Patent Publication Number: US-2022229818-A1

Title: Structured testing method for diagnostic or therapy support of a patient with a chronic disease and devices thereof

Description:
TECHNICAL FIELD 
     Embodiments of the present invention relate generally to care management of chronic diseases, and particularly to a structured testing method for diagnostic or therapy support of a patient with a chronic disease and devices thereof. 
     BACKGROUND 
     A disease which is long lasting or which reoccurs often is defined typically as a chronic disease. Known chronic diseases include, among others, depression, compulsive obsession disorder, alcoholism, asthma, autoimmune diseases (e.g., ulcerative colitis, lupus erythematosus), osteoporosis, cancer, and diabetes mellitus. Such chronic diseases require chronic care management for effective long-term treatment. After an initial diagnosis, one of the functions of chronic care management is then to optimize a patient&#39;s therapy of the chronic disease. 
     In the example of diabetes mellitus, which is characterized by hyperglycemia resulting from inadequate insulin secretion, insulin action, or both, it is known that diabetes manifests itself differently in each person because of each person&#39;s unique physiology that interacts with variable health and lifestyle factors such as diet, weight, stress, illness, sleep, exercise, and medication intake. Biomarkers are patient biologically derived indicators of biological or pathogenic processes, pharmacologic responses, events or conditions (e.g., aging, disease or illness risk, presence or progression, etc.). For example, a biomarker can be an objective measurement of a variable related to a disease, which may serve as an indicator or predictor of that disease. In the case of diabetes mellitus, such biomarkers include measured values for glucose, lipids, triglycerides, and the like. A biomarker can also be a set of parameters from which to infer the presence or risk of a disease, rather than a measured value of the disease itself. When properly collected and evaluated, biomarkers can provide useful information related to a medical question about the patient, as well as be used as part of a medical assessment, as a medical control, and/or for medical optimization. 
     For diabetes, clinicians generally treat diabetic patients according to published therapeutic guidelines such as, for example, Joslin Diabetes Center &amp; Joslin Clinic,  Clinical Guideline for Pharmacological Management of Type  2  Diabetes  (2007) and Joslin Diabetes Center &amp; Joslin Clinic,  Clinical Guideline for Adults with Diabetes  (2008). The guidelines may specify a desired biomarker value, e.g., a fasting blood glucose value of less than 100 mg/dl, or the clinician can specify a desired biomarker value based on the clinician&#39;s training and experience in treating patients with diabetes. However, such guidelines do not specify biomarker collection procedures for parameter adjustments to support specific therapies used in optimizing a diabetic patient&#39;s therapy. Subsequently, diabetic patients often must measure their glucose levels with little structure for collection and with little regard to lifestyle factors. Such unstructured collections of glucose levels can result in some biomarker measurements lacking interpretative context, thereby reducing the value of such measurements to clinicians and other such health care providers helping patients manage their disease. 
     Again, in the example of diabetes mellitus, after performing routine collections, a diabetic patient may provide the same information repeatedly to different clinicians at various times and at different locations, as the various clinicians involved in providing the patient with care, such as physicians, nurses, nutritionists, certified disease educators, and the like, are not typically located at the same place. Such information although important to chronic disease management, such as to put context to the collected biomarker data in which to help a clinician make diagnostic and/or optimization decisions, the repetitive manner of providing such information manually and/or orally can be frustrating to the diabetic patient. Additionally, a patient may be asked by different clinicians at various times to perform a number of collections in an effort to diagnose a chronic disease or to optimize therapy. However, these requests to perform such collections according to a schedule may overlap, be repeats, run counter to each other and/or provide a burden on the patient such that the patient may avoid any further attempts to diagnose their chronic disease or to optimize therapy. 
     In addition, if a requesting clinician does not evaluate the patient properly to see if the schedule of requested collections is possible and/or whether parameters for the collections are suitable and/or acceptable for the patient, having useful results from such collections may be unlikely. Still further, if there has not been enough suitable data collected to complete the requested collections, such that the data collected is helpful towards addressing the medical question and/or the interests of the clinician, such a request may waste the time and effort of the clinician and the patient as well as the consumables used to perform the collections. Again, such failure may discourage the patient from seeking further therapy advice. 
     Moreover, prior art collection devices used in facilitating a schedule of collections provide limited guidance, if any at all, and simple reminders of a collection event. Such prior art device typically need to be programmed manually by the either clinician or the patient, in which to govern the collection schedule. Such limited guidance and functionality provided by prior art collection devices can also further discourage the patient from seeking any future optimization of their therapy as performing another collection procedure in this manner may be viewed as being laborious by the patient, thereby leaving such optimization to simply guessing. 
     SUMMARY 
     It is against the above background that the embodiments of invention provide a structured testing method for diagnostic or therapy support of a patient with a chronic disease and devices thereof. The devices can implement a structured collection procedure based on a medical use case and/or medical question. The structured collection procedure can provide at least one or more parameters defining at least one entry criterion, a schedule of events, at least one adherence criterion, and at least one exit criterion. The entry criterion establishes the conditions needed to be met prior to obtaining biomarker data from the patient. Each event in the schedule of events can comprise at least one or more of a performance time, patient guidance to perform the event, a request for information from the patient, a request for collection of at least one type of biomarker data from the patient. The adherence criterion is used to assess qualitatively whether an event performed according to the schedule of events provided data which is acceptable to addressing the medical use case, and the exit criterion establishes the conditions needed to be met prior to exiting the structured collection procedure. 
     Such a structured testing method results in providing improved guidance to the patient throughout a collection procedure, and possibly a sense of accomplishment, purpose, and time well spent when completing a collection procedure. Other benefits include a reduction in consumable waste from avoiding unnecessary collection procedure requests and through ensuring that the data collected is suitable for addressing the inquiry/interest/purpose of the clinician in making the request for the collection procedure. Still other benefits include generating data which can include biomarker values that each have an interpretative context to increase the value of such measurements to clinicians in order to help improve diagnoses, and to help provide effective treatment or modification of treatment for controlling a patient&#39;s chronic disease, such as diabetes. 
     In one embodiment, a device for diagnostic or therapy support of a patient with a chronic disease is disclosed, which can comprise a display, a user interface, and a processor coupled to the display and the user interface. Program instructions can also be provided that when executed by the processor causes the processor to: prompt a plurality of medical use cases or questions related to the chronic disease for selection on the display, receive a selected medical use case or question via the user interface, select automatically a structured collection procedure for the diagnostic or therapy support of the patient with the chronic disease based on the selected medical use case or question from a plurality of structured collection procedures stored in a memory, and implement the selected structured collection procedure. The structured collection procedure can have parameters defining a schedule of events, each of said events comprising at least one or more of a performance time, guidance to perform the event, a request for patient action(s), a request for information, and a request for collection of at least one type of biomarker data. 
     In another embodiment, a device for diagnostic or therapy support of a patient with a chronic disease is disclosed and can comprise a processor, and program instructions that when executed by the processor causes the processor to retrieve automatically from memory a structured collection procedure. The structured collection procedure can be based on a medical use case or question of and can have parameters defining at least one entry criterion, which establishes conditions needed to be met prior to obtaining biomarker data, a schedule of events, each of said events comprising at least one or more of a performance time, guidance to perform the event, a request for patient action(s), a request for information, and a request for collection of at least one type of biomarker data, and at least one exit criterion which establishes the conditions needed to be met prior to exiting the structured collection procedure. The program instructions can further cause the processor to implement automatically the schedule of events of the structured collection procedure upon the entry criterion being met at some unknown time, and end automatically the structured collection procedure upon the exit criterion being met at some unknown time. 
     In still another embodiment, a device for diagnostic or therapy support of a patient with a chronic disease is disclosed and can comprise a processor, and program instructions that when executed by the processor causes the processor to retrieve automatically from memory a structured collection procedure. The structured collection procedure can be based on a medical use case or question of and can have parameters defining a schedule of events, each of said events comprising at least one or more of a performance time, guidance to perform the event, a request for patient action(s), a request for information, and a request for collection of at least one type of biomarker data, and at least one adherence criterion which is used to qualitatively assess whether an event performed according to the schedule of events provided data which is acceptable to addressing the medical use case. The program instructions can further cause the processor to implement automatically the schedule of events of the structured collection procedure, and take one or more additional actions if the adherence criterion is not met. In another embodiment, the processor can provide on a display information about the one or more additional actions. 
     In yet another embodiment, a device for diagnostic or therapy support of a patient with a chronic disease is disclosed and can comprises a processor, and program instructions that when executed by the processor causes the processor to retrieve automatically from memory a structured collection procedure. The structured collection procedure can be based on a medical use case or question of and can have parameters defining a schedule of events, each of said events comprising at least one or more of a performance time, guidance to perform the event, a request for patient action(s), a request for information, and a request for collection of at least one type of biomarker data. The program instructions can further cause the processor to receive biomarker data according to the structured collection procedure, store the biomarker data together with data of the schedule of events as contextualized biomarker data, and calculate measured noise from the contextualized biomarker data by applying a noise function, wherein the noise function comprises one or more of measured noise, procedure noise and system noise. 
     In another embodiment, a structured testing method for diagnostic or therapy support of a patient with a chronic disease is disclosed and can comprise selecting a structured collection procedure on a computer for the diagnostic or therapy support, and having a processor of the computer retrieving automatically from memory the structured collection procedure. The structured collection procedure can be based on a medical use case and can have parameters defining at least one entry criterion which establishes conditions needed to be met prior to obtaining biomarker data, a schedule of events, each of said events can comprise at least one or more of a performance time, guidance to perform the event, a request for patient action(s), a request for information, and a request for collection of at least one type of biomarker data, at least one adherence criterion which is used to qualitatively assess whether an event performed according to the schedule of events provided data which is acceptable to addressing the medical use case, and at least one exit criterion which establishes the conditions needed to be met prior to exiting the structured collection procedure. The method can further comprise prescribing the selected structured collection procedure to the patient, wherein the processor of the computer provides as output the selected structured collection procedure to the patient to perform when prescribed. 
     In another embodiment, a collection device for performing a prescribed structured collection procedure is disclosed. The device can comprise memory storing one or more structured collection procedures and for storing patient data. The patient data can comprise one or more of collected biomarker data, a date-time stamp and other data associated with each instance of the collected biomarker data. The other data may comprise contextualized data which characterizes the associated collected biomarker. The device further can comprise a clock providing the date-time stamp; and a display for providing a selection choice for selecting a structured collection procedure from the plurality of structured collection procedures stored in the memory. Each of the plurality of structured collection procedures can comprise at least one entry criterion needed to begin the structured collection procedure, at least one exit criterion to end the structured collection procedure, and a schedule of collection events, each of the collection events can comprise at least one or more of guidance to perform the collection event, a request for patient action(s), a request for information, and a request for collection of at least one type of biomarker data. The device further can comprise a user interface for selecting the structured collection procedure from the plurality of structured collection procedures that are displayed; at least one biomarker reader to provide the at least one type of biomarker data specified in the structured collection procedure that is selected; and a processor coupled to the memory, the clock, the display, the user interface, and the biomarker reader. A program is provided that has instructions that when executed by the processor can cause the processor to: provide the selection choice to the display; receive the selected structured collection procedure from the plurality of structured collection procedures displayed on the display; determine whether the entry criterion of the selected structured collection procedure is met, automatically perform the schedule of collection events; store in memory the patient data resulting from each of the collection events; check data from a performed collection event whether acceptable to help address the use case; and determine whether the at least one exit criterion has been met for the selected structured collection procedure to end. 
     In another embodiment, a computer readable storage medium storing instruction that, when executed by a processor of a computer, can cause the processor to perform a structured collection procedure to obtain contextualized biomarker data from a patient is disclosed. The method can comprise retrieving automatically from an electronic component the structured collection procedure. The structured collection procedure can be based on a medical use case and can have one or more parameters defining: at least one entry criterion which establishes conditions needed to be met prior to obtaining biomarker data, a schedule of events, each of the events can comprise at least one or more of a performance time, guidance to perform the event, a request for patient action(s), a request for information, and a request for collection of at least one type of biomarker data, at least one adherence criterion which is used to qualitatively assess whether an event performed according to the schedule of events provided data which is acceptable to addressing the medical use case, and at least one exit criterion which establishes the conditions needed to be met prior to exiting the structured collection procedure. The method can further comprise permitting adjustment of the parameters of the selected structured collection procedure; and prescribing the selected structured collection procedure to the patient, wherein the processor of the computer can provide as output the selected structured collection procedure to the patient to perform when prescribed. 
     Embodiments of the invention can be implemented, for example, as follows: a paper tool; diabetes software integrated into a collection device such as a blood glucose meter; diabetes software integrated into a personal digital assistant, handheld computer, or mobile phone; diabetes software integrated into a device reader coupled to a computer; diabetes software operating on a computer such as a personal computer; and diabetes software accessed remotely through the internet. 
     These and other advantages and features of the invention disclosed herein, will be made more apparent from the description, drawings and claims that follow. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The following detailed description of the embodiments of the present invention can be best understood when read in conjunction with the following drawings, where like structure is indicated with like reference numerals. 
         FIG. 1  is a diagram showing a chronic care management system for a diabetes patient and a clinician along with others having an interest in the chronic care management of the patient according to an embodiment of the present invention. 
         FIGS. 2 and 2A  are diagrams showing embodiments of a system suitable for implementing a structured testing method according to an embodiment of the present invention. 
         FIG. 3  shows a block diagram of a collection device embodiment according to the present invention. 
         FIG. 4  shows a depiction in tabular format of a data record embodiment created from using a structured testing method on the collection device of  FIG. 3  according to the present invention. 
         FIG. 5A  depicts a method of creating a structured collection procedure for a medical use case and/or question according to an embodiment of the present invention. 
         FIGS. 5B and 5C  show parameters defining a structured collection procedure and factors which can be considered to optimize a patient&#39;s therapy using the structured collection procedure, respectively, according to one or more embodiments of the present invention. 
         FIGS. 6A, 6B, 6C, 6D, and 6E  show various structured collection procedures embodiments defined according to the present invention. 
         FIG. 7A  depicts a structured testing method for diagnostic or therapy support of a patient with a chronic disease according to an embodiment of the present invention. 
         FIG. 7B  conceptually illustrates one example of a pre-defined structured collection procedure, and a method for customizing the pre-defined structured collection procedure according to an embodiment of the present invention. 
         FIG. 8A  shows a method for performing a structured collection procedure according to an embodiment of the present invention. 
         FIGS. 8B and 8C  show a method of implementing a structured collection procedure via a graphical user interface provided on a collection device according to an embodiment of the present invention. 
         FIG. 9  shows a method for performing a structured collection procedure to obtain contextualized biomarker data from a patient according to another embodiment of the present invention. 
         FIG. 10  shows a diagram of contextualized biomarker data intermingled with non-contextualized biomarker data according to an embodiment of the present invention. 
         FIG. 11  shows a diagram of type 2 diabetes disease progression. 
         FIG. 12  shows a method for adjusting parameters to bring a contextualized biomarker of concern into a guideline based target range according to an embodiment of the present invention. 
         FIG. 13  shows a diagram of a contextualized biomarker of concern adjusted into a guideline based target range according to an embodiment of the present invention. 
         FIG. 14  show a diagram of the relationship of the component parts of a noise function to estimated noise according to an embodiment of the present invention. 
         FIG. 15  shows a flowchart of a method for creation of a noise function according to an embodiment of the present invention. 
         FIGS. 16A and 16B  show a method of collecting contextualized biomarker data for diabetes diagnostics and therapy support according to a use case embodiment of the present invention. 
     
    
    
     DETAILED DESCRIPTION 
     The present invention will be described below relative to various illustrative embodiments. Those skilled in the art will appreciate that the present invention may be implemented in a number of different applications and embodiments and is not specifically limited in its application to the particular embodiments depicted herein. In particular, the present invention will be discussed below in connection with diabetes management via sampling blood, although those of ordinary skill will recognize that the present invention could be modified to be used with other types of fluids or analytes besides glucose, and/or useful in managing other chronic diseases besides diabetes. 
     As used herein with the various illustrated embodiments described below, the follow terms include, but are not limited to, the following meanings. 
     The term “biomarker” can mean a physiological variable measured to provide data relevant to a patient such as for example, a blood glucose value, an interstitial glucose value, an HbA1c value, a heart rate measurement, a blood pressure measurement, lipids, triglycerides, cholesterol, and the like. 
     The term “contextualizing” can mean documenting and interrelating conditions that exists or will occur surrounding a collection of a specific biomarker measurement. Preferably, data about documenting and interrelating conditions that exists or will occur surrounding a collection of a specific biomarker are stored together with the collected biomarker data and are linked to it. In particular, a further assessment of the collected biomarker data takes into account the data about documenting and interrelating conditions so that not only the data as such are evaluated but also the link between data to which it is contextualized. The data about documenting and interrelating conditions can include for example information about the time, food and/or exercises which occurs surrounding a collection of a specific biomarker measurement and/or simultaneously thereto. For example, the context of a structured collection procedure according in an embodiment to the present invention can be documented by utilizing entry criterion for verifying a fasting state with the user before accepting a biomarker value during a Basal titration optimization focused testing procedure. 
     The term “contextualized biomarker data” can mean the information on the interrelated conditions in which a specific biomarker measurement was collected combined with the measured value for the specific biomarker. In particular, the biomarker data are stored together with the information on the interrelated conditions under which a specific biomarker measurement was collected and are linked thereto. 
     The term “criteria” can mean one or more criterions, and can be at least one or more of a guideline(s), rule(s), characteristic(s), and dimension(s) used to judge whether one or more conditions are satisfied or met to begin, accept, and/or end one or more procedural steps, actions, and/or values. 
     The term “adherence” can mean that a person following a structured collection procedure performs requested procedural steps appropriately. For example, the biomarker data should be measured under prescribed conditions of the structured collection procedure. If then the prescribed conditions are given for a biomarker measurement the adherence is defined as appropriate. For examples, the prescribed conditions are time related conditions and/or exemplarily can include eating of meals, taking a fasting sample, eating a type of meal with a requested window of time, taking a fasting sample at a requested time, sleeping a minimum amount of time, and the like. The adherence can be defined as appropriate or not appropriate for a structured collection procedure or a single data point in particular of a contextualized biomarker data. Preferably, the adherence can be defined as appropriate or not appropriate by a range of a prescribed condition(s) or by a selectively determined prescribed condition(s). Moreover the adherence can be calculated as a rate of adherence describing in which extent the adherence is given for a structured collection procedure or a single data point in particular of a contextualized biomarker data. 
     The term “adherence event” can mean when a person executing a structured collection procedure fails to perform a procedural step. For example, if a person did not collect data when requested by the collection device, the adherence is determined as not appropriate resulting in an adherence event. In another example, adherence criteria could be a first criterion for the patient to fast 6 hours and a second criterion for collecting a fasting bG value at a requested time. In this example, if the patient provides the bG sampling at the requested time but fasted only 3 hours before providing, then although the second adherence criterion is met, the first adherence criterion is not, and hence an adherence event for the first criterion would occur. 
     The term “violation event” is a form of an adherence event in which the person executing the structured collection (testing) procedure (protocol) does not administer a therapeutic at a recommended time, does administer a recommended amount, or both. 
     The term “adherence criterion” can include adherence and can also mean a basis for comparison (e.g., assessment) of a measured value, a value related to a measured value and/or a calculated value with a defined value or defined range of the value wherein based on the comparison data are accepted with approval and positive reception. Adherence criterion can take into account time related values and/or adherence in one embodiment, but also can take into account noise in other embodiments, and the like. Furthermore, adherence criterion can be applied to contextualized biomarker data so that a biomarker data is accepted depending on a comparison of the contextualized data about documenting and interrelating conditions that exists or occurs surrounding the collection of the specific biomarker. Adherence criterion can be akin to a sanity check for a given piece of information, or group of information. Preferably, the single data point/information or group of data or information is rejected if the accepted criterion is not fulfilled. In particular, such rejected data are then not used for further calculations which are used to provide a therapy recommendation. Mainly the rejected data are only used to assess the adherence and/or to trigger automatically at least one further action. For example, such a triggered action prompts the user then to follow a structured collection procedure or a single requested action so that based on that the adherence criterion can be fulfilled. 
