Patent Publication Number: US-10319466-B2

Title: Intelligent filtering of health-related information

Description:
REFERENCE TO COPENDING APPLICATIONS 
     This application claims priority to U.S. Provisional Application 61/600,927, filed on Feb. 20, 2012 and entitled INTELLIGENT FILTERING OF HEALTH-RELATED INFORMATION, the entire disclosure of which is hereby incorporated by reference. 
    
    
     BACKGROUND 
     Health information exchanges are being established to promote the electronic exchange of health-related information between health care providers, which will require the participating health care providers to be able to transmit their data according to certain data standards. Examples of such data standards include the Systematized Nomenclature of Medicine—Clinical Terms (SNOMED CT) standard for diagnostic and clinical data, the Logical Observation Identifiers Names and Codes (LOINC) standard for laboratory data, the RxNorm standard for prescription drugs, and the International Statistical Classification of Diseases and Related Health Problems (ICD-9 and ICD-10) standards for billing and other purposes. Thus, a single healthcare provider may need to be able to send and receive data according to eight or nine different data standards. The data is then received by the health information exchange from the various health care providers, where it is stored and made available to other health care providers. 
     As a result of such initiatives, the medical community is quickly becoming overloaded with data. But, the data may contain vital information about a patient that the caregiver needs to know when treating that patient. For example, buried within the data may be information about the patient&#39;s current prescription drugs or preexisting conditions. It is important that relevant data be located and made available to the caregiver when it is needed so that appropriate treatment decisions can be made. 
     SUMMARY 
     In general terms, this disclosure is directed to intelligent filtering of health-related information. 
     One aspect is a method of filtering health-related information. The method includes receiving health-related information including items encoded in one or more external standard terminologies; converting the health-related information into converted health-related information including items encoded in an internal medical terminology; identifying one or more terms in the internal medical terminology related to a selected term of the internal medical terminology; and identifying items in the converted health-related information that match one or more of the terms related to the selected term 
     Another aspect is an intelligent filtering system. The system includes at least one computing device including at least one processing device; and at least one computer readable storage device comprising data instructions, which when executed by the computing device cause the at least one computing device to generate: a data extraction engine that extracts items from health-related information in a native terminology; a terminology conversion engine that converts the items from the native terminology into an internal medical terminology to generate converted health-related information; a relevancy search engine that identifies items within the converted health-related information that are related to a selected term; and a user interface engine that presents the items that are related to the selected term to a caregiver. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a schematic block diagram illustrating an exemplary healthcare information exchange network. 
         FIG. 2  illustrates an exemplary architecture of a computing device that can be used to implement aspects of the present disclosure. 
         FIG. 3  is a schematic block diagram illustrating an example of an intelligent filtering system. 
         FIG. 4  is a flow chart illustrating an example method of filtering medical data. 
         FIG. 5  is a schematic diagram illustrating the extraction of items from incoming health-related information. 
         FIG. 6  is a schematic diagram illustrating the conversion of items from the health-related information to an internal medical terminology. 
         FIG. 7  is a schematic diagram illustrating another example of the conversion of health-related information to an internal medical terminology. 
         FIG. 8  is a schematic diagram illustrating the health-related information after conversion to the internal medical terminology. 
         FIG. 9  is a schematic diagram illustrating the identification of items from the health-related information that relate to a selected term. 
         FIG. 10  is a schematic diagram further illustrating the identification of items from the health-related information. 
         FIG. 11  is a screen shot of an example user interface display showing the items in the health-related information that relate to the selected term. 
         FIG. 12  is a screen shot of another example user interface display showing the items in the health-related information that relate to another selected term. 
     
    
    
     DETAILED DESCRIPTION 
     Various embodiments will be described in detail with reference to the drawings, wherein like reference numerals represent like parts and assemblies throughout the several views. Reference to various embodiments does not limit the scope of the claims attached hereto. Additionally, any examples set forth in this specification are not intended to be limiting and merely set forth some of the many possible embodiments for the appended claims. 
       FIG. 1  is schematic block diagram illustrating an exemplary healthcare information exchange network  100 . The healthcare information exchange network  100  includes many different healthcare providers  102 , a health information exchange system  104 , and an intelligent filtering system  106 . 
