Patent Application
20150278297
Embodiments of the invention relate generally to data processing and more particularly relate to a method, apparatus and computer program product for extending data models for usage by applications and providing results of calculations that the applications may access.
Currently, analytics platforms typically require performant extensions of data models without requiring new software releases. At present, many applications require complex query operations which may compute values based on data from many sources, evaluate complex predicates, or require intensive computations. These requirements may make queries of these applications prohibitively expensive to perform during the normal operation of the applications.
For example, consider a query that relates to determining what age bracket a user such as, for example, a patient is within. In this example, the query may ask whether the age of the patient is between 18 and 25, 26 to 35, or 36 to 50. Presume further that several applications (e.g., 50 applications) request this query to be determined such that the age bracket of patients is calculated. In this regard, it may be time consuming and inefficient to process the requests of multiple applications at the same time and determine the age bracket of several patients in a health care system.
In view of the foregoing drawbacks, there may be a need for an efficient and reliable mechanism of providing an extensible framework that efficiently extends data models and provides results of calculations accessible by applications.
A method, apparatus and computer program product are therefore provided that may provide an efficient and reliable mechanism of extending data models of applications and calculates results that are accessible by applications. In this regard, an example embodiment may calculate results of queries and provide the calculated results to a database (e.g., a speed table of a database) for access by one or more requesting applications or requesting communication devices.
In this regard, an example embodiment may provide an extensible framework for creating and maintaining one or more tables that hold or store the results of calculations corresponding to one or more queries or derivations needed by applications. As such, the framework provided by an example embodiment may provide a reusable parameterized package (e.g., a reusable Extract Transform and Load (ETL) package) describing calculations without requiring a user (e.g., a developer(s)) or application(s) to be concerned about how or when the calculations are executed. The framework of an example embodiment may ensure that the calculations are properly executed per the desires of the user (e.g., a developer(s), an administrator(s), etc.).
In one example embodiment, a method for extending a data model is provided. The method may include providing information to a queue in response to a detection of an indication of new data associated with a user or an encounter or detection of modified data associated with the user or the encounter. The method may further include identifying one or more predefined patterns based in part on analyzing the information in the queue. The information may include content of the new data or the modified data. The patterns may include defined instructions specifying at least one calculation for execution based in part on usage of the content of the new data or the modified data. The method may further include enabling provision of one or more results of the calculation to a database to enable access of the results by one or more requesting applications or one or more requesting communication devices in response to executing at least one of the patterns and determining the results of the calculation.
In another example embodiment, an apparatus for extending a data model is provided. The apparatus may include a processor and a memory including computer program code. The memory and the computer program code are configured to, with the processor, cause the apparatus to at least perform operations including providing information to a queue in response to a detection of an indication of new data associated with a user or an encounter or detection of modified data associated with the user or the encounter. The memory and the computer program code may further cause the apparatus to identify one or more predefined patterns based in part on analyzing the information in the queue. The information may include content of the new data or the modified data. The patterns may include defined instructions specifying at least one calculation for execution based in part on usage of the content of the new data or the modified data. The memory and the computer program code may further cause the apparatus to enable provision of one or more results of the calculation to a database to enable access of the results by one or more requesting applications or one or more requesting communication devices in response to executing at least one of the patterns and determining the results of the calculation.
In another example embodiment, a computer program product for extending a data model is provided. The computer program product includes at least one computer-readable storage medium having computer-executable program code instructions stored therein. The computer executable program code instructions may include program code instructions configured to provide information to a queue in response to a detection of an indication of new data associated with a user or an encounter or detection of modified data associated with the user or the encounter. The program code instructions may also identify one or more predefined patterns based in part on analyzing the information in the queue. The information may include content of the new data or the modified data. The patterns may include defined instructions specifying at least one calculation for execution based in part on usage of the content of the new data or the modified data. The program code instructions may also enable provision of one or more results of the calculation to a database to enable access of the results by one or more requesting applications or one or more requesting communication devices in response to executing at least one of the patterns and determining the results of the calculation.
