Patent Publication Number: US-2007106531-A1

Title: Method and process that automatically finds patients for clinical drug or device trials

Description:
CROSS REFERENCE TO RELATED APPLICATIONS  
      This application claims the benefit of U.S. Provisional Application No. 60/453,680 filed on Mar. 11, 2003 and is a continuation-in-part of U.S. patent application Ser. No. 10/618,418 filed on Jul. 11, 2003. 
    
    
     BACKGROUND ART  
      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.  
      As the number of elderly people increase in the United States and their lifespans extend, there is an ever-increasing need for newer and safer pharmaceutical products. As such, there is a need for new drugs and medical devices to be approved more rapidly. With the mapping of the human genome it is estimated that drug targets and drugs will multiply tenfold, necessitating more clinical testing. In fact, The Pharmaceutical Research and Manufacturers of America (PhRMA) states that all drugs currently on the market are based on about 500 different targets. They expect this number to increase 600-2000%, to 3,000 to 10,000 drug targets in the coming years. However, such medical advances are outrageously expensive and have necessitated changes throughout the industry.  
      It is estimated to cost $880 million to bring one new drug to market, and it is estimated that the average pharmaceutical company has 70 new drugs in development. This has forced the pharmaceutical companies to consolidate for the purpose of underwriting the prohibitive expense of bringing a drug to market. The average drug takes 10 to 12 years to bring to market and must negotiate a series of 3 clinical trials before approval by the Food and Drug Administration (FDA) can even be granted, leaving 8 to 10 years on a drug patent to recoup costs and turn a profit. Factoring in the governmental and managed care cost containment pressures, the pharmaceutical companies must produce one blockbuster medicine every 18 months to survive.  
      In summary, 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.  
      In order to speed up this process, business efficiencies are being applied to the previously haphazard clinical trials process. According to a Tufts University study, each day a study is late a pharmaceutical company can lose $1.3 million in lost prescription drug sales and it can be as high as $10 million for a blockbuster drug. Clinical trials are for the most part paper-based; necessarily cumbersome; and slow to monitor, process and store. One of the key factors affecting the time it takes to complete a clinical trial or study is the time it takes to recruit, screen and refer patients to the study. Only when the study is completely populated with patients can testing begin. Currently, the haphazard methods to recruit patients can take up to a year and 25% of the duration of the clinical study and thus, it becomes no surprise that 75% of all clinical studies are completed late.  
      There are a number of web-based clinical trial management software programs which plan, administer, and process trials for pharmaceutical companies. Although less than 15% of drug trials are e-clinical trials, this number is expected to increase to 50% or more in the next few years. Such trials will allow realtime monitoring of trials for adverse drug reactions and quality control, as well as more efficiently, move and process the prodigious amount of data generated. However, one area which still has not been adequately addressed is patient recruitment.  
      Traditionally, patients for studies have been enrolled from an investigator&#39;s clinic or practice, via referrals or by advertising. One prior art publication that addresses this problem using the internet, is “Systems and Methods for Selecting and Recruiting Investigators and Subjects for Clinical Studies” U.S. patent application Pub. No. 2002/0002474 by Leslie Dennis Michelson and Leonard 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. patent application Pub. No. 2002/0099570 by Knight. Basically, 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.  
      These publications, however, do not utilize data as close to realtime as possible. They also do not systematically search all available places that patients may be found for drug trial enrollments. In particular, those websites that deal only with investigators comprise only 5% of all physicians, and a corresponding number of patients. Both Knight&#39;s and Michelson&#39;s methods do not systematically search for and find patients. It is believed that none of the known systems have a way to tap into the 95% of non-research preforming physicians to find and enroll their patients into studies.  
      A method that searches dictations and flags patients may be used in the offices of physicians with large practices who do research. These physicians are then paid for each patient found and for administering the study on that patient. However, these physicians are usually specialists who depend on referrals and it may take months for newly diagnosed patients to see the specialist and they comprise about 5% of the physician population.  
      Rao et al. describe methods for mining patient data in U.S. patent application Pub. Nos. 2003/0120458 and 2003/0130871. However, 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.  
      Therefore, based upon the foregoing, there is a need for a process that will tap a larger pool of patients more systematically, using data as close to realtime as possible with a level of precision not previously found and that will identify prospective patients at an earlier stage of their ailment before they see the appropriate specialist, to widen their treatment options.  
