US20230055094A1

US20230055094A1 - Capturing Detailed Structure from Patient-Doctor Conversations for Use in Clinical Documentation

Info

Publication number: US20230055094A1
Application number: US17/978,790
Authority: US
Inventors: Christopher Co; Gang Li; Philip Chung; Justin Paul; Daniel Shing Shun Tse; Katherine Chou; Diana Jaunzeikare; Alvin Rajkomar
Original assignee: Google LLC
Current assignee: Google LLC
Priority date: 2017-10-20
Filing date: 2022-11-01
Publication date: 2023-02-23
Also published as: US11521722B2; JP2020528590A; EP3602563A1; US20200152302A1; CN110692102A; KR20190140987A; JP7174717B2; KR102365621B1; WO2019078887A1

Abstract

A method and system is provided for assisting a user to assign a label to words or spans of text in a transcript of a conversation between a patient and a medical professional and form groupings of such labelled words or spans of text in the transcript. The transcript is displayed on an interface of a workstation. A tool is provided for highlighting spans of text in the transcript consisting of one or more words. Another tool is provided for assigning a label to the highlighted spans of text. This tool includes a feature enabling searching through a set of predefined labels available for assignment to the highlighted span of text. The predefined labels encode medical entities and attributes of the medical entities. The interface further includes a tool for creating groupings of related highlighted spans of texts. The tools can consist of mouse action or keystrokes or a combination thereof.

Description

CROSS-REFERENCE TO RELATED DISCLOSURE

This application is a continuation of U.S. patent application Ser. No. 16/612,879, filed Nov. 12, 2019, which is a national stage application under 35 U.S.C. § 371 of International Application No. PCT/US2017/057640, filed Oct. 20, 2017, each of which are incorporated herein by reference in their entirety.

BACKGROUND

This disclosure is directed to a method and system for facilitating the annotation of transcribed audio or audio-visual recordings of medical encounters.
Conversations between patients and medical practitioners such as doctors and nurses and their conversations are often recorded. The record of the conversation, and a transcript, are part of the patient's medical record. The transcript can be created by a speech-to-text converter or created by a trained (human) medical transcriptionist listening to the recording.
A transcript without any annotation is of limited usefulness when it is reviewed by the physician, as they have to pore over many lines or pages of the transcript to find relevant information or understand the relatedness of different comments in the transcript.
Additionally, a collection of transcripts of medical encounters can be used to train machine learning models. Training a machine learning model requires a large amount of high quality training examples, i.e., labelled data. There is a need in the art for methods for facilitating the generation of transcripts of medical encounters that are annotated, that is, relevant words or phrases are highlighted and associated with medical concepts and grouped as being related to each other. This disclosure meets that need.

SUMMARY

In a first aspect, a method of facilitating annotation of a recording of a medical practitioner-patient conversation is disclosed. The method includes a step of generating a display of the transcribed audio recording (i.e., transcript), for example on the display of a workstation used by a human (“scribe labeler”) who is performing the annotation. A tool is provided for highlighting spans of text in the transcript consisting of one or more words. The tools can be simple mouse or keyboard shortcuts for selecting or highlighting one or more words.
The method further includes a step of providing a tool for assigning a label to the highlighted spans of text. The tool includes a feature for searching through a set of predefined labels available for assignment to the highlighted span of text. For example, when the scribe labeler highlights a word such as “stomachache” in the transcript a window pops up where the user can search through available labels, e.g. by scrolling or using a search tool. The labels encode medical entities (such as symptoms, medications, lab results, etc.) and attributes of the medical entities (e.g., severity, location, frequency, time of onset of a symptom entity).
In this document, the term “medical entities” is intended to refer to categories of discrete medical topics, such as symptoms, medications, lab results, vital signs, chief complaint, medical imaging, conditions, medical equipment, and so forth. The medical entities are predefined to be relevant to the context of the labelling task, and so in this case in one embodiment they could consist of the following list: medications, procedures, symptoms, vitals, conditions, social history, medical conditions, surgery, imaging, provider, vaccine, reproductive history, examination, and medical equipment. The medical entities could be structured in a hierarchical manner, such as the medical entity “medication” could be in the form of “medication:allergy” where “allergy” is a type or subclass of the overall class “medication.” As another example, the medical entity “symptom” could be structured in a hierarchical manner of symptoms for different parts of the body, such as “symptom:eyes”, “symptom:neurological”, etc.
The term “attributes of the medical entities” simply means some descriptive property or characteristic of the medical entity, such as for example the medical entity “medical equipment” may have an attribute of “patient's actual use” meaning that the patient is currently using a piece of medical equipment. As another example, a symptom medical entity may have an attribute of “onset.” A label of “symptom/onset” would be used as an annotation when there is word or phrase in the transcript indicating when the patient first started experiencing the symptom. As another example, a label of “medical equipment/regularly” would be used as an annotation when there is a word or phrase in the transcript indicating the patient used some piece of medical equipment regularly, with “regularly” being the attribute of the medical entity “medical equipment.”
The method further includes a step of providing a tool for grouping related highlighted spans of texts. The tool could be for example a combination of mouse clicks or keyboard shortcuts to establish the grouping. The groupings allow medical entities associated with labels assigned to the highlighted spans of text to be associated as a group. For example, in a conversation in which a patient describes a sharp chest pain that started last week, the text “sharp”, “chest pain” and “last week” would be highlighted and labeled with symptom labels and attributes of severity, location, and time of onset, respectively and grouped together as all being related to each other.
In another aspect, a system is disclosed for facilitating annotation of a recording of a medical practitioner-patient conversation. The system includes a) an interface displaying a transcript of the recording; b) a tool for highlighting spans of text in the transcript consisting of one or more words; c) a tool for assigning a label to the highlighted spans of text, wherein the tool includes a feature enabling searching through predetermined labels available for assignment to the highlighted span of text, and wherein the labels encode medical entities and attributes of the medical entities; and d) a tool for creating groupings of related highlighted spans of texts.
The methods and systems are applicable to other types of transcripts, in which a set of predefined labels are created, e.g., by an operator, which are designed to be relevant to the annotation task at hand and the labels are associated with entities and attributes relevant to the transcript and annotation task. The tools of this disclosure are used in the same manner in these other possible implementations, such as for example transcripts of legal proceedings, such as deposition or trial, or transcripts of hearings before administrative bodies, such a city council, Congress, State Legislature, etc.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a flow chart showing an environment in which the method can be performed.

FIG. 2 is an illustration of workstation having a display and user interface for use by a human (“scribe labeler”) to annotate a transcript of medical encounter. The user interface includes the tools described in conjunction with FIGS. 4-6 . The term “user interface” is intended to refer to the combination of the display on the workstation and associated devices for providing user input, such as the mouse and keyboard.

FIG. 3 is an illustration of the user interface of FIG. 2 showing a list of transcripts which are ready for annotation.

FIG. 4 is an illustration of a transcript of a medical encounter in which the scribe labeler is annotating certain words or phrases in the text. FIG. 4 shows a search box which pops up which permits the scribe labeler to search for medical entities and associated attributes. Spans of text can be highlighted by use of a tool such as by clicking on the word or using drag techniques with a mouse.

FIG. 5 is an illustration of the transcript of FIG. 4 in which the scribe labeler is annotating the text “upper left” and a search box which pops up. Additionally, a proposed label for the phrase “upper left” for the medical entity “symptom” and attribute “location (on body)” is also displayed. The proposed label is generated by a pre-labelling system shown in FIG. 1 .

FIG. 6 is illustration of the transcript of FIGS. 4 and 5 when the scribe labeler forms a grouping of the two highlighted spans of text “stomachache” and “three days”. The tool for forming the grouping consists of a highlighting the two texts and then keyboard shortcut of holding down the “G” key, clicking on the highlighted spans of text, and releasing the “G” key. FIG. 6 also shows the formation of the group in the Groups tab listing all the groups in the transcript at the bottom of the display.

FIG. 7 is a more detailed illustration of the pre-labeler of FIG. 1 .

FIG. 8 is an illustration of a machine learning model training system which receives as input a multitude of annotated transcripts in accordance with the features of FIG. 1 .

