CROSS-REFERENCE TO RELATED APPLICATIONS AND PRIORITY
[001] The present application does not claim priority from any patent application.
TECHNICAL FIELD
[002] The present disclosure in general relates to the field of pharmacy and healthcare. More particularly, the present subject matter relates to a system and a method for enabling a fulfilment of a prescription.
BACKGROUND
[003] Generally, organization that provides durable medical equipment, drugs for patients, or customers does go through the prescriptions provided by the doctors or hospitals. These handwritten, typed and mixed of both prescriptions appear in various formats like pdf and images files from the hospitals or the doctors, and are difficult to comprehend to a human, due to image quality and handwriting clarity. Moreover, these prescriptions are received by the organization through multiple channels like email, fax, scanned fax and electronic data exchange.
[004] Conventional prescriptions processing systems are manual and have no to little capabilities to handle prescriptions and converting them into digital data. The manual steps in the prescriptions processing have a cost implication for the organization as well as lead delays for the patients looking for medical device or drugs. Moreover, the conventional are not robust.
SUMMARY
[005] Before the present a system and a method for enabling a fulfilment of a prescription, are described, it is to be understood that this application is not limited to the particular system, systems, and methodologies described, as there can be multiple possible embodiments, which are not expressly illustrated in the present disclosures. It is also to be understood that the terminology used in the description is for the purpose of describing the particular implementations, versions, or embodiments only, and is not intended to limit the scope of the present application. This summary is provided to introduce aspects related to a system and a method for enabling a fulfilment of a prescription. This summary is not intended to identify essential features of the claimed subject matter nor is it intended for use in determining or limiting the scope of the claimed subject matter.
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[006] In one embodiment, a method for enabling a fulfilment of a prescription is disclosed. In the embodiment, the method may comprise receiving a prescription from one or more sources. In one example, the prescription may be one of a handwritten note, a typed communication, and a combination of both and the one or more sources may be an email, a fax, a text message, an electronic data exchange and a scan copy. Further to receiving, the method may comprises identifying one or more of a medicine or a medical device based on analysis of the prescription using machine learning technique, and natural language processing and classifying the one or more medicine or the medical device into one or more categories based on a type of the one or more medicine or a medical device, a severity of an ailment associated with the one or more medicine or medical device, a profile of a patient associated with the prescription and an urgency associated with the one or more medicine or a medical device. Upon classifying, the method may comprise identifying a fulfilment agency from a set of fulfilment agencies with the availability of the one or more medicine or a medical device in the prescription based on and the category and the distance between the fulfilment agency and the patient and placing an order based on the category with the fulfilment agency for delivery of the one or more medicine or the medical device to the patient, thereby enabling a fulfilment of a prescription.
[007] In another embodiment, a system for enabling a fulfilment of a prescription is disclosed. The system comprises a memory and a processor coupled to the memory, further the processor may be configured to execute programmed instructions stored in the memory. In one embodiment, the system may receive a prescription from one or more sources. In one example, the prescription may be one of a handwritten note, a typed communication, and a combination of both and the one or more sources may be an email, a fax, a text message, an electronic data exchange and a scan copy. Upon receiving the system may identify one or more of a medicine or a medical device based on analysis of the prescription using machine learning technique, and natural language processing and classify the one or more medicine or the medical device into one or more categories based on a type of the one or more medicine or a medical device, a severity of an ailment associated with the one or more medicine or medical device, a profile of a patient associated with the prescription and an urgency associated with the one or more medicine or a medical device. Further to classifying, the system may identify a fulfilment agency from a set of fulfilment agencies with the availability of the one or more medicine or a medical device in the prescription based on and the category and the distance between the fulfilment agency and the patient and place an order based on the category with the fulfilment
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agency for delivery of the one or more medicine or the medical device to the patient, thereby enabling a fulfilment of a prescription.
BRIEF DESCRIPTION OF DRAWINGS
[008] The foregoing detailed description of embodiments is better understood when read in conjunction with the appended drawings. For the purpose of illustrating of the present subject matter, an example of construction of the present subject matter is provided as figures; however, the present subject matter is not limited to the specific method and system disclosed in the document and the figures.
[009] The present subject matter is described detail with reference to the accompanying figures. In the figures, the left-most digit(s) of a reference number identifies the figure in which the reference number first appears. The same numbers are used throughout the drawings to refer various features of the present subject matter.
