Classifications

• • 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
  • G16H50/70—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other 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
  • 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
  • G16H10/00—ICT specially adapted for the handling or processing of patient-related medical or healthcare data
  • G16H10/20—ICT specially adapted for the handling or processing of patient-related medical or healthcare data for electronic clinical trials or questionnaires
• • 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
  • 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
  • G16H70/00—ICT specially adapted for the handling or processing of medical references
  • G16H70/60—ICT specially adapted for the handling or processing of medical references relating to pathologies
• • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
  • Y10—TECHNICAL SUBJECTS COVERED BY FORMER USPC
  • Y10S—TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
  • Y10S706/00—Data processing: artificial intelligence
  • Y10S706/902—Application using ai with detail of the ai system
  • Y10S706/924—Medical

Definitions

• This invention concerns systems, methods and computer program products for guiding the selection of therapeutic treatment regimens for complex disorders such as cancer and HIV-1 infection, wherein a ranking of available treatment regimens is generated and advisory information clinically useful for treating patients is provided.
• Therapeutic treatment regimens for disorders such as HIV-1 infection (acquired immune deficiency syndrome or AIDS) and cancer are increasingly complex. New data and new therapeutic treatment regimens continue to modify the treatments available, and it is difficult for all but the specialist to remain current on the latest treatment information. Further, even those who are current on the latest treatment information require time to assimilate that information and understand how it relates to other treatment information in order to provide the best available treatment for a patient. Combination therapeutic treatment regimens exacerbate this problem by making potential drug interactions even more complex. Finally, an increasingly sophisticated patient population, in the face of a vast volume of consumer information on the treatment of disease, makes the mere statement of a treatment regime, without explanation, difficult for the patient to accept.
• U.S. Patent No. 5,672,154 to Sillen describes a method for giving patients individualized, situation- dependent medication advice.
• the recommended type of medicine may include at least two different medicines.
• No means for ranking multiple treatment options is disclosed, and no means for explaining why treatment options were rejected is given. Rather, this system is primarily concerned with generating new rules from patient information to optimize a particular therapy for diseases such as Parkinson's disease, epilepsy and abnormal blood pressure .
• U.S. Patent No. 5,694,950 to McMichael describes a method and system for use in treating a patient with immunosuppressants such as cyclosporin.
• An expert system is employed to generate a recommendation on whether the immunosuppressant dosage should be changed and, if so, how. Ranking or selection among a plurality of different combination therapeutic treatment regimens is not suggested.
• U.S. Patent No. 5,594,638 to Iliff describes a medical diagnostic system that provides medical advice to the general public over a telephone network. This system is not concerned with generating a recommendation for a combination therapeutic treatment regimen for a known disease (see also U.S. Patent No. 5,660,176 to Iliff) .
• an object of the invention is to provide systems, methods and computer program products for selecting therapeutic treatment regimens for patients in which available treatments are listed, and optionally ranked, while unavailable or rejected treatment regimens (e.g., regimens that would not be effective, or would be dangerous) are not displayed or are assigned a low rank and are indicated to a user as not likely to be efficacious, or not preferred due to patient-specific complicating factors such as drug interaction from concomitant medications.
• unavailable or rejected treatment regimens e.g., regimens that would not be effective, or would be dangerous
• a further object of the invention is to provide systems, methods and computer program products for selecting treatment regimens in which the available treatment options can be readily understood.
• a further object of the invention is to provide systems, methods and computer program products for selecting treatment regimens in which the implications of selecting a particular treatment regimen can be readily understood.
• a further object of the invention is to provide systems, methods and computer program products for selecting treatment regimens in which the reasons for rejection of a particular regimen can be readily understood.
• a still further object of the invention is to provide systems, methods and computer program products for obtaining information about the efficacy of previous treatment regimens imposed on patients.
• a method of the present invention includes providing patient information to a computing device that includes various knowledge bases.
• a first knowledge base may include a plurality of different therapeutic treatment regimens for a disease or medical condition.
• a second knowledge base may include a plurality of expert rules for selecting a therapeutic treatment regimen for the disease or medical condition.
• a third knowledge base may include advisory information useful for the treatment of a patient with different constituents of different therapeutic treatment regimens.
• a fourth knowledge base may include information about past therapies, such as how a patient has fared under previous therapies.
