WO2010057890A1 - Procédé mis en oeuvre par ordinateur pour afficher des diagnostics de maladies chroniques spécifiques d'un patient - Google Patents

Procédé mis en oeuvre par ordinateur pour afficher des diagnostics de maladies chroniques spécifiques d'un patient Download PDF

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Publication number
WO2010057890A1
WO2010057890A1 PCT/EP2009/065332 EP2009065332W WO2010057890A1 WO 2010057890 A1 WO2010057890 A1 WO 2010057890A1 EP 2009065332 W EP2009065332 W EP 2009065332W WO 2010057890 A1 WO2010057890 A1 WO 2010057890A1
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Prior art keywords
diagnosis
patient
diagnostic
medical
patient data
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PCT/EP2009/065332
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German (de)
English (en)
Inventor
Frank Gotthardt
Dierk Heimann
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Compugroup Holding Ag
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Application filed by Compugroup Holding Ag filed Critical Compugroup Holding Ag
Priority to EP09752378A priority Critical patent/EP2359280A1/fr
Priority to US13/129,996 priority patent/US20120101846A1/en
Publication of WO2010057890A1 publication Critical patent/WO2010057890A1/fr

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    • 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
    • G16H40/00—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices
    • G16H40/60—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices
    • G16H40/63—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for local operation
    • 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
    • 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
    • G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
    • G16H20/10—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients
    • 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
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
    • 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
    • G16H70/00—ICT specially adapted for the handling or processing of medical references
    • G16H70/20—ICT specially adapted for the handling or processing of medical references relating to practices or guidelines
    • 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
    • G16H40/00—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices
    • G16H40/60—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices
    • G16H40/67—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for remote operation

Definitions

  • the invention relates to a computer-implemented method for diagnosing and displaying patient-related, chronic diseases, a data processing system and a computer program product.
  • Medical information systems document, among other things, diverse, patient-related, administrative and medical data. Although the use of medical information systems makes it possible to document patient data essentially comprehensively and to store the patient data, the problem that a treating physician often has is that time problems in medical practices and hospitals arise It is rarely possible for a patient to have a complete overview of the patient's course of treatment by reviewing a patient's medical record before the start of a treatment appointment. In this case, a treating physician often only has time to spend a lot of time on the health problems and diagnoses of the patient, which have occurred in the recent past.
  • a symptom associated with a disease may vary from patient to patient.
  • One symptom can be an indicator for a large number of different Diseases are more likely to be diseases, and each disease can be characterized by a multitude of several, not always clear, symptoms.
  • the available medical expertise for the various diseases is very unevenly distributed.
  • the causes and symptoms of some diseases are well known and well described, while the causes of other diseases are still completely obscure. For some diseases at least correlation studies are available that show a statistical correlation between certain environmental factors, eating habits, physical activity, a particular genotype or the presence of other diseases (comorbidities).
  • Some diseases can be clearly attributed to one or a few causes, such as monogenic heritable diseases a gene defect.
  • Other diseases are multifactorial and can be caused by a variety of factors.
  • Articular arthrosis can be caused, for example, by age and wear-related joint wear.
  • joint arthrosis can also be the result of a corresponding genetic predisposition that comes into play at a certain age.
  • the diagnosis is complicated by the fact that various diagnostic methods for detecting a disease are possible.
  • diagnostic procedures and query standards recommended by the health insurance funds in accordance with a guideline diagnostics specific to the respective disease.
  • a computer-implemented method for displaying patient-related diagnoses of chronic diseases on a graphical user interface of a data processing system wherein the graphical UI has at least a first and a second display window.
  • the method initially comprises the step of displaying at least part of patient data of a patient in the first display window, the displayed patient data being displayed line by line in the first display window, the first display window being adapted for line-by-line tracking of the patient data to be displayed by a scroll bar is.
  • the method according to the invention has the advantage that a treating physician is assisted in making diagnoses with regard to chronic diseases of a patient in a quick and efficient manner.
  • the attending physician no longer has to undertake a thorough review of all available patient data of a patient, especially since, as already mentioned above, this is usually not possible for reasons of time.
  • diagnosis refers to a finding on a physiological condition or a disease of a patient in the following: A diagnosis was classically made on the basis of externally recognizable features (symptoms), laboratory values or various diagnostic procedures by a doctor who uses this data against the background of his or her own An important advantage of the present method according to the invention is that these evaluation steps can be automated and take into account more information than the physician can do in the short term improve diagnoses and speed up the diagnosis.
  • the invention relates to a computer-implemented method for medical diagnostic support for the prediction and presentation of chronic diseases by a data processing system.
  • the data processing system has a graphical user interface.
  • the procedure begins by accessing rules for calculating diagnostic risks of medical diagnoses.
  • the rules and the data objects representing the diagnoses are stored in a database in a manner that accommodates the heterogeneity of the knowledge about various diseases and associated symptoms described above.
  • Every diagnosis in this database is stored with a primary medical risk.
  • the primary medical risk for a diagnosis indicates the likelihood that the presence of this diagnosis in a patient may be presumed when the only evidence used for this assumption is the general statistical distribution of a disease in the general population.
  • the primary risk of having a disease affecting 10 000 people in a population of 1 million is 0.01 (1%). Age, gender or pre-existing conditions are not taken into account when calculating the primary risk. Rather, the primary risk is used according to the currently available medical knowledge (number of diseases per total population or, if unknown, number of patients within a studied patient group of a medical study). A reference to the literature source from which the value for the primary risk was taken is also stored in the database.
  • a prediction system is not only able to assign a primary risk to every diagnosis.
  • medical diagnostic risks are calculated individually for the respective patient.
  • rules are applied to the data of the patient.
  • Each rule contains one or more query conditions (age, sex, medical history, etc.).
  • Applying a rule to the data of an electronic health record means checking that all query conditions of a rule for that record are met.
  • the rules are stored in a database in such a way that a large number of possible query conditions in different combinations can be flexibly taken into account.
  • the database scheme used also makes it possible by importing appropriate updates for the medical diagnostic objects and risk calculation methods, so that the inventive method can be easily adapted to the current and ever-changing state of medical knowledge.
  • Applying the rules to the patient data results in the calculation of at least a first medical diagnostic risk for a first medical diagnosis when at least one of the rules is applicable to the patient data.
  • the database contains three rules for calculating a risk for a particular disease K, all three of which contain as a condition a patient age of at least 30 years, then in this example none of the rules is applicable to a 25 year old patient.
  • the next step is to output the first calculated diagnosis risk for the first medical diagnosis along with the first medical diagnosis on the graphical user interface and the output of a user query, whether for the first medical diagnosis interactive symptom diagnostics and / or a guideline diagnostics should be carried out.
  • Medical guidelines are systematically developed diagnostic and symptom assessment procedures to support physician decision-making. Both symptom diagnostics and guideline diagnostics serve, on the one hand, to specify the first diagnosis risk calculated by application of the rule by interactively specifying further characteristics of the patient. On the other hand, you suggest to the doctor symptoms or guideline criteria for the selection that are stored associated with the first diagnosis. These guideline criteria and symptoms, in turn, may correlate with other diagnoses that are also suggested to the physician for selection. By selecting and deselecting the one associated with a first diagnosis risk Symptoms and guideline criteria can not only be used to specify the first diagnosis risk, but also to detect further possible diagnoses in the context of the first diagnosis, which the physician can select for further analysis.
  • a symptom user query is issued by which the physician can determine which of the medical symptoms associated with the first medical diagnosis will be used for further analysis of the patient data To influence the previously determined diagnostic risk.
