Patent Application
20230048252
The present application claims priority to Indian Provisional Application No. 202141036711 entitled “METHODS AND SYSTEMS FOR TREATMENT GUIDELINE DISPLAY”, filed on Aug. 13, 2021, and to Indian Provisional Application No. 202141036688 entitled “METHODS AND SYSTEMS FOR PATIENT HISTORY SUMMARIES”, filed on Aug. 13, 2021. The entire contents of the above-identified applications are hereby incorporated by reference for all purposes.
Embodiments of the subject matter disclosed herein relate to analysis of patient information, and more particularly to patient history analysis and display of treatment guidelines.
Digital collection, processing, storage, and retrieval of patient medical records may include a conglomeration of large quantities of data configured in a wide variety of formats. In some examples, the data may include numerous medical procedures and records generated during investigations of the patient, including a variety of examinations, such as blood tests, urine tests, pathology reports, image-based scans, etc. For a large hospital facility, retrieving all the relevant information stored in different formats and presenting it to a medical practitioner in an efficient, streamlined manner for analysis is challenging.
Duration of the diagnosis of a medical condition of a subject followed by treatment may be spread over time from a few days to a few months or even years in the case of chronic disease, e.g., diseases that take more than one year to cure. Examples of chronic disease include Alzheimer's disease, cancer, asthma, and diabetes, amongst others. Over the course of diagnosing and treating chronic disease, the patient may undergo many different treatments and procedures, and may move to different hospitals and/or geographic locations. Maintaining the patients records and evaluating ongoing and future directions for treatment may be a complicated task.
Clinical standardization of patient care, with respect to chronic disease, may reduce costs and increase a quality of treatment. For example, by referring to standardized cancer treatment guidelines and pathways, physicians may be better equipped to select optimized treatment routes for the patient. Use of the standardized guidelines, however, may depend on both accurate tracking of the patient's medical history and adherence to the recommendations provided by the guidelines which may be challenging due to a complexity of data and an amount of data to process.
Physicians are increasingly relying on Electronic Health Record (EHR) systems to review historical health records of the patient during diagnosis. For patients with chronic illnesses, there are often hundreds of EHRs resulting from numerous routine visits. Sorting and extracting information from past EHRs for such patients is a slow and inefficient process, increasing a likelihood of missing records with relevant data which may be spread out across a large number of less informative routine visit records. Furthermore, the standardized treatment guidelines may include numerous options at junctions along the treatment route and selecting the most suitable choice based on patient history may be difficult. As a result, patient care may deviate from the treatment guidelines, leading to adverse effects on an accuracy of diagnosis and quality of treatment.
Systems and methods are desired for matching a diagnosis of a patient to a relevant section of care guidelines and presenting the relevant guidelines to a clinician for efficient selection of treatment.
In one embodiment, a computing system includes a display screen configured to display a patient medical path listing one or more of treatment guidelines and patient medical history, and additionally being configured to display abbreviated representations of at least one of the treatment guidelines and the patient medical history that can be reached directly from the displayed patient medical path. The abbreviated representations each display a limited list of data offered within the treatment guidelines and/or the patient medical history, each of the data in the list being selectable to launch the treatment guidelines and/or the patient medical history and enable the selected data to be seen. Furthermore, the abbreviated representations are displayed while in an unlaunched state. In this way, a duration of time between treatments and/or appointments may be reduced, high quality patient care throughput and patient satisfaction may be increased, and continuity of distributed care is enhanced. Furthermore, more accurate diagnosis and treatments may be provided, leading to more successful patient treatment outcomes as well as reductions in costs.
It should be understood that the brief description above is provided to introduce in simplified form a selection of concepts that are further described in the detailed description. It is not meant to identify key or essential features of the claimed subject matter, the scope of which is defined uniquely by the claims that follow the detailed description. Furthermore, the claimed subject matter is not limited to implementations that solve any disadvantages noted above or in any part of this disclosure.
The present invention will be better understood from reading the following description of non-limiting embodiments, with reference to the attached drawings, wherein below:
The following description relates to various embodiments of patient history analysis and display of treatment guidelines based on the patient history analysis. Diagnosis and treatment of chronic diseases, such as cancer, may include many clinical markers such as procedures, check-ups, medications, results, and as well as treatment at multiple locations and clinics. A medical history of a cancer patient may store all clinical markers in a variety of file formats and, over a course of care, an amount of data corresponding to the patient's history may become large. Locating target information from the data may be time-consuming and inefficient, leading to lower quality of care and less accurate assessments, while incurring high costs.
As such, a method to organize and sort through longitudinal patient records, e.g., over an entire patient history, and efficiently indicate decisions, previous treatments, and findings directly affecting current and future selections for patient care is desired. In one example, treatment for a patient with a chronic illness may be optimized for the patient by a patient care engine configured to parse both patient medical records and a predetermined set of treatment guidelines specific to the chronic illness. The patient care engine may be a system implemented at a processor that includes a variety of software, such as modules, databases, user-interactive fields displayed at a graphical user interface of a display device, as well as algorithms determining how information is displayed at the display device. For example, the treatment guidelines may be a document providing a consensus amongst cancer care specialists regarding recommended treatments specific to diagnostic results and observations. The treatment guidelines may encompass a large amount of information that may be burdensome for a user to locate target information. By enabling the patient care engine to scan the treatment guidelines for the target information, relevant sections of the treatment guidelines may be rapidly matched to the patient's clinical markers and visually displayed, allowing the user to provide optimized care efficiently.