• a listing (preferably a ranked listing) of therapeutic treatment regimens for a patient is generated in the computing device.
• Advisory information for one or more treatment regimens in the listing is generated in the computing device based on the patient information and the expert rules .
• the method described above further includes entering a user- defined therapeutic treatment regimen for the disease (or medical condition) that may not be displayed from the system knowledge base-generated therapeutic treatment regimens, and generating in the computing device advisory information for the user-defined combination therapeutic treatment regimen.
• the method described above further includes entering a rejected therapeutic treatment regimen for the disease (or medical condition) that is included in the first knowledge base but not recommended from the ranking (or given a very low ranking) , and generating in the computing device advisory information for the non- recommended/low ranked therapeutic treatment regimen, wherein the advisory information includes at least one reason for not recommending (or low ranking) the therapeutic treatment regimen.
• Fig. 1 illustrates a process of the instant invention, including routines for entering a user- defined therapeutic treatment regimen and for entering a "non-recommended" therapeutic treatment regimen.
• Fig. 2 schematically illustrates a system or apparatus of the present invention.
• Fig. 3 illustrates a client-server environment within which the system of Fig. 2 may operate, according to an embodiment of the present invention, and wherein a central server is accessible by at least one local server via a computer network, such as the Internet, and wherein each local server is accessible by at least one client.
• a central server is accessible by at least one local server via a computer network, such as the Internet, and wherein each local server is accessible by at least one client.
• Fig. 4 illustrates a medical history user interface for entering data about a patient ' s medical history according to the present invention.
• Fig. 5 illustrate a user interface chart for monitoring a patient's condition during a particular therapeutic treatment regimen over a period of time according to the present invention.
• Fig. 6 illustrates a therapy evaluation user interface that facilitates evaluation of various therapeutic treatment regimen options with respect to relative efficacy, individualized adjusted relative efficacy, dosage, frequency, cost, medical complications and drug interactions according to the present invention.
• Fig. 7 illustrates various symbols for providing information about a therapeutic treatment regimen option within the therapy list box of the therapy evaluation user interface of Fig. 6 according to the present invention.
• Fig. 8 illustrates the therapy details box of Fig. 6 in "full screen” mode.
• Fig. 9 illustrates a pop-up menu including an indexed electronic link to a PDR® that can be activated from within the therapy list box of the therapy evaluation user interface of Fig. 6 according to the present invention.
• Figs. 10A-10D illustrate various functions of the present invention as described in Example 1.
• Figs. 11A-11E illustrate various functions of the present invention as described in Example 2.
• Figs. 12A-12C illustrate various functions of the present invention as described in Example 3.
• the present invention may be embodied as a method, data processing system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product on a computer-usable storage medium having computer readable program code means embodied in the medium. Any suitable computer readable medium may be utilized including, but not limited to, hard disks, CD-ROMs, optical storage devices, and magnetic storage devices. The present invention is described below with reference to flowchart illustrations of methods, apparatus (systems) , and computer program products according to an embodiment of the invention.
• each block of the flowchart illustrations, and combinations of blocks in the flowchart illustrations can be implemented by computer program instructions.
• These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart block or blocks .
• These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart block or blocks.
• the computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks.
• a method of the instant invention is illustrated in Fig. 1.
• the patient is examined to determine patient information. Examples of patient information that may be gathered include one or more of gender, age, weight, CD4 + cell information, viral load information, HIV genotype and phenotype information, hemoglobin information, neuropathy information, neutrophil information, pancreatitis, hepatic function, renal function, drug allergy and intolerance information, and information for drug treatments for other conditions.
• the information may include historical information on prior therapeutic treatment regimens for the disease or medical condition. While the patient is typically examined on a first visit to determine the patient information, it will be appreciated that patient information may also be stored in the computing device, or transferred to the computing device from another computing device, storage device, or hard copy, when the information has been previously determined.
• the patient information is then provided 11 to a computing device that contains a knowledge base of treatments, contains a knowledge base of expert rules for determining available treatment options for the patient in light of the patient information, and also contains a knowledge base of advisory information.
• a list of available treatments for the patient is then generated 12 from the patient information and the available treatments by the expert rules, and advisory information for the available treatments is generated 13.
