Patent Publication Number: US-11037665-B2

Title: Generating medication orders from a clinical encounter

Description:
BACKGROUND 
     The present invention relates to computing systems, and more specifically, to computing systems which generate medication orders from clinical encounters. 
     Conventionally, medical professionals must spend time and resources to prescribe medications for patients. For example, for a prescription, a physician must write, call, and/or electronically provide an indication of the prescription to a pharmacy. Often, the physician may delegate the task to others. In any event, these manual operations waste time and resources, and may lead to errors. Reducing the time needed to prescribe a medication would increase the amount of time medical professionals have to interface with patients and increase the overall quality of patient care. 
     SUMMARY 
     According to one embodiment, a computer-implemented method comprises applying one or more natural language processing (NLP) algorithms to a text transcription of a medical encounter dialogue to determine a plurality of features of the dialogue, processing, by a machine learning (ML) model executing on a processor, the text transcription and the plurality of features of the dialogue to identify a plurality of candidate medications from a knowledge base corresponding to a first statement made by a medical professional during the medical encounter, and outputting an indication of the plurality of candidate medications for display. 
     In another embodiment, a system comprises a processor and a memory storing instructions, which when executed by the processor, performs an operation comprising applying one or more natural language processing (NLP) algorithms to a text transcription of a medical encounter dialogue to determine a plurality of features of the dialogue, processing, by a machine learning (ML) model, the text transcription and the plurality of features of the dialogue to identify a plurality of candidate medications from a knowledge base corresponding to a first statement made by a medical professional during the medical encounter, and outputting an indication of the plurality of candidate medications for display. 
     In another embodiment, a computer-readable storage medium has computer-readable program code embodied therewith, the computer-readable program code executable by a processor to perform an operation comprising applying one or more natural language processing (NLP) algorithms to a text transcription of a medical encounter dialogue to determine a plurality of features of the dialogue, processing, by a machine learning (ML) model, the text transcription and the plurality of features of the dialogue to identify a plurality of candidate medications from a knowledge base corresponding to a first statement made by a medical professional during the medical encounter, and outputting an indication of the plurality of candidate medications for display. 
    
    
     
       BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS 
         FIG. 1  illustrates a system configured to generate medication orders from a clinical encounter, according to one embodiment. 
         FIGS. 2A-2C  illustrate example text transcriptions generated based on a clinical encounter, according to one embodiment. 
         FIG. 3  is a flow chart illustrating a method to generate medication orders from a clinical encounter, according to one embodiment. 
         FIG. 4  is a flow chart illustrating a method to perform preprocessing, according to one embodiment. 
         FIG. 5  is a flow chart illustrating a method to determine historical data, according to one embodiment. 
         FIG. 6  is a flow chart illustrating a method to generate a text transcription and determine candidate medications, according to one embodiment. 
         FIG. 7  illustrates a system configured to generate medication orders from a clinical encounter, according to one embodiment. 
     
    
    
     DETAILED DESCRIPTION 
     Embodiments disclosed herein provide techniques to extract medication information from a transcript of a clinical encounter with higher accuracy than conventional approaches. Generally, embodiments disclosed herein leverage speech to text algorithms to transcribe an encounter between a medical professional (e.g., a physician, nurse practitioner, nurse, etc.) and a patient in real time. Embodiments disclosed herein then apply NLP and ML techniques to extract medication information, which can be presented to the medical professional as candidate medications. The medical professional may then select one or more candidate medications for the patient. In the case of medications requiring a prescription, embodiments disclosed herein may transmit an indication of the prescription to a pharmacy for fulfillment. 
       FIG. 1  illustrates an example system  100  configured to generate medication orders from a clinical encounter, according to one embodiment. As shown, the system  100  includes a computing system  101  and an example clinical environment  130 . The clinical environment  130  is representative of any type of patient care setting, such as a hospital, physician&#39;s office, in-home consultation, and the like. The clinical environment  130  illustratively includes a medical professional  111  and a patient  112 . An audio recording device  113  in the clinical environment  130  records audio of the encounter between the medical professional  111  and the patient  112 , and transmits the recorded audio  114  to the computing system  101  in real-time (e.g., via a network). 
