Chronic disease monitor

Details Chronic disease monitor Chronic disease monitor

1999-06-25

2001-08-21

Lacyk, John P. (Department: 3736)

Surgery

Diagnostic testing

Reexamination Certificate

active

06277071

ABSTRACT:

COPYRIGHT NOTICE
A portion of the disclosure of this patent document contains material which is subject to copyright protection. The copyright owner has no objection to the facsimile reproduction by anyone of the patent document or the patent disclosure, as it appears in the Patent and Trademark Office patent files or records, but otherwise reserves all copyright rights whatsoever.
BACKGROUND OF THE INVENTION
1. Field of the Invention
This invention relates generally to data processing systems. More specifically, this invention relates to a new and improved apparatus and method for managing chronic disease which allows multiple access to patient data by medical providers, pay organizations and/or the patient. Chronic disease data is generated through the input and creation of a chronic disease model(s), patient history, a patient treatment plan, provider parameters, tests, expected and measured outcomes, and associated data. An integrated chronic disease monitor capable of providing communication between medical providers, pay organizations and the patient is provided. The chronic disease monitor automatically plans events (clinical exams and patient services) and generates alerts to the medical provider, pay provider and patient, if a test is not performed as planned, and also if the test results do not fall within an expected range.
2. Description of the Related Art
The concept of cost containment and efficiency of medical care services, commonly known as managed care, has taken on significant importance in the health care industry. Pay providers, in the form of employers, government agencies, insurance companies, health care maintenance organizations, and the like, frequently set forth a series of thresholds which must be established before a patient may have covered access to medical services. Communication of the patient's etiology, treatment plan and updating any changes thereto, is tremendously cumbersome, requiring countless hours by medical providers and their staff to insure this information is organized and accurately communicated to the pay provider, as well as the patient, so that the patient may access covered services and optimize treatment. Further, it is often difficult for the medical provider and/or pay provider to measure the success of the services rendered to the patient and/or the patient's own follow up with the treatment plan.
Certain chronic diseases, such as diabetes, have known etiologies and associated risk factors. Guidelines for treatment have been promulgated by, e.g. the American Diabetes Association, the National Commission for Quality Assurance (NCQA) and Diabetes Quality Improvement Project (DQUIP). These guidelines incorporate known complications associated with diabetes such as retinopathy, neuropathy, nephropathy, Pulmonary Vascular Disease (PVD), Cardial Artery Disease (CAD) and cerebral vascular disease. In addition to various tests associated with monitoring the diabetes, such as HbAlc (measuring glycosolated hemoglobin levels), microalbumin (blood protein), lipids (cholesterol), etc., the physician must typically perform routine eye and foot examinations to monitor the progress of the disease. These tests are in conjunction with those examinations normally associated with an office visit, i.e. blood pressure, temperature, weight, pulse, etc. In addition, there is a significant education and behavior component to the treatment of the disease which can encompass such items as nutrition counseling, smoking cessation, and self education about the disease. The Center for Disease Control estimates that diabetes is reaching epidemic proportions in the United States. Effective treatment centers on the known parameters and risk factors associated with the disease, and insuring that the patient is meeting the objectives of the treatment plan.
The patient's ability to self monitor blood glucose values at home has significantly improved the ability of the patient (and medical provider) to control the progress of the disease. Hand held monitoring units, such as disclosed in U.S. Pat. No. 4,731,726 to Allen, III, allow the patient to have a portable monitor which generates test values for the blood glucose level and stores the test results. The data may then be downloaded and/or transferred to a computer. The monitor may generate a recommendation to the patient based on patient data, physician input data and test results, such as an increased insulin dosage. U.S. Pat. No. 5,251,126 to Kahn et al illustrates another diabetes data analysis and interpretation method which identifies insulin intake regimens and identifies statistically significant changes in blood glucose levels in relationship to the insulin levels.
The use of computers to generate a patient record registry and to record data associated with the treatment of those patients enhances the provider's ability to assess the patient's health and generate an assessment plan. U.S. Pat. No. 5,262,943 to Thibado et al discloses a system which receives standardized test data as well as a therapists's subjective evaluations to generate an assessment report for the care of an individual in the mental health field. U.S. Pat. No. 5,265,010 to Evans-Paginelli discloses a hospital patient document method and apparatus which is used to generate an initial patient health care plan, identifying the patient's problems, expected outcomes and interventions to achieve those outcomes.
The use of statistical analysis to create a diagnostic model for a given disease has been employed to create trained neural networks. U.S. Pat. No. 5,769,074 to Barnhill et al, discloses a computer based method which employs the steps of collecting data about patients (such as biological, physical, demographic, racial, environmental); digitizing the data and medical historical data; selecting digitized values that are associated with the diagnosis of a disease; scaling the data; performing tests to analyze the discriminating power of the data; grouping individual data values; preprocessing the data; inputting selected data to make pre-processed values into a computer based neural network in order to train the neural network; analyzing the contributions of the individual data inputs to the network; selecting the optimally trained neural network based on the performance, accuracy and cost; and inputting other patient data into the neural network to produce an output value which indicates whether the patient may have or be susceptible to the disease. Such technology has application to diagnostic patterns which are too subtle or too complex for humans and conventional computational methods to identify and allow for the provider to access large neural networks which are capable of recognizing diagnostic patterns. U.S. Pat. No. 5,860,917 to Comanor, et al, discloses such a neural network with a statistical model derived using a robustified similarity metrical least squares (SMILES) analysis.
In contrast to the neural network developed through statistical analysis of patient data and risk factors to create a diagnostic protocol, certain chronic diseases, such as diabetes, have a known and highly defined treatment protocol. Though incurable, the risk factors associated with diabetes and the complications of diabetes have been well studied. The diabetic patient, however, must be closely monitored to control the disease. It is estimated, however, that physicians associated with the treatment of diabetes do not use computer based data systems to manage and maintain their files with respect to the diabetic patient. Indeed, it is estimated that less than ten percent (10%) of all physicians use computers in the treatment of their patients for purposes other than billing.
According to the Center for Disease Control (CDC), advances in diabetes research now provide the clinical and therapeutic means to improve outcomes for people with diabetes. The 1993 landmark study, the Diabetes Control and Complications Trial (DCCT), conclusively showed that improved glucose control can retard the onset and progression of d

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