DETAILED DESCRIPTION

This disclosure is directed to methods and systems for facilitating annotations of recordings of medical encounters, i.e., conversations between patients and medical practitioners such as doctors or nurses. The recordings could be audio or audio-visual recordings. The recordings are transcribed into written form. The transcripts could be generated by trained medical transcriptionists, that is by hand, or by the use of speech to text converters, which are known in the art. The output of the system is an annotated version of the transcript in which relevant medical information (i.e., spans of text, such as individual words or groups of words) in the text are labeled (i.e., tagged as being associated with medical entities and attributes of such entities), and grouped to express relatedness between the labelled text.
FIG. 1 is a flow chart showing the environment in which the methods and systems of this disclosure are practiced. Patient consent for recording the encounter with the doctor or nurse is obtained at 102. Additionally, the patient is advised of the use of a transcript of the recording to be placed into the electronic health record and consent is obtained. The patient is further advised that the recording may be annotated and used for generating or training machine learning models and consent is obtained as well. In all cases where the transcripts are annotated or used for machine learning model training the transcript data is patient de-identified and used in compliance with all requirements for disclosure and use of a limited data set under HIPAA. Ethics review and institutional review board exemption is obtained from each institution. Patient data is not linked to any Google user data. Furthermore, for the system 116 using annotated transcripts for machine learning model training includes a sandboxing infrastructure that keeps each electronic health record (or transcript) dataset separated from each other, in accordance with regulation, data license and/or data use agreements. The data in each sandbox is encrypted; all data access is controlled on an individual level, logged, and audited.
At step 104, after the required patient consents are obtained, the patient consults with the medical practitioner and a recording, either audio or audio-visual, is obtained and stored in digital format.
At step 106, a written transcript of the recording is obtained, either by a trained transcriptionist or by use of a speech-to-text converter. The transcript is preferably accompanied by a time indexing, in which the words spoken in the transcript, or lines of text, are associated with elapsed time of the recording, as will be illustrated subsequently.
At step 108, an annotation of the transcript is performed by the scribe labeler in the manner described and explained in the subsequent figures. The annotations include the assignment of labels to spans of text in the transcript and groupings of spans of text to indicate their relatedness. In step 108 a display of the transcribed audio recording is generated, for example on the display of a workstation used by the scribe labeler. See FIGS. 2 and 4-6 . A tool is provided for highlighting spans of text in the transcribed audio recording consisting of one or more words. The tool can be simple mouse or keyboard shortcuts for selecting or highlighting one or more words. A tool is also provided for assigning a label to the highlighted spans of text. The tool includes a feature for searching through predetermined labels available for assignment to the highlighted span of text. For example, when the scribe labeler highlights a word such as “stomachache” in the transcript a list pops up where the user can search through available labels, and a search tool is provided for performing a word search through the list of labels. The labels encode medical entities (such as symptoms, medications, lab results, etc.) and attributes of the medical entities (e.g., severity, location, frequency, time of onset of a symptom entity).
A tool is also provided for grouping related highlighted spans of texts. The groupings allow medical entities associated with labels to be grouped together. For example, in a conversation in which a patient describes a sharp chest pain that started last week, the text “sharp”, “chest pain” and “last week” would be highlighted and labeled with symptom labels and attributes of severity, location, and time of onset, and grouped together, as they are all related to a single medical condition of the patient. This tool can consist of keyboard and/or or mouse action, as explained below.
The system may include a pre-labeler 110, shown in more detail in FIG. 7 . The pre-labeler is a computer system implementing a learned, automated word recognition model which identifies words or spans of text in the transcript which are likely to be the subject of labelling or grouping. The pre-labeler 110 provides input into annotation step 108 by providing suggested labels for highlighted spans of text when the scribe labeler performs the annotation of the transcript. This is shown in more detail in FIG. 5 .
As a result of the annotation step 108 an annotated transcript file 112 is created, which consists of the transcript as well as annotations in the form of labelled or tagged spans of text (words or phrases) and groupings of the tagged spans of text. The annotated transcript file is in digital form, with the annotations and groupings in the file as metadata or otherwise. The annotated transcript file 112 is then added to the patient's electronic health record (EHR) 114 or supplied to a machine learning model training system 116. The machine learning model training system 116 may, for example, be a system for training a machine learning model to automatically annotate transcripts of medical encounters. Alternatively, the machine learning model may use the annotated transcript as well as other data in the patient health record, for not only the individual patient, but also a multitude of other patients, to generate predictions of future medical events for example as described in the pending U.S. provisional application Ser. No. 62/538,112 filed Jul. 28, 2017, the content of which is incorporated by reference herein. The EHR 114 may be provided to the system 116 as indicated by the dashed line 114.
The annotated transcript file 112 may be fed back into the pre-labeler to enable further training the machine learning pre-labeler 110, as indicated by the dashed line 120. This aspect will be described in further detail later.
FIG. 2 is an illustration of a workstation 200 which is used by a scribe labeler during the annotation step 108 of FIG. 1 . The workstation includes a central processing unit (general purpose computer 210) executing an application which provides for display of the transcript of the medical encounter and tools by which the user interface consisting of a keyboard 212, a mouse 214 and a monitor 216 allow for the highlighting of spans of text (words or phrases 230), assigning labels to the spans of text, and grouping of the highlighted spans of text as will be discussed below. The monitor 216 includes a display 218 of a transcript 222, and a scroll bar 224 for allowing the user to navigate to various portions of the transcript. A time index 220 of the transcript is shown at the top of the display 218. The time index includes a slider 221 which when moved horizontally back and forth allows for the portion of the transcript associated with a particular elapsed time to be displayed at the top of the display 118. In this case the time index 220 indicates that the transcript is 13 minutes 24 seconds duration and the slider 221 is all the way to the left, therefore the beginning of the transcript is shown at the top of the display. The transcript is in the form of numbered lines, followed by identification of who was speaking (doctor or patient), followed by a text transcript of what was said.
FIG. 3 shows the display of a “to-do” list of transcripts in need of annotation which is provided on the user interface of FIG. 2 when the scribe labeler logs on to the workstation of FIG. 2 . The individual transcripts are patient de-identified (that is, identified only by patient number in column 302 and not by name). Column 304 shows the elapsed time, column 306 shows the number of lines of text in the transcript, column 308 shows the patient's chief complaint associated with the medical encounter, and column 310 shows the nature or type of the medical encounter. When one of the transcripts is selected in FIG. 3 (e.g., by clicking on the number in the column 302) the display of FIG. 2 is generated.
FIG. 4 is an illustration of the display 218 of the user interface along with a transcript 222, and time index 220. Time segment information for each utterance (sentence or word) is provided in the transcript and the time index 220 provides a slider tool 221 which moves right and left to jump to different portions of the transcript.
The interface provides a tool for text highlighting. In particular, mouse and keyboard shortcuts make highlighting spans of text easy. For example, a user can double click on a given word and the word is automatically highlighted on the display. Only words can be highlighted, not individual characters, reducing errors and increasing annotation speed. Other tools could be used for highlighting, such as by click and drag techniques with a mouse, a keyboard stroke (such as by putting the cursor over the word and hitting a particular key such as H, or CTRL-H), or a combination keyboard stroke and mouse action.