     The term “data event request” can mean an inquiry for a collection of data at a single point in space-time defined by a special set of circumstances, for example, defined by time-related or not time-related events. 
     The term “decentralized disease status assessment” can mean a determination of the degree or extent of progression of a disease performed by using a biomarker measurement of interest to deliver a value without sending a sample to a laboratory for assessment. 
     The term “medical use case or question” can mean at least one or more of a procedure, situation, condition, and/or question providing an uncertainty about the factuality of existence of some medical facts, combined with a concept that is not yet verified but that if true would explain certain facts or phenomena. Medical use case or question can be already deposited and stored in the system so that the user can select between different medical use cases or questions. Alternatively, the medical use case or question can be defined by the user itself. 
     The terms “focused”, “structured”, and “episodic” are used herein interchangeably with the term “testing” and can mean a predefined sequence in which to conduct the testing. 
     The terms “software” and “program” may be used interchangeably herein. 
       FIG. 1  shows a chronic care management system  10  for a diabetes patient(s)  12  and a clinician(s)  14  along with others  16  having an interest in the chronic care management of the patient  12 . Patient  12 , having dysglycemia, may include persons with a metabolic syndrome, pre-diabetes, type 1 diabetes, type 2 diabetes, and gestational diabetes. The others  16  with an interest in the patient&#39;s care may include family members, friends, support groups, and religious organizations all of which can influence the patient&#39;s conformance with therapy. The patient  12  may have access to a patient computer  18 , such as a home computer, which can connect to a public network  50  (wired or wireless), such as the internet, cellular network, etc., and couple to a dongle, docking station, or device reader  22  for communicating with an external portable device, such as a portable collection device  24 . An example of a device reader is shown in the manual “Accu-Chek® Smart Pix Device Reader User&#39;s Manual” (2008) available from Roche Diagnostics. 
     The collection device  24  can be essentially any portable electronic device that can function as an acquisition mechanism for determining and storing digitally a biomarker value(s) according to a structured collection procedure, and which can function to run the structured collection procedure and the method of the present invention. Greater details regarding various illustrated embodiments of the structured collection procedure are provided hereafter in later sections. In a preferred embodiment, the collection device  24  can be a self-monitoring blood glucose meter  26  or a continuous glucose monitor  28 . An example of a blood glucose meter is the Accu-Chek® Active meter, and the Accu-Chek® Aviva meter described in the booklet “Accu-Chek® Aviva Blood Glucose Meter Owner&#39;s Booklet (2007), portions of which are disclosed in U.S. Pat. No. 6,645,368 B1 entitled “Meter and method of using the meter for determining the concentration of a component of a fluid” assigned to Roche Diagnostics Operations, Inc., which is hereby incorporated by reference. An example of a continuous glucose monitor is shown in U.S. Pat. No. 7,389,133 “Method and device for continuous monitoring of the concentration of an analyte” (Jun. 17, 2008) assigned to Roche Diagnostics Operations, Inc., which is hereby incorporated by reference. 
     In addition to the collection device  24 , the patient  12  can use a variety of products to manage his or her diabetes including: test strips  30  carried in a vial  32  for use in the collection device  24 ; software  34  which can operate on the patient computer  18 , the collection device  24 , a handheld computing device  36 , such as a laptop computer, a personal digital assistant, and/or a mobile phone; and paper tools  38 . Software  34  can be pre-loaded or provided either via a computer readable medium  40  or over the public network  50  and loaded for operation on the patient computer  18 , the collection device  24 , the clinician computer/office workstation  25 , and the handheld computing device  36 , if desired. In still other embodiments, the software  34  can also be integrated into the device reader  22  that is coupled to the computer (e.g., computers  18  or  25 ) for operation thereon, or accessed remotely through the public network  50 , such as from a server  52 . 
     The patient  12  can also use for certain diabetes therapies additional therapy devices  42  and other devices  44 . Additionally, therapy devices  42  can include devices such as an ambulatory infusion pump  46 , an insulin pen  48 , and a lancing device  51 . An example of an ambulatory insulin pump  46  include but not limited thereto the Accu-Chek® Spirit pump described in the manual “Accu-Chek® Spirit Insulin Pump System Pump User Guide” (2007) available from Disetronic Medical Systems AG. The other devices  44  can be medical devices that provide patient data such as blood pressure, fitness devices that provide patient data such as exercise information, and elder care device that provide notification to care givers. The other devices  44  can be configured to communicate with each other according to standards planned by Continua® Health Alliance. 
     The clinicians  14  for diabetes are diverse and can include e.g., nurses, nurse practitioners, physicians, endocrinologists, and other such health care providers. The clinician  14  typically has access to a clinician computer  25 , such as a clinician office computer, which can also be provided with the software  34 . A healthcare record system  27 , such as Microsoft® HealthVault™ and Google™ Health, may also be used by the patient  12  and the clinician  14  on computers  18 ,  25  to exchange information via the public network  50  or via other network means (LANs, WANs, VPNs, etc.), and to store information such as collection data from the collection device  24  to an electronic medical record of the patient e.g., EMR  53  ( FIG. 2A ) which can be provided to and from computer  18 ,  25  and/or server  52 . 
     Most patients  12  and clinicians  14  can interact over the public network  50  with each other and with others having computers/servers  52 . Such others can include the patient&#39;s employer  54 , a third party payer  56 , such as an insurance company who pays some or all of the patient&#39;s healthcare expenses, a pharmacy  58  that dispenses certain diabetic consumable items, a hospital  60 , a government agency  62 , which can also be a payer, and companies  64  providing healthcare products and services for detection, prevention, diagnosis and treatment of diseases. The patient  12  can also grant permissions to access the patient&#39;s electronic health record to others, such as the employer  54 , the payer  56 , the pharmacy  58 , the hospital  60 , and the government agencies  62  via the healthcare record system  27 , which can reside on the clinician computer  25  and/or one or more servers  52 . Reference hereafter is also made to  FIG. 2 . 
       FIG. 2  shows a system embodiment suitable for implementing a structured testing method according to an embodiment of the present invention, which in another embodiment can be a part of the chronic care management system  10  and communicate with such components, via conventional wired or wireless communication means. The system  41  can include the clinician computer  25  that is in communication with a server  52  as well as the collection device  24 . Communications between the clinician computer  25  and the server  52  can be facilitated via a communication link to the public network  50 , to a private network  66 , or combinations thereof. The private network  66  can be a local area network or a wide are network (wired or wireless) connecting to the public network  50  via a network device  68  such as a (web) server, router, modem, hub, and the likes. 
     In one embodiment, the server  52  can be a central repository for a plurality of structured collection procedures (or protocols)  70   a ,  70   b ,  70   c ,  70   d , in which the details of a few exemplary structured collection procedures are provided in later sections. The server  52 , as well as the network device  68 , can function also as a data aggregator for completed ones of the structured collection procedures  70   a ,  70   b ,  70   c ,  70   d . Accordingly, in such an embodiment, data of a completed collection procedure(s) from a collection device of the patient  12  can then be provided from the server  52  and/or network device  68  to the clinician computer  25  when requested in response to a retrieval for such patient data. 
     In one embodiment, one or more of the plurality of structured collection procedures  70   a ,  70   b ,  70   c ,  70   d  on the server  52  can be provided over the public network  50 , such as through a secure web interface  55  ( FIG. 2A , showing another embodiment of the system  41 ) implemented on the patient computer  18 , the clinician computer  25 , and/or the collection device  24 . In another embodiment, the clinician computer  25  can serve as the interface (wired or wireless)  72  between the server  52  and the collection device  24 . In still another embodiment, the structured collection procedures  70   a ,  70   b ,  70   c ,  70   d , as well as software  34 , may be provided on a computer readable medium  40  and loaded directed on the patient computer  18 , the clinician computer  25 , and/or the collection device  24 . In still another embodiment, the structured collection procedures  70   a ,  70   b ,  70   c ,  70   d  may be provided pre-loaded (embedded) in memory of the collection device  24 . In still other embodiments, new/updated/modified structured collection procedures  70   a ,  70   b ,  70   c ,  70   d  may be sent between the patient computer  18 , the clinician computer  25 , the server  52  and/or the collection device  24  via the public network  50 , the private network  66 , via a direct device connection (wired or wireless)  74 , or combinations thereof. Accordingly, in one embodiment the external devices e.g., computer  18  and  25 , can be used to establish a communication link  72 ,  74  between the collection device  24  and still further electronic devices such as other remote Personal Computer (PC), and/or servers such as through the public network  50 , such as the Internet and/or other communication networks (e.g., LANs, WANs, VPNs, etc.), such as private network  66 . 
     The clinician computer  25 , as a conventional personal computer/workstation, can include a processor  76  which executes programs, such as software  34 , and such as from memory  78  and/or computer readable medium  40 . Memory  78  can include system memory (RAM, ROM, EEPROM, etc.), and storage memory, such as hard drives and/or flash memory (internal or external). The clinician computer  25  can also include a display driver  80  to interface a display  82  with the processor  76 , input/output connections  84  for connecting user interface devices  86 , such as a keyboard and mouse (wired or wireless), and computer readable drives  88  for portable memory and discs, such as computer readable medium  40 . The clinician computer  25  can further include communication interfaces  90  for connections to the public network  50  and other devices, such as collection device  24  (wired or wireless), and a bus interface  92  for connecting the above mentioned electronic components to the processor  76 . Reference hereafter is now made to  FIG. 3 . 
       FIG. 3  is a block diagram conceptually illustrating the portable collection device  24  depicted in  FIG. 2 . In the illustrated embodiment, the collection device  24  can include one or more microprocessors, such as processor  102 , which may be a central processing unit comprising at least one more single or multi-core and cache memory, which can be connected to a bus  104 , which may include data, memory, control and/or address buses. The collection device  24  can include the software  34 , which provides instruction codes that causes a processor  102  of the device to implement the methods of the present invention that are discussed hereafter in later sections. The collection device  24  may include a display interface  106  providing graphics, text, and other data from the bus  104  (or from a frame buffer not shown) for display on a display  108 . The display interface  106  may be a display driver of an integrated graphics solution that utilizes a portion of main memory  110  of the collection device  24 , such as random access memory (RAM) and processing from the processor  102  or may be a dedicated graphic processing unit. In another embodiment, the display interface  106  and display  108  can additionally provide a touch screen interface for providing data to the collection device  24  in a well-known manner. 
     Main memory  110  in one embodiment can be random access memory (RAM), and in other embodiments may include other memory such as a ROM, PROM, EPROM or EEPROM, and combinations thereof. In one embodiment, the collection device  24  can include secondary memory  112 , which may include, for example, a hard disk drive  114  and/or a computer readable medium drive  116  for the computer readable medium  40 , representing for example, at least one of a floppy disk drive, a magnetic tape drive, an optical disk drive, a flash memory connector (e.g., USB connector, Firewire connector, PC card slot), etc. The drive  116  reads from and/or writes to the computer readable medium  40  in a well-known manner. Computer readable medium  40 , represents a floppy disk, magnetic tape, optical disk (CD or DVD), flash drive, PC card, etc. which is read by and written to by the drive  116 . As will be appreciated, the computer readable medium  40  can have stored therein the software  34  and/or structured collection procedures  70   a ,  70   b ,  70   c , and  70   d  as well as data resulting from completed collections performed according to one or more of the collection procedures  70   a ,  70   b ,  70   c , and  70   d.    
     In alternative embodiments, secondary memory  112  may include other means for allowing the software  34 , the collection procedures  70   a ,  70   b ,  70   c ,  70   d , other computer programs or other instructions to be loaded into the collection device  24 . Such means may include, for example, a removable storage unit  120  and an interface connector  122 . Examples of such removable storage units/interfaces can include a program cartridge and cartridge interface, a removable memory chip (e.g., ROM, PROM, EPROM, EEPROM, etc.) and associated socket, and other removable storage units  120  (e.g. hard drives) and interface connector  122  which allow software and data to be transferred from the removable storage unit  120  to the collection device  24 . 
     The collection device  24  in one embodiment can include a communication module  124 . The communication module  124  allows software (e.g., the software  34 , the collection procedures  70   a ,  70   b ,  70   c , and  70   d ) and data (e.g., data resulting from completed collections performed according to one or more of the collection procedures  70   a ,  70   b ,  70   c , and  70   d ) to be transferred between the collection device  24  and an external device(s)  126 . Examples of communication module  124  may include one or more of a modem, a network interface (such as an Ethernet card), a communications port (e.g., USB, firewire, serial, parallel, etc.), a PC or PCMCIA slot and card, a wireless transceiver, and combinations thereof. The external device(s)  126  can be the patient computer  18 , the clinician computer  25 , the handheld computing devices  36 , such as a laptop computer, a personal digital assistance (PDA), a mobile (cellular) phone, and/or a dongle, a docking station, or device reader  22 . In such an embodiment, the external device  126  may provided and/or connect to one or more of a modem, a network interface (such as an Ethernet card), a communications port (e.g., USB, firewire, serial, parallel, etc.), a PCMCIA slot and card, a wireless transceiver, and combinations thereof for providing communication over the public network  50  or private network  66 , such as with the clinician computer  25  or server  52 . Software and data transferred via communication module  124  can be in the form of wired or wireless signals  128 , which may be electronic, electromagnetic, optical, or other signals capable of being sent and received by communication module  124 . For example, as is known, signals  128  may be sent between communication module  124  and the external device(s)  126  using wire or cable, fiber optics, a phone line, a cellular phone link, an RF link, an infrared link, other communications channels, and combinations thereof. Specific techniques for connecting electronic devices through wired and/or wireless connections (e.g. USB and Bluetooth, respectively) are well known in the art. 
     In another embodiment, the collection device  24  can be used with the external device  132 , such as provided as a handheld computer or a mobile phone, to perform actions such as prompt a patient to take an action, acquire a data event, and perform calculations on information. An example of a collection device combined with such an external device  126  provided as a hand held computer is disclosed in U.S. patent application Ser. No. 11/424,757 filed Jun. 16, 2006 entitled “System and method for collecting patient information from which diabetes therapy may be determined,” assigned to Roche Diagnostics Operations, Inc., which is hereby incorporated by reference. Another example of a handheld computer is shown in the user guide entitled “Accu-Chek® Pocket Compass Software with Bolus Calculator User Guide” (2007) available from Roche Diagnostics. 
     In the illustrative embodiment, the collection device  24  can provide a measurement engine  138  for reading a biosensor  140 . The biosensor  140 , which in one embodiment is the disposable test strip  30  ( FIG. 1 ), is used with the collection device  24  to receive a sample such as for example, of capillary blood, which is exposed to an enzymatic reaction and measured by electrochemistry techniques, optical techniques, or both by the measurement engine  138  to measure and provide a biomarker value, such as for example, a blood glucose level. An example of a disposable test strip and measurement engine is disclosed in U.S. Patent Pub. No. 2005/0016844 A1 “Reagent stripe for test strip” (Jan. 27, 2005), and assigned to Roche Diagnostics Operations, Inc., which is hereby incorporated by reference. In other embodiments, the measurement engine  138  and biosensor  140  can be of a type used to provide a biomarker value for other types of sampled fluids or analytes besides or in addition to glucose, heart rate, blood pressure measurement, and combinations thereof. Such an alternative embodiment is useful in embodiments where values from more then one biomarker type are requested by a structured collection procedure according to the present invention. In still another embodiment, the biosensor  140  may be a sensor with an indwelling catheter(s) or being a subcutaneous tissue fluid sampling device(s), such as when the collection device  24  is implemented as a continuous glucose monitor (CGM) in communication with an infusion device, such as pump  46  ( FIG. 1 ). In still another embodiments, the collection device  24  can be a controller implementing the software  34  and communicating between the infusion device (e.g., ambulatory infusion pump  46  and electronic insulin pen  48 ) and the biosensor  140 . 
     Data, comprising at least the information collected by the biosensor  140 , is provided by the measurement engine  138  to the processor  102  which may execute a computer program stored in memory  110  to perform various calculations and processes using the data. For example, such a computer program is described by U.S. patent application Ser. No. 12/492,667, filed Jun. 26, 2009, titled “Method, System, and Computer Program Product for Providing Both an Estimated True Mean Blood Glucose Value and Estimated Glycated Hemoglobin (HbA1C) Value from Structured Spot Measurements Of Blood Glucose,” and assigned to Roche Diagnostics Operations, Inc., which is hereby incorporated by reference. The data from the measurement engine  138  and the results of the calculation and processes by the processor  102  using the data is herein referred to as self-monitored data. The self-monitored data may include, but not limited thereto, the glucose values of a patient  12 , the insulin dose values, the insulin types, and the parameter values used by processor  102  to calculate future glucose values, supplemental insulin doses, and carbohydrate supplement amounts as well as such values, doses, and amounts. Such data along with a date-time stamp  169  for each measured glucose value and administered insulin dose value is stored in a data file  145  of memory  110  and/or  112 . An internal clock  144  of the collection device  24  can supply the current date and time to processor  102  for such use. 
     The collection device  24  can further provide a user interface  146 , such as buttons, keys, a trackball, touchpad, touch screen, etc. for data entry, program control and navigation of selections, choices and data, making information requests, and the likes. In one embodiment, the user interface  146  can comprises one or more buttons  147 ,  149  for entry and navigation of the data provided in memory  110  and/or  112 . In one embodiment, the user can use one or more of buttons  147 ,  149  to enter (document) contextualizing information, such as data related to the everyday lifestyle of the patient  12  and to acknowledge that prescribed tasks are completed. Such lifestyle data may relate to food intake, medication use, energy levels, exercise, sleep, general health conditions and overall well-being sense of the patient  12  (e.g., happy, sad, rested, stressed, tired, etc.). Such lifestyle data can be recorded into memory  110  and/or  112  of the collection device  24  as part of the self-monitored data via navigating through a selection menu displayed on display  108  using buttons  147 ,  149  and/or via a touch screen user interface provided by the display  108 . It is to be appreciated that the user interface  146  can also be used to display on the display  108  the self monitored data or portions thereof, such as used by the processor  102  to display measured glucose levels as well as any entered data. 
     In one embodiment, the collection device  24  can be switched on by pressing any one of the buttons  147 ,  149  or any combination thereof. In another embodiment, in which the biosensor  140  is a test-strip, the collection device  24  can be automatically switched on when the test-strip is inserted into the collection device  24  for measurement by the measurement engine  138  of a glucose level in a sample of blood placed on the test-strip. In one embodiment, the collection device  24  can be switched off by holding down one of the buttons  147 ,  149  for a pre-defined period of time, or in another embodiment can be shut down automatically after a pre-defined period of non-use of the user interface  146 . 
     An indicator  148  can also be connected to processor  102 , and which can operate under the control of processor  102  to emit audible, tactile (vibrations), and/or visual alerts/reminders to the patient of daily times for bG measurements and events, such as for example, to take a meal, of possible future hypoglycemia, and the likes. A suitable power supply  150  is also provided to power the collection device  24  as is well known to make the device portable. 
     As mentioned above previously, the collection device  24  may be pre-loaded with the software  34  or by provided therewith via the computer readable medium  40  as well as received via the communication module  124  by signal  128  directly or indirectly though the external device  132  and/or network  50 . When provided in the latter matter, the software  34  when received by the processor  102  of the collection device  24  is stored in main memory  110  (as illustrated) and/or secondary memory  112 . The software  34  contains instructions, when executed by the processor  102 , enables the processor to perform the features/functions of the present invention as discussed herein in later sections. In another embodiment, the software  34  may be stored in the computer readable medium  40  and loaded by the processor  102  into cache memory to cause the processor  102  to perform the features/functions of the invention as described herein. In another embodiment, the software  34  is implemented primarily in hardware logic using, for example, hardware components such as application specific integrated circuits (ASICs). Implementation of the hardware state machine to perform the feature/functions described herein will be apparent to persons skilled in the relevant art(s). In yet another embodiment, the invention is implemented using a combination of both hardware and software. 
