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WO2004081752A2 - Methode et procede permettant de trouver automatiquement des patients pour des tests cliniques de medicaments ou de dispositifs - Google Patents

Methode et procede permettant de trouver automatiquement des patients pour des tests cliniques de medicaments ou de dispositifs Download PDF

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Publication number
WO2004081752A2
WO2004081752A2 PCT/US2004/007409 US2004007409W WO2004081752A2 WO 2004081752 A2 WO2004081752 A2 WO 2004081752A2 US 2004007409 W US2004007409 W US 2004007409W WO 2004081752 A2 WO2004081752 A2 WO 2004081752A2
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database
patients
clinical
patient
criteria
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WO2004081752A3 (fr
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Daniel Deakter
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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
    • 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

Definitions

  • This invention relates generally to the field of clinical research and more specifically to a method and system that automatically matches patients to clinical drug or device trials.
  • the pharmaceutical companies are in a position where they are producing more new drug compounds than ever before; they are about to lose the patents on many of their highly profitable, blockbuster, drugs; and they are being squeezed by the managed care industry. It is therefore critical for the pharmaceutical companies to discover, test and market the maximum number of new drugs in the minimum amount of time.
  • Rosenberg. Michelson and Rosenberg utilize an online web-based system to screen and enroll investigators and patients, and match patients to an appropriate investigator by zip code.
  • Another prior art publication is entitled, "Recruiting A Patient Into A Clinical Trial", U.S. Pat. Appln. Pub. No. 2002/0099570 by Knight.
  • Knight discloses how a patient with a particular disease may find a relevant study using a computer, a web browser and an Internet connection. Otherwise, the need for recruiting patients is served by databases of patients available for drug trials, or by programs that flag key words on dictated summaries using a search engine for evaluation for eligibility in studies, or by web-based patient enrollment programs. There are a number of websites where patients may do a preliminary application for eligibility and thereby enroll by this means.
  • Rao et al. describe methods for mining patient data in U.S. Pat. App. Pub. Nos.2003/0120458 and 2003/0130871.
  • the methods of Rao et al. require the calculation of probability-based inferences of matching patients to clinical trials and not on direct matching of trial criteria with suitable patients. These methods also do not order search parameters to minimize the amount of text searching.
  • DKT.P.PC0002 DKT.P.PC0002
  • a database component operative to maintain a hospital patient database component and its plurality of hospital databases and their corresponding plurality of patient names and medical records, and a medical practice database and their corresponding plurality of specialties and their corresponding plurality of patient names and medical records, and a clinical studies database component and its corresponding plurality of clinical studies; a communications component to receive changes to said database component; a communications component to receive changes to said database component; and a processor programmed to periodically match compatible patients and clinical studies, and to generate reports to matched medical practices in said medical practice database.
  • a system for automatically matching patients to clinical trials comprising: a database component operative to maintain: one or more hospital patient database components and their one or more hospital databases and their corresponding plurality of patient names and their medical records, wherein the hospital patient database components are in communication with one or more medical practice database components and their corresponding plurality of specialties and their corresponding plurality of patient names and their medical records; a clinical studies database component and its corresponding plurality of clinical studies; a communications component to receive changes to said database component; and a processor programmed to periodically match compatible patients and clinical studies without reliance on calculation of probability-based inferences of matching, and generate reports to matched medical practices in said medical practice database component having one or more patients matched to at least one clinical study.
  • a computerized method for matching patients to clinical medical studies comprising: identifying a group of patients in a hospital database; identifying at least one clinical study; maintaining a database identifying each said patient in said hospital database and each said clinical study; and comparing said group of patients in said hospital database to said clinical studies and matching one or more patients in a hospital database to one or more clinical trials without reliance on calculation of probability-based inferences of matching.
  • FIG. 1 is a schematic diagram of the system according to the present invention.
  • DKT.P.PC0002 DKT.P.PC0002
  • Fig. 2 is a schematic of the Al (Artificial Intelligence) Module
  • Fig. 3 is a flow chart of the process according to the present invention
  • Fig. 4A is a flowchart of the process used in classifying search parameters
  • Fig. 4B is a flowchart of the process used in prioritizing search parameters and determination of search order;
  • Figs. 5A, 5B, 5C, 5D, 5E, 5F are flowcharts of variations of the Search Process.
  • Fig. 6 is a flowchart of the Text Recognition module.
  • a system and related method for identifying patients for enrollment into a clinical trial is generally designated by the numeral 10.