     The healthcare information exchange network  100  permits the exchange of healthcare-related information among multiple healthcare providers  102 . In some embodiments, the healthcare information exchange network  100  includes one or more health information exchange system  104 , but other embodiments do not include a health information exchange system  104 . For example, data can be passed directly between the healthcare providers  102  (or through other third parties), rather than, or in addition to the interaction with the health information exchange system  104 . 
     To illustrate the different healthcare providers that can participate, the exemplary healthcare information exchange network  100  includes healthcare providers such as a primary care provider  110 , a pharmacy  112 , a cardiologist  114 , and a hospital  116 . Although certain exemplary healthcare providers are explicitly identified herein, the healthcare exchange network also can include healthcare providers in addition to or in place of a primary care provider  110 , a pharmacy  112 , a cardiologist  114 , and a hospital  116 . 
     Typically, each healthcare provider  102  has at least one computing device, and oftentimes each healthcare provider has its own local area network with many computing devices, including one or more server computing devices and client computing devices. Caregivers at the healthcare providers  102  often interact with the client computing devices, or other terminal devices, which in turn communicate with the server computing devices. The server computing devices can communicate with another data communication network, such as the Internet, a local area network, or other data communication network, in which data can be communicated with the other healthcare providers  102  or with the health information exchange system  104 . 
     The one or more possible computing devices at each of the healthcare providers  102  are represented by the computing devices  120 ,  122 ,  124 , and  126 , shown in  FIG. 1 . Further, there may be additional intermediary computing devices (such as those operated by third party services, such as medical records services, coding services, and the like) that participate in the data communications within the healthcare information exchange network  100 , in at least some possible embodiments. 
     The health information exchange system  104  also includes one or more computing devices  128 , and one or more data storage devices  130 . The computing device  128  interacts with the healthcare provider computing devices  120 ,  122 ,  124 , and  126  to send or receive data. Data received from the healthcare providers  102  is stored in the one or more data storage devices  130 , as electronic health records. The electronic health records are associated with patients to which the records relate, and contain health-related information. 
     In a possible example, the healthcare providers  102  send health-related information  138  to the health-information exchange, or to each other, across the network  108 , as shown in  FIG. 1 . The primary care provider  110  provides an encounter note  140  describing the primary care provider&#39;s  110  encounter with a patient relating to the patient&#39;s asthma. The pharmacy  112  provides a prescription refill  142  describing a prescription that is currently prescribed to the patient. The cardiologist  114  provides an encounter note describing the cardiologist&#39;s  114  encounter with the patient relating to cardiology. The hospital provides a discharge summary  146  describing the patient&#39;s recent hospital visit. 
     The various health-related information  138  communicated from the healthcare providers is typically encoded using one or more of multiple different standard terminologies that are external to the filtering system  106 . These standard terminologies are sometimes referred to herein as external standard terminologies. For example, data relating to diagnostic and clinical data may be encoded using the Systematized Nomenclature of Medicine—Clinical Terms (SNOMED CT) standard; data describing laboratory test results may be encoded using the Logical Observation Identifiers Names and Codes (LOINC) standard; and prescription drug data may be encoded using the RxNorm standard. Clinical data also may be encoded utilizing one or more of the International Statistical Classification of Diseases and Related Health Problems (such as ICD-9 or ICD-10) standards. 
     Although the exemplary embodiment illustrates the health-related information as being stored in the electronic health records on the electronic storage device  130 , other embodiments are possible. For example, the health care providers  102  could provide the health-related information  138  directly to each other. 
     The intelligent filtering system  106  provides the capability to sort through a vast amount of health-related information  138 , such as from the patient&#39;s health related information stored in the data storage device  130  or received from other healthcare providers  102 , to identify relevant information. An example of the intelligent filtering system  106  is illustrated and described in more detail with reference to  FIGS. 2-12 . 
     Although the intelligent filtering system  106  is illustrated as a system separate from the healthcare providers  102  computing devices  120 ,  122 ,  124 , and  126  and from the health information exchange system  104 , in other embodiments the intelligent filtering system  106  is or operates as a part of one or more of these systems. In some embodiments, the intelligent filtering system  106  is a part of an electronic medical records system in which the caregiver interacts to generate patient notes to document a patient encounter and to review the patient&#39;s historical record. 