     As shown, the computing system  101  includes a processing engine  102 , one or more ML algorithms  104 , one or more NLP algorithms  105 , and data stores of patient data  106 , history data  107 , medications  108 , transcriptions  109 , and ML models  110 . The patient data  106  stores information for a plurality of patients, such as biographical information, electronic medical records (e.g., reflecting medical conditions, procedures, etc.), and any other type of medical information. The history data  107  reflects past orders for medications  108  by medical professionals, which may include information indicating geographical and/or organizational preferences for certain types of medications  108 . The medications  108  is a knowledge base storing information describing any type of medication information, including without limitation, prescription medicines, over the counter medicine, home remedies, physical therapy orders, stretches, exercises, etc. The transcriptions  109  store annotated transcriptions generated based on audio recordings of clinical encounters between a medical professional and a patient. The ML models  110  store machine learning models reflecting a medical professional&#39;s speech patterns, prescription histories, and the like. In at least one embodiment, an ML model  110  for a given medical professional is generated based at least in part on the transcriptions  109  of the speech (or dialogue) of the medical professional. The training of the ML models  110  considers attributes of the speech and/or dialogue of each medical professional (e.g., grammatical features, semantic features, sentiment features, etc.), how the medical professional refers to medications (e.g., by brand name, generic name, etc.), prescription history, and the like. As such, the ML models  110  are used to generate one or more candidate medications from the medications  108  corresponding to a medication a medical professional is referring to in a recorded audio dialogue of a clinical encounter. 
     In at least one embodiment, a patient may schedule an appointment to visit a medical professional. In such embodiments, the processing engine  102  may reference the patient data  106  to determine the patient&#39;s clinical history, which may include a reason for the scheduled appointment, and any medications  108  the patient is currently taking and/or previously was taking. The processing engine  102  may leverage the medication history, patient history, and reason for the appointment to determine a first set of candidate medications  108  that may be prescribed by the medical professional. For example, if the patient&#39;s nausea is reason for the visit, the processing engine  102  may identify anti-nausea medications  108  as part of the first set of candidate medications. 
     At the time of the patient&#39;s encounter with a medical professional, the recording device  113  records the conversation, and transmits the audio recording  114  to the computing system  101 . The processing engine  102  then generates a transcription of the recorded audio  114  and extracts medication information therefrom. Generally, the processing engine  102  applies speech-to-text NLP algorithms  105  to generate a text transcription of the recorded audio  114 . The processing engine  102  may then apply additional NLP algorithms  105  and/or ML algorithms  104  to extract grammatical features of the dialogue (e.g., identify tone, identify sentiment, etc.), eliminate noise, identify references to medications, generate annotations, identify concepts, and extract candidate medications from the transcription. The processing engine  102  may store the annotated transcription (and any other extracted features and/or metadata) in the transcriptions  109 . Doing so allows the processing engine  102  to compare what a given medical professional stated during a conversation to what was actually done (e.g., a physician states that she will prescribe a generic class of medication, then prescribes medication N from the class as reflected in the patient data  106  and/or annotations to the transcription). 
     The ML algorithms  104  and/or NLP algorithms  105  may further reference the history data  107  to identify patterns in medication orders made by the medical professional (e.g., the medical professional often prescribes medication Y when patients have nausea). In at least one embodiment, the processing engine  102  adds the medications  108  that the medical professional most frequently recommends for the patient&#39;s condition as candidate medications. Doing so allows the processing engine  102  to generate more accurate candidate medication recommendations. 
     The processing engine  102  may further rank each statement in the transcript to identify their relative importance as determined by one or more of the ML algorithms  104 , NLP algorithms  105 , and/or ML models  110 . For example, if the transcript includes a single sentence where the medical professional mentions a prescription medication, the processing engine  102  may rank this sentence as the most important. The processing engine  102  may then provide the text transcription to the ML model  110  for the medical professional, which processes the text to identify intent, speech patterns, etc., in the medical professional&#39;s dialogue. The ML model  110  may then determine the meaning (e.g., context, tone, sentiment) of each statement made by the medical professional. For example, the ML model  110  may map the phrase “beta-blocker” made by the medical professional to “1 mg beta-blocker Y”, as the ML model  110  is tailored to the history of statements made by the medical professional (e.g., the medical professional frequently prescribes beta-blocker Y in 1 mg doses). 
     The processing engine  102 , ML algorithms  104 , NLP algorithms  105 , and/or ML model  110  may then return a final set of candidate medications from the medications  108  to the medical professional. The medical professional may then select one or more candidate medications  108 , and optionally provide feedback regarding the candidates. The feedback may be stored as metadata to the transcription  109  of the audio recording  114 . The ML models  110  may be retrained based on the feedback. Furthermore, for each candidate medication  108  selected by the medical professional, the processing engine  102  may transmit an indication via a network. For example, the processing engine  102  may transmit an indication of a prescribed medication  108  to a pharmacy for the patient, while transmitting an email to the patient describing physical therapy exercises for the patient to attempt at home. 
