EP1841356A2 - Edition basee sur des caracteristiques pour l'electrocardiographie - Google Patents

Edition basee sur des caracteristiques pour l'electrocardiographie

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Publication number
EP1841356A2
EP1841356A2 EP06718854A EP06718854A EP1841356A2 EP 1841356 A2 EP1841356 A2 EP 1841356A2 EP 06718854 A EP06718854 A EP 06718854A EP 06718854 A EP06718854 A EP 06718854A EP 1841356 A2 EP1841356 A2 EP 1841356A2
Authority
EP
European Patent Office
Prior art keywords
ecg
data
ecg data
interpretation
features
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Withdrawn
Application number
EP06718854A
Other languages
German (de)
English (en)
Inventor
Jonathan L. Elion
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Heartlab Inc USA
Original Assignee
Heartlab Inc USA
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Heartlab Inc USA filed Critical Heartlab Inc USA
Publication of EP1841356A2 publication Critical patent/EP1841356A2/fr
Withdrawn legal-status Critical Current

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Classifications

    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/74—Details of notification to user or communication with user or patient ; user input means
    • A61B5/742—Details of notification to user or communication with user or patient ; user input means using visual displays
    • A61B5/7435—Displaying user selection data, e.g. icons in a graphical user interface
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/316—Modalities, i.e. specific diagnostic methods
    • A61B5/318—Heart-related electrical modalities, e.g. electrocardiography [ECG]
    • A61B5/339—Displays specially adapted therefor
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/316—Modalities, i.e. specific diagnostic methods
    • A61B5/318—Heart-related electrical modalities, e.g. electrocardiography [ECG]
    • A61B5/346—Analysis of electrocardiograms
    • A61B5/349—Detecting specific parameters of the electrocardiograph cycle
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/74—Details of notification to user or communication with user or patient ; user input means
    • A61B5/742—Details of notification to user or communication with user or patient ; user input means using visual displays
    • A61B5/7445—Display arrangements, e.g. multiple display units
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H15/00—ICT specially adapted for medical reports, e.g. generation or transmission thereof
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H10/00—ICT specially adapted for the handling or processing of patient-related medical or healthcare data
    • G16H10/60—ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records

Definitions

  • Electrocardiography is a technology for the detection and diagnosis of cardiac conditions.
  • An electrocardiograph is a medical device capable of recording the potential differences generated by the electrical activity of the heart.
  • An electrocardiogram (ECG or EKG) is produced by the electrocardiograph. It typically comprises the ECG wave data that describes the heart's electrical activity as a function of time.
  • the heart's electrical activity is detected by sensing electrical potentials via a series of electrode leads that are placed on the patient at defined locations on the patient's chest and limbs. Systems with ten (10) separate ECG leads and digital data capture/storage are typical. During electrocardiography, the detected electrical potentials are recorded and graphed as ECG wave data that characterize the depolarization and repolarization of the cardiac muscle.
  • the ECG interpretation is performed by analyzing the various cardiac electrical events presented in the ECG wave data.
  • the ECG wave data comprise a P wave, which indicates atrial depolarization, a QRS complex, which represents ventricular depolarization, and a T-wave representing ventricular repolarization.
  • ECG systems provide for the machine interpretation of the ECG data. These systems are designed to measure features of the ECG wave data from the patient. The various features of portions of the ECG, such as intervals, segments and complexes, including their amplitude, direction, and duration of the waves and their morphological aspects, are measured. Then all of the feature information is analyzed together. From this feature information, these systems are able to generate machine ECG interpretations diagnosing normal and abnormal cardiac rhythms and conduction patterns. These interpretations are often used by the physician/cardiologist as the basis of an ECG report for a given patient.
  • the present invention is directed to a method and system for generating electrocardiogram reports. It allows for the editing of features in the electrocardiogram interpretation process. This improves the accuracy of machine interpretation of the ECG data thereby facilitating the analysis and generation of the final report by the physician.
  • the present method and system are most useful in host-based ECG interpretation systems where the physician accesses the ECG data at a workstation including a machine interpretation that is generated typically by the host system or workstation.
  • the physician is then provided with the ability to modify the features in the ECG data and generate a new host-based interpretation based on the original ECG data and the features specified by the physician.
  • the invention features a method for generating an electrocardiogram report.