  • the first diagnostic risk calculated in the previous step is modified and specified.
  • the physician will select some or all of the suggested symptoms.
  • Each selection or selection of a symptom can increase or decrease the first diagnosis risk.
  • the symptom user query can be used by the physician to specify the first diagnostic result based on the application of rules.
  • the second diagnosis risk determined in the symptom diagnosis thus uses the first diagnosis risk as initial value in order to specify this, depending on the presence or absence of further symptoms. Finally, in a subsequent step, the second, even more precise, diagnostic risk is output together with the second diagnostic on the graphical user interface.
  • a guideline diagnostic user query is output. If the guideline diagnostics is performed immediately after the first diagnostic risk has been calculated, the first diagnostic risk is the starting point for further clarification of the diagnostic risk. If the guideline diagnostic is performed after the symptom diagnosis, the second diagnosis risk determined in the symptom diagnosis sets the initial value for further clarification of the diagnosis Diagnosis risk.
  • the diagnostic risk calculated in the course of the guideline diagnostics is referred to as the third diagnostic risk, irrespective of the actual sequence of the diagnostic steps. Similarly, the diagnostic risk determined in symptom diagnostics is referred to as a second diagnostic risk. Symptom diagnostics is therefore not a prerequisite for conducting the guideline diagnostics. Rather, both diagnostic methods can be based on each other or individually directly after the calculation of the first diagnosis risk.
  • Symptoms to be used by the physician according to a guideline diagnostic to assess the presence of a particular diagnostic are referred to below as guideline criteria.
  • the guideline criteria are linked to the diagnostic objects stored in a first database. Conducting a guideline diagnostic diagnostic means that the user is presented with the guideline criteria associated with that diagnosis for selection.
  • the guideline criteria may also include laboratory values of the patient, eg, blood glucose, serum creatinine, blood pressure, or similar data.
  • the user usually the physician, selects from the presented set of guideline criteria some that he considers relevant and that should be used to further clarify the previously determined diagnostic risk. By selecting or deselecting the presented guideline criteria by the user, the diagnostic risk calculated in the previous step is further modified and specified.
  • the physician will select some or all of the proposed guidance criteria.
  • the selection or deselection of individual guideline criteria results in a modification of the initial risk value, as a result of which a third diagnostic risk is returned and displayed.
  • the third diagnostic risk is further refined through the use of disease-specific guideline routines.
  • Guideline routines are diagnostics-specific calculation routines that ultimately result in a modification of the second diagnostic risk value.
  • the guideline routines may, for example, give greater weight to the existence of individual guideline criteria, perform complex Boolean operations (eg, AND, OR, NOR) or arithmetic functions on the selected guideline criteria, and apply the modified diagnostic risk calculated therefrom.
  • the guideline routines on the guideline criteria for calculating the diagnostic risk are heuristics that are based on a combination of several individual factors.
  • the MDRD formula commonly used to diagnose renal dysfunction takes into account serum creatinine (laboratory finding) also the age, the skin color and the gender of the patient. So factors that are known from various studies that you have an influence on the presence of kidney dysfunction, or at least correlate with it.
  • ICD codes International Statistical Classification of Diseases and Related Health Problems
  • LEZ eg according to the uniform assessment standard for the medical fee, EBM
  • ICD codes represent diagnoses that have already been made in the patient's past according to the patient record.
  • LEZ codes can aid in the calculation of the diagnostic risk, although they are not always indicative of a particular pre-existing condition. For example, if the patient presented with unexplained upper abdominal discomfort in the past to a doctor who subsequently performed a gastroscopy without findings, then this event in the patient record is not associated with a diagnosis of a disease. However, the fact that gastroscopy has been performed at all, as shown by the LEZ Code, may be an indication of the presence of upper abdominal health problems.
  • the third diagnostic risk identified in the guideline diagnostic thus uses the second diagnostic risk as the baseline value to specify it, depending on the presence or absence of the guideline criteria assigned to the diagnosis, and on the outcome of the guideline routines. Finally, in a subsequent step, the output of the thus calculated third diagnosis risk is performed together with the third medical diagnosis on the graphical user interface.
  • the physician can confirm the diagnosis, which is consequently stored in the electronic patient record of the patient.
  • the calculation of one or more first diagnostic risks by applying the rules is initiated immediately according to a preferred embodiment of the invention, when the doctor or a receptionist opens the electronic patient record.
  • the display of the calculated diagnostic risks can also be made later, eg only when the doctor opens a prescription form.
  • This embodiment is particularly advantageous since in everyday practice the electronic patient record is typically first opened by a medical assistant in order, for example, to enter laboratory values or administration data associated with the doctor's visit.
  • the fact that the risk calculation is initiated by opening the electronic file the results are already available to the doctor, which brings a further time savings.
  • the doctor can immediately switch to symptom diagnosis or guideline diagnostics.
  • the diagnoses obtained by applying the rules and further patient-related data are displayed in a pop-up window.
  • a threshold for the calculated diagnostic risk only really relevant information is displayed.
  • the present invention offers the possibility of a suspected diagnosis check.
  • This feature includes allowing the physician to directly enter a diagnosis into the system as a suspected diagnosis. This option ensures that even if the system does not suggest a diagnosis, the physician can further investigate a presumption of the presence of a particular diagnosis.
  • the suspected diagnosis check differs from the procedure explained above in that rules that are applied to the patient data do not propose the first diagnoses but the physician does so. The doctor selects a suspected diagnosis from a list of possible diagnoses in the first database.
  • patient data is understood to be any type of information which has been recorded with regard to a patient.
  • patient data In addition to structured and free-text data, this also includes electronic image data as well as medical measurement data of any kind.
  • Structured patient data refers to patient data provided according to a previously defined standard or classification.
  • the method according to the invention has the advantage that a treating physician is enabled to consider various medical diagnoses together in their entirety. In other words, he can perform an analysis of patient data in a faster and more efficient way.
  • the procedure allows a doctor to be automatically alerted to possible medical diagnoses that are not detectable by manually reviewing the patient's data because complex relationships between medical findings need to be considered.
  • medical diagnosis risks and associated diagnoses determined individually for the patient are thus displayed to a doctor. If the physician considers that a potential diagnosis could be of high relevance in the present case of treatment, he can quickly and effectively make a determination by confirming the user prompt as to whether interactive diagnosis of symptoms is to be performed for the first medical diagnosis whether a displayed medical diagnosis is actually relevant or not. In other words, a suspicion for a particular diagnosis can be substantiated or rejected.
  • it is ensured on the whole that the interaction time of the physician with the data processing system is considerably shortened. The same applies analogously to the guideline diagnostics.
  • the user has in the symptom user query according to an embodiment of the invention, the possibility of various medical symptoms associated with the linked to the first medical diagnosis for further analysis of the patient data. After selecting a symptom that he considers appropriate for the patient currently being examined, applying the symptom diagnostic rules associated with this selected symptom is performed at the previously determined diagnostic risk value for a particular diagnosis.
  • the symptom user query is interactive, that is, the physician can consult or exclude individual symptoms believed to be diagnosing the patient for diagnosis. This has the advantage that the doctor can interactively determine the influence of each individual symptom on the diagnosis result individually by selecting or deselecting the symptom.
  • the user has according to an embodiment of the invention in an analogous manner for the guideline diagnostics the ability to different guideline criteria, which may include laboratory values that are associated with the previously determined diagnosis to select for further analysis of the patient data.
  • guideline criteria which may include laboratory values that are associated with the previously determined diagnosis to select for further analysis of the patient data.
