Abstract:
Systems and methods of individualized disease management are provided and use patient-specific, physician-defined rules to assist a patient in the management of their disease. The set of physician-defined rules for a patient can be maintained within the patient&#39;s blood glucose metering system and activated when a lifestyle event or blood glucose result is expected or recorded. Pattern analysis can be performed in real-time to provide physician-generated suggestions to a patient to positively influence their behavior toward managing their disease.

Description:
TECHNICAL FIELD 
       [0001]    The present invention relates to systems and methods for managing health. More particularly, the present invention relates to systems and methods for providing individualized disease management to patients with a chronic disease. 
       BACKGROUND 
       [0002]    Diabetes is a chronic disease that requires continued monitoring and controlling of health parameters such as blood glucose levels, medication, nutritional condition, as well as weight and exercise data. For patients with diabetes and their physicians, the amount of such information can be difficult to track and use effectively to make behavioral changes that positively influence management of their disease. 
         [0003]    Further complicating matters is the fact that each patient brings a different personality to bear upon the treatment regime. That is, whereas some patients may respond quickly to reinforcement, whether positive or negative, so that very little reinforcement is required, others may require more repetition to cause a desired change. Effectiveness of positive versus negative reinforcement may also vary significantly among patients. For some patients, the necessity to interact with a medical or other device with any frequency, may be seen as a barrier that could negatively impact their ability to manage their disease, whereas others may actually enjoy such interaction and the sense of organization and control it can afford. 
         [0004]    Each individual patient also brings different physical attributes and habits that influence their behavior and that can impact the effectiveness of a treatment plan. That is, while some diabetic patients may exhibit one or more of insulin resistance, aversion to dieting, and a relatively inactive lifestyle, others may respond to insulin, maintain a healthy diet and exercise regime but have a high level of stress. Typically, each patient may exhibit a combination of relatively positive and negative physical and behavioral attributes that may vary in occurrence over the course of treatment, as well as vary in significance in the context of each individual patients overall health and treatment progress. 
         [0005]    Conventionally, several methods and systems to assist physicians and patients with the difficult task of diabetes management are available. Diabetes data management software, such as LifeScan&#39;s OneTouch™ Diabetes Management Software, for example, uploads results from a blood glucose metering system and stores this information in a database. This system, and others like it, may also attempt to integrate specific event information (i.e. tags, flags, and/or comments) or include additional lifestyle information (i.e. duration of exercise, nutritional information) that may impact a patient&#39;s blood glucose results. Subsequently, these systems can generate various reports when the physician or patient queries the database that may then be used to remind the patient, or alert a physician, of a past problem. 
         [0006]    Although each of these types of methods and/or systems has provided invaluable assistance to physicians and patients alike in the complex task of disease management, each also may be limited in the assistance it can provide. That is, conventional methods and systems are not capable of responding to a patient&#39;s behavior or being customized in the information that is provided to a patient, much less in the information that is requested, or the frequency at which the information is provided or requested. 
       SUMMARY 
       [0007]    In accordance with the present invention exception-based pattern analysis and reporting guidelines to process real-time data and provide physician-defined suggestions for disease management provide a useful alternative to conventional methods of disease management. Exception-based pattern analysis relies on a set of pre-set physician-defined rules that are patient specific to analyze in real-time all health parameters deemed necessary to track by the physician. Exception-based reporting provides real-time physician-defined suggestions based on the exceptions to the rules triggered by the exception-based pattern analysis module. Such a method may enhance both a patient&#39;s and a physician&#39;s ability to understand and actively influence patient compliance with disease state management. 
         [0008]    The present invention uses patient-specific, physician-defined rules to assist a patient in the management of their disease. The set of physician-defined rules for a patient can be maintained within the patient&#39;s blood glucose metering system and activated when a lifestyle event or blood glucose result is expected or recorded. Pattern analysis can be performed in real-time to provide physician-generated suggestions to a patient to positively influence their behavior toward managing their disease. Moreover, a professional can download all stored data records from a patient user and generate a report detailing the results of a patient user for a period between office visits. 
         [0009]    In accordance with an aspect of the present invention, a patient is assessed by a physician to define a set of rules for the management of the patient&#39;s disease. The assessment provides the physician with guidance as to setting of parameters of physician-defined rules. These parameters preferably include aspects of impact and significance. Impact may be a determination as to whether the measurement associated with the rule will have a positive or negative impact on the health of the patient. Significance may be how important the impact associated with the rule will be to the health of the patient. The significance may be viewed as a weighting of the impact to the rule. Further, the significance and impact may be viewed as rule parameters, among others that may be used to trigger reporting activities. Based on the assessment, the physician can also set parameters associated with rules that trigger a report to the patient, physician, or both. These parameters may be based on the physician&#39;s assessment of the patient and are preferably triggered based on the impact and the significance parameters of the rule being violated or complied with. If the reporting rule is triggered, a report or other output is preferably provided to the patient, physician, or both. 
         [0010]    In another aspect of the present invention, a method of individualized disease management that customizes a pattern analysis rule and reporting trigger based on a disease management characteristic of a patient is provided. The method comprising the steps of defining at least one pattern analysis rule, determining at least one disease management characteristic of a patient, and customizing the at least one pattern analysis rule. The at least one pattern analysis rule comprises a rule parameter and a reporting trigger. The reporting trigger includes a rule parameter threshold. The method thus further includes a step of customizing the reporting trigger by adjusting the rule parameter threshold based on the at least one disease management characteristic of the patient. 
         [0011]    In yet another aspect of the present invention, a system for individualized disease management is provided. The system comprises a data source, processor, memory, and program instructions. The program instructions comprise plural disease management parameters at least one of which is capable of being customized according to at least one disease management characteristic of a patient, obtain exception-based data from the data source, obtain at least one patient-specific physician-defined rule from the data source, and provide for the setting of at least one property for triggering the at least one patient-specific physician-defined rule. 
     