The patient medical records and corresponding treatment guidelines may be displayed in an overlaid manner, as shown in
The treatment path may be provided by the patient care engine as a clinical care pathway chart. For example, the patient care engine may include a plurality of modules, as shown in
Each of the relevant treatment guidelines and the patient summaries may be listed at the patient journey and depicted as abbreviated representations in an un-launched state, where a launched state refers to interaction of the user, at a display screen displaying the patient journey, with the abbreviated representations. Upon interacting with the abbreviated representations, the abbreviated representations may be altered in appearance to display selected data corresponding to each of the abbreviated representations. The abbreviated representations may, for example, be associated with limited lists of data and depicted as graphical representations such as icons, text boxes, drop-down text boxes, images, etc. The patient journey may be displayed to the user in a comprehensive graphical representation, examples of which are illustrated in
In some examples, the patient medical records may be displayed as patient summaries in the patient journey to organize and display relevant patient information in a streamlined manner. The patient summaries may be created by the patient care engine via a system shown in
An example of system 100 in which a patient care engine may be implemented for displaying treatment guidelines for a patient is depicted in
The patient care engine may include instructions and algorithms for reading EHRs from the health record database and extracting information from the EHRs based on predetermined parameters and/or input rules. The information may be used to locate relevant portions of the treatment guidelines and compared to the treatment guidelines using predefined nodes, where the nodes may also be defined based on predetermined parameters and/or rules. The patient care engine may further include instructions to display the relevant portions of the treatment guidelines as the clinical care pathway chart along with a patient event timeline in a manner that allows the user 104 to readily view a current status of the patient, past treatments, and future directions of the treatment, according to the treatment guidelines.
Two types of information may be displayed at the display device 102 including clinical information 106, herein also referred to as a patient event timeline 106, overlaid with a clinical care pathway chart 108. It will be noted that the illustrated patient event timeline 106 and the clinical care pathway chart 108 are representative depictions of the information and not replicas of how the information is displayed at the display device 102. The clinical care pathway chart 108 may be overlaid with the patient event timeline 106, thereby indicating a coupling of the two types of information at relevant points and providing a rational and robust guide to patient care. Details of a visual presentation of the information is described further below.
The patient event timeline 106 may include a plurality of electronic health records (EHRs) 112 of the patient, represented as dots in a patient journey 110. The EHRs 112 may be acquired over a relatively long duration of time, such as exceeding one year, and stored in a EHR database 122 of a computing system 120 communicatively coupled to the display device 102. The patient journey 110 may be a visual presentation of a treatment path of a patient with a chronic disease, providing a visual summary of past, current, and future medical events and may include visual representations of clinical markers, EHRs, clinical findings, organized such that a user may readily find an element of interest and navigate to additional information regarding the element of interest. The visual representation may be, for example, icons, hyperlinks, images, etc. An example of a patient journey 110 is shown in
The plurality of EHRs 112 of the patient event timeline may be stored according to a time of generation and, as shown in
As described above, data for generating the patient event timeline 106 may be acquired over time and stored in a memory 130 of the computing system 120. Generation of the patient event timeline 106 may occur during investigations of the patient and may include a variety of examination reports such as blood tests, urine tests, pathology reports, scans using various medical imaging systems such as ultrasound, magnetic resonance imaging, computed tomography systems, other radiological investigations, etc. The examination reports may be used to create the EHRs. In some examples, the patient event timeline 106 may be partitioned or segmented based on the patient's condition or treatment type, and/or the events and decisions that are clinically relevant for the user 104 may be visually indicated, e.g., by highlighting.
The clinical care pathway chart 108 may be presented according to an ordering defined by treatment guidelines. For example, the treatment guidelines may be a set of standardized protocols for treating the chronic illness developed based on consensus amongst healthcare professionals. As one example, when the chronic illness is cancer, the National Comprehensive Cancer Network guidelines may be used. Other, similar treatment guidelines may be applied to other types of chronic disease, where a primary clinical finding of a chronic disease may be tracked independently and correlated to treatment guidelines specific to the chronic disease.
The treatment guidelines may be stored at memory 130 of the computing system 120. In some examples, the patient care engine may be configured to periodically retrieve updated treatment guidelines, e.g., from a corresponding website, to be stored in addition to or, in some instances, replace the previous treatment guidelines. In other examples, the user may manually enter updated treatment guidelines for storage at memory 130.
The clinical care pathway chart 108 includes interconnected nodes 114. Relationships between the nodes 114 are indicated by connecting lines and each of the nodes may represent a junction along the treatment guidelines where a path of the clinical care pathway chart 108 may branch depending on information entered at the nodes 114. The nodes 114 may be based on diagnostic information or current or future treatment procedures include in at least one section of the treatment guidelines that is relevant to the clinical markers of the patient event timeline 106. An example of treatment guidelines 200 for cancer is illustrated in
The computing system 120 may include or be communicatively coupled to EHR database 122. EHR database 122 may be stored in a mass storage device (e.g., part of the computing system 120) configured to communicate with secure channels (e.g., HTTPS and TLS), and store data in encrypted form. Further, the computing system 120 is configured to control access to patient electronic medical records such that only authorized healthcare providers may edit and access the electronic health records. An EHR for a patient may include patient demographic information, family medical history, past medical history, lifestyle information, preexisting medical conditions, current medications, allergies, surgical history, past medical screenings and procedures, past hospitalizations and visits, etc.
The computing system 120 includes memory 130 and a processor(s) 132 to store and execute the methods disclosed herein to generate the patient journey 110, as well as send and receive communications, graphical user interfaces, medical data, and other information.