  • a modification of the previously determined risk value for a specific diagnosis is made, the strength of the modification depending on the respective guideline criterion.
  • the guideline user query is interactive, meaning that the physician may consult or exclude individual guideline criteria that he believes he can identify to the patient for the purpose of the diagnosis.
  • the previously determined diagnostic risk is modified by the execution of diagnostic-specific guideline routines.
  • the patient data is received from a second database.
  • This second database can external database, such as the database of a doctor information system.
  • medical diagnoses are issued only at a predetermined threshold.
  • the medical diagnoses are preferably output sorted according to the calculated risk level. This ensures that a user of the data processing system, i. a treating physician is not unnecessarily confronted with irrelevant medical diagnoses.
  • a threshold is selected for a diagnostic risk of 40% to be displayed to the physician, but this value can be changed by the user.
  • the first, second and third medical diagnosis risks are displayed in the form of a tachograph disc.
  • the diagnostic risk is displayed with colored gradations on the scale of the speedometer disk.
  • the primary risk is displayed as a risk probability in the form of a numerical value together with the speedometer disk.
  • a first operating element is provided together with the first medical diagnosis risk, wherein the first operating element is designed for a user confirmation, wherein in the case of a user confirmation via the first operating element the first medical diagnosis and / or the medical symptoms with the patient data linked stored in the second database.
  • a second operating element is provided together with the second medical diagnosis risk, wherein the second operating element is designed for a user confirmation, wherein in the case of a user confirmation via the second operating element, the second diagnosis risk and the second medical diagnosis as a new first Diagnostic risk and is output as a new first medical diagnosis on the graphical user interface.
  • the possibility is given of updating the diagnosis specified by the additional entry of symptoms in that overview which was originally generated with the output of the first diagnosis diagnosis for the first medical diagnosis together with the first medical diagnosis. This is particularly relevant in the event that not only a single medical diagnosis provided with a corresponding diagnostic risk was originally displayed, but a set of different diagnoses.
  • the risk is specified for the diagnosis for which the symptom diagnosis was made.
  • symptom diagnostics has the function of identifying other potentially relevant diagnoses that were not included in the list of initial diagnoses. This is done so that the user of symptom diagnostics will be presented with further diagnoses that correlate with the symptoms selected by the user. If the user considers the additionally suggested diagnoses to be relevant, he can select the diagnoses and thereby add them to the list of the first diagnoses.
  • the symptom diagnostic rules are applied again to the patient data and the medical symptoms hitherto selected by the user. Thereafter, recalculating at least one new second diagnostic risk for a new second medical diagnosis is dynamically performed, followed by an updated output of the recalculated new second diagnostic risk along with the new second medical diagnosis in the graphical user interface. Finally, an updating Issue the user query which medical symptoms associated with the new second medical diagnosis should be used for further analysis of the patient data. This enables the physician to immediately recognize the significance that the specific indication of a single symptom has for possible diagnoses with regard to their diagnostic risks.
  • the updated output of the second diagnostic risk is for a re-updated output of the symptom user query, wherein the updated output of the symptom user query indicates which of the medical symptoms associated with the further medical diagnosis previously selected by the user for further analysis patient data is to be used, maintaining previously selected medical symptoms in the updated output of the symptom user query.
  • this further restricts the list of selectable possible medical symptoms or dynamically supplements them with other possible selectable symptoms. This is relevant, for example, if the combined evaluation of patient data and selected symptoms indicates that a possible illness exists, which, however, can only be taken into account for a diagnosis risk calculation if further symptoms not previously indicated are considered.
  • the symptom user query takes place in the form of a checkbox list.
  • the first and / or second database is a database external to the data processing system or the first and / or second database is contained in the data processing system.
  • the computer-implemented diagnostic support method is implemented as a plug-in of an interface, which interface can communicate with one of a plurality of physician information systems (AIS). Since the plug-in uses this interface with the various AIS is not limited to a specific AIS. The plug-in can be used for a variety of AIS.
  • AIS physician information systems
  • At least some of the laboratory values of a patient are entered automatically, e.g. via the connection to a LIMS (Laboratory Information and Management System).
  • the data transmission preferably takes place in accordance with the LOINC (Logical Observation Identifier Names and Codes) system for the encryption and transmission of data from laboratory examinations.
  • LOINC Logical Observation Identifier Names and Codes
  • all structured medical data of the electronic patient files of a doctor or a clinic are statistically evaluated. This includes the classification of patients and their associated medical data into strata (groups whose representatives are similar in certain characteristics, such as age, gender, occupation / income, physical activity, diagnoses, etc.). From the strata connections between these characteristics and the risk for the occurrence of further diagnoses are determined by means of various data mining and interference methods. Through these methods, statistical correlations can be discovered that are not yet known to the medical world. The correlation data obtained in this way can be used to define the rules for the calculation of diagnostic risks even more precisely and better.
  • the method further comprises the step of preparing the patient data, wherein the application of the rules to the patient data is carried out only for the processed patient data.
  • data processing includes the filtering of structured data from the patient data. This reduces the amount of data to be processed and transferred for each query and significantly speeds up the corresponding query.
  • the patient data is read from a second database and processed, which includes above all the filtering of the structured data from all existing patient data.
  • patient data is stored in a third database, whereby the rules are applied to the patient data by accessing the third database.
  • the third database may again be a database external to the data processing system.
  • the third database is a cache memory of the data processing system, so that a query on the relevant patient data can be performed in a very fast manner.
  • this represents a significant advantage, since for corresponding queries the first and third database can be kept relatively small in scope - the amount of data to be transmitted or the number of queries to be performed is drastically reduced.
  • Another technical advantage of loading all structured patient data into the cache memory is that this "memory database" ensures that the patient data will always be in the same structure, even if the structure of the patient data in the second database, for example, depends on the AIS or LIMS used and can be structured differently.
  • At least a portion of the patient data is provided in a first graphical user interface display window, wherein the first and second diagnostic risk for a first and second medical diagnosis is populated along with the medical diagnosis on the graphical user interface.
  • the application of the rules to the patient data is carried out automatically after displaying the patient data in the first display window.
  • the method is performed after opening the electronic patient record, the method further comprising the step of receiving new patient data through a user input.
  • Structured data obtained can be used to automatically generate medical reports. For example, after performing a symptom diagnosis that resulted in the diagnosis of a disease based on the presence of five symptoms, the system can automatically generate a medical report that contains the information that a particular patient has arrived in practice on a particular date has identified the five or so symptoms in the patient and that based on these symptoms, a specific diagnosis has been made.
  • the automated generation of medical reports and other administrative documents significantly increases the efficiency of a doctor's office workflow and avoids errors by manually entering the diagnoses into the medical report.
  • the invention in another aspect, relates to a data processing system having a graphical user interface, wherein the data processing system is adapted to perform the method for medical diagnostic support of a patient.
  • the invention in another aspect, relates to a computer program product having processor executable instructions for performing the method of providing medical diagnostic support to a patient's patient data.
  • the graphical user interface has at least a first and a second display window.
  • the method in this case comprises the step of displaying at least a part of patient data of a patient in the first display window, wherein the displayed patient data are displayed in rows in the first display window.
  • the first display window is designed for line-by-line tracking of the patient data to be displayed by means of a scrollbar.
  • access is made to a first database, this first database containing medical diagnostic objects.
  • the medical diagnostic objects are linked to patient patient data rules and are used to automatically identify individualized diagnostic risks based on the electronic patient record.
  • the first database also contains information on whether the diseases represented by the medical diagnostic objects are usually or in individual cases are chronic.
