Patent Publication Number: US-2023139172-A1

Title: Advising Diabetes Medications

Description:
CROSS REFERENCE TO RELATED APPLICATIONS 
     This U.S. patent application is a continuation of, and claims priority under 35 U.S.C. § 120 from, U.S. patent application Ser. No. 16/222,415, filed on Dec. 17, 2018, which claims priority under 35 U.S.C. § 119(e) to U.S. Provisional Application 62/609,326, filed on Dec. 21, 2017. The disclosures of these prior applications are considered part of the disclosure of this application and are hereby incorporated by reference in their entirety. 
    
    
     TECHNICAL FIELD 
     This disclosure relates to managing anti-diabetes medications (ADMs). 
     BACKGROUND 
     Diabetes is among the most prevalent and expensive medical conditions that requires prescription therapy. Managing diabetes requires maintaining glucose levels within a prescribed goal range. For patients with type 1 diabetes, where the production of insulin is impaired, the affected individual must regularly inject insulin into the body to maintain control glucose levels. In contrast to type 1 diabetes, individuals having type 2 diabetes may produce insulin; however, the pancreas may not secrete enough insulin and/or the cells of the body may be insulin resistant. Accordingly, type 2 diabetes may be treated with one or more of: insulin injections; lifestyle changes, such as exercise and diet; and anti-diabetes medications (ADMs). 
     Anti-diabetes medications may include agents configured to increase the amount of insulin secreted by the pancreas, lower resistance of the target organs to insulin, and/or lower a rate at which glucose is absorbed from the gastrointestinal tract. Selection of anti-diabetes medications generally includes consideration of a variety of factors, including cost, efficacy, effectiveness, complexity of administration, patient lifestyle, interactions of the medication with other medications, and potential side effects, for example. Accordingly, selection and management of ADMs in combination with other treatment options can be complex. 
     Hyperglycemia is a condition that exists when blood sugars are too high. While hyperglycemia is typically associated with diabetes, this condition can exist in many patients who do not have diabetes, yet have elevated blood sugar levels caused by trauma or stress from surgery and other complications from hospital procedures. Insulin therapy is used to bring blood sugar levels back into a normal range. 
     Hypoglycemia may occur at any time when a patient&#39;s glucose level is below a preferred target. Appropriate management of glucose levels for critically ill patients reduces co-morbidities and is associated with a decrease in infection rates, length of hospital stay, and death. The treatment of hypoglycemia may differ depending on whether or not a patient has been diagnosed with Type 1 diabetes mellitus, Type 2 diabetes mellitus, gestational diabetes mellitus, or non-diabetic stress hypoglycemia. The glucose target range BG TR  is defined by a lower limit, i.e., a low target BG TRL  and an upper limit, i.e., a high target BG TRH . 
     SUMMARY 
     One aspect of the disclosure provides a method for determining a therapy regimen. The method includes obtaining, by data processing hardware, prescribing drug information and published guidelines for each of a plurality of Anti-Diabetes Medications (ADMs) available for managing glucose levels and receiving, at the data processing hardware, patient information associated with a patient seeking selection and dosing of one or more of the available ADMs. For each of the available ADMs, the method further includes: determining, by the data processing hardware, an adverse demerit value, a guideline demerit value, and an instruction demerit value based on the patient information, the prescribing drug information, and the published guidelines for the corresponding available ADM; and determining, by the data processing hardware, a total demerit value by summing the adverse demerit value, the guideline demerit value, and the instruction demerit value. The method also includes ordering, by the data processing hardware, the total demerit values for the available ADMs from lowest to highest; selecting, by the data processing hardware, a predetermined number of recommended ADMs associated with the lowest total demerit values from the plurality of available ADMs; determining, by the data processing hardware, a recommended dosage for each recommended ADM based on the patient information, the prescribing drug information, and the published guidelines; and transmitting the therapy regimen from the data processing hardware to a patient device associated with the patient. The therapy regimen includes the recommended ADMs and the recommended dosage for each recommended ADM. 
     Implementations of the disclosure may include one or more of the following optional features. In some implementations, the patient information includes at least one of treatment preference information, treatment guideline ratings, a current medications list, current medical conditions associated with the patient, permanent medical conditions associated with the patient, one or more glucose values for the patient, or an A1c value for the patient. The treatment preference information includes at least one of a target glucose range for the patient, a target A1c value for the patient, a preferred minimum monthly treatment cost, or a preferred maximum monthly treatment cost. The treatment guideline ratings are each assigned by the patient and measure a subjective level of importance to the patient for a corresponding treatment guideline. The treatment guideline ratings include at least one of a cost rating, a body weight rating, a treatment regimen complexity rating, a treatment efficacy rating, a mealtime coverage needs rating, or a hypoglycemia rating. The current medications list includes a list of medications and corresponding dosages the patient is currently prescribed. The one or more glucose values for the patient are measured by a glucometer or a continuous glucose monitor in communication with the data processing hardware. 
     The method may include receiving, at the data processing hardware, exercise data and adjusting, by the data processing hardware, the recommended dosage for at least one of the recommended ADMs based on the received exercise data. The exercise data may be received from a fitness tracker associated with the patient. In some implementations, determining the adverse demerit value includes obtaining one or more contraindicating conditions associated with the corresponding available ADM based on the prescribing drug information and the published guidelines, obtaining a list of medications that interact with the corresponding available ADM based on the prescribing drug information, determining whether the patient currently has any of the contraindicating conditions associated with the corresponding available ADM based on the patient information that includes lab results associated with the patient, determining whether the patient is currently taking at least one of the medications that interact with the corresponding available ADM based on the patient information that include a list of medications the patient is currently taking, assigning an adverse demerit increment value when the patient currently has any of the contraindicating conditions associated with the corresponding available ADM, assigning the adverse demerit increment value when the patient is currently taking at least one of the medications that interact with the corresponding available ADM, and determining the adverse demerit value for the corresponding available ADM based on a sum of each assigned adverse demerit increment value. 
     In some examples, determining the guideline demerit value includes obtaining treatment guideline ratings each assigned by the patient that measures a subjective level of importance to the patient for a corresponding treatment guideline, obtaining scaled guideline values for the corresponding available ADM based on the prescribing drug information and the published guidelines where each scaled guideline value is associated with a corresponding treatment guideline rating, and, for each treatment guideline rating, multiplying the treatment guideline rating times the corresponding scaled guideline value and a guideline demerit increment value. In these examples, the treatment guideline ratings include at least one of a cost rating, a body weight rating, a treatment regimen complexity rating, a treatment efficacy rating, a mealtime coverage needs rating, or a hypoglycemia rating. 
     For each of the available ADMs, the method may also include determining, by the data processing hardware, whether the patient is currently taking the corresponding available ADM based on the patient information, wherein the patient information includes a list of medications the patient is currently taking. When the patient is currently taking the corresponding available ADM, the method may further include assigning, by the data processing hardware, a low modified demerit value to the corresponding available ADM and adding, by the data processing hardware, the corresponding available ADM having the low modified demerit value to the predetermined number of recommended ADMs. 
     In some examples, for each of the available ADMs, the method further includes obtaining, by the data processing hardware, a list of excluded ADMs that the patient is either allergic to or is excluded from the treatment regimen for the patient and determining, by the data processing hardware, whether the corresponding available ADM is on the list of excluded ADMs. In these examples, when the corresponding available ADM is on the list of excluded ADMs, the method includes assigning, by the data processing hardware, a high modified demerit value to the corresponding available ADM and replacing, by the data processing hardware, the total demerit value for the corresponding available ADM with the assigned high modified demerit value. 
     In some implementations, the therapy regimen, when received by the patient device, causes the patient device to display the recommended ADMs and the recommended dosage for each recommended ADM on a patient interface executing on the patient device. 
     Additionally or alternatively, the method may also include transmitting the recommended dosage for at least one of the recommended ADMs to an administration device associated with the recommended ADM and in communication with the data processing hardware. Here, the administration device includes a doser and an administration computing device in communication with the doser. The administration computing device may be configured to cause the doser to administer the recommended dosage to the patient. In some examples, the administration device includes a smart pill bottle and the doser includes a locking/dispensing mechanism configured dispense one or more ADM pills based on the recommended dosage. In other examples, the administration device includes a smart pen that includes a cartridge containing the recommended ADM, and the doser includes a needle for insertion into the patient for administering the recommended ADM to the patient via the cartridge. 
     Another aspect of the disclosure provides a system for determining a therapy regimen. The system includes a patient device associated with a patient and a dosing controller in communication with the patient device. The dosing controller includes data processing hardware and memory hardware in communication with the data processing hardware. The dosing controller is configured to perform operations that include obtaining prescribing drug information and published guidelines for each of a plurality of Anti-Diabetes Medications (ADMs) available for managing glucose levels and receiving patient information from the patient device. The patient information is associated with the patient seeking selection and dosing of one or more of the available ADMs. For each of the available ADMs, the operations further include: determining an adverse demerit value, a guideline demerit value, and an instruction demerit value based on the patient information, the prescribing drug information, and published guidelines for the corresponding available ADM; and determining a total demerit value by summing the adverse demerit value, the guideline demerit value, and the instruction demerit value. The operations also include: ordering the total demerit values for the available ADMs from lowest to highest; selecting a predetermined number of recommended ADMs associated with the lowest total demerit values from the plurality of available ADMs; determining a recommended dosage for each recommended ADM based on the patient information, the prescribing drug information, and the published guidelines; and transmitting the therapy regimen from the data processing hardware to the patient device. The therapy regimen includes the recommended ADMs and the recommended dosage for each recommended ADM. 
     Implementations of the disclosure may include one or more of the following optional features. In some implementations, the patient information includes at least one of treatment preference information, treatment guideline ratings, a current medications list, current medical conditions associated with the patient, permanent medical conditions associated with the patient, one or more glucose values for the patient, or an A1c value for the patient. The treatment preference information includes at least one of a target glucose range for the patient, a target A1c value for the patient, a preferred minimum monthly treatment cost, or a preferred maximum monthly treatment cost. The treatment guideline ratings are each assigned by the patient and measure a subjective level of importance to the patient for a corresponding treatment guideline. The treatment guideline ratings include at least one of a cost rating, a body weight rating, a treatment regimen complexity rating, a treatment efficacy rating, a mealtime coverage needs rating, or a hypoglycemia rating. The current medications list includes a list of medications and corresponding dosages the patient is currently prescribed. The one or more glucose values for the patient are measured by a glucometer or a continuous glucose monitor in communication with the data processing hardware. 
     In some implementations, the operations further include receiving exercise data from a fitness tracker associated with the patient and adjusting the recommended dosage for at least one of the recommended ADMs based on the received exercise data. In some examples, determining the adverse demerit value includes obtaining one or more contraindicating conditions associated with the corresponding available ADM based on the prescribing drug information and the published guidelines, obtaining a list of medications that interact with the corresponding available ADM based on the prescribing drug information, and determining whether the patient currently has any of the contraindicating conditions associated with the corresponding available ADM based on the patient information that includes lab results associated with the patient. In these examples, determining the adverse demerit value further includes determining whether the patient is currently taking at least one of the medications that interact with the corresponding available ADM based on the patient information that includes a list of medications the patient is currently taking, assigning an adverse demerit increment value when the patient currently has any of the contraindicating conditions associated with the corresponding available ADM, assigning the adverse demerit increment value when the patient is currently taking at least one of the medications that interact with the corresponding available ADM, and determining the adverse demerit value for the corresponding available ADM based on a sum of each assigned adverse demerit increment value. 
     In some implementations, determining the guideline demerit value includes obtaining treatment guideline ratings each assigned by the patient that measures a subjective level of importance to the patient for a corresponding treatment guideline, obtaining scaled guideline values for the corresponding available ADM based on the prescribing drug information and the published guidelines where each scaled guideline value is associated with a corresponding treatment guideline rating, and, for each treatment guideline rating, multiplying the treatment guideline rating times the corresponding scaled guideline value and a guideline demerit increment value. In these implementations, the treatment guideline ratings includes at least one of a cost rating, a body weight rating, a treatment regimen complexity rating, a treatment efficacy rating, a mealtime coverage needs rating, or a hypoglycemia rating. 
     For each of the available ADMs, the operations may further include determining whether the patient is currently taking the corresponding available ADM based on the patient information, wherein the patient information includes a list of medications the patient is currently taking. When the patient is currently taking the corresponding available ADM, the operations may also include assigning a low modified demerit value to the corresponding available ADM and adding the corresponding available ADM having the low modified demerit value to the predetermined number of recommended ADMs. 
     In some implementations, for each of the available ADMs, the operations also include obtaining a list of excluded ADMs that the patient is either allergic to or is excluded from the treatment regimen for the patient and determining whether the corresponding available ADM is on the list of excluded ADMs. In these implementations, when the corresponding available ADM is on the list of excluded ADMs, the operations also include assigning a high modified demerit value to the corresponding available ADM and replacing the total demerit value for the corresponding available ADM with the assigned high modified demerit value. 
     In some examples, the therapy regimen when received by the patient device causes the patient device to display the recommended ADMs and the recommended dosage for each recommended ADM on a patient interface executing on the patient device. In some implementations, the operations also include transmitting the recommended dosage for at least one of the recommended ADMs to an administration device associated with the recommended ADM and in communication with the data processing hardware. Here, the administration device includes a doser and an administration computing device in communication with the doser. The administration computing device is configured to cause the doser to administer the recommended dosage to the patient. In some examples, the administration device includes a smart pill bottle and the doser includes a locking/dispensing mechanism configured dispense one or more ADM pills based on the recommended dosage. In other examples, the administration device includes a smart pen that includes a cartridge containing the recommended ADM and the doser includes a needle for insertion into the patient for administering the recommended ADM to the patient via the cartridge. 
    