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0012]    These and other features, aspects, and advantages of the present invention will become better understood with regard to the following description, appended claims, and accompanying drawings where: 
           [0013]      FIG. 1  is a schematic diagram illustrating an exemplary disease management system in accordance with the present invention; 
           [0014]      FIG. 2  is a schematic view of an exemplary dialog window for interfacing with a user for providing settings for pattern analysis in a computer implemented method in accordance with the present invention; 
           [0015]      FIG. 3  is a schematic view of an exemplary dialog window for interfacing with a user for providing settings for reporting rates in a computer implemented method in accordance with the present invention; 
           [0016]      FIG. 4  is a schematic view of an exemplary output that may be sent to a professional user&#39;s output device in a computer implemented method in accordance with the present invention; 
           [0017]      FIG. 5  is a schematic view of an exemplary output that may be sent to a patient user&#39;s output device in a computer implemented method in accordance with the present invention; and 
           [0018]      FIG. 6  is a flowchart illustrating a sequence of steps in a method in accordance with the present invention. 
       
    
    
     DETAILED DESCRIPTION 
       [0019]      FIG. 1  illustrates an exemplary system  100  that implements a computer program  112  for exception-based pattern analysis and exception-based reporting in accordance with the present invention. System  100 , as shown, includes a data source  102 , a communications link  104 , and a processing station  106  preferably connected to one or more data input devices  108 , a visual display  110 , and an output device  114 . Examples of data source  102  include a blood glucose metering system and a continuous metering system for detecting glucose in blood or interstitial fluid such as described in U.S. patent application Ser. No. 10/432,827, filed on Dec. 29, 2003, which is fully incorporated herein by reference for all purposes. Other representative examples include metering systems for detecting analytes or indicators (e.g. cholesterol or HbA1c,) in any bodily fluid (e.g. blood, urine, interstitial fluid, etc). Generally, data source  102  may comprise any type of data input, including metering and measuring devices designed to test for physical characteristics. Data source  102  may further include input devices (e.g., buttons, keys, touch screens, on screen menus, user interfaces, etc.) to input lifestyle information such as, for example, quality and duration of exercise, weight data, type and quantity of diabetes medication, and general nutritional information. 
         [0020]    As shown, data source  102  is connected to processing station  106  via communications link  104  and may comprise any known or future developed wired or wireless communications link. Examples of communications link  104  include a direct serial or USB cable, a TCP/IP or Ethernet based network connection and a wireless connection using protocols such as IEEE 802.11, InfraRed or Bluetooth. Alternatively, data source  102  can be connected directly to processing station  106  via an appropriate cable or the like. 
         [0021]    Processing station  106  preferably includes a device to save and store information (e.g., a memory, a disk drive, or other removable storage device, a database, etc.) and a device to process data (e.g., a central processing unit or CPU) from data source  102  using algorithms within and desired software, such as within program  112 . Examples of processing station  106  include a personal or networked computer, a personal digital assistant (PDA), a blood glucose metering system, and a mobile telephone. Examples of input devices  108  include, a keyboard, keypad, a mouse, a joystick, a stylet, as well as others which are usable with central processing unit devices. Examples of visual display  110  include, a display monitor for a personal or networked computer, and a Liquid Crystal Display (LCD) for a personal digital assistant (PDA), mobile telephone, and a blood glucose metering system. Alternatively, one or more lights, such as LED&#39;s, may be used on the device to communicate information by glowing and/or blinking. Examples of output devices  114  include, a printer, a fax machine, an email message, a text message, and a file that is stored to memory on processing station  106 . 
         [0022]    Processing station  106  further includes computer program or instructions  112  for providing exception-based pattern analysis in combination with exception-based reporting in accordance with the present invention. Exception-based pattern analysis can automatically notify the professional user (e.g. a physician or nurse practitioner, or anyone with an administrative function) or a patient user when any physician-defined metric or condition established in advanced is not being met or is being met. Exception-based reporting is designed to focus the attention of the professional user or a patient user on the exceptions to planned compliance to treatment and/or to praise appropriate behavior. 
         [0023]    In general, data is collected by metering device  102  over a time period and typically includes plural samples. Exception based pattern analysis is preferably used to analyze the collected data and provide alerts, messages, or other information to a user (patient or professional). Pattern analysis preferably is used to identify general trends or patterns in data that is collected over time or from a number of samples. Exception based pattern analysis is generally a way in which to identify data which may fall outside acceptable data limits, and preferably exceptions to data limits of the disease management regimen. Pattern analysis rules are used to set acceptance levels for data in disease management. By looking for occurrences and/or average of occurrences that are outside acceptance limits, it may be apparent that the patient is not subscribing to the treatment regimen, or that there are other problems in the way that the disease is being treated or managed. 