  • the check is made as to whether at least one of the rules for the patient data is fulfilled. If this is the case, a display element is displayed on the graphical user interface, wherein the display element has at least one of the first diagnostic objects for which the first rule is fulfilled. If the first diagnosis thus determined is recorded in the first database as a possible permanent diagnosis (chronic illness), the output of a user query on the graphical user interface is made as to whether a medical diagnosis associated with the diagnosis object should be adopted as a chronic permanent diagnosis. If the medical diagnosis linked to the diagnostic object is to be adopted as a permanent diagnosis, the permanent diagnosis in the second display window is displayed regardless of the position of the scrollbar.
  • the method further comprises storing the permanent diagnosis in the second database, which also contains the patient data, linked to the patient data.
  • diagnosis object is understood to mean any type of information that makes it possible to describe a medical diagnosis, including free-text information, which, for example, names the diagnosis by name or which describes in detail a clinical picture associated with the chronic disease Furthermore, diagnostic but also the ICD codes already mentioned above or generally structured information.
  • the graphical user interface also has a third display window, wherein the method, if the medical diagnosis associated with the diagnostic object is to be adopted as a permanent diagnosis, further comprises the following steps: First, it should be noted that in this embodiment, the first database information contains which active substances to prescribe in the presence of a diagnosis. Furthermore, the first or a fourth database contains information about which medicaments and associated medicament objects contain which active ingredients. Further, the electronic patient record contains information about which medications have been prescribed to the patient in the past. First, after confirmation by the physician that the present disease / diagnosis is a chronic diagnosis, the first database is searched for active substances which should be prescribed if this diagnosis is present.
  • these drugs are associated with drugs (or drug-presenting objects), and the electronic health record is analyzed to see if any medications containing this drug have been prescribed in the past. If so, another display element is displayed on the graphical user interface, which further display element comprises at least one of the drug objects that have been previously prescribed and can also be used to treat a patient's chronic diagnosis. Thereafter, the output of another user query on the graphical user interface, whether a drug associated with the drug object is to be adopted as a preparation for long-term medication. Corresponding preparations are referred to below as permanent drugs.
  • the medicament linked to the medicament object is to be adopted as a permanent medicament
  • a permanently visible indication of the permanent medicaments in the third display window takes place, likewise independent of the position of the scrollbar.
  • the medical diagnosis associated with the diagnostic object is to be taken as a permanent diagnosis, that is, if a chronic disease is considered safe by the physician
  • the next step is a review of whether medications already used to treat the chronic illness have been used according to the patient record, that is the patient data previously prescribed to the patient. If the system detects a relevant chronic illness and if drugs or medicaments are found in the individual patient file which are appropriate for these chronic diseases, the physician is advised to transfer the respective preparation to the section "long-term medication" in the third display window.
  • this procedure ensures that the physician, if he / she fails has occasionally provided a case-by-case diagnosis, although it would actually be apparent from the patient record that it is a chronic disease, receives an indication of the presence of a chronic disease and possibly a permanent medication.
  • the permanent drugs confirmed by the physician can be stored linked to the patient data as a permanent medication.
  • the first and / or second and / or fourth database is a database external to the data processing system, but it is also possible for the first and / or second and / or fourth database in the data processing system itself is included. According to a preferred embodiment, however, the patient data is located on a second database, for example a doctor information system.
  • the first database is identical to the second and fourth databases and will be For example, provided together with the above-mentioned data processing system.
  • the second database can be contained in a doctor information system, wherein this doctor information system can carry out the method according to the invention as described above.
  • the doctor information system accesses a web service via a network, which can be called up by a server.
  • This web service provides, for example, a service in the form of a servlet, which makes it possible to apply the method according to the invention to the patient data.
  • the web service may access the first and fourth databases representing external databases regarding the doctor information system, but the web service may be executed either on the doctor information system or server side on a server operated by a medical service provider , The first and fourth databases can be assigned to this server of the medical service provider.
  • the method according to the invention can also be carried out on an external server, the graphical user interface being part of a client, via which patient data are entered and which can be operated by a corresponding doctor.
  • the rules for determining the first diagnosis risk are linked to a time constant for a maximum age of the patient data.
  • the time constant includes at least the date and possibly other time information indicating when a data entry was made into the patient's electronic file, wherein the data entry may constitute the creation of a particular diagnosis, the prescription of a drug, or the performance or billing of a medical examination .
  • the check as to whether the patient file contains indications of the existence of a diagnosis, in particular of a permanent diagnosis is applied only to the patient data having a younger time stamp than the maximum age.
  • the doctor may discontinue only those diagnoses, medications and treatments in the Patient file at diagnosis to play role, which were entered into the file within a predetermined period of time.
  • This predetermined period is initially predetermined by the system, but can also be adapted by the physician in a corresponding manner.
  • this predefined period is preferably dependent on the type of medical diagnosis, so that the time constant for each query condition is set individually. Nonetheless, it is possible to set a global upper limit as to the age of eligible patient data.
  • the check of whether some of the rules for calculating first diagnostic risks can be applied to the patient data is made automatically after displaying the at least a part of the patient data in the first display window.
  • the method is preferably performed in real time, the method further comprising the step of receiving new patient data through a corresponding user input.
  • the check of whether at least one of the first rules for the patient data is fulfilled is carried out in the order of decreasing diagnosis risk of the respective rule.
  • the retrieval of the rules can be performed according to the above-mentioned prioritization.
  • Such a prioritization may also include, for example, only those rules for a specific diagnosis that are associated with the highest diagnostic risk being performed. If a rule executed according to this prioritization applies, the query for further rules for the same diagnosis can be omitted, since with a higher risk. kowert is no longer to be expected even if other rules for this diagnosis apply. This can further reduce the computation time required for risk calculation.
  • the invention relates to the function of prescription medication.
  • the drug objects in the first database are, according to this embodiment, stored with information on package size (number of dosage units present in the package, measured, for example, in milliliters, drops, tablets, or others).
  • each drug object is provided with information about the standard dosage, that is information about how many dosage units per day, week or month usually need to be taken.
  • this additional feature reads the medication items that were previously prescribed to the patient, and stores information about the size of the package and the default dose prescribed with these medication items.
  • the medication prescription aid function can calculate how long the prescribed drug is still sufficient and whether the doctor may need to prescribe another pack.
  • this medication prescription aid primarily relates to permanently prescribed medications.
  • the display of the remaining time until the prescription of another pack is required is preferably displayed in the form of a color-coded scale or dial, with red signals that the drug must now be re-prescribed, green, that the currently prescribed pack still sufficient is and yellow that the new prescription is discretionary thing of the doctor.
  • the invention relates to a data processing system having a graphical user interface, wherein the data processing system is designed to carry out the method for displaying patient-related diagnoses of chronic diseases.
  • the invention relates to a computer program product with instructions executable by a processor for carrying out the method according to the invention for displaying patient-related diagnoses of chronic diseases.
  • FIG. 1 shows a block diagram of a data processing system according to the invention
  • FIG. 2 is a schematic view of a graphical user interface
  • FIG. 3 shows a flow diagram of a method for displaying patient-related diagnoses of chronic diseases
  • FIG. 4 shows a computer-implemented method for medical diagnostic support for patient data of a patient
  • FIG. 5 Steps of a method for medical diagnostic support for patient data of a patient.
  • FIG. 6 shows a database table with rules for calculating the first diagnosis risks
  • FIG. 7 shows a database table for symptom diagnostics
  • FIG. 8 shows a database table for guideline diagnostics
  • FIG. 9 shows a computer-readable storage medium.
