be edited to remove unnecessary information based on information about the patient. This improves and further contextualizes the patient-specific explanative content so that it is more relevant for a particular patient.

By way of example only, generic explanative content may contain information pertinent to patients over the age of 65. This information may be filtered out if the patient is under the age of 65 (as indicated in the patient-specific data), to provide more relevant information. The method may be adapted wherein the step of obtaining the generic explanative content comprises obtaining the generic explanative content from a first database; and the step of obtaining the patient-specific data comprises obtaining the patient-specific data from a patient-specific data module comprising a second, different database, a user interface and/or a patient monitoring device.

In some embodiments, the method further comprises displaying a visual representation of the generic explanative content at a user interface.

In some embodiments, the method further comprises, subsequent to the step of processing the generic explanative content to generate patient-specific explanative content: receiving a user input from a user interface; and processing the generic explanative content and/or the patient-specific explanative content, using one or more computer-based rules or algorithms, based on the user input to generate adapted patient- specific explanative content.

This enables an audience member to request further information, which can also be made patient-specific and thereby further increase their understanding.

In some embodiments, the step of processing the generic explanative content and/or the patient-specific explanative content based on the user input comprises processing the generic explanative content based on the user input and the patient-specific data to generate adapted patient-specific explanative content.

An additional advantage of the present invention is the explanative content is provided without necessitating a laborious database search by the user. In common practice the clinician (user) would not only need to review recent data stored in the electronic medical records (which might not even be real-time data), but, in parallel, search an internal database for a vast plurality of possible guidance, which would then to be further aligned by the user with generic medical practice.

According to examples in accordance with an aspect of the invention, there is provided a computer program product comprising computer program code which, when executed by one or more processing systems, cause the one or more processing systems to perform all of the steps of the method herein described. The computer program product may be stored on a non-transitory computer readable medium According to examples in accordance with an aspect of the invention, there is provided an explanative content processor for providing a patient-specific explanation of medical subject-matter associated with a patient.

The explanative content processor is adapted to: obtain generic explanative content providing generic descriptions and/or illustrations of different possible aspects of medical subject-matter; obtain patient-specific data associated with a patient; and process the generic explanative content, using one or more computer-based rules or algorithmic processes, based on the patient-specific data to generate patient-specific explanative content of medical subject-matter associated with the patient.

According to examples in accordance with an aspect of the invention, there is provided an explanative content system. The explanative content system comprises the explanative content processor previously described; a first database storing generic explanative content; and a patient-specific data module adapted to provide patient-specific data, wherein the patient-specific data module comprises a second, different database, a user interface and/or a patient monitoring device, wherein the explanative content processor is adapted to obtain the generic explanative content from the first database and obtain the patient-specific data from the patient-specific data module.

According to examples in accordance with an aspect of the invention, there is provided an explanative content display system. The explanative content display system comprises an explanative content processor or an explanative content system as previously described; and a user interface adapted to provide a visual representation of the patient- specific explanative content generated by the explanative content processor.

These and other aspects of the invention will be apparent from and elucidated with reference to the embodiment(s) described hereinafter.

BRIEF DESCRIPTION OF THE DRAWINGS

For a better understanding of the invention, and to show more clearly how it may be carried into effect, reference will now be made, by way of example only, to the accompanying drawings, in which:

Figure 1 conceptually illustrates an embodiment of the invention; and

Figure 2 is a flowchart illustrating a method according to an embodiment of the invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

The invention will be described with reference to the Figures.

It should be understood that the detailed description and specific examples, while indicating exemplary embodiments of the apparatus, systems and methods, are intended for purposes of illustration only and are not intended to limit the scope of the invention. These and other features, aspects, and advantages of the apparatus, systems and methods of the present invention will become better understood from the following description, appended claims, and accompanying drawings. It should be understood that the Figures are merely schematic and are not drawn to scale. It should also be understood that the same reference numerals are used throughout the Figures to indicate the same or similar parts.

The invention provides a process for producing patient-specific explanative content. Generic explanative content is processed, based on patient-specific data, to thereby generate patient-specific explanative content that can be used to improve an understanding of an audience member (of the patient-specific explanative content) to medical subject-matter associated with the patient.