         [0024]    In a system in which the disease management can be customized to a patient, it is desirable that characteristics of a patient and their disease be assessed by a physician or other medical personnel such that the impact and significance associated with each of the pattern analysis rules is customized to the particular patient. For example, for glucose management, a rule might be set to track the number of times a glucose measurement or an average of all measurements is outside of a range, such as indirectly hypoglycemic or hyperglycemic conditions such a rule may be “report when averages for last 14 days are 20% below/over target or above upper target” where the impact is set to negative and the significance is set to the middle between low and high. Thus, every time the average over the last 14 days is outside the predetermined range, the rule is considered violated and, an exception is triggered. Once the exception is triggered, program  112  is configured to determine, based on the impact and significance parameters and the settings for reporting, whether a report, alert, or other indicator should be provided to a patient and/or professional user. The impact level and significance parameters and the settings for reporting are typically set by the professional user as detailed and explained below. 
         [0025]    Computer program  112  preferably controls processing station  106  to perform many steps. Computer program  112  preferably utilizes standard user interfaces (e.g. menus and dialogs) to permit a user to access its functions. Computer program  112  may be written in any computer language, such as, for example, structured query language (SQL), Visual Basic, C++, as a matter of design choice and may be stored on any computer-readable memory device such as a hard drive coupled with a computer processing unit such as processing station  106 . 
         [0026]    Computer program  112  preferably includes both an exception-based pattern analysis unit and an exception-based reporting unit. Each of these units may be viewed as subroutines, or subprograms of computer program  112 . Alternatively, these units can be separate programs which are called and initialized by computer program  112 . Pattern analysis and reporting units may provide access to algorithms provided in software  112  or other separate software also provided in memory of device  106  for data sorting and analysis as well as expert system tools to help users control processes of computer program  112 . Input data from data source  102  is incorporated into computer program  112  and the exception-based pattern analysis unit analyzes input data to determine if specific pattern criteria are met. The exception-based reporting unit then preferably generates reports for a patient user and an associated professional user (e.g., a physician, a diabetes educator, or a nurse). Such reports may be generated and viewed on any of a variety of devices, including device  102 , processing station  106 , display  110 , and printer  114 . 
         [0027]      FIG. 2  illustrates an exemplary dialog window  200  that can be used for a professional user to set rules for pattern analysis, i.e. SETTINGS FOR PATTERN ANALYSIS. Window  200  may be displayed on visual display  110  or alternatively on another visual display that may be networked with processing station  106 . Though listed in sequence, the selections which activate these rules may be selected at any time and can be changed interactively by a professional user at any time during the course of treatment of a patient user. In accordance with the present invention, the rules are preferably pre-defined in the software package by patient type such as a type 2 diabetic on a diet, a type 2 diabetic on oral medication, and gestational. Alternatively, the type of rules can preferably be changed/programmed by a professional user so that the software can be customized to a particular patient or physician&#39;s use. Implementation of changes to settings for pattern analysis typically occur at a communication link  104  between processing station  106  and data source  102 . In other words, the settings are preferably downloaded to data source  102 . 
         [0028]    Optionally, the professional user may select in any order the physician-defined pattern analysis rules and provide settings therefore. The professional user may be a physician, nurse, other medical technician, data input personnel, any other administrative personnel, etc., having access to computer program  112 . Also, a physician or other healthcare provider may redefine the physician-defined pattern analysis rules to customize to a particular disease or individual. The professional user may chose to select or not to select any of the pattern analysis rules, as well, depending upon the course of treatment for a patient user. 
         [0029]    For each pattern analysis rule that a professional user selects, the professional user may set the limits for reporting the exceptions to and the properties of that pattern analysis rule. Limits are preferably set according to the rule and may include, for example, a duration (e.g., hours or days) and a percentage. For example, a physician may wish to set up the properties of a rule such that if the data source  102  measurement is on average at least 20% below the target for 2 days, then action (alarm, alert, report, etc.) is triggered. Properties that may be set up may also include an impact  210  and a significance  216 . Impact may be a determination as to whether the measurement associated with the rule will have a positive or negative impact on the health of the patient. Significance may be how important the impact associated with the rule will be to the health of the patient. The significance may be viewed as a weighting of the impact to the rule. Further, the significance and impact may be viewed as rule parameters, among others that may be used to trigger reporting activities. The professional user may set impact  210  by clicking on a radio button preceding either Negative  212  or Positive  214  for each pattern analysis rule. The professional user may set significance  216  of a pattern analysis rule at either Low  218  or High  220  or