     In an example software embodiment of the invention, the methods described hereafter can be implemented in the C++ programming language, but could be implemented in other programs such as, but not limited to, Visual Basic, C, C#, Java or other programs available to those skilled in the art. In still other embodiment, the program  34  may be implemented using a script language or other proprietary interpretable language used in conjunction with an interpreter. Reference hereafter is also made to  FIG. 4 . 
       FIG. 4  depicts in tabular form a data file  145  containing data records  152  of self-monitored data  154  resulting from a structured collection procedure according to an embodiment of the present invention. The data records  152  (e.g., rows) along with the self-monitoring data  154  (e.g., various one of the columns) can also provide associated therewith contextual information  156  (e.g., other various ones of the columns as well as via row and column header information). Such contextual information  156  can be collected either automatically, such as for example via input received automatically from the measurement engine, the biosensor, and/or any one of the other devices, or via input received from the user interface which was manually enter by the patient in response to a collection request (e.g., a question displayed by the processor  102  on the display  108 ) during the structured collection procedure. Accordingly, as such contextual information  156  can be provided with each data record  152  in a preferred embodiment, such information is readily available to a physician and no further collection of such information is necessarily needed to be provided again by the patient either manually or orally after completing the structured collection procedure. In another embodiment, if such contextual information  156  and/or additional contextual information is collected after completion of a structured collection procedure according to the present invention, such information may be provided in the associated data file and/or record  145 ,  152  at a later time such as via one of the computers  18 ,  25 . Such information would then be associated with the self-monitored data in the data file  145 , and thus would not need to be provided again orally or manually. Such a process in the latter embodiment may be needed in the situation where the structured collection procedure is implemented as or partly as a paper tool  38  which is used with a collection device incapable of running the software  34  implementing such a structured collection procedure. 
     It is to be appreciated that the date file  145  (or portions thereof, such as only the self-monitored data  154 ) can be sent/downloaded (wired or wireless) from the collection device  24  via the communication module  124  to another electronic device, such the external device  132  (PC, PDA, or cellular telephone), or via the network  50  to the clinician computer  25 . Clinicians can use diabetes software provided on the clinician computer  25  to evaluate the received self-monitored data  154  as well as the contextual information  156  of the patient  12  for therapy results. An example of some of the functions which may be incorporated into the diabetes software and which is configured for a personal computer is the Accu-Chek® 360 Diabetes Management System available from Roche Diagnostics that is disclosed in U.S. patent application Ser. No. 11/999,968 filed Dec. 7, 2007, titled “METHOD AND SYSTEM FOR SETTING TIME BLOCK,” and assigned to Roche Diagnostics Operations, Inc., which is hereby incorporated by reference. 
     In a preferred embodiment, the collection device  24  can be provided as portable blood glucose meter, which is used by the patient  12  for recording self-monitored data comprising insulin dosage readings and spot measured glucose levels. Examples of such bG meters as mentioned above previously include but are not limited to, the Accu-Chek® Active meter and the Accu-Chek® Aviva system both by Roche Diagnostics, Inc. which are compatible with the Accu-Chek® 360° Diabetes management software to download test results to a personal computer or the Accu-Chek® Pocket Compass Software for downloading and communication with a PDA. Accordingly, it is to be appreciated that the collection device  24  can include the software and hardware necessary to process, analyze and interpret the self monitored data in accordance with predefined flow sequences (as described below in detail) and generate an appropriate data interpretation output. In one embodiment, the results of the data analysis and interpretation performed upon the stored patient data by the collection device  24  can be displayed in the form of a report, trend-monitoring graphs, and charts to help patients manage their physiological condition and support patient-doctor communications. In other embodiments, the bG data from the collection device  24  may be used to generated reports (hardcopy or electronic) via the external device  132  and/or the patient computer  18  and/or the clinician computer  25 . 
     The collection device  24  can further provide the user and/or his or her clinician with at least one or more of the possibilities comprising: a) editing data descriptions, e. g. the title and description of a record; b) saving records at a specified location, in particular in user-definable directories as described above; c) recalling records for display; d) searching records according to different criteria (date, time, title, description etc.); e) sorting records according to different criteria (e.g., values of the bG level, date, time, duration, title, description, etc.); f) deleting records; g) exporting records; and/or h) performing data comparisons, modifying records, excluding records as is well known. 
     As used herein, lifestyle can be described in general as a pattern in an individual&#39;s habits such as meals, exercise, and work schedule. The individual additionally may be on medications such as insulin therapy or orals that they are required to take in a periodic fashion. Influence of such action on glucose is implicitly considered by the present invention. 
     It is to be appreciated that the processor  102  of the collection device  24  can implement one or more structured collection procedures  70  provided in memory  110  and/or  112 . Each structured collection procedure  70  in one embodiment can be stand-alone software, thereby providing the necessary program instructions which when executed by the processor  102  causes the processor to perform the structure collection procedure  70  as well as other prescribed functions. In other embodiments, each structured collection procedure  70  can be part of the software  34 , and can be then be selectively executed by the processor  102  either via receiving a selection from a menu list provided in the display  108  from the user interface  146  in one embodiment or via activation of a particular user interface, such as a structured collection procedure run mode button (not shown) provided to the collection device  24  in another embodiment. It is to be appreciated that the software  34 , likewise, provides the necessary program instructions which when executed by the processor  102  causes the processor to perform the structure collection procedure  70  as well as other prescribed functions of the software  34  discussed herein. One suitable example of having a selectable structured collection procedure provided as a selectable mode of a collection meter is disclosed by in U.S. patent application Ser. No. 12/491,523, filed Jun. 25, 2009, titled “Episodic Blood Glucose Monitoring System With An Interactive Graphical User Interface And Methods Thereof,” assigned to Roche Diagnostics Operations, Inc., which is hereby incorporated by reference. 
     In still another embodiment, a command instruction can be sent from the clinician computer  25  and received by the processor  102  via the communication module  124 , which places the collection device  24  in a collection mode which runs automatically the structured collection procedure  70 . Such a command instruction may specify which of the one or more structured collection procedures to run and/or provide a structured collection procedure to run. In still another embodiment, a list of defined medical use cases or medical questions can be presented on the display  108  by the processor  102 , and a particular structured collection procedure  70  can be automatically chosen by the processor  102  from a plurality of structured collection procedures (e.g., procedures  70   a ,  70   b ,  70   c , and  70   d ) depending on the selection of the defined medical use cases or medical questions received by the processor  102  via the user interface  146 . 
     In still another embodiment, after selection, the structured collection procedure(s)  70  can be provided through the computer readable medium e.g.,  40  and loaded by the collection device  24 , downloaded from computer  18  or  25 , the other device(s)  132 , or server  52 . Server  52 , for example, may be a healthcare provider or company providing such pre-defined structured collection procedures  70  for downloading according to a selected defined medical use case or question. It is to be appreciated that the structured collection procedure(s)  70  may be developed by a healthcare company (e.g. company  64 ) and implemented via the public network  50  through a webpage and/or made available for downloading on server  52 , such as illustrated in  FIG. 2 . In still other embodiments, notices that a new structured collection procedure  70  is available for use on the collection device  24  to help address a particular use case/medical question that a user (e.g., healthcare provider and patient) may have can be provided in any standard fashion, such for via postal letters/cards, email, text messaging, tweets, and the likes. 
     In some embodiments, as mentioned above previously, a paper tool  38  can perform some of the functions provided by the diabetes software  34 . An example of some of the functions which may be incorporated into the diabetes software  34  and which is configured as a paper tool  38  is the Accu-Chek® 360 View Blood Glucose Analysis System paper form available from Roche Diagnostics also disclosed in U.S. patent application Ser. No. 12/040,458 filed Feb. 29, 2007 entitled “Device and method for assessing blood glucose control,” assigned to Roche Diagnostic Operations, Inc., which is hereby incorporated by reference. 
     In still another embodiment, the software  34  can be implemented on the continuous glucose monitor  28  ( FIG. 1 ). In this manner, the continuous glucose monitor  28  can be used to obtain time-resolved data. Such time-resolved data can be useful to identify fluctuations and trends that would otherwise go unnoticed with spot monitoring of blood glucose levels and standard HbA1c tests. Such as, for example, low overnight glucose levels, high blood glucose levels between meals, and early morning spikes in blood glucose levels as well as how diet and physical activity affect blood glucose along with the effect of therapy changes. 
     In addition to collection device  24  and software  34 , clinicians  14  can prescribe other diabetes therapy devices for patients  12  such as an ambulatory insulin pump  46  as well as electronically based insulin pen  48  ( FIG. 1 ). The insulin pump  46  typically includes configuration software such as that disclosed in the manual “Accu-Chek® Insulin Pump Configuration Software” also available from Disetronic Medical Systems AG. The insulin pump  46  can record and provide insulin dosage and other information, as well as the electronically based insulin pen  48 , to a computer, and thus can be used as another means for providing biomarker data as requested by the structured collection procedure  70  ( FIG. 2 ) according to the present invention. 
     It is to be appreciated that, and as mentioned above previously, one or more of the method steps discussed hereafter can be configured as a paper tool  38  ( FIG. 1 ), but preferably all the method steps are facilitated electronically on system  41  ( FIG. 2 ) or on any electronic device/computer, such as collection device  24 , having a processor and memory as a program(s) residing in memory. As is known, when a computer executes the program, instructions codes of the program cause the processor of the computer to perform the method steps associated therewith. In still other embodiments, some or all of the method steps discussed hereafter can be configured on computer readable medium  40  storing instruction codes of a program that, when executed by a computer, cause the processor of the computer to perform the method steps associated therewith. These method steps are now discussed in greater detail hereafter with reference made to  FIGS. 5A and 5B . 
     Create a Structured Collection Procedure 
       FIG. 5A  depicts a method  200  of creating a structured collection procedure  70  illustrated by  FIG. 5B  for a medical use case or question which may be implemented in any one of the above described devices  18 ,  24 ,  25 ,  26 ,  28 ,  36 ,  52  as stand alone software, as part of the diabetes software  34  or portions there of as part of paper tool  38 . In step  202 , a medical use case or question, hereafter referred to generally as use case(s), is selected and/or can be defined. It is to be appreciated that a use case may be, for example, one selected from the following medical use cases or questions: a desire to know the effects of eating a particular food; a desire to know the best time to take medication before and/or after with a meal; and a desire to know the effects of exercise on bG levels. Other use cases may be questions concerning finding a diagnosis, how best to initialize therapy for a patient, finding a determination of status of a patient disease progression, finding the best ways to optimize a patient therapy, and the like. Still other examples can be providing such structured collection procedures  70  which can be used to help address medical questions regarding fasting blood glucose, pre-prandial glucose values, postprandial glucose values, and the like. Other medical questions can be to control the biomarker in a predefined context, to optimize the biomarker in a predefined context, related to therapy onset, type of therapy, oral mono-therapy, oral combination therapy, insulin therapy, lifestyle therapy, adherence to therapy, therapy efficacy, insulin injection or inhalation, type of insulin, split of insulin in basal and bolus, and the likes. For example, medical questions regarding oral mono-therapy and oral combination could include those involving sulfonylureas, biguanides, thiazolidinediones, alpha-glucosidase inhibitors, meglitinides, dipeptidyl peptidase IV inhibitors, GLP-1 analogs, taspoglutide, PPAR dual alpha/gamma agonists, aleglitazar. The selected use case can be assigned to a medical use case parameter  220  depicted in  FIG. 5B . 
     In step  204 , the situation or problem surrounding the selected use case can be defined. This can be accomplished via looking at all the factors which may affect a change in the use case. For example, in the use case of desiring to know how best to optimize a patient&#39;s therapy some factors to look at may include stress, menstrual cycle, pre-dawn effect, background insulin, exercise, bolus timing with respect to a meal, basal rate, insulin sensitivity, post-prandial behavior, and the like such as shown by  FIG. 5C . 
     In step  206 , a determination can be made as to what kinds of analysis can be used to address or shed light on the situation or the problem. Such analysis may be, for example, selected from the following: evaluating the change in fasting blood glucose (FPG) values over the course of the collection procedure  70 , monitoring one or more particular value over the duration of the collection procedure  70 , determining an insulin to carbohydrate (I:C) ratio, determining insulin sensitivity, determining best time for administering a drug with respect to another variable, such as meal(s), and the like. In step  208 , a sampling group determination can be made as to which information has to be collected, such as what biomarker(s) and the context(s) in which the biomarkers shall be collected, as well as when this information needs to be collected to conduct the analysis. For example, the sampling group can be defined as a string of data objects, each of which consists of: target type, e.g., time based which can use a target time (e.g., used for an alerting feature), a lower time window bound, an upper time window bound, etc., or data based which defines a data type (single, aggregate, or formula), the conditions for accepting the data (e.g., none, below a value, above a value, a formula, etc.), the type of collection (e.g., user input, sensor, data, etc.), as well as any reminder screen text (e.g., static, and/or dynamic in both formatting and value insertion) for each collection. The result of this process is a schedule of collection events  222  ( FIG. 5B ). Next in step  210 , the manner in which each or a group of the schedule of collection events  222  is/are to be conducted in order to be useful for addressing the situation or problem of the selected use case is then determined. This results in one or more adherence criterions  224 . In addition to and/or instead of the manner for performing a collection, the adherence criterion(s)  224  may also be based on one or more biomarker values falling into a pre-defined range or is equal to a certain pre-defined value. In other embodiments, the adherence criterion(s) can be a formula(s) which uses a biomarker datum or group of such data to determine if the resulting value falls into the pre-defined range or is equal to a certain pre-defined value. 
     For example, adherence criteria  224  can describe the parameters around the events  237  that the patient  12  needs to perform such as tests within a certain window, fasting for a given amount of time, sleeping for a given amount of time, exercise, low stress, not menstruating, etc. As such, an adherence criterion  224  can establish the context of the information about to be provided. Adherence criteria  224  can also be used as mentioned above previously in another context to provide an assessment of whether the data is acceptable, and when used in such a context may be referenced to as “acceptance” criteria. For example, before a sample is taken, the adherence criteria  224  can establish whether steps leading up to taking of the sample are accomplished. For example, the processor  102  in response to a request  240  displays the question, “Have you been fasting for the last 8 hours?”, wherein a “Yes” response received by the processor via the user interface  146  meets the adherence criterion  224  for this step. In another example, after the sample is taken, the processor  102  can assess the received data for reasonableness using other adherence (acceptance) criterion(s). For example, based on prior data, a fasting bG sample should be between 120-180 mg/dl, but the receives value was of 340 mg/dl, and thus fails such adherence (acceptance) criteria since being out of the predefined range for an acceptable value. In such an example, an adherence event  242  occurs wherein the processor  102  could prompt for an additional sample. In such a case, if the re-sampling fails too (i.e., not between 120-180 mg/dl), the assessment provided by the processor  102  is that the patient  12  has not fasted, and thus the processor  102  as instructed by the adherence criterion upon a failing of the re-sampling extend automatically the events  237  in the schedule of events  222  accordingly. 
     Next in step  212 , the condition(s) and context(s) in which the schedule of events  222  is to be started and ended can be determined. This results in one or more entry criterions  226  and exit criterions  228  being provided for the schedule of events  222  as well as possibly for a group of other schedule of events to which the schedule of events  222  belongs if providing a package of structured collection procedures, e.g., procedures  70   a ,  70   b ,  70   c , and  70   d , which may run concurrently and/or sequentially one after the other. 
     For example, the one or more entry criterions  226  can be used to determine whether the patient meets the conditions to use the collection procedure by the processor  102  checking that, for example, the patient  12  meets the entry criterion  226  based on current age being in a range, HbA1c being in a range, that the patient has a particular disease, has had the disease over a minimum period of time, has a Body Mass Index (BMI) in a range, had a Fasting Plasma Glucose (FPG) in a range, had a particular drug sensitivity, is taking a particular drug, taking a particular drug dosage, meets one or more prerequisites of another structured collection procedure, has completed one or more of another structured collection procedure, does not have one or more particular pre-conditions, e.g., pregnant, not fasting, or contraindications, e.g., feeling ill, feverish, vomiting, etc., and combinations thereof. Entry criterion  226  can also initiate the schedule of events  222  by an initiation event such as a time of day, a time of week, meal, taking a meal with a time offset, exercise, and exercise with a time offset, use of a therapeutic drug, use of a therapeutic drug with time offset, physiological circumstances, biomarker range, and biomarker within a predetermined range calculated as an offset from a prior biomarker value. Example of a physiological circumstance can be that entry criterion will be met to start a structured collection procedure when a pre-determined number of a physiological event, e.g., hyperglycemia, hypoglycemia, a certain temperature at a certain of day, and the like, occur within a pre-defined amount of time, e.g., hours, day, weeks, etc. Accordingly, the entry criterion can be used to support the use of need to met prerequisites, indications for usage, and/or contraindications for usage. For example, an entry criterion  226  could define a prerequisite condition which in order for the structure collection procedure  70  to run an Insulin Sensitivity optimization, the processor  102  must verify first that a structured collection procedure for a Basal titration is completed and/or has a desired result and/or as well as another structured collection procedure for an insulin to carbohydrate ratio is completed and/or has a desired result. In another example, an entry criterion  226  could be defined with needing to meet certain indications for usage in which certain structured collection procedures could provide segregated uses for diabetics who are Type 1 vs. Type 2 as well as types of structure collection procedures which can be used to titrate for specific drugs. In another example, the entry criterion  226  could be defined with needing to meet certain contraindications for usage, in which for example, certain structured collection procedures  70  will not run if the patient  12  is pregnant, sick, etc. 
     Examples of the one or more exit criterions  228  can be based on the processor  102  determining that a particular value is reached, that a mean average of the primary samples values are in a range, that a particular event(s) and/or condition(s) have or have not occurred, and combinations thereof. Other conditions when the procedure may stop can include adverse events such as a hypoglycemic event, the patient is sick, the patient undergoes a therapy change, etc. Additional detail may also by provided by the processor  102  on the display  108  to the patient  12  based on what the specific exit criterion has been met. For example, in one example, if the patient  12  measures a glucose value indicating hypoglycemia, upon exiting the procedure, the processor  102  run automatically another alternative procedure which instructs the patient  12  to ingest carbohydrates and measure his blood glucose value every half an hour until the blood glucose exceeds 120 mg/dL. For this alternative procedure, the patient  12  can also be requested by the processor  102  to document his meals, activity, stress, and other relevant details to ensure that the conditions that led to hypoglycemia are recorded. The patient  12  may also be instructed by the processor  102  to contact the clinician  14  in this and other such special cases as deemed fit. Exit criteria can also include, for example, criterion for ending such as exiting after a successful completion, or exiting after an indeterminate completion, such as expiration of a predetermined timeout (logistical end), e.g., no result after n days, where n=1 to 365 days, or by termination e.g., exit with unsuccessful termination due to a fail-safe. It is to be appreciated that the structured collection procedure  70  can also be defined to end automatically not only based on meeting the exit criterion  228 , but also when the patient  12  fails to perform a request to an acceptable level of compliance and/or when a patient physiological state has changed such that the patient is should not carry out the schedule of events  222 , thereby failing adherence criteria  224 , wherein the adherence event  242  is to end the structured collection procedure. 