    
     
       DESCRIPTION OF DRAWINGS 
         FIG.  1 A  is a schematic view of an example system for managing glucose levels of a patient. 
         FIG.  1 B  is a schematic view of an example system for managing glucose levels of a patient. 
         FIG.  1 C  is a schematic view of an example administration device in communication with a dosing controller. 
         FIG.  1 D  is a schematic view of example components of the system of  FIGS.  1 A- 1 C . 
         FIG.  2    is a schematic view of an example dosing controller configured to execute instructions to evaluate and select Anti-Diabetes Medications (ADMs) to be included in a treatment regimen for a patient. 
         FIG.  3 A  is a schematic view of an example patient data table including a schedule of all patients treated by a respective Health Care Provider (HCP). 
         FIG.  3 B  is a schematic view of a permanent condition table for a respective patient including a list of permanent medical conditions associated with the patient. 
         FIG.  4 A  is a schematic view of a patient preferences table listing treatment preferences associated with a patient. 
         FIG.  4 B  is a schematic view of an allergies and exclusions table including a list of one or more ADMs that a patient is allergic to or that have been excluded from a treatment regimen for the patient. 
         FIG.  4 C  is a schematic view of a current medications table including a list of medications a patient is currently taking. 
         FIG.  4 D  is a schematic view of a patient device calibration table listing patient devices associated with a patient and calibration parameters associated with each patient device. 
         FIG.  4 E  is a schematic view of a patient device table including health data and exercise data obtained from one or more patient devices associated with the data. 
         FIG.  4 F  is a schematic view of a current conditions table including a list of conditions associated with lab test results for a patient. 
         FIG.  4 G  is a schematic view of a current labs table including a record of lab results for a patient. 
         FIG.  5 A  is a schematic view of an ADM table including a list of ADMs and pertinent information for each ADM. 
         FIG.  5 B  is a schematic view of a drug interactions table including a list of drugs/medications that interact with one of the ADMs from the ADM table of  FIG.  5 A . 
         FIG.  5 C  is a schematic view of an available dosages table for one of the ADMs from the ADM table of  FIG.  5 A . 
         FIG.  5 E  is a schematic view of a contraindications table including a list of contraindications associated with ADMs. 
         FIG.  5 F  is a schematic view of a guideline refreshment conversion process table including a list of guideline values assigned by a patient. 
         FIG.  5 G  is a schematic view of a configurable constants table. 
         FIG.  6 A  is a schematic view of a patient preferences screen. 
         FIG.  6 B  is a schematic view of an allergies and conditions screen indicating ADMs a patient is allergic to. 
         FIG.  6 C  is a schematic view of an energy-based dose adjustment screen for adjusting ADM dosages based on exercise. 
         FIG.  6 D  is a schematic view of an ADM selection screen displaying a treatment regimen for a patient that includes a list of recommended ADMs and recommended dosages for each recommended ADM. 
         FIG.  7    is a schematic view of an ADM selection process for selecting recommended ADMs for inclusion in a treatment regimen for a patient. 
         FIG.  8    is a schematic view of an ADM selection table including a list of available ADMs. 
         FIG.  9    is an exemplary arrangement of operations for selecting recommended ADMs and dosing for administration to a patient. 
     
    
    