  • the system includes various organizations or entities that cooperate with one another for the purpose of identifying patients to be enrolled in medical studies.
  • sponsors of clinical trials in order to eliminate bias from clinical testing, have to outsource their research to outside entities that actually do the research.
  • One of the first steps to perform the trial is to find and enroll patients.
  • One of the sources for finding patients are medical practices generally designated by the numeral 20 wherein any number of specific medical practices are provided with an alphabetic suffix.
  • the patient population for each medical practice is generally designated by the numeral 22 and specifically each practice has a corresponding patient population each designated by a corresponding alphabetic suffix.
  • patient populations may be accessed through one or more hospitals to which the patients are referred.
  • patient populations may be accessed through the hospitals without reference to a DKT.P.PC0002 referring medical practice.
  • the hospitals are generally designated by the numeral 24 with each individual hospital represented by alphabetic suffixes.
  • the identifier consists of a communications component 28 capable of receiving and sending communications in any number of forms, including but not limited to facsimile, page, email, voice text, website data entry and instant messaging.
  • the identifier 26 includes a computer processor 30 which includes the necessary hardware, software and memory to implement the system and methodologies disclosed herein.
  • the processor 30 is programmed, using a Conversion Module 44, to convert database information from incompatible operating systems to the operating system data types used by the processor.
  • the processor 30 is programmed to load the eligibility criteria, implement a best search strategy based on prioritization of search criteria, utilizing the Al Module 46 also disclosed herein, and to output a report of matched patient clinical study and physicians.
  • each processor 30 is designed to access a database 34 each of which is designated by the same alphabetic suffixes as its corresponding hospital.
  • the database comprises a studies database component 36, which contains the eligibility criteria for all the studies; a patient database component 38, also designated by the same alphabetic suffix as its corresponding hospital, containing clinical and demographic information that is a duplicate of the corresponding hospital database; and a physician database component 40, also designated by the same alphabetic suffix as its corresponding hospital, and comprising a plurality of medical practices.
  • the processor 30 and communications component 28 are operative to maintain and update the database components. The selection process begins when clinical study criteria are transmitted to the communications component 28 of identifier 26.
  • the Al Module 46 and the process by which it is used in implementing system 10 is generally designated by the numeral 100.
  • the external database information from hospitals 24 is input into the identifier 26 at step 102.
  • the processor 30 evaluates the data to DKT.P.PC0002 determine if it is in a compatible format. If it is incompatible, the processor uses the conversion module 44 at step 104 to convert the data to a compatible format, such as conversion of 64 bit data from a VMS operating system to UNIX/LINUX 64. In either case, compatible data is then used to populate the various tables within the database 34.
  • a compatible format such as conversion of 64 bit data from a VMS operating system to UNIX/LINUX 64. In either case, compatible data is then used to populate the various tables within the database 34.
  • the conversion module employs a software emulator or other program which reads and converts files from one operating system to another to change the format of the data into a compatible format.
  • the converted data files are then input into an extracted converted database at step 38, which is a duplicate of the information from each hospital 24.
  • the study criteria 42 are input into the Al module 46 and in particular to a First Expert System at step 106, which classifies the criteria.
  • the criteria is then input into a Second Expert System 108 which sorts the order of the criteria to search more efficiently.
  • the search begins utilizing the prioritized criteria list.
  • the output of step 110 is a reduced subset of patients of the database 34 matching one or more of the criteria. This subset is then further searched at step 112 using a text extraction module which is detailed herein.
  • step 112 is then passed to the text analysis module 113, and the output of step 112 is further searched.
  • This is the most compiler/CPU intensive part of the process and is, therefore, the last step before final matches are output, as the pool of candidates has, at this point, been maximally reduced.
  • the text analysis increases the precision of the search process by extracting and processing data from text not revealed by the previous steps.
  • the text analysis module may use semantic processing, contextual extraction, semantic networks, neural networks and the like.
  • VisualText' M Text Analysis International, Inc., Sunnyvale, CA
  • This module 113 may be used to extract patient information from text such as histories and physicals, operative notes, pathology and radiology reports and the like.
  • VisualTextTM can scan a typical text document in about 0.25 seconds, and hence, should optimally be used as the last step in the search process for obtaining precise results as quickly as possible. For example, for a database having a size of 350 gigabytes, it is estimated that a text search of the entire database would take approximately 40 hours. However, if text DKT.P.PC0002 searching is performed last in a series of inclusion and/or exclusion criteria, the text search is estimated to take approximately 90 minutes.