     In step  214 , guidance  230  for the user during collection can be determined as well as any options  232  for customizing the collection. For example, for guidance  230 , the clinician  14  can use a default list of messages, or tailor messages to guide the patient  12  during execution of the collection procedure  70 . As an example, one message could be provided on a successful data acquisition (i.e., meets the adherence criteria  224 ) would read, “Thank you. Your next scheduled measurement is at 1230 pm.” Alarms, such as provided by indicator  148 , can also be associated with the collection procedure  70  that remind the patient  12  to take a measurement and can include a snooze functionality should the patient  12  need additional time to conduct the measurement. The snooze functionality as well as other device features are discussed further in later sections. 
     The result of steps  208 - 214  is the structured collection procedure  70  being created in step  216  which associates together the use case parameter  220 , the scheduled of events  222 , the adherence criterion(s)  224 , the entry criterion(s)  226 , the exit criterion(s)  228 , guidance  230 , and the options  232 . In one embodiment, at the time of generating a collection procedure  70  the clinician  14  also generates printed material that explains to the patient the following aspects (at a minimum): the purpose of the collection procedure  70  and expected ideal outcome, i.e., setting a goal for the collection procedure  70 ; the collection procedure  70  design and the number of measurements needed; the entry criteria  226  that the patient  12  must satisfy before initiating the collection procedure  70  and before taking each reading; and the exit criteria  228  under which the patient  12  should cease to continue the collection procedure  70 . Such printed material as well as the guidance  230  that can be provided during the execution of the collection procedure  70  ensures that the patient is fully aware of why the data collection procedure is being carried out. 
     Examples, of the structured collection procedure  70  may be, for example, a structured collection procedure for determining an insulin-to-carbohydrate ratio, for determining bolus timing in respect to meal start, and for determining an exercise equivalent to ingested carbohydrates. In step  218 , the structured collection procedure  70  is then made available for implementation and use in the system  41 , such as in any of the above discussed manners mentioned with regards to  FIGS. 1, 2, and 3 . A structured collection procedure  70  accordingly may be provided via the above process, such as by either the medical community or healthcare companies  64 , to help the clinician  14  address and/or investigate a defined medical use case or problem. 
       FIG. 5B  shows the interactions of the parameters  222 ,  224 ,  226 , and  228  of the structured collection procedure  70  for obtaining contextualized biomarker data from a diabetic patient to address a medical use case upon which the structured collection procedure is based. As mentioned above, the use case parameter  220  may be provided to identify the medical use case or question to which the parameters  222 ,  224 ,  226 , and  228  address. For example, the processor  76  of the clinician computer  25 , the processor  102  of the collection device  24 , and/or the server  52  may read the medical use case parameters  220  from a plurality of structured collection procedures  70   a ,  70   b ,  70   c ,  70   d  ( FIG. 2 ), such as provided on these devices and/or within the system  41 , and provide a list of the available structured collection procedures, such as on the display  82  of the clinician computer  25  or the display  108  of the collection device  24 . Additionally, the clinician computer  25 , the patient computer  18 , and/or the server  52  can use the medical use case parameter  220  for locating/sorting/filtering such structured collection procedures according to a medical use case(s). 
     As mentioned above, the entry criterion(s)  226  establishes the requirements for initiating the structured collection procedure  70  to obtain patient data which includes biomarker data, particularly, collected in a predefined context. In one embodiment, the processor  102  of the collection device  24  can use the entry criterion(s)  226  to determine when an associated structured collection procedure  70  is appropriate for the patient&#39;s physiological context and to ensure that all of the necessary inputs to the associated structured collection procedure have been established. Therefore, it is to be appreciated that the start date and/time of a structured collection procedure may dynamically change automatically by the processor  102  of the collection device  24  if the predefined condition(s) of the entry criterion(s)  226  is not satisfied. Accordingly, until the entry criterion  226  is satisfied, the start date and/time of the associated structured collection procedure  70  can be at some unknown time in the future. 
     For example, in one embodiment, a structured collection procedure  70  can be chosen automatically by the processor  102  from a plurality of structured collection procedures  70   a ,  70   b ,  70   c ,  70   d , such as provided in memory  110  of the collection device  24 , memory of the computer  18 ,  25  and/or from server  52 , based on satisfying the condition(s) of a defined entry criterion  226  for an associated structured collection procedure. For example, in one embodiment, a first structured collection procedure, such as procedure  70   d , is useful for showing trends in blood glucose levels (“bG Level Trending”). Therefore, an entry criterion  226  for the first structured collection procedure  70   d  may be for the patient to have a bG level mean which has elevated over a defined period (e.g., a past number of days, weeks, and months from the current date) above a certain pre-defined rate. For a second structured collection procedure, such as procedure  70   a , its entry criteria  226  may require a particular number of bG measurement for a pre-breakfast measurement over a defined period (e.g., a past number of days, weeks, months, from the current date) being below a pre-defined bG value. In such an example, the processor  102  upon start up in one embodiment when commanded, such as via input received via the user interface, in another embodiment, or at a scheduled time as programmed by the software  34  in another embodiment, can run through the various entry criteria  226  provided by the various structured collection procedures  70   a  and  70   d  that are, for example, provided in memory  110  of the collection device  24  and determine whether the stated condition(s) for the entry criteria  226  of a particular procedure  70  is satisfied. In this example, the processor  102  determines that the historical data from past measurements in memory  110  indicate that the patient&#39;s bG level mean has been elevating, and that the entry criterion  226  for the first collection procedure  70   d  has been met, but not the entry criteria for the second collection procedure  70   a . In this example, the processor  102  then automatically selects and starts the first structured collection procedure  70   d  based on the above-mentioned analysis. 
     It is also to be appreciated that the use of the entry criterion  226  can help to reduce the misallocation of medical expenses by assuring that the indications of use for the structured collection procedure  70  have been met before starting the schedule of collection events  222 . The entry criterion  226  as well can help assure that any requests to perform multiple structured collection procedures do not overlap if incompatible, are not unnecessary repeats of each other, or provide a significant burden on the patient. In this manner, many of the noted problems in which a patient may avoid any further attempts to diagnose their chronic disease or to optimize therapy can be both addressed and avoided automatically by the processor  102  of the collection device  24  via use of the entry criterion  226 . 
     As shown by  FIG. 5B , the entry criteria  226  can include context specific entry criterion  234 , procedure specific entry criterion  236 , and combination thereof. Examples of context specific entry criterion  234  can include one or more variables to identify meals, low blood glucose events, insulin type and dosage, stress, and the like. In another example, the context specific entry criterion  234  can be defined such as in the form of a specific question(s), to which the processor  102  requires a specific answer to be received from patient via input from the user interface  146 . For example, the processor  102  in executing the entry criterion  226  may display on the display  108  the question of whether the patient is willing and able to perform the structured collection procedure  70  over the required period. If the patient responses affirmatively via the user interface  146 , then the entry criterion  226  has been satisfied and the processor  102  continues automatically with performing the collection events  237  according to the their associated timing as defined in the structured collection procedure  70 . If the patient responses in the negative to the displayed question, then the processor  102  will not continue with the structured collection procedure  70 , and may for example, re-schedule the asking of such a question to a future time, such as if designated by an options parameter. 
     Examples of procedure specific entry criterion  236  can include one or more variables to identify disease state, disease status, selected therapy, parameter prerequisites, insulin to carbohydrate ratio prior to testing insulin sensitivity, incompatible collection procedures, and the like. The procedure specific entry criterion  236  can be defined such that the processor  102  will continue automatically with the structured collection procedure  70  with one of three initiators—the patient  12 , the clinician  14 , or data, e.g., if the condition(s) of the entry criterion  226  is satisfied. For example, the procedure specific entry criterion  236  can be satisfy if the clinician  14  has prescribed the structured collection procedure  70 , such as via an authorized user entering via the user interface  146  a valid password to unlock the particular structured collection procedure for use, in one embodiment. In another embodiment, the clinician  14  can send the password or an authorization code from clinician computer  25  and/or server  52  to the collection device  24  which prescribes (authorizes) the collection procedure  70  for use by the patient  12  on the collection device  24 . It is to be appreciated that one or more structured collection procedure  70  can be provided in memory  110  of the collection device  24  which cannot be used by the patient  12 , and which can be also hidden from being viewed on the display  108 , such as in a selection list, by the patient until authorized by the clinician  14 . 
     The procedure specific entry criterion  236  can be satisfy by a user for example, by the user selecting a particular structured collection procedure  70  from a listing of structured collection procedures  70   a ,  70   b ,  70   c ,  70   d  provided on the display  108 . An example of a data initiated procedure for criterion  236  would be that a biomarker measurement(s) provided to the processor  102  indicates a certain condition which must have occurred or be present in order for the entry criteria  226  for the particular structured collection procedure to be satisfied. Such a condition, for example, can be the occurrence of a single event, such as a severe hypoglycemic event, or a series of events, such as hypoglycemic events within a given, a predetermined time frame, such as in 24 hours from a start time, in one week from a start time, etc, a calendar date-time, and the like. 
     Accordingly, the entry criteria  226  can be a single criterion or multiple criteria that establish context and/or condition of the patient&#39;s physiology that are relevant to the medical use case being addressed by the structured collection procedure  70 . In another embodiment, the entry criteria  226  can be assessed after patient data has been collected, such as, on historical patient data. 
     The schedule of events  222  specifies one or more events  237  which each comprises at least one or more variables defining a performance time  238 , the guidance  230  to perform the event, requests  240  for patient actions, which may include a request for information from the patient and/or a request for collection of at least one type of biomarker data from the patient, and combinations thereof. For performance time  238 , the schedule of events  222  can specify timing of each event  237 , such as for a biomarker sampling at a particular time on three consecutive work days, or one sample at time of wake-up, one sample thirty minutes later, and another sample one hour later. 
     The guidance  230  for each event  237  and for any criteria  224 ,  226 ,  228  may include, for example, providing electronic reminders (acoustic, visual) to start, end and/or wake up at a particular time, to perform a bG collection at a particular time, to ingest a particular meal or food(s) at a particular time, to perform a certain exercise(s) at a particular time, take medication at a particular time, and the like. Guidance  230  may also include information, questions and requests to record particular information about physiology, health, sense of well-being, etc., at a particular time, suggestion to improve compliancy with the collection procedure, encouragement, and positive/negative feedback. 
     It is to be appreciated that the events  237  define all the steps that are necessary to be preformed in advance of as well as after a biomarker sampling according to a request  240 , such that a reproducible set of circumstances, i.e., context before and/or after the sampling, is created in the biomarker data for the biomarker sampling. Examples of such biomarker data, in the context of diabetes, include fasting blood glucose values, pre-prandial glucose values, postprandial glucose values, and the like. Examples of a set of circumstances can include data associated with the biomarker value which identifies collected information in the patient data about meals, exercises, therapeutic administration, sleep, hydration, and the likes. 
     Each of the events  237  in the schedule of events  222  can be time-based, event-based, or both. An event  237  can also be a start of a meal, a wake-up time, start of exercise, a therapeutic administration time, a relative offset used with a prior glucose value, or a time indicating movement above or below a predetermined biomarker value threshold. The events  237  can also include any required patient actions necessary to be performed in advance of and during biomarker sampling such that reproducible circumstances are created at the time of biomarker sampling. This can includes one or more of meals, exercise, therapeutic administration, sleep, hydration, and the like. Additionally, the events  237  in the schedule of events  222  can be adjusted (number, types, timing, etc.), to accommodate work schedule, stressors, and the like of the patient  12 . 
     As mentioned above previously, the adherence criteria  224  is used to assess qualitatively whether an event  237  performed according to the schedule of events  222  provided data which is acceptable to addressing the medical use case upon which the structured collection procedure  70  is based. In particularly, the adherence criteria  224  can provide variables and/or values used to validate data from a performed event  237 . For example, an adherence criterion  224  can be a check performed by the processor  102  of the collection device  24  that a value collected in response to an event  237  is within a desired range, or is above, below, or at a desired value, wherein the value may be a time, a quantity, a type, and the like. The same or different adherence criteria  224  may be associated with each of the events  237  within the schedule of events  222  as well with the entry criterion(s)  226  in one embodiment, and as being the exit criterion  228  in another embodiment, such as illustrated by  FIG. 6D  (i.e., “stop exercising when bG back in target range” which defines both the adherence and exit criteria). In one embodiment, one or more events  237  in the schedule of events  222  may be modified (e.g., added, deleted, delayed, etc.) if a particular event or events fail to met the adherence criterion  224  for the particular event or events. In one embodiment, the failure of the adherence criterion(s)  224  can trigger an adherence event  242 . In one embodiment, upon occurrence of an adherence event  242  due to the associated adherence criterion  224  for an event  237  not being met or satisfied, the processor  102  may be required one or more additional actions as a consequence. For example, the processor  102  may prompt on the display  108  additional information to the patient, and/or prompt a question to determine whether the patient  12  is sick, stressed, or unable to perform the request e.g., eat the meal, or exercise. If the patient answers “Yes”, e.g., via the user interface  146 , then as part of the adherence event  242  the processor  102  can provide a delay to the schedule of event (i.e. suspend). In one embodiment, the delay can continue until the patient indicated that he or she is better in response to another question prompter by the processor  102 , such as the next day or after a predefined amount of time as also part of the adherence event. For example, the patient  12  is prompted by the processor  102  to administer a drug, but the patient is not at home, such as for example, where his/her insulin is located. The patient  12  can select the delay via the user interface  146 , wherein the processor  102  re-prompts the patient after a predetermined amount of time. This delay may also have an upper limit in which if the schedule of events is not re-started within a certain amount of the time, the structure collection procedure  70  in such a circumstance may just end. In another embodiment, another form of an adherence event is a violation event, which results when the person executing a structure collection procedure  70  fails to make a recommended change in response to a request. For example, the request may be for the patient to adjust a drug dosage from 10 U to 12 U, wherein the patient answers in the negative to a question on the displayed on the display  108  asking if the patient will or has complied with such a change. In response to such a violation event, the processor  102  may also send a message and/or provide a delay as previously discussed above concerning the adherence event. 
     In another example and in one embodiment, a bG measurement must be collected before each meal in order for a structured collection procedure  70  to provide data that is useful in addressing the medical use case or question for which it was designed, such as identified by the use case parameter  220 . If, in this example, the patient fails to take a bG measurement for the lunch meal in response to a request  240  for such a collection according to the schedule of the event  222 , and hence the adherence criteria  224  for that event  237  fails to be satisfied, the processor  102  in response to the associated adherence event  242  can be programmed according to instructions in the collection procedure  70  to cancel all remaining events  237  in the schedule of events  222  for that day, mark the morning bG measurement stored in the data file (such as data file  145  ( FIG. 4 ) as invalid, and reschedule for the schedule of event  222  for the next day. Other examples of further actions in which the processor  102  may take in response to an adherence event  242  may be to dynamically change the structure testing procedure by switch to a secondary schedule of event, which may be easier for the patient to perform, provide additional events for measurements to make up the missing data, change the exit criteria from a primary to a secondary exit criterion providing modified criterion(s), change the adherence criteria from a primary to a secondary adherence criterion, fill in the missing data for the failing event with (an estimate from) historical data, perform a particular calculation to see if the structured collection procedure  70  can still be successfully performed, send a message to a particular person, such as a clinician, of the failing event, provide a certain indication in the associated data record  152  to either ignore or estimate the missing data point, and the likes. In still another embodiments, the adherence criteria  224  can be dynamically assessed, such as for example, based on one or more biomarker values and/or input received from the user interface in response to one or more questions, via an algorithm which determines whether the collected data provides a value which is useful in addressing the medical use case or case. In this example, if the calculated adherence value is not useful, for example, does not fall into a desired range or meet a certain pre-define value, then further processing as defined by the resulting adherence event would then take place, such as any one or more of the processes discussed above. 
     The exit criteria  228  as mentioned previously above establishes the requirements for exiting or completing the structured collection procedure  70 , so that the structured collection procedure  70  has adequate contextual data to answer the medical question addressed by the structured collection procedure  70 . The exit criterion  228  can help increase the efficiency of the structured collection procedure  70  by minimizing the number of required samples needed to address the medical use case. By “addressing”, it is meant that sufficient patient data has been collected in which the clinician  14  may render an assessment to the medical use case. In other embodiments, the assessment may be indicated by a given confidence interval. A confidence interval is a group of discrete or continuous values that is statistically assigned to the parameter. The confidence interval typically includes the true value of the parameter at a predetermined portion of the time. 
     As with the entry criteria  226 , the exit criteria  228  can comprise one or more of context specific exit criterion  244 , procedure specific entry criterion  246 , and combinations thereof. Examples of context specific exit criterion  244  can include one or more variables to identify mood, desired blood glucose events (i.e., blood glucose level), to indicate stress, illness, contraindications, such as for example, hyperglycemia, hypoglycemia, vomiting, a fever, and the likes. Examples of procedure specific entry criterion  246  can include one or more variables to identify a number of events meeting the adherence criteria, biomarker values being in a desired pre-determined range and/or at a desired pre-determined value, a desired disease state, desired disease status, no change in the biomarker after a pre-determined period, or no significant progress over a pre-determined period to a desired biomarker value, and the like. It is to be appreciated that in one embodiment the exit criterion  228  can establish the condition(s) needed to be met for entry criterion  226  of a second structured collection procedure  70 . For example, upon having a suitable Insulin-to-Carbohydrate (I:C) determined with a first collection procedure, such as for example, structure collection procedure  70   b  ( FIG. 6B ), running a structured test for determining the best time for administering a bolus in regards to a start of a meal, such as for example, structured collection procedure  70   c  ( FIG. 6C ), which needs a current I:C ratio, can be conditioned such that the processor  102  can implement automatically a schedule of events of the second structured collection procedure  70   c  upon meeting the exit criterion of the first structured collection procedure  70   b  at some unknown time. In other embodiment, for example, the exit criterion  228  of a first structured collection procedure  70  that is being run by the processor  102  according to the schedule of events  222  and the entry criterion  226  of the second structured collection procedure  70  both can be based on the same one or more contraindications, such as mentioned above. In such an embodiment, upon occurrence of a contraindication being provided to and/or detected by the processor  102 , such as via the user interface  146  and/or the biosensor  140 , respectively, which in this example meets the exit criterion  228  of the first structured collection procedure  70 , the processor  102  would automatically start the schedule of events of the second structured collection procedure  70  as the entry criterion  226  of the second structured collection procedure  70  has also been met. An example of such a second structured collection procedure  70  which can be started via exiting a first structured collection procedure can be one which has a schedule of events  222  which requests a biomarker samplings at a routine interval, e.g., every 30 minutes, every hour, every day at a particular time, etc., until the contraindication(s) clears (e.g., biomarker value(s) reaches a desire range or value, patient  12  indicates to processor  102  via user interface  146  no longer having a contraindication(s), expiration of a predefined period, etc.). Such an embodiment is useful if recording the context and values of the events after the occurrence of the contraindication(s) is a desire and in which the first collection procedure should be exited when a contraindication(s) occurs. 
     The exit criteria  228  can be a single criterion or multiple criteria that establish the conditions to exit the structured collection procedure  70 . The conditions are provided in a preferred embodiment such to ensure that adequate contextualized biomarker data has been obtained to answer the medical question being addressed by the collection method. For example, such that a predetermined number of valid samples have been acquired, or that the variability in the samples is below a predetermined threshold. Therefore, it is to be appreciated that the end date and/time of the collection procedure  70  may be dynamic and be changed automatically by the processor  102  if the predefined condition(s) of the exit criterion(s)  228  is not satisfied. Likewise, the conditions of the exit criterion  228  may be dynamic and be changed automatically be the processor  102  such for example if a particular adherence criterion  224  is satisfied or not satisfied. For example, in one embodiment if adherence criterion  224  for a particular collection event  237  is met, then the processor  102  is instructed to use a first exit criterion and if not met, then the processor  102  is instructed to use a second exit criterion that is different from the first exit criterion. Accordingly, until the exit criterion  228  is satisfied, the end date and/time of the structured collection procedure  70  can be at some unknown time in the future. In another embodiment, the exit criteria  228  can be assessed after patient data has been collected, such as, on historical patient data. 