In the example of FIG. 4 , the user has highlighted the word “stomachache” (see 400). The user interface provides a tool for text tagging, i.e., labelling the highlighted term. Labels are applied to the highlighted spans of text essentially allowing the scribe labeler to inject information into the transcript, for example to indicate that the highlighted text “stomachache” is a symptom, or a gastrointestinal symptom. In particular, when the user has highlighted the term “stomachache”, a box (tool) 402 pops up which shows a list 404 of medical entities and associate attributes, a search term entry field 405 by which they can search the list 404, and a scroll bar 406 allowing the scribe labeler to scroll through the list and select a medical entity and associate attribute which is appropriate for the highlighted test. In the example FIG. 4 , the medical entity “Symptom:GI” and associated attribute “abdominal pain” was found in the list 404 and the user clicked on that combination of medical entity and attribute. The display includes a Table tab 410 at the bottom of the display which lists the labelled spans of text, including medical entity, attribute, location in the transcript (line 4) and the associated text span (“stomachache”).
The scribe labeler does the same process and uses the same tools to highlight the span of text “three days”, assign a label of medical entity “SymAttr” and attribute “duration” (“Symattr/duration”) to the highlighted span of text “three days” and this additional annotation shows up in the Table of annotations 410.
The scribe labeler then proceeds to highlight the span of text “upper left”, 412. The scribe labeler again uses the tool 402 to ascribe a label to the span of text “upper left.” Again this could be done using the tools described in FIG. 4 . As shown in FIG. 5 , in one embodiment where there is pre-labelling of the transcript, when the user highlights the span of text “upper left” a suggested label is shown in the box 502. This suggested label was assigned to the span of text “upper left” by the pre-labeler of FIG. 1 . The user can accept this suggestion by clicking on the box 502, or reject the suggestion by clicking on the X icon 504. In the situation of FIG. 5 the scribe labeler accepted the suggestion by a mouse click (or any other alternative suitable user interface action, such as keyboard shortcut etc.) and the annotation is added to the Table 410 as shown in FIG. 5 at 506. If the scribe labeler rejects the suggestion they can use the pop-up search tool 402 or scroll through the list of labels to find a suitable label.
It will be appreciated that the search tool 402 could pop up when the scribe labeler is taking action to highlight a span of text, and disappear after the label has been assigned, or alternatively it could be a persistent feature of the user interface during annotating.
As noted previously, the user interface of FIGS. 2 and 4-6 includes a tool for permitting the scribe labeler to group together highlighted and labelled spans of text which are conceptually or causally related to each other. For example, in FIG. 6 the spans of text “stomachache”, and “three days” are related to a gastrointestinal symptom, namely the type of symptom and the duration of the symptom. To make this grouping, the interface provides a tool in the form of combination of key strokes and mouse actions in the illustrated embodiment. In particular, the scribe labeler holds down the “G” key, clicks on the two highlighted spans of text, and then releases the “G” key. Of course, variations from this specific example of the tool for forming a grouping are possible and within the scope of this disclosure, such as combinations of mouse actions alone (e.g., selecting spans of text with a left click and then a right click to form the group), key strokes alone (e.g., ALT-G to select the highlighted spans of text and then ENTER to form the group), or other various possible combinations of mouse actions and key strokes. In FIG. 6 , the “2” icon 602 indicates the number of elements in the grouping (here two). The “X” icon 604 is click target to delete the grouping. The user has toggled the Groups tab 606 and the group of “stomachache” and “threedays” is shown as indicated at 608, along with the location in the transcript (line 4 for the location first element in the group in this example).
The search tool 402 of FIG. 4 makes the process of locating the relevant label easy to navigate. In the example of medical transcripts, there may many hundreds of possible labels to choose from. For example, there may be ten or twenty predefined different medical entities and ten or twenty or more different attributes for each of the medical entities. The medical entities may be customized and organized in a hierarchical manner, as explained previously. These labels encode a medical ontology that is designed specifically for medical documentation. These labels encode medical entity information, such as medication, procedures, symptoms, conditions, etc., as well as attributes of the entities, such as onset, severity, frequency, etc., of a symptom, and whether or not the patient declined or refused (attributes) a medical procedure (entity).
The text grouping as shown in FIG. 6 allows the scribe labeler to inject additional information into the transcript and in particular identify relationships or relatedness between concepts. For example the system and method of this disclosure allows the scribe labelers to specify groups of highlighted text such that entities can be associated with the attributes as a group.
The pre-labelling system 110 of FIG. 1 is shown in more detail in FIG. 7 . The input to the system 110 is a text transcript 702 generated at step 108 of FIG. 1 . The system 110 uses a machine learning medical named entity recognition (NER) model 703 which identifies candidate information (words or phrases) in the transcript and suggested labels for such words or phrases based on supervised learning from trained examples, in the form of a pre-annotated transcript 704. Named entity recognition models are well known in the field of machine learning and are described extensively in the scientific literature. The NER model 703 needs its owned labelled training data. For this training data we use a large corpus of medical text books (over 120,000 medical text books) using deep learning word embedding, in conjunction with a large lexicon of existing medical ontologies, e.g., UMLS (unified medical language system) and SNOMED (systemized nomenclature of medicine).
Additionally, the NER can be trained from annotated medical encounter transcripts. A NER model can also be trained from a hybrid of data sources, which may include medical and clinical text books, annotated transcripts from doctor-patient conversations, and clinical documentation contained in anonymized electronic health records of a multitude of patients. The NER model may further be trained from feedback of the annotation of the transcript as performed in FIG. 1 and FIG. 7 . For example, after the pre-labeling system generates the pre-annotated transcript 704 and the scribe labeler has proceeded to complete the annotation at step 108, there can be feedback of corrections between the suggested annotations in pre-annotated transcript 704 and annotated transcript 112 back into the NER model.
As shown in FIG. 8 , the annotated transcripts 112 can be supplied to a machine learning model training system. In one form, the model training system 116 uses the transcripts, along with other patient data, from a multitude of patients to generate machine learning models to make health predictions. Alternatively, the annotated transcripts could be used in the system 116 to develop deep learning models for automating the process of generating annotated transcripts of medical encounters.
The system and method of this disclosure has several advantages. In many natural language processing text annotation tools, relationships between must be identified in an explicit and cumbersome manner. In contrast, in this disclosure the labels (including predefined labels relevant to the annotation task) and labelling and groupings tools permit such relationships to be readily specified. The user can quickly search for labels by means of the search tools as shown in the Figures and select them with simple user interface action such as a click of a mouse. Moreover, groupings of conceptually or causally related highlighted spans of text can be created very quickly with simple user interface actions using a keyboard, mouse, or combination thereof, as explained above.
While the illustrated embodiment has described an interface and tools for assisting in labeling transcripts of medical encounters, the principles of this disclosure could be applied to other situations. In particular, a predefined list of labels is generated for entities and attributes of those entities, e.g., listing all the possible categories or classes of words of interest in a transcript and attributes associated with each of the categories or classes, analogous to the attributes of medical entities. The user interface actions described above would generally be performed in the same way, that is the scribe labeler would read the transcript and highlight words or other spans of text that are relevant to the annotation task, using simple user interface tools, and then tools would be enabled by which the scribe labeler could search through the available labels and assign them to the highlighted spans of text.
Additionally, grouping tools are provided to form groups of related highlighted spans of text. The result is an annotated transcript. The methods could have usefulness in other types of transcripts, such as deposition or trial transcripts in the context of the legal profession, hearing transcripts of testimony of governmental bodies, etc.
An example of a list of labels for use in annotation of medical transcripts is set forth below in Table 1. It will be understood of course that variation from the list is possible and that in other contexts other labels will be defined. In the list, Entity 1 is a medical entity and Entity 2 is either a subcategory of the medical entity of Entity 1 or an attribute of the medical entity, and Entity 3 is either an attribute of the medical entity or a further subcategory of the medical entity of Entity 1 in a hierarchical schema.