       FIG. 2  illustrates an exemplary architecture of a computing device that can be used to implement aspects of the present disclosure, including any of the plurality of healthcare provider  102  computing devices  120 ,  122 ,  124 , and  126 , the health information exchange computing device  128 , and the intelligent filtering computing device  132 . More specifically, the computing device illustrated in  FIG. 2  can be used to execute the operating system, application programs, and software modules (including the software engines) described herein. By way of example, the computing device will be described below as the intelligent filtering system  106  computing device  132 . To avoid undue repetition, this description of the computing device will not be separately repeated herein for each of the other computing devices, including computing devices  120 ,  122 ,  124 ,  126 , and  128 , but such devices can also be configured as illustrated and described with reference to  FIG. 2 . 
     The computing device  132  includes, in some embodiments, at least one processing device  180 , such as a central processing unit (CPU). A variety of processing devices are available from a variety of manufacturers, for example, Intel or Advanced Micro Devices. In this example, the computing device  132  also includes a system memory  182 , and a system bus  184  that couples various system components including the system memory  182  to the processing device  180 . The system bus  184  is one of any number of types of bus structures including a memory bus, or memory controller; a peripheral bus; and a local bus using any of a variety of bus architectures. 
     Examples of computing devices suitable for the computing device  132  include a desktop computer, a laptop computer, a tablet computer, a mobile computing device (such as a smart phone, an iPod® or iPad® mobile digital device, or other mobile devices), or other devices configured to process digital instructions. 
     The system memory  182  includes read only memory  186  and random access memory  188 . A basic input/output system  190  containing the basic routines that act to transfer information within computing device  132 , such as during start up, is typically stored in the read only memory  186 . 
     The computing device  132  also includes a secondary storage device  192  in some embodiments, such as a hard disk drive, for storing digital data. The secondary storage device  192  is connected to the system bus  184  by a secondary storage interface  194 . The secondary storage devices  192  and their associated computer readable media store nonvolatile storage of computer readable instructions (including application programs and program modules), data structures, and other data for use or communication by the computing device  132 . 
     Although the exemplary environment described herein employs a hard disk drive as a secondary storage device, other types of computer readable storage media are used in other embodiments. Examples of these other types of computer readable storage media include magnetic cassettes, flash memory cards, digital video disks, Bernoulli cartridges, compact disc read only memories, digital versatile disk read only memories, random access memories, or read only memories. Some embodiments include non-transitory media. 
     A number of program modules can be stored in secondary storage device  192  or memory  182 , including an operating system  196 , one or more application programs  198 , other program modules  200  (such as the engines  230 ,  232 ,  234 , and  236  described herein with reference to  FIG. 4 ), and program data  202 . The computing device  132  can utilize any suitable operating system, such as Microsoft Windows™, Google Chrome™, Apple OS, and any other operating system suitable for a computing device. Other examples can include Microsoft, Google, or Apple operating systems, or any other suitable operating system used in tablet computing devices. 
     In some embodiments, a user provides inputs to the computing device  132  through one or more input devices  204 . Examples of input devices  204  include a keyboard  206 , mouse  208 , microphone  210 , and touch sensor  212  (such as a touchpad or touch sensitive display). Other embodiments include other input devices  204  such as a digital pen. The input devices are often connected to the processing device  180  through an input/output interface  214  that is coupled to the system bus  184 . These input devices  204  can be connected by any number of input/output interfaces, such as a parallel port, serial port, game port, or a universal serial bus. Wireless communication between input devices and the input/output interface  214  is possible as well, and includes infrared, BLUETOOTH® wireless technology, 802.11a/b/g/n, cellular, or other radio frequency communication systems in some possible embodiments. 
     In this example embodiment, a display device  216 , such as a monitor, liquid crystal display device, projector, or touch sensitive display device, is also connected to the system bus  184  via an interface, such as a video adapter  218 . In addition to the display device  216 , the computing device  132  can include various other peripheral devices (not shown), such as speakers or a printer. 
     When used in a local area networking environment or a wide area networking environment (such as the Internet), the computing device  132  is typically connected to the network  108  through a network interface  220 , such as an Ethernet interface. Other possible embodiments use other communication devices. For example, some embodiments of the computing device  132  include a modem for communicating across the network  108 . 
     The computing device  132  typically includes at least some form of computer readable media. Computer readable media includes any available media that can be accessed by the computing device  132 . By way of example, computer readable media include computer readable storage media and computer readable communication media. 