  • the output at step 114 consists of the candidates identified for potential entry into clinical trials.
  • the process which is used in implementing system 10 may be further illustrated in Fig. 3, and generally designated by the numeral 200.
  • the process utilizes the following steps to match patients to clinical studies.
  • the study criteria 42 are input into the database 38 of the identifier 26.
  • the database typically includes such components as a laboratory result database component 204, a radiology and pathology report database component 206, dictated history and physical database component 208, dictated progress notes database component 210, physiological studies database component 212 which may include, but are not limited to, pulmonary function studies, cardiac catheterizations, electrocardiogram results, cardiac stress tests, esophageal manometry, hysterosalpingogram, bladder capacity test, nerve conduction tests and the like.
  • the database may also include a genetic database component 214, which contains identified genes which are needed for studies that correct a disease caused by deficient gene.
  • the Al Module processes the criteria and searches the extracted database.
  • the processor 30 finds matches between the study criteria parameters and the patients.
  • selected patient study matches are paired with the admitting or ordering physician.
  • the processor can be programmed to choose matches of 100% of criteria or another variable preset percentage.
  • a report is generated at step 222 which may contain: patient name, title of the study that the patient quantifies for, a listing of the criteria that the patient has met and any criteria not met, if any, and the name of the admitting or ordering physician.
  • Step 224 utilizes the communications component 28 and transmits a report to the physician via secure means, which includes but is not limited to encrypted email, sealed confidential envelopes handed to physician by a specially cleared person at the hospital similar to the current mechanism that confidential HIV results are transmitted to physicians in the hospital in accordance with the Privacy Rules of The Health Insurance Portability Act.
  • secure means which includes but is not limited to encrypted email, sealed confidential envelopes handed to physician by a specially cleared person at the hospital similar to the current mechanism that confidential HIV results are transmitted to physicians in the hospital in accordance with the Privacy Rules of The Health Insurance Portability Act.
  • the physician may verify the accuracy of the criteria, discuss treatment options with his or her patient, and DKT.P.PC0002 obtain consent either to enroll the patient into a study or to refer the patient to a research site that does the study.
  • This part of the system and method is generally designated by the numeral 300A and describes the specific classifying processes of First Expert System 106. Efficient use of processor time and resources depend on minimizing the number of free text searches. Therefore it can be seen that by matching patients based on other criteria first and free text last, whenever possible, the pool of patients that will be searched for free text criteria will be greatly reduced.
  • This part of the process commences with the input of study eligibility criteria 42 to the processor 30.
  • the processor extracts the first or next criteria.
  • the processor checks to see if the criteria is free text such as dictations of histories and physicals, discharge summaries and progress notes.
  • the criteria is free text
  • this information is stored on a separate list of free text criteria 310A, which is then input at step 344A to an updated list of criteria, and summed to create one list of categorized criteria at step 348A.
  • the list of categorized criteria is then fed back to the processor 30 at step 305A to complete one iteration of the cycle.
  • the cycle continues with a new comparison of the eligibility criteria to the list of criteria. If the criteria is not free text, other criteria categories are checked, such as diagnosis at step 312A, demographic data at step 316A, laboratory result at step 320A, allergy at step 324A, current medication patient is taking at step 328A, prior treatments at step 332A, physiological function test result at step 336A and lastly genotype test result at step 340A.
  • Each of the foregoing steps 308A to 340A has a corresponding list 314A, 318A, 322A, 326A, 330A, 334A, 338A, and 342A that is updated depending on which criteria is matched. All the lists are fed into updated lists at step 344A DKT.P.PC0002 and feedback to the processor at 350A.
  • the processor again compares its master list to the study eligibility criteria 42. Each parameter is examined as described above until all parameters have been examined.
  • the processor determines that the list is completed at step 304A and then the classified unprioritized list is output to a Second Expert System 108 at step 352A, to determine a sorting order such that free text searches are placed last on the list.
  • the classified, unprioritized list 360 is determined at step 362B to be one of four types of studies. It can be a study where most of the inclusion/exclusion criteria are contained in the laboratory criteria such as that shown at step 364B, in which case its corresponding search order is enumerated by the list at 372B. Alternatively, it can have most of the inclusion/exclusion criteria in Free Text, as at step 366B, with its corresponding search order 374B. In another alternative, most of the criteria can be physiological, as in step 368B, with its corresponding search order 376B.
  • the predominant criteria are genetic, as in 370B, in which case the priority list at 378B reflects the importance of genetic and allelic data.
