Patent Publication Number: US-2017364640-A1

Title: Machine learning algorithm to automate healthcare communications using nlg

Description:
CROSS-REFERENCE TO PRIOR APPLICATIONS 
     This application claims benefit of U.S. Provisional Patent Application No. 62/350,851, filed on 16 Jun. 2016. These applications are hereby incorporated by reference herein. 
    
    
     BACKGROUND OF THE INVENTION 
     Physicians spend a significant amount of time manually writing or dictating clinical notes regarding diagnoses, laboratory results, examination notes, and prescribed treatments. Many physicians, nurses, and caregivers manually cut and paste notes from other charts to streamline the process. Carelessness and demanding workloads can lead to errors that may lead to potentially incoherent, incompatible, and even inapplicable notes. 
     To improve the efficiency of medical note writing, solutions have been proposed which would automate the document generation process. Typically, these automated document generation systems incorporate algorithms that analyze a patient&#39;s medical record and generate a narrative text. For instance, U.S. Patent Application No. 2013/0304507 describes clinical note generator which enables creation, storage, and editing of a narrative based on patient measures. One problem with such a system is that in the event that the notes of the narrative text are inconsistent with the intent of the writer, much of the auto-generated text would have to be deleted and rewritten. So despite the promise of natural language processing, such systems, in practice, often leave the writer with the same problem they are faced with when not using a natural language generator and end up having to rewrite much of the text. Accordingly, what is needed is a system and method which overcomes one or more of the deficiencies in the art. 
     BRIEF SUMMARY OF THE INVENTION 
     The present invention relates to a system having a server for executing an analysis algorithm for generating an electronic narrative document and a user device for modifying the generated electronic narrative document to suggest text based on user input. 
     The present invention also relates to a method for automated analysis of medical records. The method of the present invention comprises executing an analysis algorithm to analyze the medical records and to generate an electronic narrative document. After generating an electronic narrative document, it is stored in the patient database. The stored electronic narrative document is then retrieved and viewed by a user. This document is to be modified by the user, wherein the analysis algorithm predicts and suggests input during the modification step based on the patient&#39;s medical records. The electronic narrative document is then updated in the patient database. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The accompanying drawings, which are included to provide a further understanding of the invention, are incorporated herein to illustrate embodiments of the invention. Along with the description, they also serve to explain the principle of the invention. In the drawings: 
         FIG. 1  illustrates a system for analyzing patient medical record according to the preferred embodiment of the present invention. 
         FIG. 2  illustrates a flowchart for analyzing patient medical record according to the preferred embodiment of the present invention. 
         FIG. 3  illustrates an electronic document showing the initial diagnosis generated by the medical analysis algorithm. 
         FIG. 4  illustrates an alternative electronic document showing the medication suggestions based on the initial diagnosis. 
         FIG. 5  illustrates an alternative electronic document showing the suggested corrections in spelling. 
     
    
    
     DETAILED DESCRIPTION OF THE VARIOUS EMBODIMENTS OF THE INVENTION 
     The following are definitions of terms as used in the various embodiments of the present invention. 
     The term “natural language” refers to some type of language that approximates or is more or less recognizable as human-based language. 
     The term “Natural Language Generation” or “NLG” as used herein refers to a system or process for automatically generating narrative texts from computer accessible data. 
     The term “patient medical record” as used herein refers to a print or electronic document that includes information or data relating to a patient&#39;s age, weight, symptoms, diagnosis, laboratory tests, vital signs, prescribed medications, care plan, discharge instructions and other relevant information or data relating to the patient. 
     The term “database” as used herein refers to a collection of data and information or data organized in such a way as to allow the data and information or data to be stored, retrieved, updated, and manipulated and to allow them to be presented into one or more formats such as in table form or to be grouped into text, numbers, images, and audio data. The term “database” as used herein may also refer to a portion of a larger database, which in this case forms a type of database within a database. “Database” as used herein also refers to conventional databases that may reside locally or that may be accessed from a remote location, e.g., remote network servers. The database typically resides in computer memory that includes various types of volatile and nonvolatile computer memory. Memory wherein the database resides may include high-speed random access memory or non-volatile memory such as magnetic disk storage devices, optical storage devices, and flash memory. Memory where the database resides may also comprise one or more software for processing and organizing data received by and stored into the database. 