  • FIG. 1 shows a block diagram of a data processing system 100 according to the invention.
  • the data processing system 100 has a processor 104 and input means 102, such as a mouse, keyboard, etc.
  • the input means may also be medical devices by means of which corresponding medical image and / or measurement data of a patient are acquired and can be stored.
  • the data processing system 100 has a memory 116 in which there is a computer-executable code for an application program, for example for carrying out the method according to the invention.
  • the data processing system 100 has a graphical user interface 106, which is output on a corresponding display device 108.
  • This display device 108 may be, for example, an LCD or CRT screen.
  • the data processing system 100 can communicate with databases 122, 132 and 142, for example via the network 118.
  • the interface communicates with the physician information system AIS using a data encryption method, e.g. a hash process.
  • the databases 122, 132, and 142 may also be part of the data processing system 100 itself.
  • code for execution by the processor 104 can also be retrieved from a server 144, in which case the code for carrying out the method of the invention is e.g. provided by means of a web service. The code can be executed either on the server 144 or in the data processing system 100.
  • a treating doctor first opens a patient file.
  • This patient record contains patient data 134 stored in the database 132.
  • the patient data 134 are first transmitted to the data processing system 100 via the network 118.
  • the last-entered patient data is then displayed line by line in the display window 114 shown, wherein this display window has a scrollbar. With this, the doctor is able to scroll through all entries of the patient data by moving the scrollbar.
  • the data processing system 100 and / or its processor 104 will initially proceed to open the patient record in such a way that is accessed via the network 118 to the database 122.
  • This database 122 contains medical diagnostic objects 124.
  • the data processing system 100 may determine if there is a high likelihood of a patient's chronic illness being present.
  • the first database 122 contains information about which of the medical diagnostic objects may or may occur as persistent diagnoses. If one of the rules 128, which determines the diagnosis risk for the existence of a specific diagnosis based on the patient data 134, is met, and if the thus determined diagnosis object is stored in the first database as a possible permanent diagnosis, the display element, for example a popup, is displayed. on the graphical user interface 106. This pop-up contains further information regarding the possibility of the presence of a chronic disease, and thus in particular information contained in the medical diagnostic object 124.
  • This may be, for example, an ICD code or the name of a corresponding chronic disease.
  • additional information and, where appropriate, also links in the form of hyperlinks to other databases can be specified, by means of which the attending physician can continue to learn in detail about the corresponding chronic disease.
  • the data processing system 100 offers the attending physician the opportunity to monitor the corresponding chronic illness.
  • this diagnosis is permanently displayed in the display window 110 of the graphical user interface 106, irrespective of a scrolling movement within the various rows of the patient data in the display window 114. If so, the physician will do the same confirmed, it is then preferably this permanent display of medical diagnosis, for example in the form of an ICD code, in the display window 110 and also storing this display option for the patient in the patient's file in the database 132. In other words, so the patient data 134 with the permanent diagnosis "chronic illness". The next time the medical records are opened by the attending physician, the data processing system 100 can thus display this permanent diagnosis permanently in the display window 110.
  • the data processing system 100 first of all accesses the database 122 in which information is available which active substances are generally to be presumed in the presence of a specific diagnosis.
  • the database 142 is accessed.
  • the fourth database 142 includes medical drug objects 136 and information about which drugs 138 are contained in which drugs. Access to the database is to identify those drug objects that, according to the drug and drug mapping information, contain the drugs to be prescribed according to the information from the database 122 in the presence of a particular diagnosis.
  • the patient data 134 is analyzed as to whether one or more of the drugs thus determined have already been prescribed to the patient in the past.
  • a corresponding user query will be output on the graphical user interface 106.
  • the determined medical drug objects for example in the form of drugs or drug names, possibly represented by PZN numbers, whereupon the attending physician can select from the list made available to him one or more drugs, which he used for long-term medication of the respective patient wants to add to the patient's file. After selecting one or more drugs, these are then displayed permanently in the display window 112 of the graphical user interface 106.
  • the list of preparations proposed to the physician as a permanent medication is not restricted to those preparations which have already been prescribed, so that the described function can also be used to determine suitable medicaments for the treatment of a chronic disease have not yet been prescribed.
  • the data processing system 100 still further allows a treating physician to make diagnoses in a reliable manner.
  • the data processing system 100 can in turn access the database 122 in order to retrieve rules 128 with calculation of diagnostic risks for medical diagnoses, wherein in the database 122 the medical diagnoses are also stored linked to medical symptoms 130.
  • this diagnostic risk may be displayed on the graphical user interface 106, again in the form of a pop-up to the physician, for example.
  • the diagnosis risk is preferably presented to the physician together with the medical diagnosis.
  • different risks for different medical diagnoses, if present, can be displayed here, preferably according to the risk probability.
  • a user query is output on the graphical user interface 106 as to whether a medical diagnosis of 646. If such is confirmed by the physician, an output of a symptom user query is made which of the medical symptoms associated with the medical diagnosis 130 for further analysis the patient data 134 should be used. For example, for a diagnosis selected by the doctor, various symptoms of illness are displayed in the form of a list of checkboxes, with each activation of a checkbox, that is to say the confirmation of the presence of a disease symptom, a dynamic updating and recalculation of the diagnostic risk for the corresponding diagnostic result.
  • diagnosis findings can also be supplemented with further, even more precise diagnostic findings on the graphical user interface. For example, if a medical diagnosis was initially labeled as a "60% risk for diabetes," the additional specification on the graphical user interface 106 indicates that the risk for "type I diabetes is 80%" and that " Risk of Type II Diabetes at 40% ".
  • a treating physician now considers one of the medical diagnoses to be secure, he can confirm this in a corresponding manner and thus store it in the database 132 linked to the patient data 134.
  • FIG. 2 shows a schematic view of a graphical user interface 106.
  • the graphical user interface 106 has display windows 110, 112 and 114.
  • the display window 110 is for displaying persistent diagnoses, whereas the display window 112 is for displaying continuous medications.
  • the display window 114 is used for line by line display of patient data, wherein preferably when opening a patient file only the few, most recently made entries are displayed in the patient record. Nevertheless, access to further entries is possible by moving a corresponding element 202 of a scroll bar 200 vertically up and down so that it is possible to scroll through the various entries of the patient file. By clicking on arrows 204 it is also possible to scroll in the form of line Make jumps. Further shown in FIG. 2 is a popup 206 in which further information can be provided to a user. Such a popup can be, for example, a display element with diagnostic objects, queries, medication objects, or else diagnostic findings in connection with medical diagnoses, a window for performing interactive symptom diagnostics, or a corresponding query window.
  • FIG. 3 shows a flow chart of the method according to the invention in accordance with a further embodiment of the invention for displaying patient-related diagnoses of chronic diseases on a graphical user interface of a data processing system.
  • the medical diagnostic objects 124 are linked stored with additional information about whether a diagnosis can usually be chronic or in individual cases.
  • the method begins in step 300 with displaying the patient data in a display window 114, this display window having a scroll bar and only a portion of the patient data displayed in that display window.
  • step 302 the reading and applying of rules to the patient data occurs, the rules containing query conditions and applied to the available patient data of a patient.
  • the rules 128 are stored linked to medical diagnostic objects 124 in a first database 122. The structure of the rules is shown in detail in FIG.
  • step 304 the check is made as to whether at least one of the rules for the patient data has been fulfilled.
  • this step checks whether the medical diagnoses are diagnoses that may also be chronic. If one of the two criteria is not fulfilled, the method then ends in step 322. If, however, one of the rules for the patient data is fulfilled in step 304, and the diagnosis thus determined may be chronic, then the method sets the display in step 306 of a display element on the graphical user interface, this display element having at least one of the diagnostic objects, for example an ICD code, which is part of the corresponding diagnostic object for which the rule is fulfilled.