Having thus described the invention in general terms, reference will now be made to the accompanying drawings, which are not necessarily drawn to scale, and wherein:
Some embodiments of the present invention will now be described more fully hereinafter with reference to the accompanying drawings, in which some, but not all embodiments of the invention are shown. Indeed, various embodiments of the invention may be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Like reference numerals refer to like elements throughout. As used herein, the terms “data,” “content,” “information” and similar terms may be used interchangeably to refer to data capable of being transmitted, received and/or stored in accordance with embodiments of the invention. Moreover, the term “exemplary”, as used herein, is not provided to convey any qualitative assessment, but instead merely to convey an illustration of an example. Thus, use of any such terms should not be taken to limit the spirit and scope of embodiments of the invention.
As defined herein a “computer-readable storage medium,” which refers to a non-transitory, physical or tangible storage medium (e.g., volatile or non-volatile memory device), may be differentiated from a “computer-readable transmission medium,” which refers to an electromagnetic signal.
General System Architecture
Referring now to
In one exemplary embodiment, the computing devices 100, 105, 110, 115, 117 and 120 may be utilized by users such as, for example, clinicians (e.g., physicians, nurses, pharmacists, psychologists, social workers, physical therapists, laboratory technicians, etc.) and/or any other suitable health care professionals. The computing devices 100, 105, 110, 115, 117, 120 may be maintained by one or more health care institutions. For instance, the computing device 100 may be maintained by a medical entity 1 (e.g., a medical clinic), the computing device 105 may be maintained by a pharmacy 3, the computing device 110 may be maintained by a laboratory 5. In addition, the computing device 115 may be maintained by a medical entity 7 (e.g., a hospital), the computing device 117 may be maintained by a health care facility 9 (e.g., a psychotherapy entity (e.g., a psychiatric office, an office of a social worker, etc.) and the computing device 120 may be maintained by the pharmacy 11. In an exemplary embodiment, the communication device 125 may be maintained by a health care institution 14. The communication device 125 may be utilized by one or more users (e.g., clinicians).
The communication device 125 may communicate with the computing devices 100, 105, 110, 115, 117, and 120. In this regard, the communication device 125 may receive one or more requests or queries from computing devices 100, 105, 110, 115, 117, and 120 or applications (e.g., health applications, billing applications, etc.) of computing devices 100, 105, 110, 115, 117, and 120. The requests/queries may include data requesting one or more calculations. The communication device 125 may also receive information ( ) from and may transmit medical information to the computing devices 100, 105, 110, 115, 117, and 120.
It should be pointed out that although
Referring now to
For example, the memory 86 may store content transmitted from, and/or received by, the computing devices 100, 105, 110, 115, 117 and 120. In this regard, in an exemplary embodiment, the memory 86 may store data received from various disparate sources. For example, the memory 86 may store medical information received by the communication device 125 from the computing devices of the medical entity 1, the pharmacy 3, the laboratory 5, the medical entity 7, the health care facility 9 and the pharmacy 11, etc. The medical information may include, but is not limited to, prescriptions, visits to a health care facility, medications, medical diagnoses, laboratory results, medical tests or measurements, medical chart information, medical imaging information (e.g., magnetic resonance imaging (MRI) of the human body), alert/notification data and any other suitable information.
The medical information received by the communication device 125 from the computing devices 100, 105, 110, 115, 117, 120 may include one or more patient identifiers of respective patients. For example, medical record numbers (MRNs) may be utilized as patient identifiers to identify respective patients. In addition, or alternatively, patient demographic data (e.g., biographical data (e.g., name, date of birth, etc.), race, age, gender, etc.) may be utilized to identify one or more patients. These patient identifiers and patient demographic data may be stored in the memory 86 (e.g., in a history table 83 of the memory 86).