     Computer readable storage media includes volatile and nonvolatile, removable and non-removable media implemented in any device configured to store information such as computer readable instructions, data structures, program modules or other data. Computer readable storage media includes, but is not limited to, random access memory, read only memory, electrically erasable programmable read only memory, flash memory or other memory technology, compact disc read only memory, digital versatile disks or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to store the desired information and that can be accessed by the computing device  132 . 
     Computer readable communication media typically embodies computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media. The term “modulated data signal” refers to a signal that has one or more of its characteristics set or changed in such a manner as to encode information in the signal. By way of example, computer readable communication media includes wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, radio frequency, infrared, and other wireless media. Combinations of any of the above are also included within the scope of computer readable media. 
     The computing device illustrated in  FIG. 2  is also an example of programmable electronics, which may include one or more such computing devices, and when multiple computing devices are included, such computing devices can be coupled together with a suitable data communication network so as to collectively perform the various functions, methods, or operations disclosed herein. 
       FIG. 3  is a schematic block diagram illustrating an example of the intelligent filtering system  106 , shown in  FIG. 1 . The intelligent filtering system includes a computing device  132  and a data storage device  134 . 
     The data storage device  134  can be a part of the computing device  132  (such as memory  182  or secondary storage device  192 , shown in  FIG. 2 ), or can be a separate data storage device. For example, the data storage device  134  can be a separate database, which can itself include one or more computing devices, in some embodiments. In any event, the data storage device  134  includes one or more computer readable storage devices that store digital data. Examples of computer readable storage devices are described herein. 
     The computing device  132  includes one or more engines that are executed by the computing device to perform particular functions. In this example, the computing device  132  includes a data extraction engine  230 , a terminology conversion engine  232 , a relevancy search engine  234 , and a user interface engine  236 . A brief exemplary description of each engine  232 ,  234 ,  236 , and  238  is provided below, while a more detailed explanation and additional examples are provided herein. 
     The data extraction engine  230  receives incoming health-related information  138 , extracts items from the information, and saves it in a common format. 
     The terminology conversion engine  232  converts the items from the health-related information  138  from the native terminologies into an internal medical terminology  244 . The internal medical terminology is terminology used internal to the intelligent filtering system as opposed to terminology that received from sources external to the intelligent filtering system such as ICD codes. 
     The relevancy search engine  234  searches through the converted health-related information to identify those items that are related to a particular term or otherwise relevant to a particular situation. 
     The user interface engine  236  presents the relevant items from the health-related information  138  to the caregiver, while selectively choosing not to present certain information to the caregiver that is determined to be irrelevant to the present item. Presenting the relevant items to the caregiver can include displaying the items on a display device  216  of the computing device  132 , or sending the relevant items across the network  108  for display on another computing device (e.g., computing devices  120 ,  122 ,  124 , or  126 ). In another possible embodiment, the items are stored on a computing device or sent to another computing device without displaying the items to the caregiver. For example, the items can be used for subsequent processing. 
     The data storage device  134  includes, for example, external standard terminologies definitions  240 ; a knowledge base  242  including an internal medical terminology definition  244  and diagnostic relationship data  246 ; and terminology mapping data  248 . 
     The external standard terminologies definitions  240  includes data that describes the multiple different external standard terminologies that may be used to encode health-related information  138  that is received by the intelligent filtering system. Examples of external standard terminologies include SNOMED-CT, LOINC, RxNorm, ICD-9, and ICD-10. Other standard terminology definitions can also be included. 
     As one example, the external standard terminology definitions can include a separate table for each standard terminology. Within each table, the standard terms are listed along with the corresponding codes that are used to represent the standard terms. The data used to populate the external standard terminology definitions  240  is obtained from the respective organization that manages each standard. 
     The knowledge base  242  contains data that is used by the intelligent filtering system for, at least, internal processing of the data, as described herein. The knowledge base includes an internal medical terminology  244  and diagnostic relationship data  246 . 
     The internal medical terminology  244  is a single terminology that is used by the intelligent filtering system  106 . In other words, incoming data is converted into the internal medical terminology  244  so that all data uses a common terminology. An example of a suitable internal medical terminology  244  is the MEDCIN standard medical terminology developed by Medicomp Systems, Inc. of Chantilly, Va. The MEDCIN standard medical terminology includes approximately 280,000 terms. Each of the terms is associated with an internal medical terminology code that uniquely identifies each term. 