• the advisory information may include warnings to take the patient off a contraindicated drug or select a suitable non contraindicated drug to treat the condition before initiating a corresponding treatment regimen and/or information clinically useful to implement a corresponding therapeutic treatment regimen .
• the treatment regimen when the known disease is HIV-1 infection, the treatment regimen includes antiretroviral drugs, and the treatment regimen or advisory information may also include contraindicated or potentially adversely interacting non-antiretroviral drugs.
• the treatment regimen includes a protease inhibitor.
• a contraindicated drug may be terfenadine.
• a contraindicated drug is cisapride.
• antiretroviral drugs are listed below in Table 1.
• Exemplary advisory information that can be displayed to a user is summarized below in Table 2 .
• Diseases are those for which multiple different therapy options are available for selection and treatment.
• diseases and medical conditions include, but are not limited to, cardiovascular disease (including but not limited to congestive heart failure, hypertension, hyperlipidemia and angina) , pulmonary disease (including but not limited to chronic obstructive pulmonary disease, asthma, pneumonia, cystic fibrosis, and tuberculosis) , neurologic disease (including but not limited to Alzheimer's disease, Parkinson's disease, epilepsy, multiple sclerosis, amyotrophic lateral sclerosis or ALS, psychoses such as schizophrenia and organic brain syndrome, neuroses, including anxiety, depression and bipolar disorder) , hepatitis infections (including hepatitis B and hepatitis C infection) , urinary tract infections, venereal disease, cancer (including but not limited to breast, lung, prostate, and colon cancer), etc.
• cardiovascular disease including but not limited to congestive heart failure, hypertension, hyperlipidemia and angina
• pulmonary disease including but not limited to chronic ob
• the present invention is useful for known diseases such as HIV-1 infection (acquired immune deficiency syndrome or "AIDS"), or where the known disease is any medical condition for which a combination therapeutic treatment regimen can be used.
• the invention is particularly useful when the list of available treatments includes a plurality (e.g., 2, 10 or 15 or more) of treatment, combination therapeutic treatment regimens ⁇ e. g. , therapeutic treatment regimens incorporating two or more active therapeutic agents) , where the potential for drug interactions is increased and/or the complexity involved in selecting the best available treatment is multifactorial .
• the list of available treatments and advisory information may be regenerated in a number of ways.
• the patient information may be simply modified 18.
• a user-defined therapy may be entered 14 and advisory information generated 15 based on the user- defined therapy.
• the non-recommended therapeutic treatment regimen may be entered 16 and advisory information generated 17 for the non-recommended therapeutic treatment regimen. This may indicate to the user that they should discontinue use of a non-critical drug for another condition or select a suitable substitute that does not create a conflict/non-recommended situation so that they can then proceed with the therapy of choice.
• the advisory information can be generated automatically for non-recommended therapeutic treatment regimens.
• therapy and “therapeutic treatment regimen” are interchangeable herein and, as used herein, mean any pharmaceutical or drug therapy, regardless of the route of delivery (e.g., oral, intraveneous, intramuscular, subcutaneous, intraarterial, intraperitoneal, intrathecal, etc.), for any disease (including both chronic and acute medical conditions, disorders, and the like) .
• route of delivery e.g., oral, intraveneous, intramuscular, subcutaneous, intraarterial, intraperitoneal, intrathecal, etc.
• disease including both chronic and acute medical conditions, disorders, and the like
• the present invention is not limited to facilitating or improving the treatment of diseases.
• the present invention may be utilized to facilitate or improve the treatment of patients having various medical conditions, without limitation.
• the present invention may be embodied as an expert system that provides decision support to physicians (or other health care providers) treating patients with a known disease, such as HIV infection.
• a system according to the present invention calculates combination antiretroviral therapy options and attaches all relevant information to those options.
• an expert system also known as artificial intelligence (Al)
• Al artificial intelligence
• An expert system typically contains a knowledge base containing accumulated experience and a set of rules for applying the knowledge base to each particular situation that is described to the program.
• Expert systems are well known to those of skill in the art and need not be described further herein.
• the antiretroviral therapy options (combinations of antiretroviral drugs) , are derived using a knowledge base consisting of a number of expert system rules and functions which in turn take into account a given patient's treatment history, current condition and laboratory values.