[0010] Figure 1 illustrates multiple embodiments of a network implementation of a system for enabling a fulfilment of a prescription, in accordance with an embodiment of the present subject matter.
[0011] Figure 2 illustrates the system for enabling a fulfilment of a prescription, in accordance with an embodiment of the present subject matter.
[0012] Figure 3 illustrates a method for enabling a fulfilment of a prescription, in accordance with an embodiment of the present subject matter.
DETAILED DESCRIPTION
[0013] Some embodiments of this disclosure, illustrating all its features, will now be discussed in detail. The words "comprising," "having," "containing," and "including," and other forms thereof, are intended to be open ended in that an item or items following any one of these words is not meant to be an exhaustive listing of such item or items, or meant to be limited to only the listed item or items. It must also be noted that as used herein and in the appended claims, the singular forms "a," "an," and "the" include plural references unless the context clearly dictates otherwise. Although any a system and a method for enabling a fulfilment of a prescription, similar or equivalent to those described herein can be used in the practice or
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testing of embodiments of the present disclosure, the exemplary, a system and a method for enabling a fulfilment of a prescription are now described.
[0014] Various modifications to the embodiment will be readily apparent to those skilled in the art and the generic principles herein may be applied to other embodiments for enabling a fulfilment of a prescription. However, one of ordinary skill in the art will readily recognize that the present disclosure for enabling a fulfilment of a prescription is not intended to be limited to the embodiments described, but is to be accorded the widest scope consistent with the principles and features described herein.
[0015] The present subject matter relates to a system and method for enabling a fulfilment of a prescription. In another embodiment, a prescription may be received from one or more sources. In one example, the prescription may be one of a handwritten note, a typed communication, a combination of both and the one or more sources may be an email, a fax, a text message, an electronic data exchange and a scan copy. Upon receiving, one or more of a medicine or a medical device may be identified based on analysis of the prescription using machine learning technique, and natural language processing and the one or more medicine or the medical device may be classified into one or more categories based on a type of the one or more medicine or a medical device, a severity of an ailment associated with the one or more medicine or medical device, a profile of a patient associated with the prescription and an urgency associated with the one or more medicine or a medical device. Further to classification, a fulfilment agency may be identified from a set of fulfilment agencies with the availability of the one or more medicine or a medical device in the prescription based on and the category and the distance between the fulfilment agency and the patient, and an order may be placed based on the category with the fulfilment agency for delivery of the one or more medicine or the medical device to the patient, thereby enabling a fulfilment of a prescription.
[0016] Referring now to Figure 1, multiple embodiment of a network implementation 100 of a system 102 for enabling a fulfilment of a prescription is disclosed. Although the present subject matter is explained considering that the system 102 is implemented on a server 110, it may be understood that the system 102 may also be implemented in a variety of computing systems, such as a laptop computer, a desktop computer, a notebook, a workstation, a mainframe computer, a server, a network server, and the like. In one implementation, the system 102 may be implemented in a cloud-based environment. It will be understood that multiple users may access the system 102 through one or more user device or applications
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residing on the user device 104-1… 104-N, herein after individually or collectively referred to as 104. Examples of the user device 104 may include, but are not limited to, a portable computer, a personal digital assistant, a handheld system, and a workstation. The device 104 may be communicatively coupled to a server 110 through a network 106.
[0017] In one implementation, the network 106 may be a wireless network, a wired network or a combination thereof. The network 106 may be implemented as one of the different types of networks, such as intranet, local area network (LAN), wide area network (WAN), the internet, and the like. The network 106 may be either a dedicated network or a shared network. The shared network represents an association of the different types of networks that use a variety of protocols, for example, Hypertext Transfer Protocol (HTTP), Hypertext Transfer Protocol Secure (HTTPS), Secure File Transfer Protocol (SFTP), Transmission Control Protocol/Internet Protocol (TCP/IP), Wireless Application Protocol (WAP), and the like, to communicate with one another. Further, the network 106 may include a variety of network systems, including routers, bridges, servers, computing systems, storage systems, and the like.
