Patent Application
20230096820
The present inventive concepts relate generally to health care systems and services and, more particularly, to managing clinical records for patients to reduce privacy violations.
Health care service providers record clinical information associated with patients under their care in clinical charts, which are typically stored as electronic health records. These charts or health records may pass through many different health care professionals and other entities in the process of providing care to the patients. Clinical charts or health records for multiple patients may be consolidated for batch transfer between entities. Once a transfer is complete, the consolidated batch document is split back into the individual patient charts or records. This consolidation and subsequent splitting may not always be performed accurately resulting in the charts or records for multiple patients remaining grouped together. Other times one or more pages from one patient's medical chart or record may end up in another patient's medical chart or record. Considering the size and scope of many health care systems, there are numerous ways for patient records to become intermingled. If a patient's chart or record refers to another patient, however, this could represent a protected health information (PHI) violation, if the party accessing the chart or record for any of a variety of different administrative tasks, such as data entry, medical coding, and/or auditing, does not have the rights to access the second patient's chart or record. Moreover, the co-mingling of chart or record information between patients could lead to errors in patient care by medical professionals.
According to some embodiments of the inventive concept, a method comprises: receiving a record comprising at least one page and containing clinical information associated with a first patient; receiving respective first patient identification values for one or more patient identification parameters corresponding to the first patient; automatically processing the record to identify first example instances referencing the patient identification parameters including values therefor; automatically processing the record to identify second example instances of the first patient identification values; automatically processing the record to determine whether any of the at least one page contained therein cannot be semantically linked to another one of the at least one page in the record; and assigning a grade to each of the at least one page indicating a degree of confidence that the record does not include clinical information associated with a second patient based on the first example instances, the second example instances, and the determination whether any of the at least one page contained therein cannot be semantically linked to another one of the at least one page in the record.
In other embodiments, the method further comprises converting the record into a text record; wherein automatically processing the record to identify first example instances, automatically processing the record to identify second example instances, and automatically processing the record to determine whether any of the at least one page contained therein cannot be semantically linked to another one of the at least one page in the record, comprises: processing the text record to identify first example instances, processing the text record to identify second example instances, and processing the text record to determine whether any of the at least one page contained therein cannot be semantically linked to another one of the at least one page in the text record.
In still other embodiments, converting the record into the text record comprises converting the record into the text record using optical character recognition (OCR).
In still other embodiments, the patient identification parameters comprise: patient demographic parameters, pharmacy information parameters, diagnosis code parameters, and provider encounter parameters.
In still other embodiments the patient demographic parameters comprise: patient name, patient address, patient mobile phone number, patient home phone number, patient work phone number, patient guardian name, patient guardian address, patient guardian mobile phone number, patient guardian home phone number, patient guardian work phone number, patient gender, patient date of birth, patient social security number, patient provider identification number, patient marital status, patient email, patient preferred language, patient race, patient primary care physician name, patient emergency contact name, patient emergency contact phone number, patient employer name, and/or patient employer address.
In still other embodiments, the pharmacy information parameters comprise: pharmacy name, pharmacy address, and/or pharmacy phone number.
In still other embodiments, the provider encounter parameters comprise: encounter date, name of medical professional, and/or identification of service performed.
In still other embodiments, automatically processing the record to identify the first example instances referencing the patient identification parameters including the values therefor comprises: determining a Levenshtein distance between each of the first example instances referencing the patient identification parameters and the patient identification parameters, respectively.
In still other embodiments, determining the Levenshtein distance comprises: determining the Levenshtein distance using fuzzy matching, regular expression analysis, and/or language modeling.
In still other embodiments, automatically processing the record to identify the second example instances of the first patient identification values comprises: determining a Levenshtein distance between each of the second example instances of the first patient identification values and the first patient identification values, respectively.
In still other embodiments, determining the Levenshtein distance comprises: determining the Levenshtein distance using fuzzy matching, regular expression analysis, and/or language modeling.
In still other embodiments, automatically processing the record to identify the first example instances referencing the patient identification parameters including the values therefor comprises: generating a patient identification parameter extraction model based on historical records containing historical clinical information for patients in which associations are learned between the patient identification parameters and manners in which the historical clinical information is organized in the historical records.