     Like reference symbols in the various drawings indicate like elements. 
     DETAILED DESCRIPTION 
     Diabetic outpatients affected by type 2 diabetes may maintain their glucose levels within desired ranges by using various combinations of therapies that includes injection dosages of insulin, dietary and exercise management, and anti-diabetes medications (ADMs). However, a wide variety of ADMs are available for treating type 2 diabetes, each of which may be associated with various characteristics. Therefore, it is desirable to have a clinical support system  100  ( FIGS.  1 A and  1 B ) that advises and manages selection and administration of ADMs. 
     Referring to  FIGS.  1 A and  1 B , in some implementations, a clinical support system  100  analyzes inputted patient condition parameters for an outpatient  10  and selects and manages a personalized treatment regimen to adjust and maintain a glucose level or target A1C of the outpatient  10  within a target range. As used herein, the patient  10  refers to an outpatient that may be located at some remote location, such as the patient&#39;s  10  residence or place of employment. As used herein, the term “clinic” or “clinical” may refer to a location in which care managers provide healthcare services to patients. The system  100  includes a first program implemented in connection with one or more of: a personal computer  110 ,  110   a  of a patient  10 ; a patient device  110 ,  110   b  (e.g., mobile phone, tablet); a smart wearable  110 ,  110   c  (e.g., smart watch, fitness tracker); an insulin pump  123 ,  123   a ; a smart pen  123 ,  123   b ; smart pill bottle  123   c ; a smart pill  123   d  configured to detect and communicate ingestion; glucose meter (commonly referred to as “glucometer”)  124 ; continuous glucose monitor (CGM)  127 ; a body weight scale  125 , a service provider or health care professional (HCP) device  140 ; and/or a service provider  130 . The glucose meter  124  and CGM  127  may be collectively referred to as a glucose measurement device  124 ,  127 . 
     The system  100  further includes a second program, or dosing controller  160 , that may reside in one or more of the patient device  110 , the service provider device  140 , and or the service provider  130 . The dosing controller  160  provides advice on the selection and dosing of Anti-Diabetes Medications (ADMs). The dosing controller  160  may also advise and/or select dosing for insulin injections to manage the patient&#39;s  10  glucose values. Selection and dosing advice is determined by comparing a health status of the patient  10  to prescribing drug information  196  and published guidelines  198 . The health status incudes: real-time data transmitted by the patient device(s)  110 ,  123 ,  124 ,  125 ,  127 ; digital downloads from the patient device(s)  110 ,  123 ,  124 ,  125 ,  127 ; laboratory tests; and judgement-based assessments by the HCP  40  and the patient  10 . The prescribing drug information  196  and published guidelines  198  may be from published advisory literature including, but not be limited to, two types: 1) the Food and Drug Administration (FDA) approved labeling provided by the manufacturer of the ADM as a package insert, and 2) guidelines published by advisory institutions such as the American Diabetes Association (ADA) and the American Association of Clinical Endocrinologists (AACE). 
     The comparison of the health status to the aforementioned references  196 ,  198  is accomplished by the dosing controller  160 , which then provides an output corresponding to selection and dosing of a treatment regimen. The results are used to improve glycemic control of the patient  10  by adjusting the selection and dosing of the ADMs. Selection and dosing may be controlled automatically by the dosing controller  160 , or may include communicating information to the patient  10  in real-time so that he/she can manually change his/her ADM regimen. 
     In addition to selecting and managing ADMs, the dosing controller  160  may advise or prescribe changes in a dietary and exercise regimen of the patient  10 . This is accomplished by calculating a net-energy budget that compares grams of carbohydrate consumed and calories of energy burned by regimented exercise and in the process of normal living. An excess or deficit of caloric energy would cause an increase or decrease in the Hemoglobin A1c of the patient  10 , which is monitored as an indicator. The HCP  40  can prescribe changes in diet and exercise that will adjust the A1c of the patient  10  toward a target range. 
     Referring to  FIGS.  1 A and  1   , the clinical support system  100  includes a glycemic management module  50 , an integration module  60 , a surveillance module  70 , and a reporting module  80 . Each module  50 ,  60 ,  70 ,  80  is in communication with the other modules  50 ,  60 ,  70 ,  80  via a network  20 . In some examples, the network  20  (discussed below) provides access to cloud computing resources that allows for the performance of services on remote devices instead of the specific modules  50 ,  60 ,  70 ,  80 . The glycemic management module  50  executes the program  160  (e.g., an executable instruction set) on a computing device  112 ,  132 ,  142  or on the cloud computing resources. The integration module  60  allows for the interaction of users  40  and patients  10  with the system  100 . The integration module  60  receives information inputted by a user  40  and allows the user  40  to retrieve previously inputted information stored on a storage system (e.g., one or more of cloud storage resources  24 , a non-transitory memory  144  of an electronic medical system  140  of a clinic  42  or telemedicine facility, a non-transitory memory  114  of the patient device  110 , a non-transitory memory  134  of the service provider&#39;s system  130 , or other non-transitory storage media in communication with the integration module  60 ). The storage resources  24  and non-transitory memory  114 ,  134 ,  144  may individually or collectively be referred to as memory hardware. Therefore, the integration module  60  allows for the interaction between the HCPs  40 , patients  10 , and the system  100  via a display  116 ,  146 . The surveillance module  70  considers patient information received from a HCP  40  via the integration module  60  and information received from a glucometer  124  or CGM  127  that measures a patient&#39;s glucose value and determines if the patient  10  is within a threshold glucose value. Generally, the glucometer  124  measures capillary “blood glucose” values and the CGM  127  measures “interstitial glucose” values that can be correlated to blood glucose values. As used herein, the term “glucose value” refers to either one of blood glucose or interstitial glucose. Moreover, use of the term “blood glucose” is not meant to imply that the CGM  127  was not used due to the correlation between interstitial glucose and blood glucose. In some examples, the surveillance module  70  alerts the user  40  if a patient&#39;s glucose values are not within a threshold glucose value. The surveillance module  70  may be preconfigured to alert the user  40  of other discrepancies between expected values and actual values based on pre-configured parameters. For example, when a patient&#39;s glucose value drops below a lower limit of the threshold glucose value. The reporting module  80  may be in communication with at least one display  116 ,  146  and provides information to the user  40  determined using the glycemic management module  50 , the integration module  60 , and/or the surveillance module  70 . In some examples, the reporting module  80  provides a report that may be displayed on a display  116 ,  146  and/or is capable of being printed. 
     The system  100  is configured to evaluate a glucose level, a nutritional intake, and lifestyle of a patient  10 . Based on the evaluation and analysis of the data, the system  100  selects and executes a treatment regimen, which is administered to the patient  10  to adjust and maintain the glucose value of the patient  10  into a glucose target range. The system  100  may be applied to various devices, including, but not limited to, patient devices  110 , subcutaneous insulin infusion pumps  123   a , smart pens  123   b , smart pill bottles  123   c , smart pills  123   d , glucometers  124 , CGM  127 , and smart scales  125 . Smart pens  123   b  may include ADM pens for injecting ADMs to the patient subcutaneously or may include insulin pens for injecting insulin to the patient  10  subcutaneously. 
     In some examples, the clinical support system  100  includes the network  20 , the patient device  110 , the dosing controller  160 , a service provider  130 , and a glucose device manufacturer provider  180 . The patient device  110  may include, but is not limited to, desktop computers  110   a  or portable electronic device  110   b  (e.g., cellular phone, smartphone, personal digital assistant, barcode reader, personal computer, or a wireless pad), activity trackers  110   c  (e.g., smart watch, fitness band) or any other electronic device capable of sending and receiving information via the network  20 . In some implementations, one or more of the patient&#39;s glucometer  124 , CGM  127 , insulin pump  123   a , pen  123   b , or bottle/cap  123   c  are capable of sending and receiving information via the network  20 . 
     The patient device  110   a ,  110   b ,  110   c  includes a data processor  112   a ,  112   b ,  112   h  (e.g., a computing device that executes instructions), non-transitory memory  114   a ,  114   b ,  114   h  and a display  116   a ,  116   b ,  116   h  (e.g., touch display or non-touch display) in communication with the data processor  112   a ,  112   b ,  112   h . In some examples, the patient device  110  includes a keyboard  118 , speakers  122 , microphones, mouse, and a camera. 
     The insulin pump  123   a , pen  123   b , glucometer  124 , and CGM  127  associated with the patient  10  may include a data processor  112   c ,  112   d ,  112   e ,  112   i  (e.g., a computing device that executes instructions), and non-transitory memory  114   c ,  114   d ,  114   e ,  114   i , and/or a display  116   c ,  116   d ,  116   e  (e.g., touch display or non-touch display) in communication with the data processor  112   c ,  112   d ,  112   e ,  112   i . The devices  123   a ,  123   b ,  124 ,  127  may also communicate wirelessly through the network  20  and/or with any other patient device  110 ,  123   a ,  123   b ,  123   c ,  124 ,  125 ,  127  through the same or different network  20 . 
     The smart scale  125  and the smart bottle  123   c  each include a data processor  112   f ,  112   g , (e.g., a computing device that executes instructions). The smart scale  125  and the smart bottle  123   c  further include non-transitory memory  114   f ,  114   g  and a display  116   f ,  116   g  (e.g., touch display or non-touch display) in communication with the data processor  112   f ,  112   g.    
     The clinical support system  100  may also include a glucose device manufacturer provider  180  including a data processor  182  in communication with non-transitory memory  194 . The data processor  192  may execute a proprietary download program for downloading glucose data from the memory  114   c  of the patient&#39;s glucometer  124  and/or from the memory  114   i  of the patient&#39;s CGM  127 . In some implementations, the heal care provider  140  implements the proprietary download program on a computing device  142  or the proprietary download program is implemented on the patient device  110  for downloading the glucose data from the memory  114   c . In some examples, the download program exports a glucose data file for storage in the non-transitory memory  24 ,  114 ,  144 . The data processor  182  may execute a web-based application for receiving and formatting glucose data transmitted from one or more patient devices  110   a ,  110   b ,  124 ,  123   a ,  123   b ,  123   c ,  127  and storing the glucose data in non-transitory memory  24 ,  114 ,  144 . 