Memory 130 includes one or more data storage structures, such as optical memory devices, magnetic memory devices, or solid-state memory devices, for storing programs and routines executed by processor(s) 132 to carry out various functionalities disclosed herein. Memory 130 may include any desired type of volatile and/or non-volatile memory such as, for example, static random access memory (SRAM), dynamic random access memory (DRAM), flash memory, read-only memory (ROM), etc. Processor(s) 132 may be any suitable processor, processing unit, or microprocessor, for example. Processor(s) 132 may be a multi-processor system, and, thus, may include one or more additional processors that are identical or similar to each other and that are communicatively coupled via an interconnection bus.
In some examples, the computing system 120 may be implemented over a cloud or other computer network. For example, the computing system 120 is shown in
As used herein, the term “module” may include a hardware and/or software system that operates to perform one or more functions. For example, a module may include a computer processor, controller, or other logic-based device that performs operations based on instructions stored on a tangible and non-transitory computer readable storage medium, such as a computer memory. Alternatively, a module may include a hard-wired device that performs operations based on hard-wired logic of the device. Various modules shown in the attached figures may represent the hardware that operates based on software or hardwired instructions, the software that directs hardware to perform the operations, or a combination thereof.
“Modules” may include or represent hardware and associated instructions (e.g., software stored on a tangible and non-transitory computer readable storage medium, such as a computer hard drive, ROM, RAM, or the like) that perform one or more operations described herein. The hardware may include electronic circuits that include and/or are connected to one or more logic-based devices, such as microprocessors, processors, controllers, or the like. These devices may be off-the-shelf devices that are appropriately programmed or instructed to perform operations described herein from the instructions described above. Additionally or alternatively, one or more of these devices may be hard-wired with logic circuits to perform these operations.
Turning to
For example, an example clinical care pathway chart 300, including examples of nodes 350 are shown in
The patient event timeline 302 may include EHRs 308 arranged automatically in chronological order. For example, a first EHR 308a of the EHRs 308 may be acquired prior to a second EHR 308b of the EHRs 308 in time. The patient care engine may identify a finding of a nodule indicative of lung cancer, as indicated at the first EHR 308a, and locate matches to the finding in standardized treatment guidelines, such as the NCCN guidelines, based on defined entry and exit markers for the nodes 350. For example, an entry marker for a first node 350a of the nodes 350 may include a type of finding in the patient event timeline 302 indicative of cancer, such as lung cancer. The entry marker may trigger entry to the first node 350a which may be a risk assessment node 350a. A set of exit markers 351 is shown at the risk assessment node 350a which may be a list of target information needed to proceed to a next step, e.g., a subsequent node, in the treatment guidelines, via a first path 301 of the clinical care pathway chart 300.
The set of exit markers 351 may be used to scan the EHRs of the patient event timeline 302 to locate relevant information and provide corresponding confirmations or values to the exit markers. For example, the patient care engine may locate an age of the patient, a smoking history, a previous cancer history, etc. at the second EHR 308b. In one example, the second EHR 308b may be a patient summary created by the patient care engine according to a process for generating patient summaries described further below with reference to
The nodule in the patient's lungs may be a primary diagnostic finding and, in some examples, more than one primary diagnostic finding may be found. Additionally, in some examples, diagnostic procedures may uncover secondary diagnostic findings, such as liver metastasis, as shown at the third EHR 308c (where an application of the finding to the clinical care pathway chart 30 is indicated by arrow 306). Identification of the secondary diagnostic findings may provide entry markers which may trigger entry to a different section of the treatment guidelines specific to liver metastasis, defined as a third node 350c, as indicated by arrow 306, generating a second path 303 of the clinical care pathway chart.
In examples where more than one primary diagnostic finding is presented, each primary diagnostic finding may be tracked independently. Recommendations based on the treatment guidelines may be provided for each primary diagnostic finding, e.g., individual clinical care pathway charts may be generated for each primary diagnostic finding, which may be displayed according to user selection amongst the more than one primary diagnostic findings.
The first path 301 and the second path 303 of the clinical care pathway chart 300 may be treatment paths for different findings originating from a common node (e.g, the first node 350a). Each of the first and the second paths 301, 303 may proceed to complete subsequent nodes (e.g., provide entry markers and exit markers for each node) of each pathway until the pathways each reach points where exit markers of the nodes 350 cannot be completed. In other words, each of the paths may terminate at a node where insufficient exit markers are present to exit the node. An inability to complete the exit markers may indicate a current status of the treatment and the incomplete exit markers may represent current, ongoing procedures and/or future procedures.
In this way, relevant sections of the treatment guidelines may be located and used to assess a status of the patient's ongoing care by displaying the sections at a display device, overlaid with a patient event timeline, in real-time. In some examples, the treatment guidelines may provide vast amounts of information with many treatment paths included. Treatment guideline documents may be available in a variety of formats, such as a pdf, html, an image, etc., which may complicate efficient scanning to locate target sections, such as scanning based on a keyword or phrase. By using the patient care engine, parsing of the treatment guidelines for relevant information may be streamlined and a suitable path of care may be readily selected from many possible paths. For example, as shown in
The section 400 of the treatment guidelines includes multiple junctions and outcomes. Without well-defined criteria for selecting a path at each junction, a resulting outcome may deviate from an optimal treatment path for the patient. However, by implementing the patient care engine, a specific, targeted path, as indicated by arrow 402, is selected with high accuracy, with respect to an optimal treatment path for the patient, by utilizing the patient's medical history and providing rigorous criteria for proceeding to each subsequent step.