       FIG. 1  illustrates a system for analyzing patient medical records according to the preferred embodiment of the present invention. The main server  100  comprises an input device  102 , processor  104 , communications module  106 , and a memory  108 . The memory  108  further comprises patient medical record  110 , electronic narrative database  112 , disease database  114 , medication and treatment plan database  116 , and a suggestion database  117 . While these databases are depicted as being located in the memory  108  of the main server  100 , it is also contemplated that the databases may be remotely located and connected to the main server via the network  122 . 
     The patient medical record  110  includes data relating to the attending physician&#39;s initial diagnosis, laboratory tests, vital signs, prescribed medications, care plan, biometrics, and other relevant information relating to the patient&#39;s medical history. Information relating to diseases and corresponding symptoms are stored in the disease database  114 . This database may classify the diseases such as by ICD-10 or via other similar classification system. The medication and treatment plan database  116  includes recommended treatment, such as prescribed medicines, dietary instructions, bed rest instructions, and prescribed physical activities. The medication and treatment plan database  116  may also include dietary and activity restrictions or instructions deemed to be therapeutic and/or supportive The narrative text database  112  includes both complete documents associated with patients as well as a suggestions which may be searchable by algorithm  200 . And, the suggestions database  117  includes suggestions to be provided to the user  218  in real-time while editing the narrative text document. These suggestions may be complete words which are suggested as characters are typed by the user. Or, the suggestions, may correct spellings to detected mistyped words. Or, the suggestions may be common phrases when the user has typed the first few words of the phrase. Or, the suggestions may be synonyms of words or alternative drug names, diseases, etc. 
     The main server  100  is accessed by a user device  120  via a network. The user device  120  comprises a user interface  124 , a processor  126 , communications module  128 , and a memory  130 . The user device  120  may be a desktop, a laptop, a tablet, a smartphone, or any other devices that comprise at least one or more processors, one or more types of memory, and at least one computer program or operating system. The user interface  124  may be a touchscreen display, voice prompt, or keypad, including buttons and/or a gesture-driven interface. The medical device  132  used for acquiring a patient&#39;s health-related information may comprise a health sensor  134  (including but not limited to blood pressure, weight, activity, respiration, pulse/oximetry and other similar sensors well-known in the art), communications module  136  (including but not limited to wireless communications such as WiFi, Bluetooth, or Zigbee and wired communications such as Ethernet), and memory  138 . The main server  100  is accessible to any authorized personnel  118  including physicians, nurses, caregivers, case managers, or administrators. 
     The patient&#39;s health-related information is acquired via at least one of the following methods: audio recognition, manual input, and direct measurement of health data using a medical device. The data may come from a variety of sources such as the patient&#39;s electronic medical records, insurance providers, sensors and the like. Preferably, the device  120  used by the user  118  is a computing device that can be used for inputting or acquiring the patient&#39;s health-related information, such as a tablet, smartphone, and desktop computer, among others. The acquired data may be initially stored in the one or more device&#39;s memory and later transmitted via wireless or wired connections to the hospital main server  100 . 
       FIG. 2  is a flowchart that illustrates a process involving the storing and editing of the electronic narrative document. A patient&#39;s health-related information or data is acquired using one or more devices  122  (step  200 ). The acquired information may be initially stored in the device&#39;s memory  132  and later transmitted  130  to the hospital network server  100 . Using an analysis algorithm, an electronic narrative document about the patient is generated based on the patient&#39;s health-related information (step  202 ). The narrative document could be a variety of different documents commonly drafted by users such as but not limited to a note to the patient&#39;s medical record, a diagnosis, a care plan, or discharge instructions. The analysis algorithm may use a variety of techniques for generating the narrative document. For instance, the narrative document may be created by simply selecting a similar document from another patient in the database who is most closely related to the current patient based on one or more criteria such as disease, medication, treatment, or medical records. Or, the narrative document could be generated based on, in the case of a diagnosis, the disease database could be queried for the disease diagnosed and pre-drafted text could be selected which describes the disease or provides other relevant medical information. Or, in the case of providing instructions on the use of medication, a pre-drafted medication description and dosing regimen could be selected. Or, in the case of a treatment plan, a pre-drafted care plan could be generated, which is based on a determined best practice. 