     The drug manufacturer provider  190  may include a data processor  192  in communication with non-transitory memory  194 . The memory  194  may store the prescribing drug information  196  and the published guidelines  198 , and the data processor  192  may provide the prescribing drug information  196  and the published guidelines  198  to the dosing controller  160  for outputting a corresponding selection and dosing of a treatment regimen for the patient  10  based on the health status of the patient  10 . 
     The services provider  130  may include a data processor  132  in communication with non-transitory memory  134 . The service provider  130  provides the patient  10  with a program  162  (see  FIG.  1 D ) (e.g., a mobile application, a web-site application, or a downloadable program that includes a set of instructions) executable on a computing device  112 ,  132 ,  142  of the dosing controller  160  and accessible through the network  20  via the patient device  110 , health care provider electronic medical record systems  140 , portable glucose measurement devices  124 ,  127  (e.g., glucose meter, glucometer, or CGM), or portable administration devices  123   a ,  123   b ,  123   c.    
     In some implementations, the HCP medical record system  140  is located at a doctor&#39;s office, clinic  42 , or a facility administered by a hospital (such as a hospital call center) and includes a data processor  142 , a non-transitory memory  144 , and a display  146  (e.g., touch display or non-touch display). The non-transitory memory  144  and the display  146  are in communication with the data processor  142 . In some examples, the HCP electronic medical system  140  includes a keyboard  148  in communication with the data processor  142  to allow a user  40  to input data, such as fixed patient data  300  ( FIG.  2   ). The non-transitory memory  144  maintains patient records capable of being retrieved, viewed, and, in some examples, modified and updated by authorized hospital personal on the display  146 . 
     The dosing controller  160  is in communication with the glucose measurement devices  124 ,  127  and the administration devices  123 , and includes a computing device  112 ,  132 ,  142  and non-transitory memory  114 ,  134 ,  144  in communication with the computing device  112 ,  132 ,  142 . The dosing controller  160  executes the program  162 . The dosing controller  160  stores patient related information retrieved from the glucose measurement devices  124 ,  127 , patient devices  110 , and/or smart scale  125  to determine ADM selections and dosing parameters (and insulin dosing parameters in some scenarios) based on the received glucose measurement and other factors associated with the patient  10 , such as activity level, weight, and/or meal consumption. 
     Referring to  FIG.  1 C , in some implementations, the administration device  123  (e.g., insulin pen, smart pill bottle/cap, smart pill), in communication with the dosing controller  160 , is capable of executing instructions for administering insulin and/or ADM(s) according to an anti-diabetes treatment regimen selected by the dosing controller  160 . The administration device  123  may include the insulin pump  123   a , the pen  123   b , or the smart pill bottle/cap  123   c . The administration device  123  is in communication with the patient devices  110 , the glucometer  124 , the CGM  127 , and the smart scale  125  and includes a computing device  112   d ,  112   e ,  112   g  and non-transitory memory  114   d ,  114   e ,  114   g  in communication with the computing device  112   d ,  112   e ,  112   g . The administration device  123  includes a doser  223   a ,  223   b ,  223   g  in communication with the administration computing device  112   d ,  112   e ,  112   g  for administering an ADM or insulin to the patient  10 . For instance, the doser  223   a  of the insulin pump  123   a  includes an infusion set including a tube in fluid communication with an insulin reservoir and a cannula inserted into the patient&#39;s  10  body and secured via an adhesive patch. The doser  223   b  of the pen  123   b  of the pen  123   b  includes a needle for insertion into the patient  10  for administering an ADM or insulin to the patient via a cartridge. The doser  223   g  of the smart pill bottle/cap  123   c  may include a locking mechanism that unlocks the bottle  123   c  for administering an ADM pill by the patient  10 . Additionally or alternatively, the doser  223   g  may include a dispensing mechanism that dispenses one or more ADM pills for administering to the patient  10 . In some examples, the doser  223   g  communicates with the display  116   g  and/or speaker for presenting a visual and/or audio alert to notify the patient  10  it is time to administer a specified dosage of one or more ADM pills. The administration device  123  is in communication with the dosing controller  160 , and receives instructions from the dosing controller relating to administration of recommended dosages of insulin or ADMs. Here, the administration computing device  112   d ,  112   e ,  112   g  may execute the anti-diabetes treatment regimen selected by the dosing controller  160  and need not be pre-programmed to execute various anti-diabetes treatment regimens/programs stored within memory  114   d ,  114   e ,  114   g , thereby reducing memory usage while increasing processing speeds thereof. Thus, executing the anti-diabetes treatment regimen by administration computing device  112   d ,  112   e ,  112   g  causes the doser  223   a ,  223   b ,  223   b  to administer doses of ADMs or insulin specifically tailored for the patient  10  as specified by the anti-diabetes treatment regimen. Accordingly, the administration devices  123   a ,  123   b ,  123   c  may be “smart” administration devices capable of communicating with the dosing controller  160  to populate recommended doses of ADMs or insulin for administering to the patient  10 . In some examples, the administration devices  123   a ,  123   b ,  123   c  execute the dosing controller  160  on the administration computing devices  112   d ,  112   e ,  112   g  to calculate the recommended doses of ADMs or insulin for administering to the patient  10 . 
     The network  20  may include any type of network that allows sending and receiving communication signals, such as a wireless telecommunication network, a cellular telephone network, a time division multiple access (TDMA) network, a code division multiple access (CDMA) network, Global system for mobile communications (GSM), a third generation (3G) network, fourth generation (4G) network, Long-Term Evolution (LTE) network, fifth generation (5G) network, a satellite communications network, and other communication networks. The network  20  may include one or more of a Wide Area Network (WAN), a Local Area Network (LAN), and a Personal Area Network (PAN). In some examples, the network  20  includes a combination of data networks, telecommunication networks, and a combination of data and telecommunication networks. The patient device  110 , the service provider  130 , and the hospital electronic medical record system  140  communicate with each other by sending and receiving signals (wired or wireless) via the network  20 . In some examples, the network  20  provides access to cloud computing resources, which may be elastic/on-demand computing and/or storage resources  24  available over the network  20 . The term ‘cloud’ services generally refers to a service performed not locally on a user&#39;s device, but rather delivered from one or more remote devices accessible via one or more networks  20 . 
       FIG.  1 D  is a schematic view of exemplary components of the system  100 . In some implementations, the administration device  123  associated with the patient  10  includes a smart pen  123   b  or smart pill bottle  123   c  that is capable of communicating (e.g., syncing) with a patient device  110  such as a smart phone  110   b . In the example shown, the smart pen  123   b  and smart pill bottle  123   c  communicate with the smart phone  110   b  via Bluetooth, however, other wireless or wired communications are possible. The smart pen  123   b  and/or smart pill bottle  123   c  may include an associated smart cap  23  that removably attaches to the respective smart pen  123   b  or smart pill bottle  123   c . For instance, the smart cap  23  may attach to the smart pen  123   b  to enclose and protect the doser  223   b  when not being used to administer the ADM or insulin, and then removed from the pen  123   b  to expose the doser  223   b  when the patient  10  is administering and ADM or insulin. Similarly, the smart cap  23  may attach to the smart pill bottle  123   c  to enclose/seal the ADM pills within the smart pill bottle  123   c  and be removed to provide access to the bottle when the patient  10  is administering one or more ADM pills. In some implementations, the smart cap  23  implements some or all of the functionality of the respective smart pen  123   b  or smart pill bottle  123   c . For instance, the smart cap  23  may include the processor  112   e ,  112   g , the non-transitory memory  114   e ,  114   g  and/or the display  116   e ,  116   g  instead of the smart pen and smart pill bottle  123   b ,  123   c , or the pen  123   b  and/or bottle  123   c  may each implement at least one of the processor  112   e ,  112   g  the non-transitory memory  114   e ,  114   g  and/or the display  116   e ,  116   g . Accordingly, the smart cap  23  may communicate with the patient device  110  (e.g., smart phone  110   b ) via Bluetooth or through other wireless or wired communications. 
     In some configurations, the fitness tracker  110   c  communicates exercise data to the smart phone  110   b  via Bluetooth, infrared, cable, or other communications. The mobile application (e.g., program)  162  may execute on the computing device  112   b  of the smart phone  110   b  to provide the exercise data to the dosing controller  160 . The exercise data may include, without limitation, calories burned, walking steps, running steps, miles run, miles walked, and resistance repetitions. The dosing controller  160  may use exercise data when determining a recommended dose of an ADM or insulin for the patient to administer. The patient  10  may additionally or alternatively input the exercise data into the smart phone  110   b  or other device in communication with the smart phone  110   b.    
     The glucometer  124  and CGM  127  may also communicate glucose measurements to the smart phone  110   b  via Bluetooth, infrared, cable, or other communications. The mobile application  1198  executing on the computing device  112   b  of the smart phone for communicating with the dosing controller  160  such that information can be communicated over the network  20  between the dosing controller  160  and each of the smart pill bottle  123   c  (and/or cap  23 ), smart pen  123   b  (and/or cap  23 ), the glucometer  124 , the CGM  127 , and the fitness tracker  110   c . For example, dosing parameters (dosing information) adjusted by the dosing controller  160  may be transmitted to the smart phone  110   b  and stored within memory  114   b  ( FIG.  1 B ). The dosing parameters may include, but are not limited to: TargetBG; target A1c, recommended basal/bolus doses of insulin; recommended ADM doses and types; and scheduled administration times for administering doses of ADMs or insulin. The dosing parameters may be adjusted automatically or manually initiated by the user/HCP  40  or patient  10 . 