For example, a plurality of stages 404 of treatment criteria are depicted in the section 400 of the treatment guidelines. The plurality of stages 404 are ordered sequentially and include junctions that form branches in the treatment guidelines. As an example, a patient factor stage 404a of the stages 404 may be a first junction in the section 400 of the treatment guidelines. The patient factor stage 404a may include a list of clinical markers 405, such as age, smoking history, previous cancer history, etc. The treatment guidelines may proceed to a first branch 420 or a second branch 430, as shown in
Determination of which of the first branch 420 or the second branch 430 is more suitable for the patient, and which of the respective sub-branches therein, may be achieved by parsing additional information provided in the treatment guidelines. For example, information provided in a subscript link 426 of the second sub-branch 424 of the first branch 420 may be analyzed for clinical markers corresponding to clinical markers of a patient event timeline (e.g., the patient event timeline 302 of
Parsing the additional information provided in the treatment guidelines may also include scanning information associated with hyperlinks embedded in the treatment guidelines. For example, a hyperlink 452 is shown in box 450, corresponding to a linked document displaying information regarding Principles of Diagnostic Evaluation. The linked document (not shown) may include relevant information for clinical markers such as sputum cytology, bronchoscopy with biopsy and TBNA, image-guided transthoracic needle core biopsy or FNA, thoracentesis, mediastinoscopy, etc. The matching of the clinical markers from the treatment guidelines, including information embedded in the treatment guidelines thus determines which information from the branches and sub-branches of the section 400 of the treatment guidelines to display in nodes of a clinical care pathway chart (e.g., the clinical care pathway chart 300 of
A system 500 for a patient care engine, the system 500 configured to display a clinical care pathway chart (such as the clinical care pathway chart 300 of
The guideline information collection form 502 may be provided offline, e.g., accessible to the user prior to implementation of the patient care engine, and configured as a form with user-interactive, fillable fields into which the user may either enter terms for each variable or select from a list of possible entries. For example, information requested by the guideline information collection form 502 may include a node name, a category or type of the node, confirmation of whether the node is to be displayed, and visual effects of the displayed node, such as a display color. The node name may be determined from, as one example, a table or list of possible nodes according to treatment guidelines specific to an identified chronic illness of the patient.
The guideline information collection form 502 may also include fields for defining entry and exit markers for each of the nodes. In some examples, selection of a name of the node may automatically link the name to a predetermined set of entry and exit markers from which a specific set of markers may be selected. The entry and exit markers may also be determined based on the treatment guidelines.
The guideline information collection form 502 may further include fields for defining ordering constraints (e.g., node display constraints), which may be used to modify a display of a patient care journey overlaid with a clinical care pathway chart, described further below. For example, as shown in
In some examples, the fields of the guideline information collection form 502 may be filled manually, e.g., by the user. In other examples, the fields may be automatically filled, such as by script. For example, a set of lists may be provided, each list including information for completely filling the fields. Upon entry of a specific node name, the patient care engine may automatically identify a corresponding list and use the information from the list to fill the remaining fields.
The entries from the guideline information collection form 502 may be used in conjunction with standardized treatment guidelines for the chronic illness. The standardized treatment guidelines may be entered and stored at a guideline database 504 in a variety of formats and may include one or more sets of treatment guidelines. Entry of the treatment guidelines may be conducted offline by the user. In some examples, the treatment guidelines may be periodically updated as new findings emerge and are incorporated into the treatment guidelines. In some instances, the user may manually enter the updated treatment guidelines at the guideline database 504 when a newer version of the treatment guidelines is provided at, for example, a website or saved in a memory of a computing system at which the patient care engine is implemented. In other instances, the patient care engine may include instructions to automatically search for updated treatment guidelines and upload the updated guidelines to the guideline database 504. For example, the patient care engine may use script to recognize updated versions of the treatment guidelines based on, as one example, a new version or edition number in a title of the treatment guidelines document.
The patient care engine may retrieve data input to the guideline database 504 from the guideline information collection form 502, the patient care journey information, and the treatment guidelines and send the input data to modules such as a clinical marker detection module 506, a guideline selection module 508, a node exit time-point detection module 510, and an ordering constraint enforcement module 512. Each of the modules may utilize a set of rules defined by the guideline information collection form 502 (except for the clinical marker detection module 506) to analyze the input data and provide outputs according to the set of rules. The outputs of the modules may be used to generate the clinical care pathway chart which may be displayed at a display device 514, similar to the display device 102 of
The clinical marker detection module 506 may utilize a clinical marker list, such as the list of clinical markers 405 of
As described above, the clinical marker detection module 506 may be configured to locate the clinical markers from the patient's EHRs and/or the treatment guidelines to create the clinical markers list. Upon identifying one or more of the clinical markers in the patient event timeline, the clinical marker detection module 506 may search for the identified clinical markers in the treatment guidelines document to locate relevant sections of the treatment guidelines. The relevant sections may be delivered to the guideline selection module 508.