     It is to be appreciated that the entry and exit criteria  226 ,  228  together with the adherence criteria  224  can help to reduce both the time to perform the structured collection procedure  70  and the expense associated with the collection by defining one or more of the acceptable conditions, values, structure and context needed to perform the schedule of events  222  in an effort to make every collection event  237  count and/or reduce consumption of test strips  30  with unneeded collections that do not help address the medical use case or question. Hereafter reference is made to  FIGS. 6A-6E . 
     Structured Collection Procedure Examples 
       FIGS. 6A-E  illustrate examples of some structured collection procedures  70   a ,  70   b ,  70   c , and  70   d  depicting their functions which can easily be translated by one of ordinary skill in the related art into instruction code which may be implemented on any one of the devices the above described devices  18 ,  24 ,  25 ,  26 ,  28 ,  36 ,  52 . Therefore, for brevity, no discussion is provided in regard to pseudo-code or actual code relating to these illustrated functions. 
       FIG. 6A  diagrammatically illustrates an embodiment of a structured collection procedure  70   a  used to obtain contextualized biomarker data from a diabetic patient. The horizontal axis shows the performance times  238  of the various events  237 , and the vertical axis shows adherence criterion  224  without values. In the illustrated embodiment, the events  237  can include recording information regarding a meal  248  and sleep  250  in which to provide context  252  for the five-biomarker samplings  254  also events  237  that are part of the schedule of events  222 . In this example, the adherence criterion  224  for the meal  248  can be a value which must be greater than a minimum value, e.g., for a carbohydrate amount. The entry criterion  226 , for example, can comprise a biomarker value being above a particular value such as required to meet contextualization requirements to begin the structured collection procedure  70   a . The exit criterion  228  as well can comprise a biomarker values being below a particular value such as also required to meet contextualization requirements to end the structured collection procedure  70   a . Such a structured collection procedure  70  is useful for helping to address a number of medical use cases. 
     GLP1 Structured Testing Procedure 
     For example, several epidemiological studies have confirmed that elevated postprandial glucose (PPG) levels are a significant predictor of cardiovascular mortality and morbidity in type 2 diabetes (T2D). For this reason, there is a family of human once-weekly long acting glucagon-like peptide-1 (GLP 1) drugs which can be prescribed to T2Ds who show high post prandial bG values. These GLP 1 drugs are similar to the natural hormone GLP-1 which has a key role in blood sugar regulation by stimulating insulin secretion and suppressing glucagon secretion. Therefore, a structured collection procedure  70  can be provided in one embodiment which proposes an intensive measurement of bG values during the time after one or more meals over time allows therapy efficacy to be shown by means of observed reduced postprandial bG values. Based on such observed values, doses recommendation for a GLP 1 drug and/or whether a particular GLP 1 drug is the right drug at all for the patient can be determined. 
     For example, the structured collection procedure  70  could be provided on a collection device  24  for when a patient has been prescribed to administer a particular drug, e.g., a GLP 1 drug. In the case of a GLP 1 drug, in which determination of drug efficacy is desired, the entry criterion  226  for such a structured collection procedure could then be that the patient must affirm to the processor  102  in response to a question displayed on the display  108  to perform the structured collection procedure  70  over a period of time (e.g., over the next 4 to 24 weeks) and/or the processor  102  has determined that the mean PPG level of the patient from prior post prandial bG values over a period (e.g., week, month, etc.) are high (e.g., greater than 141 mg/dl). Still other factors could be used as the entry criterion(s)  226 , such as fasting blood glucose being more than a certain value, e.g., 126 mg/dl or less than a certain value, e.g., 240 mg/dl. 
     After the conditions of the entry criterion(s)  226  have been satisfied and confirmed by the processor  102 , the schedule of events  222  is then automatically run by the processor  102 . The schedule of events  222  would specify desired collection events  237  in which the processor  102  would automatically prompt the patient for entering post prandial bG values after breakfast, lunch, and dinner (i.e., performing a bG measurement on a sample provided to a test strip that is read by the measurement engine and provided to the processor for storing in a data record and display). As customized by the prescribing physician, the schedule of events  222  could also define a collection event  237  with a performance time  238  in which the patient must administer the drug as well as to provide a reminder of the dosage and a request  240  for confirmation from the patient when the drug has been administered. For example, the processor  102  in executing the schedule of events  222  would automatically prompt the patient to administer dosages at the times specified by the collection events  237  in the schedule of events  222 , e.g., 10 mg of Taspoglutide on a certain day of the week, and then after a period, a second dosage according to a second interval, e.g., after 4 weeks, then 20 mg also on a certain day of the week. A collection event  237  could also be defined in the schedule of events  222  in which the processor  102  makes a request on the display  108  for information, such as whether the patient is feeling well, to provide an indication of energy level, to provide an indication of size of meals consumed, and the like. 
     A condition(s) for the adherence of each entered post prandial bG value could be provided via the use of adherence criteria  224  in which any post prandial bG value entered (i.e., measured) an amount of time before or after the prompting, e.g., a testing window of ±30 minutes, such a measured value would not be accepted as a valid measurement for the schedule of events  222  by the processor  102 . In one embodiment, the processor  102  can take further action automatically based on the adherence criteria  224  assessment preformed automatically by the processor  102 . For example, if a bG measurement was taken before a measurement prescribed by a collection event in the schedule of events  222  and outside the defined testing window, e.g., −30 minutes before the collection event time, the processor  102  in such a case will automatically notify the patient that a measurement is still needed at the prescribed time as the previous measurement was not accepted since outside the testing window. Likewise, if after the testing window, e.g., the collection event time+30 minute, the processor  102  can automatically notify the patient that the previous measurement was not accepted since outside the testing window and provide encouragement on the display  108  to the patient to make an effort take a measurement within the testing window. 
     The exit criterion  228  for such a GLP 1 structured collection procedure  70  could be an indication that the mean bG value, in using a minimum amount of time (e.g., days, weeks, months, etc.), a minimum number of accepted measurements, or both, has reached a desire value. Likewise, the exit criterion  228  could be an indication that the mean bG value, after a maximum amount of time (e.g., days, weeks, months, etc.), a maximum number of accepted measurements, or both, has not reached a desire value. Still further, the exit criterion  228  can be other factors which indicate that the drug or dosage is not at all right for the patient, such as the patient responding as having nausea and/or vomiting each day for a minimum number of days in response to a collection event for such information prompted by the processor  102  on the display  108 . Still other factors could be used as the exit criteria  228 , such as fasting blood glucose being less than a certain value, e.g., 126 mg/dl or greater than a certain value, e.g., 240 mg/dl. The data collected from such a drug base structured collection procedure  70  can then be used by a physician to make a dosage recommendation for the GLP 1 drug and/or determine whether the particular GLP 1 drug is the right drug or not for the patient. 
     Another example is diagrammatically depicted by  FIG. 6B  which shows a structured collection procedure  70   b  which has a defined medical use case parameter  220  indicating that the procedure can be helpful for determining suitability of an insulin to carbohydrate (I:C) ratio. As illustrated, the entry criterion  226  is defined as having the patient simply acknowledge guidance  230  of selecting a fast-acting meal, to note that the insulin dose is calculated with the current I:C ratio as well as agreeing not to exercise, take additional food or insulin during the testing period. For example, the processor  102  can present on the display  108  such guidance  230 , which the user can then acknowledge after reading with either a “Yes” or a “No” entered via using the user interface  146  for the desired entry choice. If the user enters “Yes”, then the entry criterion  226  is satisfied, and the processor  102  automatically starts the schedule of events  222  defined in the structured collection procedure  70   b . In another embodiment, the entry criterion  226  may be or include satisfying a request  237  for selecting a fast-acting meal. For example, the request  237  for selection can be the processor  102  displaying on the display  108  a selection menu providing a listing of fast-acting meals to which input of such a selection via the user interface  146  is needed. For example, selection of a fast-acting meal may be made via a press of one of the buttons  147 ,  149  or via the touch screen interface if provided by display  108 . Such a selection can then be stored in memory  110  of the collection device  24  such as setup data  163  ( FIG. 4 ) which may be part of the data file  145  ( FIG. 4 ) for the structured collection procedure  70   b . In an alternative embodiment, a particular fast-acting meal may be recommended by the structured collection procedure  70   b.    
     As shown, the schedule of events  222  can comprise one or more events, such as the plurality of events  237   a - k  illustrated and with each having associated performance times  238   a - k  and requests for action  240   a - k . As shown, the requests for action  240   a - c , and  240   f - k  are requests for the user to take a bG level measurement, request  240   d  is to take an insulin dose, and request  240   e  is to eat the fast acting meal. Also shown is that events  238   f - k  each have an adherence criterion  224 , which must be met if the data for events  238   f - k  are to be recorded in the data file  145 . In this example, the adherence criteria  224  requires that the actions  240   f - k  be completed within ∀20 minutes of their corresponding performance times  238   f - k  in order for a data record  152  recording the received value(s) for the corresponding event  237   f - k  to count towards completing the collection procedure  70   b . In one embodiment, the processor  102  will make each of the requests  240   a - k  at their associated performance times  238   a - k  in order to obtain resulting data values e.g., data values  256   a - k  ( FIG. 4 ) at the time the requests are performed. 
     For example, the processor  102  can prompt the patient  12  with a request  240   a  to take a bG level (biomarker) measurement at performance time  238   a . The resulting measurement when received by the processor  102 , such as automatically from the measurement engine  138  after reading the test strip (biosensor)  140  for the desired biomarker, is then recorded automatically by the processor  102  in the date file  145  as a corresponding data value  256   a  for the associated event  237   a . For actions  240   d  and  240   e , at a required time, the processor  102  can automatically prompt the patient  12  to take the prescribed action at the required time, and again automatically prompt the patient thereafter to confirm that the required action has been taken, or that a predefine status has been achieved. A date-time stamp  169  can also be provided in the date record  152  automatically by the processor  102  upon triggering of the requests  240   a - k , acknowledgement of the requests  240   a - k , upon completion of the event  237   a - k , upon receiving a data value  256   a - k  for the event  237   a - k , and combinations thereof. Additionally, in another embodiment, the patient  12  can record data values  256   a - k  for one or more events  237   a - k  by entering the data directly into the device  24  via the user interface  146 , wherein the processor  102  stored the entered data values/information in the associated data record  152  for the event  237   a - k , or in other embodiments can record a voice message with the information for later transcription into digital data. In still other embodiments, the patient  12  can be guided by the collection device  24  to record data for an event  237   a - k  using a paper tool  38 . 
     As mentioned previously above, each event  237  can be a recording of a biomarker value, or a request for a required patient action that is necessary in order to create a context for the biomarker value, such as for example, meals, exercise, therapeutic administration, and the like. In the illustrated embodiment, the context  252  for completing events  237   a - c  is to establish a pre-prandial baseline and a no-trend condition, and for events  237   f - k  to establish a post-prandial excursion and tail. Such context  252  for these events may also be associated with the corresponding data records  152  for each event as contextual information  156  ( FIG. 4 ). Such information is useful later when reconstructing the data and/or when there is a desire to know the context for which the data record was created. 
     It is to be appreciated that any patient action taken outside of the required requests for patient actions  240   a - k  can also be recorded by the processor  102  but will not be considered by the processor  102  as part of the collection procedure  70   b . Data  256   a - k  for events  237   a - k  that are prospective can be identified based on a type of event, the time of the event, the trigger of the event, and combination thereof. Each of the performance times  238   a - k  can be fixed or variable based on prior data. Some of the event  237   a - k  in other embodiments can also be a past, current, or a future event such as for meals, exercise, and the like, or data values such as for hypoglycemic events, hyperglycemic events, or data of a specific value of interest. In some embodiments, the events  237   a - k  can be identified via a paper tool  38  that is procedure based. 
     As also shown, the structured collection procedure  70   b  will end if the condition of the exit criterion  228  is satisfied. In this example, the exit criterion  228  is satisfied if at least three of the actions  240   f - k  met the adherence criterion  224 . For example, the processor  102  may provide a unique identifier (e.g. an incremental count)  167  ( FIG. 4 ) in the data file  145  for each event  237   a - k  performed and to which satisfied the adherence criterion  224  if required. In the illustrated embodiment of  FIG. 4 , events  237   a - c  and  237   e - k  each receive a unique identifier but not event  237   d , e.g., &lt;null&gt;, since not satisfying an associated adherence criteria (not shown). In addition, analysis logic  258  and resulting recommendations  260  can also be provided in the structured collection procedure  70   b  which the processor  102  may apply automatically to the data collected upon satisfying the exit criterion  228  in one embodiment. 
     Similar features are also provided in the examples illustrated by  FIGS. 6C and 6D , wherein  FIG. 6C  depicts a structured collection procedure  70   c  which has a defined medical use case parameter  220  indicating that the procedure is helpful for determining suitability of a bolus in regards to a meal start. Likewise,  FIG. 6D  depicts a structured collection procedure  70   d  which has a defined medical use case parameter  220  indicating that the procedure is helpful for determining suitability of an exercise equivalent to a carbohydrate intake. In addition to the above examples, other such structured collection procedures may be designed to address other various medical use cases such as, for example, the following: determining the effects of eating a particular food on a biomarker level of a patient; determining the best time to take medication before and/or after a meal; and determining the affect of a particular drug on a biomarker level of a patient. Still other structured collection procedures can be provided which may be useful in addressing questions concerning how best to initialize therapy for a patient, finding a determination of status of a patient disease progression, finding the best ways to optimize a patient therapy, and the like. For example, the clinician  14  can define and/or use a pre-defined structured collection procedure  70  which looks at factors which may have an effect on the therapy of the patient. Such factors can include, for example, stress, menstrual cycle, pre-dawn effect, background insulin, exercise, bolus timing with respect to a meal, basal rate, insulin sensitivity, post-prandial behavior, and the like. 
       FIG. 6E  shows a diagram structured collection procedure  70  comprising one or more multiple sampling groupings  262  each comprising a recurring schedule of events  222  provided between the entry criterion  226  and the exit criterion  228 . In this example, the schedule of events  222  comprises one or more events  237  occurring each day at consistent times of day. As the structured collection procedure  70  in the process of obtaining contextualized biomarker data from a diabetic patient  12  can span over multiple days, even week and/or months before the exit criterion  228  is met, one or more checks  264 , such as for parameter adjustment, and/or evaluation of whether to re-run the sampling groupings  262 , can also be provided between the entry and exit criterions  226 ,  228  in one embodiment. The duration between such checks  264  can be used for physiological system equilibration, evaluation of treatment efficacy, or convenience. For example, either between each sample grouping  262  or after a predefined number such sampling grouping  262  (as shown), an analysis for the check  264  can be performed by the processor  102  to determine whether an adjustment to any parameter in the collection procedure  70  is needed. 
     For example, such analysis may be either for a parameter optimization or efficacy assessment. For the parameter optimization, the processor  102  can run calculations on the samples provided within a previous schedule of events  222  or sample grouping  262 , using information from prior optimizations, clinician set parameters, and a collection or therapy strategy, recommends a new parameter value. For the efficacy assessment, the processor  102  can evaluate data not utilized by the optimization analysis. Additionally, it is to be appreciated that after a group of samples, i.e., sampling group  262 , are taken the processor  102  can also evaluate the data from the sampling group  262 , such as if such data is need in order to alter/optimize a person&#39;s therapy. Adherence criteria can be applied to the perform this evaluation to the data of the sampling group  262 . For example, a first adherence criterion  224  can be used by the processor  102  to assess whether a minimum amount of data is provided by the sampling group  262  and if not, for example, the alteration/optimization of the patient&#39;s therapy will not take place. Another adherence criterion  224  could permit the processor  102  assess whether the data is acceptable to permit an adjustment called for by the check  264 , such as looking at spread of the data, whether these is too much variability (noise), as well as other data attributes to use the data. In this example, if meeting such adherence criterion, then processor  102  has assessed that there is minimum risk that adjusting a parameter of the procedure could readily result in a severe event, e.g., hyper- or hypoglycemic event. Lastly, an adherence criterion can be used by the processor to assess the exit criteria based on the data of sampling group, for example, the exit criterion is met when the data from the sampling group  262  satisfies the adherence criterion, such as for example, discussed above, for the sampling group. 
     It is to be appreciated that collection or therapy strategies can be categorized into scale based (sliding or fixed) assessments or formula based assessments. As input to the collection or therapy strategy, the processor  102  in one embodiment can utilize the data collected from a predetermined number of prior sample grouping(s)  262 . This data can be either used as individual points (only the formula based collection or therapy strategies), or combined with filtering for use in a scale based assessment. In another embodiment, for example, the result of a check  264  performed by the processor  102  can also result in a status or recommendation being provided by the processor  102  automatically. Such status or recommendation may be e.g., a status of continuing with current parameter values, a recommendation to change particular parameters, a recommendation to change the adherence and/or exit criterion, a status that the processor  102  switched to a secondary adherence and/or exit criterion based on the analysis performed on the data from a prior schedule of events or prior sample grouping, or a recommendation to terminate the collection procedure, and the likes. A discussion of performing a structured testing method using a structured collection procedure according to an embodiment of the present invention is provided hereafter with reference made to  FIG. 7A . 
     Structured Testing Method 
       FIG. 7A  depicts a structured testing method  300  for diagnostic or therapy support of a patient with a chronic disease. The method  300  may be implemented as instruction codes of a program running on a computer with a processor and memory, such as preferably clinician computers  25  ( FIG. 2 ) as stand-alone software, as part of software  34 , or as software provided as a service by server  52  via a secure web implementation over public network  50 . Upon a processor  76  executing the program from memory  78  of the clinician computer  25 , as one function among others, the processor  76  after receiving a query for a medical use case and/or question, searches memory  78 , computer readable medium  40 , and/or server  52  for all structured collection procedures  70   a - d , which matches the submitted query in step  302 . For example, the processor  76  may read the medical use case parameter  220  of each available structured collection procedures  70   a - d  and using a conventional search algorithm (e.g., list, tree, heuristics, etc.), provide on a display  82  a selection choice for those structured collection procedure matching the query in step  304  in one embodiment. 
     In one embodiment, the list displayed can reflect, for example, the structured collection procedures  70   a ,  70   b ,  70   c , and  70   d  available for use from the server  52 . In still another embodiment, the list of selection choices displayed can be dynamically created based on a type of medical use case the clinician  14  wishes to investigate. For example, prior to step  302 , a list of selectable medical use cases can be displayed on the display  82  by the processor  76 . In such an embodiment, the clinician  14 , using the user interface device(s)  86  may selected from among the displayed medical use cases, for example, the medical use case “Determining meal effect on patient&#39;s therapy.” After the clinician makes such a selection, which the processor  76  receives as input from the user interface device(s)  86 , the processor  76  after using decision logic (e.g., if . . . then) provided by the software  34  would then display in step  304 , for example, structured collection procedure  70   b  (e.g., a structured collection procedure to determine a more accurate insulin-to-carbohydrate ratio) and  70   c  (e.g., a structured collection procedure to determine bolus timing in regards to meal start), and not structured collection procedures  70   a  and  70   d , which are structured collection procedures unrelated to the medical use case. Likewise, a “show all structured collection procedures” could also be a choice among the displayed medical use cases, in which the complete list of available structured collection procedures would then be displayed in step  304 . In another embodiment, step  302  may be skipped and the processor  76  in step  304  can just provide a display of the structured collection procedures  70   a - d  available in memory  78  of the clinician computer  25 . 
     In step  306 , a clinician using the user interface devices  86  can select a structured collection procedure  70  on the computer  25  for diagnostic or therapy support. For example, the selecting process can include choosing from the list displayed in step  304 , which provided one or more structured collection procedures. After the clinician makes such a selection in step  306 , which the processor  76  receives as input from the user interface device(s)  62 , the processor  76  of the computer  25  retrieves automatically from an electronic component, e.g., computer memory  78 , server  52 , or computer readable medium  40 , and displays the selected structured collection procedure  70  on display  82  for viewing. 