     SUMMARY OF THE INVENTION  
      In light of the foregoing, it is a first object of the invention to provide a system to rapidly and precisely identify patient candidates for clinical trials comprising: 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.  
      It is another object of the invention to provide a computerized method for matching patients to clinical medical studies, comprising: identifying a group of medical practices; identifying at least one clinical study; identifying a group of patients from a hospital database; maintaining a database identifying each said medical practice and each patient of said group of patients from said hospital database and each said clinical study; and comparing said medical practices and said clinical studies and matching one to the other.  
      Other objects and advantages of the present invention will become apparent from the following descriptions, taken in connection with the accompanying drawings, wherein, by way of illustration and example, an embodiment of the present invention is disclosed.  
      In accordance with a preferred embodiment of the invention, there is disclosed, 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.  
      In accordance with a preferred embodiment of the invention, there is disclosed 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. 
    
    
     BRIEF DESCRIPTION OF THE DRAWINGS  
      The drawings constitute a part of this specification and include exemplary embodiments to the invention, which may be embodied in various forms. It is to be understood that in some instances various aspects of the invention may be shown exaggerated or enlarged to facilitate an understanding of the invention.  
       FIG. 1  is a schematic diagram of the system according to the present invention;  
       FIG. 2  is a schematic of the AI (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 ,  5 C,  5 D,  5 E,  5 F are flowcharts of variations of the Search Process; and  
       FIG. 6  is a flowchart of the Text Recognition module. 
    
    
     BEST MODE FOR CARRYING OUT THE INVENTION  
      Detailed descriptions of the preferred embodiment are provided herein. It is to be understood, however, that the present invention may be embodied in various forms. Therefore, specific details disclosed herein are not to be interpreted as limiting, but rather as a basis for the claims and as a representative basis for teaching one skilled in the art to employ the present invention in virtually any appropriately detailed system, structure or manner.  
      Referring now to  FIG. 1  it can be seen that 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. As discussed previously, 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. These patient populations may be accessed through one or more hospitals to which the patients are referred. Optionally, patient populations may be accessed through the hospitals without reference to a referring medical practice. The hospitals are generally designated by the numeral  24  with each individual hospital represented by alphabetic suffixes. In the preferred embodiment of this invention, there is an identifier generally designated by the numeral  26  and specifically one associated with each hospital and designated by the same alphabetic suffix as its corresponding hospital. 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 AI Module  46  also disclosed herein, and to output a report of matched patient clinical study and physicians. Moreover, 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 .  
      Referring now to  FIGS. 1 and 2 , the AI 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 . At step  103 , the processor  30  evaluates the data to 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 . 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 AI 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. At step  110 , 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. The output of 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™ (Text Analysis International, Inc., Sunnyvale, Calif.) and similar natural language text analysis software is suitable for use as a text extraction module. 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. VisualText™ 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 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. At step  202 , 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. At step  216 , the AI Module processes the criteria and searches the extracted database. At step  218 , the processor  30  finds matches between the study criteria parameters and the patients. At step  220 , 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. Then, at step  226 , the physician may verify the accuracy of the criteria, discuss treatment options with his or her patient, and obtain consent either to enroll the patient into a study or to refer the patient to a research site that does the study.  
      Referring now to  FIG. 4A  and to the Examples below, a detailed explanation of the generation of a prioritized list of search criteria will be discussed in detail. This part of the system and method is generally designated by the numeral  300 A 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 . As the process is iterative, it is a necessary first step  302 A to compare the eligibility criteria  42  to a predetermined categorized list of criteria. At the beginning, there will be no matches between the study criteria  42  and the categorized list of criteria. At all times where the prioritized list is incomplete, the match will not be complete and at the next step  306 A the processor extracts the first or next criteria. At step  308 A, the processor checks to see if the criteria is free text such as dictations of histories and physicals, discharge summaries and progress notes. If the criteria is free text, this information is stored on a separate list of free text criteria  310 A, which is then input at step  344 A to an updated list of criteria, and summed to create one list of categorized criteria at step  348 A. The list of categorized criteria is then fed back to the processor  30  at step  305 A 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  312 A, demographic data at step  316 A, laboratory result at step  320 A, allergy at step  324 A, current medication patient is taking at step  328 A, prior treatments at step  332 A, physiological function test result at step  336 A and lastly genotype test result at step  340 A. Each of the foregoing steps  308 A to  340 A has a corresponding list  314 A,  318 A,  322 A,  326 A,  330 A,  334 A,  338 A, and  342 A that is updated depending on which criteria is matched. All the lists are fed into updated lists at step  344 A and feedback to the processor at  350 A. At step  302 A, 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. When the categorized list matches the study eligibility list, the processor determines that the list is completed at step  304 A and then the classified unprioritized list is output to a Second Expert System  108  at step  352 A, to determine a sorting order such that free text searches are placed last on the list.  