     Alternatively, in another embodiment, Natural language generation (NLG) or Natural language processing (NLP) may be used to generate the narrative document. As known to those skilled in the art, NLG/NLP is a machine processing task that converts pieces of data or information into human-readable format. Data or information undergoes levels of organization before it can be rendered by the machine into a narrative that resembles natural language. The hospital main server  100  is preferably integrated with NLG in a software, in which the medical analysis algorithm identifies data or information about the patient, such as name, age, address, gender, patient room assignments, patient medical history, diagnosis, treatment plans, prescribed medication, known allergies, family history, and other relevant information or data relating to the patient. The identified data or information is then organized using one or more data structures or template. Then, the medical analysis algorithm constructs sentences using the acquired or retrieved patient-related data or information while following the medical analysis algorithm&#39;s pre-defined grammar, syntax, and morphology rules. If the medical analysis algorithm generates ideas or sentences that are similar or related, the algorithm tries to combine them into a single sentence. When describing events or the severity of a patient&#39;s medical condition, the algorithm chooses appropriate words, for example, choosing mild or severe when describing the patient&#39;s cough. The machine then incorporates expressions of referral to improve the context of the sentences. Using syntax and language conventions, the sense of the narrative is completed by combining the different ideas and sentences into an actual text. One of ordinary skill in the art can appreciate that a variety of technics can be used to generate the electronic narrative document without departing from the scope of this invention. 
     In a preferred embodiment of the present invention, the generation of the electronic narrative document is automated once the patient-related information or data is received by the one or more devices connected wirelessly to the hospital main server  100 . Alternatively, the generation of the electronic narrative document can initially be stored in the one or more devices&#39; memory, then preferably later require a prompt from a medical personnel  118 . The electronic narrative document comprise at least one of the patient&#39;s vital status upon admission, symptoms related to patient&#39;s medical condition, sequence of events that describe the development of symptoms, prior medications or admissions, and medical notes recommending further actions for and by the patient. 
     Once generated, the electronic narrative document is stored in the electronic narrative database  112  (step  204 ) associated with the patient. A user  118  then retrieves the stored electronic narrative document using a user device  120  (step  206 ) and checks for errors (step  208 ). If the retrieved electronic narrative document requires no corrections, the user may approve the narrative text which is then stored in the hospital main server  100  (step  210 ). 
     If the retrieved electronic narrative document requires corrections, the user can determine the corrections to be made (step  212 ). Once the corrections are identified, the user  118  selects or edits the elements to be edited. While the user  118  makes corrections, the analysis algorithm searches the narrative text database and presents suggestions for corrections in real-time (step  214 ). This may be done via a variety of techniques. One such technique would be for the algorithm to identify the characters typed (which may be complete words, partial-words, phrases or even misspellings). It may suggest a complete word based on the characters typed. It may suggest synonyms based on the characters typed which are deemed to be likely, or often selected, alternatives. It may suggest conceptually similar phrases. 
     Next, the user  118  determines the inputs or corrections to be made (step  216 ) and compares them with the suggestions provided by the analysis algorithm (step  218 ). If the user  118  agrees with the one or more suggestions provided by the analysis algorithm, the user  118  selects the matching one or more suggestions which is incorporated into the narrative document and then saves the revised patient electronic narrative document (step  220 ). If the user  118  does not agree with the one or more suggestions provided by the medical analysis algorithm, the new inputs or corrections made by the user  118  are incorporated in the electronic narrative document (step  222 ) and saved by the analysis algorithm in the narrative text database  112 . 