     It is to be appreciated that each structured collection procedures  70   a ,  70   b ,  70   c , and  70   d  is based on a medical use case and has parameters defining entry criteria  226 , a schedule of events  222 , adherence criteria  224 , and exit criteria  228 . As mentioned above, the entry criteria  226  establish the conditions needed to be met prior to obtaining biomarker data from the patient. Each event  237  of the schedule of events  222  comprises a performance time, patient guidance to perform the event, patient actions, a request for information from the patient, a request for collection of at least one type of biomarker data from the patient, and combinations thereof. The adherence criteria  224  is used to qualitatively assess whether an event  237  performed according to the schedule of events  222  provided data which is acceptable to addressing the medical use case upon which the structured collection procedure  70  is based. Additionally, as mentioned above, the exit criteria  228  establish the conditions needed to be met prior to exiting the structured collection procedure  70 . 
     In step  310 , after the processor  76  displays the selected structured collection procedure  70 , the clinician  14 , to meet the needs of the patient  12  and/or interests of the clinician, may adjust any one of the parameters  222 ,  224 ,  226 , and  228  which are also displayed on the display  82 . Safe guards may be implemented to ensure that only the clinician  14  can modify such parameters and/or run the software  34 , such as via password protection. The processor  76  receives any such changes to the parameters  222 ,  224 ,  226 , and  228  as input via user interface devices  86  and saves the revised structured collection procedure  70  in memory  78 . Next, in step  312 , the selected structured collection procedure  70  is prescribed on the computer  25  to the patient  12  by the clinician  14 , wherein the processor  76  of the computer  25  provides as output the selected structured collection procedure  70  to the patient  12  to perform. For example, in step  314 , the prescribed structured collection procedure  70  is implemented electronically on a processor based device, such as collection device  24 , or any of the other above described devices  18 ,  28 , and  36  ( FIG. 1 ), as part of the software  34  or in other embodiment, portions thereof as part of paper tool  38 . 
     In one embodiment, the prescribed structured collection procedure  70  may be implemented from the clinician computer  25  ( FIG. 2 ) to the collection device  24  via communication link  72 , via the public network  50  through a webpage and/or made available for downloading on server  52 . In still other embodiments, the prescribed structured collection procedure  70  can be provided through the computer readable medium  40  and loaded by one of the devices  18 ,  24 ,  28 , and  36 , downloaded from another one of the devices  18 ,  24 ,  25 ,  26 ,  28 , and  36 , or downloaded via cell phone or telephone connection from server  52 . Notice that a new/updated/prescribed structured collection procedure  70  available for use on the devices  18 ,  24 ,  25 ,  26 ,  28  and  36  may be provided in any standard fashion, such for via postal letters/cards, email, text messaging, tweets, and the likes. 
     Customizing a Structured Collection Procedure 
       FIG. 7B  conceptually illustrates one example of a pre-defined structured collection procedure  70 , which has a defined medical use case parameter  220  indicating that the procedure is helpful for medical use cases or questions which need to know the trends in blood glucose (bG) levels of a patient and/or the relationships between blood glucose values and time of day, meal size, and energy level. As mentioned above previously, the use case parameter  220  can be used as an identity tag in which the processor  102  may locate the associated structured collection procedure  70  in response to a search query, such as, for entered use case or question. For example, the search query can be entered into the collection device  24  via the user interface  146  and/or received from the clinician computer  25 . Such a search query may result from a desire to know which uses case can be addressed by the structured collection procedures  70  currently available on the collection device  24 , or to know which structured collection procedure  70  would be useful to address a particular use case or question. Therefore, the use case parameter  220  in one embodiment permits a structured collection procedure  70  to be automatically chosen by the processor  102  from a plurality of structured collection procedures  70   a - d , such as provided in memory  110 , memory  78 , computer readable medium  40 , and/or server  52  based on a selection, such as from a displayed list on the display  108  provided by the processor  102 , or from input received by the processor  102  from the user interface of a defined medical question. In other embodiments, the use case parameter  220  may also indicate the structured collection procedure  70  is also useful for showing relationships between bG level values and time of day, meal size, and/or energy level. 
     In one embodiment, the pre-defined parameters of the structured collection procedure  70  can be displayed for modification/customization by the processor  102  of the collection device  24  on the display  108  and/or by the processor  76  of the clinician computer  25  on the display  82  by an authorized user. Such an authorized user may be identified, for example, on the collection device  24  and/or the clinician computer  25  by a password entered via the user interface  146 ,  86 , respectively. In such an embodiment, the pre-define parameters of structured collection procedure  70  can be displayed on the display  108 ,  82  in which customizable parameters can provide editable or selectable variables via drop-down boxes with various selection choices, radio buttons, check boxes, formatted fields requesting a specific type of information (mm-dd-yyyy, number, letter, etc.), text boxes to enter messages to be displayed, and the likes. The structured collection procedure  70  can be displayed for editing in tabular format (as illustrated) in one embodiment or in a sequential manner listing one parameter at a time in a scroll-through fashion in another embodiment. In still another embodiment, structured collection procedures can be provided which cannot be modified. 
     As shown by  FIG. 7B , the structured collection procedure  70  may further comprise parameters defining one or more criterions setting the conditions needing to be met by the patient  12  to start of the structured collection procedure, i.e., entry criterion(s)  226 , to end the structured collection procedure i.e., exit criterion(s)  228 , and combinations thereof. In one embodiment, the processor  102  of the collection device  24  uses the one or more criterions to automatically start, evaluate, and end the structured collection procedure  70  if the condition(s) defined by the structured collection procedure are met. In still another embodiment, adherence criterion(s)  224 , which are the conditions needing to be met in order for the collected datum/data to be accepted, can also be provided in the structured collection procedure  70 . 
     As also shown in  FIG. 7B , the structured collection procedure  70  further comprise parameters defining one or more (collection) events  237  which together form the schedule of events  222 . Each of the events  237  comprises one or more requests  240 , e.g., for a measurement from the measurement engine  138  of a biomarker value for a sample provided to the biosensor  140 , and/or for information to be entered by the patient via the user interface  146  such as in response to a question presented by the processor  102  on the display  108 . In the illustrated embodiment, the requests  240  are for a bG measurement, a meal size indication (S, M, or L), and an energy level indication (1, 2, 3, 4, 5), in which 1 is lowest and 5 is highest. Other such requests  240  can include indicating whether the patient exercised, indicating a particular food that was consumed, indicating which medicine was administered, indicating dosage of the medicine administered, and the like may also be provided in other structured collection procedures  70 . In the illustrated embodiment, the collection events can be customized by selecting which request  240  the processor  102  should perform via a yes/no selection box. 
     The structured collection procedure  70  may also include guidance  230  and timing or performance time  238  associated with each of the collection events  237  as well as with each of the entry, exit, and adherence criterion/criteria  226 ,  228 , and  224 . Such guidance  230  is provided by the processor  102  to the display  108  upon the occurrence of the associated collection event  237  or other parameters. For example, a collection event  237  for a bG measurement before breakfast may also have a request  240  for an indication of the energy level of the patient. Therefore, in this example, the associated guidance  230  which states, “Please indicate energy level” is provided on the display  108  by the processor  102 . It is to be appreciated that the guidance  230  is a text box, field, area, which enables for information to be provided to the patient to help the patient in performance of the structured collection procedure  70 . In this example, selection of a number from 1 to 5 may be made via press of one of the buttons  147 ,  149  or via the touch screen interface if provided by display  108  as a data entry for such a request  237 , which is then stored by the processor  102  in memory  110  of the collection device  24  as part of a data file  145  ( FIG. 4 ) for the structured collection procedure  70 . 
     The timing parameter  238  of the structured collection procedure  70  is used to specify for any one of the associated collection event  237 , the entry, exit, and adherence criterion/criteria  226 ,  228 ,  224 , either a specific date and/or time (mm-dd-yyyy, hh:mm), or a period (n) after a preceding collection event in which to perform the associated collection event. The periods n 1 , n 2 , n 3  in the illustrated embodiment for the respective collection events  237  indicate hours, but in other embodiments can be indicated in minutes or seconds. In another embodiment, the timing or performance time parameter  238  for an associated collection event  237  and for the entry, exit, and adherence criterion/criteria  226 ,  228 ,  224  can be modified by another collection event and/or by the criterion/criteria. 
     For example, in the illustrate embodiment, the entry criterion  226  is modified by the adherence criterion  224  by adding a day if the guidance  230  provided in the form of a question “Are you willing to conduct a test over 3 consecutive days?” is not affirmed by the patient  12  e.g., via a “No” selection provided on the collection device  24 . In this illustrated example, the “Affirms guidance” may be a drop down selection provided in a combo box for customizing the adherence criterion  224  of the associated collection event  237 , which when selected causes the processor  102  to wait for the accepted/not accepted input (e.g., via buttons  147 ,  149 ) before executing the remaining logic (“if not add 1 day to timing”) of the adherence criterion  224 . Still further in this example, the processor  102  in accordance with the logic provided in the adherence criterion  224  associated with the exit criterion  228 , can set the timing or performance time parameter  238  of the exit criterion  228  to the date (mm-dd-yyyy) that is 3 days after completing the entry criterion  226 . It is to be appreciated that the various possible combinations of logic statements which may be performed by the structured collection procedure  70  can be pre-defined and selected by a drop down box in order to be customized in one embodiment, and/or logic statements can be built in another embodiment. 
     The structured collection procedure  70  can also includes an options parameter  232  associated with each of the collection events  237  as well as with each of the entry, exit, and adherence criterion/criteria  226 ,  228 ,  224 . The options parameter  232  can have a customizable value(s) to govern whether the data and/or results of the associated collection event  237  or any of the other parameters e.g., entry, exit, and adherence criterion/criteria  226 ,  228 ,  224 , in the structured collection procedure  70  meets a particular condition such that still further processing may be carried out by the processor  102  if such a condition(s) is meet. For example, such options can be to have the processor  102  automatically send a message to the physician indicating that the patient has started the structured collection procedure  70  via satisfying the entry criterion  226 , or to provide a message to the patient and/or the physician if the patient fails a collection event  237  by not satisfying an adherence criterion, or to provide a message to the physician when the patient completes the structured collection procedure  70  when the exit criterion is satisfied, or combinations thereof. For example, such an options parameter  232  can have a global list of such actions which is selected on the display  108 , for example, by a selected value from a range of values associated with each option. For example, the options for each parameter can be customized via selecting from a drop down box having option choices (e.g., 1, 2, 3, 4, 5, . . . , A, B, C, etc.) and in which, for example, Option 1 of having the processor  102  provide a message to the physician if the patient fails a collection event  237  (e.g., by not satisfying an adherence criterion), is shown selected for the before breakfast collection event  237 . An example in the context of patient  12  being diabetic is provided hereafter to illustrate further such features provided on a collection device  24  according to the present invention. 
     A typical patient with Type 2 diabetes may measure his/her blood glucose once per day after waking up in the morning. At a routine office visit, the patient&#39;s HbA1C result is found to be elevated. The physician recommends that the person goes through three days of intensified glucose monitoring, and selects the structured collection procedure which is useful for this purpose. The structured collection procedure  70  is then customized as discussed above such that during these three days collection events  237  are defined with a number bG measurement requests  240  such that the patient can be requested by the processor  102  to measure his/her blood glucose before and two hours (e.g., n 1 =2) after breakfast, before and two hours (n 2 =2) after lunch, before and two hours (n 3 =2) after supper, and at bedtime. Additionally, the patient  12  can be requested via other associated requests  240  for each collection event  237  to provide an assessment of the relative size of the ingested meals at the appropriate times as well as an indication how he/she feels with regard to energy level. In the illustrate embodiment of  FIG. 7B , the processor  102  can request the indication of energy level with each collection event  237  and the assessment of the relative size of the ingested meals every other collection event  237  (i.e., after the meal). Furthermore, the physician has provided a condition via adherence criterion  224  of having to perform the meal assessment within ±30 minutes of period (n) of the associated collection event  237  in order for such information to be useful in the assessment. Such information is useful to contextualize the collected data and for the analysis performed on the collected data. 
     Additionally, the physician would like to be notified when the patient has failed to complete the “before breakfast” collection event  237 . Therefore, to facilitate the notification option, the physician customizes the structured collection procedure  70  by set the options parameter  232  associated with the “before breakfast” collection event, via a drop down box to “Send a message to the physician if adherence criterion fails.” All other collection events  237  have their associated options parameter  232  default to indicate that the processor  102  is not to take any additional action with regards to the options parameter. It is to be appreciated that the above described features and arrangements illustrated embodiment of  FIG. 7B , provides a simply and convenient interface and method for customizing a structured collection procedure, such as for parameter adjustments carried out in step  310  of method  300  previously discussed above in reference to  FIG. 7A . 
     Implementing and Performing a Structured Collection Procedure 
       FIG. 8A  shows a flowchart of the method for implementing and performing a structured collection procedure  70  to obtain contextualized biomarker data from a patient  12  according to an embodiment of the invention. It is to be appreciated that a number of structured collection procedures  70   a - d  ( FIG. 2 ) prescribed in step  312  and implement in step  314  ( FIG. 7A ) may be stored in memory  110  ( FIG. 3 ) of the device  24  and selected for execution at any desired time. For example, upon pressing a certain combination of the buttons  147 ,  149 , the patient can select a desired structured collection procedures  70   a - c  and the date when to start a structured testing collection i.e., a set mode function. For example, a date range to choose from may be to begin the testing tomorrow and end at today +90 days, which the processor  102  can also recorded in the data file  145  ( FIG. 4 ) as part of the setup data  163 . In such an implementation, the processor  102  as instructed by the software  34  reads the setup data  163  for the selected structured collection procedure  70  and indicates on the display  108  that the device  24  is in a structured testing mode, for example, from one day before the chosen focused testing start date until the end of the structured collection procedure. 
     It should be appreciated that multiple structured collection procedures  70   a - d  can be executed sequentially or simultaneously at any given time. However, in one embodiment, the software  34  permits the user only to schedule another structured collection procedure  70  if the start date is later than the end date of the current structure collection procedure  70  being executed. The software  34  also permits the user to override a scheduled date for a structured collection procedure  70 . If a structured collection procedure  70  is scheduled and the user enters the set mode function again, the software  34  causes the processor  102  to display the scheduled date on the display  108  as the default date; if the user exits the set mode without modifying the date, the previously scheduled date stays active. If a structured collection procedure  70  has started, the software  34  permits the user to enter the set mode and cause the processor  102  to cancel the current structured collection procedure  70 . Upon cancellation, in one embodiment, the software  34  causes the processor  102  to de-tag (e.g., null the unique identifiers  167 ) the data records  152  in the data file  145  for the data collected for the cancelled structured collection procedure  70 . 
     Upon reaching the procedure start in step  316  ( FIG. 8 a   ), the processor  102  evaluates the whether entry criterion(s)  226  is met in step  318  to begin the structured collection procedure  70  selected to obtain biomarker data to address a predefined use case or question (e.g., use case parameter  220 ). In step  320 , the processor  102  specifies requests  240  according to their associated timing  238  for each event  237  in the schedule of events  222  for the structured collection procedure  70 . It is to be appreciated that the schedule of events  222  provides a sampling plan for biomarker data collection that is performed by the processor  102  to obtain biomarker data in a predefined context. In performing the schedule of events  222  in step  320 , the software  34  causes the processor  102  to assign a unique identifier (e.g. incremental count)  167  in a date record  152  which corresponds to each event  237  in the structured collection procedure  70 . Optionally, each criterion  226 ,  228 ,  224  may also be provide with a date time stamp  169  to indicate when such criterion was satisfied, if desired. 
     Adherence criterion  224  is then applied to the input received (e.g., biomarker data or information) in response to an request  240  to determine whether the input received meets the adherence criterion  224 . When a structure collection procedure  70  has started, all data collected according to requests  240  in the structured collection procedure  70  and which satisfy the adherence criterion  224 , if required in step  322 , are then assigned (tagged) in the data file  145  by the processor  102  with the unique identifier  167  in step  324 . It is to be appreciated that the unique identified also serves to associates the collected data e.g., data values  256  with their event  237 , the request  240 , and a date-time stamp  169  to indicate when the collection in response to the request  240  was received by the processor  102 . While a structured collection procedure  70  is being executed, in one embodiment the software  34  permits the user to perform a measurement on the device  24  at any time without interfering with the episode. 
     In one embodiment, the software  34  permits reminders for biomarker measurements to be ‘snoozed’ as mentioned above for a period, such as for example, 15 minutes and up to a number of times, for non-critical measurements. In another embodiment, biomarker measurements or data entries that are performed close enough in time to a request  240  in step  320  are designed as valid measurements or data entry for the request  240  by the software  34 . As such, the processor  102  will tag the associated data record  152  for the event  237  with the unique identifier  167  for such a biomarker measurement or data entry accordingly. In the case of biomarker measurements, if the measurement is accepted as valid for the request  240 , the software  34  causes the processor  102  to prompt the user to input additional information if needed by the structured collection procedure  70  to provide context  252  for data resulting from the request  240 . Such additional input, may include, for example, a rating of energy level from 1 to 5, where 1 is low and 5 is high; meal size from 1 to 5 where 1 is small and 5 is large, and exercises from yes or 1 to mean over 30 minutes, and no or 2 to mean less than 30 minutes. Such additional information or contextual information  156  when inputted via the user interface  146  is stored by the processor  102  in the data file  145  associated with the unique identifier  167  for the data event request  240  requiring the additional information also in step  324 . 
     In one embodiment, biomarker measurements determined by the processor  102  as not being close enough in time to the data event request  240  defined by the structured collection procedure  70  will not be tagged with a unique identifier  167  in the data file  145  by the processor  102 . Such is illustrated in the shown data file  145  with request  240   d  and data values  256   d  not being associated with a unique identifier  167  e.g., &lt;null&gt;. An example of a definition of ‘close enough in time to the collection procedure’ as instructed by the structured collection procedure  70  and/or software  34  to cause the processor  102  to make such a determination may be defined as being relative to a prescheduled time or a snoozed time. For example, for pre-prandial measurements up to 15 minutes in anticipation is acceptable; for post-prandial measurements, up to 10 minutes in anticipation is acceptable; and for bedtime measurements, up to 15 minutes in anticipation is acceptable. Other definitions may be provided in other structured collection procedures  70  and/or software  34 . 
     In step  326 , the processor  102  then evaluates whether the exit criterion  228  for the selected structured collection procedure  70  is satisfied. If not, then the processor  102  continues with performance the schedule of events  222  until the exit criterion  228  is satisfied. Upon satisfying the exit criterion  228 , the collection procedure  70  ends in step  328 . In one embodiment, the structured collection procedure  70  may also end if in step  318 , the entry criterion  226  is also not met. 
     In some embodiments, the structured collection procedure  70  can be configured for performance as a paper tool  38 ; diabetes software  34  integrated into a collection device  24  such as a blood glucose meter  26 ; diabetes software  34  integrated into the computing device  36 , such as a personal digital assistant, handheld computer, or mobile phone; diabetes software  34  integrated into a device reader  22  coupled to a computer; diabetes software  34  operating on a computer  18 ,  25  such as a personal computer; and diabetes software  34  accessed remotely through the internet, such as from a server  52 . When diabetes software  34  is integrated into a collection device  24  or a computing device  36 , the diabetes software  34  can prompt the patient to record diary information such as meal characteristics, exercise, and energy levels. The diabetes software  34  can also prompt the patient to obtain biomarker values such a blood glucose values. 