     In some implementations, upon the glucometer  124  or CGM  127  determining a glucose measurement, the glucometer  124  or CGM  127  transmits the glucose measurement to the smart phone  110   b . The smart phone  110   b  may render the glucose measurement upon the display  116   b  and permit the patient  10  to select the BGtype associated with the glucose measurement. The BGtype or BG Interval corresponds to a label or tag chosen by the patient  10  from a dropdown list upon the display  116   b  of the smart phone  110   b . Alternatively, the patient  10  may select the BG Interval from a dropdown list displayed on the display  116   c  of the glucometer. The smart phone  110   b  may transmit the glucose measurement and the BG type to the dosing controller  160  via the network  20 . In some examples, the glucometer  124  or CGM  127  is configured to transmit the glucose measurement and/or BG type directly to the dosing controller  160  via the network  20 . The patient  10  may also input meal information, such as carbohydrates consumed for breakfast, lunch, or dinner, to the smart phone  110   b.    
     In some examples, the patient  10  may enter a number of carbohydrates for a current meal into the glucometer  124 , the CGM  127 , or fitness tracker  110   c  for transmission to the smart phone  110   b  or directly into the smart phone  110   b  when a glucose measurement is received. For instance, upon receiving the glucose measurement from the glucometer  124  or the CGM  127 , the smart phone  110   b  may render an interactive graphic upon the display  116   b  that enables the patient to enter the number of carbohydrate grams the patient  10  plans to ingest. The mobile application  1198  executing on the smart phone  110   b  may provide the glucose measurement and the number of carbohydrate grams to the dosing controller  160  for calculating the recommended dose for display on the display  116   b.    
     In some implementations, a recommended dose is determined by the dosing controller  160  and sent to the smart phone  110   b  during each adjustment transmission and stored within the memory  114   b . The recommended dose may include one or more ADM pills or a dosage of insulin for the patient  10  to administer. Accordingly, upon receiving the recommended dose, the mobile application  1198  sends the appropriate number of ADM pills, doses of ADM, or doses of insulin to the smart pill bottle  123   c  or the smart pen  123   b . In some examples, the smart pen  123   b  (using the administration computing device  112   e ) automatically dials in the total number of units for the recommended dose of ADM or insulin for the doser  223   b  to administer. The patient  10  may interact with the smart pen  123   b  (or cap  23 ) or smart pill bottle  123   c  (or cap  23 ) to accept the recommended dose displayed upon the display  116   e  or manually change the recommended dose. The doser  223   b  of the smart pen  123   b  may include an electro-mechanical stop that actuates a plunger to only administer the recommended dosage of ADM or insulin accepted by the patient  10  or dosage of ADM or insulin manually entered by the patient  10 . Likewise, the doser  223   g  of the smart pill bottle  123   c  may include a locking mechanism that unlocks to dispense a number of ADM pills corresponding to the recommended dosage of ADM. In some examples, upon administration of an ADM or insulin dose by the administration device  123  (e.g., smart pen  123   b  or smart pill bottle  123   c ), the administration device  123  transmits the value of the administered dose (or bottle access data) and the time of the administered dose (or bottle access data) to the smart phone  110   b  for storage within memory  114   b  along with the associated BG measurement. Additionally, the smart phone  110   b  may transmit the administered dose (or bottle access data) and the time of the administered dose (or bottle access data) to the dosing controller  160  via the network  20 . In some configurations, the smart pen  123   b  (or cap  23 ) and/or smart pill bottle  123   c  (or cap  23 ) forms a direct communication link with the dosing controller  160  via the network  20  for receiving the recommended dosing information and/or transmitting the administered dose and the time of the administered dose to the dosing controller  160 . 
     In some implementations, an ADM pill includes the ADM smart pill  123   d  that includes the ADM as well as an ingestible sensor  113  that activates when in contact with stomach fluid to detect when the patient  10  administers the pill. Subsequently, the pill is configured to transmit activation by the sensor  113  to a wearable patch  115  (or other transceiver) that transmits the ingestion data to the smart phone  110   b . The application  162  executing on the smart phone  110   c  may log the received ingestion data along with a corresponding time stamp to allow the HCP  40  to access the ingestion data to determine if the patient  10  is being compliant. The patch  115  may include an adhesive for attaching to the patient skin near the stomach, and a transceiver for receiving an indication that the ingestible sensor  113  has been activated upon ingestion and transmitting the ingestion data to the smart phone  110   b  or other patient device  110 . In some examples, if ingestion data is not received by a time threshold for administering the ADM smart pill  123   d , the dosing controller  160  may send an alert to the administration device  123  to remind the patient  10  to administer a recommended dosage of the ADM pill  123   d  in case the patient  10  forgot to administer the pill. 
     With reference to  FIG.  2   , the dosing controller  132 ,  160  is configured to execute instructions to evaluate and select ADMs to be included in a treatment regimen based on a plurality of linked tables maintained in data storage  200  of the memory  24 ,  114 ,  134 ,  144 . Each of the tables can be classified into one of three categories: (i) fixed patient data  300 ; (ii) dynamic patient data  400 ; and (iii) reference data  500 . Tables including fixed patient data  300  are shown in  FIGS.  3 A and  3 B  and contain data permanently associated with each individual patient  10 , such as identification, demographics, and permanent medical information, for example. Tables including dynamic patient data  400  contain date-stamped data associated with dates-of-service and changes in the health status and therapy of the patient  10 . Examples of tables including dynamic patient data  400  are shown in  FIGS.  4 A- 4 G . Tables including reference data  500  are applied universally throughout the system  100  for all patients  10 . The tables including reference data  500  contain published information from third-party resources, and are periodically updated based on revisions by the third-party resource. Examples of tables including reference data  500  are shown in  FIGS.  5 A- 5 G . 
     Referring to  FIG.  3 A , a patient data table  310  of the fixed patient data  300  is provided and includes a schedule of all patients  10  treated by a respective HCP  40 . The patient data table  310  is linked to a plurality of sub-tables  320 ,  410 ,  420 ,  430 ,  440 ,  450 ,  450 ,  470  in a few-to-many relationship, whereby data related to each record  312 ,  312   a - c  (i.e., patient) in the patient data table  310  is stored in each of the various sub-tables corresponding to the record. For example, the second record  312 ,  312   b  associated with Tilly Typical in the patient data table  310  of  FIG.  3 A  may be linked to the permanent conditions table  320  shown in  FIG.  3 B . The permanent conditions table  320  includes a schedule of permanent conditions associated with patient Tilly Typical. 
     Referring back to  FIG.  2   , the patient data table  310  is further linked to a plurality of sub-tables including dynamic patient data  400 . As shown in  FIG.  4 A , a patient preferences table  410  includes treatment preference information  411 ,  411   a - d  and treatment guideline ratings  412 ,  412   a - f  for a single one of the patients  10  in the patient data table  310 . For example, the treatment preference information  411 ,  411   a - d  may include a target glucose (BG Target)  411   a , a target A1c (A1c_Target)  411   b , a preferred minimum monthly treatment cost (ADM_$_perMo_Low)  411   c , and a preferred maximum monthly treatment cost (ADM_$_perMo_Hi)  411   d.    
     The treatment guideline ratings  412  of the patient preferences table  410  are associated with an importance of corresponding treatment guidelines. In the illustrated example, the treatment guideline ratings  412  include cost (Cost Importance)  412   a , effect on body weight (Weight Importance)  412   b , treatment regimen complexity (Complexity Importance)  412   c , treatment efficacy (Efficacy Importance)  412   d , mealtime coverage needs (Mealtime_Coverage_Importance)  412   e , and risk of hypoglycemia (Hyopglycemia_Importance)  412   f . Each treatment guideline rating ( 412 ) is assigned a numeric rating based on the patient&#39;s  10  subjective level of importance for the treatment guideline. In the illustrated example, the importance of the treatment guidelines are rated using a binary scale, whereby a rating of “0” corresponds to a treatment guideline having little or no importance to the patient, and a rating of “1” corresponds to a treatment guideline having high importance. In some implementations, importance of each treatment guideline is indicated based on a scaled rating. For example, importance may be indicated based on a scale from  1  to  10 , with a value of “1” being associated with a lowest level of importance to the patient  10  and a value of “10” being associated with a highest level of importance to the patient  10 . 
     The dosing controller  160  may periodically update the patient preferences table  410  based on feedback received from the patient  10 . Here, the patient  10  may provide patient preference feedback to his/her healthcare provider(s) during office visits, phone consultations, or electronic communications, and the HCP  40  may provide the patient preference feedback to the dosing controller  160  to update the patient preferences table  410 . For example, as shown in  FIG.  4 A , on Jun. 17, 2016 the HCP  40  with the surname Pepper updated the patient preferences table  410  to indicate that treatment cost  412   a  was now of high importance to the patient, and on Jun. 28, 2016 another HCP  40  with surname Livingston updated the patient preferences table  410  to indicate that effect on body weight  412   b  was of high importance to the patient  10 . 
     The patient preferences table  410  may be updated via an interactive patient preferences screen  610 , as shown in  FIG.  6 A . The patient preferences screen  610  presents the HCP  40  or a patient  10  with a series of questions corresponding to the treatment guideline ratings  412 . For example, the patient preferences screen  610  may present a first series of questions to be answered by the patient  10 , including questions related to the importance of an effect on body weight guideline, the treatment cost guideline, and, if necessary, the minimum and maximum monthly treatment costs. The patient preferences screen  610  may also include questions to be answered by the HCP  40 . For example, the interactive input may include questions relating to the HCP&#39;s judgment with respect to the requirement for additional mealtime coverage and the ability of the patient to handle a complex treatment regimen. As provided above, the responses to these questions are stored in the patient preferences table  410  as ratings  412  of 0 (i.e., “no”) or 1 (i.e., “yes”). 
     With continued reference to the patient preferences screen  610 , the HCP may be presented with one or more advisory notes  612  including data relevant to determining and selecting treatment guideline ratings  412  for the patient. For example, the advisory notes  612  may include a first advisory note  612   a  displaying a calculated glucose (BG) ratio for consideration when determining whether the patient requires additional mealtime coverage. The BG ratio  612   a  is calculated by taking a mean of all BG measurements taken during lunch (BG Lunch ), dinner (BG Dinner ), and bedtime (BG Bedtime ) intervals, over a mean of all BG measurements taken during a fasting interval prior to breakfast (BG Breatfast ). For instance, the BG ratio  612   a  may be expressed by the following formula: 
     