Additionally, for example, the memory 86 typically stores client applications, instructions, patterns, keys, algorithms or the like for execution by the processor 70 to perform steps associated with operation of the communication device 125 in accordance with embodiments of the invention. The patterns may include logic (e.g., software code (e.g., computer code/instructions)) to perform derivations or calculations when executed (e.g., by a processor (e.g., processor 70) and/or a speed table service module (e.g., speed table service module 78)) and may include metadata corresponding to the logic, as described more fully below. As explained below, for example, the memory 86 may store one or more client applications such as, for example, software (e.g., software code also referred to herein as computer code). The memory 86 may include a queue (e.g., signal queue 81), a history table 83 and a speed table database 85 that includes one or more speed tables 87. As data (e.g., source data) changes, the materializer manager 75 may generate signals that are provided to the signal queue 81 (e.g., a Structured Query Language (SQL) signal queue) which may be read by the speed table service module, as described more fully below.
The one or more speed tables 87 (also referred to herein as one or more speed table entities 87 or as speed table(s) 87) may be the destination for data generated by the speed table service module 78 and any application(s) may read from the one or more speed tables 87. However, the speed table service module 78 may write to the one or more speed tables 87. In an example embodiment, the speed table service module 78 may manage the properties and/or functions of the one or more speed tables 87, as described more fully below. The history table 83 may store data (e.g., medical data) associated with one or more patients (e.g., lab results of corresponding patients, number of visits to a health care facility, etc.). In addition, the history table 83 may store one or more patient identifiers corresponding to the patients. In some example embodiments, the patient identifiers may be analyzed by the speed table service module 78 to merge data (e.g., medical data) corresponding to a patient, as described more fully below.
The speed table service module 78 may poll (e.g., periodically polls) the signal queue 81 which the materializer manager 75 may write data (e.g., newly received patient data (e.g., new lab results, new information indicating visits to a health care facility)) to and as the data is read from the signal queue 81, the speed table service module 78 may delete the read data from the signal queue 81. However, the speed table service module 78 may include the read data in an application (app) storage 73 (also referred to herein as local app storage 73 or app storage 73) of a memory device 71, as described more fully below.
The processor 70 may be embodied in a variety of ways. For instance, the processor 70 may be embodied as a controller, coprocessor, microprocessor of other processing devices including integrated circuits such as, for example, an application specific integrated circuit (ASIC), a field programmable gate array (FPGA). In an exemplary embodiment, the processor may execute instructions stored in the memory 86 or otherwise accessible to the processor 70.
The communication device 125 may include one or more logic elements for performing various functions of one or more client applications. In an exemplary embodiment, the communication device 125 may execute the client applications. The logic elements performing the functions of one or more client applications may be embodied in an integrated circuit assembly including one or more integrated circuits (e.g., an ASIC, FPGA or the like) integral or otherwise in communication with a respective network entity (e.g., computing system, client, server, etc.) or more particularly, for example, a processor 70 of the respective network entity.
In addition to the memory 86, the processor 70 may also be connected to at least one interface or other means for displaying, transmitting and/or receiving data, content or the like. The interface(s) can include at least one communication interface 88 or other means for transmitting and/or receiving data, content or the like. In this regard, the communication interface 88 may include, for example, an antenna and supporting hardware and/or software for enabling communications with a wireless communication network. For example, the communication interface(s) may include a first communication interface for connecting to a first network, and a second communication interface for connecting to a second network. In this regard, the communication device is capable of communicating with other devices such as, for example, computing devices 100, 105, 110, 115, 117, 120 over one or more networks (e.g., network 140) such as a Local Area Network (LAN), wireless LAN (WLAN), Wide Area Network (WAN), Wireless Wide Area Network (WWAN), the Internet, or the like. Alternatively, the communication interface may support a wired connection with the respective network.
In addition to the communication interface(s), the interface(s) may also include at least one user interface that may include one or more earphones and/or speakers, a display 80, and/or a user input interface 82. The user input interface, in turn, may comprise any of a number of devices allowing the entity to receive data from a user, such as a microphone, a keypad, keyboard, a touch display, a joystick, image capture device, pointing device (e.g., mouse), stylus or other input device.