TABLE 1

			Combined Label that will show
Entity 1	Entity 2	Entity 3	up in labeling tool

NOS			NOS:
SymAttr	Time of Onset		SymAttr: Time of Onset
SymAttr	Frequency/Tempo		SymAttr: Frequency/Tempo
SymAttr	Duration		SymAttr: Duration
SymAttr	Improving/Worsening		SymAttr: Improving/Worsening
SymAttr	Location (on body)		SymAttr: Location (on body)
SymAttr	Severity/Amount		SymAttr: Severity/Amount
SymAttr	Characteristic/Quality		SymAttr: Characteristic/Quality
SymAttr	Provoking Factor		SymAttr: Provoking Factor
SymAttr	Alleviating Factor		SymAttr: Alleviating Factor
SymAttr	Radiation		SymAttr: Radiation
SymAttr	Not Experienced		SymAttr: Not Experienced
SymAttr			SymAttr:
Chief			Chief Complaint::
Complaint
Meds			Meds::
Meds	Physician's		Meds: Physician's Intended
	Intended Status		Status:
Meds	Physician's	Active, Continued	Meds: Physician's Intended
	Intended Status		Status: Active, Continued
Meds	Physician's	Active, Modified	Meds: Physician's Intended
	Intended Status		Status: Active, Modified
Meds	Physician's	Recommended/To	Meds: Physician's Intended
	Intended Status	Start	Status: Recommended/To
			Start
Meds	Physician's	Completed/Finished/	Meds: Physician's Intended
	Intended Status	Stopped	Status: Completed/Finished/
			Stopped
Meds	Patient's actual		Meds: Patient's actual use:
	use
Meds	Patient's actual	Yes, Regularly	Meds: Patient's actual use:
	use		Yes, Regularly
Meds	Patient's actual	Yes, Intermittently	Meds: Patient's actual use:
	use		Yes, Intermittently
Meds	Patient's actual	Yes, as Needed	Meds: Patient's actual use:
	use		Yes, as Needed
Meds	Patient's actual	Stopped	Meds: Patient's actual use:
	use		Stopped
Meds	Patient's actual	No	Meds: Patient's actual use:
	use		No
Meds	Side Effect		Meds: Side Effect:
Meds	Side Effect	Experienced	Meds: Side Effect: Experienced
Meds	Side Effect	No	Meds: Side Effect: No
Meds	Benefit		Meds: Benefit:
Meds	Benefit	Experienced	Meds: Benefit: Experienced
Meds	Benefit	No	Meds: Benefit: No
Meds	Dosage		Meds: Dosage:
Meds	Quantity		Meds: Quantity:
Meds	Frequency/Duration		Meds: Frequency/Duration:
Meds	Instructions/Directions		Meds: Instructions/Directions:
Meds	Route of		Meds: Route of Administration:
	Administration
Meds	Indication		Meds: Indication:
Meds	Allergy		Meds: Allergy:
Meds	Allergy	Yes	Meds: Allergy: Yes
Meds	Allergy	No	Meds: Allergy: No
Meds	Allergy	Reaction	Meds: Allergy: Reaction
Medical			Medical Equipment::
Equipment
Medical	Physician's		Medical Equipment:
Equipment	Intended Status		Physician's Intended
			Status:
Medical	Physician's	Active, Continued	Medical Equipment:
Equipment	Intended Status		Physician's Intended
			Status: Active, Continued
Medical	Physician's	Active, Modified	Medical Equipment:
Equipment	Intended Status		Physician's Intended
			Status: Active, Modified
Medical	Physician's	Recommended/To	Medical Equipment:
Equipment	Intended Status	Start	Physician's Intended
			Status: Recommended/To
			Start
Medical	Physician's	Completed/Finished/	Medical Equipment:
Equipment	Intended Status	Stopped	Physician's Intended
			Status: Completed/Finished/
			Stopped
Medical	Patient's actual		Medical Equipment: Patient's
Equipment	use		actual use:
Medical	Patient's actual	Yes, Regularly	Medical Equipment: Patient's
Equipment	use		actual use: Yes, Regularly
Medical	Patient's actual	Yes, Intermittently	Medical Equipment: Patient's
Equipment	use		actual use: Yes, Intermittently
Medical	Patient's actual	Yes, as Needed	Medical Equipment: Patient's
Equipment	use		actual use: Yes, as Needed
Medical	Patient's actual	Stopped	Medical Equipment: Patient's
Equipment	use		actual use: Stopped
Medical	Patient's actual	No	Medical Equipment: Patient's
Equipment	use		actual use: No
Medical	Side Effect		Medical Equipment: Side
Equipment			Effect:
Medical	Side Effect	Experienced	Medical Equipment: Side
Equipment			Effect: Experienced
Medical	Side Effect	No	Medical Equipment: Side
Equipment			Effect: No
Medical	Benefit		Medical Equipment: Benefit:
Equipment
Medical	Benefit	Experienced	Medical Equipment: Benefit:
Equipment			Experienced
Medical	Benefit	No	Medical Equipment: Benefit:
Equipment			No
Medical	Dosage		Medical Equipment: Dosage:
Equipment
Medical	Quantity		Medical Equipment: Quantity:
Equipment
Medical	Frequency/Duration		Medical Equipment:
Equipment			Frequency/Duration:
Medical	Instructions/Directions		Medical Equipment:
Equipment			Instructions/Directions:
Medical	Indication		Medical Equipment:
Equipment			Indication:
Condition			Condition::
Condition	Status		Condition: Status:
Condition	Status	Active	Condition: Status: Active
Condition	Status	Recurrence	Condition: Status: Recurrence
Condition	Status	Inactive	Condition: Status: Inactive
Condition	Status	Remission	Condition: Status: Remission
Condition	Status	Resolved	Condition: Status: Resolved
Condition	Time of Onset/		Condition: Time of Onset/
	Duration		Duration:
Condition	Physician certainty		Condition: Physician certainty:
Condition	Physician certainty	Provisional/	Condition: Physician certainty:
		Differential	Provisional/Differential
Condition	Physician certainty	Confirmed	Condition: Physician
			certainty: Confirmed
Condition	Physician certainty	Refuted	Condition: Physician
			certainty: Refuted
Condition	Severity	New	Condition: Severity: New
Condition	Severity	Stable	Condition: Severity: Stable
Condition	Severity	Improved	Condition: Severity: Improved
Condition	Severity	Worsening	Condition: Severity: Worsening
Condition	Family		Condition: Family:
Condition	Family	History of (First	Condition: Family: History of
		Degree)	(First Degree)
Condition	Family	History of (Non-first-	Condition: Family: History of
		degree)	(Non-first-degree)
Surgery			Surgery::
Surgery	Status		Surgery: Status:
Surgery	Status	Completed	Surgery: Status: Completed
Surgery	Status	Planned/Anticipated	Surgery: Status: Planned/
			Anticipated
Procedures/			Procedures/Other Tests::
Other Tests
Procedures/	Status		Procedures/Other Tests:
Other Tests			Status:
Procedures/	Status	Scheduled/Upcoming	Procedures/Other Tests:
Other Tests			Status: Scheduled/Upcoming
Procedures/	Status	Completed	Procedures/Other Tests:
Other Tests			Status: Completed
Procedures/	Status	Not done	Procedures/Other Tests:
Other Tests			Status: Not done
Procedures/	Status	Declined/Refused	Procedures/Other Tests:
Other Tests			Status: Declined/Refused
Procedures/	Status	Recommended	Procedures/Other Tests:
Other Tests			Status: Recommended
Procedures/	Result		Procedures/Other Tests:
Other Tests			Result:
Procedures/	Result	Value/Result/Finding	Procedures/Other Tests:
Other Tests			Result: Value/Result/Finding
Procedures/	Result	Normal	Procedures/Other Tests:
Other Tests			Result: Normal
Procedures/	Result	Abnormal	Procedures/Other Tests:
Other Tests			Result: Abnormal
Labs			Labs::
Labs	Status		Labs: Status:
Labs	Status	Scheduled/Upcoming	Labs: Status: Scheduled/
			Upcoming
Labs	Status	Completed	Labs: Status: Completed
Labs	Status	Declined/Refused	Labs: Status: Declined/
			Refused
Labs	Status	Recommended	Labs: Status: Recommended
Labs	Result	Value/Result/Finding	Labs: Result: Value/Result/
			Finding
Labs	Result	Normal	Labs: Result: Normal
Labs	Result	Abnormal	Labs: Result: Abnormal
Imaging			Imaging::
Imaging	Status		Imaging: Status:
Imaging	Status	Scheduled/Upcoming	Imaging: Status: Scheduled/
			Upcoming
Imaging	Status	Completed	Imaging: Status: Completed
Imaging	Status	Declined/Refused	Imaging: Status: Declined/
			Refused
Imaging	Status	Recommended	Imaging: Status: Recommended
Imaging	Result	Value/Result/Finding	Imaging: Result: Value/Result/
			Finding
Imaging	Result	Normal	Imaging: Result: Normal
Imaging	Result	Abnormal	Imaging: Result: Abnormal
Vaccine			Vaccine::
Vaccine	Status		Vaccine: Status:
Vaccine	Status	Scheduled/Upcoming	Vaccine: Status: Scheduled/
			Upcoming
Vaccine	Status	Completed	Vaccine: Status: Completed
Vaccine	Status	Declined/Refused	Vaccine: Status: Declined/
			Refused
Vaccine	Status	Recommended	Vaccine: Status: Recommended
Provider			Provider::
Provider	Type		Provider: Type:
Provider	Type	Physician/Practitioner	Provider: Type: Physician/
			Practitioner
Provider	Type	Other Health	Provider: Type: Other Health
		Professional	Professional
Provider	Status of Referral		Provider: Status of Referral:
Provider	Status of Referral	Recommended/To	Provider: Status of Referral:
		Start	Recommended/To Start
Provider	Status of Referral	On-going	Provider: Status of Referral
			On-going
Provider	Status of Referral	Discontinued/Stopped	Provider: Status of Referral:
			Discontinued/Stopped
Provider	Status of Referral	Requested	Provider: Status of Referral:
			Requested
Provider	Urgent/Emergency		Provider: Urgent/Emergency
	Care		Care:
Provider	Hospital		Provider: Hospital:
Provider	Follow-up Visit		Provider: Follow-up Visit:
Patient			Patient instructions/
instructions/			education/recommendation::
education/
recommendation
Social Hx			Social Hx::
Social Hx	Lifestyle/Wellness		Social Hx: Lifestyle/
	Habits		Wellness Habits:
Social Hx	Tobacco		Social Hx: Tobacco:
Social Hx	Tobacco	Active	Social Hx: Tobacco: Active
Social Hx	Tobacco	Second Hand	Social Hx: Tobacco: Second
		Smoking	Hand Smoking
Social Hx	Tobacco	Former	Social Hx: Tobacco: Former
Social Hx	Tobacco	Never	Social Hx: Tobacco: Never
Social Hx	Tobacco	Current	Social Hx: Tobacco: Current
		Quantity/Freq	Quantity/Freq
Social Hx	Tobacco	Former Quantity/Freq	Social Hx: Tobacco: Former
			Quantity/Freq
Social Hx	Tobacco	Counseling	Social Hx: Tobacco: Counseling
Social Hx	Alcohol		Social Hx: Alcohol:
Social Hx	Alcohol	Active	Social Hx: Alcohol: Active
Social Hx	Alcohol	Denies	Social Hx: Alcohol: Denies
Social Hx	Alcohol	Former	Social Hx: Alcohol: Former
Social Hx	Alcohol	Never	Social Hx: Alcohol: Never
Social Hx	Alcohol	Current	Social Hx: Alcohol: Current
		Quantity/Freq	Quantity/Freq
Social Hx	Alcohol	Former Quantity/Freq	Social Hx: Alcohol: Former
			Quantity/Freq
Social Hx	Alcohol	Counseling	Social Hx: Alcohol: Counseling
Social Hx	Marijuana or Drug		Social Hx: Marijuana or Drug
	Use		Use:
Social Hx	Marijuana or Drug	Active	Social Hx: Marijuana or Drug
	Use		Use: Active
Social Hx	Marijuana or Drug	Former	Social Hx: Marijuana or Drug
	Use		Use: Former
Social Hx	Marijuana or Drug	Never	Social Hx: Marijuana or Drug
	Use		Use: Never
Social Hx	Marijuana or Drug	Current	Social Hx: Marijuana or Drug
	Use	Quantity/Freq	Use: Current Quantity/Freq
Social Hx	Marijuana or Drug	Former Quantity	Social Hx: Marijuana or Drug
	Use		Use: Former Quantity
Social Hx	Marijuana or Drug	Counseling	Social Hx: Marijuana or Drug
	Use		Use: Counseling
Social Hx	Socio Economic		Social Hx: Socio Economic
	Status		Status:
Social Hx	Socio Economic	Home	Social Hx: Socio Economic
	Status		Status: Home
Social Hx	Socio Economic	Occupation	Social Hx: Socio Economic
	Status		Status: Occupation
Social Hx	Socio Economic	Insurance	Social Hx: Socio Economic
	Status		Status: Insurance
Social Hx	Logistics		Social Hx: Logistics:
Social Hx	Logistics	Transportation	Social Hx: Logistics:
			Transportation
Social Hx	Sexual History		Social Hx: Sexual History:
Social Hx	Sexual History	Active	Social Hx: Sexual
			History: Active
Social Hx	Sexual History	Inactive	Social Hx: Sexual
			History: Inactive
Social Hx	Sexual History	Never	Social Hx: Sexual
			History: Never
Social Hx	Sexual History	Quantity of Partners	Social Hx: Sexual
			History: Quantity of Partners
Social Hx	Travel History		Social Hx: Travel History:
Code Status/	Code Status/End		Code Status/End of Life:
End of Life	of Life		Code Status/End of Life:
Reproductive			Reproductive Hx::
Hx
Reproductive	Gravida (Number		Reproductive Hx: Gravida
Hx	of Pregnancies)		(Number of Pregnancies):
Reproductive	Parity (Number of		Reproductive Hx: Parity
Hx	Births Carried to a		(Number of Births Carried to
	Viable Gestational Age)		a Viable Gestational Age):
Reproductive	Number of		Reproductive Hx: Number of
Hx	Premature Births		Premature Births:
Reproductive	Number of Natural		Reproductive Hx: Number of
Hx	Abortions/Miscarriages		Natural Abortions/Miscarriages:
Reproductive	Number of Living		Reproductive Hx: Number of
Hx	Children		Living Children:
Reproductive	Currently Pregnant		Reproductive Hx: Currently
Hx			Pregnant:
Reproductive	Current		Reproductive Hx: Current
Hx	Gestational Age		Gestational Age:
Reproductive	Anticipating		Reproductive Hx: Anticipating
Hx	Planned or Unplanned		Planned or Unplanned
	Pregnancy		Pregnancy:
Reproductive	Infertility Issue		Reproductive Hx: Infertility
Hx			Issue:
Reproductive	IVF		Reproductive Hx: IVF:
Hx
Reproductive	Last Menstrual		Reproductive Hx: Last
Hx	Period		Menstrual Period:
Reproductive	Menarche (Time		Reproductive Hx: Menarche
Hx	of First Period)		(Time of First Period):
Vitals	Ht		Vitals: Ht:
Vitals	Ht	Value/Result/Finding	Vitals: Ht: Value/Result/Finding
Vitals	Ht	Normal	Vitals: Ht: Normal
Vitals	Ht	Abnormal	Vitals: Ht: Abnormal
Vitals	Wt		Vitals: Wt:
Vitals	Wt	Value/Result/Finding	Vitals: Wt: Value/Result/Finding
Vitals	Wt	Normal	Vitals: Wt: Normal
Vitals	Wt	Abnormal	Vitals: Wt: Abnormal