     In addition to the internal medical terminology  244 , the knowledge base  242  also includes diagnostic relationship data  246 . The diagnostic relationship data  246  defines relationships between the terms of the internal medical terminology  244 . For example, the internal medical term for “asthma” is linked to other terms that are diagnostically related to asthma, such as “difficulty breathing,” “cough,” and “wheezing.” In this example, each of the related terms are common findings that are associated with a diagnosis of asthma in a patient. In some embodiments the diagnostic relationship data  246  includes a separate table for each diagnosis included in the internal medical terminology  244 . For example, the diagnosis of asthma has a table which includes a list of all terms within the internal medical terminology  244  that have a relationship to the diagnosis of asthma. An example of such a table is illustrated in  FIG. 9 . 
     Terminology mapping data  248  defines the relationships between the items in the external standard terminologies definitions  240  and the internal medical terminology  244 . As one example, each term of the internal medical terminology  244  has a table that includes a list of each of the corresponding terms in the external standard terminologies definitions  240 . This permits the computing device  132  to convert between the external standard terminologies  240  and the internal medical terminology  244  utilizing the terminology conversion engine  232 . 
     The engines  230 ,  232 ,  234 , and  236  utilize the data stored in the data storage device in order to perform the operations of the intelligent filtering system  106 , as illustrated in  FIG. 4 . 
       FIG. 4  is a flow chart illustrating exemplary operations performed by the intelligent filtering system  106 .  FIG. 4  also illustrates an exemplary method  260  of filtering health-related information. In this example, the method  260  includes one or more operations  262 ,  264 ,  266 , and  268 . 
     The method  260  begins when health-related information  138  is received from an external source, such as from a healthcare provider  102 , from the health information exchange system  104  (both shown in  FIG. 1 ). Examples of the health-related information  138  includes the encounter note  140 , prescription refill  142 , encounter note  144 , and discharge summary  146 , shown in  FIG. 1 . In alternative embodiments, the health-related information  138  can be received from sources other than the health information exchange. For example, the health-related information could be receive directly from the healthcare provider  102  for processing, extracted from patient health records hosted by or stored at the intelligent filtering system itself, or from some other source. 
     Once the health-related information  138  has been received, the operation  262  extracts items from incoming data in the native terminology. In some embodiments the operation  262  is performed by the data extraction engine  230 , shown in  FIG. 3 . 
     As previously discussed, the health-related information  138  can be encoded utilizing one or more of a variety of external standard terminologies. In addition to the use of different terminologies, the health-related information  138  is also provided in various different formats. The operation  262  removes the differences in formatting by extracting the items from the health-related information  138  and storing the items in a common format. The operation  262  utilizes the external standard terminology definitions  240  ( FIG. 3 ) to identify the items within the health-related information  138 . An example of operation  262  is illustrated and described in more detail herein with reference to  FIG. 5 . 
     After items have been extracted from the health-related information  138 , the items are then converted in operation  264  into the internal medical terminology. In some embodiments, the operation  264  is performed by the terminology conversion engine  232 , shown in  FIG. 3 . 
     The conversion between the native terminologies in which the health-related information  138  is received and the internal medical terminology  244 , is performed utilizing the terminology mapping data  248  (shown in  FIG. 3 ). Examples of operation  264  are illustrated and described in more detail with reference to  FIGS. 6-8 . 
     After the items have been converted into the internal medical terminology  244 , operation  266  is performed to identify the items in the health-related information  138  that are related to a selected term. The selected term is, in some embodiments, the clinical problem that the caregiver is currently evaluating for the patient. In some embodiments, the operation  266  is performed by the relevancy search engine  234 . 
     In some embodiments, operation  266  begins by receiving from a caregiver one or more selected terms. For example, the user interface engine  236  ( FIG. 3 ) is used to prompt the caregiver to select one or more diagnoses that are being considered or for which the patient is being evaluated. Terms related to things other than a diagnosis also can be selected by the caregiver, such as a particular finding (e.g., “cough”). 
     In another possible embodiment, the term is pre-selected by the computing device. For example, if the intelligent filtering system  106  is being used by the cardiologist  114  ( FIG. 1 ), the intelligent filtering system  106  may automatically select one or more cardiology terms. As another example, the term may be selected based on the patient&#39;s stated purpose for the visit, such as “chest pain.” In another possible embodiment, the term is automatically selected by the computing device based on predetermined criteria. 
     Once the one or more terms are selected, a search is performed through the converted health-related information (which is encoded in the internal medical terminology  244 ), to identify any items that are related to the selected term or terms. 