In an exemplary embodiment, the processor 70 may be in communication with and may otherwise control the materializer manager 75. The materializer manager 75 may be any means such as a device or circuitry operating in accordance with software or otherwise embodied in hardware or a combination of hardware and software thereby configuring the device or circuitry (e.g., a processor, controller, microprocessor or the like) to perform the corresponding functions of the materializer manager 75, as described below. In examples in which software is employed, a device or circuitry (e.g., processor 70 in one example) executing the software forms the structure associated with such means. As such, for example, the materializer manager 75 may be configured to, among other things, to detect one or more modifications of data and/or newly received data (e.g., received from one or more of computing devices 100, 105, 110, 115, 117, 120), stored in a database (e.g., database 86), corresponding to one or more patients. In response to detecting one or more items of modified data (e.g., medical data) or new data corresponding to a patient(s), the materializer manager 75 may write or provide an indication of the modified data or new data (e.g., new lab results, an updated number of visits to a health care facility) to a signal queue (e.g., signal queue 81) for detection by the speed table service module 78, as described more fully below.
Additionally, in an example embodiment, the processor 70 may be in communication with and may otherwise control the speed table service module 78. The speed table service module 78 may be any means such as a device or circuitry operating in accordance with software or otherwise embodied in hardware or a combination of hardware and software thereby configuring the device or circuitry (e.g., a processor, controller, microprocessor or the like) to perform the corresponding functions of the speed table service module 78, as described below. In examples in which software is employed, a device or circuitry (e.g., processor 70 in one example) executing the software forms the structure associated with such means. As such, for example, the speed table service module 78 may be configured to, among other things, periodically poll the signal queue 81 indicating the modified data or new data corresponding to one or more patients (e.g., Jane Doe, a fictitious person as referred to herein), written/provided to the signal queue 81 by the materializer manager 75.
In an example embodiment, the speed table service module 78 may perform one or more calculations (e.g., calculate all visits of a patient to a health care facility, etc.) defined by a pattern (e.g., logic (e.g., defined computer program code for performing the calculation(s)) associated with a corresponding key, as described more fully below. The speed table service module 78 may provide the results of the calculation(s) to the speed table(s) 87 of the speed table database 85 to enable one or more requesting applications (e.g., health care applications, billing applications, etc.) or devices (e.g., computing devices 100, 105, 110, 115, 117, 120) hosting the applications to access/obtain the results of the calculations, as described more fully below.
Referring now to
The memory device 36 may store medical information (e.g., medical diagnoses, laboratory results, medications, prescriptions, medical visit information, etc.) associated with one or more patients. The medical information may include one or more patient identifiers (e.g., MRNs) identifying respective patients (e.g., Jane Doe, a fictitious patient) and/or biographic data.
In an instance in which medical information (e.g., new medical data, updates and/or changes to medical data) of one or more of the patients is sent to the communication device 125, by the processor 34, the materializer manager 75 of the communication device 125 may detect a patient identifier(s) (e.g., a MRN(s), biographic data, etc.) to identify respective patients and corresponding medical data. In this manner, the materializer manager 75 may detect data received from various sources/entities (e.g., medical entities, pharmacies, laboratories health care facilities, etc.) pertaining to one or more patients.
The processor 34 may be connected to at least one communication interface 38 or other means for displaying, transmitting and/or receiving data, content, information or the like. In this regard, the communication interface 38 may be capable of connecting to one or more networks. The computing device may also include at least one user input interface 32 that may include one or more speakers, a display 30, and/or any other suitable devices. For instance, the user input interface 32 may include any of a number of devices allowing the computing device to receive data from a user, such as a keyboard, a keypad, mouse, a microphone, a touch screen display or any other input device. In an example embodiment, in response to being executed by processor 34 one or more applications (e.g., health care applications, billing applications, etc.), stored in the memory 36, may send a request(s) for one or more results of calculations to the communication device 125, as described more fully below.
Exemplary embodiments of the invention may provide an efficient and reliable mechanism for extending data models of applications. In this regard, an exemplary embodiment may calculate results of queries or other derivations (e.g., defined by one or more patterns) and provide the calculated results to a table (e.g., a speed table) of a database (e.g., a speed table database) for access (e.g., fast access) by one or more requesting applications. As such, an exemplary embodiment may enable multiple applications to access or obtain the calculated results from the table (e.g., speed table(s) 87) based on calculations that were previously performed in response to detecting data (e.g., newly received medical data, updates/changes to medical data (e.g., changes to existing medical data)) associated with one or more patients, as described more fully below.