Vitals	BMI		Vitals: BMI:
Vitals	BMI	Value/Result/Finding	Vitals: BMI :Value/Result/Finding
Vitals	BMI	Normal	Vitals: BMI: Normal
Vitals	BMI	Abnormal	Vitals: BMI: Abnormal
Vitals	Temp		Vitals: Temp:
Vitals	Temp	Value/Result/Finding	Vitals: Temp: Value/Result/Finding
Vitals	Temp	Normal	Vitals: Temp: Normal
Vitals	Temp	Abnormal	Vitals: Temp: Abnormal
Vitals	HR		Vitals: HR:
Vitals	HR	Value/Result/Finding	Vitals: HR: Value/Result/Finding
Vitals	HR	Normal	Vitals: HR: Normal
Vitals	HR	Abnormal	Vitals: HR: Abnormal
Vitals	BP		Vitals: BP:
Vitals	BP	Value/Result/Finding	Vitals: BP: Value/Result/Finding
Vitals	BP	Normal	Vitals: BP: Normal
Vitals	BP	Abnormal	Vitals: BP: Abnormal
Vitals	Resp Rate		Vitals: Resp Rate:
Vitals	Resp Rate	Value/Result/Finding	Vitals: Resp Rate:
			Value/Result/Finding
Vitals	Resp Rate	Normal	Vitals: Resp Rate: Normal
Vitals	Resp Rate	Abnormal	Vitals: Resp Rate: Abnormal
Vitals	O2		Vitals: O2:
Vitals	O2	Value/Result/Finding	Vitals: O2: Value/Result/Finding
Vitals	O2	Normal	Vitals: O2: Normal
Vitals	O2	Abnormal	Vitals: O2: Abnormal
Exam	General		Exam: General:
Exam	General	Value/Result/Finding	Exam: General: Value/Result/Finding
Exam	Const		Exam: Const:
Exam	Const	Value/Result/Finding	Exam: Const: Value/Result/Finding
Exam	Eyes		Exam: Eyes:
Exam	Eyes	Value/Result/Finding	Exam: Eyes: Value/Result/Finding
Exam	ENMT		Exam: ENMT:
Exam	ENMT	Value/Result/Finding	Exam: ENMT : Value/Result/Finding
Exam	Dental		Exam: Dental:
Exam	Dental	Value/Result/Finding	Exam: Dental: Value/Result/Finding
Exam	Neck		Exam: Neck:
Exam	Neck	Value/Result/Finding	Exam: Neck: Value/Result/Finding
Exam	Resp/Pulm		Exam: Resp/Pulm:
Exam	Resp/Pulm	Value/Result/Finding	Exam: Resp/Pulm: Value/Result/
			Finding
Exam	CV		Exam: CV:
Exam	CV	Value/Result/Finding	Exam: CV: Value/Result/Finding
Exam	Lymph		Exam: Lymph:
Exam	Lymph	Value/Result/Finding	Exam: Lymph: Value/Result/Finding
Exam	GU		Exam: GU:
Exam	GU	Value/Result/Finding	Exam: GU: Value/Result/Finding
Exam	MSK		Exam: MSK:
Exam	MSK	Value/Result/Finding	Exam: MSK: Value/Result/Finding
Exam	Derm		Exam: Derm:
Exam	Derm	Value/Result/Finding	Exam: Derm: Value/Result/Finding
Exam	Neuro		Exam: Neuro:
Exam	Neuro	Value/Result/Finding	Exam: Neuro: Value/Result/Finding
Exam	Abd		Exam: Abd:
Exam	Abd	Value/Result/Finding	Exam: Abd: Value/Result/Finding
Exam	Breast		Exam: Breast:
Exam	Breast	Value/Result/Finding	Exam: Breast: Value/Result/Finding
Exam	Rectal		Exam: Rectal:
Exam	Rectal	Value/Result/Finding	Exam: Rectal: Value/Result/Finding
Exam	Prostate		Exam: Prostate:
Exam	Prostate	Value/Result/Finding	Exam: Prostate: Value/Result/Finding
Exam	Hernia		Exam: Hernia:
Exam	Hernia	Value/Result/Finding	Exam: Hernia: Value/Result/Finding
Exam	Bimanual/GYN		Exam: Bimanual/GYN:
Exam	Bimanual/GYN	Value/Result/Finding	Exam: Bimanual/GYN:
			Value/Result/Finding
Exam	Psych		Exam: Psych:
Exam	Psych	Value/Result/Finding	Exam: Psych: Value/Result/Finding
Exam	Extremities		Exam: Extremities:
Exam	Extremities	Value/Result/Finding	Exam: Extremities: Value/Result/
			Finding
Sym	Const		Sym: Const::
Sym	Const	Fever	Sym: Const: Fever:
Sym	Const	Chills	Sym: Const: Chills:
Sym	Const	Night Sweats	Sym: Const: Night Sweats:
Sym	Const	Body Aches	Sym: Const: Body Aches:
Sym	Const	Pain (Non-specific)	Sym: Const: Pain (Non-specific):
Sym	Const	Fatigue	Sym: Const: Fatigue:
Sym	Const	Lightheadedness	Sym: Const: Lightheadedness:
Sym	Const	Difficulty Sleeping	Sym: Const: Difficulty Sleeping:
Sym	Const	General Weakness	Sym: Const: General Weakness:
Sym	Const	Weight Loss	Sym: Const: Weight Loss:
Sym	Const	Weight Gain	Sym: Const: Weight Gain:
Sym	Eyes		Sym: Eyes::
Sym	Eyes	Change in Vision	Sym: Eyes: Change in Vision:
Sym	Eyes	Double Vision	Sym: Eyes: Double Vision
		(Diplopia)	(Diplopia):
Sym	Eyes	Flashers (Photopsia)	Sym: Eyes: Flashers
			(Photopsia):
Sym	Eyes	Sensitivity to Light	Sym: Eyes: Sensitivity to Light
		(Photophobia)	(Photophobia):
Sym	Eyes	Eye Pain	Sym: Eyes: Eye Pain:
Sym	Eyes	Eye Discharge	Sym: Eyes: Eye Discharge:
Sym	Eyes	Red Eye	Sym: Eyes: Red Eye:
Sym	Eyes	Dry Eye	Sym: Eyes: Dry Eye:
Sym	ENMT		Sym: ENMT::
Sym	ENMT	Ear Pain (Otalgia)	Sym: ENMT: Ear Pain (Otalgia):
Sym	ENMT	Ear Discharge	Sym: ENMT: Ear Discharge
		(Otorrhea)	(Otorrhea):
Sym	ENMT	Swollen Ear	Sym: ENMT: Swollen Ear:
Sym	ENMT	Hearing Loss	Sym: ENMT: Hearing Loss:
Sym	ENMT	Sensitivity to Sounds	Sym: ENMT: Sensitivity to
		(Phonophobia or	Sounds (Phonophobia or
		Hyperacusis)	Hyperacusis):
Sym	ENMT	Ear Ringing	Sym: ENMT: Ear Ringing
		(Tinnitus)	(Tinnitus):
Sym	ENMT	Nose Bleeding	Sym: ENMT: Nose Bleeding
		(Epistaxis)	(Epistaxis):
Sym	ENMT	Nasal Discharge	Sym: ENMT: Nasal Discharge
		(Rhinorrhea)	(Rhinorrhea):
Sym	ENMT	Nasal Congestion	Sym: ENMT: Nasal Congestion:
Sym	ENMT	Loss of Smell	Sym: ENMT: Loss of Smell:
Sym	ENMT	Sinus Issue	Sym: ENMT: Sinus Issue:
Sym	ENMT	Sore Throat	Sym: ENMT: Sore Throat:
Sym	ENMT	Oral Sores/Lesions	Sym: ENMT: Oral Sores/Lesions:
Sym	ENMT	Painful Swallowing	Sym: ENMT: Painful Swallowing
		(Odynophagia)	(Odynophagia):
Sym	ENMT	Loss of Taste	Sym: ENMT: Loss of Taste:
Sym	ENMT	Tooth Pain	Sym: ENMT: Tooth Pain:
Sym	ENMT	Bleeding Gums	Sym: ENMT: Bleeding Gums
		(Gingival Hemorrhage)	(Gingival Hemorrhage):
Sym	ENMT	Hoarse Voice	Sym: ENMT: Hoarse Voice:
Sym	ENMT	Change in Voice	Sym: ENMT: Change in Voice:
Sym	ENMT	Neck Pain	Sym: ENMT: Neck Pain:
Sym	ENMT	Change of Taste	Sym: ENMT: Change of Taste
		(Dysgeusia)	(Dysgeusia):
Sym	CV		Sym: CV::
Sym	CV	Chest Pain (Angina)	Sym: CV: Chest Pain (Angina):
Sym	CV	Palpitations	Sym: CV: Palpitations:
Sym	CV	Leg Swelling (Edema)	Sym: CV: Leg Swelling (Edema):
Sym	CV	Leg Pain with walking	Sym: CV: Leg Pain with walking
		(Claudication)	(Claudication):
Sym	CV	Fainting/Syncope	Sym: CV: Fainting/Syncope:
Sym	Resp		Sym: Resp::
Sym	Resp	Cough	Sym: Resp: Cough:
Sym	Resp	Hemoptysis	Sym: Resp: Hemoptysis:
Sym	Resp	Wheezing	Sym: Resp: Wheezing:
Sym	Resp	SOB Lying Flat	Sym: Resp: SOB Lying Flat
		(Orthopnea)	(Orthopnea):
Sym	Resp	SOB when Waking	Sym: Resp: SOB when Waking
		up (Paroxysmal	up (Paroxysmal Nocturnal
		Nocturnal Dyspnea)	Dyspnea):
Sym	Resp	SOB	Sym: Resp: SOB:
Sym	GI		Sym: GI::
Sym	GI	Decreased Appetite	Sym: GI: Decreased Appetite:
Sym	GI	Nausea	Sym: GI: Nausea:
Sym	GI	Vomiting	Sym: GI: Vomiting:
Sym	GI	Difficulty Swallowing	Sym: GI: Difficulty Swallowing
		(Dysphagia)	(Dysphagia):
Sym	GI	Jaundice	Sym: GI: Jaundice:
Sym	GI	Abdominal Pain	Sym: GI: Abdominal Pain:
Sym	GI	Abdominal Distension	Sym: GI: Abdominal Distension:
Sym	GI	Change in Bowel	Sym: GI: Change in Bowel