     GUI Interface Providing a Selectable Structured Collection Procedure 
       FIG. 8B  shows a method of implementing the structured collection procedure via a graphical user interface provided on a collection device  24 , which when executed on the collection device, cause the processor  102  to perform the following steps. Upon pressing a certain combination of the buttons  147 ,  149 , the patient  12  can scroll to the structured collection procedure  70  available for selection in a list  329  provided by the processor  102  on the display  108  of the collection device  24  in step  330 . If desiring to start the structured collection procedure, the patient  12 , for example, selects via pressing an OK button  151  in step  332 , the desired structured collection procedure  70 . In this example, the entry criteria  226  ( FIG. 6 ) of the structured collection procedure  70  provides information in step  334  which the processor  102  displays to the user on the display  108 . After reading the displayed information, the user presses any button in step  336  in which the next procedure in the entry criteria  226  is performed by the processor  102 . In this illustrated example, as part of the entry criteria  226 , a question is then asked in step  338  by the processor  102 . If the patient  12  is still desirous of starting the structured collection procedure, the patient  12  selects the OK button  151  in step  340 ; otherwise, any other press via button  147 ,  149  will cause the processor to go back to the list  329 , thereby stopping the set-up procedure for the structured collection procedure  70 . 
     After the patient  12  presses the OK button  151 , the processor  102  in step  342  will provide on the display  108  an alarm clock  343  for setting the time to begin the selected structured collection procedure  70 . It is to be appreciated that all the required events  237  for biomarker sampling, patient information, etc., is automatically schedule by the processor  102  in accordance with the schedule of events  222  for the structured collection procedure  70  in which timing, values, questions, etc., therein may have been adjusted by the clinician  14  as discussed previously above in reference to  FIGS. 7A and 7B . Therefore, other than entering the start time as permitted by the entry criteria  226 , no other parameter adjustments in the structured collection procedure  70  is required by the patient  12  (or permitted in one embodiment). 
     In the illustrated embodiment, the patient in step  344  can adjust the start time of the structured collection procedure for the next day, e.g., Day 1, via buttons  147 ,  149 . Upon confirming the start time in step  346  via pressing the OK button  151 , the start time is recorded in memory  110  as part of the setup data  163  in the data file  145  ( FIG. 4 ) for the structured collection procedure  70  by the processor  102 . The processor  102  then displays the selection list  329  on the display  108  in step  348 , thereby completing the set-up procedure, which satisfies the entry criterion  226 , and indicates on the display  108  that the collection device  24  is in a structured testing mode  349 . 
     It should be appreciated that in on embodiment multiple structured collection procedures can be executed sequentially or simultaneously at any given time, and hence in one embodiment the mode  349  provided on the display  108  will indicated which structured testing is being performed. However, in one preferred embodiment, the software  34  does not permits the user to schedule another structured collection procedure, unless the start date is later than the end date of the current structured collection procedure being executed via the user interface  146 . It is to be appreciated that processor  102  may re-schedule the following structured collection procedures automatically if the current structured procedure is still running due to the exit criteria  228  not being met. The software  34  in another embodiment may also permit the user to override a scheduled date for a structured collection procedure. If a structured collection procedure is scheduled and the user enters the set mode function again, the software  34  causes the processor  102  to display the scheduled date on the display  108  as the default date; if the user exits the set mode without modifying the date, the previously scheduled date stays active. If a structured collection procedure has started, the software  34  permits the user to enter the set mode and cause the processor  102  to cancel the current structured collection procedure, if desired. 
     In step  350 , an alarm condition  351  can be provided by the processor  102  the next day (as indicated by the symbol Day1) as was set in the above-mentioned procedure the previous day (as indicted by the symbol Start Up). Upon the user selecting any button  147 ,  149 ,  151  in step  352 , the processor  102  as instructed by schedule of events  222 , provides a first scheduled event  237  which is information  353  to be displayed on display  108  in step  354 , which the patient  12  acknowledges with any button  147 ,  149 ,  151  being pressed in step  356 . Next in step  358 , the processor  102  is instructed by the schedule of events  222  to execute a second scheduled event, which is to display on the display  108  a question  359  for the patient, which the patient  12  acknowledges with any button  147 ,  149 ,  151  pressed in step  360 . In the illustrated embodiment, the patient in step  362  indicates the start time of breakfast in minutes from the wake up alarm  351  previously acknowledged in step  352 . Upon confirming the meal start time in step  364  to the processor  102 , via pressing the OK button  151 , the meal start time is recorded in memory  110 . For example, the meal start time is recorded in the data file  144  in the associated data record  152  as data for the event  237  by the processor  102 . Additionally, in step  366 , the processor  102  displays to the patient  12  the information regarding the timing for the next schedule event as a reminder. In step  368 , upon reaching the next scheduled event indicted by the schedule of events  222 , the processor  102  provides a request  240  on the display  108  for the patient to take a measurement, e.g., a blood glucose measurement. Additionally, in step  370 , the processor  102  also makes a request  240  for information on the size of the meal that is to be ingested as required by the schedule of events  222  in order to provide contextual information  156  to the measurement value. 
     As mentioned above previously, for each event the software  34  causes the processor  102  to assign a unique identifier (e.g. incremental count)  167  ( FIG. 4 ) to the data of each request  240  provided in the schedule of events  222  which meet the adherence criterion  224  in the associated date record  152  for the event  237 . Therefore, while the structured collection procedure is being executed, the software  34  permits the user to perform a measurement on the collection device  24  at any time out side the schedule of events  222 . Such a measurement since not being performed according to a request  240  will not be evaluated for the adherence criterion  224 , and thus will not be provided with a unique identifier  167  in the date file but will only be provided with a date-time stamp and its measurement value. Such data is still recorded in the data file  145 , as such data may still be useful for another analysis. 
     In another embodiment, the software  34  also permits reminders for biomarker measurements, such as provided in step  238 . For example, in one embodiment, the processor  102  provides an alarm and/or alert message for a reminder via the indicator  148  and/or on the display  108 , respectively, to provide a measurement. For example, at the time  238  of a particular request  240  for taking a biomarker measurement (or reading), the processor  102  prompts the patient  12  by al least displaying on the display the message, “It is now time for your reading.” An audible alarm and/or tactile alarm (vibrations) can be provided by the processor  102  via indicator  148  in another embodiment. For example, in one embodiment, the collection device  24  will provide such a prompt even when already powered on, such as by the patient  12  for another reason, e.g., to conduct a non-scheduled event, when in, for example, a window of time in which to take the requested measurement/reading, or even when powered downed, such as in a standby mode, by waking up to provide the reminder via the prompt. In another embodiment, the provided reminder or prompt can be ‘snoozed’ for a pre-defined period as mentioned above, that still falls within the window of time in which to take the requested (critical) measurement/reading such as for example, 15 minutes or any other such suitable time that falls in the window of time. It is to be appreciated that the snooze feature for a measurement/reading that is considered critical to the structured collection procedure  70 , e.g., a measurement/reading needed for helping to address the medical use case or question, needed to meet adherence criteria  224 , and/or needed in subsequent analysis for some determination, etc., the snooze feature will not extend the request  240  beyond the window of time provided by the collection procedure  70  via, e.g., adherence criterion  224  for the request  240 . For example, in one embodiment one or more events  237  in the schedule of events  222  can be pre-defined as critical as well as being a primary sample via use of the options parameter  232  ( FIG. 7B ) provided in the structured collection procedure  70 . For example, an event  237  which is designated as critical is one that cannot be missed, but if missed can be replaced by another sample already in the date file  145 . An event  237  which is designated as a primary sample is one that cannot be missed, and which cannot be replaced by another sample, even if available in the date file  145 . In still another embodiment, the snoozing can be up to a number of times, for non-critical measurements. For example, certain events  237  in the structured collection procedure  70  could be designated as having a non-critical request  240 , which can be snoozed, such as via selecting such an option that is provided as one of the options parameter  232  ( FIG. 7B ). The options parameter  232  in this embodiment could for example provide the snooze option as well as a selectable time interval (e.g., 1-60 minutes, etc.) and a selectable number of times (e.g., 1-5, etc.) that the user is permitted to snooze the request  240 . In still another embodiment, the collection device  24  permits for an alarm shut off i.e., the indicator  148  if providing the reminder (audible, vibratory) can be shut off for the entire window of time via the user interface  146 , but wherein processor  102  still accepts the measurement/reading as long as it is made in the window of time. In still another embodiment, the collection device  24  provides a skip reading option also received by the processor  102  via a selection entered using the user interface  146 , e.g., from a list of selectable options, such as for example, snooze, alarm shut off, skip reading, provided on the display  108 , in which again no reminder/prompt will be provided as patient  12  has indicated to the processor  102  that he/she does not want to take that particular requested measurement/reading. It is to be appreciated that selecting the skip reading selection option can result in an adherence event  242  resulting in further processing, such as discussed previously above in early sections, if adherence criterion  224  had been associated with the event  237  prompting the request  240 . 
     In still another embodiment, the adherence criteria  224  can require biomarker measurements to be performed close enough in time to a data event request  240 . Therefore, if such biomarker measurements are performed within the period specified by the adherence criteria  224 , the processor  102  can indicate that the measurements or data entry for the event is acceptable and tags (i.e., assigns the unique identifier  167 ) the value of the biomarker measurement or data entry in the data file  145  accordingly. In the case of biomarker measurements, if the measurement is accepted as valid for the data event request  240  (i.e., meets the adherence criterion(s)  224 ), the schedule of events  222  may causes the processor  102  to prompt the user to input additional information if needed by the structured collection procedure  70 , such as mentioned above regarding step  370  to provide contextual information  156  (i.e., context) to the measurement received in response to a request  240 . 
     Such contextual information  156  when inputted via the user interface  146  can be stored by the processor  102  in the data file  145  associated with the unique identifier  167  for the data event request  240  requiring the additional information. Biomarker measurements determined by the processor  102  as not being close enough in time to the data event request  240  as defined by the adherence criteria  224  will not be tagged in the data file  145  by the processor  102 . Such is illustrated in the shown data file  145  ( FIG. 4 ) with data event request  240   d  and data values  256   d  not being associated with a unique identifier  167 . An example of a definition of ‘close enough in time to the collection procedure’ as instructed by the adherence criteria  224  to cause the processor  102  to make such a determination may be defined as being relative to a prescheduled time or a snoozed time. For example, for pre-prandial measurements up to 15 minutes in anticipation is acceptable; for post-prandial measurements, up to 10 minutes in anticipation is acceptable; and for bedtime measurements, up to 15 minutes in anticipation is acceptable. Other definitions may be provided in other adherence criteria  224  for other events in the schedule of events  222  as well as in other structured collection procedure. 
     In the illustrated embodiment, the user uses the buttons  147 ,  149  to scroll to a selection, which is entered by the processor in the data record  152  for the associated request  240  via pressing Okay button  151  in step  372 . In one embodiment, the meal size can be indicated via a number range, such as for example, from 1 to 5, where 1 is small and 5 is large. In the illustrated embodiment, additional input for contextual information  156  regarding a rating of energy level from 1 to 5, where 1 is low and 5 is high is requested in step  374 , which is entered in the data file  145  as mentioned previously above via the processor  102  receiving input for the request  240  by using the user interface  146  in step  376 . In other embodiment, other contextual information  156  may include indicating whether the patient exercised and/or how long. For example, the user interface  146  may be use in which yes or 1 to mean over 30 minutes, and no or 2 to mean less than 30 minutes. In the illustrated embodiment, as the exit criterion  228  is now meet via successfully performing steps  368 - 376 , the structured collection procedure  70  ends in step  378 , wherein the processor  102  again displays the list  329 , such that the patient  12  may perform other tasks on the collection device  24  if so desired. Reference is now made to  FIG. 9  hereafter. 
     Method of Contextualizing Biomarker Data 
       FIG. 9  depicts a method  388  of contextualizing biomarker data for diabetes diagnostics and therapy support according to an embodiment of the invention. It is to be appreciated that in the previous embodiments discussed above with reference to  FIGS. 8A and 8B , the contextual information  156  was requested and recorded with the associated biomarker value by the processor automatically during the structured collection procedure  70 . However, in embodiments where such automation is not provided on the collection device  24 , and the patient is using a paper tool  38 , the collection data can be later associated with its contextual information  156  after, for example, the structured collection procedure  70  is performed in step  390  to create at least data event values  256 . If not already done by the collecting device  24 , such as in the case of a device with limited memory and processing power or when recordings are made on paper tool  38 , such data may be provided to another one of the devices  18 ,  25 ,  36  that is running the software  34  and has the ability to associate at least the data event values  256  ( FIG. 4 ) with their respective data event requests  240 . This associating of at least the data event values  256  with their respective data event request  240 , the date-time stamp  169 , and the contextual information  156  results in contextualized (self-monitoring) data  170  in step  392 . 
     With the contextualized data  170 , the physiological state of the patient  12  at the time of the measurement can be described. The patient&#39;s physiological state can influence a biomarker value, so knowledge of the patient&#39;s physiological state aids in the understanding of a biomarker value. The biomarker data can be contextualized because the biomarker data is collected in the context of predetermined events such as last time of meal, meal type, meal distribution, exercise information, sleep quality, sleep duration, waking time, and stressors such as illness and the like. Time-resolved data permits interpreting the biomarker data in context with other information, such as compliance with a structured collection procedure  70  and patient lifestyle events. 
     Next in step  394 , the contextualized data  170  is evaluated using adherence criterion (or criteria)  224  to generate accepted contextualized data  395  that meets the adherence criterion. As the adherence criterion  224  provides a basis for comparison of a data event value  256  with a standard, so the data event value can be either accepted and used or rejected and not used, the adherence criteria can be used to filter data in one embodiment. 
     For example,  FIG. 10  shows a diagram of accepted contextualized data  395  intermingled with non-acceptable contextualized data  397 . The diagram vertical axis shows biomarker values  256  including context  250  in the form of a biomarker setpoint, a biomarker upper limit, and a biomarker lower limit. The diagram horizontal axis shows performance times  238  of measurement requests  240  and a sleep period event  237  in which the actual sleep surpassed a recommended minimum amount of sleep as indicated by the dashed line. The accepted contextualized data  395  is that which met the adherence criterion  224 . The non-acceptable contextualized biomarker data  397  are either not within the structured collection procedure  70  or did not meet adherence criterion  224 . By excluding the non-acceptable contextualized biomarker data  397 , the accepted contextualized biomarker data  395  can help improve decision-making. Statistical techniques can be used to view the accepted contextualized biomarker data  395  in a form that conveys additional information to a clinician  14 . Examples of statistical techniques include regression methods, variance analysis, and the like. 
     Referring back to  FIG. 9 , in another embodiment, step  394  may precede step  392 . The accepted contextualized biomarker data  395  in another embodiment can also be used to provide a decentralized disease status assessment in step  396  for diabetes diagnostics and therapy support. For example, the processor of a cable device may be requested to run the assessment discussed previously above regarding the check  264  ( FIG. 6E ). Based on the results of the assessment and/or the accepted contextualized biomarker data, the parameters  226 ,  222 ,  224 ,  228  as well as the timing  238  and requests  240  in the structured collection procedure  70 , may optionally be adjusted in step  398 , such as discussed above in reference to  FIGS. 7A and 7B . Additionally, the data may be characterized using a noise function in step  400 . Further details regarding another process embodiment for parameter adjustment and the noise function are provided hereafter in later sections. It is to be appreciated that the steps  398  and  400  are optionally, but if applied, then the structured collection procedure  70  may again being prescribed to the patient  12 . A brief discussion on the stages of type 2 diabetes disease progression is provided now hereafter in reference to  FIG. 11 , such that a better understanding of the parameter adjustment is provided in the sections thereafter. 
       FIG. 11  shows that pre-diabetes  410  is a period prior to a diagnosis of type 2 diabetes where the patient  12  is exhibiting symptoms of impaired glucose tolerance (IGT) which transitions into postprandial hyperglycemia  412 . About the time of a patient being medically diagnosed as a type 2 diabetic (phase I)  414 , the patient typically begins oral mono-therapy. However, oral mono-therapy can begin in later stages of postprandial hyperglycemia  412 . As type 2 diabetes progresses, the patient enters phase II  416  that includes oral combination therapy. Finally, the type 2 diabetic enters phase III  418  that includes insulin therapy. Although type 1 diabetes is a much more stable disease state because pancreatic beta cells have essentially ceased production of insulin, some type 1 patients also develop type 2 diabetes. 
     Parameter Adjustment 
     Referring to  FIG. 12  shows a flowchart of another method  420  for adjusting parameters (e.g., in step  398 ,  FIG. 9 ), and more specifically to bring a contextualized biomarker value of concern  422  into a guideline based target range is shown. The method  420  is preferably facilitated on the clinician computer  25  as an analysis function of the software  34  to assess patient data, e.g., contained in a patient data file  145  that was collected from a completed structured collection procedure  70  and in which the contextualized biomarker value of concern  422  fell outside a target range provided by known guidelines and standards. In many of the structured collection procedures  70 , there can be a number of parameter available for adjustment by the clinician therefore a systemic approach implemented on the clinician computer  25  should help to improve the time and effort taken in hoping to bring the biomarker value of concern  422  into the desired target range. With reference also made to  FIG. 2 , the method  420  comprises in step  424 , selecting on the display  82  a contextualized biomarker value of concern  422 . The processor  76  will then display a list of parameters in step  424  from which to select. Although any parameter can be selected, in the this example, the parameter selected by the clinician is a parameter which is believed to potentially move the selected contextualized biomarker value of concern  422  from outside of the target range and into the target range. Examples of parameters may include one or more of drugs, dosages, exercise frequency and duration, and meal frequency, timing, size and type provided by the processor as dedicated by the software  34 . Other examples of parameters can include one or more of metformin dosage, insulin-to-carbohydrate ratio, basal insulin dosage, exercise, and meals. Still other examples of the parameters can include one or more of taspoglutide, aleglitazar, sulfonylureas, bifuanides, biguanides, thiazolidinediones, alpha-glucosidase inhibitors, meglitinides, dipeptidyl peptidase IV inhibitors, GLP-1 analogs, and PPAR dual alpha/gamma agonists. 
     In step  426 , the selected parameter(s) is adjusted automatically by the processor  76  using an algorithm in an attempt to bring the contextualized biomarker value of concern into a guideline based target range, such as specified by guidelines and standards  428 , while avoiding adverse side effects. The clinician  14  has the option to accept, modify or cancel the adjustment made by the processor in step  426 . 
     Next, the method  420  waits to receive a new value of the selected contextualized biomarker value of concern  422  in step  430 . Once a new value is noted in the patient data, such from, e.g., a second run of the structured collection procedure  70  with the modified parameter, the processor in step  432  evaluates the new value. After evaluating the contextualized biomarker value in step  432 , the processor  76  then determines in step  434  whether the new contextualized biomarker value of concern  422  is now within the guideline based target range. The parameter adjustment is completed in step  436  once the contextualized biomarker is within range. If not, then the process repeats to adjust the parameter algorithmically in step  426 . 
       FIG. 13  shows a report diagram of a contextualized biomarker value of concern  422  being adjusted into a guideline based target range  438 . The horizontal axis shows time  440  with a procedure run one, procedure run two, and procedure run three. The vertical axis show a scale  442  of contextualized biomarker values, and the target range  438  comprising a biomarker setpoint, upper limit, and lower limit. In procedure run one, a first contextualized biomarker value  422  is obtained that is outside the target range  438 . After procedure run one, a parameter such as metformin dosage of a drug is adjusted. In procedure run two, a second contextualized biomarker value  422 N is obtained that continues to be outside the target range  438  although much closer to the target range than the first contextualize biomarker value from procedure run one. Again, the same parameter is adjusted. In procedure run three, a third contextualized biomarker value  422 O is obtained that is now within the target range  438 . 