In still other embodiments, generating the patient identification parameter extraction model comprises: using an Artificial Intelligence (AI) system to learn the associations between the patient identification parameters and the manners in which the historical clinical information is organized in the historical records.
In still other embodiments, automatically processing the record to determine whether any of the at least one page contained therein cannot be semantically linked to another one of the at least one page in the record comprises: determining whether adjacent pages in the record have sequential numbers; determining whether any of the at least one page contained in the record references a different case number than other ones of the at least one page contained in the record; and/or determining whether content between adjacent ones of the at least one page in the record is continuous.
In still other embodiments, assigning the grade to each of the at least one page indicating the degree of confidence that the record does not include clinical information associated with the second patient comprises: assigning the grade of confirmed when the page satisfies a threshold criterion that the page does not include clinical information associated with the second patient indicating the page does not need manual review; assigning the grade of suspicious when the page does not satisfy the threshold criterion and is predicted to include clinical information associated with the second patient and needs correction; and assigning the grade of unknown when a prediction whether the page includes clinical information associated with the second patient cannot be made and manual review is recommended.
In still other embodiments, assigning the grade to each of the at least one page indicating the degree of confidence that the record does not include clinical information associated with the second patient comprises: assigning a grade to the record based on the grades assigned to each of the at least one page indicating a degree of confidence that the record does not include clinical information associated with the second patient.
In some embodiments of the inventive concept, a system comprises a processor; and a memory coupled to the processor and comprising computer readable program code embodied in the memory that is executable by the processor to perform operations comprising: receiving a record comprising at least one page and containing clinical information associated with a first patient; receiving respective first patient identification values for one or more patient identification parameters corresponding to the first patient; automatically processing the record to identify first example instances referencing the patient identification parameters including values therefor; automatically processing the record to identify second example instances of the first patient identification values; automatically processing the record to determine whether any of the at least one page contained therein cannot be semantically linked to another one of the at least one page in the record; and assigning a grade to each of the at least one page indicating a degree of confidence that the record does not include clinical information associated with a second patient based on the first example instances, the second example instances, and the determination whether any of the at least one page contained therein cannot be semantically linked to another one of the at least one page in the record.
In further embodiments, automatically processing the record to identify the first example instances referencing the patient identification parameters including the values therefor comprises: determining a Levenshtein distance between each of the first example instances referencing the patient identification parameters and the patient identification parameters, respectively; and automatically processing the record to identify the second example instances of the first patient identification values comprises: determining a Levenshtein distance between each of the second example instances of the first patient identification values and the first patient identification values, respectively.
In still further embodiments, assigning the grade to each of the at least one page indicating the degree of confidence that the record does not include clinical information associated with the second patient comprises: assigning the grade of confirmed when the page satisfies a threshold criterion that the page does not include clinical information associated with the second patient indicating the page does not need manual review; assigning the grade of suspicious when the page does not satisfy the threshold criterion and is predicted to include clinical information associated with the second patient and needs correction; and assigning the grade of unknown when a prediction whether the page includes clinical information associated with the second patient cannot be made and manual review is recommended.
In some embodiments, a computer program product comprises a non-transitory computer readable storage medium comprising computer readable program code embodied in the medium that is executable by a processor to perform operations comprising: receiving a record comprising at least one page and containing clinical information associated with a first patient; receiving respective first patient identification values for one or more patient identification parameters corresponding to the first patient; automatically processing the record to identify first example instances referencing the patient identification parameters including values therefor; automatically processing the record to identify second example instances of the first patient identification values; automatically processing the record to determine whether any of the at least one page contained therein cannot be semantically linked to another one of the at least one page in the record; and assigning a grade to each of the at least one page indicating a degree of confidence that the record does not include clinical information associated with a second patient based on the first example instances, the second example instances, and the determination whether any of the at least one page contained therein cannot be semantically linked to another one of the at least one page in the record.