[0018] In the embodiment, a system 102 for enabling a fulfilment of a prescription is disclosed. In the said embodiment, the system 120 may receive a prescription from one or more sources. In one example, the prescription may be one of a handwritten note, a typed communication, a combination of both and the one or more sources may be an email, a fax, a text message, an electronic data exchange and a scan copy from the device 104. Upon receiving the system 102 may identify one or more of a medicine or a medical device based on analysis of the prescription using machine learning technique, and natural language processing and classify the one or more medicine or the medical device into one or more categories based on a type of the one or more medicine or a medical device, a severity of an ailment associated with the one or more medicine or medical device, a profile of a patient associated with the prescription and an urgency associated with the one or more medicine or a medical device. Further to classifying, the system 102 may identify a fulfilment agency from a set of fulfilment agencies with the availability of the one or more medicine or a medical device in the prescription based on and the category and the distance between the fulfilment agency and the patient and place an order based on the category with the fulfilment agency for delivery of the one or more medicine or the medical device to the patient, thereby enabling a fulfilment of a prescription.
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[0019] Referring now to figure 2, the system 102 for enabling a fulfilment of a prescription is illustrated in accordance with an embodiment of the present subject matter. The system 102 may include at least one processor 202, an input/output (I/O) interface 204, and a memory 206. The at least one processor 202 may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, logic circuitries, and/or any systems that manipulate signals based on operational instructions. Among other capabilities, at least one processor 202 may be configured to fetch and execute computer-readable instructions stored in the memory 206.
[0020] The I/O interface 204 may include a variety of software and hardware interfaces, for example, a web interface, a graphical user interface, and the like. The I/O interface 204 may allow the system 102 to interact with the user directly or through the user device 104. Further, the I/O interface 204 may enable the system 102 to communicate with other computing systems, such as web servers and external data servers (not shown). The I/O interface 204 may facilitate multiple communications within a wide variety of networks and protocol types, including wired networks, for example, LAN, cable, etc., and wireless networks, such as WLAN, cellular, or satellite. The I/O interface 204 may include one or more ports for connecting a number of systems to one another or to another server.
[0021] The memory 206 may include any computer-readable medium known in the art including, for example, volatile memory, such as static random access memory (SRAM) and dynamic random access memory (DRAM), and/or non-volatile memory, such as read only memory (ROM), erasable programmable ROM, flash memories, hard disks, optical disks, and magnetic tapes. The memory 206 may include modules 208 and data 210.
[0022] The modules 208 may include routines, programs, objects, components, data structures, and the like, which perform particular tasks, functions or implement particular abstract data types. In one implementation, the module 208 may include a receiving module 212, a primary identification module 214, a classification assistant module 216, a secondary identification module 218, an order module 220 and other modules 224. The other modules 224 may include programs or coded instructions that supplement applications and functions of the system 102.
[0023] The data 210, amongst other things, serve as a repository for storing data processed, received, and generated by one or more of the modules 208. The data 210 may also include a
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system data 226, and other data 228. In one embodiment, the other data 228 may include data generated as a result of the execution of one or more modules in the other module 224.
[0024] In one implementation, a user may access the system 102 via the I/O interface 204. The user may be registered using the I/O interface 204 in order to use the system 102. In one aspect, the user may access the I/O interface 204 of the system 102 for obtaining information, providing inputs or configuring the system 102.
[0025] In one implementation, the receiving module 212 may receive a prescription from one or more sources. In one example, the prescription may be a handwritten note, a typed communication, a combination both and the one or more sources may be an email, a fax, a text message, an electronic data exchange and a scan copy. Further, the receiving module 212 may store the received prescription in the system data 226.
[0026] Upon receiving the prescription, the primary identification module 214, may identifying one or more of a medicine or a medical device based on analysis of the prescription using machine learning technique, and natural language processing. In one example, the machine learning technique may be a convolutional deep neural network or a deep recurrent neural network is applied to process the prescription file which contains handwritten, typed or mixed of both text and extract the raw data written in prescription, further natural language processing or recurrent neural networks are applied to extract the key information such as medicine name, medical device name, patient profile etc. healthcare ontology is applied on top of this to correct the out and identify the urgency of a particular medicine or medical device prescribed by the doctor or hospital. Further, the primary identification module 214 may store the details of the identified a medicine or a medical device in the system data 226 for order fulfilment.