       
         
           
             
               
                 
                   
                     BG 
                     ⁢ 
                         
                     Ratio 
                   
                   = 
                   
                     
                       Average 
                       ⁢ 
                           
                       
                         ( 
                         
                           
                             BG 
                             Lunch 
                           
                           , 
                           
                             BG 
                             Dinner 
                           
                           , 
                           
                             BG 
                             Bedtime 
                           
                         
                         ) 
                       
                     
                     
                       Average 
                       ⁢ 
                           
                       
                         ( 
                         
                           BG 
                           Breakfast 
                         
                         ) 
                       
                     
                   
                 
               
               
                 
                   ( 
                   1 
                   ) 
                 
               
             
           
         
       
     
     Additional concepts and features related to average BG measurements for each of the BG intervals can be found in U.S. Patent Application Publication No. 2017/0228518, the disclosure of which is incorporated herein in its entirety. A BG ratio  612   a  greater than 1.00 indicates that the average meal-related BG measurements (BG Lunch , BG Dinner , BG Bedtime ) are higher than the average fasting BG measurements taken before breakfast (BG Breakfast ). Conversely, for BG Ratios less than or equal to 1.00, the HCP may identify the patient as not requiring additional mealtime coverage. Accordingly, an advisory note showing the BG ratio  612   a  is provided to the HCP in the patient preferences input screen  610  so that the HCP may identify the patient as needing additional mealtime coverage. 
     Referring still to the patient preferences screen  610  of  FIG.  6 A , the HCP may also be presented with an advisory note  612  indicating a treatment compliance rate  612   b  for the patient  10 , which can be considered by the HCP  40  in determining whether the patient  10  is capable of handling complex treatment regimens. The system calculates the treatment compliance rate based on information obtained from the patient device data table  450  shown in  FIG.  4 E . For example, as shown in  FIG.  4 E , the patient  10  may be associated with a smart pill bottle (eBottle_Rx)  123   c  capable of tracking each instance of the bottle  123   c  being opened (e.g., the bottle access data of  FIG.  1 D ). The number of bottle openings (Bottle_Openings_wk) is then stored in the device data table  450 . Treatment compliance rate  612 ,  612   b  is then calculated as a ratio of the number of measured bottled openings per week over the scheduled doses per week by the following formula. 
     
       
         
           
             
               
                 
                   
                     Treatmant 
                     ⁢ 
                         
                     Compliance 
                   
                   = 
                   
                     
                       Measured 
                       ⁢ 
                           
                       Bottle 
                       ⁢ 
                           
                       Openings 
                     
                     
                       Scheduled 
                       ⁢ 
                           
                       Doses 
                     
                   
                 
               
               
                 
                   ( 
                   2 
                   ) 
                 
               
             
           
         
       
     
     If more than one medication is currently prescribed to the patient  10 , the treatment compliance rate  612 ,  612   b  may be calculated as an average of the treatment compliance rate for each one of the prescribed medications. 
     Referring to  FIG.  4 B , an allergies and exclusions table  420  includes a listing of all ADMs that a patient is either allergic to or that have been excluded from the treatment regimen for other reasons. For example, ADMs may be excluded by the patient  10  or HCP  40  based on the undesirable side-effects or contraindications. The allergies and exclusions table  420  is in reciprocal communication with an allergies and conditions screen  620  ( FIG.  6 B ). Here, the data included in the allergies and exclusions table  420  is presented to the patient  10  or HCP  40  in the allergies and conditions screen  620  on the display  116 ,  146 . The allergies and exclusions table  420  may update based on feedback received from inputs to the allergies and conditions screen  620  by the patient  10  or HCP  40 . This interactive relationship is described in greater detail below. 
       FIG.  4 C  illustrates an example of a current medications table  430  including a listing of all medications currently being taken by the patient  10 . The current medications table  430  may also be referred to as a current medications list  430 . As shown in rows  2  and  3  of the illustrated current medications table  430 , non-ADM medications may also be included in the current medications table  430 . The current medications table  430  is queried by the program  160  as part of determining potentially adverse interactions between suggested treatment regimens and medications currently taken by the patient  10 . Further, once a treatment regimen is selected and implemented, the ADM selection program  160  may update the current medications table  430  to include changes or additions to the listed medications. 
     Referring to  FIGS.  2 ,  4 D and  4 E , the data storage  200  further includes a patient device calibration table  440  and the patient device data table  450  discussed above. The patient device data table  450  may be provided as a linked child ( FIG.  2   ) to the patient device calibration table  440 , whereby the patient device data table  450  is used by the system  100  to maintain calibration of each of the devices. For example, the patient may use a fitness tracker  110   c , a smart phone  110   b , a BG monitor  124 , a smart pill bottle  123   c , and a smart scale  125  all listed by the patient device calibration table  440  and the patient device data table  450 . The data for each of the devices  110   c ,  110   b ,  124 ,  123   c ,  125  in the patient device data table  450  is communicated to the system  100  from each device  110   c ,  110   b ,  124 ,  123   c ,  125 . Accordingly, the patient device data table  450  may be updated in real-time, at regular intervals, or on-demand. 
     Based on the data provided in the patient device data table  450 , each of the devices  110   c ,  110   b ,  124 ,  123   c ,  125  can be calibrated. For example, the parameter of Calories-per-Mile-by-GPS can be calibrated by taking the actual calories burned by GPS for the previous week divided by the actual miles by GPS for the previous week. For instance, the Calories-per-Mile-by-GPS can be calculated by the following formula: 
     
       
         
           
             
               
                 
                   
                     Calories_per 
                     ⁢ 
                     _Mile 
                     ⁢ 
                     _by 
                     ⁢ 
                     _GPS 
                   
                   = 
                   
                     
                       Calores_by 
                       ⁢ 
                       _GPS 
                       ⁢ 
                       _wk 
                     
                     
                       Miles_by 
                       ⁢ 
                       _GPS 
                       ⁢ 
                       _wk 
                     
                   
                 
               
               
                 
                   ( 
                   3 
                   ) 
                 
               
             
           
         
       
     
     The calculated value of this calibration constant, (Calories-per-Mile_by_GPS), is stored in the patient device calibration table  440 . Another example is (Calories_per_rep_per-Lb_WeightMachine_A), which also is dependent on a resistance weight machine&#39;s weight load, in Lb. For instance, the Calories_per_rep_per-Lb_WeightMachine_A can be calculated using the following formula: 
     
       
         
           
             
               
                 
                   
                     Calories_per 
                     ⁢ 
                     _rep 
                     ⁢ 
                     _per 
                     ⁢ 
                     _lb 
                     ⁢ 
                     _WeightMachine 
                     ⁢ 
                     _A 
                   
                   = 
                   
                     
                       Calories_by 
                       ⁢ 
                       _WeightMachine 
                       ⁢ 
                       _A 
                       ⁢ 
                       _wk 
                     
                     
                       ( 
                       
                         
                           Reps_by 
                           ⁢ 
                           _WeightMachine 
                           ⁢ 
                           _A 
                           ⁢ 
                           _wk 
                         
                         
                           WeightMachine_A 
                           ⁢ 
                           _weightload 
                         
                       
                       ) 
                     
                   
                 
               
               
                 
                   ( 
                   4 
                   ) 
                 
               
             
           
         
       
     