Other methods, systems, articles of manufacture, and/or computer program products according to embodiments of the inventive concept will be or become apparent to one with skill in the art upon review of the following drawings and detailed description. It is intended that all such additional systems, methods, articles of manufacture, and/or computer program products be included within this description, be within the scope of the present inventive subject matter and be protected by the accompanying claims.
Other features of embodiments will be more readily understood from the following detailed description of specific embodiments thereof when read in conjunction with the accompanying drawings, in which:
In the following detailed description, numerous specific details are set forth to provide a thorough understanding of embodiments of the inventive concept. However, it will be understood by those skilled in the art that embodiments of the inventive concept may be practiced without these specific details. In some instances, well-known methods, procedures, components, and circuits have not been described in detail so as not to obscure the inventive concept. It is intended that all embodiments disclosed herein can be implemented separately or combined in any way and/or combination. Aspects described with respect to one embodiment may be incorporated in different embodiments although not specifically described relative thereto. That is, all embodiments and/or features of any embodiments can be combined in any way and/or combination.
As used herein, the term “provider” may mean any person or entity involved in providing health care products and/or services to a patient.
Embodiments of the inventive concept are described herein in the context of a protected health information (PHI) violation detection system that includes an artificial intelligence (AI) engine, which uses machine learning. It will be understood that embodiments of the inventive concept are not limited to a machine learning implementation of the PHI violation detection system and other types of AI systems may be used including, but not limited to, a multi-layer neural network, a deep learning system, a natural language processing system, and/or computer vision system. Moreover, it will be understood that the multi-layer neural network is a multi-layer artificial neural network comprising artificial neurons or nodes and does not include a biological neural network comprising real biological neurons.
Some embodiments of the inventive concept stem from a realization that the handling of clinical care charts or health records for patients by different medical professionals and/or other entities may result in a patient's chart or record including one or more pages from another patient's chart or record, which could result in a PHI violation. Traditionally, such charts or records would be manually reviewed page-by-page to verify that the entire chart or record is associated with the same patient. If one or more pages are detected that correspond to another patient, then these pages are flagged and removed to ensure each patient's privacy is not violated. Embodiments of the inventive concept may provide an automated PHI violation detection system in which, in response to a request for a first patient's clinical record, for example, the first patient's clinical record may be converted into text form for processing and evaluation. The PHI violation detection system may include an extraction component in which the record is processed to identify instances that reference metadata corresponding to patient identification parameters. The extraction component may, therefore, identify examples in the record that identify any patient based on the identification parameters including the first patient to which the record belongs and any other patient whose clinical record page(s) may have found their way into the first patient's record. The PHI violation detection system may further include a matching component in which the record is processed to identify instances in which the first patient's identification values (i.e., values for one or more patient identification parameters corresponding to the first patient to which the record belongs) are found in the record. In addition to the extraction component and the matching component, the PHI violation detection system may use a linking component to identify whether any of the pages in the record are not semantically linked with one another. For example, pages with page numbers or other type of identifier that can be identified as being part of a sequence may be identified or flagged as being linked together. Pages that cannot be identified as belonging to such a linked sequence are excluded from the link flag or identification and may, therefore, be considered as potentially belonging to a different patient, chart, or record. Based on the analysis from the extraction, matching, and linking components, an assembly component may assign a grade to each of the pages in the record indicating a degree of confidence that the page does not include clinical information associated with a patient other than the first patient to which the record belongs. Based on this grade, the record may be confirmed as not needing a manual review, may be identified as likely containing one or more PHI errors that require correction, or may be identified as unknown or indeterminate with a recommendation that the page be reviewed manually. The record as a whole may be assigned a grade based on the individual grades assigned to its respective pages including a summary of the number of pages in each grade category. The automated PHI violation detection system, according to some embodiments of the inventive concept, may reduce the amount of manual review required for detecting PHI violations in patients' clinical records, which can result in significant time and expense savings due to the quantity of patient records and the potentially large number of pages in each record.