Embodiments may be employed in a clinical setting, to aid in the explanation of medical subject-matter associated with the patient within the clinical setting, or in devices connected to a medical system, e.g. to aid in the explanation of medical subject-matter to persons outside of the medical system (such as friends or family) who may still have an interest or desire to understand the medical subject-matter of the patient.

Figure 1 illustrates an explanative content processor 100 according to an embodiment of the invention. The explanative content processor is explained in the context of an explanative content display system 1, which is itself an embodiment of the invention.

The explanative content processor 100 generates patient-specific explanative content 105 from generic explanative content 140 and patient-specific data 150. In other words, the generic explanative content is contextualized using the patient- specific data to provide more relevant and tailored explanative content. This helps improve the understanding of an audience member viewing the explanative content, or aids in enabling an audience member to explain the explanative content to another person. The patient-specific explanative content 105 is then passed to a destination device. In the illustrated examples, the destination device is a user interface 110 for displaying (a visual representation of) the patient-specific explanative content. In other examples, the destination device is a server or storage facility (not shown), e.g. for later accessing by a user interface.

The patient-specific explanative content may therefore be displayed at or otherwise output by one or more user interfaces 110 of the explanative content display system. In particular embodiments, the user interface(s) 110 may provide a visual representation of the patient-specific explanative content, e.g. at a screen 111. The user interface 110 may also provide an input mechanism 112, e.g. a keyboard or a touch- sensitive screen, for enabling a user to provide a user input.

The explanative content processor 100 is adapted to receive generic explanative content 140, for example, from a first database 145.

The generic explanative content provides generic descriptions and/or illustrations of different aspects of medical subject-matter. For example, the generic explanative content may comprise generic descriptors of possible conditions, diagnoses, treatment strategies or (clinical) environments and/or illustrations of possible conditions, diagnoses, treatment strategies or (clinical) environments.

The generic explanative content may, for example, comprises one or more textual data files, each textual data file providing a generic explanation of a different possible patient condition, treatment strategy, diagnosis or environment. The generic explanative content may, for example, comprise one or more default charts or graphs which can be adapted based on the patent-specific data to generate explanative content for aiding in understanding a condition or status of the patient.

The explanative content processor 100 is also adapted to receive patient- specific data 150 from a set patient-specific data module 151, which comprises one or more data sources 152-154. Any of these elements may form part of the overall explanative content display system 1.

A first data source 152 may be a second database. The second database 152 may, for example, store information about a specific patient, such as an electronic medical record of the patient. A second data source 153 may be a user interface. The user interface 153 may allow a user to directly input information about the patient to the explanative content processor 100. This may, for example, be in the form of answers to a questionnaire for defining the patient-specific explanative content. In embodiments, the second data source 153 comprises or is formed as an aspect of the user interface 110.

A third data source 154 may be a patient monitor. Use of a patient monitor enables real-time monitoring data of the patient to influence the patient-specific explanative content, thereby ensuring that the patient-specific explanative content is up to date.

The data sources may further or otherwise comprise one or more, e.g. a plurality of, medical sensors coupled to the patient. In this case, the patient specific data module 151 can be a patient monitoring device associated with the present patient. Therefore, the patient-specific data 150 would be received real-time and enable the user to get an instantaneous guideline representative of the patient’s state. In another embodiment, the patient monitoring device associated with the patient can be one of the data sources 152-154, as is the case of the third data source 154. Other examples of suitable data sources include one of or a combination of, for example, a database of lab tests and/or historical information from one or more electrical medical records.

Thus, the patient-specific data may comprise any combination of: medical history information (e.g. from the first/second data source); patient demographic information (e.g. from the first/second data source) and/or real-time medical monitoring information (e.g. from the third data source). Other examples of patient-specific data would be apparent to the skilled person.

Any combination of one or more of these data sources may be used, according to various embodiments.

The explanative content processor 100 processes the generic explanative content, based on the patient-specific data, in order to produce the patient-specific explanative content. The explanative content processor 100 performs this step using one or more computer based rules or algorithmic processes.