[0027] Further to identification of the one or more medicine or medical device, the classification module 216, may compute type of the one or more medicine or medical device, a severity of an ailment associated with the one or more medicine or medical device, and an urgency associated with the one or more medicine or a medical device based on an analysis of the prescription and a healthcare database. In one example, the analysis may comprises comparison of the identified data with a stored data in the healthcare database. In one other example healthcare databased comprises data associated with one or more ailments, medicines,
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and medical devices, and data associated with the patient. Further, the classification module 216 may store all the computed data in the system data 226.
[0028] Subsequent to computation, the classification module 216 may classify the one or more medicine or the medical device into one or more categories. Further the classification module 216 may classify based on a type of the one or more medicine or a medical device, the severity of an ailment associated with the one or more medicine or medical device, the profile of a patient associated with the prescription and the urgency associated with the one or more medicine or a medical device. In one example, the categories may comprise high medium and low or very urgent, urgent, and regular. Further, the classification module 216 may store all the classification data in the system data 226.
[0029] After to classification, the secondary identification module 218 may identify a fulfilment agency from a set of fulfilment agencies with the availability of the one or more medicine or a medical device in the prescription. Further the secondary identification module 218 may identify based on and the category and the distance between the fulfilment agency and the patient. Furthermore, the secondary identification module 218 store the identified data in system data 226.
[0030] Finally, upon identification of the fulfilment centre, the order module 220 may place an order, with the fulfilment agency for delivery of the one or more medicine or the medical device to the patient, based on the category, thereby enabling a fulfilment of a prescription.
[0031] Exemplary embodiments for enabling a fulfilment of a prescription discussed above may provide certain advantages. Though not required to practice aspects of the disclosure, these advantages may include those provided by the following features.
[0032] Some embodiments of the system and the method enable automation of prescription processing, analysis and order placement.
[0033] Some embodiments of the system and the method enable faster services on medical equipment and drugs.
[0034] Some embodiments of the system and the method enables accurate and efficient prescription processing.
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[0035] Some embodiments of the system and the method enables reduction in manual error, manual effort and cost of processing.
[0036] Referring now to figure 3, a method 300 for enabling a fulfilment of a prescription, is disclosed in accordance with an embodiment of the present subject matter. The method 300 for enabling a fulfilment of a prescription may be described in the general context of device executable instructions. Generally, device executable instructions can include routines, programs, objects, components, data structures, procedures, modules, functions, and the like, that perform particular functions or implement particular abstract data types. The method 300 for enabling a fulfilment of a prescription may also be practiced in a distributed computing environment where functions are performed by remote processing systems that are linked through a communications network. In a distributed computing environment, computer executable instructions may be located in both local and remote computer storage media, including memory storage systems.
[0037] The order in which the method 300 for enabling a fulfilment of a prescription is described is not intended to be construed as a limitation, and any number of the described method blocks can be combined in any order to implement the method 300 or alternate methods. Additionally, individual blocks may be deleted from the method 300 without departing from the spirit and scope of the subject matter described herein. Furthermore, the method 300 can be implemented in any suitable hardware, software, firmware, or combination thereof. However, for ease of explanation, in the embodiments described below, the method 300 for enabling a fulfilment of a prescription may be considered to be implemented in the above-described system 102.
[0038] At block 302, a prescription may be received from one or more sources. In one example, the prescription may be one of a handwritten note, a typed communication, a combination of the handwritten note and typed communication and the one or more sources may be an email, a fax, a text message, an electronic data exchange and a scan copy. In one embodiment, the receiving module 212 may receive a prescription from one or more sources. Further, the receiving module 212 may store the prescription in the system data 226.
[0039] At block 304, one or more of a medicine or a medical device may be identified based on analysis of the prescription using machine learning technique, and natural language processing. In one embodiment, the primary identification module 214 may identify one or
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more of a medicine or a medical device based on analysis of the prescription using machine learning technique, and natural language processing. Further, the primary identification module 214 may store the one or more of a medicine or a medical device in the system data 226.
[0040] At block 306, the one or more medicine or the medical device may be classified into one or more categories based on a type of the one or more medicine or a medical device, a severity of an ailment associated with the one or more medicine or medical device, a profile of a patient associated with the prescription and an urgency associated with the one or more medicine or a medical device. In one embodiment, the classification module 216 may classify the one or more medicine or the medical device into one or more categories. Further, the classification module 216 may store the classified categories in the system data 226.