      Referring now to  FIG. 4B , the Second Expert System  108  is generally designated by the numeral  300 B. The classified, unprioritized list  360  is determined at step  362 B 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  364 B, in which case its corresponding search order is enumerated by the list at  372 B. Alternatively, it can have most of the inclusion/exclusion criteria in Free Text, as at step  366 B, with its corresponding search order  374 B. In another alternative, most of the criteria can be physiological, as in step  368 B, with its corresponding search order  376 B. Lastly, it may be that the predominant criteria are genetic, as in  370 B, in which case the priority list at  378 B reflects the importance of genetic and allelic data. In all cases a prioritized list is generated at  380  and searches can now commence.  
      The search process is generally designated by the numeral  400 A,  400 B,  400 C, or  400 E, shown in  FIGS. 5A, 5B ,  5 C and  5 E, respectively, depending on the predominant search criteria type. If the sorted prioritized list  380  consists predominantly (60% or more) of laboratory test inclusion/exclusion criteria, the search follows the process of  400 A. List  380  is input and examined at step  408 A to determine if a new diagnosis is required (step  402 A) or if an existing disease is required (step  406 A). If a new diagnosis is required, the diagnostic criteria are examined and it is immediately searched for at step  404 A. Only those patients whose records match this criteria are retained. Non-matching records are eliminated. If the diagnosis is known, then a search for an International Statistical Classification of Diseases and Related Health Problems (or ICD) code can be used to retain only those patients with the disease of interest. At step  410 A, the list of exclusionary nontextual criteria is populated and then queried at step  412 A. If the patient is not excluded, the processor checks to see if the criteria list has been exhausted at step  414 A, 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  416 A and checked against patient records at step  418 A. The list is then checked at step  420 A to see if it has been exhausted. If not, the remaining patient records are checked again at step  418 A 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  422 A utilizing the text extraction module  112  and later, the text analysis module  113 . At step  423 A, patients are determined to be included or excluded according to the text criteria. Of the subset that remains, the list of textual inclusion criteria is then checked for exhaustion at step  424 A and if not exhausted, another text criteria is searched at steps  422 A/ 423 A 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  424 A and will recycle iteratively until the text inclusionary criteria list is exhausted. At step  426 A, the text exclusionary criteria are searched, the patient is excluded or included at step  427 A, 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  430 A, a partial match at step  432 A (because of missing data) or  433 A 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.  
      If the list type is predominantly text inclusion/exclusion criteria, the search follows the process of  400 B shown in  FIG. 5B . List  380  is examined at step  408 B to determine if a new diagnosis is required (step  402 B) or if an existing disease is required (step  406 B). If a new diagnosis is required, the diagnostic criteria are examined and it is immediately searched for at step  404 B. Only those 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. At step  410 B the list of inclusionary textual criteria is populated and then queried at step  412 B. If the patient is not included, the processor checks to see if the list has been exhausted at step  414 B, 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. When the list has been exhausted, exclusionary text criteria are listed at step  416 B and checked against patient records at step  418 B. The list is checked at step  420 B to see if it has been exhausted. If not, the remaining patient records are checked again at step  418 B 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  422 B and checked for inclusion at step  423 B. Of the subset that remains, patient records are checked against the list of laboratory test result inclusion criteria for exhaustion at step  424 B and if not exhausted, another lab criteria is searched at steps  422 B/ 423 B and the list rechecked at step  424 B. This will cycle until the laboratory test result inclusionary criteria list is exhausted. At step  426 B the laboratory test result exclusionary criteria are searched, the patient list checked for exclusion at step  427 B, 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  428 B is passed to the text analysis module at step  429 B. 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  429 B is a complete match at step  430 B, a partial match at step  432 B (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.  