  • This method comprises receiving ECG data at a physician workstation and enabling the physician to review, specify, and, if needed, correct the values for features of the ECG data.
  • An interpretation of the ECG data is then generated based on the values of the features specified by the physician. Finally, the interpretation is stored after physician editing in a patient database.
  • the ECG data include the ECG wave data and an ECG machine interpretation of that ECG wave data.
  • the ECG wave data are generated at an ECG cart that is operated by a nurse or technician and the interpretation is generated by the host system.
  • the physician selects whether to edit the final host machine interpretation and finalize the report or specify new values for the features.
  • the physician will select to edit the final machine interpretation if it is determined to be basically or largely accurate after having reviewed the ECG wave data.
  • the step of enabling feature value specification comprises presenting machine-generated values for features of the ECG wave data and enabling the physician 5 to specify different values for at least one of the features. Then, the final steps of the host-based interpretation are re-performed in which a new interpretation of the ECG is generated using the specified values for the features from the physician and the machine-generated values for other features.
  • the invention features a system for generating an l o electrocardiogram (ECG) report.
  • the system comprises a workstation that receives ECG data and enables specification of values for features of the ECG data and an interpretation system for evaluating of the ECG data based on the values of the features specified by a user and generating an inteipretation for storage in a patient database.
  • the interpretation system runs on the workstation or host system.
  • the invention features a computer software product for generating an electrocardiogram (ECG) report.
  • the product comprises a computer-readable medium, such as a compact disk, in which program instructions are stored. These instructions, when read by a computer, cause the computer to receive ECG data at a physician workstation, and enable a user to specify values for features of the ECG data.
  • the instructions also provide for the
  • Fig. 1 is a schematic diagram illustrating the electrocardiogram (ECG) workflow in a typical hospital
  • Fig. 2 is a flow diagram illustrating the machine interpretation process in a conventional ECG device or host-based interpretation system
  • FIG. 3 shows prototypical ECG wave data illustrating the various portions of the wave
  • Fig. 4 shows a conventional interface in an ECG report editing system
  • Fig. 5 shows a series of text statements as would be generated by machine interpretation for an exemplary ECG report as is conventional
  • Fig. 6 is a flow diagram illustrating the process for ECG feature specification and ECG report generation according to the present invention
  • Fig. 7 is a flow diagram illustrating the process for ECG scoring and complexity sorting i o according to the present invention.
  • Fig. 8 is a flow diagram illustrating the process for ECG data quality assurance according to the present invention.
  • Fig. 1 illustrates the electrocardiogram (ECG) workflow in a typical hospital.
  • a nurse or ECG technician 112 interacts with the patient 110 to acquire the ECG data.
  • the ECG machine 114 is an ECG cart that is moved throughout the hospital between patient, examining, and operating rooms.
  • the ten (10) leads 118 of the ECG device 114 are placed on the limbs and torso of the patient 110. Then, a printout of the ECG wave data 116 is generated at the cart using twelve 20 (12) combinations of the leads that have been placed on the patient. Also, ECG data 120 including the wave data, identifying information about the patient, and possibly the machine-generated ECG interpretation are digitally stored in the ECG cart 114 and/or transmitted to a central hospital records data storage and host system 130.
  • the present invention generally applies to a host-based interpretation and editing systems. 25
  • a cardiologist 122 accesses the ECG data 125 from the records database and host systeml30 via a workstation 124.
  • These hospital records will store preliminary ECG data and machine interpretations and the subsequent final reports that are the product of the editing process by the cardiologist 122 at the workstation 124. The final reports will then be entered into the patients' records.
  • the workstation 124 is provided with standard software for accessing and editing the ECG data and machine-generated interpretations reports from the host system 130 and generating the final cardiologist-reviewed ECG reports 126.
  • Fig. 2 illustrates the general process by which these machine interpretations are generated by the host system 130 or possibly by the workstation 124.
  • the digital ECG signals or wave data 150 are acquired in step 150 and stored such as by the ECG cart 114. Measurements of portions of this ECG wave data are made in step 154 and low-level features 152 are typical identified in the wave data at the host system 130. This information is then combined in step 156 when high-level features are determined. Based on these calculated features, the final interpretation is generated in step 158.