• a system according to the present invention supports the entry, storage, and analysis of patient data in a large central database.
• a system according to the present invention has a flexible data driven architecture and custom reporting capabilities designed to support patient therapy management and clinical drug trial activities such as screening, patient tracking and support. It is anticipated that a system according to the present invention may be used by health care providers (including physicians) , clinical research scientists, and possibly healthcare organizations seeking to find the most cost-effective treatment options for patients while providing the highest standard of care.
• a system 20 for carrying out the present invention is schematically illustrated in Fig. 2.
• the system 20 comprises a knowledge base of treatment regimens 21, which may be ranked for efficacy ( e . g. , by a panel of experts) or ranked according to system rules, a knowledge base of expert rules 22, a knowledge base of advisory information 23, a knowledge base of patient therapy history 24 and patient information 25.
• Patient information is preferably stored within a database and is configured to be updated.
• the knowledge bases and patient information 21-25 may be updated by an input/output system 29, which can comprise a keyboard (and/or mouse) and video monitor. Note also that, while the knowledge bases and patient data 21-25 are shown as separate blocks, the knowledge bases and patient data 21-25 can be combined together ( e . g.
• the expert rules and the advisory information can be combined in a single database) .
• the information from blocks 21-25 is provided to an inference engine 26, which generates the listing of available treatments and the corresponding advisory information from the information provided by blocks 21- 25.
• the inference engine 26 may be implemented as hardware, software, or combinations thereof. Inference engines are known and any of a variety thereof may be used to carry out the present invention. Examples include, but are not limited to, those described in U.S. Patents Nos. 5,263,127 to Barabash et al . (Method for fast rule execution of expert systems); 5,720,009 to Kirk et al . (Method of rule execution in an expert system using equivalence classes to group database objects); 5,642,471 to Paillet (Production rule filter mechanism and inference engine for expert system) ;
• the inference engine 26 may be a separate block from the knowledge bases and patient information blocks 21-25, or may be combined together in a common program or routine. Note that the advisory information that is generated for any available therapy may differ from instance to instance based on differences in the patient information provided.
• the present invention can be implemented as a system running on a stand alone computing device .
• the present invention is implemented as a system in a client-server environment.
• a client application is the requesting program in a client-server relationship.
• a server application is a program that awaits and fulfills requests from client programs in the same or other computers.
• Client-server environments may include public networks, such as the Internet, and private networks often referred to as "intranets", local area networks (LANs) and wide area networks (WANs) , virtual private networks (VPNs) , frame relay or direct telephone connections.
• LANs local area networks
• WANs wide area networks
• VPNs virtual private networks
• a client application or server application including computers hosting client and server applications, or other apparatus configured to execute program code embodied within computer usable media, operates as means for performing the various functions and carries out the methods of the various operations of the present invention.
• the illustrated client-server environment 30 includes a central server 32 that is accessible by at least one local server 34 via a computer network 36, such as the Internet.
• a computer network 36 such as the Internet.
• a variety of computer network transport protocols including, but not limited to TCP/IP, can be utilized for communicating between the central server 32 and the local servers 34.
• the central server 32 includes a central database 38, such as the Microsoft® SQL Server application program, version 6.5 (available from Microsoft® SQL Server application program, version 6.5 (available from Microsoft® SQL Server application program, version 6.5 (available from Microsoft® SQL Server application program, version 6.5 (available from Microsoft® SQL Server application program, version 6.5 (available from Microsoft® SQL Server application program, version 6.5 (available from Microsoft® SQL Server application program, version 6.5 (available from Microsoft® SQL Server application program, version 6.5 (available from Microsoft® SQL Server application program, version 6.5 (available from Microsoft® SQL Server application program, version 6.5 (available from Microsoft® SQL Server application program, version 6.5 (available from Microsoft® SQL Server application program, version 6.5 (available from Microsoft® SQL Server application program, version 6.5 (available from Microsoft® SQL Server application program, version 6.5 (available from Microsoft® SQL Server application program, version 6.5 (available from Microsoft® SQL Server application program, version 6.5 (available from Microsoft® SQL Server application program, version 6.5 (available from Microsoft® SQL Server application program, version 6.5 (available from Microsoft® SQL Server application program
• the central server 32 ensures that the local servers 34 are running the most recent version of a knowledge base.