		Habits	Habits:
Sym	GI	Diarrhea	Sym: GI: Diarrhea:
Sym	GI	Constipation	Sym: GI: Constipation:
Sym	GI	Stool Incontinence	Sym: GI: Stool Incontinence
		(Encopresis)	(Encopresis):
Sym	GI	Bright Red Blood Per	Sym: GI: Bright Red Blood Per
		Rectum (Hematochezia)	Rectum (Hematochezia):
Sym	GI	Black Tarry Stools	Sym: GI: Black Tarry Stools
		(Melena)	(Melena):
Sym	GI	GI Bleeding (Non-	Sym: GI: GI Bleeding (Non-
		specific)	specific):
Sym	GI	Anal Pain	Sym: GI: Anal Pain:
Sym	GU		Sym: GU::
Sym	GU	Pelvic Pain	Sym: GU: Pelvic Pain:
Sym	GU	Sexually Transmitted	Sym: GU: Sexually Transmitted
		Disease Exposure	Disease Exposure:
Sym	GU	Frequent Urination	Sym: GU: Frequent Urination:
Sym	GU	Decreased Urination	Sym: GU: Decreased Urination:
Sym	GU	Urgent Urination	Sym: GU: Urgent Urination:
Sym	GU	Urinary Hesitancy	Sym: GU: Urinary Hesitancy:
Sym	GU	Burning with	Sym: GU: Burning with
		Urination (Dysuria)	Urination (Dysuria):
Sym	GU	Blood in urine	Sym: GU: Blood in urine
		(Hematuria)	(Hematuria):
Sym	GU	Changes in Urine	Sym: GU: Changes in Urine
		Quality (Non-bloody)	Quality (Non-bloody):
Sym	GU	Incomplete Bladder	Sym: GU: Incomplete Bladder
		Emptying (Urinary	Emptying (Urinary Retention):
		Retention)
Sym	GU	Urinary Incontinence	Sym: GU: Urinary Incontinence:
Sym	GU	Penile Discharge	Sym: GU: Penile Discharge:
Sym	GU	Penile Ulcers	Sym: GU: Penile Ulcers:
Sym	GU	Testicular Pain	Sym: GU: Testicular Pain:
Sym	GU	Scrotal	Sym: GU: Scrotal
		Mass/Swelling	Mass/Swelling:
Sym	GU	Difficulty Obt. or	Sym: GU: Difficulty Obt. or
		Maint. an Erection	Maint. an Erection (Erectile
		(Erectile Dysfunction)	Dysfunction):
Sym	GU	Heavy Menstrual	Sym: GU: Heavy Menstrual
		Bleeding (Menorrhagia)	Bleeding (Menorrhagia):
Sym	GU	Menstrual Regularity	Sym: GU: Menstrual Regularity:
Sym	GU	Irregular Menstrual	Sym: GU: Irregular Menstrual
		Bleeding (Metrorrhagia)	Bleeding (Metrorrhagia):
Sym	GU	Vaginal Bleeding	Sym: GU: Vaginal Bleeding
		after Menopause	after Menopause:
Sym	GU	Menstrual Pain	Sym: GU: Menstrual Pain
		(dysmenorrhea)	(dysmenorrhea):
Sym	GU	Vaginal Discharge	Sym: GU: Vaginal Discharge:
Sym	GU	Vaginal/Vulva ulcers	Sym: GU: Vaginal/Vulva ulcers:
Sym	GU	Pain with Sexual	Sym: GU: Pain with Sexual
		Intercourse (Dyspareunia)	Intercourse (Dyspareunia):
Sym	GU	Vaginal Dryness	Sym: GU: Vaginal Dryness:
Sym	MSK		Sym: MSK::
Sym	MSK	Pain	Sym: MSK: Pain:
Sym	MSK	Swelling	Sym: MSK: Swelling:
Sym	MSK	Decreased Range Of	Sym: MSK: Decreased Range Of
		Motion	Motion:
Sym	Skin/Br		Sym: Skin/Br::
Sym	Skin/Br	Rash	Sym: Skin/Br: Rash:
Sym	Skin/Br	Stria	Sym: Skin/Br: Stria:
Sym	Skin/Br	Wounds	Sym: Skin/Br: Wounds:
Sym	Skin/Br	Incisions	Sym: Skin/Br: Incisions:
Sym	Skin/Br	Scrapes	Sym: Skin/Br: Scrapes:
Sym	Skin/Br	Sores/Ulcers	Sym: Skin/Br: Sores/Ulcers:
Sym	Skin/Br	Skin Darkening	Sym: Skin/Br: Skin Darkening:
Sym	Skin/Br	Hair Loss	Sym: Skin/Br: Hair Loss:
Sym	Skin/Br	Thinning Hair	Sym: Skin/Br: Thinning Hair:
Sym	Skin/Br	Sun sensitivity	Sym: Skin/Br: Sun sensitivity:
Sym	Skin/Br	Skin Itch (Pruritis)	Sym: Skin/Br: Skin Itch
			(Pruritis):
Sym	Skin/Br	Breast Lumps	Sym: Skin/Br: Breast Lumps:
Sym	Skin/Br	Breast Pain	Sym: Skin/Br: Breast Pain:
Sym	Skin/Br	Nipple Discharge	Sym: Skin/Br: Nipple Discharge:
Sym	Skin/Br	Fingers/Toes/Extremities	Sym: Skin/Br: Fingers/Toes/
		Turn Colors in Cold	Extremities Turn Colors in Cold:
Sym	Skin/Br	Nail Issue	Sym: Skin/Br: Nail Issue:
Sym	Skin/Br	Dry Skin	Sym: Skin/Br: Dry Skin:
Sym	Skin/Br	Scalp Tenderness	Sym: Skin/Br: Scalp
			Tenderness:
Sym	Neuro		Sym: Neuro::
Sym	Neuro	Pain	Sym: Neuro: Pain:
Sym	Neuro	Sensation changes	Sym: Neuro: Sensation changes
		(Numbness/Coldness/	(Numbness/Coldness/Crawling/
		Crawling/Prickling/	Prickling/Parasthesias):
		Parasthesias)
Sym	Neuro	Memory Loss	Sym: Neuro: Memory Loss:
Sym	Neuro	Difficulty Thinking/	Sym: Neuro: Difficulty Thinking/
		Changes in Mentation	Changes in Mentation:
Sym	Neuro	Seizures	Sym: Neuro: Seizures:
Sym	Neuro	Tremor	Sym: Neuro: Tremor:
Sym	Neuro	Dizziness	Sym: Neuro: Dizziness:
Sym	Neuro	Speech Problems	Sym: Neuro: Speech Problems:
Sym	Neuro	Non-general	Sym: Neuro: Non-general
		Weakness	Weakness:
Sym	Neuro	Muscle Cramps	Sym: Neuro: Muscle Cramps:
Sym	Neuro	Jaw Pain with	Sym: Neuro: Jaw Pain with
		Chewing	Chewing:
Sym	Neuro	Headache	Sym: Neuro: Headache:
Sym	Psych		Sym: Psych::
Sym	Psych	Anxiety	Sym: Psych: Anxiety:
Sym	Psych	Depressed Mood	Sym: Psych: Depressed Mood:
Sym	Psych	Feeling of Failure	Sym: Psych: Feeling of Failure:
Sym	Psych	Psychomotor	Sym: Psych: Psychomotor
		Agitation or	Agitation or Retardation:
		Retardation
Sym	Psych	Sadness	Sym: Psych: Sadness:
Sym	Psych	Anhedonia	Sym: Psych: Anhedonia:
Sym	Psych	Manic Episodes	Sym: Psych: Manic Episodes:
Sym	Psych	Change in	Sym: Psych: Change in
		Personality	Personality:
Sym	Psych	Paranoia	Sym: Psych: Paranoia:
Sym	Psych	Hallucinations	Sym: Psych: Hallucinations:
Sym	Psych	Irritability/Mood	Sym: Psych: Irritability/Mood
		Swings	Swings:
Sym	Psych	Wake up	Sym: Psych: Wake up
		Unrefreshed	Unrefreshed:
Sym	Psych	Stress	Sym: Psych: Stress:
Sym	Psych	Suicidality	Sym: Psych: Suicidality:
Sym	Psych	Homicidality	Sym: Psych: Homicidality:
Sym	Psych	Changes in Sexual	Sym: Psych: Changes in Sexual
		Arousal	Arousal:
Sym	Endo		Sym: Endo::
Sym	Endo	Heat Intolerance	Sym: Endo: Heat Intolerance:
Sym	Endo	Cold Intolerance	Sym: Endo: Cold Intolerance:
Sym	Endo	Excessive Thirst	Sym: Endo: Excessive Thirst
		(Polydipsia)	(Polydipsia):
Sym	Endo	Excessive Appetite	Sym: Endo: Excessive Appetite
		(Polyphagia)	(Polyphagia):
Sym	Endo	Excessive Sweating	Sym: Endo: Excessive
			Sweating:
Sym	Endo	Flushing	Sym: Endo: Flushing:
Sym	Endo	Hot Flashes	Sym: Endo: Hot Flashes
		(Vasomotor	(Vasomotor symptoms):
		symptoms)
Sym	Heme/Lymph		Sym: Heme/Lymph::
Sym	Heme/Lymph	Lymph Node	Sym: Heme/Lymph: Lymph Node
		Enlargement/Tenderness	Enlargement/Tenderness:
Sym	Heme/Lymph	Easy Bruising	Sym: Heme/Lymph: Easy
			Bruising:
Sym	Heme/Lymph	Easy Bleeding	Sym: Heme/Lymph: Easy
			Bleeding:
Sym	Immuno		Sym: Immuno::
Sym	Immuno	Anaphylaxis	Sym: Immuno: Anaphylaxis:
Sym	Immuno	Hives (Urticaria)	Sym: Immuno: Hives (Urticaria):
Sym	Immuno	Frequent Sneezing	Sym: Immuno: Frequent
			Sneezing:
Sym	Immuno	Seasonal Allergies	Sym: Immuno: Seasonal
			Allergies:
Sym	Immuno	Environmental	Sym: Immuno: Environmental
		Allergies	Allergies:
Sym	Immuno	Exposure to	Sym: Immuno: Exposure to
		infectious diseases	infectious diseases (TB, HIV,
		(TB, HIV, etc.)	etc.):
Sym			Sym:::
Sym	Suggest Entity		Sym: Suggest Entity::