An example of a suitable computer-based rule or algorithmic process is a neural network or other machine learning algorithm. A machine-learning algorithm is any self-training algorithm that processes input data in order to produce or predict output data. Here, the input data comprises generic explanative content and patient specific data, and the output data comprises patient-specific explanative data.

Suitable machine-learning algorithms for being employed in the present invention will be apparent to the skilled person. Examples of suitable machine-learning algorithms include decision tree algorithms and artificial neural networks. Other machine- learning algorithms such as logistic regression, support vector machines or Naive Bayesian model are suitable alternatives.

The structure of an artificial neural network (or, simply, neural network) is inspired by the human brain. Neural networks are comprised of layers, each layer comprising a plurality of neurons. Each neuron comprises a mathematical operation. In particular, each neuron may comprise a different weighted combination of a single type of transformation (e.g. the same type of transformation, sigmoid etc. but with different weightings). In the process of processing input data, the mathematical operation of each neuron is performed on the input data to produce a numerical output, and the outputs of each layer in the neural network are fed into the next layer sequentially. The final layer provides the output.

Methods of training a machine-learning algorithm are well known. Typically, such methods comprise obtaining a training dataset, comprising training input data entries and corresponding training output data entries. An initialized machine-learning algorithm is applied to each input data entry to generate predicted output data entries. An error between the predicted output data entries and corresponding training output data entries is used to modify the machine-learning algorithm. This process can repeated until the error converges, and the predicted output data entries are sufficiently similar (e.g. ±1%) to the training output data entries. This is commonly known as a supervised learning technique.

For example, where the machine-learning algorithm is formed from a neural network, (weightings of) the mathematical operation of each neuron may be modified until the error converges. Known methods of modifying a neural network include gradient descent, backpropagation algorithms and so on. The training input data entries correspond to examples the patient data and corresponding generic explanative data. The training output data entries correspond to corresponding examples of patient-specific explanative data.

Preferably, the processing of the generic explanative content comprises filtering the generic explanative content using the one or more computer-based rules or algorithmic processes.

Thus, there may be a larger body of generic explanative content that is filtered (i.e. elements removed) by the one or more computer-based rules or algorithmic processes to generate the patient-specific explanative content. This effectively enables explanative content to be prepared in advance (in the generic explanative content), with only the patient-appropriate portions of the generic explanative content being provided in the patient-specific explanative content.

This provides a simple and low processing demand method of generating patient-specific explanative content from a large body of generic explanative content.

By way of example only, generic explanative content may contain two versions of a certain aspect of explanative content, a first version pertinent to patients at or over the age of 65 and a second version pertinent to patient under the age of 65. The first version of this certain aspect may be filtered out if the patient is under the age of 65 (as indicated in the patient-specific data), to provide the more relevant second version as an aspect of the patient-specific explanative content.

By way of another example, generic explanative content may contain two different explanations of a certain treatment, a first explanation being suitable for a medical professional (who would understand complex medical terms/jargon) and a second explanation being suitable for a non-medical professional (who would not understand complex medical terms/jargon). This decision on which explanation to select or filter may depend upon the intended audience for the patient-specific explanative content (as will be later described).

By way of yet another example, generic explanative content may contain explanations for multiple different possible conditions of the patient. The one or more rules/algorithms may process the patient-specific information to identify any conditions of the patient and select only those explanations associated with any identified condition of the patient to include in the patient-specific explanative content. By way of yet another example, generic explanative content may comprise a template for explaining a condition of the patient, and the patient-specific data may be used to complete or fill out missing portions of the template.

The explanative content processor 100 may be adapted to receive additional information, to assist in the generation of the patient-specific data.

The additional information may be used as an additional input for the computer-based rules and/or algorithms (e.g. new features for a neural network).

In other examples, the additional information may be used to control a mode setting for the explanative content processor 100, e.g. to define which rules and/or algorithms, aspects of the input data or aspects of the possible output data (for the rules/algorithms) are used when generating the patient-specific data. Examples of mode settings will be described later.

Various embodiments of suitable additional information will now be described.

In embodiments in which the patient-explanative content is displayed by a user interface 110, the explanative content process 100 may be adapted to obtain information about the user interface for displaying the patient-specific explanative content.

Information about the user interface may be obtained directly from the user interface (e.g. by requesting information from the user interface or by monitoring how communications are made to the user interface).