  • a user query is issued which asks the user to make a decision 308 as to whether the determined possible permanent diagnosis actually takes place in the patient record as a permanent diagnosis. should be. If the medical diagnosis is not accepted as a chronic permanent diagnosis, then the method jumps back to step 304, where it is checked whether another rule for the patient data is fulfilled. Steps 304-308 are thus cycled for all rules.
  • step 310 the permanent diagnosis is displayed, for example in the form of the ICD code, in a second display window 110 independently of the position of the scroll bar of the first display - window 114.
  • step 312 access to the first database 122, in which the medical diagnostic objects are stored with information about which active substances are to be presumed in the presence of a diagnosis.
  • the information about which drugs to administer in the case of a particular diagnosis may alternatively be stored in a fourth database (142). If at least one corresponding active ingredient has been determined, an access (312) to another database of medical drug objects takes place, wherein the medical drug objects are linked to information about contained active ingredients. In this step, all drugs containing at least one of the previously identified active ingredients are determined. In step 314, a check is made on the patient data as to whether the previously determined drugs have already been prescribed to the patient once.
  • this step may also be associated with a review of the time constant of the prescription of the drug, which may be determined from the patient data 134. If the prescription of the drug has been around for a very long time, the drug in this case will be disregarded in 314. If the medication has not yet been prescribed, or if its prescription is too long ago, the method returns to step 304, where it is further cyclically checked with steps 304, 306, and 308 to see if at least one of the other chronic disease compliance rules is met are.
  • step 316 the method continues to display a graphical user interface display element that suggests at least one of the drug objects to be selected by the user, with the proposed drug objects having at least one drug against the patient User-confirmed continuous diagnosis and already prescribed for the patient. Also, only parts of the data associated with a medicine object may be displayed, such as a central pharmaceutical number or a drug description or a drug name.
  • the query in step 318 is to allow a physician to decide whether to use the indicated medication for long-term medication. If it does not want to do so, the process returns to step 304.
  • step 318 is followed by step 320 of displaying the drug in a third graphical user interface window 112 in a persistent, ie independent manner from the position of the scrollbar. After step 320, the method again jumps to step 304.
  • step 300 it is also possible to carry out a data processing of the patient data with an intermediate step 301.
  • those data which are structured are filtered out of the patient data.
  • These structured data are then held in a corresponding memory, for example a cache memory, indicated by the reference numeral 140 in FIG.
  • a first diagnosis risk there is the primary risk for each diagnosis in the system or, after applying the rules, a first diagnosis risk.
  • the risk value which indicates the probability with which a diagnosis is chronically pronounced in the event of its occurrence
  • the risk for the existence of a chronic diagnosis can be predicted even more precisely.
  • FIG. 4 shows a flowchart of a method for medical diagnostic support for patient data of a patient by a data processing system.
  • FIG. 4 a shows the method of calculating the first diagnoses and diagnostic risks by applying rules to the patient data.
  • FIG. 4b shows the further specification of the diagnostic risk of a previously calculated diagnosis, eg a diagnosis, which was calculated in FIG. 4a by means of symptom diagnostics.
  • FIG. 4 c shows the further refinement of the diagnostic risk of a previously calculated diagnosis, eg a diagnosis, which was calculated in FIG. 4 a or 4 b, by means of guideline diagnostics.
  • the method begins in step 400 with reading patient data from a database.
  • step 402 of data preparation is available, either after step 402 or directly after step 400, accessing the first database to retrieve rules for calculating diagnostic risks for medical diagnostics.
  • step 406 the check is made as to whether at least one of the rules is applicable to the patient data. If this is not the case because, for example, too little patient data is available or because the patient data available to If, however, at least one of the rules is applicable in step 406, step 408 then follows, in which the rules are applied to the patient data, thereby performing a diagnostic risk calculation for a first medical diagnosis.
  • This first medical diagnosis is output in step 410 along with the first diagnostic risk on the graphical user interface.
  • step 412 a check is made in step 412 to see if all risks have been calculated for all possible medical diagnoses. If not, the process continues with steps 408 and 410, again followed by step 412.
  • FIG. 4 does not show the additional possibility of limiting an output of diagnostic risks to a corresponding minimum probability, from which corresponding diagnostic risks are first output on the graphical user interface.
  • step 416 the process continues with the issuance of a user query as to whether the diagnosis designated with a certain risk can be adopted as a confirmed diagnosis in the patient data. If this is not the case for any of the calculated diagnostic results, the method ends in step 414. However, it is also possible to directly display one of the displayed diagnostic results, for example with a high probability of diagnosis of more than 90%, either automatically or after confirmation by the attending physician Store doctor associated with the patient data in the corresponding patient database in step 420, and then after step 420, the process ends in step 414.
  • step 416 it may be possible to provide the physician at step 418 or 436 the opportunity to perform interactive symptom diagnostics or guideline diagnostics. If the physician does not wish to carry out such an analysis, the step 420 already mentioned above takes place after step 418/436 with the saving of the diagnosis as a confirmed diagnosis, linked to the patient data in the patient database. This in turn is followed by step 414 with the completion of the process. If the physician wishes to perform interactive symptom diagnostics in step 418, the method continues in step 422. However, if the physician wishes to perform interactive guideline diagnostics in step 436, the method continues in step 438.
  • steps 416 and 450 serve to give the physician a choice between a) directly adopting one of the diagnostic results as a confirmed diagnosis, b) discarding all diagnostic results, or c) performing additional interactive symptom diagnostics or guideline diagnostics for one or more of the first diagnostic results.
  • a checklist of symptoms is output, which are linked to the medical diagnosis selected in step 418 in the first database.
  • This may be done, for example, by accessing the first database at step 422, wherein a query of the first database for possible symptoms for a given and selected medical diagnosis is made.
  • those diagnoses that correlate statistically significantly with certain symptoms are stored linked to each other, whereby the link also contains information about the source of literature underlying this link.
  • Figure 800 shows e.g. A database table in which several symptoms are stored linked to a specific diagnosis ID 68. These symptoms associated with the diagnosis to be specified are then transmitted to or retrieved from the data processing system and displayed in step 422 to the user in the form of a checklist.
  • the user may now select one or more of the symptoms, or alternatively provide additional details on symptoms, for example in the form of numeric inputs. For example, if a symptom is "high blood pressure," the physician may specify this by adding a corresponding blood pressure value for that symptom.
  • the link between symptoms and correlating diagnoses is thus used to describe the query elements, eg checkboxes, dynamically from the database for the symptom diagnostics of a specific patient To build up symptoms.
  • the link also serves to find further diagnoses 628 based on the current symptom selection of the user 642, which correlate with the respective symptom selection.
  • an updated calculation of the diagnostic risk for the currently selected diagnosis is dynamically performed by applying the symptom diagnostic rules 800 to the previously determined diagnostic risk.
  • the correlation between the selected symptoms and the diagnoses is, as previously mentioned, literature-based and stored in the first database.
  • the additional diagnoses can be adopted by the user according to a further embodiment of the invention in the list of first diagnoses (suspected diagnoses hypertension and CPOD in Fig. 5-1, for example, after the symptom diagnosis by the suspected diagnosis of renal insufficiency stage II supplemented by selection of the user).