      If the prioritized list type is predominantly physiologic inclusion/exclusion criteria, the search follows the process generally designated by the numeral  400 C in  FIG. 5C . The sorted prioritized list is examined at step  408 C to determine if a new diagnosis is required (step  402 C) or if an existing disease is required (step  406 C). If a new diagnosis is required, the diagnostic criteria are immediately searched for at step  404 C. 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. At step  410 C the list of inclusionary textual criteria is populated and then queried at step  412 C utilizing the text extraction module  112 . If the patient is not excluded, the processor checks to see if the list has been exhausted at step  414 C 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, exclusionary text criteria are listed at step  416 C and checked against patient records at step  418 C. The list is checked at step  420 C to see if it has been exhausted. If not, the remaining patients are checked again at step  418 C 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 . Then, at step  422 C, a list of inclusionary laboratory tests are populated and the remaining patient records are examined at step  423 C. 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  424 C and if not exhausted, another text criteria is searched at steps  422 C/ 423 C and the list rechecked at step  424 C. This will cycle until the text inclusionary criteria list is exhausted. At step  426 C, the lab and ICD exclusionary criteria list is populated, searched at step  427 C, 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  430 C, a partial match at step  432 C (because of missing data) or no match at step  433 C, 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.  
      Referring now to  FIG. 5D , the physiologic inclusion/exclusion module is generally designated by the numeral  400 D. Once the list of text exclusions have been exhausted at step  420 C, as shown in  FIG. 5C , the subset of patients remaining are examined. At step  432 D, the physiologic inclusion criteria list is populated and patients are determined to be included or excluded at step  434 D. At step  436 D the list is check for exhaustion and if not exhausted, the remaining patients are checked for the next criteria on the list at  432 D/ 434 D. When the list is exhausted at step  436 D the remaining patients are then checked for physiological exclusion criteria. The list of physiological exclusion criteria is populated at  438 D and the remaining subset of patients are checked at step  440 D for exclusions. At step  442 D the list is checked for exhaustion. If there are remaining criteria to be checked the process iterates at steps  438 D and  440 D on the ever decreasing subset of patients. When the list of physiological exclusions is exhausted, inclusion labs criteria are checked at step  422 C of  FIG. 5C .  
      If the sorted prioritized list  380  is predominantly (60% or more) genetic inclusion/exclusion criteria, the search follows the process generally designated by numeral  400 E as shown in  FIG. 5E . The list  380  is examined at step  408 E to determine if a new diagnosis is required (step  402 E) or if an existing disease is required (step  406 E). If a new diagnosis is required, the diagnostic criteria are immediately searched for at step  404 E. 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  409 E and further detailed in  FIG. 5F . At step  410 E, the list of exclusionary nontextual laboratory test results/ICD criteria is populated and queried at step  412 E. If the patient is not excluded, the processor checks to see if the list has been exhausted at step  414 E 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  416 E and checked at step  418 E. The list is checked at step  420 E to see if it has been exhausted. If not the remaining patients are checked again at step  418 E 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  422 E. At step  423 E, patients are determined to be included or excluded. Of the subset that remains, the list of textual inclusion criteria is then checked for exhaustion at step  424 E and if not exhausted, another text criteria is searched at step  422 E/ 423 E 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  424 E and will recycle iteratively until the text inclusionary criteria list is exhausted. At step  426 E, the text exclusionary criteria are searched, excluded or included at step  427 E, 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  431 E 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  430 E, a partial match at step  432 E (because of missing data) or no match at step  433 E, 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.  
      Referring now to  FIG. 5F , the genetic module is generally designated by the numeral  400 F. Once the inclusionary diagnoses have been met at step  408 E, shown in  FIG. 5E , the subset of patients remaining are examined. At step  432 F, the genetic inclusion criteria list is populated and patients are determined to be included or excluded at step  434 F. At step  436 F, the list is checked for exhaustion and if not exhausted, the remaining patients are checked for the next criteria on the list at steps  432 F/ 434 F. When the list is exhausted at step  436 F, the remaining patients are then checked for genetic exclusion criteria. The list of genetic exclusion criteria is populated at  438 F and the remaining subset of patients are checked at step  440 F for exclusions. At step  442 F, the list is checked for exhaustion. If there are remaining criteria to be checked the process iterates at steps  438 F and  440 F on the ever decreasing subset of patients. When the list of genetic exclusions is exhausted, inclusion labs criteria are checked at step  410 E of  FIG. 5E .  