       FIG. 2A  illustrates an example text transcription  109  generated based on an audio recording  114  of a clinical encounter between a medical professional and a patient, according to one embodiment. The transcription  109  depicted in  FIG. 2A  is a speech-to-text transcription generated by the NLP algorithms  105 , where each word spoken during the encounter is transcribed into text. As previously stated, however, the transcription  109  is further processed by the ML algorithms  104 , NLP algorithms  105 , and/or ML model  110  to remove noise, rank statements, identify concepts, and the like. 
       FIG. 2B  illustrates an annotated text transcription  109 . As shown, the text transcription  109  has been annotated with statements of sentiment in brackets. At least some portions of the text of the transcription  109  have been modified to reflect the relative importance of each portion of text. For example, statements that are of little clinical value and do not provide details on the topic of conversation are italicized, while irrelevant statements (e.g., noise) are removed from the transcription  109  as reflected by the strikethrough. Statements that are of some relevance are ranked higher than the irrelevant statements and statements of little clinical value, and are reflected by standard font in  FIG. 2B . Statements that reference medications may receive the highest rank, and are bolded in  FIG. 2B . Of the highly ranked statements, the statements that reference medications  108  the patient is currently taking and/or have side effects related to the topic of conversation (e.g., patient&#39;s chief complaint) are the most highly ranked. 
       FIG. 2C  illustrates additional annotations to the text transcription  109 . As shown, the brackets now include any determined sentiment and/or medical conditions of the corresponding statements  210 - 213 . For example, the statement  210  in the transcription  109  of  FIG. 2C  includes an annotation reflecting that the doctor was joking, and was not being serious when discussing “medicine Y”. This indicates to the ML algorithms  104 , NLP algorithms  105 , and/or ML models  110  that “medicine Y” is a very low candidate medication. Similarly, when processed by the ML model  110  for the medical professional, this statement will receive little consideration as the ML model  110  specifies that the medical professional has no history of prescribing medicine Y for watching sports, and does not use the phrase “handing out [medicine name]” when referencing a prescription the medical professional is about to prescribe. 
     For the statement  211 , the ML model  110  may identify “let&#39;s try increasing the dose” as a common way the medical professional writes a new prescription for an existing medication  108  the patient is currently taking. Furthermore, in statement  211 , the statement “I&#39;d rather have you on a higher dose of the beta blockers than putting you on an ACE” may be used by the NLP algorithms  105  and/or ML models  110  to determine intent to increase a dosage of an existing medication. Furthermore, the NLP algorithms  105  and/or ML models  110  may determine that “ACE” is not a class of medications, while determining that the statement actually refers to angiotensin-converting-enzyme inhibitors (“ACEI”), which is a known class of medication. Therefore, the NLP algorithms  105  and/or ML models  110  may eliminate “ACEI” as a candidate medication  108  by identifying the concept of “ACEI” based on the statement that included “ACE”. For the statement  212 , the ML algorithms  104 , NLP algorithms  105 , and/or ML models  110  may identify one or more concepts that correspond to instructions for taking medications. Furthermore, because the concepts in statement  212  are in the same conversation context, the ML algorithms  104 , NLP algorithms  105 , and/or ML models  110  may associate the instructions with the prescription. For the statement  213 , the ML algorithms  104 , NLP algorithms  105 , and/or ML models  110  may identify the term “prescription” to indicate that a prescription should exist. Therefore, the candidate medications  108  are returned to the medical professional via a computing device. 
     In at least one embodiment, each candidate medication  108  is scored, and ranked according to the score. For example, the “beta blocker” the patient is currently taking may be ranked based on the dosage (e.g., higher doses may be scored/ranked higher than the current dose and/or lesser doses). Other types of beta blockers (e.g. different brands of beta blocker, beta blockers used for other purposes, etc.) may be ranked lower than at least one of the beta blocker the patient is currently taking. The medical professional may then select the appropriate medication, and a prescription identifying the medical professional, the patient, the medication, dose, route (e.g., oral, intravenous, etc.), and any other instructions may be transmitted to a pharmacy for fulfillment. The transcriptions  109  depicted in  FIGS. 2A, 2B , and/or  2 C may be stored in the transcriptions  109  with an indication of the medication selected by the medical professional. Similarly, the patient data  106  may be updated to reflect the new prescription. 