  • the calculation of a second diagnosis and a second diagnosis risk mentioned in 428 is also performed by applying the symptom diagnostic rules in Table 800 and, as shown in the display window 510, may include several second diagnoses with second diagnosis risks that correlate with the symptom selection. For the sake of simplicity, only a single second diagnosis is shown in 426 in FIG. 4b and only a single third diagnosis in 442, and FIG. 4c.
  • Figure 630 shows, however, that multiple diagnoses can correlate with the first diagnosis.
  • correlated diagnoses are displayed 628 for all user-selected symptoms, such as shown in 624.
  • FIG. 4b assumes only one further diagnosis, which is referred to as a second diagnosis with a second risk.
  • the risk of the second diagnosis is calculated analogously from the first risk determined by the rules 128 for this second diagnosis, which was additionally modulated by the current symptom selection according to table 800.
  • step 432 is followed by step 416, which returns to the physician the original display window, in which the diagnostic risks calculated in steps 408 through 412 are displayed for different diagnoses become. If, on the other hand, the physician confirms one of the diagnoses issued in step 428; 630 in step 432, an acquisition of this diagnosis takes place in step 434, the latter now being specified to the physician in step 416 together with the further ones calculated in steps 408 to 412 Diagnoses and their diagnostic risks are provided for selection for storage associated with the patient data, further interactive symptom diagnostics or even complete discarding of all calculated diagnostic risks.
  • an intermediate step 402 may also follow step 400, in which a data processing of the patient data may take place.
  • FIG. 4c Further clarification of the diagnostic risk by the guideline diagnostics FIG. 4c and the calculation of a third diagnostic risk are analogous to the symptom diagnostics shown in FIG. 4b.
  • the display 438 will be of those guideline criteria stored linked to the user-selected diagnosis in the first database. Some or all of these guideline criteria may be selected by the user. Guideline criteria that correlate with the diagnostic objects are also stored in the first database linked to the diagnostic objects.
  • the diagnostic risk is specified and a third diagnosis with associated diagnostic risk is output 444, 644.
  • a third diagnosis with associated diagnostic risk is output 444, 644.
  • FIG. 4c assumes a third diagnostic risk.
  • FIG. 5 shows various outputs on a graphical user interface in the event that a medical diagnostic support for patient data of a patient is performed by the data processing system. This is therefore preceded by the fact that a corresponding patient has been selected by the attending physician and thus the patient data has been made available to the data processing system.
  • the data processing system then automatically analyzes the patient data and, as shown in Figure 4, applies rules to the patient data to calculate at least a first diagnostic risk for a first medical diagnosis.
  • the data processing system determines the probability or relative frequencies of vide comorbidities or frequently associated diseases on a transparent guideline and literary basis, this compares with the already known diagnoses and displays the previously unlisted or recognized diseases to the doctor patient-individualized, ordered by probability.
  • the screen output 500 shows such an output of a diagnosis risk in the form of a "tachometer disc” 602.
  • the tachograph disc can be visualized equally well with probabilities and / or relative frequencies.
  • the tachometer disc consists of a scale with colored graduations, whereby the tachometer disc preferably has red graduation parts, It has a medium probability that it has yellow scales and, with low probability, green scales, which allows a clinician to quickly and easily visually assess the likelihood of a relevant comorbidity, and to specify a likelihood for a diagnosis risk in the center of the tachograph disc, the primary risk is given in the form of a percentage probability 604 or a relative percentage frequency 604.
  • the diagnostic risk is represented by the orders 600 in the form of speedometer disc and numerical value.
  • the physician is given the option of hiding the display 500 for a certain period of time by actuating the "remember later” button 606 or, by pressing the “do not remember” button, completely masking out the display 500 of the probability of relevant comorbidities.
  • the display element 500 thus serves the purpose of informing the doctor in a clear and general manner about whether or not there is a particular diagnostic risk for a relevant medical diagnosis at all. A clarification as to what this medical diagnosis looks like or more Possible relevant medical diagnoses are not given by the display element 500.
  • the criteria for determining a certain probability are displayed to the physician in a manner that is related to the indication, if desired, as shown in display element 502.
  • This display including the literature and study sources used, forms the basis of the respective diagnostic procedure (application of the rules for determining the first diagnosis risk, symptom diagnostics and guideline diagnostics).
  • the physician reaches the display element 504 by pressing the button 608 "more", on which a summary of the patient's name "Maria Test 74" ( Reference numeral 618) finds possible comorbidities.
  • the possible first diagnosis is presented in the form of a textual description together with the respective ICD 10 code (reference numeral 16), together with the respective first diagnosis risk in the form of a tachograph disc (reference numeral 612).
  • the first diagnosis or diagnoses are referred to in the display 504 and the following displays as the basic risk.
  • the physician is given the option of using the selection elements 620 to determine whether these displayed possible diagnoses individually represent only a suspected diagnosis or a confirmed diagnosis.
  • the diagnosis can be saved individually or all diagnoses can be stored at one go, i. into the patient record.
  • the "show all" button 614 is used to display other possible diagnoses whose diagnostic risk is below a predetermined threshold, in the present case, for example, the threshold is 40%, so that only possible diagnoses are displayed which have a diagnostic risk of at least 40%.
  • the doctor wishes to investigate the respective diagnosis risk, he can call up the indication-related, in each case literature-supported symptoms and with findings of the Have patients matched or supplemented with a checklist. This is done by the physician clicking on the corresponding "GO" button in column 610 to perform a symptom diagnosis for the respective possible diagnosis, for which the sources of symptom diagnostics are also stored and transparently displayed to the physician, as illustrated with display element 506. Structurally stored findings are recorded and "preselected" in a different color coding. If the doctor moves the mouse over such a marked advertisement, he will be shown a text with his own file source (for example, free text input "consultation of 01.11.2008” or “laboratory value of 15.10.2007”). If the doctor clicks on the corresponding "GO” button in column 646, a guideline diagnostics will be carried out for each possible diagnosis.
  • the physician By pressing the "GO" button in the display element 504, column 610, the physician first arrives at the symptom diagnostics display element 508.
  • the display element 508 has a button 622, via which the physician arrives at the display element 506.
  • the display element 622 has a Checklist 624 with various symptoms (findings) which are symptomatic of the possible diagnosis 616 for which the corresponding "GO" button has been selected in display element 504.
  • a diagnosis suggestion 626 is displayed in the display element 508 for the selected symptoms together with a corresponding match in the form of a newly calculated diagnosis risk as a tachograph disc.
  • the display element 510 With each further selection of one of the check elements, an update of the diagnosis proposal and corresponding match takes place, which in turn results in a probability-sorted arrangement 628 of diagnostic risks.
  • the first diagnosis proposal made is also specified more precisely by selecting further findings.
  • display element 508 only a determination was possible that there may be renal failure stage I or stage II, whereas in display element 510 due to a more precise specification of the present findings, a recalculation of diagnostic risks for various medical diagnoses could be made, now as an additional Diagnoses of renal failure stage III or stage IV is eligible.
  • the Probabilities in the form of the tachograph discs 628 shown in the display element 510 calculated calculated.
  • the attending physician may supplement the necessary symptoms / findings - by questioning the patient, examining or adding information already known in the checklist.
  • the diagnosis suggestions appear together with ICD 10 codes, i. in plain text and coding which, according to the literature cited, i. appropriate symptom diagnostic rules, correlate with the findings described.
  • the display changes dynamically, i. the degree of filling of the previously described tachograph disc and insertion of the plausible ICD diagnoses, depending on further findings or degree of correlation.
  • the respective diagnostic proposal can be taken directly into the central overview, wherein the process can be performed with one or more diagnoses.
  • Such a precise central overview is shown with display element 512.