      Referring now to  FIG. 6 , 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. Also, 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. String matches are checked for at step  520  and if matches are not found, then the next criteria on the list is obtained at step  526  from the list  380  and the search iterates until all of the text criteria are exhausted. If there is a match at step  520 , the desired text is extracted and the patient kept in the working subset of patients. When all textual criteria are exhausted, those records that matched the criteria are either output to be searched for other lab criteria or for further text analysis by any commercial text analysis software or output as a list of likely candidates for entry into a clinical trial, as in the latter case all other criteria have been exhausted.  
     EXAMPLES  
      The examples below are lists of study eligibility and exclusion criteria for selected clinical drug trials. A study is listed by the title of the study in bold letters. The category of the criteria for the study is designated in bold brackets [category].  
     Example 1  
      A Phase II Safety and Efficacy Study of Clarithromycin in the Treatment of Disseminated  M. avium  Complex (MAC) Infections in Patients With AIDS  
      Eligibility  
      Ages Eligible for Study: 13 Years and above, Genders Eligible for Study: Both Criteria  
      Inclusion Criteria  
      [CURRENT MEDICATION] Concurrent Medication: Allowed:  
      Didanosine (ddI).  
      Dideoxycytidine (ddC).  
      Zidovudine (AZT).  
      Acetaminophen.  
      Acyclovir.  
      Fluconazole.  
      Erythropoietin (EPO).  
      [DIAGNOSIS] Systemic  Pneumocystis carinii  pneumonia (PCP) prophylaxis (aerosolized or oral pentamidine, trimethoprim/sulfamethoxazole, or dapsone).  
      [CURRENT MEDICATION] Maintenance ganciclovir therapy (permitted only if dose and clinical and laboratory parameters have been stable for at least 4 weeks prior to study entry).  
      [CURRENT MEDICATION] Maintenance treatment for other opportunistic infections if the dose and clinical and laboratory parameters have been stable for 4 weeks prior to study entry. Patients must have:  
      [LABORATORY RESULT] Positive results for HIV by ELISA confirmed by another method.  
      [LABORATORY RESULT] Positive blood culture for  Mycobacterium avium  complex within 2 months of study entry and clinical symptoms of MAC infection.  
      [FROM FREE TEXT] Discontinued all mycobacterial drugs (approved and investigational) for at least 4 weeks prior to the start of drug therapy (with the exception of isoniazid prophylaxis which should be discontinued at Study Day minus 14 to Study Day minus 7)  
      [THIS WILL BE DONE AFTER THE PATIENT IS COUNSELED AND WILL NOT BE A SEARCH ENGINE CRITERION] Given written informed consent to participate in the trial.  
      Met the listed laboratory parameters in the pre-treatment visit.  
      [TREATMENT HISTORY] Prior Medication: Allowed:  
      Didanosine (ddI).  
      Dideoxycytidine (ddC).  
      Zidovudine (AZT).  
      Acetaminophen.  
      Acyclovir.  
      Fluconazole.  
      Erythropoietin (EPO).  
      [DIAGNOSIS] Systemic  Pneumocystis carinii  pneumonia (PCP) prophylaxis (aerosolized or oral pentamidine, dapsone, trimethoprim/sulfamethoxazole).  
      [CURRENT MEDICATION] Maintenance ganciclovir therapy (permitted only if dose and clinical and laboratory parameters have been stable for at least 4 weeks prior to study entry).  
      Exclusion Criteria  
      Co-existing Condition: Patients with the following conditions or symptoms are excluded:  
      [DIAGNOSIS] Active opportunistic infections. Maintenance treatment for other opportunistic infections will be permitted if the dose and clinical and laboratory parameters have been stable for 4 weeks prior to study entry.  
      [CURRENT MEDICATION] Concurrent Medication: Excluded:  
      Aminoglycosides.  
      Ansamycin (rifabutin).  
      Quinolones.  
      Other macrolides.  
      Clofazimine.  
      Cytotoxic chemotherapy.  
      Rifampin.  
      Ethambutol.  
      Immunomodulators (except alpha interferon).  
      Investigational drugs (except ddI, ddC, and erythropoietin).  