some designation in between Low  218  or High  220  by moving a sliding bar  221  on a sliding scale. Thus, based upon a professional user&#39;s analysis or assessment of a patient, a professional user may decide whether violation (or compliance) of the rule has a negative or positive effect on a patient&#39;s treatment and how significant the violation (or compliance) with that rule is to the particular patient&#39;s treatment, including consideration of individual characteristics of the patient, (i.e., how the patient is likely to respond during disease management). Alternatively, other user interface setting options and mechanisms may be used, such as numerical choices, drop down menus, buttons, not limited to the radio buttons and slider bar illustrated in  FIG. 2 . Through a patient assessment by a physician or other medical personnel, the impact  210  and significance  216  for each pattern analysis rule are preferably set according to the patient characteristics determined during the assessment. For example, as in  FIG. 2 , hypoglycemia is viewed as being more significant by the physician than hyperglycemia. Thus, for a pattern of low results (i.e., low glucose readings over some duration), the significance  216  would be set at a High  220  level. Low results have a negative impact on the patient user&#39;s health. Therefore, a Negative  212  impact  210  would be selected in window  200 . If a pattern analysis rule is triggered (based on the parameter set for the rule) and predetermined reporting properties of impact  210  and significance  216  are met or exceeded, a reporting exception to the rule may be triggered as described below with respect to  FIG. 3 . For example, when the pattern analysis rule and the reporting rule is triggered, one option is that the patient would receive a message about the exception. Typical messages to the patient user are provided below with respect to the descriptions of exemplary rules in Table 1. 
         [0030]    Still referring to  FIG. 2 , window  200  for settings for pattern analysis rules, as shown, includes tabs for a TARGET AND LIMITS  222 , a MEASURE OF OVERALL CONTROL  224 , a MEASURE OF CONTROL BY TIME SLOT  226 , a TRENDS AND SHIFTS  228 , and a PATTERN OF TESTING  230 . Although these are the tabs depicted in  FIG. 2 , more or less tabs are contemplated. 
         [0031]    In accordance with the present invention, TARGET AND LIMITS  222  tab preferably includes sections for setting one or more rules for an OVERALL AVERAGE OUTSIDE OF TARGET  232 , an OVERALL TESTING IS WITHIN TARGET  234 , a PATTERN OF LOW RESULTS  236 , and a PATTERN OF HIGH RESULTS  238 . For OVERALL AVERAGE OUTSIDE OF TARGET  232 , a professional user may select limits X and Y for reporting exceptions by checking a box  240  preceding “Report when overall average for last X day(s) is Y % below lower target or above upper target” and by entering values for X and Y in boxes  242  and  244  ( FIG. 2  depicts those values as being an exemplary 14 days and 20% respectively). For patients dealing with low blood sugar tracking, X may range from about 1 day to 14 days and Y may range from 0 percent to 100 percent, and more typically from about 10 percent to 20 percent. A professional user may select a number of days by toggling up and down arrows next to a day(s) box  242 . A professional user may also select a percent by toggling up and down arrows next to a percent box  244 . A professional user can also select impact  210  by clicking a radio button preceding either Negative  212  or Positive  214  (as discussed previously) and by sliding bar  221  for significance  216  (as discussed previously). 
         [0032]    If an exception to this pattern analysis rule is triggered by collecting data over time, then a patient user may be prompted with the following exemplary statement: “Your overall average is not within your target range (you may want to discuss with your physician ways to improve your level of control by changes to your diet, insulin, and/or medication),” as shown in a reporting box  246 . Interface window  200  provides customization of the trigger messages, e.g. as a text box allowing input thereto, or alternatively by a drop down or other access to predetermined message lists. A professional user may set the messages in reporting boxes  246 ,  250 ,  256  and  260  to be customized to a particular patient and to a particular disease. Computer program  112  may determine if the criteria for an exception to this pattern analysis rule are met and then may determine if the criteria for reporting this exception to a patient user are met. As an alternative to conventional systems, the present invention advantageously provides customization of the rules by the use of the impact and significance parameters determined via the physician&#39;s assessment of the patient&#39;s characteristics and the disease. The significance parameter is used in such a way that only rule violations that have at least a specified minimum significance are aggregated until a time is reached when the reporting criteria are met. Once the reporting criteria are met, the message is provided to the patient or professional user in order to affect behavior of the patient or for use by the physician as a way to better manage the patient&#39;s disease treatment. 
         [0033]    For OVERALL TESTING IS WITHIN TARGET  234 , a professional user may select limits Y and X for reporting exceptions by checking a box  248  preceding “Report when Y % or more of all results for the last X day(s) is (are) within lower and upper targets” and by entering values for Y and X. Y may range from about 0 percent to 100 percent and X may range from about 0 days to 21 days. A professional user may also select a percent by toggling up and down arrows adjacent to percent box  244 . A professional user also may select a number of days by toggling up and down arrows adjacent to days box  242 . A professional user can also select impact  210  by clicking a radio button preceding either Negative  212  or Positive  214  (as discussed previously) and by sliding bar  221  for significance  216  (as discussed previously). If an exception to this pattern analysis rule is triggered, then a patient user may be prompted with the following exemplary statement: “Congratulations, overall you are staying within your target range (you may want to discuss with your physicians the reasons for your success and the benefits to your health)” as shown in a reporting box  250 . In this case, the message is positive which, when provided to a patient user may provide positive reinforcement to the patient user in order to reinforce the patient&#39;s good management of the patient&#39;s disease. Computer program  112  preferably determines if the criteria for an exception to this pattern analysis rule are met and may determine if the criteria for reporting this exception to a patient user are met, as described in more detail below. 