     In some embodiments, operation  266  utilizes the knowledge base  242  to identify such items. For example, if the search term is “asthma,” the diagnostic relationship data  246  is used to identify the set of terms in the internal medical terminology  244  that are related to asthma. The relevancy search engine  234  obtains the set of terms, and then conducts a search across the health-related information  138  to determine whether any of those terms can be found. The relevant portions of the health-related information  138 , which include at least items containing terms related to the selected term, are then stored for subsequent use. An example is illustrated in  FIG. 9-10 . 
     Once the relevant items of the health-related information  138  have been identified, operation  268  is performed in some embodiments to present the items identified within the health-related information  138  to the caregiver. In some embodiments, operation  268  is performed by the user interface engine  236 . 
     As an example, the caregiver is presented with a user interface display that shows the items from the health-related information  138  that are relevant to asthma. In this way the caregiver is able to quickly review the relevant portions of the patient&#39;s medical record without having to search through a large volume of information that is irrelevant to the present encounter with the patient. Examples of displays generated by the user interface engine  236  and during operation  268  are shown in  FIGS. 11-12 . 
       FIG. 5  is a schematic diagram illustrating an example of operation  262 , shown in  FIG. 4 , such as performed by the data extraction engine  230 , shown in  FIG. 3 . In this example, the operation  262  involves the use of the external standard terminology definitions  240  to extract items from the health-related information  138  and to generate the health-related information  280 . 
     As shown in the example of  FIG. 1 , the health-related information  138  includes data from a variety of sources, including an encounter note  140  from the primary care provider  110 , a prescription refill  142  from the pharmacy  112 , an encounter note from a cardiologist  114 , and a discharge summary  146  from the hospital  116 . 
     The health-related information  138  can be in a variety of different formats. As a result, it is desirable to extract the items from the health-related information  138  and save the items in a common format as health-related information  280 . In this example, the items from the encounter note  140  are extracted and saved in a list  282 . Similarly, the items from the prescription refill  142 , the encounter note  144 , and the discharge summary  146  are extracted and saved in lists  284 ,  286 , and  288 . 
     To generate the lists, the external standard terminology definitions  240  can be used to identify items within the health-related information that corresponds to a term from one of the external standard terminologies. A scan of the health-related information is performed, and if an item is found that matches a term in one of the external standard terminologies, that item is added to the respective list  282 ,  284 ,  286 , or  288  of health-related information  280 . 
     In the example, the encounter note  140  was scanned and four items were identified, including items  292 ,  294 ,  296 , and  298 . Items  292  and  294  were identified as terms in the SNOMED-CT standard terminology, while item  296  was identified as a term in the ICD-9 standard terminology, and term  298  was identified as a term of the LOINC terminology. For each term, the list  282  includes an identifier of the standard terminology in which the term was found, a code for the term, and a description of the term. Some embodiments include additional information, such as values or other information retrieved from the health-related information  138 . In yet other embodiments, the list  282  includes only the codes, or a combination of the codes and any other desired information. 
     The lists  284 ,  286 , and  288  are similarly generated, such that the list  284  includes items  302  and  304 , the list  286  includes items  306 ,  308 ,  310 ,  312 ,  314 ,  316 , and  318 , and the list  288  includes items  322 ,  324 ,  326 ,  328 , and  330 . 
       FIG. 6  is a schematic diagram illustrating an example of operation  264 , shown in  FIG. 4 , such as performed by the terminology conversion engine  232 , shown in  FIG. 3 , in some embodiments. In this example, the operation  264  involves the use of the terminology mapping data  248 , such as a terminology mapping table  340  associated with asthma, to convert the items from the health-related information  138  (e.g., the list  282 ) into the internal medical terminology. 
     In this example, the items in the list  282  are to be converted into the internal medical terminology, as defined by the internal medical terminology definition  244  ( FIG. 3 ). More specifically, this example illustrates the conversion of the item  296 . 
     Item  296  is an ICD-9 code for asthma, having the code 493.9. To convert this code into the internal medical terminology, the terminology conversion engine retrieves the terminology mapping data for the ICD-9 external standard terminology, and conducts a search within that terminology for the code “493.9.” 
     The search finds a matching cell  342  of the terminology mapping table  340 . 
     The terminology conversion engine then determines the term of the internal medical terminology that is associated with the terminology mapping table  340 . In this case, the term is asthma, which has the internal code of 328881. This code is then saved in computer readable storage medium as the conversion of the item  296 . 