Referring to
According to some embodiments of the inventive concept, a PHI violation detection system may be provided to assist entities, such as providers, payors, auditors, data entry personnel, and others to automatically identify potential PHI violations in patient records. The PHI violation detection system may include a health care facility interface server 130, which includes an EMR interface system module 135 to facilitate the transfer of information between the EMR system 120, which the providers use to manage patient charts and records and issue orders, and a PHI violation detection server 140, which includes an AI/Rules engine module 145. The PHI violation detection server 140 and AI/Rules engine module 145 may be configured to receive patient records along with patient identification values for one or more patient identification parameters corresponding to the patient from the EMR system 120 by way of the health care facility interface server 130 and EMR interface module 135. The PHI violation detection server and AI/Rules engine 145 may process each page of each patient clinical record using a multi-phase approach based on an extraction component, a matching component, a linking component, and an assembly component to assign a grade to each page that is indicative of a degree of confidence that the record does not include clinical information associated with a patient other than the patient to whom the record belongs. Based on the grade, the page in the record may be confirmed as exempt from manual review to identify PHI violations, identified as needing correction of one or more likely PHI violations, or identified as needing manual review due to the automated review of the page being indeterminate with respect to the existence of a PHI violation.
It will be understood that the division of functionality described herein between the PHI violation detection server 140/AI/Rules engine module 145 and the health care facility interface server 130/EMR interface module 135 is an example. Various functionality and capabilities can be moved between the PHI violation detection server 140/AI/Rules engine module 145 and the health care facility interface server 130/EMR interface module 135 in accordance with different embodiments of the inventive concept. Moreover, in some embodiments, the PHI violation detection server 140/AI engine module 145 and the health care facility interface server 130/EMR interface module 135 may be merged as a single logical and/or physical entity.
A network 150 couples the health care facility server 105 to the health care facility interface server 130. The network 150 may be a global network, such as the Internet or other publicly accessible network. Various elements of the network 150 may be interconnected by a wide area network, a local area network, an Intranet, and/or other private network, which may not be accessible by the general public. Thus, the communication network 150 may represent a combination of public and private networks or a virtual private network (VPN). The network 150 may be a wireless network, a wireline network, or may be a combination of both wireless and wireline networks.
The PHI violation detection service provided through the health care facility interface server 130, EMR interface module 135, PHI violation detection server 140 and AI/Rules engine module 145 to automatically detect a potential PHI violation in a patient clinical record may, in some embodiments, be embodied as a cloud service. For example, entities may integrate their clinical record processing system with the PHI violation detection service and access the service as a Web service. In some embodiments, the PHI violation detection service may be implemented as a Representational State Transfer Web Service (RESTful Web service).
Although
Referring to
Returning to
The pattern matching rules 345 may evaluate the structure of the record itself to implement the linking component by processing the record to determine whether any of the pages contained therein cannot be semantically linked to another one of the pages at block 225. Example operations of the linking phase are illustrated, for example, in
The grading logic 345 may then take the outputs from the extraction phase, matching phase, and linking phase to assign a grade to each of the pages indicating a degree of confidence that the record does not include clinical information associated with a second patient at block 230. For example, a grade of confirmed may be assigned when the page satisfies a threshold criterion that the page does not include clinical information associated with the second patient. The threshold criterion for a page may, for example, be that the page contains sufficient matched metadata associated with a patient or can be linked to a page that contains sufficient matched metadata for a patient and cannot be linked to an extracted reference to another patient. The threshold criterion for a document or record may be that all pages can be linked to a page with sufficient matched metadata associated with a patient and no page contains extracted references to another patient. The confirmed grade may indicate the page does not need manual review. A grade of suspicious may be assigned when the page does not satisfy the threshold criterion and is predicted to include clinical information associated with the second patient and needs correction. A grade of unknown may be assigned when a prediction whether the page includes clinical information associated with the second patient cannot be made and manual review is recommended. The unknown grade may be assigned, for example, if the PHI violation detection system cannot determine whether the page relates to any patient at all (e.g., the page is a handwritten note with patient identifying information contained thereon). A grade may be assigned to the record based on the grades assigned to each of the pages in the record. The grade may include a summary of the grades for each of the pages in the record.