  • a prioritized list is generated at 380 and searches can now commence.
  • the search process is generally designated by the numeral 400A, 400B,
  • the search follows the process of 400A.
  • List 380 is input and examined at step 408A to determine if a new diagnosis is required (step 402A) or if an existing disease is required (step 406A). If a new diagnosis is required, the diagnostic criteria are examined and it is immediately searched for at step 404A. Only those patients whose records match this criteria are retained. Non-matching records are eliminated.
  • step 410A the list of DKT.P.PC0002 exclusionary nontextual criteria is populated and then queried at step 412A. If the patient is not excluded, the processor checks to see if the criteria list has been exhausted at step 414A, and if not, it is iteratively utilized for matching. However, in this case, all matches are removed from the working subset of patients and are utilized in the next search step, leaving those who have not met any exclusions. When the list has been exhausted, inclusionary laboratory tests are listed at step 416A and checked against patient records at step 418A.
  • the list is then checked at step 420A to see if it has been exhausted. If not, the remaining patient records are checked again at step 418A and those who remain when the list is exhausted, a still smaller subset of the original, are then sent to the text search inclusion module at step 422A utilizing the text extraction module 112 and later, the text analysis module 113.
  • the list of textual inclusion criteria is then checked for exhaustion at step 424A and if not exhausted, another text criteria is searched at steps 422A/423A and the patient is determined to be included or excluded. Again, only those patients who are included will be kept in the working subset.
  • the list is then rechecked at step 424A and will recycle iteratively until the text inclusionary criteria list is exhausted.
  • the text exclusionary criteria are searched, the patient is excluded or included at step 427A, and again, the remaining patients of that list are checked for exclusion and the search again iterates until the all of the criteria have been searched.
  • the output of which is either a complete match at step 430A, a partial match at step 432A (because of missing data) or 433A where there are no matches, in which case, the search ends.
  • the entire list of remaining patients is matched to their physicians of record and a report is generated and sent to their corresponding physicians.
  • the search follows the process of 400B shown in Fig. 5B.
  • List 380 is examined at step 408B to determine if a new diagnosis is required (step 402B) or if an existing disease is required (step 406B). If a new diagnosis is required, the diagnostic criteria are examined and it is immediately searched for at step 404B. Only those DKT.P.PC0002 patients whose records match these criteria are retained. If the diagnosis is known, then a search for an ICD code can be used to retain only those patients with the disease of interest.
  • the list of inclusionary textual criteria is populated and then queried at step 412B.
  • the processor checks to see if the list has been exhausted at step 414B, and if not, it is iteratively utilized for matching. However, in this case, all matches are removed from the working subset of patients, leaving those who have not met any inclusions.
  • exclusionary text criteria are listed at step 416B and checked against patient records at step 418B. The list is checked at step 420B to see if it has been exhausted. If not, the remaining patient records are checked again at step 418B and those who remain when the list is exhausted, a still smaller subset of the original, are then sent to the LAB inclusion module at step 422B and checked for inclusion at step 423B.
  • step 424B patient records are checked against the list of laboratory test result inclusion criteria for exhaustion at step 424B and if not exhausted, another lab criteria is searched at steps 422B/423B and the list rechecked at step 424B. This will cycle until the laboratory test result inclusionary criteria list is exhausted.
  • step 426B the laboratory test result exclusionary criteria are searched, the patient list checked for exclusion at step 427B, and again of the remaining patients that list lab exclusions are checked for exhaustion and the search again iterates until the last criteria has been searched. After the exclusions list has been exhausted, the output of step 428B is passed to the text analysis module at step 429B.
  • the text analysis step is the last step before final matches are output, again, to enhance precision and to analyze text for the smallest possible subset of patients.
  • the output of step 429B is a complete match at step 430B, a partial match at step 432B (because of missing data) or no match at step 433 B, in which case, the search ends.
  • the entire list of remaining patients is matched to their physicians of record and a report is generated and sent to their corresponding physicians.
  • the search follows the process generally designated by the numeral 400C in Fig. 5C.
  • the sorted prioritized list is examined at step 408C DKT.P.PC00O2 to determine if a new diagnosis is required (step 402C) or if an existing disease is required (step 406C). If a new diagnosis is required, the diagnostic criteria are immediately searched for at step 404C. Only those patients matching this criteria are retained. If the diagnosis is known, then an ICD code search can be used to retain only those patients with the disease of interest.