The information about the user interface may act as an additional input for the computer-based rules and/or algorithms, e.g. a new feature for a neural network. Thus, the step of processing the generic explanative content may be further based upon the information about the user interface.

Embodiments employing information about the user interface enables the patient-specific explanative content to be contextualized or adapted for a particular user interface (e.g. adapting the explanative content for a display capability or size of the user interface), thereby improving the understandability of the medical subject-matter when displayed by the user interface.

The explanative content processor 100 may be adapted to obtain information about an intended audience for the explanative content, i.e. information about who is going to read/interpret the explanative content and/or who will be involved in a discussion concerning the explanative content.

Information about an intended audience for the explanative content may, for example, be obtained from a user interface 110 which may allow a user to input information on the intended audience for the explanative content. In other embodiments, information about an intended audience for the explanative content may be obtained from a camera (not shown), e.g. by performing a facial recognition process on images/videos obtained by the camera.

In yet other embodiments, information about an intended audience may be obtained from metadata associated with the (device housing the) explanative content processor or a destination device for the patient-specific explanative content, such as the user interface 110. Thus, information about an intended audience may be derived by determining an owner or viewer of a user interface that will display the patient-specific explanative information.

In some examples, information about an intended audience may be inferred by a location or network associated with the destination device for the patient-specific explanative content (e.g. the user interface 110). By way of example, it may be inferred that the intended audience is a medical professional if the destination device is in a hospital, whereas it may be inferred that the intended audience is a family member if the destination device is outside of a clinical setting.

The information about the intended audience for the explanative content acts as an additional input for the computer-based rules and/or algorithms, e.g. a new feature for a neural network. Thus, the step of processing the generic explanative content may be further based upon the information about the intended audience for the explanative content.

Embodiments employing information about the intended audience enables the patient-specific explanative content to be contextualized for an intended audience, thereby improving the intended audience’s understanding of the medical subject-matter described by the patient- specific explanative content.

The explanative content process 100 may be adapted to obtain information about an intended purpose for the patient-specific explanative content, i.e. information about the reason that patient-specific information is being generated. For example, the purpose of the patient-specific information may include a purpose of discussing future treatment options for the patient, a purpose of making a treatment decision about the patient, a purpose of explaining information to an audience, a purpose of using the patient- specific explanative content for a research paper and so on.

Information about an intended purpose for the patient-specific information may be obtained from a user input, e.g. enabling a user to input information on the intended purpose. In some examples, information about an intended purpose may be inferred from a content in which the patient-specific explanative content is to be generated (e.g. for display at a bedside user interface, which may imply that a purpose is for discussing treatment options with the patient). Other methods will be apparent to the skilled person.

The information about the intended purpose for the patient-specific explanative content acts as an additional input for the computer-based rules and/or algorithms, e.g. a new feature for a neural network. Thus, the step of processing the generic explanative content is further based upon the information about the intended purpose for the patient-specific explanative content.

Embodiments employing information an intended purpose for the patient- specific information enables the patient-specific information to be contextualized for a particular reason or purpose of the explanative content, thereby improving the audience’s understanding of the medical subject-matter and ensuring that more relevant explanative content is provided to the audience. This results in a reduced length of time in achieving the intended purpose for the explanative information.

The explanative content processor may be adapted to be operable in a number of modes. In other words, a mode setting may be used to control how the patient- specific explanative content is generated.

Mode settings may be used to define how the additional information (e.g. information beyond the patient-specific data and/or generic explanative content, such a user input, information about an intended audience or information about a purpose of the patient-specific explanative content) is used to control or define the content of the patient- specific explanative content. This may be instead or, or complimentary to, using such information as input to the one or more rules and algorithms. In some examples, the mode setting may define which of a plurality of different sets of one or more rules or algorithms are applied to the generic explanative content and the patient-specific data in order to generate the patient-specific explanative content.

In some examples, the mode setting may define the output features of the patient-specific explanative content, e.g. what type of information is included in the patient-specific explanative content.

In some examples, the mode setting may define the input data for the one or more rules or algorithms (e.g. what portion of available patient-specific is used) to generate the patient-specific explanative content.