       FIG. 3  is a flow chart illustrating a method  300  to generate medication orders from a clinical encounter, according to one embodiment. As shown, the method  300  begins at block  310 , where the processing engine  102  receives an indication of a clinical encounter between a patient and a medical professional. The indication may reflect a date and time of the clinical encounter, identifiers of the associated patient and medical professional, and one or more complaints and/or problems of the patient. For example, the patient may have a chief complaint of chest pains. At block  320 , described in greater detail with reference to  FIG. 3 , the processing engine  102  optionally performs preprocessing prior to the clinical encounter. The preprocessing may include determining the patient&#39;s medical history, medications  108  the patient is currently taking, and any candidate medications  108  that may be prescribed for the current complaints and/or problems of the patient. For example, for the patient having the complaint of chest pains, the preprocessing may determine that the patient has a history of diabetes and hypertension, and takes medications  108  to treat each condition. The preprocessing may identify the diabetes and hypertension medications  108  as candidate medications. 
     At block  330 , the audio recording device  113  captures an audio recording  114  of the dialogue of a clinical encounter between the medical professional and the patient. At block  340 , described in greater detail with reference to  FIG. 5 , the processing engine  102  determines any historical data, which may be related to the medical professional, the patient, the patient&#39;s conditions, and the like. For example, the processing engine  102  may determine how the medical professional speaks when he is prescribing medications, and the most frequently prescribed hypertension and/or diabetes medicines the medical professional prescribes. At block  350 , described in greater detail with reference to  FIG. 6 , the processing engine  102  generates a text transcription of the audio recording  114  and determines one or more candidate medications  108 . Generally, the processing engine  102  applies ML algorithms  104 , NLP algorithms  105 , and/or the ML models  110  to generate an annotated text transcription  109  and one or more candidate medications  108  based on the statements in the annotated text transcription  109 . For example, the processing engine  102  may return a higher dose of hypertension medicine and a lower dose of the diabetes medicine as the candidate medications  108 . 
     At block  360 , the processing engine  102  outputs the candidate medications  108  for display to the medical professional. At block  370 , the medical professional provides input selecting a first candidate medication  108 , and the processing engine  102  generates a prescription for the first candidate medication. The processing engine  102  may then transmit the prescription to a pharmacy and/or the user. At block  380 , the processing engine  102  optionally receives feedback from the medical professional. For example, the feedback may exclude certain medications  108  from further consideration, annotate other medications  108  as preferred medications, and the like. At block  390 , the ML models  110  are optionally updated based on the selection of the first prescription, the transcription  109 , and any feedback provided by the medical professional. 
       FIG. 4  is a flow chart illustrating a method  400  corresponding to block  320  to perform preprocessing, according to one embodiment. As shown, the method  400  begins at block  410 , where the processing engine  102  receives the patient data  106  for the patient. At block  420 , the processing engine  102  determines whether an indication of the current complaint is in the patient data  106 . For example, the patient complaining of chest pains may have a history of chest pains as reflected in the patient data  106 . At block  430 , the processing engine  102  may extract medications  108  that the patient is currently and/or previously has taken from the patient data  106 , and extract medications  108  that are most closely related to the patient&#39;s complaints (e.g., chest pains). The related medications  108  may be determined based on metadata associated with each medication  108 , medical literature, or any other means. At block  440 , the processing engine  102  may filter the medications  108  extracted at block  430  based on any number of factors. For example, the processing engine  102  may filter the medications based on known adverse side effects, contraindications, known patient allergies, etc. At block  450 , the processing engine  102  returns one or medications  108  as candidate medications for the patient prior to the clinical encounter. 
       FIG. 5  is a flow chart illustrating a method  500  corresponding to block  340  to determine historical data, according to one embodiment. As shown, the method  500  begins at block  510 , where the processing engine  102  receives an ML model  110  describing the current medical professional. At block  520 , the processing engine  102  determines speech patterns of the medical professional based on the ML model  110 . For example, the ML model  110  may specify how the medical professional frequently refers to medications, the medical professional&#39;s speech mannerisms, etc. At block  530 , the processing engine  102  determines patterns derived for the medical professional specified in the ML model  110  (and/or extracted from the history data). For example, the medical professional may frequently prescribe generic medications. The processing engine  102  may further determine any patterns in the geographic location and/or organization associated with the medical professional. At block  540 , the processing engine  102  determines the most frequently prescribed medications for the patient&#39;s condition. For example, an electronic medical dictionary may specify which medications are most frequently prescribed for chest pains. At block  550 , the processing engine  102  maps each patient complaint to one or more diagnoses and prescriptions. 