[0041] At block 308, a fulfilment agency may be identified from a set of fulfilment agencies with the availability of the one or more medicine or a medical device in the prescription based on the category and the distance between the fulfilment agency and the patient. In one embodiment, the secondary identification module 218 may identify a fulfilment agency and store the identification in the system data 226.
[0042] At block 310, an order may be placed on the category with the fulfilment agency for delivery of the one or more medicine or the medical device to the patient, thereby enabling a fulfilment of a prescription. In one embodiment, the order module 220 may place an order for delivery of the one or more medicine or the medical device to the patient and store the order in the system data 226.
[0043] Although implementations for methods and systems for enabling a fulfilment of a prescription have been described in language specific to structural features and/or methods, it is to be understood that the appended claims are not necessarily limited to the specific features or methods for enabling a fulfilment of a prescription described. Rather, the specific features and methods are disclosed as examples of implementations for enabling a fulfilment of a prescription.

WE CLAIM:
1. A method for enabling a fulfilment of a prescription, the method comprises steps of:
receiving, by a processor, a prescription from one or more sources, wherein the prescription is one of a handwritten note, a typed communication, and a combination, wherein the one or more sources is an email, a fax, a text message, an electronic data exchange and a scan copy;
identifying, by the processor, one or more of a medicine or a medical device based on analysis of the prescription using machine learning technique, and natural language processing;
classifying, by the processor, the one or more medicine or the medical device into one or more categories based on a type of the one or more medicine or a medical device, a severity of an ailment associated with the one or more medicine or medical device, a profile of a patient associated with the prescription and an urgency associated with the one or more medicine or a medical device;
identifying, by the processor, a fulfilment agency from a set of fulfilment agencies with the availability of the one or more medicine or a medical device in the prescription based on the category and the distance between the fulfilment agency and the patient; and
placing, by the processor, an order, for delivery of the one or more medicine or the medical device to the patient, based on the category with the fulfilment agency, thereby enabling a fulfilment of the prescription.
2. The method of claim 1, further comprising computing type of the one or more medicine or medical device, a severity of an ailment associated with the one or more medicine or medical device, a profile of the patient and an urgency associated with the one or more medicine or a medical device based on an analysis of the prescription and a healthcare database.
3. The method of claim 1, wherein the categories comprises high medium and low.
4. The method of claim 1, wherein the machine learning technique is one of a convolutional deep neural network or a deep recurrent neural network.
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5. The method of claim 2, wherein the healthcare databased comprises data associated with one or more ailments, medicines, and medical devices, and data associated with the patient.
6. A system for enabling a fulfilment of a prescription, the system comprising:
a memory; and
a processor coupled to the memory, wherein the processor is configured to:
receiving a prescription from one or more sources, wherein the prescription is one of a handwritten note, a typed communication, and a combination, wherein the one or more sources is an email, a fax, a text message, an electronic data exchange and a scan copy;
identifying one or more of a medicine or a medical device based on analysis of the prescription using machine learning technique, and natural language processing;
classifying the one or more medicine or the medical device into one or more categories based on a type of the one or more medicine or a medical device, a severity of an ailment associated with the one or more medicine or medical device, a profile of a patient associated with the prescription and an urgency associated with the one or more medicine or a medical device;
identifying a fulfilment agency from a set of fulfilment agencies with the availability of the one or more medicine or a medical device in the prescription based on the category and the distance between the fulfilment agency and the patient; and
placing an order, based on the category, with the fulfilment agency for delivery of the one or more medicine or the medical device to the patient, thereby enabling a fulfilment of a prescription.
7. The system of claim 6 further comprising computing type of the one or more medicine or medical device, a severity of an ailment associated with the one or more medicine or medical device, a profile of the patient and an urgency associated with the one or more medicine or a medical device based on an analysis of the prescription and a healthcare database.
8. The system of claim 6, wherein the categories comprises high, medium and low.
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9. The system of claim 6, wherein the machine learning technique is one of a convolutional deep neural network or a deep recurrent neural network.
10. The system of claim 7, wherein the healthcare databased comprises data associated with one or more ailments, medicines, and medical devices, and data associated with the patient.