A couple of examples of possible mode settings is hereafter described for the sake of improved understanding. The skilled person would be readily capable of identifying alternative mode settings.

The mode settings may include at least a professional mode, for providing patient-specific explanative content suitable for a professional medical practitioner, and a patient mode for providing patient-specific explanative content suitable for a patient, carer or friend/relative of the patient (i.e. non-medically trained persons).

When operating in the professional mode, a first set of one or more rules or algorithms (e.g. a first machine-learning algorithm or first neural network) may be applied to the generic explanative content and the patient-specific data to generate the patient- specific explanative content.

When operating in the patient mode, a second set of one or more rules or algorithms (e.g. a second machine-learning algorithm or a second neural network) may be applied to the generic explanative content and the patient-specific data to generate the patient-specific explanative content.

These two modes enable different patient-specific explanative content to be generated depending upon who is to view the patient-specific explanative content.

These exemplary mode settings effectively enable the explanative content processor to determine information about an intended audience for the patient-specific explanative content (e.g. whether the audience is “professional” or “non-professional”) and generate the patient-specific explanative content based on an outcome of the determination. The mode settings may, for example, comprise a “simple processing” mode and a “complex processing” mode.

When operating in the simple processing mode, only a first portion of the possible patient-specific data is made available for processing by the one or more rules or algorithms to generate the patient-specific explanative content.

When operating in the complex processing mode, a second (larger than the first) portion of possible patient-specific data is made available for processing by the one or more rules or algorithms to generate the patient-specific explanative content.

These two modes effectively enable the explanative content processor to process different amounts of input data to generate the patient-specific explanative content, thereby enabling control over the processing power and/or demand required to generate the patient-specific explanative content.

Other suitable modes or mode settings would be apparent to the skilled person, and the skilled person would be capable of further adapting any previously described modes or mode settings depending upon implementation details.

By way of example only, a “simple processing” mode may further restrict the number of rules/algorithms applied to the (first portion of the) patient-specific data, to further reduce a processing power/demand.

Various ways for determining or selecting a mode setting are envisaged.

In a simple example, the mode setting may be provided by a user input, e.g. via the user interface 110.

It has previously been explained how the explanative content processor 100 may be adapted to receive additional information, to assist in the generation of the patient- specific data. This additional information may be used to select a mode setting of the explanative content processor.

For example, if the additional information indicates that an intended audience for the patient-specific explanative content is the patient themselves or a family/friend of the patient, the selected mode setting may be the “patient” setting.

The patient-specific explanative content may be updated or adapted. This may be performed by either re-generating the patient specific explanative content using the generic explanative content (i.e. “updating” the patient-specific explanative content) or further processing the patient-specific explanative content using at least patient-specific data (i.e. “adapting” the patient-specific explanative content).

Updating the patient-specific explanative content may comprise, for example, performing any previously described process to generate new patient-specific explanative content. This enables the patient-specific explanative content to be re generated based on, for example, changes in the patient-specific information.

Adapting the patient-specific explanative content may comprise, for example, processing both the patient-specific explanative content and patient-specific information, using one or more computer-based rules or algorithms, to adapt the patient- specific explanative content. This enables the patient-specific explanative content to be adapted based on, for example, changes in the patient-specific information, and may reduce a processing cost compared to a step of updating the patient-specific explanative content.

An in-depth description of how to process the patient-specific explanative content and patient-specific information to adapt the patient-specific explanative content will not be described, for the sake of conciseness, as the skilled person would readily understand how such processing methods could be implemented (e.g. by consulting the previous description on how the patient-specific explanative content may be initially generated).

Updating or adapting may be performed automatically or periodically, in response to detection of a change of state of the user interface 110 or explanative content processor 100, detection of a change in the patient-specific data and/or in response to a user input.

In one example, patient-specific explanative content is updated and/or adapted at periodic intervals, e.g. once a day, once every 12 hours, once every 4 hours and so on.

In another example, patient-specific explanative content may be updated and/or adapted to coincide with shift changes of a caregiver (e.g. by consulting a calendar or monitoring the movement of caregivers throughout a clinical setting, e.g. using a camera or badge tracking system). In another example, patient-specific explanative content is updated or adapted in response to detecting a state of the user interface, e.g. in response to a new user interacting with (e.g. logging in) to a user interface.