  • the display element 512 again shows the name of the patient 618 and the possible diagnoses 616.
  • the central overview now allows the physician to display all comorbidity probabilities (button 614), and discard appropriate diagnoses (click on the cross 639, and if necessary reactivate later) or to save all the displays (click on Element 634). All diagnoses can also be rejected at once (click on element 636) or all diagnoses and displays can be accepted by clicking on element 638. In the latter case, the possible diagnoses and symptoms are not transferred to the patient database, but the system only remembers the view 512 so that the doctor can restore this view identically at a later time.
  • a further alternative is to let the preselection "suspect" 620 persist until a threshold probability, which is preferably very high (over 90%), is exceeded, at which point the choice automatically changes to "secured".
  • the physician can have the respective proposed guideline diagnostics displayed and run through to conclusively secure the diagnosis.
  • a corresponding display window is given by the display element 514.
  • a display window 516 is opened, in which the corresponding guideline diagnostics with corresponding literature sources for the necessary or recommended diagnostics and their interpretation are named.
  • correlating indications are displayed and again provided with a degree of graphic correlation. The most plausible diagnosis (or another) can be taken directly into the overview and in sequence into the file.
  • FIGS. 6, 7 and 8 show the database tables on which the individual risk calculation methods are based in accordance with a preferred embodiment of the invention in a simplified form.
  • Database table 700 in Figure 6 contains the rules 128 which are used to calculate the first diagnostic risks immediately upon opening the patient record.
  • Each rule has an ID (column 702), a value that indicates how much the primary risk changes if the rule can be applied to a patient (Column 716), and a diagnosis associated with the rule, which has a Diagnostic ID ( Column 716) is identified in table 700.
  • the table contains additional columns that contain conditions for the application of the rule, such as the medication that the patient has taken so far (column 704), ICD code (column 706), LEZ codes (column 708), the Age (column 710) and gender (column 712) of the patient.
  • the database table 700 corresponds to a preferred embodiment of the invention, further embodiments with additional or occasionally deviating features are possible.
  • Not every feature requires a rule to have a data value (Rule 1988, for example, has no value for an ICD Code).
  • a specific diagnosis eg the diagnosis with the ID 23, can be assigned several rules (rule IDs 1987-1989). If a rule can be applied to a patient, it will modify the patient's primary risk for having a particular diagnosis. For example, if Rule ID 1987 applies to a patient, it increases their ID 23 risk by 15.23%.
  • the diagnostic risks may also be given relative values, eg "x 1, 2.” Such values should be understood to mean that the diagnostic risk, when the rule is applicable, is calculated by multiplying the primary risk by those in rule Related Diagnosis by Factors 1, 2.
  • a rule is applicable if all the conditions in each column are met, Rule 1987 is then applicable and modifies the level of diagnosis risk for ID 32 diagnosis if the patient is a male, is between 35 and 45 years old, if the patient's electronic patient record has already been marked with the ICD Code 706 and the LEZ Code 54. Whether the patient is taking certain medications will not be considered in this rule.
  • the rules for each diagnosis are applied to the patient data in order of the magnitude of their effect on the primary risk. As soon as a diagnosis is made, the application of the rules for this diagnosis is aborted.
  • FIG. 7 shows a detail of a simplified database table according to a preferred embodiment of the invention, which is used for symptom diagnostics.
  • Table 800 which contains symptom diagnostic rules to further clarify the diagnostic risk
  • one or more symptoms via their Symptom IDs 802 are associated with a diagnosis via their diagnostic ID.
  • Diagnostic ID 68 is associated with multiple symptoms (ID 1321-1323) in the example shown. If the diagnostic method according to the invention has established a diagnosis risk for a specific illness after opening a patient file, for example a risk of 60% for diagnosis with ID 68, and the user has chosen to perform an interactive symptom diagnosis, then the user is initially provided with a selection presented with the first diagnosis associated symptoms.
  • the descriptions of all the symptoms associated with ID 68 diagnostic according to Table 800 would be suggested to the user for selection.
  • the entries (rows) of the table 800 thus correspond in each case to a graphical selection option by the physician on a display.
  • the option is implemented in the form of a check box. This means that the user would be presented with the description 804 of the symptoms 1321-1323 in the form of checkbox elements of a graphical user interface if he had previously selected to carry out interactive symptom diagnostics in order to further clarify the diagnosis risk for the diagnosis 68.
  • the first diagnostic risk which is the baseline for symptom diagnostics, is modified.
  • FIG. 8 shows a detail of a simplified database table according to a preferred embodiment of the invention, which is used for guideline diagnostics.
  • Table 900 identifies one or more symptoms and laboratory findings with guideline criterion IDs 902 as diagnostic criteria. Diagnostic ID 68 is thus associated with guideline criterion IDs 1421-1423.
  • guideline diagnostics include the possibility to formulate guideline routines (values of column 904 for entries ID 1426-1430) specifically for a diagnosis whose risk is to be specified.
  • These guideline routines may, for example, include complex Boolean or arithmetic functions that are applied to the data provided by the user by selecting relevant guideline elements on a graphical user interface.
  • a guideline routine could interrogate the presence of two particular guideline criteria in the presence of a given laboratory value and, if the query conditions were applied, modify the diagnostic risk for which the guideline diagnostic is performed until then.
  • the laboratory value could, for example, be applied to the diagnostic risk as a multiplication factor if the risk height directly correlates with the laboratory value.
  • the guideline routine thus checks whether the required guideline criteria constellation is given and causes a corresponding modification of the previously known diagnosis risk in accordance with a computation routine which is contained in the code of these guideline routines and therefore does not appear in the database table.
  • table 900 thus contains guidelines criteria which serve to reduce the previously determined diagnostic risk by further clarify the relationship between the existence of certain guidelines.
  • the guideline diagnostic 900 also includes diagnostic-specific guideline routines.
  • FIG. 9 uses JavaScript code to run the guideline routines in a user's browser.
  • other embodiments of the invention may use any other programming language for the implementation of the guideline routines.
  • Another possibility is to propose guideline substances according to organ systems, if a guideline diagnostics, as described in display element 514 of FIG. 5, is carried out.
  • a guideline diagnostics as described in display element 514 of FIG. 5
  • the function of displaying a temporal range of medicine packaging is extended to the extent that - is present at presentation of chronic diseases - guideline diagnostics - in BroVerowski of recommended drugs - despite a given indication on literary basis - ordered by organ systems, the doctor recommended lead substances can be displayed to ensure adequate care of the patient.

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Abstract

L'invention concerne un procédé mis en oeuvre par ordinateur pour afficher des maladies chroniques sur une interface graphique utilisateur (100) d'un système de traitement de données, l'interface graphique utilisateur (100) présentant au moins une première (114) et une deuxième (110) fenêtre d'affichage.
PCT/EP2009/065332 2008-11-19 2009-11-17 Procédé mis en oeuvre par ordinateur pour afficher des diagnostics de maladies chroniques spécifiques d'un patient WO2010057890A1 (fr)

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EP09752378A EP2359280A1 (fr) 2008-11-19 2009-11-17 Procédé mis en oeuvre par ordinateur pour afficher des diagnostics de maladies chroniques spécifiques d'un patient
US13/129,996 US20120101846A1 (en) 2008-11-19 2009-11-17 Computer-Implemented Method For Displaying Patient-Related Diagnoses Of Chronic Illnesses

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EP08169431A EP2192509A1 (fr) 2008-11-19 2008-11-19 Procédé d'affichage de diagnostics relatifs aux patients de maladies chroniques
EP08169431.7 2008-11-19

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