Claims

What is claimed is:

1. A computer-implemented method comprising:

receiving a training dataset comprising a plurality of labeled transcripts of respective medical practitioner-patient conversations, each labeled transcript having been generated by a user interaction with the transcript, the user interaction having comprised of a user relating two different highlighted spans of text by utilizing an input device of a computing device, and the computing device having been configured to interpret the user interaction to be an indication that the two user-related different highlighted spans of text are medically related to a same health condition of the patient;

training, based on the training dataset, a machine learning model to automatically annotate an input transcript of a recording of a medical conversation; and

outputting the trained machine learning model.

2. The method of claim 1, wherein the transcript of the recording is indexed to time segment information.

3. The method of claim 1, wherein the automatic annotation of the input transcript comprises storing the annotation of the input transcript as metadata associated with the input transcript.

4. The method of claim 1, wherein each highlighted span of text in the transcript is associated with a label encoding a medical entity and one or more attributes of the medical entity appearing in the highlighted span, wherein medical entities indicate categories of medical topics, and wherein medical attributes indicate descriptive properties or characteristics of an associated medical entity.

5. The method of claim 4, wherein the medical entity comprises one or more of medications, procedures, symptoms, vitals, lab results, chief complaint, social history, medical conditions, surgery, imaging, provider, vaccine, reproductive history, examination, and medical equipment.

6. The method of claim 5, wherein the medical entity is a symptom medical entity comprising an attribute of at least severity, frequency, onset, or location of a medical condition.

7. The method of claim 1, further comprising:

generating the training dataset by providing a plurality of unlabeled transcripts to a pre-labeling system and receiving, from the pre-labeling system a pre-annotated transcript containing suggested labels for spans of text in each of the plurality of unlabeled transcripts.

8. The method of claim 7, wherein the pre-labeling system includes a named entity recognition model trained on at least one of medical textbooks, a lexicon of clinical terms, clinical documentation in electronic health records, and annotated transcripts of doctor-patient conversations.

9. The method of claim 1, further comprising:

providing the annotated version of the input transcript to another computing device for another user interaction with the annotated version, wherein the other user interaction comprises modifications including one or more additional annotations of the annotated version;

receiving, from the other computing device, the modified version of the annotated transcript; and

fine-tuning the trained machine learning model based on the modified version.

10. A computer-implemented method comprising:

receiving an input transcript of a recording of a medical practitioner-patient conversation;

applying a trained machine learning model to automatically annotate the input transcript, the machine learning model having been trained on a training dataset comprising a plurality of labeled transcripts of respective medical conversations, each labeled transcript having been generated by a user interaction with the transcript, the user interaction having comprised of a user relating two different highlighted spans of text by utilizing an input device of a computing device, and the computing device having been configured to interpret the user interaction to be an indication that the two user-related different highlighted spans of text are medically related to a same health condition of the patient; and

providing the annotated version of the input transcript.

11. The method of claim 10, further comprising:

generating the input transcript of the recording.

12. The method of claim 10, further comprising:

generating a display of the input transcript of the recording.

13. The method of claim 10, wherein the input transcript is indexed to time segment information.

14. The method of claim 10, wherein the automatic annotation of the input transcript comprises storing the annotation of the input transcript as metadata associated with the input transcript.

15. The method of claim 10, wherein the providing of the annotated version comprises associating each highlighted span of text in the input transcript with a label encoding a medical entity and one or more attributes of the medical entity appearing in the highlighted span, wherein medical entities indicate categories of medical topics, and wherein medical attributes indicate descriptive properties or characteristics of an associated medical entity.

16. The method of claim 15, wherein the medical entity comprises one or more of medications, procedures, symptoms, vitals, lab results, chief complaint, social history, medical conditions, surgery, imaging, provider, vaccine, reproductive history, examination, and medical equipment.

17. The method of claim 15, wherein the medical entity is a symptom medical entity comprising an attribute of at least severity, frequency, onset, or location of a medical condition.

18. A system comprising:

a computing device; and

data storage, wherein the data storage has stored thereon computer-executable instructions that, when executed by the one or more processors, cause the computing device to carry out functions comprising:

providing the annotated version of the input transcript.

19. The system of claim 18, further comprising:

generating the input transcript of the recording.

20. The system of claim 18, wherein the providing of the annotated version comprises associating each highlighted span of text in the input transcript with a label encoding a medical entity and one or more attributes of the medical entity appearing in the highlighted span, wherein medical entities indicate categories of medical topics, and wherein medical attributes indicate descriptive properties or characteristics of an associated medical entity.