     Another parameter suitable for adjustment in method  420  is adjusting for an estimated insulin-to-carbohydrate (I:C) ratio. This adjustment can be performed by correlating the effect of a meal to the bolus insulin dosage for the determination of an I:C ratio estimate. The I:C ratio estimate is used to cancel out a meal effect. For performing the insulin-to-carbohydrate ratio estimate adjustment, meals having predefined ratios of carbohydrates, fat, protein, predefined uptake speed, and predefined calories adjusted to a person&#39;s weight are used. Any deviation from the expected blood glucose range is used to calculate an updated estimated insulin-to-carbohydrate ratio. Two consecutive measurements within the acceptance range are typically required to establish a robust insulin-to-carbohydrate ratio estimate. The estimate can be confirmed by skipping meals and verifying expected blood glucose effects. 
     For example, another parameter suitable for adjustment in method  420  is adjusting for an oral agent to treat type 2 diabetes such as metformin. For example, in adjusting a metformin dosage, which is the parameter, the biomarker of concern would be fasting blood glucose. Adjustments to the metformin dosage would attempt to bring the fasting blood glucose measurements within the desired fasting blood glucose range while also avoiding significant gastro-intestinal side effects and in rare cases the risk of lactic acidosis. In this case, the fasting blood glucose measurements would need to be further contextualized with patient information on side effects. 
     In other example, another parameter suitable for adjustment in method  420  is adjusting for basal insulin delivery dosage(s) for type 1 and type 2 diabetics on insulin therapy. In adjusting the basal insulin delivery dosage(s), which is the parameter, the biomarker of concern would be fasting blood glucose. Adjustments to the basal insulin delivery dosage(s) would attempt to bring the fasting blood glucose measurements within the desired fasting blood glucose range. 
     Still other parameters suitable for adjustment in method  420  is adjusting for dosages or switching drugs, such as for example, parameter adjustment embodiments can also be configured for adjusting one or more of sulfonylureas, biguanides such as metformin, thiazolidinediones such as pioglitazone hydrochloride, rosiglitazone maleate, ciglitazone, troglitazone or balaglitazone, alpha-glucosidase inhibitors, meglitinides, dipeptidyl peptidase IV (DPP-IV) inhibitors such as sitagliptin phosphate, saxagliptin, vildagliptin, alogliptin, carmegliptin or denagliptin, and combinations. 
     Still other parameters suitable for adjustment in method  420  is adjusting for GLP-1 analogs such as Exenatide (Byetta™), NN2211 (Liraglutide), GLP-1(7-36) amide, GLP-1(7-37), AVE-0010 (ZP-10), R1583 (Taspoglutide), GSK-716155 (albiglutide, GSK/Human Genome Sciences), BRX-0585 (Pfizer/Biorexis) and CJC-1134-PC (Exendin-4:PC-DAC™) and for PPAR dual alpha/gamma agonists such muraglitazar, aleglitazar or peliglitazar. Specific parameter adjustments can be configured for taspoglutide or aleglitazar. The characteristics of taspoglutide are disclosed in WO 2000/34331 “Analogues of GLP-1” assigned to Societe de Conseils de Recherches et d′Applications Scientifiques S.A., which is hereby incorporated by reference. The characteristics of aleglitazar are disclosed in WO 2002/092084 “Carboxylic acid substituted oxazole derivatives for use as par-alpha and -gamma activators in the treatment of diabetes” assigned to F. Hoffmann-La Roche AG, which is hereby incorporated by reference. Noise is another factors upon which adjustments to the parameters of a structured collection procedure  70  can be based which is further discussed hereafter in reference to  FIGS. 14 and 15 . 
     Noise 
     To understand better the effect of noise on biomarker measurements,  FIG. 14  shows a diagram of the relationship of the component parts of total noise. As shown, total noise  450  comprises system noise  452 , procedural noise  454 , and measurement method noise  456 . Total noise  450  is a function relating measurement noise, procedural noise, and system noise, and can be described by the following equation: 
       Total Noise= f (Measurement,Procedural,System), 
     where Measurement, Procedural, and System noise are the constituent parts of the function. In  FIG. 14 , the horizontal axis shows time  458 , and the vertical axis shows biomarker values  460 . The vertical axis further shows a biomarker setpoint  462 , upper range  464 , and lower range  466 . A new noise characterized biomarker value  468  is shown which has been adjusted for system noise  452 , procedural noise  454 , measurement noise  456 , and combinations thereof. 
     Measurement noise  456  is the noise of the collection device  24  used to take measurements, and is typically readily available directly from the literature provided by the manufacturer of the collection device. Such measurement noise  456  is typically caused by variability in a measurement system or collection device. Such variability includes variations among test strip batches, collection device such as in the calibration of a blood glucose meter, and in the specific limitations of particular collection devices. For example, a typical blood glucose measuring device has a reported measurement variance of below 100 mg/dL±15 mg/dL and at or above 100 mg 15% of the actual value. The measurement noise  456  may also be caused by variations in the surrounding such as temperature, humidity and pressure. 
     Procedural noise  454  is the noise contributed by the structured testing procedure provided in the structured collection procedure  70 . Contributions of such procedural noise can include the manner in which data is collected, such as magnitude (size), velocity (speed), and timing of the steps in the structured collection procedure  70  as well as the patient&#39;s activity before the application of such step such as meals, exercise, effect of medications, variations in the sampling method such as sample variations due to location of lancing sites, and the like. 
     System noise  452  is the variability in a biomarker value caused by variations in each individual&#39;s physiology. It is to be appreciated that chronic diseases, such as diabetes, manifests itself differently in each patient because of each patient&#39;s unique physiology that is influenced by variable lifestyle factors such as diet, exercise, and sleep. When a patient collects biomarker data such as glucose values, variations in the patient&#39;s physiological state can create system noise that can complicate a clinician&#39;s interpretation of the biomarker data. 
     It is to be appreciated that the use of both system and procedural noise  452 ,  454  by the structured collection procedure  70  is endemic. Therefore, by understanding the pattern of both system and procedural noise  452 ,  454 , the determination of such constituents can be streamlined through the use of a personal procedure in which these noise constituents can be calculated. For example, total noise can be measured by performing a typically collection for a biomarker value, and then the processor  102  in using the available measurement noise  456  of the collection device  24  from the manufacture&#39;s literature and a population based value for procedural noise  454 , calculate an estimate of the system noise  454 . Alternatively, the processor  102  can first calculate the system noise  452  through use of a specific protocol, a measurement as described above for total noise  450 , and the available measurement noise  456 , to calculate an estimate the procedural noise  454 . For example, a specific protocol for measuring system noise  456  could be to test for the magnitude of a patient&#39;s physiological response to exercise, by comparing bG level measurement with exercise performed and without exercise performed. Similar protocols can also be developed for testing the affect of site location selection on sample measurements, affect of certain meal types and sizes, affect of variations for sleep, affect of stress, and for any other such factors affecting a patient&#39;s physiological response. In one embodiment, such system noise  452  is determined as described above and then applied as a constant for the purposes of a single, short duration protocol, and in another embodiment as a value with continual long term drifts over the life of the patient. 
     A population-based procedural noise  454  can be measured by the application of a protocol in a clinical trial that is designed to measure this noise. For example, a simple two-tiered study could be developed which first establishes the system noise  452  of the study participant as described above, then, using this information, combined with the measurement noise  456 , would establish the procedural noise  454  as seen by that specific study participant. Executing this procedure across multiple study participants yields a population based procedural noise  454  that then can be used for general application. The procedural noise contribution can be shaped by constraints that are placed on the structured collection procedure, but can never be eliminated. An example of constraining the procedural noise  454  would be to require predefined meals of known loading being consumed in place of ad-hoc meals. In another example, a clinical trial to measure the procedural noise  454  can be created which is iterated along with making changes to reduce specifically the procedural noise. 
       FIG. 15  shows a flowchart of a method  470  for creation of a noise modeling function, which can be used to characterize noise thereby enabling one to modify parameters such as criterion and events in a structured collection procedure  70 . In step  472 , contextualized biomarker data of concern  422  in received data, such as provided in the data file  145 , are selected such as in the manner previously discussed above in reference to  FIG. 12 . In one embodiment, the contextualized biomarker data is contextualized biomarker data collected from using a specialized noise collection procedure  70  or from a contextualized biomarker data set collected using a structured collection procedure  70  used to help address a medical use case or question. In some embodiments, the selected contextualized biomarker data can also be filtered in step  472 , such as applying adherence criterion  224  to the contextualized biomarker values in the received data to produce accepted contextualized biomarker values of concern in the received data, as previously discussed above in reference to  FIG. 9 . In step  474 , the processor  76  calculates measured noise from the contextualized biomarker data by statistically assessing the variation within the data set. Measured noise is an instance of total noise in the actual data set that is caused by variations that exists about a measured value attributable to random events. 
     Next, a noise modeling function is created in step  476  based on the total noise function algorithm using the contextualized biomarker data  422 , measurement noise  456 , procedural noise  454 , and a number of biomarker values at various data points. An example of a noise modeling function follows the use of statistical covariance between a known measurement method for producing a noise estimate, a procedural noise estimate based on population based or personal historical data, and system noise to create a functional relationship between the three noise sources. The biomarker values at the various data points in the data set are then used with the noise function to estimate the noise of a dataset with the same sample count. If the calculated noise estimate is equal to or within a threshold window based on sample counts, then the noise function is considered valid. 
     Some embodiments can further comprise validating the noise modeling function in step  478  by calculating total noise using the quantity of contextualized biomarker data points and the noise modeling function, and comparing the calculated noise estimate against the measured noise of the contextualized biomarker data to determine whether they are equal within a given confidence interval. Total noise thus is defined as the variability in the biomarker data as obtained from a measurement device. Total noise should be characterized, minimized, and ideally removed. The noise estimate is calculated utilizing the noise modeling function, the measurement method and procedural noise values, combined with the estimated system noise and the calculated or predetermined number of data points in a prospective data structured collection procedure. The calculated total noise estimate is compared against the measured noise of the supplied data set and evaluated for equality within a given confidence interval. The result of method  470  is the validated noise function in step  480 . Reference hereafter is made to  FIGS. 16A and 16B . 
     Use Case Example 
     To help illustrate the above benefits provided by the embodiments of the present invention, the following use case example is provided. Although the contextualization of biomarker data for diabetes diagnostics and therapy support is discussed hereafter with reference to a structured collection procedure using a biomarker of concern, namely glucose, various other embodiments can be configured for other biomarkers, such as for example, triglycerides, cholesterol, low density lipids, high density lipids, and the like, from the elements of creating a structured collection procedure ( FIGS. 5A and 5B and 7A and 7B ), performing a structured collection procedure ( FIGS. 8A and 8B ), adjusting parameters ( FIGS. 9 and 12 ), and creating a noise modeling function ( FIG. 15 ). 
     For this use case example,  FIGS. 16A and 16B  show various interactions among a clinician  14 , patient  12 , and operations  500  of selected embodiments for contextualizing biomarker data for diabetes assessment and therapy support. The various interactions can occur in a clinical setting, in a patient&#39;s setting, such as a home, with clinician interaction via communications ( FIG. 2 ), or in a patient&#39;s setting to collect biomarker data and then in a clinician&#39;s setting for optimization. The operations  500  can be performed on a paper tool  38 , on a device  24  or  36  having diabetes software  34 , or on a computer  18  or  25  operating installed diabetes software or operating remote diabetes software from a server  52  through the network  50 . 
     In step  502 , the clinician  14  selects a biomarker of concern such as, for example, glucose. After selecting the biomarker of concern, the clinician can have the patient  12  perform a decentralized assessment of diabetes disease status. A current assessment of diabetes disease status can aid the clinician  14  in determining which parameter to adjust in step  504 . The clinician defines a structured collection procedure that can be tailored for the patient in step  506 , and verifies that entry criterion is met in step  508 . For example, one particular structured collection procedure may be used to determine an effective carbohydrate to insulin (C:I) ratio of the patient  12 , or another to determine the patient&#39;s sensitivity to insulin. In the carbohydrate to insulin (C:I) ratio example, the entry criterion may be to ensure that the patient is willing and able to perform an 8-day testing challenge, has a blood glucose level above 130 mg/dL, and had no recent low blood sugar events. In the insulin sensitivity example, the entry criterion may be to ensure that the patient is willing and able to perform a 4-day testing challenge, has a blood glucose level above 150 mg/dL, and had no recent low blood sugar events. 
     Optionally in step  510 , the clinician  14  can have the system (e.g., clinician computer  25 ) in its operations  500  select a noise characterization procedure, e.g., to help define a noise function, such as provided above in reference to  FIG. 15 . In some embodiments, the selected noise characterization procedure will require the patient  12  to collect biomarker data in step  512  for the noise function in order to establish a noise dependent resolution for each biomarker value collected. In other embodiments, the noise dependent resolution can be established without collection of biomarker data from the patient in step  514 . If established, the noise dependent resolution is then used by the system (e.g., the collection device  24  and/or the clinician device  25 ) in its operation to evaluate and present the significance of any potential change to the biomarker value in step  516 . 
     In step  518 , the patient then performs the structured collection procedure  70 . For example, and with reference to  FIG. 3 , the patient is provided with and performs the structured collection procedure  70  implemented on the portable collection device  24  for this step. In this example, the collection device  24  comprises a display  108 , a user interface  146 , a measurement engine  138  to measure a biomarker value, and memory  110  containing the data file  145  and a structured collection procedure  70 . The structured collection procedure  70  can have one or more parameters defining entry, exit, and adherence criterion  226 ,  228 ,  224  as well as one or more collection events  237  and guidance  230 , timing  238  and optionally an options parameter  232  associated with each of the collection events  237 . 
     In the example of determining an effective carbohydrate to insulin (C:I) ratio of the patient  12 , such guidance (and instructions)  230  may include information provided in preparing the patient for the testing and information for what to do after each test. Such information for preparing the patient in the carbohydrate to insulin (C:I) ratio example may include: eat nothing for at least 4 hours before starting the test; drinking water is permitted; have no recent low blood sugar events; take no fast acting insulin (meal bolus or correction bolus) for at least 4 hours; any long acting insulin (basal) must be taken between 2 and 12 hours prior to the start of the test; do not begin test if you are ill or experiencing abnormal stress; do not consume coffee, tea, food, or alcohol before or during the test; awake early enough to begin the test between 7 am and 8 am; familiarize yourself with all steps of the structured collection procedure before beginning the test; and eat normally for at least three days prior to the test. Information for what to do after each test performed in accordance to the requested collection event in this example may include call your physician if you experience a low blood glucose event, or if bG is within 25 mg/dL of starting, and this is the third consecutive test with this C:I ratio, testing is complete. 
     The collection device  24  may further comprise a processor  102  connected to the display  108 , the user interface  146 , the measurement engine  138 , and the memory  110  as well as power supply  150  for powering the collection device  24 . Software  34  is also provided on the collection device  24 , and provides instructions that when executed by the processor  102  causes the processor to perform the following processing steps. In step  518 , the processor  102  reads the structured collection procedure  70  from memory  110 , and runs the structured collection procedure automatically in step  518 , such as for example, if the entry criterion  226  is satisfied. In step  518 , the processor  102  sends automatically the requests  240  to the display  108  for the measurement of the biomarker value and/or for the information according to the timing  238  of each of the collection events  237  prescribed in the structured collection procedure  70 . 
     In step  518 , the processor  102  stores automatically each biomarker value measured by the measurement engine  138  or information entered via the user interface  146  in an associated data record  152  in the data file  145  in response to a sent request  240 . The processor  102  also assesses the adherence criterion  224  and whether further processing is required as defined by the options parameter  232 . 
     For example, in the above C:I ratio example, the structured collection procedure  70  conducted by the patient may include the following processes. First, at start of each daily test, which is to begin 30 minutes before a planned (and first) morning meal, the patient  12  uses the collection device  24  to confirm that his/her blood glucose level is &gt;130 mg/dL. If not, the processor  102  advises that the patient  12  should stop and try again the following day. If okay, then the date-time and glucose value of the first biomarker value is recorded by the processor  102 . The patient  12  then uses the user interface  146  of the collection device  24  to record the starting Carbohydrate to Insulin (C:I) ratio used in the first daily test. Next, the patient will record the total carbohydrates and carbohydrates from fiber via the user interface  146 . Fiber is then subtracted from the total amount of carbohydrates by the processor  102  to indicate an amount of carbohydrates requiring insulin. This amount is then divided by the carbohydrates indicated in the starting C:I ratio and then times by the insulin indicated in the starting C:I ratio by the processor  102  to give a new insulin dose for a meal bolus. It is to be appreciated that the insulin dose may be rounded down to the nearest unit supported by the injection device. For example, if supported unit is 1, then if the resulting insulin dose is 1.26, such a dosage is rounded down to 1 unit. The patient  12  then 30 minutes after collection of the first biomarker value uses the collection device  24  to confirm again that his/her blood glucose level is &gt;130 mg/dL. If not, the processor  102  advises on the display  108  that the patient  12  should stop and try again the following day. If okay, then the date-time and glucose value of the second biomarker value is recorded by the processor  102 . The patient  12  is then instructed to eat the meal by the processor  102 . After two hours from the collection of the second biomarker value, the processor  102  may prompt the patient  12 , via the indicator  148  and/or display  108 , wherein the patient  12  uses the collection device  24  to measure his/her blood glucose level, wherein the date-time and glucose value of the third biomarker value is recorded by the processor  102 . 
     Finally, in step  518 , the processor  102  ends the structured collection procedure  70  automatically when the exit criterion  228  is satisfied. For example, to determine whether the exit criterion  228  is met, the processor  102  can evaluate and display on the display  108  the significance of any noted change in the biomarker data. For example, in the above C:I ratio example, the processor  102  subtracts the second biomarker value from the third biomarker value to determine the effectiveness of the C:I ratio used. If the resulting difference of the subtraction is greater than 25 mg/dl, then the carbohydrates in the C:I ratio used for the next day testing is incremented by 1 unit wherein the structured collection procedure  70  repeats the sampling plan i.e., schedule of evens  222 , again for the next day. For example, if the starting C:I ratio is 30:1, then after the results of the first test day, the C:I ratio would be 31:1 for the next day test. Likewise, if the difference is less than −25 mg/dL, then the carbohydrates in the C:I ratio used for the next day testing is decreased by 1 unit. However, if the resulting difference is within ±25 mg/dL, then the C:I ratio is not changed for the next test. Additionally, as mentioned above in the instruction example, if the resulting difference is within ±25 mg/dL and the completed test is the third consecutive test with the same C:I ratio, then in this case, the exit criteria  228  is satisfied in step  518 , wherein the processor  102  automatically ends the structured collection procedure  70 . 
     With the data from the structured collection procedure  70 , the clinician then can evaluate the presented significance of the change in step  520  on the clinician computer  25  to determine if a parameter change is required. For example, in the above C:I ratio example, a parameter change may be indicated if no effective C:I ratio was determined by the structured collection procedure in step  518 . If the clinician determines that a parameter change is required, the clinician will use a method for adjusting parameters (e.g., the method depicted by  FIGS. 9 and 12 ) to adjust the parameter in step  522 . For example, in step  524 , optionally, the operations  500  of the system (e.g., the clinician computer  25 ) can provide better control procedure execution, such as by providing increased guidance, customized reminders, and addition flexibility in testing that take into account other lifestyle factors of the patient if such is determined as necessary by the clinician  14  in step  522 . In step  526 , the patient  12  then performs the adjusted procedure using the collection device  24 . Next, in step  528 , the operations  500  of the system (e.g., the collection device  24  and/or the clinician computer  25 ) calculates and/or estimates, assesses any change in the contextualized biomarker value  410 , and in step  530  evaluates and presents the significance of the change to the clinician  14 . In some embodiments, the clinician  14  can repeat the method for adjusting parameter to optimize parameter adjustment as discussed above previously. 
     Thus, by the above disclosure embodiments concerning a structured testing method for diagnostic or therapy support of a patient with a chronic disease and devices thereof are disclosed. One skilled in the art will appreciate that the teachings can be practiced with embodiments other than those disclosed. The disclosed embodiments are presented for purposes of illustration and not limitation, and the invention is only limited by the claims that follow.