  • the features typically relate to the length and amplitude of the various components of a selected ECG wave from one typical cardiac cycle out of the usually very long wave data set that the machine acquires. In other cases, an average ECG wave is calculated from a series of waves to form the basis of the interpretation. Features can be identified for each of the individual 12 leads, or combined to derive features of the overall ECG.
  • Fig. 3 illustrates a prototypical ECG wave. It generally comprises a P wave, a QRS wave complex, a T-wave, and a U wave.
  • the features that the typical system uses can be dependent on specific characteristics of that system but will include intervals, segments and complexes, including amplitude, direction, and duration of the waves and their morphological aspects.
  • Table I lists a number of exemplary features and feature information that are generated in order to enable the subsequent machine ECG interpretation.
  • RR intervals • Average RR-interval Time duration • Local avg. RR-interval between two consecutive R waves of the ECG
  • IA [IB] ECG morphology between QRS offset and T-wave offset
  • Fig. 4 illustrates a typical interface 250 for an ECG report editor running on workstation 124.
  • it displays a window 252 that provides general information on the patient "R, Joseph.” It has another window 254 that provides a workspace for creating the final ECG report.
  • these ECG reports are a set of specific codes, displayed in window 256 that correspond to different conditions.
  • Fig. 5 illustrates an exemplary draft report 258 as generated by a machine interpretation. It comprises a series of lines that correspond to different conditions. Typically, they are ordered in their relative importance. The physician, at the workstation, will review the specific ECG wave data and revise the draft report generated from the machine-generated interpretation. These series of statements 01-07 (280), providing specific diagnoses, will then be edited in order to generate the final report 126 that is stored in the patient database 130.
  • Fig. 6 illustrates a process for feature-leveling editing according to the present invention.
  • the digital ECG data including the interpretation are received at the workstation 124 in step 210 from the host 130.
  • the ECG data including the interpretations are then displayed in step 212 to the cardiologist or other user/reader 122 on the workstation 124 using interfaces as illustrated in Figs. 4 and 5.
  • step 214 the physician/cardiologist reviews the actual ECG wave data and determines whether or not the interpretations of the report are useful. Most often, the interpretations will be largely correct and only require minor editing. In this case, the physician will simply edit the text statements in step 216 and then store the final ECG report in step 218 in the database 130.
  • the workstation 124 displays the ECG data along with the calculated and otherwise determined features in step 230.
  • the inventive feature-based editing permits the cardiologist or other reader/user to review and edit at the intermediate step of the machine-generated ECG interpretation. This is accomplished with a user interface that permits the display, review and editing of the measurements and features derived by the computer analysis (feature-based editing), along with the capability of resubmitting these features and measurements to the interpretive algorithms to generate a new list of interpretive statements. Often, the features are as listed in Table I. According to the invention, the cardiologist or other user edits the specific values for the features.
  • one example may be the length of the QRS complex - in the situation where the interval was improperly measured by the algorithm.
  • the physician manually edits feature or features in step 232.
  • the workstation 124 or host system 130 runs a host-based interpretation based on the cardiologist-edited features and the remaining calculated features in step 234.
  • the interpretation algorithm is a system licensed by the Glasgow Royal Infirmary. This generates a new ECG draft report, which the, physician edits in step 216 for submission in step 218.
  • the system also allows the option of selecting one or more specific pulses in the ECG wave data for analysis.
  • features are typically pulled or calculated from a selected ECG wave from one typical cardiac cycle out of the usually very long wave data set that the machine acquires.
  • Other systems calculated features base on an average ECG wave derived a series of waves to form the basis of the interpretation.
  • these processes do not always create the best basis for the analysis.
  • the cardiologist may want to force analysis of some other, atypical, for example, wave.
  • the user/cardiologist is also provided with an opportunity to select a specific wave for analysis of a specific cardiac cycle.
  • Example 1 The duration of one portion of the ECG wave, called the QRS complex, is used to determine if there is an interruption in the flow of electricity in the heart's conduction system.
  • the QRS duration is a feature that directly leads to specific diagnoses on the ECG report. If the QRS duration feature is calculated to be greater than 120 milliseconds (and the QRS configuration matches a certain pattern), the condition is referred to as Left Bundle Branch Block, or "LBBB.” This means that the electricity is not conducting properly through the Left Bundle of the conducting system. In the presence of LBBB on the ECG report, it is not possible to make any definitive, statements about other clinical conditions such as anterior myocardial infarction ("AMI”) or left ventricular hypertrophy (“LVH").