• the central server 32 also stores all patient data and performs various administrative functions including adding and deleting local servers and users to the system (20, Fig. 2) .
• the central server 32 also provides authorization before a local server 34 can be utilized by a user.
• Patient data is preferably stored on the central server 32, thereby providing a central repository of patient data. However, it is understood that patient data can be stored on a local server 34 or on local storage media.
• Each local server 34 typically serves multiple users in a geographical location.
• Each local server 34 includes a server application, an inference engine, one or more knowledge bases, and a local database 39.
• Each local server 34 performs artificial intelligence processing for carrying out operations of the present invention.
• the user logs on to a local server 34 via a client 35, the user is preferably authenticated via an identification and password, as would be understood by those skilled in the art. Once authenticated, a user is permitted access to the system (20, Fig. 2) and certain administrative privileges are assigned to the user.
• Each local server 34 also communicates with the central server 32 to verify that the most up-to- date version of the knowledge base(s) and application are running on the requesting local server 34. If not, the requesting local server 34 downloads from the central server 32 the latest validated knowledge base(s) and/or application before a user session is established. Once a user has logged onto the system (20, Fig. 2) and has established a user session, all data and artificial intelligence processing is preferably performed on a local server 34.
• An advantage of the illustrated client-server configuration is that most of the computationally intensive work occurs on a local server 34, thereby allowing "thin" clients 35 (i.e., computing devices having minimal hardware) and optimizing system speed.
• each local server database 39 is implemented via a Microsoft® SQL Server application program, Version 6.5.
• the primary purpose of each local database 39 is to store various patient identifiers and to ensure secure and authorized access to the system (20, Fig. 2) by a user. It is to be understood, however, that both central and local databases 38, 39 may be hosted on the central server 32.
• Each local client 35 also includes a client application program that consists of a graphical user interface (GUI) and a middle layer program that communicates with a local server 34.
• Program code for the client application program may execute entirely on a local client 35, or it may execute partly on a local client 35 and partly on a local server 34.
• GUI graphical user interface
• Program code for the client application program may execute entirely on a local client 35, or it may execute partly on a local client 35 and partly on a local server 34.
• a user interacts with the system (20, Fig. 2) by entering (or accessing) patient data within a GUI displayed within the client 35.
• the client 35 then communicates with a local server 34 for analysis of the displayed patient information.
• Computer program code for carrying out operations of the present invention is preferably written in an object oriented programming language such as JAVA ® , Smalltalk, or C++.
• the computer program code for carrying out operations of the present invention may also be written in conventional procedural programming languages, such as the "C" programming language, in an interpreted scripting language, such as Perl, or in a functional (or fourth generation) programming language such as Lisp, SML, or Forth.
• the middle layer program of the client application includes an inference engine within a local server 34 that provides continuous on-line direction to users, and can instantly warn a user when a patient is assigned drugs or a medical condition that is contraindicated with, or antagonistic of, the patient's current antiretroviral therapy. Every time patient data is entered into the system (20, Fig. 2) or updated, or even as time passes, the inference engine evaluates the current status of the patient data, sorting, categorizing, ranking and customizing every possible antiretroviral therapy for a patient according to the specific needs of the patient.
• Each knowledge base used by an inference engine according to the present invention is a collection of rules and methods authored by a clinical advisory panel of HIV-treating physicians and scientists.
• a knowledge base may have subjective rules, objective rules, and system-generated rules.
• Objective rules are based on industry established facts regarding the treatment of HIV using antiretroviral therapy and are drawn from the package insert information of antiretroviral drug manufacturers and from peer reviewed and published journal articles.
• An example of an objective rule would be an antiretroviral to antiretroviral contraindication such as :
• rule #1 the term "eval therapy” refers to the therapy currently being analyzed by the system (20, Fig. 2) . Rule #1 then states that if this therapy contains both AZT and d4T, then this therapy should not be displayed in a list of potential therapy options for the patient.
• the present invention can be configured so as to prevent a user from receiving recommendations on new therapy options when certain crucial data on the patient has not been entered.
• the present invention does not prevent a health care provider, such as a physician, from recording his/her therapy decisions, even if the system (20, Fig. 2) has shown reasons why that therapy may be harmful to the patient.