       FIG. 6  is a flow chart illustrating a method  600  corresponding to block  350  to generate a text transcription and determine candidate medications, according to one embodiment. As shown, the method  600  begins at block  610 , where the processing engine  102  applies one or more NLP algorithms  105  to the audio recording  114  to generate a text transcription. At block  620 , the processing engine  102  applies one or more ML algorithms  104 , NLP algorithms  105 , and or ML models  110  to remove noise from the transcription (e.g., text including concepts that are not related to the patient&#39;s medical issues). By removing noise, the processing engine  102  refrains from processing irrelevant statements when generating candidate medications. At block  630 , the processing engine  102  applies one or more ML algorithms  104 , NLP algorithms  105 , and or ML models  110  to determine tone, sentiment, medical concepts, and/or other grammatical features in the text transcription. At block  640 , the processing engine  102  annotates the text transcription with indications of the tone, sentiment, medical concepts, and/or other features determined at block  630 . At block  650 , the text transcription is processed by one or more ML models  110  to determine the intent of the medical professional. For example, the ML model  110  may determine that the medical professional&#39;s statement that “you need new pills” means the medical professional will prescribe new medication and/or a different brand of an existing medication. 
     At block  660 , the ML models  110  may compute a score for each fragment (e.g., sentence) of the text transcription. The scores may be computed based on whether the sentence includes medical language, refers to medication and/or prescriptions, and the like. At block  670 , the ML models  110  generate at least one candidate medication from the medications  108  based on the text transcription and the intent determined at block  650 . For example, based on the patient&#39;s complaint, patient history, prescription patterns, and analysis of the text transcription, the ML models  110  may return a ranked list of blood pressure medication for the patient having chest pains. The processing engine  102  may further receive medications  108  related to the candidate medications  108  generated at block  670 , e.g., generics, different doses, different brand names, etc. At block  680 , the processing engine  102  returns the candidate medications to the medical professional for review. 
       FIG. 7  illustrates a system  700  configured to generate medication orders from a clinical encounter, according to one embodiment. The networked system  700  includes a computing system  101 . The computing system  101  may also be connected to other computers via a network  730 . In general, the network  730  may be a telecommunications network and/or a wide area network (WAN). In a particular embodiment, the network  730  is the Internet. 
     The computing system  101  generally includes a processor  704  which obtains instructions and data via a bus  720  from a memory  706  and/or a storage  708 . The computing system  101  may also include one or more network interface devices  718 , input devices  722 , and output devices  724  connected to the bus  720 . The computing system  101  is generally under the control of an operating system (not shown). Examples of operating systems include the UNIX operating system, versions of the Microsoft Windows operating system, and distributions of the Linux operating system. (UNIX is a registered trademark of The Open Group in the United States and other countries. Microsoft and Windows are trademarks of Microsoft Corporation in the United States, other countries, or both. Linux is a registered trademark of Linus Torvalds in the United States, other countries, or both.) More generally, any operating system supporting the functions disclosed herein may be used. The processor  704  is a programmable logic device that performs instruction, logic, and mathematical processing, and may be representative of one or more CPUs. The network interface device  718  may be any type of network communications device allowing the computing system  101  to communicate with other computers via the network  730 . 
     The storage  708  is representative of hard-disk drives, solid state drives, flash memory devices, optical media and the like. Generally, the storage  708  stores application programs and data for use by the computing system  101 . In addition, the memory  706  and the storage  708  may be considered to include memory physically located elsewhere; for example, on another computer coupled to the computing system  101  via the bus  720 . 
     The input device  722  may be any device for providing input to the computing system  101 . For example, a keyboard and/or a mouse may be used. The input device  722  represents a wide variety of input devices, including keyboards, mice, controllers, and so on. Furthermore, the input device  722  may include a set of buttons, switches or other physical device mechanisms for controlling the computing system  101 . The output device  724  may include output devices such as monitors, touch screen displays, and so on. 
     As shown, the memory  706  contains the processing engine  102 , ML algorithms  104 , and NLP algorithms  105 , each described in greater detail above. The storage  708  contains the patient data  106 , history data  107 , medications  108 , transcriptions  109 , and ML models  110 , each described in greater detail above. As shown, a medical professional system  750  includes a GUI  751  for outputting candidate medications returned by the processing engine  102 , and for receiving selection of candidate medications from the medical professional. Once selected for a prescription, an indication of a prescription for the medication may be transmitted to a prescription interface  761  of a pharmacy system  760 . 