     The process continues for each item in the health-related information  138 .  FIG. 7  illustrates another example. 
       FIG. 7  is a schematic diagram illustrating another example of the conversion of health-related information items into an internal medical terminology. The conversion utilizes the terminology mapping data  248 . 
     In this example, an item  306  from the list  286  is to be converted into the internal medical terminology. Item  306  was determined to contain a SNOMED-CT code associated with difficulty breathing, having the code 267036007. 
     To convert this item into the internal medical terminology, the terminology conversion engine  232  conducts a search within the terminology mapping data  248  associated with the SNOMED-CT standard terminology, to identify a term having the code 267036007. The search results in a match in cell  352  of table  350 . 
     The terminology conversion engine  282  then determines the term of the internal medical terminology that is associated with the table  350 . In this case, the term is difficulty breathing, having an internal medical code  353 . Accordingly, the internal medical code  353  is stored as the conversion of item  306  into the internal medical terminology. 
     The process is repeated for items  308 ,  310 ,  312 ,  314 ,  318 , and any other items to be evaluated from the health-related information  138 , such as those in lists  284  and  288  ( FIG. 5 ). The result is illustrated in  FIG. 8 . 
       FIG. 8  is a schematic diagram illustrating the health-related information  360  after conversion to the internal medical terminology. The health-related information  360  includes lists  362 ,  364 ,  366 , and  368 . 
     List  362  is a list of items  292 ,  294 ,  296 , and  298  ( FIG. 5 ) after conversion into the internal medical terminology. For example, item  296 ′ now includes the internal medical code (IMC) 328881 for asthma, as shown in  FIG. 6 . 
     Lists  364 ,  366 , and  368  were similarly converted. For example, item  306 ′ now includes the internal medical code  353  for difficulty breathing. 
       FIG. 9  is a schematic diagram illustrating an example of operation  266 , shown in  FIG. 4 , such as performed by the relevancy search engine  234 , shown in  FIG. 3 , in some embodiments. In this example, the operation  266  is performed for a selected term of asthma, to identify items within the health-related information  360  ( FIG. 8 ) relating to asthma. 
     The operation  266  involves the use of the diagnostic relationship data  246 , which defines the relationships between the terms in the internal medical terminology  244 . In some embodiments, the diagnostic relationship data  246  includes tables including a table  382 . 
     Once a selected term has been identified, the relevancy search engine  234  retrieves the table  382  from the diagnostic relationship data  246  associated with the selected term. For example, the table  382  associated with asthma (internal medical code 32881) is retrieved, as shown by label  384 . 
     Table  382  includes a list of 281 terms  388  (see count  386 ) that are related to the asthma term in the diagnostic relationship data  246 . The intelligent prompting column  387  identifies how strong the relationship is between the selected term (asthma) and the other listed terms  388 . An intelligent prompting value of 1 indicates that the listed term is one of the hallmark findings associated with asthma. An intelligent prompting value of 2 indicates a more detailed finding associated with asthma. A term that has no value listed in the intelligent prompting column is a term that a physician would not typically think of as a primary indicator for asthma, but is still considered to have some correlation (or even an inverse correlation) to asthma. 
     The listed terms  388  include term  390  for difficulty breathing, term  392  for recent difficulty breathing rapidly progression, term  394  for chronic difficulty breathing, a term  396  for recurrent episodes of acute difficulty breathing, etc. In some embodiments, table  382  includes the internal medical code for each term. For example, table  382  could include the internal medical code  353  for difficulty breathing. In yet another embodiment, the description of the term is replaced by the internal medical code. 
     The relevancy search engine  234  then utilizes the terms  388  to conduct a search of the health-related information  360  to find items in the health-related information  360  that match one of the listed terms  388 , and are therefore associated with asthma. An example is shown in  FIG. 10 . 
       FIG. 10  is a schematic diagram further illustrating a portion of operation  266 , shown in  FIG. 4 . The operation  266  utilizes the diagnostic relationship data  246 , including a table  382 , to search within the health-related information  360  for items associated with a selected term, such as asthma. 
     After identifying the table  382  associated with the selected term, the health-related information  360  (shown in  FIG. 8 ) is searched to determine if any of the listed terms  388  within the table  382  can be found. 