         [0034]    For PATTERN OF LOW RESULTS  236 , a professional user may select limits Z and X for reporting exceptions by checking a box  252  preceding a selection for “Report when Z or more (or less) low results in last X day(s)” and by entering values for Z and X in boxes  254  and  242  respectively. Z may range from about 0 to about 5 and X may range from about 0 days to about 21 days. A professional user may also select a number of low results by toggling up and down arrows adjacent to a numbers box  254 . A professional user may also select a number of days by toggling up and down arrows adjacent to days box  242 . A professional user can also select impact  210  by clicking a radio button preceding either Negative  212  or Positive  214  (as discussed previously) and by sliding bar  221  for significance  216  (as discussed previously). If an exception to this pattern analysis rule is triggered, then the patient user may be prompted with the following exemplary statement: “You are experiencing a pattern of low results (you may want to discuss with your physician the reasons for this fall and whether changes to insulin and/or medications is required to reduce the risk of complications)” as shown by a reporting box  256 . In this case, the message is negative which, when provided to a patient user may provide negative reinforcement to the patient user in order to reinforce the patient&#39;s poor management of the patient&#39;s disease or to alert the patient that the treatment provided by a medical personnel is not effective and may need to be modified. Computer program  112  preferably determines if the criteria for an exception to this pattern analysis rule are met and may determine if the criteria for reporting this exception to a patient user are met, as described in more detail below. 
         [0035]    For PATTERN OF HIGH RESULTS  238 , a professional user may select limits Z and X for reporting exceptions by checking a box  258  preceding a selection for “Report when Z or more (or less) high results in last X day(s),” and by entering values for Z and X. Z may range from about 0 to about 10 and X may range from about 0 day to about 21 days. A professional user may also select a number of high results by toggling up and down arrows adjacent to numbers box  254 . A professional user may also select a number of days by toggling up and down arrows adjacent to days box  242 . A professional user can then select impact  210  by clicking a radio button preceding either Negative  212  or Positive  214  (as discussed previously) and by sliding bar  221  for significance  216  (as discussed previously). If an exception to this pattern analysis rule is triggered, then a patient user may be prompted with the following statement: “You are experiencing a pattern of high results (you may want to discuss with your physician the reasons for this fall and whether changes to insulin and/or medications is required to reduce the risk of complications)” as shown in a reporting box  260 . In this case, the message is negative which, when provided to a patient user may provide negative reinforcement to the patient user in order to reinforce the patient&#39;s poor management of the patient&#39;s disease or to alert the patient that the treatment provided by a medical personnel is not effective and may need to be modified. Computer program  112  preferably determines if the criteria for an exception to this pattern analysis rule are met and may determine if the criteria for reporting this exception to a patient user are met, as described in more detail below. 
         [0036]    It should be noted that for all of the pattern analysis rules  232 ,  234 ,  236  and  238 , the system may not be limited to the rules shown and described. Any number of pattern analysis rules may be implemented in accordance with the present invention. The system shown and described provides a great deal of user flexibility by giving the users the ability to create nearly any type of rule. 
         [0037]    Still referring to  FIG. 2 , to accept all of the appropriate pattern analysis settings for each patient, a professional user may click an APPLY button  262  or an OK button  264  and to cancel without accepting any changes to the pattern analysis setting options, a professional user may click a CANCEL button  266 . When settings are changed, the metering device  102  needs to be updated with the new settings and may be done so at any later time including during the next communication between processing station  106  and device  102 . If the setting changes are canceled, the device remains as-is. To obtain more information about any feature in this window a professional user may click on an information button  268 . 
         [0038]    As shown in  FIG. 2 , other tabs for setting rules may include a MEASURE OF OVERALL CONTROL  224 , a MEASURE OF CONTROL BY TIME SLOT  226 , a TRENDS AND SHIFTS  228 , and a PATTERN OF TESTING  230 . The format for the windows in each tab may be similar to the window for TARGETS AND LIMITS  222  in that each rule preferably includes a section for setting limits for reporting exceptions and sections for setting properties such as impact  210  and significance  216 . Exemplary tab windows with rules and exemplary limit statements with limits for reporting exceptions are listed in Table I below. As described previously, the limits are preferably determined by a professional user and if an exception to a rule is triggered, a patient user is preferably prompted with an appropriate statement. 
         [0000]    
       