The at least one core 711 may be configured to execute computer program instructions. For example, the at least one core 711 may execute an operating system and/or applications represented by the computer readable program code 716 stored in the memory 713. In some embodiments, the at least one core 711 may be configured to instruct the AI accelerator 715 and/or the HW accelerator 717 to perform operations by executing the instructions and obtain results of the operations from the AI accelerator 715 and/or the HW accelerator 717. In some embodiments, the at least one core 711 may be an ASIP customized for specific purposes and support a dedicated instruction set.
The memory 713 may have an arbitrary structure configured to store data. For example, the memory 713 may include a volatile memory device, such as dynamic random-access memory (DRAM) and static RAM (SRAM), or include a non-volatile memory device, such as flash memory and resistive RAM (RRAM). The at least one core 711, the AI accelerator 815, and the HW accelerator 717 may store data in the memory 713 or read data from the memory 713 through the bus 719.
The AI accelerator 715 may refer to hardware designed for AI applications. In some embodiments, the AI accelerator 715 may include a machine learning engine configured to extract instances of a patient identification parameters including values therefor from a patient clinical record. The AI accelerator 715 may generate output data by processing input data provided from the at least one core 715 and/or the HW accelerator 717 and provide the output data to the at least one core 711 and/or the HW accelerator 717. In some embodiments, the AI accelerator 715 may be programmable and be programmed by the at least one core 711 and/or the HW accelerator 717. The HW accelerator 717 may include hardware designed to perform specific operations at high speed. The HW accelerator 717 may be programmable and be programmed by the at least one core 711.
The pattern matching rules/grading logic module 915 may provide the pattern matching rules and grading logic 345 for detecting possible PHI violations in a patient's clinical record as described above with respect to
Although
Computer program code for carrying out operations of data processing systems discussed above with respect to
Moreover, the functionality of the health care facility interface server 130 of
The data processing apparatus described herein with respect to
Some embodiments of the inventive concept may provide a PHI violation detection system that can be used by entities, such as providers, payors (e.g., insurers), clinical record auditors, and/or data entry personnel, to process patient clinical records and identify pages in the records that can be confirmed as not needing manual review to check for possible PHI violations while also identifying those pages that likely need correction and/or manual review to ensure the record does not include pages associated with different patients thereby causing a PHI violation. This may improve efficiency and costs associated with the review of patient records for PHI violations as most clinical record pages may be confirmed using the automated PHI violation detection system as not requiring manual review leaving a relatively small portion to be inspected manually.
In the above-description of various embodiments of the present inventive concept, it is to be understood that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this inventive concept belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of this specification and the relevant art and will not be interpreted in an idealized or overly formal sense expressly so defined herein.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various aspects of the present inventive concept. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The terminology used herein is for the purpose of describing particular aspects only and is not intended to be limiting of the inventive concept. As used herein, the singular forms “a”, “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises” and/or “comprising,” when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. As used herein, the term “and/or” includes any and all combinations of one or more of the associated listed items. Like reference numbers signify like elements throughout the description of the figures.
In the above-description of various embodiments of the present inventive concept, aspects of the present inventive concept may be illustrated and described herein in any of a number of patentable classes or contexts including any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof. Accordingly, aspects of the present inventive concept may be implemented entirely hardware, entirely software (including firmware, resident software, micro-code, etc.) or combining software and hardware implementation that may all generally be referred to herein as a “circuit,” “module,” “component,” or “system.” Furthermore, aspects of the present inventive concept may take the form of a computer program product comprising one or more computer readable media having computer readable program code embodied thereon.
Any combination of one or more computer readable media may be used. The computer readable media may be a computer readable signal medium or a computer readable storage medium. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an appropriate optical fiber with a repeater, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain or store a program for use by or in connection with an instruction execution system, apparatus, or device.
The description of the present inventive concept has been presented for purposes of illustration and description, but is not intended to be exhaustive or limited to the inventive concept in the form disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the inventive concept. The aspects of the inventive concept herein were chosen and described to best explain the principles of the inventive concept and the practical application, and to enable others of ordinary skill in the art to understand the inventive concept with various modifications as are suited to the particular use contemplated.