In this manner, an example embodiment may provide an extensible framework for creating and maintaining one or more tables that hold or store the results of calculations in response to one or more queries for access by one or more requesting applications or requesting communication devices. As such, the framework provided by an example embodiment may enable developers to provide a reusable package (e.g., a reusable ETL package) describing calculations of the queries without requiring a user (e.g., a developer(s)) to be concerned about the manner in which the calculations are executed. The framework of an example embodiment may ensure that the calculations are properly executed per the desires of a user (e.g., a developer(s), an administrator(s), etc.).
By utilizing an example embodiment, extensible analytical models may be driven by content. For instance, an example embodiment may enable a data model to grow based upon determined calculations (e.g., number of visits by a patient to a health care facility, new lab results for a patient). The data model may be extended automatically in order to incorporate a new element(s) of information (e.g., a result for a new type of lab test). In this regard, the data model may include one or more tables which include rows and columns of information, wherein each column represents a new type of calculation, and each row represents one instance of each of the calculations which corresponds to a specific patient or encounter or other keyed value. For example, in an instance in which results of calculations are determined by the speed table service module 78, the results of the calculations may be output to a memory (e.g., a speed table(s) 87 of a speed table database 85) and as such the results of these calculations may be new items of information that may be added to the data model without altering or destroying the underlying data model. In this regard, new items of information may be added to the data model automatically and as such the data model may be extended.
Furthermore, generalized analytical computations driven by content may be obtained by utilizing an example embodiment. For instance, calculations may be automatically determined by the speed table service module 78 in an instance in which new or updated data is detected (e.g., detection of another visit to a health care facility by patient Jane Doe) corresponding to a patient or an encounter that is relevant to one or more queries (e.g., a query specifying calculation of a count or number of all visits by patients to the health care facility) received from an application(s) or a device (e.g., computing device 115).
Moreover, by utilizing an example embodiment, normalization of computation of similar derivations is provided. In an example embodiment, normalization of computation of similar derivations may involve a calculation defined to be performed but which is parameterized such that the calculation may be performed in many different ways. For purposes of illustration and not of limitation, suppose patient Jane Doe visits a hospital and a user (e.g., a developer) desires a calculation of patient Jane Doe's latest blood test(s). Suppose further that patient Jane Doe recently had a blood test and a user (e.g., a developer, etc.) always wants to know the most recent results of the blood test. In this regard, a calculation may be defined, via processor 70, to calculate the latest blood test results of patient Jane Doe. Moreover, the calculation may be defined such that the calculation is driven by content specifying that the latest glucose, the latest white blood cell count, the latest blood gases results, etc. are determined. In this example, a generalized normalization calculation/computation may be defined as latest lab results that are parameterized and driven by specific contents of lab results such as, for example, latest glucose, the latest white blood cell count, and/or latest blood gases results.
In an example embodiment, the speed table service module 78 may execute one or more defined patterns in response to detecting information written to the signal queue 81 by the materializer manager 75. The patterns may include all the logic (e.g., computer code, computer instructions) to perform a derivation(s) or calculation(s) (e.g., “LatestResults” (e.g., calculate the latest lab results of a patient (e.g., Jane Doe)) designated to be performed. The patterns may also include metadata about the calculation(s). The metadata may include, but is not limited to: (1) a unique identifier that is constant across environments; (2) a name; (3) a version number; (4) a subject entity (e.g., an object that the result of the calculation(s) is about (e.g., a person or an encounter) (e.g., in an instance in which the calculation is about a person, the subject entity may be “Person” or in an instance in which the calculation is about a particular visit or encounter, the subject entity may be “Encounter”, or in the case when the calculation is about any other entity this other entity may be specified as the subject entity)); (5) a set of source entities the pattern(s) uses to perform the calculation(s) (e.g., the result of the most recent lab test may make use of the Encounter, Result, and/or Order entities to perform the calculation) where the source entities are related to the subject entity by means of a foreign key relationship; (6) a set of parameters; (7) a set of outputs; (8) a target speed table entity; (9) an algorithm (e.g., logic (e.g., computer code, computer instructions)) for performing the calculation(s); or (10) any other suitable metadata. The patterns may be stored in in a database (e.g., database 86) of a health care system (e.g., health care system 4). As referred to herein, an entity, or source entity may be defined as a collection of information related to a single concept and may be embodied as a table(s) of information in a database wherein each instance(s) (or row(s)) of such related information includes multiple properties (or columns) and the instance(s) may be referenced by a unique key.