  • the list of inclusionary textual criteria is populated and then queried at step 412C utilizing the text extraction module 112.
  • the processor checks to see if the list has been exhausted at step 414C and if not, it is iteratively utilized for matching. However, in this case, all matches are removed from the working subset of patients, leaving those who have not met any exclusions.
  • exclusionary text criteria are listed at step 416C and checked against patient records at step 418C. The list is checked at step 420C to see if it has been exhausted. If not, the remaining patients are checked again at step 418C and those who remain when the list is exhausted, a still smaller subset of the original, are then sent to the physiologic inclusion/exclusion module shown in Fig. 5D.
  • a list of inclusionary laboratory tests are populated and the remaining patient records are examined at step 423C.
  • the subset that remains that is, those patient records that satisfy one or more of the inclusionary lab test criteria, is checked against the list of textual inclusion criteria for exhaustion at step 424C and if not exhausted, another text criteria is searched at steps 422C/423C and the list rechecked at step 424C. This will cycle until the text inclusionary criteria list is exhausted.
  • the lab and ICD exclusionary criteria list is populated, searched at step 427C, and again the remaining patient records that list text exclusions are checked for exhaustion and the search again iterates until the last criteria has been searched.
  • the output is a complete match at step 430C, a partial match at step 432C (because of missing data) or no match at step 433C, in which case, the search ends.
  • the entire list of remaining patients is matched to their physicians of record and a report is generated and sent to their corresponding physicians.
  • the physiologic inclusion/exclusion module is generally designated by the numeral 400D. Once the list of text exclusions have DKT.P.PC0002 been exhausted at step 420C, as shown in Fig. 5C, the subset of patients remaining are examined.
  • the physiologic inclusion criteria list is populated and patients are determined to be included or excluded at step 434D.
  • the list is check for exhaustion and if not exhausted, the remaining patients are checked for the next criteria on the list at 432D/434D.When the list is exhausted at step 436D the remaining patients are then checked for physiological exclusion criteria. The list of physiological exclusion criteria is populated at 438D and the remaining subset of patients are checked at step 440D for exclusions . At step 442D the list is checked for exhaustion. If there are remaining criteria to be checked the process iterates at steps 438D and 440D on the ever decreasing subset of patients. When the list of physiological exclusions is exhausted, inclusion labs criteria are checked at step 422C of Fig. 5C.
  • the search follows the process generally designated by numeral 400E as shown in Fig. 5E.
  • the list 380 is examined at step 408E to determine if a new diagnosis is required (step 402E) or if an existing disease is required (step 406E). If a new diagnosis is required, the diagnostic criteria are immediately searched for at step 404E. Only those patients matching these criteria are retained. If the diagnosis is known, then an ICD code can be used to retain only those patients with the disease of interest.
  • the genetic inclusion/exclusion criteria are checked by the genetic module at step 409E and further detailed in Fig. 5F.
  • the list of exclusionary nontextual laboratory test results/ICD criteria is populated and queried at step 412E. If the patient is not excluded, the processor checks to see if the list has been exhausted at step 414E and if not, it is iteratively utilized for matching. However, in this case, all matches are removed from the working subset of patients leaving those who have not met any exclusions. When the list has been exhausted, inclusionary labs are listed at step 416E and checked at step 418E. The list is checked at step 420E to see if it has been exhausted.
  • step 422E If not the remaining patients are checked again at step 418E and those who remain when the list is exhausted, a still smaller subset of the original, are then sent to the text search inclusion module at step 422E.
  • step 423E patients are determined to be included or excluded .
  • the list of textual inclusion criteria is then checked for exhaustion at step 424E and if not exhausted, another text criteria is searched at step 422E/423E and the patients are determined to be included or excluded. Again only those patients who are included will be kept in the working subset.
  • the list is then rechecked at step 424E and will recycle iteratively until the text inclusionary criteria list is exhausted.
  • the text exclusionary criteria are searched, excluded or included at step 427E, and again of the remaining patients that list of text exclusions are checked for exhaustion and the search again iterates until the last criteria has been searched.
  • the reduced set of patients are then searched at step 43 IE for a genetic data match, such as a DNA sequence match, PCR product match, or restriction fragment length polymorphism (RFLP), for example.
  • the output is either a complete match at step 430E, a partial match at step 432E (because of missing data) or no match at step 433E, in which case, the search ends.
  • the entire list of remaining patients is matched to their physicians of record and a report is generated and sent to their corresponding physicians.
  • the genetic module is generally designated by the numeral 400F.