• the present invention allows a health care provider to be the final authority regarding patient therapy.
• Subjective rules are based on expert opinions, observations and experience. Subjective rules are typically developed from "best practices" information based on consensus opinion of experts in the field. Such expert opinion may be based on knowledge of the literature published or presented in the field or their own experience from clinical practice, research or clinical trials of approved and unapproved medications. A number of experts are used so that personal bias is reduced. System generated rules are those derived from the outcomes of patients tracked in the system who received known and defined therapies and either improved, stabilized or worsened during a defined period. Because of the large number of potential combinations usable in HIV infection, this system generated database and rules derived from them are likely to encompass data beyond that achievable from objective or subjective rules databases.
• the rules which comprise the various knowledge bases (21-24, Fig. 2) of the present invention each have two main parts : a premise and a conclusion - also referred to as the left side and the right side, respectively.
• a premise of a rule is found to be true, the action specified in the conclusion is taken. This is known to those of skill in the art as "firing" the rule. For example, consider the following rule:
• the premise of the above rule is for the inference engine to determine whether or not a therapy being evaluated (i.e., "eval therapy") contains the antiretroviral drug "ddC” . If a therapy does contain the antiretroviral drug ddC, the action called for by the conclusion of the rule is to attach "Commentary 18" to the therapy. Commentary 18 may be a piece of text that provides a user with the necessary information about therapies containing ddC.
• Therapy initiation/change Rules that provide information on therapy change or initiation
• Boundary condition rules Limits for values, intervals for values to be updated
• Comment Data Aging rules These rules warn the user that the data in certain fields is getting old and that the most current values in the system will be used.
• the inference engine (26, Fig. 2) can evaluate potential therapy options for a patient based on a patient's medical history (including therapy history) and current laboratory values. Accordingly multiple antiretroviral drug combinations can be quickly and accurately analyzed for a particular patient. Furthermore, the inference engine can quickly provide guidance in the areas listed below in Table 4.
• a medical history user interface 50 for entering data about a patient's medical history according to the present invention is illustrated.
• the medical history user interface 50 can be displayed by activating the "Medical History" tab 50a.
• the illustrated medical history user interface 50 allows a user to create, save, update and print patient records.
• the medical history user interface 50 appears with empty data entry fields.
• Data entry fields for receiving information via a GUI are well known to those of skill in the art and need not be described further herein.
• the medical history user interface 50 appears with patient data in the various fields. Preferably color is used to highlight critical or required information in a patient record.
• Important elements in the illustrated medical history user interface 50 include a "print” button 51 for printing a patient record and therapeutic treatment regimen details; a “save” button 52 for saving a patient record; and a "speed entry” check box 53 for allowing a user to move quickly between entry fields.
• group headings 54 that divide a patient's medical history into related categories. Each group contains entry fields in which a user can add patient information.
• An “add” button 55 allows a user to add new information to a patient record for a selected group.
• a “delete” button 56 allows a user to delete patient information for a selected group (although the original information is still recorded in the database) .
• a “history” button 57 allows a user to review a patient's historical data for each selected group.
• an inference engine analyzes the data and suggests whether a therapeutic treatment regimen is indicated; if an existing therapeutic treatment regimen should be continued or changed; and the best drug therapies for the selected patient. Often, more than one drug therapy is presented to the user. These drug therapies are preferably ranked according to expected efficacy, frequency in dosage, pill count, and cost. All of these factors can help the user make a decision about what therapy to use for the selected patient.
• a user clicks on a drug therapy in the presented list information is provided about the dosage regimens. Also, various warnings, such as drug interaction warnings, and notes about each drug, are presented. An appropriate drug therapy can then be selected.
• FIG. 5 an exemplary user interface chart 60 for monitoring a patient's condition during a particular drug therapy over a period of time is illustrated.
• the user interface chart 60 can be displayed by activating the "Chart" tab 60a.
• the illustrated user interface chart 60 tracks the CD4 level against viral load. Along the left-hand side of the Y-axis 61 the CD4 count is plotted. Along the right-hand side of the Y-axis 61 the viral load count is plotted.
• the lines 62 represent the CD4 test and the viral load test as would be understood by those having skill in the art.
• Drug therapy for a time period is indicated within the area of the chart user interface 60 indicated as 63. Time is plotted along the X-axis 64, as illustrated.