     The calibration ratios are considered permanent but may be re-calculated and re-saved with each therapy update. The ratio enables the HCP  40  to prescribe exercise with knowledge of the calories it will burn. 
       FIG.  4 F  shows a current conditions table  460  linked as a child to the patient preferences table  410  ( FIG.  2   ) and populated based on information provided from a current labs table  470  ( FIG.  4 G ), contraindications table  550  ( FIG.  5 E ), and the allergies and conditions screen  620  ( FIG.  6 B ). More specifically, the current conditions table  460  is populated by comparing each of the records (i.e., lab results) of the current labs table  470  against each of the records of the contraindications table  550  to identify commonality. If one of the lab results listed in the current labs table  470  satisfies one of the contraindicating conditions listed in the contraindications table  550 , then the dosing controller  160  identifies the corresponding condition for input to the current conditions table  460 . For example, the current labs table  470  shown in  FIG.  4 G  shows a Glomerular Filtration Rate (GFR) measurement of 55%, which is shown in row  1  of the contraindicating conditions table  550  of  FIG.  5 E  as a resulting contraindicating condition. Accordingly, the contraindicating condition is listed in the current conditions table  460 . The current conditions table  460  may be further populated based on responses provided by the HCP  40  in the allergies and conditions screen  620  of  FIG.  6 B . For example, the allergies and conditions screen  620  may include fields for entering current conditions and side effects of the patient  10 . 
     The current conditions table  460  serves two purposes: first, to resolve conflicts between the inputs from the allergies and conditions screen  620  and the current labs table  470 ; and second, to provide for the recording and storing of the conditions of the patient  10  on the date of the update. Accordingly, the current conditions table  460  is provided as an interactive screen, whereby the resolution of conflicts is accomplished by a process of verification or concurrence, which is done by the HCP  40  using corresponding graphical radio buttons  462  provided in the HCP Assessment Positive column. The current conditions table  460  allows the HCP  40  to view the conditions along with the applicable lab results and make a judgment-based decision about the condition. The conditions that are fed into the current conditions table  460  from the allergies and conditions screen  620  are automatically filled with the values from the allergies and conditions screen  620 . 
     Referring to  FIG.  5 A , the ADM table  510  includes a schedule of all available ADMs, which are indexed to be linked to a plurality of subtables  520 ,  530 ,  550  ( FIG.  5 E ),  560  ( FIG.  5 F ), as described in greater detail below. The ADM table  510  is populated with prescribing drug information  512  and scaled guidelines  514  derived from the references  196 ,  198  discussed above. Drug information  512  may include a Food and Drug Administration National Drug Code (FDA-NDC) number  512   a , an ADM classification  512   b , a generic name  512   c , and a delivery method  512   d . The ADM table  510  is also populated with respective scaled guidelines  514 ,  514   a - f  for each of the ADMs. 
     The scaled guidelines  514 ,  514   a - f  in the illustrated ADM table  510  include, but are not limited to, guidelines  562 ,  562   a - f  shown in the table entitled guideline refreshment conversion table  560  ( FIG.  5 F ). The guidelines  562  in the illustrated example of the guideline refreshment conversion table  560  include efficacy  562   a , hypoglycemia risk  562   b , effect on body weight  562   c , cost  562   d , complexity  562   e , and mealtime coverage  562   f . Efficacy  562   a  describes how well the ADM reduces glucose concentration and hemoglobin A1c. Hypoglycemia risk  562   b  is the probability that the ADM will cause hypoglycemia. Weight effect  562   c  is the effect of the ADM on patient&#39;s weight, ranging from weight-loss at the lower end of the parameter&#39;s range to weight-gain at the upper end. Cost  562   d  corresponds to the dollar-cost of the ADM. Complexity  562   e  relates to the amount of trouble and inconvenience incurred by a patient taking the ADM. Meal coverage  562   f  is the degree to which an ADM is more active at meals. 
     Several of the guidelines  562  are provided by the references  198  in scaled form (e.g. Low, Medium, High). However, the guidelines  562  are translated to number scaled guideline values  514  between 0 and 1 in accordance with the guidance in the tabulated guideline refreshment conversion process table  560  ( FIG.  5 F ). These numeric scaled guidelines values  514  are given names such as Scaled_Hypo_Risk, and Scaled_Weight_Effect. These scaled guideline values  514  are sent to the ADM Table  510  for storage. The ADM table  510  may occasionally be refreshed or updated to reflect revisions to the scaled guideline values  514  based on changes to the guidelines  562  in the guideline refreshment conversion table  560 . 
     The principal of the ADM selection system  100  is to assess the applicability of each available ADM to the health status of the patient  10  based on several criteria, including patient preferences, patient medical conditions, published treatment guidelines, and availability of alternative treatment regimens, for example. An example of an ADM selection table  800  is provided in  FIG.  8    for the purpose of illustrating an implementation of the ADM selection system  100 . However, in practice the ADM selection system  100  may determine recommended ADMs  810  without the use of the ADM selection table  800 . 
     Referring to  FIG.  7   , in some implementations, the dosing controller  160  executes an ADM selection process  700  to select available ADMs  810  for the treatment regimen of a patient  10 . The ADM selection process  700  includes a first step  710  of populating an ADM selection table  800  ( FIG.  8   ) with a listing of available ADMs  810 ,  810   a - i , which are obtained from the ADM table  510 . The ADM selection table  800  of  FIG.  8    shows each ADM  810  associated with one or more demerit values  812 , including an adverse demerit value  812   a , an instruction demerit value  812   b , a guide demerit value  812   c , a modified demerit value  812   d , and a total demerit value  812   e . While available ADMs  810   a - i  are shown, the ADM selection table  800  may include more or less ADMs  810 , including different types of ADMs  810  presently available or that may become available in the future for managing glucose levels. Although represented as a table  800  in the example shown, the list of available ADMs  810  may be implemented in any format. In some instances, the ADM selection table  800  is prefilled from prior iterations of the ADM selection process  700 . In such cases, the first step  710  of the ADM selection process  700  includes an initialization step, whereby each of the demerit values  812  is “zeroed” and the dose notes are cleared. Each of the ADMs  810  in the ADM selection table  800  may also be associated with one or more dose notes  814 ,  814   a - b  assigned by the ADM selection process  700 . 
     The ADM selection process  700  calculates the demerit values  812  using predetermined increment values  572  obtained from the configurable constants table  570  ( FIG.  5 G ). As shown in  FIG.  5 G , the configurable constants table  570  includes an adverse demerit increment value  572   a , an instruction demerit increment value  572   b , and a guideline demerit value increment value  572   c , along with other configurable constants, which are discussed further below. The increment values  572  for calculating each of the demerit values  812   a - 812   c  can be modified in the configurable constants table  570  by the HCP depending on a desired weight to be given to each type of demerit. In the illustrated example, the adverse demerit increment value  572   a  is larger than the other demerit increment values  572   b ,  572   c . The adverse demerit increment value  572   a  is used for the steps of checking for adverse interactions between drugs in the ADM selection table  800  and the drug interactions table  520  ( FIG.  5 B ), and in the step for checking for contraindicating conditions associated with each of the ADMs  810 . These two steps are considered highly important and, accordingly, are configured to confer more demerits than other processes. By assigning a high value to the adverse demerits increment value  572   a , ADMs  810  that are identified as having adverse interactions or contraindicating conditions are less likely to be recommended by the ADM selection system  100 . In the illustrated example, the adverse demerit increment value  572   a  is assigned a value of 60 demerits in the configurable constants table  570 . This compares with the illustrated value of 10 demerits for guideline demerits. In the current example, there are six guidelines  412   a - 412   f  (see patient preferences table  410 ). Accordingly, if each receives a maximum value of 10, the total guideline demerit value  812   c  would equal 60 demerits, which equals the total demerits of an ADM having one adverse interaction or contraindicating condition. The result of this tiered system of demerit increments  572  is that the contraindicating conditions and adverse interactions provide a coarse evaluation of the ADM under consideration and the guidelines provide a fine evaluation. 
     Referring back to  FIG.  7   , once the ADM selection table  800  is populated and initialized, a second step  720  of the ADM selection process  700  includes assigning the modified demerit values  812   d  for each of the ADMs  810 . Here, the ADM selection process  700  queries  722  the current medications table  430  ( FIG.  4 C ) for each ADM  810  listed in the ADM selection table  800 . If an ADM  810  is included in the current medications table  430 , the ADM selection process  700  assigns  723  the corresponding ADM  810  a negative (low) modified demerit value  812   d , such as −200, for example. The assigning of a negative (low) modified demerit value  812   d  ensures that the corresponding ADM  810  will have a low total modified demerit value  812   d , which will, in turn, ensure that the corresponding ADM  810  will be included among the most suitable ADMs  810  for selection from the list. In addition to adjusting the modified demerit value  812   d , the ADM selection process  700  may also edit a first dose note  814   a  to indicate that the corresponding ADM  810  will be selected as part of the current treatment regimen for the patient  10 . 
     The second step  720  of the ADM selection process  700  further queries  724  the allergies and exclusions table  420  ( FIG.  4 B ) for each of the ADMs  810 ,  810   a - 810   i  in the ADM selection table  800 . If an ADM  810 ,  810   a - 810   i  is listed within the allergies and exclusions table  420 , then the ADM selection process  700  assigns  725  the modified demerits value  812   d  with a relatively high value (e.g. 200 demerits). By contrast to assigning a relatively low (e.g., negative) value (e.g., −200 demerits), a relatively high value for the demerits associated with the inclusion in the allergies and exclusions table  420  ensures that the corresponding ADM  810  will be ranked low on the list. 
     A third step  730  of the ADM selection process  700  includes incrementing adverse and/or instruction demerit values  812   a ,  812   b  for each of the ADMs  810 . Here, the ADM selection process  700  queries  732  the contraindications table  550  ( FIG.  5 E ) for each ADM  810  and the current conditions table  460  ( FIG.  4 F ) to determine whether any contraindicating conditions listed in the contraindications table  550  are present in the current conditions table  460  for the patient  10 . If a contraindicating condition associated with an ADM  810  is listed in the current conditions table  460 , the corresponding graphical radio button  462  in the current conditions table  460  is selected, and if there are not any special dosing instructions associated with the ADM  810 , then the ADM selection process  700  increments  733  the adverse demerit value  812   a  of the ADM by 60 demerits. On the other hand, if the corresponding ADM  810  listed in the current conditions table  460  does include special dosing instructions, then the ADM selection process  700  increments  735  the instruction demerit value  812   b  by 30 demerits and adds a corresponding note indicating “conditional dosing” to the dosing notes  814  ( FIG.  8   ). 
     The third step  730  of the ADM selection process  700  also queries  734  the drug interactions table  520  ( FIG.  5 B ) for each ADM  810  listed in the ADM selection table  800  to determine if any of the medications in the ADM selection table  800  interact with any of the medications that are part of the current treatment regimen. If a first ADM  810  in the selection table  800  has an adverse interaction with a second ADM  810 , and the second ADM  810  is listed in the current medications table  410 , the ADM selection process  700  increments  737  the adverse demerit value  812   a  for the first ADM by 60 demerits. 
     In some examples, the third step  730  of the ADM selection process  700  also queries  736  the permanent conditions table  320  ( FIG.  3 B ) and the contraindications table  550  ( FIG.  5 E ). The contraindicating conditions for each of the ADMs  810  in the ADM selection table  800  are compared with the permanent conditions listed in the permanent conditions table  320 . If a permanent condition is included in the contraindications table  550  for the corresponding ADM and the condition appears with HCP concurrence in the current conditions table  460  ( FIG.  4 D ), then the ADM selection process  700  increments  739  the adverse demerits value  812   a  for the corresponding ADM  810  by 60 demerits. 
     The third step  730  of the ADM selection process  700  may further assign  738  the guideline demerit value  812   c  for each ADM  810  in the ADM selection table  800 . The assigning of the guideline demerit value  812   c  includes querying  738   a  each of the patient preferences table  410 , the ADM table  510 , and the configurable constants table  570  to obtain the treatment guideline rating values  412 ,  412   a - f , the scaled guideline values  514 ,  514   a - f , and a configurable guideline demerit increment value  572   c  for the corresponding ADM  810 . The ADM selection process  700  may calculate  738   b  the guideline demerit value  812   c  by multiplying each of the scaled guideline values  514  by the corresponding treatment guideline rating value  412  and by the guideline demerit increment value  572   c  (i.e.,  10 ) from the configurable constants table  570  for all of the guidelines listed. Accordingly, the guideline demerit value  812   c  for each ADM is the sum of the calculated demerit values for each of the guidelines, as provided in the following equation: 
       Value GuidelineDemerit =Σ(Value Scaled (Guideline)*Value Importance (Guideline)*10)  (5)
 