  • the genetic inclusion criteria list is populated and patients are determined to be included or excluded at step 434F.
  • the list is checked for exhaustion and if not exhausted, the remaining patients are checked for the next criteria on the list at steps 432F/434F.
  • the list is exhausted at step 436F, the remaining patients are then checked for genetic exclusion criteria.
  • the list of genetic exclusion criteria is populated at 438F and the remaining subset of patients are checked at step 440F for exclusions.
  • step 442F the list is checked for exhaustion. If there are remaining criteria to be checked the process iterates at steps 438F and 440F on the ever decreasing subset of patients. When the list of genetic exclusions is exhausted, inclusion labs criteria are checked at step 410E of Fig. 5E. DKT.P.PC0002
  • a textual search module is generally designated by the numeral 500.
  • the prioritized list 380 is input and the first or next criteria is selected at step 504 and used to search the textual data at step 506.
  • the textural data is checked against a table of similar diagnoses at step 512 or for similar phrases or against a table 518. The latter will take raw clinical information and classify it into standard disease conditions.
  • a gene allele table 514 which checks for membership in a gene family, may be checked. The relevant criteria together with its appropriate modifiers/staging/gene allele/mutation are compared to the parsed textual data.
  • EPO Erythropoietin
  • PCP Systemic Pneumocystis carinii pneumonia (PCP) prophylaxis (aerosolized or oral pentamidine, trimethoprim / sulfamethoxazole, or dapsone) .
  • EPO Erythropoietin
  • PCP Systemic Pneumocystis carinii pneumonia (PCP) prophylaxis (aerosolized or oral pentamidine, dapsone, trimethoprim / sulfamethoxazole).
  • Clofazimine Other macrolides. Clofazimine.
  • Immunomodulators except alpha interferon. Investigational drugs (except ddl, ddC, and erythropoietin).
  • CRITERIA If female, subject is either not of childbearing potential, defined as postmenopausal for at least 1 year or surgically sterile (bilateral tubal ligation, bilateral oophorectomy or hysterectomy), or is of childbearing potential and practicing one of the following methods of birth control: condoms, sponge, foams, jellies, diaphragm or intrauterine device (IUD), a vasectomized partner, total abstinence from sexual intercourse
  • Subject has a plasma HIV RNA level of greater than 400 copies/mL at screening.
  • ALT Alanine amino transferase
  • AST aspartate amino transferase
  • the patient has uncontrolled seizure disorder, active neurological disease, or Grade
  • the present system can find most if not all of the criteria from patient's hospital records. This can be done faster, accurately and with more up to date information, than by hand searching of charts, advertising, weekly or monthly updates of a centralized database searched via its own search engine. In addition the system will be able to draw upon the practices of vast number of physicians and hospitals and therefore make available to the general population treatments that might not have previously been available.

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  • Engineering & Computer Science (AREA)
  • Epidemiology (AREA)
  • General Health & Medical Sciences (AREA)
  • Medical Informatics (AREA)
  • Primary Health Care (AREA)
  • Public Health (AREA)
  • Medical Treatment And Welfare Office Work (AREA)

Abstract

L'invention concerne un procédé et un système permettant d'identifier rapidement et précisément des patients candidats pour des tests cliniques comprenant un composant de base de données pour conserver un composant de base de données de patients d'hôpital et une pluralité de bases de données d'hôpital et la pluralité correspondante de noms de patients et de dossiers médicaux, en communication avec un ou plusieurs composants de bases de données de cabinets médicaux et la pluralité correspondante de spécialités et la pluralité correspondante de noms de patients et de dossiers médicaux. Ce procédé et ce système comportent aussi des composants de bases de données d'études cliniques et la pluralité correspondante d'études cliniques, un composant de communication afin de recevoir des changements apportés aux composants de la base de données, et un processeur programmé pour faire correspondre périodiquement les patients compatibles et les études cliniques, et pour générer des rapports aux cabinets médicaux dans la base de données de cabinets médicaux possédant les patients correspondants. Ce processeur peut être programmé afin de rechercher des mots de passe de textes libres et des phrases.
PCT/US2004/007409 2002-11-08 2004-03-11 Methode et procede permettant de trouver automatiquement des patients pour des tests cliniques de medicaments ou de dispositifs Ceased WO2004081752A2 (fr)

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US45368003P 2003-03-11 2003-03-11
US60/453,680 2003-03-11
US61841803A 2003-07-11 2003-07-11
US10/618,418 2003-07-11

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