In some examples, the patient-specific explanative content may be updated (e.g. the process for generating such content repeated) in response to a user input. Thus, a user interface 110, 152 may be adapted to generate a user input (responsive to an action of the user) and pass this user input to the explanative content processor 100.

In further embodiments, the process of updating or adapting the patient- specific explanative content is further based on this user input. Thus, a user input may act as an additional input or feature for the computer-based rules or algorithms in generating the patient-specific explanative content. This enables the user to direct the updating/adapting of the patient-specific explanative content.

In embodiments in which a user input triggers the updating or adapting of the patient specific explanative content, the explanative content processor may be adapted to process the patient-specific explanative content and the user input, using one or more computer-based rules or algorithms, based on the user input to generate adapted patient- specific explanative content. Thus, there may be no need to use the patient-specific data in adapting the patient-specific explanative content. This allows the user to direct how the patient-specific explanative content is adapted or filtered.

In some examples, patient-specific explanative content is updated or adapted in response to an update or change in the patient-specific data. It will be apparent that the patient-specific data can be automatically or manually updated over time. For example, patient-specific data may comprise real time patient monitoring data, such as heartrate or other vital sign information.

In some examples, the patient-specific explanative content is updated or adapted in response to a significant update or change in the patient-specific data. A significant change or update may, for example, be an update or change that indicates a change in a value of the patient-specific of more than a predetermined percentage (e.g. more than 5% or 10% change in the patient-specific data). In other examples, a significant change or update might be a change or update of the patient-specific data that results in a one or more values of the patient-specific data breaching predetermined (clinically acceptable) thresholds. It will therefore be clear that a “significant update or change” may indicate a medically significant change or update in the patient-specific data, e.g. a change that might require clinical attention or a change that would affect a clinical decision for the patient. The rules for whether such a change is significant may be predefined, e.g. in clinical compliance guidelines or the like.

Of course, a combination of these features may be performed. For example, the patient-specific explanative content may be updated or adapted periodically, but only if there has been a significant update or change in the patient-specific data. In another example, the patient-specific explanative content may be updated/adapted in response to a user input, but only if a certain time period has elapsed since the last update/adaptation. Such embodiments may save or reduce unnecessary processing power.

It will be apparent that a user or operator of the explanative content processor may be able to modify and control the updating/adapting policy, e.g. whether, how and when the patient-specific explanative content is updated or adapted.

It has previously been described how a user interface 110 may be used to display or otherwise output the patient-specific explanative content.

The user interface 110 may, for example, comprise a screen 111 for displaying the patient-specific explanative content, as would be well known to the skilled person. The user interface 110 may comprise a processor 110 adapted to process the patient-specific explanative content (e.g. textual data or graphical information) to generate display data for controlling the screen 111 to provide a visual representation of the patient- specific explanative content.

Suitable methods of controlling and displaying explanative content via a user interface will be apparent to the skilled person.

In some examples, the user interface comprises a speaker 113 for providing an audio output of the patient-specific explanative content. This audio output may be performed, for example, by performing a text-to-speech operation on textual information of the patient-specific explanative content, or by the patient-specific explanative content itself comprising an audio file.

As previously explained, the user interface may comprise an input mechanism 112 for enabling a user to provide a user input. The input mechanism may comprise any known input mechanism, e.g. a mouse or keyboard. In some examples, the input mechanism is integrated with the screen 111, e.g. a touch-sensitive screen.

The user interface 110 may be integrated or an aspect of, for example, a bedside monitor, a workstation, a mobile device (e.g. mobile/cellular smartphone, smartwatch, tablet or smart glasses), a projection device, a laptop, a computer and so on.

In some embodiments, the user interface 110 may be adapted to allow an operator of the user interface, such as a friend or family member of the patient, to provide additional information about the patient (e.g. in response to the patient-specific explanative content). This may be provided via the input mechanism 112.

This additional information about the patient may be used to supplement the patient-specific data originally used to generate the patient-specific explanative content. The additional information may (also or alternatively) be made available to clinical staff to assist in the clinical staff making a clinical decision.