As one example, as shown in
A second step 1106 of the NLP pipeline 1100 may include assertion recognition. The NLP pipeline 1100 may be trained to identify positive and negative assertions of clinical markers, such as presence or absence of symptoms, from the text of the EHRS 1102. As shown in
The NLP pipeline 1100 may further include a third step 1108 for relation recognition, where relationships between keywords in the text of the EHRs 1102 may be identified. For example, as shown in
Continuing to
For example, as shown in
At a fifth step 1112 of the NLP pipeline 1100, clinical markers may be recognized, e.g., clinical marker recognition, and extracted from the first, second third, and fourth steps 1104, 1106, 1108, 1110, based on the clinical marker list generated by the clinical marker detection module 506, as described above. For example, all clinical markers may be identified and extracted from the EHRs 1102 by the NLP pipeline 1100 and the clinical markers may be compared to the clinical marker list. Clinical markers that match items on the clinical marker list may be further processed by retrieving relevant treatment guidelines from the guideline database 504 and outputting the relevant sections of the treatment guidelines to the guideline selection module 508, as shown in
Returning to
At the node exit time-point detection module 510, nodes may be identified and located in the relevant sections according to a set of node traversal rules. The node traversal rules may be generated based on the guideline information collection form 502. For example, node names and types, as entered at the guideline information collection form 502, may be used to scan the relevant sections of the treatment guidelines to locate text-based matches to the node names and types. Additionally, defined entry markers and exit markers at the guideline information collection form 502 may be incorporated to determine how much of the relevant sections corresponds to past, current, and future clinical markers of the patient's ongoing treatment plan. The relevant sections of the treatment guidelines may be clipped into segments according to the exit and entry markers that are satisfied. By evaluating whether the patient care journey includes sufficient information to satisfy the entry markers and exit markers, a path of the clinical care pathway chart may be determined according to the nodes with information that satisfies the exit markers, allowing the path to proceed to subsequent nodes until one or more treatment recommendations are attained.
The satisfied nodes and treatment recommendations may be sent to the ordering constraint enforcement module 512 to be modified for display at a display device according to the entries at the guideline information collection form 502, such as which nodes are to be displayed, the color, and the ordering constraints, e.g., which nodes are previous and sequential to a specific node. For example, a sequence of the nodes may be adjusted according to the ordering constraints such that the order adheres to an order shown in the treatment guidelines. As an example, a biopsy node is depicted as a last node in the first path 301 of the clinical care pathway chart of
The segments of the treatment guideline may be displayed as the clinical care pathway chart at the display device 514 according to the guideline information collection form 502 and information processed by the modules. At the display device 514, the clinical care pathway chart may be overlaid with the patient event timeline. In some examples, each of the clinical care pathway chart and the patient event timeline may be depicted as graphical representations, such as icons, drop-down text boxes, images, etc. For example, the clinical care pathway chart, depicted as a series of nodes as shown in
A first example of a visual display of a patient journey 600 is shown in
The patient event timeline 602 may include segments 608 of treatment guidelines, the segments 608 selected based on matching of the treatment guidelines to nodes identified by a patient care engine, as described above. The segments 608 are connected by arrows to denote a sequential ordering of the segments 608. It will be appreciated that the segments 608 are depicted arranged in a non-linear layout in
The plurality of clinical markers 604 are shown grouped according to a type of clinical marker. Each group of clinical markers may be displayed as graphical representations along a lane 610 and spaced along the lane 610 to align with a corresponding segment of the segments 608 of the clinical care pathway chart 606. As such, the patient event timeline 602 and the clinical care pathway chart 606 are displayed in a clear and organized layout with minimal amounts of text that may otherwise lead to a cluttered appearance of the patient journey 600. The user may easily locate clinical markers or segments of the treatment guidelines and also identify a relationship between the clinical markers and segments.
In some examples, the patient care journey may include clinical markers that deviate from the treatment guidelines. When such instances are identified, a visual indicator, such as a message, and/or a visual modification to the EHR, such as highlighting or a color change, may be used to notify the user of the deviation.
In other examples, the clinical care pathway chart 606 may also be depicted as abbreviated representations, as described above for the patient event timeline 602, presented to the user as visual objects, such as icons, text boxes, images, etc. The abbreviated representations of the clinical care pathway chart 606 may be displayed proximate to a corresponding clinical marker of the clinical markers 604 when in an unlaunched state. Alternatively, the clinical care pathway chart 606 (and/or the patient event timeline 602) may be visible in the unlaunched state until the user interacts with the clinical markers 604. For example, when the user adjusts a position of a cursor over one of clinical markers 604, corresponding abbreviated representations of the patient event timeline 602 and/or the clinical care pathway chart 606 may appear. If the user selects one of the abbreviated representations, the abbreviated representation may be launched to display the selected data associated therewith, which may be one of summaries 603 and/or one of the segments 608 of the treatment guidelines. As such, the patient journey 600 displayed at the display screen provides the user to directly reach, e.g., acquire to observe, data listed in each of the patient event timeline 602 and the clinical care pathway chart 606 in real-time and with reduced processing of data.
A second example of a visual display of a patient journey 700 is shown in
A clinical care pathway chart 710 may be presented below the patient event timeline 704 (only partially shown in
In the patient journey 700, the plurality of clinical markers 706 may be ordered chronologically along the patient event timeline 704. In some examples, as shown in
It will be appreciated that the visual displays of the patient journeys shown in
A process 800 for processing information from a patient's EHRs 801 via a patient care engine is depicted schematically in
At 805 of the process 800, the marker sequence may be processed according to guideline selection rules at the guideline selection module 508. The guideline selection rules may be input by a user, e.g., via the guideline information collection form 502 of
At 806, the process 800 includes comparing the marker sequence to nodes 809 from the sections of the treatment guidelines at the node exit time-point detection module 510. The nodes 809 may be presented with a visual attribute to indicate a state of each node. For example, each node may include data satisfying exit markers, e.g., the node is a past node, or the node may be a currently entered node or a future node. As an example, the nodes 809 may be shown in different colors (e.g., represented by different shading in
In this way, information from a patient's medical records may be extracted and matched with treatment guidelines for a chronic illness to provide treatment recommendations efficiently. By implementing a patient care engine configured to identify clinical markers from the medical records and rapidly locate relevant sections of the treatment guidelines based on the clinical markers, an amount of time spent sorting through large quantities of information may be reduced while an accuracy of the treatments may be increased.