     In addition, the corrections drafted by the user may be stored in the suggestion database  117  which is searchable by the analysis algorithm for future use. In one embodiment, the changes made by the user can be converted into suggestions that can be ranked by metadata associated with the suggestion via one or more criteria such as the frequency of use, the user  118  who drafted the correction, or other criteria including but not limited to healthcare provider or facility, department, field of practice, or country. In this way, the analysis algorithm is able to learn and provide more relevant tailored suggestions overtime based on the detected context. In one embodiment, once the suggestions are ranked, if a particular suggestion becomes ranked first it may be incorporated into the initial narrative text document generated in step  202  while the alternatives may be presented in real-time as alternatives in step  214 . In another embodiment, the deemed best practice could be retained as the initial electronic narrative document suggested by the system and the highest ranked suggestions could be presented during the modification step  220 . 
       FIG. 3  shows an exemplary electronic narrative document according to an embodiment of the invention. Shown in the document  300  of the exemplary embodiment is the patient chart of a patient named Jane Doe  302  with Dr. John Smith as the attending physician  304 . The document includes a timestamp comprising the date  306  and the time  308  of admission. Other types of electronic narrative documents include admission forms, release forms, lab results, physical examination results, treatment plans, and prescriptions. A transcript of the patient symptoms, results of medical tests, initial diagnosis, and prescribed treatment generated by the medical analysis algorithm based on the acquired patient data using one or more devices is shown in field  312 . As shown in  FIG. 3 , the attending physician is trying to modify the patient&#39;s initial diagnosis  314  to a more specific diagnosis. When the user positions the cursor over the phrase “Pulmonary Hypertension,” the analysis algorithm interprets that as a signal that the medical personnel is trying to edit that phrase. In response, the analysis algorithm generates a pop-up text box  316  showing suggestions for editing. The suggestions made by the analysis algorithm are based on the patient medical records, as well as on a database of diseases  114 . Thereafter, the user chooses the appropriate modification to the patient electronic narrative document  300 . 
       FIG. 4  illustrates another electronic narrative document in the form of a medical record  400  according to the embodiment of the present invention. The medical record  400  comprises a timestamp, patient information, chief complaints, vitals, symptoms, diagnosis and prescribed treatment. The timestamp comprises the date  402  and time  404  indicating the version of the document. The patient information fields, which includes the name  406 , age  408  and physician  410 , the chief complaint field  412  and the symptoms field  414  display data retrieved from the patient medical records  110 . The vitals fields for blood pressure  416 , pulse rate  418 , temperature  420  and breathing rate  422  display corresponding data retrieved from the medical device  132 . The diagnosis field  424  displays an initial diagnosis, which is determined by the medical analysis algorithm using the symptoms from the patient medical records and data from the disease database  114 . The user  118  inputs the prescribed medication in the prescribed treatment field  426 . As the physician inputs the first few characters, the medical analysis algorithm generates a suggestion box  428  showing the medication suggestions derived from the medicine and treatment database  116 . 
       FIG. 5  shows another screenshot  500  of the graphical user interface of a device used by the medical personnel  118 . The screenshot  500  is that of an electronic narrative document, which comprises a timestamp, patient information, chief complaint, vitals, symptoms, and diagnosis. The timestamp comprises the date  502  and time  504  that show the most recent creation or modification date and time of the document. The patient information fields (which include the name  506 , age  508  and attending physician  510 ), the chief complaint field  512  and the symptoms field  514  display data retrieved from the patient medical records  110 . The vitals fields blood pressure  516 , pulse rate  518 , temperature  520  and breathing rate  522  display data retrieved from the medical device  132 . In this example, the diagnosis entered by the physician in field  524  was misspelled. Thus, the medical analysis algorithm automatically generates a suggestion box  526  showing the appropriate suggested corrections. 
     In one embodiment, after the user  118  retrieves the patient electronic narrative document using a device, such as a tablet, to review and modify the electronic narrative document, natural language generation provides suggestions for modifications by parsing the text, which includes identifying symbols in either natural or computer language, and the syntax of the sentences or expressions. The meaning of each symbol is defined by identifying each as an event, time, a list of elements, quantity, diagnosis, illness, medication, prescription, or opinion as they are structured in the electronic narrative document. Thereafter, the medical analysis algorithm relates the patterns and statistics derived from the information contained the electronic narrative document to information stored in a hospital main server database. For example, the medical analysis algorithm identifies the patient symptoms from, for example, the patient medical record  110  and searches likely diseases from a compilation of diseases from the disease database  114  corresponding to the patient&#39;s symptoms. 