  • AMI anterior myocardial infarction
  • LH left ventricular hypertrophy
  • the cardiologist reviewing the ECG may determine that the two ECGs are nearly identical, and that the pathology in the heart has not changed. On a conventional ECG editing system, for i o example, the cardiologist would have to change the QRS duration, delete the LBBB line, and add the LVH and AMI lines, resulting in excess effort and a time expenditure.
  • Example 2 P- waves are very tiny, and hard for the computer to distinguish on an ECG, especially if the patient is moving or other noise is present. The computer might also incorrectly think that P-waves are present, getting confused by the presence of noise. In this latter case, the reading might be "Sinus rhythm" and "1st degree AV block.” A measurement of the PR Interval 20 will be reported (the time duration between the occurrence of the P-wave and the occurrence of the QRS complex).
  • the correct next step is for the cardiologist with convention editing systems is to remove measurement of the PR interval, and delete the line that says "1st degree AV block.”
  • the cardiologist is performing a rather clerical function in the statement-based editing environment.
  • a feature-based editing environment is provided 3 o that enables the removal of feature data relating to the presence of the P-waves, specifically that the P-waves were found. Then, when the interpretation is re-run on the host, the reading will now automatically be changed to "atrial fibrillation," the PR interval will be removed, and the statement “1st degree AV Block" will be removed. As a result, the cardiologist has been able to interact as a cardiologist, not as a clerk. Inconsistencies and mutually exclusive findings have been avoided in the reading.
  • ECGs The standard clinical practice in most hospitals in the United States and elsewhere is for ECGs to be collected by technicians in the ECG department and presented to the responsible cardiologists to be interpreted. These cardiologists are often tasked with reviewing large numbers of ECGs from many different patients. But to ease this task, it is common that the ECGs will have already been read by a computer algorithm, and the computer's interpretation (a list of interpretive statements) will only need to be reviewed (“over-read") by the cardiologist and any necessary changes noted. In this common model of "batch reading," the cardiologist is often confronted with over- reading a large number of electrocardiograms in one sitting. And, the cardiologist will encounter some degree of mental fatigue after reading for an extended sitting.
  • ECGs are presented for reading based on the patient name or based on the time that the ECGs were recorded.
  • the ECG management system is not able to sort the ECGs in a way that is useful to the cardiologists or facilitate their work.
  • the present invention is also directed to a system that allows for the prioritization of ECGs. This can be performed by the ECG management system and/or at the instruction .of the cardiologist or other reader.
  • the system will allow for the sorting of the ECGs so that the more complex interpretations are presented first, when the reader is not suffering from fatigue, saving the simpler readings for later in the session as fatigue might begin to become a factor.
  • ECGs for a patient are examined and read as a group since the patient often has more than one ECG taken between the last reading session and the current one.
  • the simplest over-reading situation is the one where there is only one ECG to read for the patient.
  • the more ECGs that have accumulated for a patient and that need to be read the more complex the reading task becomes, since as ECGs have to be compared to each other, and this comparison is time consuming.
  • Complexity also increases in direct proportion to the number of interpretive statements on each machine-generated ECG interpretation.
  • certain diagnoses require more careful review than others do, and these diagnoses can be scored based on the differences in difficulty.
  • Fig. 7 illustrates a method for presenting electrocardiogram (ECG) data to a reader.
  • ECG electrocardiogram
  • the process of requesting the job assignment can be relatively simple or complex depending on the type of system used.
  • the reader requests a job assignment simply by accessing a file that has the batch of ECGs that are pending be read.
  • the database and host system 130 compiles the batches of ECGs from the different patients and then distributes them among the cardiologists/readers that are working on batch over-reads.
  • this distribution of the patients among the cardiologists is based upon which individuals are patients of the various cardiologists.
  • the system will assign the ECGs to be read among the various cardiologists to achieve an even workload distribution.
  • the ECG data for the different patients are then compiled by the database system 130 or by the workstation 1122 accessing the pending jobs based on the cardiologist request in step 1230.
  • the cardiologist or other reader sets the sorting criteria according to the invention.