By indicating the source entities in the metadata of a pattern(s), the speed table service module 78 may analyze the indications of the source entities to determine where the source data is stored (e.g., database 86) for performing a calculation(s) and to enable the materializer manager 75 to track changes in the source data and perform the calculation(s) again at an appropriate time.
The speed table service module 78 may also analyze a key(s) associated with a pattern(s). For example, a key(s) may be an instance(s) of a pattern(s) applied to a specific situation. In this regard, the speed table service module 78 may enable implementation by using a general use pattern and make the general use pattern specific for a given set of data and/or a given set of desired outputs. In this manner, a normalized pattern/algorithm (e.g., latest lab results) may be applied across specific data (e.g., latest glucose results, latest white blood cell count results, the latest blood gases results).
In an example embodiment, keys may serve as callers of corresponding patterns. For instance, the speed table service module 78 may use a key to call or retrieve a corresponding pattern for execution. An example of a key for a “LatestResults” pattern may be “LatestHBA1C”. Each key may include, but is not limited to, a unique identifier that is constant across environments, a name, a set of parameter values for the key corresponding to inputs for a pattern(s), a status flag, a status reason comment, or any other suitable data. The keys may be stored, in memory (e.g., database 86), as separate metadata related to a corresponding pattern object. In an example embodiment, although the keys may be stored as separate metadata from the corresponding pattern object, the keys may be linked to corresponding patterns.
The parameter values of the key may alter the execution of a corresponding pattern for each key. For example, in an instance in which a key is created for latest glucose results, an International Statistical Classification of Diseases (ICD)-9 code for a glucose test may be included in the parameter values. In this example, in an instance in which the speed table service module 78 may execute the corresponding pattern, the speed table service module 78 may detect glucose values.
The speed table service module 78 may provide generated data (e.g., calculated results) to one or more speed table entities (e.g., speed table(s) 87). In other words, the speed table entities may be the destination for data generated by the speed table service module 78. In an example embodiment, any application(s) may read/retrieve data from the speed table entities (e.g., speed table(s) 87) but the application(s) may be unable to provide or write data to the speed table entities. Instead, the speed table service module 78 may be able to write or provide data to the speed table entities. The speed table service module 78 may handle the management and functions (e.g., administrative functions) of the speed table entities (e.g., speed table(s) 87).
In an example embodiment, the speed table entities may serve as the location in which data is added to a data model (e.g., which extends the data model). In other words, for a created data model, new properties, and/or new items of data may be included in a speed table entity (e.g., speed table(s) 87). For example, when a pattern is initially created one of the items of metadata of the pattern is specified as a target speed table entity, as described above. Suppose, for example, that a new pattern “Latest Results” is being defined for the first time. In this regard, in an instance in which corresponding keys are added/linked to the latest results pattern such as, for example, a key for glucose, a key for white blood cell counts and/or a key for blood gases, the new pattern “Latest Results” may begin to have new properties. In this manner, the latest glucose value, the latest glucose observation date and time, the latest glucose unit, the latest white blood cell count value, the latest white blood cell count units, etc. may be included in a speed table entity (e.g., speed table(s) 87) and added to a data model for the pattern “Latest Results” which may be read or accessed by any application(s) (e.g., a health care application(s), a billing application(s), etc.).
