A system for monitoring a chronic disease is disclosed. The monitor includes a database for storing a plurality of patient data entries and sorts the patient data entries according to whether a test threshold is crossed. Each of the patient data entries includes personal information of a patient and a set of guidelines concerning the patient's care. The guideline represents a plurality of rules concerning a patient's care derived from accepted tests used to monitor the disease represented in an algorithm. A processor separates the patient entries designated by the user according to the test thresholds, such as for HbA1c, lipids, liver enzyme and microalbumin, for the disease of diabetes. If the test threshold value derived from the guideline is crossed, an alert sequence is activated, in which the patient is categorized as a high risk patient, the physician is notified, the patient is notified, the health care provider is notified, and the patient's treatment plan is altered to treat the high risk patient.
A diabetes management system and method used to manage the blood glucose level of a diabetes patient. The system includes at least one portable electronic device and a database system. The portable electronic device allows the patient to input different types of data into the processor to calculate insulin and carbohydrate intake recommendations for the patient. A time/date stamp is individually generated and stored for each type of data inputted by the patient. The diabetes management system also includes a database system which stores (i) activity data associated with the physical activity of the patient, (ii) blood glucose data associated with the blood glucose level of the patient, (iii) meal intake data associated with the food intake of the patient, and (iv) insulin intake data associated with the insulin intake of the patient.
The present invention offers a comprehensive solution to care management which aggregates, integrates and stores clinical information from disparate sources. The system finds at-risk individuals before they experience preventable, high-cost medical events and stratifies high risk populations according to clinical criteria, which can include severity of disease states and co-morbidities. The system also compares the actual care an individual is receiving to established standards of clinical excellence and, if necessary, suggests pertinent medical care considerations to improve the care of medically mismanaged individuals. Another feature of the present invention may include a secure, patient-specific Web page which is automatically populated with a patient's own clinical information and can be personalized with customized, relevant healthcare information. The system allows users to design, facilitate and monitor clinical care plans and increase communications among physicians, nurses and patients. The system also predicts and analyzes the outcome of disease or case management for populations and individual patients.
A method is disclosed for providing early detection, classification, and reporting of health-related events in a population. The method includes capturing sets of specific emergency room patient information from a subset of the population as the patient information is first electronically entered into, for example, an electronic medical record (EMR). The patient information is pre-processed, transmitted to and stored in a central database in a central computer facility. The patient information is sorted and analyzed by the central computer facility to detect any health-related events in the population and to generate corresponding alerts. The alerts are electronically reported to designated authorities such as health officials and other government authorities such as the CDC.
Disclosed is a method, system, program, and data structure for maintaining electronic patient medical information. An electronic patient data structure is generated to include patient biographical information and one of medical history information, medication schedule information, and appointment schedule information. The patient data structure is electronically transmitted between a physician computer and a portable patient device. The patient data structure is capable of being modified.
A method of presenting glucose data to a person with diabetes from a blood glucose meter is provided in which an effective meal average (EMA) value is presented, followed by two or more of the individual values that make up the EMA, to provide improved feedback data for clinical decisions by patients who need to alter their dose of insulin. The EMA can also comprise a measure of the variability of its constituent values. The EMA encompasses those values that occur at specified times such as 1 hour before and 1 hour after a specified meal time. The EMA is calculated over a limited number of days previous to the calculation (e.g., 3 days) and has a minimum number of values that must be obtained within the time and date ranges. An algorithm allows for exclusion of any given reading from the average (e.g., post-prandial or control solution readings). Patients can use 1 to 8 EMA on any given date range (e.g., preferably 4, that is, breakfast, lunch, supper and bedtime snack).