      Patients with the following are excluded:  
      [ALLERGY] History of allergy to macrolide antimicrobials.  
      [CURRENT MEDICATION] Currently on active therapy with any anti-mycobacterial drugs listed in Exclusion Prior Medications.  
      [CURRENT MEDICATION] Currently on active therapy with carbamazepine or theophylline, unless the investigator agrees to carefully monitor blood levels. Inability to comply with the protocol or judged to be near imminent death by the investigator.  
      [DIAGNOSIS] Active opportunistic infections.  
      [DIAGNOSIS] Requiring any of the excluded concomitant medications.  
      prior Medication: Excluded for at least 4 weeks prior to study entry:  
      [TREATMENT HISTORY] All anti-mycobacterial drugs (approved and investigational) with the exception of isoniazid  
     Example 2  
      A phase II study of lopinavir/ritonavir in combination with saquinavir mesylate or lamivudine/zidovudine to explore metabolic toxicities in antiretroviral HIV-infected subjects Eligibility  
      [DEMOGRAPHIC] Ages Eligible for Study: 18 Years and above, Genders Eligible for Study: Both  
      Criteria  
      Inclusion Criteria:  
      [TREATMENT HISTORY] 1. Subject is naïve to antiretroviral treatment (subjects may not have more than 7 days of any antiretroviral treatment).  
      [DEMOGRAPHIC] 2. Subject is at least 18 years of age, inclusive.  
      [WILL BE CHECKED BY MD AND WILL NOT BE PART OF SEARCH 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  
      [LABORATORY RESULT] If female, the results of a urine pregnancy test performed at screening (urine specimen obtained no earlier than 28 days prior to study drug administration) is negative.  
      [WILL BE CHECKED BY MD AND WILL NOT BE PART OF SEARCH CRITERIA] Subject is not breast-feeding.  
      [FREE TEXT FROM PHYSICAL EXAMINATION] Vital signs, physical examination and laboratory results do not exhibit evidence of acute illness.  
      [DIAGNOSIS]. Subject has no significant history of cardiac, renal, neurologic, psychiatric, oncologic, endocrinologic, metabolic or hepatic disease that would in the opinion of the investigator adversely affect his/her participating in this study.  
      [CURRENT MEDICATION] Subject does not require and agrees not to take any of the following medications for the duration of the study: midazolam, triazolam, terfenadine, astemizole, cisapride, pimozide, propafenone, flecainide, certain ergot derivatives (ergotamine, dihydroergotamine, ergonovine, and metheylergonovine), rifampin, lovastatin, simvastatin, and St. John&#39;s wort.  
      [TO BE PART OF CONSENT AND WILL BE REMOVED FROM SELECTION CRITERIA] Subject agrees not to take any medication during the study, including over-the-counter medicine, alcohol or recreational drugs without the knowledge and permission of the principal investigator.  
      [DIAGNOSIS] Subject has not been treated for an active AIDS-defining opportunistic infection within 30 days of screening.  
      [LABORATORY RESULT] Subject has a plasma HIV RNA level of greater than 400 copies/mL at screening.  
      [TO BE PART OF CONSENT AND WILL BE REMOVED FROM SELECTION CRITERIA] Subject agrees to take all doses of the study drug from the bottles provided by the sponsor (rather than other containers, i.e., “pill box”).  
      [TO BE PART OF CONSENT AND WILL BE REMOVED FROM SELECTION CRITERIA] Subject has voluntarily signed and dated an informed consent form, approved by an Institutional Review Board (IRB)/Independent Ethics Committee (IEC), after the nature of the study has been explained and the subject has had the opportunity to ask questions. The informed consent must be signed before any study-specific procedures are performed.  
      Exclusion Criteria:  
      [ALLERGY] Subject has a history of an allergic reaction or significant sensitivity to LPV/r, INV or Combivir.  
      [DIAGNOSIS] Subject has a history of substance abuse or psychiatric illness that could preclude adherence with the protocol.  
      [LABORATORY RESULT] Screening laboratory analyses show any of the following abnormal laboratory results: •Hemoglobin &gt;10.0 g/dL •Absolute neutrophil count &gt;1000 cells/μL •Platelet count &gt;50,000 per mL •ALT or AST&lt;3.0×Upper Limit of Normal (ULN) •Creatinine&lt;1.5×Upper Limit of Normal (ULN)  
      [TREATMENT HISTORY] Subject has received any investigational drug within 30 days prior to study drug administration.  
