Data processing: database and file management or data structures – Database design – Data structure types
Reexamination Certificate
2001-05-02
2004-09-07
Robinson, Greta (Department: 2177)
Data processing: database and file management or data structures
Database design
Data structure types
C707S793000
Reexamination Certificate
active
06789091
ABSTRACT:
BACKGROUND OF INVENTION
1. Field of the Invention
The present invention relates to a system and method for a web-based assessment and analysis of the risks of adverse effects resulting from the use of a particular drug, either alone or in combination with other drugs, nutrients, supplements, and other substances.
2. Description of the Related Art
In September 1997, information regarding cardiopulmonary disease related to the use of fenfluramine and phentermine (“fen-phen”) prompted the United States Food and Drug Administration (FDA) to request the manufacturers of these drugs to voluntarily withdraw both treatments for obesity from the market. Subsequent studies show a 25 percent incidence of heart valve disease apparently resulting from diet drug use. Thus, up to 1,250,000 people may have sustained heart valve damage from these diet drugs and the FDA indicates that this may be the largest adverse drug effect the agency has ever dealt with.
Current estimates are that some 2.2 million hospital patients had serious adverse drug reactions and more than 100,000 people die each year from adverse reactions to prescription drugs. Accordingly, federal officials have recommended that the FDA require hospitals to report all serious drug reactions to the agency. The Inspector General of the Department of Health and Human Services has also indicated that the FDA should also work to identify harmful effects of new drugs and encourage health-care providers to rapidly call the FDA with information about drug side effects. As new drugs are introduced at increasing rates, the FDA will likely need additional resources to protect the public from hazardous drug side effects.
If one or two adverse drug reactions slip through the FDA's reporting process, the results can be tragic for some patients. That is especially true when the adverse reactions are rare but serious—such as in the case of liver failure caused by medication. All drugs have the potential to harm or kill the people they are designed to help. An injection of penicillin can kill in minutes if the recipient is allergic to this life-saving drug. Even common aspirin can be deadly.
Clinical trials of a new drug often involve a few hundred patients and therefore may not reveal that a drug can cause serious injury or death in one patient in 10,000 or even 1,000 patients. Accordingly, it is critical for researchers and drug companies to be able to analyze and predict adverse reactions among patients in their studies.
In addition, in clinical trials for drugs used to treat diseases such as diabetes, which affects so many people and is difficult to treat, FDA officials often face tremendous pressure to accelerate their approval process. Often, in this “fast-track” process, cases of adverse drug effects may slip through reporting procedures.
An even bigger challenge to the FDA is the occurrence of adverse drug reactions after the drug is on the market. In this case the drug is prescribed to a much larger population of patients, many of whom are taking other substances such as extracts, nutrients, vitamins, hormones or drugs that might have an adverse effect with the prescribed drug.
Thus, there is a need for effective analysis of adverse drug effects. Unfortunately, such a system has not been available.
U.S. Pat. No. 5,860,917 to Comanor et al., “Method And Apparatus For Predicting Therapeutic Outcomes” discloses methods, software, and systems for evaluating the response of a patient afflicted with a disease to a therapeutic regimen for the disease. In one aspect, the methods, systems, and software are provided for evaluating the utility of a treatment regimen for treating a patient afflicted with a disease. In one embodiment of this aspect, the value of at least one diagnostic variable relating to a statistical model describing the utility of the treatment regimen is determined. The statistical model is derived using a more robust similarity metric least squares (SMILES) analysis of the response to the treatment regimen which has been adapted to include discriminant and logistical analysis. The value of the diagnostic variable is then applied to the model to provide an estimated utility of the treatment regimen in treating the patient. Using the methods, software, and apparatus described therein, robust, statistically significant models of patient responsiveness that reduce the problems associated with present treatment response prediction methods that are brittle and oversimplify the complex interactions among treatment variables can assist patients and clinicians in determining therapies.
U.S. Pat. No. 6,000,828 to Leet, “Method Of Improving Drug Treatment” discloses a computer implemented method and system for improving drug treatment of patients in local communities by providing drug treatment protocols for particular disease states, such as Diagnosis Related Group (DRG) classifications. The protocol contains ranked recommendations for drug treatments of the disease state, and the computer system collects information about the risks and benefits of the drug treatments. The information collected about the treatments is used to modify the rankings of the drug treatments in the protocol. In one specific embodiment of the system, where the disease state has a microbial etiology and the treatments are anti-microbial drugs, the emergence of drug resistance is quickly detected by determining the percentage of microbial isolates that are found to be resistant to anti-microbial therapy in the community where the therapy is being provided (such as a community hospital or city-wide health care system). An increase in the percentage of resistant isolates produces a re-ranking of recommended drug therapies to avoid further use of the drug to which resistance has developed, and helps quickly introduce more effective drugs that will improve the effectiveness and lower the cost of treatment. In yet other embodiments, a sum of medication (e.g. dosing) errors and adverse effects (e.g. allergic reactions) are tracked by the system to identify drugs that are poorly tolerated in particular populations served by the hospital where the treatment is being provided. Data is collected about the safety and effectiveness of all types of drug therapies in the community being served, and this data is used to modify the drug protocols.
U.S. Pat. No. 6,014,631 to Teagarden et al., “Computer Implemented Patient Medication Review System And Process For The Managed Care, Health Care And/Or Pharmacy Industry” discloses an interactive computer assisted method for review and analysis of a patient who needs one or more medications. The method includes the steps of pre-selecting patients to obtain a preliminary set of patients eligible for the interactive computer assisted method responsive to first predetermined criteria, and filtering the preliminary set of patients to identify and form a secondary set of patients from the preliminary set of patients having a greater likelihood of benefiting from the interactive computer assisted method responsive to second predetermined criteria. The method includes the steps of enrolling a patient from the secondary set of patients, and communicating with the patient to obtain information to assist the user in determining whether therapy and/or medication issues are relevant. The method also includes the steps of preliminarily evaluating whether the therapy and medication issues are relevant responsive to the information, and communicating to a physician same. The method also includes the steps of determining whether the therapy and/or medication issues are relevant, and suggesting therapy changes, medication changes, or no changes for the patient.
U.S. Pat. No. 6,082,776 to Feinberg, “Storing Personal Medical Information” discloses a system and method for data compression of structured medical history information using multiple, updatable, static dictionaries in conjunction with an advanced probability-based model. The system is not a free text, word or phrase compressor as is presented in generalized or universal data compression s
Rayyan Susan F.
Shaw Pittman LLP
LandOfFree
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