In order to generate a patient event timeline and clinical care pathway chart, as shown in
A patient care engine configured to generate the patient event timeline and clinical care pathway chart may also be configured to create the summaries from a common EHR database, such as EHR database 122 of
The system 900 shares components in common with the system 500 of
In one example, the user may input a summarization rule to summarize any medical event, lab result, patient status/demographic information, medicines or treatments administered, pathology results, imaging results, etc. The user may enter a plurality of summarization rules for any preference of specificity relating to the patient and EHRs of the patient. The user may input summarization rules to summarize data from the EHRs based on a medical event addition, a medical event absence, a medical event reordering, a medical event parameter change, and the like. In one example, the summarization rules may be partially auto-generated, such that if the user is searching for clinical markers relating to lung cancer, all clinical markers related to lung cancer according to known medical guidelines may be automatically included in the summarization rules. The summarization rules may include three components, as shown in
The clinical marker list 1002 may be generated, may include various markers, and may be used in the system 900 as described above, with reference to
The summarization templates 1006 may be predefined templates for generating a summary output 1005, as indicated by a box, at a summary generation module 908. For example, the summarization templates 1006 may be textual phrases with empty slots to be filled with values sent from the clinical marker filter module 906 to complete the summary output. Thus, the empty slots are filled based on the clinical markers that are not removed by the clinical marker filter module 906. As one example, as shown in
In some instances, the generation of the summarization rules may include auto-generation of the summarization rules from treatment guidelines, such as the section 400 of the treatment guidelines shown in
The filtering rules 1004 may be auto-generated based on one or more decision factors following a section of treatment guidelines, such as the section 400 of the treatment guidelines of
Returning to
The clinical marker filter module 906 may search through the EHRs and/or extracted clinical markers sent by the clinical marker detection module 506 to filter out any information unrelated to clinical markers specified by the filtering rules 1004 stored in the summary rules database 904. Clinical markers that are not related to a diagnosis or treatment of the current patient, or are otherwise determined not to be relevant to the current summary, may be removed. In one example, the patient may be classified as a high risk patient for lung cancer and associated clinical markers for the high risk determination, according to selected guidelines, may include age, smoking status, family history of cancer, and exposure to toxins. The clinical marker detection module 506 may extract clinical markers in an EHR from a patient visit where the patient age, smoking status, family history of cancer, and exposure to toxins were recorded. If the current patient has no family history of cancer, the clinical marker filter module 906 may filter the clinical markers with the indication that the patient has no family history of cancer, such that only the clinical marker(s) relevant for the high risk determination (e.g., age and smoking status) are included in the corresponding summary output 1005.
The clinical marker filter module 906 may communicate with the summary generation module 908 by sending clinical markers and any associated information from the EHRs that pass the filter rules 1004 sent by the summary rules database 904, as requested by the user. The clinical marker filter module 906 may expedite generation of the summary output 10005 by removing EHRs and information from the included EHRs that are not relevant to the current diagnosis, treatment, and guidelines, thereby presenting a concise and comprehensive summary to the user when displayed at the display device 514.
The summary generation module 908 may take the clinical markers filtered by clinical marker filter module 906, e.g., via the filter module output 1003, and the summarization templates 1006 from the summary rules database 904 to create summaries of the EHRs to be presented to the user. In one example, the summarization templates 1006 may include natural language sentences with regions configured to receive the filtered clinical markers from the summary generation module 908. In another example, the summary generation module 908 may receive two inputs: the filter module output 1003 from the clinical marker filter module 906 and the summarization templates 1006.
The summary generation module 908 may send the summary output 1005 to the display device 514, where the summary output 1005 may be presented similar to the second EHR 308b of
In this way, a current health condition of a patient may be determined efficiently and accurately by configuring a patient care engine to generate summaries highlighting notable events and statuses of the patient and presenting the summaries to a user in a concise and comprehensive visual display. The summaries may be exhibited in a patient event timeline that is overlaid with a clinical care pathway chart to allow the user to easily obtain information regarding past and current treatments prescribed to the patient and corresponding effects of the treatment. Large quantities of data which would otherwise demand burdensome durations of time and cognitive energy for the user to examine, analyze, and compile, leading to increased likelihood of errors, may be rapidly processed and updated. Relevant information is identified and extracted for display, thereby mitigating obscuring of more useful data by less useful data. A user may thereby assess an adherence of the patient's treatment to standardized, pre-determined treatment guidelines.
The technical effect of generating a patient medical path, e.g., a patient journey, to display patient history and treatment via a patient care engine, as described herein, is that relevant information may be automatically extracted from a large pool of information and sorted to be presented to a user more quickly and in real-time in a comprehensive and concise visual representation displayed to the user with less processing. In response to a request for patient information, a timeline of health events may be automatically generated and correlated to a treatment pathway in real-time, based on common clinical markers, thus avoiding additional processor processing of data that is not relevant to the inquiry. The timeline may be overlaid with the treatment pathway in the visual representation, allowing the user to readily observe a current and past condition of the patient, previous treatments, current treatments, and recommended treatments. The information may be retrieved by parsing and crawling EHRs and treatment guidelines stored in one or more database to rapidly output the patient journey. Creation of the patient journey, in some examples, may be aided by leveraging machine learning algorithms, such as NLP, to generate summaries and select treatment guideline segments displayed in the timeline. As a result, clinical workflow is faster and more efficient, allowing the user to spend less time searching for and retrieving patient information and more time directly interfacing with the patient and a clinical care team.