  • the reader sets sorting criteria that are based on the complexity of the ECG data to be read. Specifically, the reader 122 will often request that the batch of ECG data from the different patients be sorted in decreasing complexity in terms of the process of reading the ECG data from the different patients. In other examples, the reader may present sorting criteria that requests ECG data to be sorted based on increasing complexity.
  • the database or management system sorts the ECG data from the different patients based on the sorting criteria.
  • the station 122 or database hosting system 130 calculates a complexity score for the ECG data from each of the patients. This complexity score is a metric characterizing the complexity of task of reading the ECG data and generating the report for that patient.
  • the complexity of the ECG data for a given patient there are a number of ways of characterizing the complexity of the ECG data for a given patient.
  • the number of previous ECGs that exist for each of the different patients is used as a metric.
  • the complexity of reading ECG data increases as the number of other ECG data sets from that patient increases since more ECG data sets must be compared to each other in order to determine how the patient's health is changing.
  • the complexity of the ECG report is characterized based on the number of machine-generated interpretive statements present in the ECG data.
  • each of the different potential diagnoses for all of the patients is given a score by a reviewing physician, based on the assessment of the complexity of the different diagnoses.
  • the ECG data for the different patients are sorted based upon that complexity list, and specifically the machine-generated interpretation of the ECG data.
  • the ECG data of the patients is presented to the reader in the order generated from the sorting in step 1234.
  • the reader 122 then reviews the ECG data from the management system database 130 and drafts the ECG reports for the different patients in step 1214.
  • the final interpreted ECG reports 5 from the reader are then stored in the database management system 130 in step 1236.
  • a complexity scores is assigned to the ECG data, usually based on the result of the machine-generated interpretation. These complexity scores are made available to the cardiologists/readers 122 allowing the readers to thereby sort their reports during a batch reading, o for example, based on this complexity score.
  • the management systems database 130 uses the complexity scores to affect load distribution across a number of cardiologists or other readers working at a hospital, for example. This will allow the system, in some examples, to assign the more difficult reading tasks to the more experienced cardiologists.
  • the management system/database 130 5 compares the complexity scores of the ECG data and then creates batches of ECG data to be read by the cardiologist such that all cardiologists have a similar mix of difficult and easy ECG data over-reading tasks.
  • each interpretive statement is assigned a complexity score between 0 to 4, easy to hard respectively.
  • the score for a given ECG is equal to the sum of the complexity scores of each interpretive statement that has been provided by the 5 computer analysis of the machine-generated interpretation; the complexity score for the patient is equal to the sum of the complexity scores for each of the ECGs to be over-read.
  • the ECG reading workstation 124 presents a list of ECGs to be reviewed to the over-reading cardiologist or other user 122. The order in which these are presented is based on the ECG reading complexity score, presented in decreasing complexity order in one embodiment. By 0 simply requesting "Next Patient,” the patient with the highest complexity score is selected to be reviewed next. This assures that the more difficult interpretive tasks are presented at the beginning of the over-reading session while the cardiologist is still fresh, while the simpler interpretive tasks are saved for the end of the reading session when fatigue may be a significant factor. In the typically hospital, ECGs are taken at carts throughout the hospital or institution and received at a records storage location for filing and possibly over-reading by staff cardiologists.
  • ECGs are associated with the proper patient file.
  • ECGs are often taken in situations where patient names or identifiers have not been provided to the 5 ECG cart, usually from a central computer facility. This means that the nurse or technician, who operates the ECG cart and does the ECG acquisition, has to enter the patient's demographic information (name, medical record number, etc.). This leads to several possibilities for errors, including, but not limited to: 1) the identifiers are incorrect or incomplete; 2) the identifiers are missing; and/or 3) the identifiers from the previous patient to have an ECG are inadvertently used. o When the identifiers are incorrect or incomplete, it is usually possible to reconstruct the correct information using searches of the hospital's patient database. Whereas, when the identifiers are missing or the identifiers from the previous patient are used, traditional information technology is unlikely to help resolve the problem.
  • ECGs taken around the same time could be searched. For example, it would be helpful to know that an unlabeled ECG from the emergency room is a close match to an ECG taken on the Coronary Care Unit one hour later.