     The descriptions of the various embodiments of the present invention have been presented for purposes of illustration, but are not intended to be exhaustive or limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein was chosen to best explain the principles of the embodiments, the practical application or technical improvement over technologies found in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein. 
     In the foregoing, reference is made to embodiments presented in this disclosure. However, the scope of the present disclosure is not limited to specific described embodiments. Instead, any combination of the recited features and elements, whether related to different embodiments or not, is contemplated to implement and practice contemplated embodiments. Furthermore, although embodiments disclosed herein may achieve advantages over other possible solutions or over the prior art, whether or not a particular advantage is achieved by a given embodiment is not limiting of the scope of the present disclosure. Thus, the recited aspects, features, embodiments and advantages are merely illustrative and are not considered elements or limitations of the appended claims except where explicitly recited in a claim(s). Likewise, reference to “the invention” shall not be construed as a generalization of any inventive subject matter disclosed herein and shall not be considered to be an element or limitation of the appended claims except where explicitly recited in a claim(s). 
     Aspects of the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment (including firmware, resident software, micro-code, etc.) or an embodiment combining software and hardware aspects that may all generally be referred to herein as a “circuit,” “module” or “system.” 
     The present invention may be a system, a method, and/or a computer program product. The computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present invention. 
     The computer readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device. The computer readable storage medium may be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. A non-exhaustive list of more specific examples of the computer readable storage medium includes the following: a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanically encoded device such as punch-cards or raised structures in a groove having instructions recorded thereon, and any suitable combination of the foregoing. A computer readable storage medium, as used herein, is not to be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiber-optic cable), or electrical signals transmitted through a wire. 
     Computer readable program instructions described herein can be downloaded to respective computing/processing devices from a computer readable storage medium or to an external computer or external storage device via a network, for example, the Internet, a local area network, a wide area network and/or a wireless network. The network may comprise copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. A network adapter card or network interface in each computing/processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium within the respective computing/processing device. 
     Computer readable program instructions for carrying out operations of the present invention may be assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, or either source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C++ or the like, and conventional procedural programming languages, such as the “C” programming language or similar programming languages. The computer readable program instructions may execute entirely on the user&#39;s computer, partly on the user&#39;s computer, as a stand-alone software package, partly on the user&#39;s computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user&#39;s computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider). In some embodiments, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate arrays (FPGA), or programmable logic arrays (PLA) may execute the computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform aspects of the present invention. 
     Aspects of the present invention are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer readable program instructions. 
     These computer readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer readable program instructions may also be stored in a computer readable storage medium that can direct a computer, a programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer readable storage medium having instructions stored therein comprises an article of manufacture including instructions which implement aspects of the function/act specified in the flowchart and/or block diagram block or blocks. 
     The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other device to produce a computer implemented process, such that the instructions which execute on the computer, other programmable apparatus, or other device implement the functions/acts specified in the flowchart and/or block diagram block or blocks. 
     The flowchart and block diagrams in the Figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts or carry out combinations of special purpose hardware and computer instructions. 
     Embodiments of the invention may be provided to end users through a cloud computing infrastructure. Cloud computing generally refers to the provision of scalable computing resources as a service over a network. More formally, cloud computing may be defined as a computing capability that provides an abstraction between the computing resource and its underlying technical architecture (e.g., servers, storage, networks), enabling convenient, on-demand network access to a shared pool of configurable computing resources that can be rapidly provisioned and released with minimal management effort or service provider interaction. Thus, cloud computing allows a user to access virtual computing resources (e.g., storage, data, applications, and even complete virtualized computing systems) in “the cloud,” without regard for the underlying physical systems (or locations of those systems) used to provide the computing resources. 
     Typically, cloud computing resources are provided to a user on a pay-per-use basis, where users are charged only for the computing resources actually used (e.g. an amount of storage space consumed by a user or a number of virtualized systems instantiated by the user). A user can access any of the resources that reside in the cloud at any time, and from anywhere across the Internet. In context of the present invention, a user may access applications or related data available in the cloud. For example, the processing engine  102  could execute on a computing system in the cloud and generate candidate medication prescriptions for a patient. In such a case, the processing engine  102  could store the text transcriptions  109  at a storage location in the cloud. Doing so allows a user to access this information from any computing system attached to a network connected to the cloud (e.g., the Internet). 
     While the foregoing is directed to embodiments of the present invention, other and further embodiments of the invention may be devised without departing from the basic scope thereof, and the scope thereof is determined by the claims that follow.