• a therapy evaluation user interface 70 that facilitates evaluation of various therapy options with respect to relative efficacy, dosage, frequency, cost, medical complications and drug interactions is illustrated.
• the therapy evaluation user interface 70 can be displayed by activating the
• "Therapy Evaluation” tab 70a Important elements in the illustrated therapy evaluation user interface 70 include an "Evaluate Current Therapy” button 71 for initiating an evaluation of a current therapy and a "Current Therapy” field 72 that lists a patient's current therapy. Detailed information about a patient's therapy is displayed in the therapy details box 73. A therapy displayed within box 73 is identified in box 74. Multiple check boxes 75 are provided that allow a user to control how information is displayed within the therapy evaluation user interface 70. Within the therapy list box 76, a list of available therapies for a patient can be displayed. In the illustrated embodiment the drugs are listed in standard abbreviated form. Other information displayed with each drug may include that listed below in Table 5.
• a list of available antiretroviral drugs is displayed within box 77.
• a user desiring to evaluate a particular combination of drugs can click the appropriate check boxes 77a to review information in the therapy details box 73.
• a "Use as Current Therapy" button 78 allows a user to apply a particular therapy to a patient.
• Various hyperlinks 79 within the therapy details box 73 allow a user to display specific information about a therapy evaluation. For example, a user can be allowed to view a rule which is associated with the displayed text .
• Resistance evaluation alerts 80 can be provided adjacent each available antiretroviral drug displayed within box 77.
• a blue "G” icon can be used to indicate that a patient's last genotype test contains mutations which are known to be associated with full or partial resistance to the antiretroviral drug.
• a red “P” icon can be used to indicate that a patient's last phenotype test demonstrates resistance to the antiretroviral drug.
• various symbols can be utilized to provide information about a drug therapy option. These symbols provide an instant graphical warning level for each therapy option. Some symbols, such as a red exclamation point, indicate that there is critical, possibly life threatening information in the therapy details box 73 for that therapy which must be read in order for that therapy to be properly utilized.
• the therapy details box 73 of Fig. 6 can be displayed in "full screen” mode as illustrated in Fig. 8.
• Important elements in the illustrated therapy details box 73 include an identification box 73a for identifying the therapy being evaluated; a "Use as Current Therapy” button 78 that allows a user to apply a particular therapy to a patient; and a "Show Therapies” button 73b that returns the therapy details box 73 back to half- screen size as illustrated in Fig. 6.
• various hyperlinks may be embedded within text displayed within the therapy details box 73 that can be activated by a user to display various types of information.
• Eye catching alert banner (s) 73c and text 73d can be displayed at the top of the therapy details box 73 as illustrated.
• Dosages 73e of each drug, along with special administration instructions, can be displayed within the therapy details box 73 as illustrated.
• Dosage adjustment information 73f and various warnings and advisories 73g can also be displayed within the therapy details box 73 as illustrated.
• therapeutic treatment regimens are not displayed to a user if an invalid drug is selected for treatment of a patient .
• Physicians Desk Reference® According to a preferred embodiment of the present invention, the Physicians Desk Reference®
• PDR® 28 which is a known drug reference source, is fully integrated with the system 20 of Fig. 2. Users can access the PDR® drug abstracts for antiretroviral drugs listed in the therapy list box 76 of the therapy evaluation user interface 70 of Fig. 6. In addition, users can access the PDR® on-line Web database to obtain additional information about a specific drug or to research a substitute for a contraindicated drug.
• a web browser preferably is launched and the PDR® on-line Web database is accessed. Information can also be extracted from the PDR® on-line Web database to provide drug selection lists for non-antiretroviral drugs that a patient may be taking and to define relationships between brand name and generic drugs .
• a PDR® pop-up menu 90 may be provided that can be activated from within the therapy list box 76 of the therapy evaluation user interface 70 of Fig. 6. From the PDR® pop-up menu 90 a user can access various information from the PDR® including, but not limited to, drug abstracts, and generic components contained within a brand name drug.
• the following non-limiting examples illustrate various aspects of the present invention. These examples are provided for illustrative purposes only, and are not intended to be limiting of the invention.
• Figs. 10A-10D a medical history user interface 50 containing evaluated data for patient "demol" is illustrated.