     The analysis algorithm also preferably generates suggestions for misspelled English word, medical term, diseases, or medicines. In the case of diseases and medicines, the suggested corrections are preferably generated based on information or data stored in the disease database  114  and the medicine and treatment database  116 . 
     In an exemplary embodiment of the present invention, a patient showing symptoms of watery diarrhea, vomiting, stomach pain, nausea, headache, and a fever consults with a physician  118 . The disease is initially diagnosed by the physician  118  to be gastroenteritis. A physician uses a medical device  132  to record the symptoms and initial diagnosis in a patient medical record  110  and transmits the record to the main server  100  via a cloud network  122 . In one preferred embodiment, the main server  100  runs the medical analysis algorithm to generate an electronic narrative document that includes a summary of the patient&#39;s symptoms and vitals along with the initial diagnosis. The physician  118  then retrieves and views the document on a tablet  120 . In one embodiment, the user interface  224  immediately displays the words “viral gastroenteritis” and “bacterial gastroenteritis” in a drop-down suggestion box as the cursor hovers over the word “gastroenteritis”. The physician  118  identifies the disease to be viral gastroenteritis due to the watery diarrhea symptom and clicks the word “viral gastroenteritis” in the suggestion box. After the physician  118  selects “viral gastroenteritis,” the phrase automatically replaces “gastroenteritis” in the text. Having completed the editing of the electronic narrative document, the physician  118  then uses the tablet  120  to transmit the modified electronic narrative document to the server  100  via a cloud network  122 . 
     In another embodiment of the present invention, a patient enters an emergency room and is interviewed and examined by a nurse, or other user of the present system,  118 . The patient&#39;s vital signs are obtained and recorded using one or more medical devices  132 . The nurse  118  then retrieves the patient&#39;s previously stored medical records from the main server  100  and enters the newly acquired patient information into the retrieved patient&#39;s medical record. However, the nurse  118  forgets to include the vital signs information into the patient&#39;s medical record. When the user  118  tries to save the updated electronic narrative document, the medical analysis algorithm detects the incomplete data and automatically prompts the user  118  about the missing information. The user  118  then enters the missing information and saves the revised electronic narrative document into the electronic narrative database  112 . 
     In yet another embodiment, a patient consults with a physician, or other such user  118 . The user  118  retrieves and reviews the patient&#39;s previously stored electronic narrative document, which previously listed “pulmonary hypertension” as the initial diagnosis. Based on the symptoms that the patient is currently experiencing, the user  118  identifies the disease to be a more specific variation, which is pulmonary arterial hypertension. The physician  118  then modifies the diagnosis field of the narrative document. As the user  118  begins to input the prescribed treatment field  426 , the algorithm is unable to provide suggestions for the required medication because the disease corresponding to the updated diagnosis is not found in the disease database  114 . The user  118  then connects the user device  120 , which may be a laptop, to the main server  100  to access the disease database  114  and the medicine and treatment database  116 . The physician  118  then edits the disease  114  database to add “pulmonary arterial hypertension” and also edits the medicine and treatment database  216  to add the appropriate medicines, such as iloprost, epoprostenol and bosentan for the disease. 
     In a further embodiment of the present invention, a patient consults with a physician or other such user  118 . The user  118  arrives at an initial diagnosis of dengue fever. Unsure of the physician&#39;s initial diagnosis, the physician  118  runs the medical analysis algorithm to confirm the physician&#39;s initial diagnosis. The analysis algorithm matches the symptoms with the information contained in the disease database  114  and generates an electronic narrative document that confirms the physician&#39;s initial diagnosis. The physician  118  then starts to type in the prescribed treatment field  426 , at which point a suggestion box that lists “aspirin” and “paracetamol” appears. The physician  118  then chooses “aspirin,” but the patient happens to have an allergy to aspirin according to the patient&#39;s medical record. The analysis algorithm then generates a pop-up notification to notify the physician  118  that the patient is allergic to aspirin. The physician  118  then changes the prescription to “paracetamol.” 