In some embodiments, the additional information generated by the user interface is passed to the patient-specific data module 151, and in particular embodiments, to the second database of the patient-specific data module 152. Information in this second database 152 may be accessible by clinical staff in order to aid them in making a clinical decision.

By way of example only, additional information about the patient may enable the provision of sleep preferences, home-based observations, activities of daily living (and so on) of the patient to be provided to the clinical staff.

This additional information may be used, in some example, to update or adapt the patient-specific explanative content, although this is not essential.

These embodiments enables a patient and their friends/family, to obtain patient-specific explanative content that is tailored towards them (i.e. to improve their understanding of the status/diagnosis/condition of the patient), and enables them to provide improved additional information that may assist a medical staff member in making a clinical decision.

The herein proposed embodiments thereby enable the provision of improved and more relevant information for assisting in making a clinical decision (e.g. diagnosing or treating) a patient. The skilled person would be readily capable of adapting any above- described process or concept into a (computer-based) method of generating patient-specific explanative content.

Nonetheless, for the sake of clarity, Figure 2 illustrates a flowchart of a method 200 according to an embodiment of the invention. The method 200 is for generating explanative content for providing a patient-specific explanation of medical subject-matter associated with a patient

The method 200 comprises a first step 201 of obtaining generic explanative content providing generic descriptions and/or illustrations of different possible aspects of medical subject-matter.

The method 200 further comprises a second step 202 of obtaining patient- specific data associated with a patient.

The method 200 further comprises a third step 203 of processing the generic explanative content, using one or more computer-based rules or algorithmic processes, based on the patient-specific data to generate patient-specific explanative content of the medical subject-matter associated with the patient.

The skilled person would be readily capable of developing a processing system for carrying out any herein described method or concept. Thus, each step of the flow chart may represent a different action performed by a processing system, and may be performed by a respective module of the processing system.

Embodiments may therefore make use of a processing system. The processing system can be implemented in numerous ways, with software and/or hardware, to perform the various functions required. A processor is one example of a processing system which employs one or more microprocessors that may be programmed using software (e.g., microcode) to perform the required functions. A processing system may however be implemented with or without employing a processor, and also may be implemented as a combination of dedicated hardware to perform some functions and a processor (e.g., one or more programmed microprocessors and associated circuitry) to perform other functions.

Examples of processing system components that may be employed in various embodiments of the present disclosure include, but are not limited to, conventional microprocessors, application specific integrated circuits (ASICs), and field-programmable gate arrays (FPGAs).

In various implementations, a processor or processing system may be associated with one or more storage media such as volatile and non-volatile computer memory such as RAM, PROM, EPROM, and EEPROM. The storage media may be encoded with one or more programs that, when executed on one or more processors and/or processing systems, perform the required functions. Various storage media may be fixed within a processor or processing system or may be transportable, such that the one or more programs stored thereon can be loaded into a processor or processing system.

It will be understood that disclosed methods are preferably computer- implemented methods. As such, there is also proposed the concept of computer program comprising code means for implementing any described method when said program is run on a processing system, such as a computer. Thus, different portions, lines or blocks of code of a computer program according to an embodiment may be executed by a processing system or computer to perform any herein described method. In some alternative implementations, the functions noted in the block diagram(s) or flow chart(s) may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved.

Variations to the disclosed embodiments can be understood and effected by those skilled in the art in practicing the claimed invention, from a study of the drawings, the disclosure and the appended claims. In the claims, the word "comprising" does not exclude other elements or steps, and the indefinite article "a" or "an" does not exclude a plurality. A single processor or other unit may fulfill the functions of several items recited in the claims. The mere fact that certain measures are recited in mutually different dependent claims does not indicate that a combination of these measures cannot be used to advantage. If a computer program is discussed above, it may be stored/distributed on a suitable medium, such as an optical storage medium or a solid-state medium supplied together with or as part of other hardware, but may also be distributed in other forms, such as via the Internet or other wired or wireless telecommunication systems. If the term "adapted to" is used in the claims or description, it is noted the term "adapted to" is intended to be equivalent to the term "configured to". Any reference signs in the claims should not be construed as limiting the scope.