• the group heading "Hemoglobin” 54a has changed colors to indicate to a user that the patient has an abnormally low hemoglobin value from a previous (historical) blood sampling.
• the therapy evaluation tab 70a is activated to display the therapy evaluation user interface 70 (Fig. 10B) the associated medical condition warning of a history of anemia and the caution notification if using drugs known to be associated with hematopoetic toxicity is triggered as illustrated in the therapy details box 73 of Fig. 10B.
• Fig. IOC the "Chart" user interface 60 has been activated. HIV RNA (viral load) is plotted on a log scale, the CD4 count is plotted on a linear scale, and the drug treatments are shown as Gantt bars on the horizontal date scale at the bottom of the chart user interface 60.
• HIV RNA viral load
• CD4 count is plotted on a linear scale
• drug treatments are shown as Gantt bars on the horizontal date scale at the bottom of the chart user interface 60.
• This box represents the processing of the data from the "Medical History” tab and the knowledge base output, including objective rules derived from published treatment guidelines, indicating that initiation of therapy, or a change of therapy in this case, may be called for if the other variable (s) indicated in the message have been addressed.
• the list of available therapies and associated ranking order may be shown within the therapy details box 73 of Fig. 10B. This represents the output of the knowledge base for therapy selection. Included with the list of therapies can be any of the following: safety advisories (dosage adjustment, drug interaction, etc.) with a yellow triangle or red exclamation warning symbols; number of pills; daily cost of all three drugs; dosing regimen (q 8h, q 12 h, etc.) ; and dosages for all drugs in a regimen (including dosage adjustments if necessary) and pertinent information specific to the patient is listed in the dialog box.
• safety advisories dosage adjustment, drug interaction, etc.
• dosing regimen q 8h, q 12 h, etc.
• dosages for all drugs in a regimen including dosage adjustments if necessary
• Example 2 will be explained with reference to Figs. 11A-11E, and relates to patient file "ARV naivel" which is an example of an HIV- infected patient who has not been treated with anti-HIV drugs previously.
• Fig. 11A a medical history user interface 50 containing evaluated data for patient "ARV naivel” is illustrated.
• Fig. 11B when the "Therapy Evaluation" tab 70a is activated to display the therapy evaluation user interface 70, a "Boundary and Prequalification Messages" message box MB2 pops up indicating that according to the current, published, HIV treatment guidelines, the patient should be initiated on antiviral therapy and that the current guidelines recommend combinational therapy.
• the therapy evaluation user interface 70 has been activated and demonstrates features/functions associated with therapy evaluation including a general warning Wl and advisories Al, A2, and A3 for the patient related to treatment of the disease ( e . g . , whether therapy should be initiated or changed) or related to a specific therapy selected from the list box which is being evaluated by the user.
• Fig. 11D illustrates various information that is displayable by clicking on an individual therapy in the therapy list box 76 of Fig. 11C. Information displayed includes dosages of all drugs with general and patient-specific warnings and advisories.
• FIG. 11C are illustrated in Fig. HE and include: linking to an electronic PDR® to show drug package insert information or perform drug search information; showing or hiding columns of information displayed within the therapy list box; linking to a publication or abstract associated with a therapy that has a "book” icon associated therewith; and various printing functions.
• Figs. 12A-12C and relates to patient file "Featuresl" which illustrates some important functions/features that a system according to the present invention can provide for highly drug experienced patients who may have developed resistance associated with the use of several antiviral drugs.
• Features, including functions attributed to the new resistance and historical therapy rules are illustrated and includes:
• Warning W3 is for the interaction between Nevirapine and rifabutin (which was selected from the list of non-antiretroviral drugs available as part of the medical history user interface 50) .
• the drug interaction warning message may be viewed from the medical history user interface 50 by "right- clicking" the non-ARV title bar 54C, which has turned yellow indicating the presence of an ARV-nonARV drug interaction. This information is also prominently displayed for the user on the therapy evaluation user interface 70 as a text message ( 3 , Fig. 12B) as well as in the "Safety Considerations" section of the drug list box (76, Fig. 12B) ; and
• the chart user interface 60 (Fig. 12A) illustrates the viral load, CD4 , drug therapies, and associated drug resistance in graphic form for the user to evaluate .

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