     In this example, the listed term  390  for difficulty breathing, is first considered. A search is conducted of the items in the health-related information  360 , and a matching item  306 ′ in list  366  is found. Accordingly, the relevancy search engine  234  ( FIG. 3 ) determines that item  306 ′ in the health-related information  360  is related to the selected term of asthma. 
     The process is then continued for each of the listed terms  388  in the table  382 , until all items within the health-related information  360  that are related to asthma are identified. The results are shown in  FIG. 11 . 
       FIG. 11  is a screen shot of an example user interface display  410  providing the results of the intelligent filtering performed by the intelligent filtering system  106 . In some embodiments, the display  410  is generated by the user interface engine  236 , for example. 
     User interface display  410  includes a selected term display  412  and a results display  414 . The selected term display  412  indicates that a search was conducted for the selected term. The results display  414  identifies all (or a subset) of the items within the health-related information  360  that are related to the selected term. 
     In this example, the selected term was asthma. Upon completion of the search for items relating to asthma, the results are generated and displayed in the results display  414 . The results include a list of 16 items that were located in the health-related information  360  that are related to the selected term (shown in selected term display  412 ) of asthma. In addition, the items in the results display  414  are sorted in some embodiments to permit the caregiver to more quickly review the results. In this example, the categories include symptoms, physical exam, assessment, tests, and therapy. The appropriate category is determined from the internal medical terminology  244 , which assigns one of these categories to each of the terms in the terminology. 
     The user interface display  410  permits the caregiver to view a subset of the health-related information that is relevant to the selected term, rather than requiring the caregiver to look through all of the health-related information  138 , such as shown in FIG.  5 . This display  410  provides only that information that is needed by the caregiver, and does not display items that are determined to have no relevance to the selected term. 
     If duplicate items are found in the health-related information  360 , the user interface display  410  can remove such duplicates, if desired. For example, results display  414  includes only a single item for CBC with differential, even though the item was listed in both the encounter note for asthma (list  362  in  FIG. 8 ) and the discharge summary (list  368  also in  FIG. 8 ). 
     In some embodiments, each item in the results display  414  is associated with a date. The date is assigned to the item according to the date of the health-related information from which it was retrieved. For example, the cough item was retrieved from the encounter note for asthma (list  362  in  FIG. 8 ), and is therefore assigned the date of that encounter. The date can be used for a variety of purposes. For example, the date can be displayed in the result display  414  for each item. As another example, the date can be used to display the results in a table format, where each column in the table represents a date of an encounter or other health-related event, and the rows include a list of internal medical terms that are present in the health-related information. A plus symbol represents a positive finding on that date, while a minus symbol represents a negative finding on that date. 
     In some embodiments terms are associated with an expiration period in the knowledge base  242 . If the date associated with an item in the health-related information was so long ago that the expiration period has now passed, the health-related information is not included in the result display  414 , in some embodiments. 
       FIG. 12  illustrates another example user interface display  420  including the results of another search conducted across the health-related information. The user interface display  420  includes selected term display  412  and results display  414 . 
     The previous example illustrated a search involving a search term that was already present in the patient&#39;s medical record. In other words, the health-related information  138  ( FIG. 1 ) already included an encounter note  140  for asthma (which contained the item  296  for Asthma, shown in  FIG. 5 ), and so it is not surprising that the patient&#39;s medical record contained items associated with asthma. 
     This example illustrates a search conducted for a term that is not present in the patient&#39;s health-related information. More specifically, mitral stenosis is chosen as the selected term in selected term display  412 . 
     Operation  266  ( FIG. 4 ) is then performed utilizing this selected term. For example, the table associated with mitral stenosis is retrieved from the diagnostic relationship data  246 , in the same way that table  382  was retrieved for asthma. The listed terms  388  are then identified, and a search is conducted of the health-related information  360  ( FIG. 8 ) for any of the listed items. 
     The results of the search are displayed in results display  414 . The results include four items, including difficulty breathing, accentuated p2 heart sounds, edema, and 2-d electrocardiography. 
     This illustrates how important information within the health-related information  138  can be quickly identified by a caregiver utilizing the intelligent filtering system  106 , even if the patient has never been evaluated for the condition identified by a selected term. 
     The various embodiments described above are provided by way of illustration only and should not be construed to limit the claims attached hereto. Those skilled in the art will readily recognize various modifications and changes that may be made without following the example embodiments and applications illustrated and described herein, and without departing from the true spirit and scope of the following claims.