The speed table service module 78 may poll (e.g., periodically poll) a queue (e.g., a SQL queue (e.g., signal queue 81)) in which the materializer manager 75 may write or provide data. As data is read or accessed from the queue (e.g., signal queue 81), the speed table service module 78 may delete the read/accessed data from the queue and the speed table service module 78 may provide the data read/accessed from the queue to a local storage (e.g., application (app) storage 73) of a memory 71 until the data is successfully processed.
As changes are made to source data (e.g., stored in memory 86), the materializer manager 75 may detect the changes to the source data (also referred to herein as changed sets of data) and may generate one or more signals which are provided to a queue (e.g., SQL queue (e.g., signal queue 81)). As described above, the speed table service module 78 may read the data (e.g., changed sets of data) from the queue (e.g., signal queue 81). In this regard, in response to detecting that the source data is being modified or updated based on reading the data (e.g., changed sets of data) from the queue, the speed table service module 78 may perform a calculation(s) of a corresponding pattern associated with a key(s) for a corresponding patient(s) or encounter(s) in an instance in which the pattern depends on the source data. For example, in an instance in which a calculation has been defined to pre-calculate the latest Glucose lab test result, whenever the speed table service module 78 is notified that a new lab result has arrived, a calculation may be performed to determine whether the result is for a Glucose test and if the result is for a Glucose test, the latest value previously stored may need to be updated. The calculation(s) may be performed/determined by the speed table service module 78 based in part on the parameter(s) associated with the key(s).
For purposes of illustration and not of limitation consider an example in which a patient such as, for example, Jane Smith (e.g., a fictitious patient) has been in a health care facility such as, for example, a hospital several times. Since patient Jane Smith has been in the hospital several times, the speed table service module 78 may have previously detected data about patient Jane Smith. In this example, presume that patient Jane Smith is currently admitted to the emergency room of the hospital and that patient Jane Smith is unconscious. As such, data for a new patient such as Jane Doe (e.g., a placeholder patient name) may be entered and stored (e.g., stored in a memory (e.g., memory 86)) in a health care system (e.g., health care system 4) since health care professionals (e.g., nurses, doctors) by convention use this name to indicate she is an unknown patient. However, the patient is presently unconscious and is unable to confirm her identity.
Presume further that the health care professionals (e.g., doctors, nurses) do not know what is wrong with patient Jane Doe and as such the health care professionals begin performing some lab results on her to obtain some lab results. In response to the materializer manager 75 detecting that Jane Doe is a new patient, the materializer manager 75 may send a signal(s) indicating detection of new patient Jane Doe to a queue (e.g., signal queue 81).
In response to polling the queue (e.g., signal queue 81), the speed table service module 78 may detect the signal(s) provided to the queue by the materializer manager 75 and may specify that the data relates to a new visit and/or a common encounter. As such, the speed table service module 78 may run/execute an admission statistics package or a calculation for a common encounter pattern to update data for new patient Jane Doe in this example. In response to running/executing the admission statistics package, the speed table service module 78 may identify one or more patterns that are concerned about common encounter data and that are specified to calculate admission statistics.
In this regard, the speed table service module 78 may calculate the number of visits over the course of the year for Jane Doe which is one new patient visit in this example. The speed table service module 78 may write or provide the results of the calculation pertaining to the number of visits to the speed table(s) 87 of a speed table database 85.
Subsequently, the materializer manager 75 may write or provide, via a new signal(s), the lab results indicating, for example, glucose and white blood cells count and/or blood gases to the queue (e.g., signal queue 81). In this regard, the signal(s) provided to the queue (e.g., signal queue 81) by the materializer manager 75 may indicate that the data is common results and that the data is for Jane Doe. In an example embodiment, in response to reading the data from the queue (e.g., signal queue 81), the speed table service module 78 may delete the data from the queue and include the read/accessed data in a local storage (e.g., app storage 73 of memory device 71) until the data is successfully processed.
Additionally, in response to detecting the signal(s) in the queue (e.g., signal queue 81), the speed table service module 78 may send a notification to a latest results package for patterns specifying information about common results and in this regard the speed table service module 78 may calculate the latest blood glucose, the latest white blood cell count, etc. based on the lab results for Jane Doe. As such, the speed table service module 78 may write or provide the results (e.g., values) of the calculation(s) of the common results to speed table(s) 87.