Furthermore, a display of the patient journey at a display screen of a display device may gather a pool of information offered by the timeline and the treatment pathway and present the information as abbreviated representations. When the abbreviated representations are in an unlaunched state, selected data listed by each of the abbreviated representations may be at least partially hidden from view, thereby condensing a visual footprint of the abbreviated representations. When engaged with by the user, e.g., the user clicks on or position a cursor over thereat, one or more of the abbreviated representations may be launched to expand and display the corresponding list of selected data. The selected data may be a limited quantity of data determined to have a high correlation with a clinical marker of interest. As a result, the display screen may maintain an organized, comprehensive appearance, allowing the user to quickly retrieve desired information. The display of the patient journey enables associations between lists of selected data to be identified in real-time and presented at the display in a manner that allows the user to directly access the information without spending prolonged periods of time locating the information.
An example of a method 1200 is shown in
Turning first to
At 1206, method 1200 includes identifying clinical markers from the EHRs and correlating the identified clinical markers to sections of one or more treatment guidelines. Identifying the clinical markers may include generating a clinical markers list from the EHRs, as described above, at a clinical marker detection module such as the clinical marker detection module 506 of
Method 1200 includes, at 1208, locating nodes in the sections of the treatment guidelines and clipping the sections into segments. For example, criteria such as node names, types, display parameters, as well as entry and exit markers, input by the user into the guideline information collection form, may be used to identify nodes in the sections of the treatment guidelines via a node exit time point detection module (as shown in
At 1210, method 1200 includes applying display constraints to the segments and nodes of the treatment guidelines. For example, the segments corresponding to nodes to be displayed (as defined by the entries to the guideline information collection form) may be ordered for display according to a sequence shown in the treatment guidelines and to input criteria for displaying the segments, including colors, shapes, etc., of visual representations of the nodes. The display constraints may be applied to the segments and nodes by an ordering constraint enforcement module, for example, as shown in
Method 1200 further includes obtaining patient summaries at 1212. For example, the patient summaries may be generated according to method 1300 of
At 1214, method 1200 includes constructing a visual display of the patient care journey, the visual display including a patient event timeline incorporating the selected patient summaries and a clinical care pathway chart incorporating the segments and nodes, and displaying the patient care journey at a display device. For example, the patient event timeline and the clinical care pathway chart may be overlaid at a display screen of the display device, allowing relationships between patient summaries, segments, and nodes to be readily observed. Method 1200 then ends.
Turning now to
At 1306, summary rules are obtained for the identified health condition. The summary rules may be stored in a summary rules database, such as the summary rules database 904 of
At 1310, clinical markers in the EHRs are identified as specified by the summary rules. For example, the summary rules may include a set of clinical markers that, if present in an EHR, are to be identified and extracted. The clinical markers may include certain test results, measurements, pathology findings, patient information, treatments given, tumor staging, presence of secondary tumors, and the like. The text in the EHRs may be processed to identify and extract the clinical markers. For example, as described above, methods to extract the clinical markers include using various extraction methods that rely on lookup tables, neural networks, etc., to identify the clinical markers in the text of the EHRs. At 1312, the identified clinical markers (as extracted from the EHRs) are filtered as specified by the summary rules. As explained previously, the extracted clinical markers may be filtered to remove clinical markers that are not relevant to the patient condition or guidelines for treating the patient condition.
At 1314, a patient history summary is populated with the identified, filtered clinical markers. A summary template may be populated with the clinical markers, which may format the clinical markers into natural language or another suitable format. At 1316, the patient history summary is output for display on a display device. The summary may be displayed along with a timeline of patient events and/or EHRs, at least in some examples. Method 1300 then ends.
As used herein, an element or step recited in the singular and preceded with the word “a” or “an” should be understood as not excluding plural of said elements or steps, unless such exclusion is explicitly stated. Furthermore, references to “one embodiment” of the present invention are not intended to be interpreted as excluding the existence of additional embodiments that also incorporate the recited features. Moreover, unless explicitly stated to the contrary, embodiments “comprising,” “including,” or “having” an element or a plurality of elements having a particular property may include additional such elements not having that property. The terms “including” and “in which” are used as the plain-language equivalents of the respective terms “comprising” and “wherein.” Moreover, the terms “first,” “second,” and “third,” etc. are used merely as labels, and are not intended to impose numerical requirements or a particular positional order on their objects.
The disclosure also provides support for a computing system, comprising: a display screen configured to display a patient medical path listing one or more of treatment guidelines and patient medical history, and additionally being configured to display abbreviated representations of at least one of the treatment guidelines and the patient medical history that can be reached directly from the displayed patient medical path, wherein the abbreviated representations each display a limited list of data offered within the treatment guidelines and/or the patient medical history, each of the data in the list being selectable to launch the treatment guidelines and/or the patient medical history and enable the selected data to be seen, and wherein the abbreviated representations are displayed while in an unlaunched state. In a first example of the system, segments of the treatment guidelines are selected for the abbreviated representations according to nodes of a treatment path provided in the treatment guidelines, and wherein the nodes are junctions along the treatment path where a continuation of the treatment path is determined based on clinical markers located in the patient medical history. In a second example of the system, optionally including the first example, the patient medical path is displayed based on input entered into a guideline information collection form, and wherein the input entered into the guideline information collection form includes one or more of node name, node type, node display constraints, node entry and exit markers, and node ordering constraints. In a third example of the system, optionally including one or both of the first and second examples, the input entered into the guideline information collection form is stored at a guideline database and used to generate guideline selection rules for a guideline selection module, node traversal rules for a node exit time point detection module, and node ordering rules for an ordering constraint enforcement module, and wherein the said modules include algorithms for displaying the patient medical path. In a fourth example of the system, optionally including one or more or each of the first through third examples, the patient medical path is displayed based on identification of one or more primary clinical findings and tracking a treatment path for each of the one or more primary clinical findings independently. In a fifth example of the system, optionally including one or more or each of the first through fourth examples, the abbreviated representations are user-interactive graphical representations, and wherein the abbreviated representations are in a launched state when a user engages the abbreviated representations by interacting with the abbreviated representations with a cursor.