         
               
               
               
             
           
               
                 TABLE I 
               
               
                   
               
               
                 TAB 
                 RULE 
                 LIMIT STATEMENT 
               
               
                   
               
             
             
               
                 Measure of Overall Control 
                 Excessive Fluctuation of 
                 Report when overall standard 
               
               
                   
                 Results 
                 deviation for last      day(s) is greater 
               
               
                   
                   
                 than      mg/dL 
               
               
                   
                 Over-Treating of Below 
                 Report when      below target result(s) 
               
               
                   
                 Target Results 
                 followed by result above target within      hour(s) 
               
               
                   
                 Over-Treating of Above 
                 Report when      above target result(s) 
               
               
                   
                 Target Results 
                 followed by result below target within      hour(s) 
               
               
                 Measure of Control by Time 
                 Time Slots with Excessive 
                 Report when standard deviation of any 
               
               
                 Slot 
                 Fluctuation of Results 
                 time slot for last      day(s) is greater 
               
               
                   
                   
                 than      mg/dL 
               
               
                   
                 Time Slots with Results 
                 Report when time slots with greater 
               
               
                   
                 Outside of Target 
                 than      % of results are outside of 
               
               
                   
                   
                 target for last      day(s) 
               
               
                   
                 Time Slots with Average 
                 Report when average for any time slot 
               
               
                   
                 Above Upper Target 
                 is      % greater than the upper target for 
               
               
                   
                   
                 last      day(s) 
               
               
                   
                 Time Slots with Average 
                 Report when average for any time slot 
               
               
                   
                 Below Lower Target 
                 is      % less than the lower target for 
               
               
                   
                   
                 last      day(s) 
               
               
                 Trends and Shifts 
                 Recent Condition/Compliance 
                 Report when a change of      % between 
               
               
                   
                 Shift 
                 most current      days and the same 
               
               
                   
                   
                 number of days prior 
               
               
                   
                 Weekdays vs. Weekend Trend 
                 Report when      % difference between 
               
               
                   
                   
                 weekday and weekend for last      days 
               
               
                   
                 Current vs. Previous 
                 Report when      % more lows, more 
               
               
                   
                 Encounter Trend and/or Shift 
                 highs, less lows, and/or less highs 
               
               
                   
                   
                 between encounters 
               
               
                 Pattern of Testing 
                 Pattern of Skipped Testing 
                 Report when      consecutive hour(s) or 
               
               
                   
                   
                 more within the last      day(s) without 
               
               
                   
                   
                 testing 
               
               
                   
                 Insufficient Overall Frequency 
                 Report when the average number of 
               
               
                   
                 of Testing 
                 results per day in the last      day(s) is 
               
               
                   
                   
                 less than —   
               
               
                   
                 Insufficient Testing by Time 
                 Report when the total number of 
               
               
                   
                 Slot 
                 results per time slot in the last      day(s) 
               
               
                   
                   
                 is less than —   
               
               
                   
                 Repeated Testing During 
                 Report when      or more results within      minute(s) 
               
               
                   
                 Lows to Improve Overall 
                 are below lower target 
               
               
                   
                 Average 
                 within last      day(s) 
               
               
                   
               
             
          
         
       
     