      [TO BE DETERMINED BY RESEARCH SITE] For any reason, subject is considered by the investigator to be an unsuitable candidate for the study  
     Example 3  
      Iressa/Docetaxel in Non-Small-Cell Lung Cancer  
      Eligibility  
      [DEMOGRAPHIC] Genders Eligible for Study: Both  
      Criteria  
      Inclusion:  
      [DIAGNOSIS] Pathologically confirmed non-small cell lung cancer.  
      [DIAGNOSIS] Measurable, evaluable disease outside of a radiation port.  
      [PHYSIOLOGIC] ECOG performance status 0-2.  
      [LABORATORY RESULT] Adequate hematologic function as defined by an absolute neutrophil count &gt;=1,500/mm3, a platelet count &gt;=100,000/mm3, a WBC &gt;=3,000/mm3, and a hemoglobin level of &gt;=9 g/dl.  
      [TREATMENT HISTORY] One prior chemotherapy regimen. This may include chemoradiation treatment.  
      [FROM FREE TEXT] Disease progression or recurrence within 6 months of last dose of chemotherapy in first chemotherapy regimen.  
      [TREATMENT HISTORY] At least a 2-week recovery from prior therapy toxicity.  
      [TO BE DONE WILL BE REMOVED FROM SELECTION CRITERIA] Signed informed consent.  
      [FROM FREE TEXT] Prior CNS involvement by tumor are eligible if previously treated and clinically stable for two weeks after completion of treatment.  
      Exclusion:  
      [TREATMENT HISTORY] Prior Iressa or other EGFR inhibiting agents  
      [TREATMENT HISTORY] Prior docetaxel therapy  
      [DIAGNOSIS] Other co-existing malignancies or malignancies diagnosed within the last 5 years with the exception of basal cell carcinoma or cervical cancer in situ.  
      [TREATMENT HISTORY] Any unresolved chronic toxicity greater than CTC grade 2 from previous anti-cancer therapy.  
      [FREE TEXT FROM DICTATIONS] Incomplete healing from previous oncologic or other major surgery.  
      [CURRENT MEDICATIONS] Concomitant use of phenyloin, carbamazepine, barbiturates, rifampicin, St John&#39;s Wort, anticoagulants.  
      [LABORATORY VALUE] Absolute neutrophil counts less than 1500×109/liter (L) or platelets less than 100,000×10 9 /liter (L).  
      [LABORATORY VALUE] Serum bilirubin greater than 1.25 times the upper limit of reference range (ULRR).  
      [DIAGNOSIS] In the opinion of the investigator, any evidence of severe or uncontrolled systemic disease, (e.g., unstable or uncompensated respiratory, cardiac, hepatic, or renal disease).  
      [LABORATORY VALUE] A serum creatinine &gt;=1.5 mg/dl and calculated creatinine clearance &lt;=60 cc/minute.  
      [LABORATORY VALUE] Alanine amino transferase (ALT) or aspartate amino transferase (AST) greater than 2.5 times the ULRR if no demonstrable liver metastases or greater than 5 times the ULRR in the presence of liver metastasis.  
      [LABORATORY VALUE] Evidence of any other significant clinical disorder or laboratory finding that makes it undesirable for the patient to participate in the trial.  
      [TO BE DETERMINED BY CONSENTING MD] Pregnancy or breast feeding The patient has uncontrolled seizure disorder, active neurological disease, or Grade &gt;=2  
      neuropathy  
      [TREATMENT HISTORY] The patient has received any investigational agent(s) within 30 days of study entry.  
      [DIAGNOSIS] The patient has signs and symptoms of keratoconjunctivitis sicca or incompletely treated eye infection.  
      Expected Total Enrollment: 50  
      As can be seen from the above examples criteria vary widely from one study to the next. Currently there are about 4,000+ studies that are being conducted. In addition, finding patients for these studies is like looking for a needle in a haystack.  
      Based upon the foregoing, the present system can find most if not all of the criteria from patient&#39;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.  
      While the invention has been described in connection with a preferred embodiment, it is not intended to limit the scope of the invention to the particular form set forth, but on the contrary, it is intended to cover such alternatives, modifications, and equivalents as may be included within the spirit and scope of the invention as defined by the appended claims.