     In another embodiment, the physician  118  misspells an annotation, for example accidentally writes “three day” instead of either “three times a day” or “for three days.” In this case, the analysis algorithm generates a drop-down box indicating the suggested correct phrases. The physician  118  then selects from the suggested phrases to correct the annotation. 
     In another embodiment, a patient consults with a physician or other such user  118  in a care facility wherein the patient&#39;s electronic narrative document was generated and stored in the database  112 . The patient then consults with another physician  118  in a different affiliated hospital regarding a different illness. After being authenticated, the second physician  118  is able to access the first hospital&#39;s main server  100  using a user device  120  to retrieve the patient&#39;s information or data in the patient medical record  110 , as well as the electronic narrative document from the electronic narrative document database  112 . 
     With reference to  FIG. 2 , the present invention also contemplates a method for automated analysis of medical records. A patient&#39;s health-related information or data is acquired from the one or more devices (step  200 ). Using a medical analysis algorithm (preferably a natural language generation or NLG algorithm), an electronic narrative document about the patient is generated based on the acquired patient health-related information (step  202 ). The generated electronic narrative document is then stored in the electronic narrative database (step  204 ). Medical personnel later retrieve the stored electronic narrative document using a user device (step  206 ) to check for errors ( 208 ). If the retrieved electronic narrative document requires no corrections, the retrieved electronic narrative document is stored in the hospital main server and updated or linked to the patient database ( 110 ). 
     If the retrieved electronic narrative document requires corrections, the medical personnel determines the corrections to be made (step  212 ). Once the corrections are identified, the medical personnel selects or edits the elements to be edited. While the user  118  makes corrections, the analysis algorithm searches the narrative text database and presents suggestions for corrections in real-time (step  214 ). This may be done via a variety of techniques. One such technique would be for the algorithm to identify the characters typed (which may be complete words, partial-words, phrases or even misspellings). It may suggest a complete word based on the characters typed. It may suggest synonyms based on the characters typed which are deemed to be likely, or often selected, alternatives. It may suggest conceptually similar phrases. Or use any other similar technique known in the art. 
     Next, the user  118  determines the inputs or corrections to be made (step  216 ) and compares them with the suggestions provided by the analysis algorithm (step  218 ). If the user  118  agrees with the one or more suggestions provided by the analysis algorithm, the user  118  selects the matching one or more suggestions which is incorporated into the narrative document and then saves the revised patient electronic narrative document (step  220 ). If the user  118  does not agree with the one or more suggestions provided by the medical analysis algorithm, the new inputs or corrections made by the user  118  are incorporated in the electronic narrative document (step  222 ) and saved by the analysis algorithm in the narrative text database  112 . 
     In addition, the corrections drafted by the user may be stored in the suggestion database  117  which is searchable by the analysis algorithm for future use. In one embodiment, the changes made by the user can be converted into suggestions that can be ranked by metadata associated with the suggestion via one or more criteria such as the frequency of use, the user  118  who drafted the correction, or other criteria including but not limited to healthcare provider or facility, department, field of practice, or country. In this way, the analysis algorithm is able to learn and provide more relevant tailored suggestions overtime based on the detected context. In one embodiment, once the suggestions are ranked, if a particular suggestion becomes ranked first it may be incorporated into the initial narrative text document generated in step  202  while the alternatives may be presented in real-time as alternatives in step  214 . In another embodiment, the deemed best practice could be retained as the initial electronic narrative document suggested by the system and the highest ranked suggestions could be presented during the modification step  220 . The modification step  220  may be performed using one or any combinations of manual typing, gesture-driven recognition, and voice recognition. After being presented with the suggested corrections (step  218 ), the medical personnel may choose to accept or override one or more of the suggested corrections (step  218 ). 
     The present invention is not intended to be restricted to the several exemplary embodiments of the invention described above. Other variations that may be envisioned by those skilled in the art are intended to fall within the disclosure. For instance, while this invention is preferably employed in a networked environment with separate medical device(s)  132 , user device(s)  120  and server ( 100 ) connected via a network. It is also possible that the unique features of this invention could be housed in a single device such as the medical device  132 , user device  120 , or server.