         [0039]    In addition to the rules listed in Table I and described previously, those skilled in the art will recognize that the limit statements can be provided as “positive” rules instead of “negative” rules. For example, the first limit statement in Table I could be written as “Report when overall standard deviation for last_day(s) is less than_mg/dL. Rules may also be included for other measured values (e.g., HbAlc results for a diabetic patient) or to implement an intensive insulin therapy protocol for use by nurses at the point-of-care. Also, the impact setting may be characterized as a less than or greater than setting. 
         [0040]      FIG. 3  illustrates an exemplary dialog window  300  for a professional user to set rates for reporting, i.e. SETTINGS FOR REPORTING. For a patient user, a professional user may identify the rate at which positive and negative exceptions to pattern analysis rules are reported to a patient user. Window  300  for SETTINGS FOR REPORTING preferably includes sections for setting rules for NEGATIVE AND/OR POSITIVE EXCEPTIONS REPORTED  310 , MINIMUM THRESHOLD FOR REPORTING EXCEPTIONS  312 , and MAXIMUM REPEATABILITY OF EXCEPTIONS REPORTED  314 . For NEGATIVE AND/OR POSITIVE EXCEPTIONS REPORTED  310 , a professional user selects by checking a box  316  preceding the selection for “Report to a maximum of Z negative impact exception(s) every D hour(s) or day(s)” and/or by checking a box  318  preceding the selection for “Report to a maximum of Z positive impact exception(s) every D hour(s) or day(s)” where Z may range from about 0 to about 99 and D may range from about 0 hours to about 24 hours or from about 0 days to about 21 days, for the glucose monitoring example provided. A professional user may set how many positive or negative exceptions are reported within the time period by toggling up and down arrows adjacent to a numbers box  320 . A professional user may also set the rate at which either positive or negative exceptions are reported by toggling up and down arrows adjacent to a time period box  322  and by clicking a radio button  324  preceding hour(s) or by clicking a radio button  326  preceding day(s). By adjusting these settings, a professional user is able to set, based on the characteristics of the patient, how often and with what intensity the patient is alerted as to not staying within the treatment regimen or is provided with positive reinforcement for staying within the treatment regimen. If for example, a professional user has determined that the patient benefits from frequent reminders, then the settings may be adjusted such that the frequency of reminders is likely to be high. However, some patients may be annoyed by frequent reminders and alerts such that they will tend to ignore them. If this patient characteristic can be determined from a patient assessment, the settings may be configured in such a way as to provide, in most instances, less frequent reminders and/or alerts. 
         [0041]    For MINIMUM THRESHOLD FOR REPORTING EXCEPTIONS  312 , a professional user may activate the rule either by checking a box  328  preceding the selection for “Report only negative impact exceptions with a minimum significance of:” and/or by checking a box  330  preceding a selection for “Report only positive impact exceptions with a minimum significance of.” In either case, a professional may select a significance  216  by dragging a tab  332  of a sliding scale toward either a Low  334  or a High  336  end of the scale. The significance setting here may be generally seen as a rule parameter threshold setting. Upon assessing the patient characteristics, a physician preferably determines what rules, when violated, may most significantly impact the patient&#39;s disease management. Therefore by changing the significance settings, a professional user is able to control which rules should be monitored to manage the patient&#39;s disease in the most significant way. 
         [0042]    For MAXIMUM REPEATABILITY OF EXCEPTIONS REPORTED  314 , a professional user may set either by checking a box  338  preceding a selection for “Report a specific negative impact exception a maximum of Z time(s) for every E exception(s) reported or X day(s)” and/or by checking a box  340  preceding a selection for “Report a specific positive impact exception a maximum of Z time(s) for every E exception(s) reported or X day(s)” where Z may range from about 1 to about 10, E may range from about 1 to 5, and X may range from about 1 day to about 21 days. A professional user may set a number of times a specific negative or positive impact exception is reported by toggling up and down arrows adjacent to numbers box  320 . The professional user may set the rate at which a specific negative or positive impact is reported by toggling up and down arrows adjacent to a frequency box  342  and by clicking on a radio button  344  preceding “exceptions” or by clicking on a radio button  346  preceding “day(s).” To accept all of the appropriate reporting rates for all patients, the professional user clicks an APPLY button  348  or an OK button  350  and to cancel without accepting any changes to the reporting rates the professional user clicks a CANCEL button  352 . 
         [0043]    To obtain more information about any feature in this window a professional user may click on an information button  354 . Although a number of specific settings and ranges of settings have been provided, the invention is not limited to those disclosed. Other settings and ranges of settings in accordance with the present invention may be used. 
         [0044]      FIG. 4  illustrates an example of a professional report  400  that may be sent to a professional user&#39;s output device  114  by computer program  112 . Professional report  400  preferably includes a title  410 , a summary  412 , and a data block  414 . Title  410  preferably includes a means to identify a patient for which professional report  400  is generated. For exemplary purposes only, title  410  lists the patient&#39;s name, however, title  410  may include such information as metering system serial number, patient chart number, or other means to track patient information. Data block  414 , as shown, includes multiple rows  416 , each of which preferably includes data from one day of recording and multiple columns  418 , each of which preferably includes data from one time period of recording. Examples of professional reports are further described in U.S. Provisional Patent Application 60/624,804, filed on Nov. 2, 2004 and which is fully incorporated by reference herein for all purposes. Summary  412  preferably includes a list of statements based on physician-defined, pre-set criteria and generated by computer program  112  as described in more detail below. 