In this example, presume further that the patient referred to as Jane Doe wakes up the next morning from being admitted to the emergency room of the hospital and informs the health care professionals (e.g., doctors, nurses) that her name is Jane Smith. In response to correcting the name of the patient to Jane Smith, the health care professionals, for example, may determine that there are prior entries related to this same patient, Jane Smith, in a database (e.g., memory 86) of the health care system (e.g., health care system 4) and may instruct the health care system to perform a merge operation. As such, the processor 70 and/or the speed table service module 78 may receive a message indicating that the processor 70 and/or the speed table service module 78 should merge the set of data associated with Jane Doe with the set of data stored for Jane Smith. Since Jane Smith has visited this hospital multiple times in the past, prior data associated with Jane Smith may be stored in the history table 83 of a memory (e.g., memory 86). The history table 83 may store patient identifiers of all patients in which the health care system (e.g., health care system 4) stores data.
The speed table service module 78 may poll (e.g., periodically poll) the history table 83 to detect changes. In this regard, the speed table service module 78 may detect the changes associated with the merged data which now corresponds to patient Jane Smith. As such, the speed table service module 78 may determine that recalculation of data associated with Jane Smith may be required since new data (e.g., data pertaining to Jane Doe) was merged with the stored data for Jane Smith.
As such, the speed table service module 78 may run multiple patterns and perform various corresponding calculations across multiple source entities since Jane Smith may have data across multiple source entities and not just data for common results or new visit data as was the case for patient Jane Doe. In this manner, the speed table service module 78 may calculate all of Jane Smith's visits in the last year and may calculate all of her latest lab results as well as perform other calculations since Jane Smith may have data across various source entities. In this regard, the speed table service 78 may respond appropriately to patient(s) merge events.
The speed table service module 78 may provide the results of the calculations pertaining to the merged data across multiple source entities to the speed table(s) 87 of the speed table database 85. Once the results of the calculations are provided to the speed table(s) 87, one or more applications (e.g., 1 application, 10 applications, 100 applications, etc.) may read or access the calculated results and may utilize the results without performing the calculations themselves since the results are already calculated by the speed table service module 78. In this regard, the speed table service module 78 may enhance efficiencies and perform calculations faster as data associated with a patient(s) or encounter(s) is received and detected.
Referring now to
At operation 410, the apparatus (e.g., communication device 125) may enable provision of one or more results of the calculation to a database (e.g., speed table database 85) or a table (e.g., speed table(s) 87) of the database (e.g., speed table database 85) to enable access of the results by one or more requesting applications (e.g., health care applications, billing applications, etc.) or one or more requesting communication devices (e.g., computing devices 100, 105, 110, 115, 117 and/or 120) in response to executing at least one of the patterns and determining the results of the calculation.
It should be pointed out that
Accordingly, blocks or steps of the flowchart support combinations of means for performing the specified functions and combinations of steps for performing the specified functions. It will also be understood that one or more blocks or steps of the flowchart, and combinations of blocks or steps in the flowchart, can be implemented by special purpose hardware-based computer systems which perform the specified functions or steps, or combinations of special purpose hardware and computer instructions.
In an exemplary embodiment, an apparatus for performing the methods of
Many modifications and other embodiments of the inventions set forth herein will come to mind to one skilled in the art to which these inventions pertain having the benefit of the teachings presented in the foregoing descriptions and the associated drawings. Therefore, it is to be understood that the inventions are not to be limited to the specific embodiments disclosed and that modifications and other embodiments are intended to be included within the scope of the appended claims. Moreover, although the foregoing descriptions and the associated drawings describe exemplary embodiments in the context of certain exemplary combinations of elements and/or functions, it should be appreciated that different combinations of elements and/or functions may be provided by alternative embodiments without departing from the scope of the appended claims. In this regard, for example, different combinations of elements and/or functions than those explicitly described above are also contemplated as may be set forth in some of the appended claims. Although specific terms are employed herein, they are used in a generic and descriptive sense only and not for purposes of limitation.