The disclosure also provides support for a method, comprising: providing a user-interactive electronic form for a user to input rules for displaying a treatment path for a patient, automatically acquiring, via a processor, health records of the patient and treatment guidelines corresponding to a condition of the patient, based on the input rules, and identifying clinical markers from the health records, matching the clinical markers to relevant sections of the treatment guidelines according to implementation of the input rules by the processor, and displaying, at a graphical user interface of a display device in real-time, the relevant sections of the treatment guidelines overlaid with summaries of the health records, the summaries correlated to the relevant sections based on the clinical markers, to provide treatment recommendations based on a path of the treatment guidelines. In a first example of the method, displaying the relevant sections of the treatment guidelines overlaid with the summaries of the health records includes presenting the relevant sections of the treatment guidelines as graphical representations at the graphical user interface. In a second example of the method, optionally including the first example, displaying the relevant sections of the treatment guidelines with the summaries includes ordering the summaries according to a sequence of the treatment guidelines and aligning a corresponding summary of the summaries with each of the graphical representations. In a third example of the method, optionally including one or both of the first and second examples, matching the clinical markers to the relevant sections of the treatment guidelines includes identifying nodes of the treatment guidelines based on a set of guideline selection rules of the input rules. In a fourth example of the method, optionally including one or more or each of the first through third examples, matching the clinical markers to the relevant sections of the treatment guidelines further includes comparing entry markers and exit markers for each of the nodes with information from the clinical markers, and wherein the entry markers and the exit markers are defined by a set of node traversal rules of the input rules. In a fifth example of the method, optionally including one or more or each of the first through fourth examples, comparing the exit markers for each of the nodes with the information from the clinical markers includes determining if the information from the clinical markers satisfies the exit markers of a first node of the nodes and wherein entry markers for a subsequent, second node are compared with the information from the clinical markers to determine entry to the second node when the exit markers of the first node are satisfied by the information from the clinical markers. In a sixth example of the method, optionally including one or more or each of the first through fifth examples, the method further comprises: generating a clinical marker list at a clinical marker detection module by identifying target clinical markers in the treatment guidelines and/or the health records, and wherein identifying the clinical markers from the health records includes locating the target clinical markers in corresponding records of the health records to the clinical marker list and selecting the corresponding records from the health records for display. In a seventh example of the method, optionally including one or more or each of the first through sixth examples, locating the target clinical markers in the corresponding records includes utilizing natural language processing to analyze the corresponding records, and wherein utilizing the natural language processing includes applying one or more of entity recognition, assertion recognition, relation recognition, ontology linking, and clinical marker recognition to the corresponding records. In an eighth example of the method, optionally including one or more or each of the first through seventh examples, identifying the clinical markers from the health records includes identifying one or more of clinical findings, procedures, and biomarkers from the health records. In a ninth example of the method, optionally including one or more or each of the first through eighth examples, the method further comprises: generating the summaries based on one or more of a clinical marker list, filter rules, and summarization templates. In a tenth example of the method, optionally including one or more or each of the first through ninth examples, the treatment guidelines are one or more documents providing standardized treatment recommendations for a health diagnosis, and wherein the relevant sections of the treatment guidelines are updated according to updates to the standardized treatment recommendations.
The disclosure also provides support for a system for displaying a patient journey, comprising: a guideline information collection form providing parameters for displaying a treatment path for a patient stored at a guideline database, treatment guidelines corresponding to the treatment path for the patient stored at a memory of a processor, and a set of modules also stored at the memory of the processor and implemented at the processor, the set of modules including with executable instructions that, when executed, cause the processor to: process the parameters from the guideline information collection form, identify the treatment path from the treatment guidelines based on the parameters, and display the treatment path at a display device along with corresponding summaries from medical records of the patient, the summaries correlated to the treatment path based on common clinical markers. In a first example of the system, the summaries are generated based on input received at a summary rules collection, and stored at a summary rules database, and wherein the input is used to create filter rules for a clinical marker filter module of the set of modules and summarization templates for a summary generation module of the set of modules. In a second example of the system, optionally including the first example, the clinical marker filter module is configured to filter clinical markers received from a clinical marker detection module of the set of modules, and wherein the summary generation module is configured to complete summarization templates with values received from the clinical marker filter module to create the summaries.
This written description uses examples to disclose the invention, including the best mode, and also to enable a person of ordinary skill in the relevant art to practice the invention, including making and using any devices or systems and performing any incorporated methods. The patentable scope of the invention is defined by the claims, and may include other examples that occur to those of ordinary skill in the art. Such other examples are intended to be within the scope of the claims if they have structural elements that do not differ from the literal language of the claims, or if they include equivalent structural elements with insubstantial differences from the literal languages of the claims.
| Number | Date | Country | Kind |
|---|---|---|---|
| 202141036688 | Aug 2021 | IN | national |
| 202141036711 | Aug 2021 | IN | national |