         [0045]      FIG. 5  illustrates an example of a patient report  500  that may be sent to a patient user&#39;s output device  504  (e.g., a visual display) by computer program  112  in accordance with the present invention. Patient report  500  may also be sent to any output device, such as output device  114 . Patient report  500  may include a location for a current date  506 , a record time  508 , a blood glucose result  510 , and a patient summary  512 . Patient summary  512  may include a statement based on physician-defined, pre-set criteria and generated by computer program  112 . 
         [0046]      FIG. 6  is a flowchart illustrating an exemplary method  600  in accordance with the present invention. Method  600  preferably includes first providing a system  100  as described above with respect to  FIGS. 1-5  and as set forth in step  610 . The provided system preferably includes an input device, a processing device, and a reporting device for inputting, processing and reporting information associated with diabetes management. During process  600 , individual lifestyle events and blood glucose results are integrated (e.g. uploaded or accessed) into computer program  112 . Computer program  112  then analyzes the information based on a set of physician-defined rules for analysis and reports exceptions to physician-defined pattern analysis and reporting rules to a professional user and a patient user, as will be described below. 
         [0047]    At least one patient-specific, physician-defined pattern analysis rule is defined and programmed into the system  100  and at least one limit for reporting the at least one patient-specific, physician-defined rule is set, as set forth in steps  620  and  630 , respectively, and as illustrated in  FIG. 2 . A physician preferably selects one or more pattern analysis rules that are appropriate for the specific patient by, for example, checking a box  240  preceding the selection for “Report when overall average for last X day(s) is Y % below lower target or above upper target” where X may range from about 1 day to about 14 days and Y may range from about 10 percent to about 20 percent. The physician toggles up and down arrows adjacent to a days box  242  to set the appropriate number of days and adjacent to a percent box  244  to set the appropriate percent for this pattern analysis rule. 
         [0048]    At least one property for triggering the at least one patient-specific, physician-defined rule is set by a physician as set forth in step  640  and as illustrated in  FIG. 2 . The physician preferably selects an impact  210  and a significance  218  for each pattern analysis rule established in step  620 . To set impact  210 , the physician clicks on a radio button preceding either Negative  212  or Positive  214  depending upon the needs of the specific patient user. To set significance  218 , the physician slides a tab  221  on a sliding scale between Low  218  and High  220  depending on the needs of the specific patient user. 
         [0049]    At least one rate for reporting the at least one patient-specific, physician-defined rule is preferably set by the physician as set forth by step  650  and as illustrated in  FIG. 3 . The physician sets the rate at which exceptions to reporting rules are triggered. To set a rate at which an exception patient-specific, physician-defined pattern analysis rule is reported to a patient user, the physician selects the rate by, for example, checking a box  316  preceding the selection for “Report to a maximum of Z negative impact exception(s) every D hours(s) or day(s)” where Z may range from about 0 to about 99 and D may range from about 0 hours to about 24 hours or range from about 0 days to about 21 days. The physician toggles up and down arrows adjacent to numbers box  320  to set the appropriate number and adjacent to time period box  322  to set the appropriate time period for reporting the exception to the pattern analysis rule established in step  620 . 
         [0050]    Next, at least one patient-specific, physician-defined rule is triggered when at least one result or at least one lifestyle event is expected or recorded as set forth by step  660 . Computer program  112  preferably tracks exceptions and whether or not they are reported, which can be used for processing future pattern analysis exceptions. When input data is recorded or expected, computer program  112  analyzes pattern analysis rules and determines if an exception to any pattern analysis rule is triggered. If a pattern analysis rule is triggered then computer program  112  preferably determines if an exception to a reporting rate is triggered. 
         [0051]    Computer program  112  then preferably provides an exception-based pattern analysis report to the physician or displays a suggestion to the patient on a metering system or to a physician or patient on an alternative output device as set forth by step  670 . When computer program  112  determines an exception to a pattern analysis rule and a reporting rate is triggered, computer program  112  preferably generates a patient summary  512  that is displayed on a visual display  502  of a metering system  504  for a patient user. Computer program  112  also preferably generates the professional report  400  for the professional user when data from data source  102  is transferred to processing station  106  of professional user. 
         [0052]    The software components as described above may comprise a stand alone computer program  112  or a computer module integrated into an existing computer program  112  such as, for example, the OneTouch™ Diabetes Management Software from LifeScan, Inc. In either configuration, computer program  112  preferably allows processing station  106  to accept data from data sources  102 , to store incoming data, to process accepted and stored data using a main computer program  112  and a plurality of associated plug-ins in conjunction with a set of physician-defined control options, and to generate statements for both a patient user and the professional user to see. 
         [0053]    Criteria of analysis and criteria of reporting are preferably stored in a non-volatile semiconductor storage element such as a ROM, flash memory, or a non-volatile storage device such as a hard disk or the like so that individual criterion can be added, deleted or modified as needed by the professional user. The function of each unit is realized by cooperative operation of hardware and computer program  112 . 
         [0054]    The present invention has now been described with reference to several embodiments thereof. The entire disclosure of any patent or patent application identified herein is hereby incorporated by reference. The foregoing detailed description and examples have been given for clarity of understanding only. No unnecessary limitations are to be understood therefrom. It will be apparent to those skilled in the art that many changes can be made in the embodiments described without departing from the scope of the invention. Thus, the scope of the present invention should not be limited to the structures described herein, but only by the structures described by the language of the claims and the equivalents of those structures.