  • the paper chart can be reviewed, where a paper copy of the ECG may be hand-labeled (but the identifier had not been entered into the computer); this allows complete resolution of the error.
  • the identifiers from the previous patient it is sometimes apparent that an ECG labeled as belonging to one patient does not match one recorded for that patient before or after the ECG in question. An ability to confirm the difference automatically assists the technicians, who would then consider the ECG in question to not have a valid identifier, and would undertake a corrective procedure. 5
  • the present invention can also function as part of a comprehensive ECG management
  • Fig. 8 illustrates a process for ECG quality assurance according to the present invention.
  • the ECG data for different patients are received in step 2210 at the management system 130.
  • this is a central location typically tasked with filing the ECGs and also distributing the ECGs to cardiologist for batch over-reading.
  • the ECGs will typically be generated throughout the hospital, in such varied environments as the emergency room and patient examining rooms.
  • This ECG data received at the management system 130 include the ECG wave data and patient identification information.
  • the patient identification information is useful for filing the 5 ECG data with the proper patient's file.
  • the management system 130 performs quality assurance testing.
  • Reasons that an ECG might fail this test include:
  • Demographic information such as age or gender do not match those on the previous o ECGs
  • the ECG is substantially different from the previous ECG for that patient.
  • the database management system 130 compares each of the ECGs to prior ECGs of the named patient in step 2212. Specifically, when the ECGs are originally o taken at the cart, typically the nurse or technician enters the patient name or more typically a patient number or the cart receives the information from a centralized system such as the management system 130. This patient number travels with the ECG data to the database as a mechanism for ensuring that the ECGs are put in the correct patient's file. Specifically, in step 2214, a similarity between the new ECGs and prior ECGs for the same named patient is assessed. 5 Generally, the objective is to bias the comparison to generating false negatives. That is, the system should tend to indicate that the named patient on the ECG is or could be wrong even if there is a somewhat strong similarity to previous ECGs from the same patient.
  • the exemplary algorithms for similarity include:
  • this may be a first ECG for the specific named patient. Thus, there is no prior ECG to generate a comparison. More often, the patient name/number may be invalid or uses a "John Doe" identifier. In still other cases, demographic information in the ECG data may not match data for the named patient. In each of theses situations, there is possibility of or indication of error. As a result, in step 2216, the incoming ECGs are also compared to ECGs from different named
  • these ECG against which the comparisons are made are ECGs that have been received recently at the database/management system 130.
  • step 2220 a determination is made whether each of the comparisons in steps 212 or 216 suggest error.
  • step 2224 the ECG is flagged for review if either of the comparisons suggests possible error.
  • step 2226 the results of the flagged ECG comparison is presented to a technician or cardiologist. There, the technician or cardiologist will confirm whether there is in fact similarity. It there is a suggestion that the ECG has an incorrectly named patient or a previous ECG has an incorrectly named patient, then a review is begun in step 2228, which can include contacting the individuals responsible for collecting the ECGs to resolve the apparent discrepancy. Finally, if the comparison suggests no error in step 220, or after research as to whether or not the ECG is correct, the ECG is filed as normally in step 222, either for the named patient or for the corrected patient name.

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Abstract

L'invention concerne un procédé et un système pour générer des électrocardiogrammes avec édition de caractéristiques dans le processus d'interprétation d'électrocardiogrammes, ce qui permet d'améliorer la précision de l'interprétation machine des données d'un ECG, facilitant ainsi l'analyse et la génération d'un rapport final par le chirurgien. Le procédé et le système de l'invention sont particulièrement utiles pour les système d'interprétation basés sur un ECG, lorsque le chirurgien accède aux données de l'ECG sur un poste de travail comprenant une interprétation machine générée sur un chariot ECG. Le chirurgien peut alors modifier les caractéristiques des données de l'ECG et générer une nouvelle interprétation basée sur l'appareil hôte, sur la base des données et des caractéristiques originales de l'ECG spécifiées par le chirurgien.
EP06718854A 2005-01-18 2006-01-18 Edition basee sur des caracteristiques pour l'electrocardiographie Withdrawn EP1841356A2 (fr)

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US64487605P 2005-01-18 2005-01-18
PCT/US2006/001846 WO2006078785A2 (fr) 2005-01-18 2006-01-18 Edition basee sur des caracteristiques pour l'electrocardiographie

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