     Once ADM selection process  700  assigns the corresponding guideline demerit values  812   c  for each ADM  810 , a fourth step  740  of the ADM selection process  700  calculates the total demerit value  812   e  by summing the adverse demerit value  812   a , the instruction demerit value  812   b , and the guideline demerit value  812   c  for the respective ADM. Additionally, in instances where an ADM  810  does not have a modified demerit value  812   d , the total demerit value  812   e  will also be used as the modified demerit value  812   d . Similarly, ADMs having an assigned high (e.g., positive) modified demerit value  812   d  (e.g.,  200 ) may replace the corresponding total demerit value  812   e.    
     In some implementations, a fifth step  750  of the ADM selection process  700  filters and sorts the ADMs  810  in the ADM selection table  800  based on the total demerit values  812   e  calculated during the fourth step  740 . In some examples, the fifth step  750  of the ADM selection process  700  initially sorts  752  the ADM selection table  800  based on the total demerit values  812   e  and the modified demerit values  812   d . Here, the initial sorting  752  orders total demerit values  812   e  for the ADMs  810  from lowest to highest. In some examples, any ADM  810  having a corresponding low (e.g., negative) modified demerit value  812   d  assigned during the second step  720  may be added to the ordered list to appear at the lowest position. For example, the ADM  810  included in the current medications table  430  ( FIG.  4 C ) that was assigned a modified demerit value of −200, as discussed above, would appear at the top of the sorted ADM selection table  800 . In some examples, an ADM having an assigned high (e.g., positive) modified demerit value  812   d  replaces the corresponding total demerit value  812   e  to ensure that the corresponding ADM  810  is ordered at the highest position. For instance, the ADM in the allergies and exclusions table that was assigned a modified demerit value of 200 would appear at the bottom of the sorted ADM selection table  800 . 
     In lieu of the initial sorting  752  from lowest to highest based on the total demerit values  812   e  or the modified demerit values  812   d  (when applicable), the fifth step  750  of the ADM selection process  700  may optionally execute two sorting steps  753 ,  753   a - b . The first sorting step  753   a  includes filtering out each ADM  810  from the ADM selection table  800  that includes a corresponding total demerit value  812   e  that satisfies (e.g., greater than or equal to) a demerit threshold value. As used herein, “filtering out” refers to removing an ADM  810  from the ADM selection table  800  so that the corresponding ADM  810  will not be selected as part of the treatment regimen for the patient  10 . In some examples, the demerit threshold value is equal to 60 demerits and is satisfied when the total demerit value  812   e  is greater than or equal to 60 demerits threshold. Thus, the demerit threshold value may be selected to filter out any ADMs having contraindicating conditions listed in the contraindications table  550  that are also present in the current conditions table  460  and/or the permanent conditions table  320  for the patient  10  and/or to filter out any ADMs that interact (e.g., by accessing the drug interactions table  520 ) with medications the patient  10  is currently taking (e.g., by accessing the current medications table  430 ). The second sorting step  753   b  includes sorting the remaining ADMs  810  (i.e. ADMs having a total demerit value  812   e  less than or equal to 60 demerits) from low-to-high based on their respective guideline demerit values  812   c . Accordingly, the optional sorting steps  753  sort the ADMs  810  in the ADM selection table  800  from lowest to highest based on the guideline demerit values  812   c  after filtering out (e.g., removing) all ADMs associated with corresponding total demerit values  812   e  satisfying the demerit threshold value. 
     With the ADM selection table  800  sorted via the initial sorting  752  based on the total demerit values  812   e  and/or assigned modified demerit values  812   d , or the optional sorting steps  753  based on the guideline demerit values  812   c  after filtering out any ADMs associated with corresponding total demerit values  812   e  satisfying the demerit threshold value, the fifth step  750  of the ADM selection process  700  selects  754  a predetermined number of recommended ADMs  810  having the lowest total demerit values  812   e  or lowest guideline demerit values  812  from the sorted ADM selection table  800  for display on the display  146  associated with the HCP  40 . The HCP  40  may view the predetermined number of recommended ADMs  810  to determine whether or not some or all should be included in the treatment regimen for the patient  10 . The predetermined number of ADMs  810  selected may be set by the N-Finalists constant  574  (e.g., “3”) in the configurable constants table  570  ( FIG.  5 G ). Here, the number of ADMs  810  recommended by the ADM selection process  700  is in addition to any ADMs  810  that the patient  10  is currently taking (e.g., included in the current medications table  430 ). For instance, an ADM  810  included in the current medications table  430  may have a modified demerit value  812   d  equal to −200, while the next lowest-scoring ADMs  810  not included in the current medications table  430  may have total demerit values  812   e  equal to “10”, “20”, and “30”, respectively. Thus, if N_Finalists is configured to a value of 3, then all four of these ADMs will be displayed in the ADM selection table  800  as recommended ADMs for inclusion in the treatment regimen of the patient  10 . In this way, the HCP  40  will be able to see any ADMs the patient  10  is currently taking even if these ADMs would not have been one of predetermined number of ADMs  810  selected from the sorted ADM selection table  800  based on the initial sorting  752  or the sorting steps  753 . 
     Once the recommended ADMs  810  are identified, the ADM selection process  700  executes a dosage step  760  to determine/calculate a dosage for each of the recommended ADMs  810  based on a comparison between a target A1c value (Target_A1c)  411   b  and an energy-adjusted A1c value (Energy-Adjusted_A1c)  611 . The target A1c value  411   b  is obtained from the patient preferences table  410  ( FIG.  4 A ) and the energy-adjusted A1c value  611  for the patient  10  is calculated using Equation 11 below. 
     Referring to  FIG.  6 C , an energy-based dosage screen  630  determines the energy-adjusted A1c value  611  by adjusting a current A1c value  632  based on fitness-related data received from the patient devices  110 . The current A1c value  632  may be obtained from the current labs table  470  ( FIG.  4 G ) and converted to a BG value (eBG) by a function subroutine that contains a published correlation as follows: 
       eBG=eBG[FUNCTION( A 1 c )]  (6)
 
     The eBG may then be converted to a value of excess carbohydrate grams per day (Carbs_XS) as follows: 
       Carbs_XS=(eBG−TargetBG)*HTF[FUNCTION(Weight)]  (7)
 
     where HTF is a hypoglycemia treatment factor based on a weight of the patient  10 . If the patient has a linked scale device  125 , then the weight (eWeight) obtained from the smart scale  125  is substituted for clinic-measured weight throughout the program. 
     The excess carbohydrate grams per day (Carbs_XS) may be converted to excess energy (Calories_XS)  634  by multiplying by a Calories_Per_Carb constant  576  (e.g.,  4 ) provided in the configurable constants table  570  ( FIG.  5 G ). The parameter for remaining energy surplus value (Remaining_Calories_XS)  636  is initialized to the excess energy value. 
     The HCP  40  uses the energy-based dosing screen  630  of  FIG.  6 C  to provide an energy-based dose adjustment for the patient  10 . Here, the energy-based dose adjustment may change a dosing for each recommended ADM based on an exercise regimen for the patient  10 . The exercise regimen may be obtained by tracking exercise data from the patient devices  110 . The tracked exercise data may be used to determine a frequency, intensity, duration, and types of exercises associated with the patient&#39;s  10  exercise regimen. In some examples, dosing prescribed to a patient is reduced when the patient is more active. The remaining energy surplus value (Remaining_Calories_XS)  636  is adjusted by successive changes to the exercise regimen and dietary carb intake as entered by the HCP  40 . This process involves a deliberate trial-and-error process, which is done interactively, preferably while the HCP  40  and the patient  10  are communicating with one another. This insures that the HCP  40  does not prescribe an exercise regimen that the patient is unwilling to comply with. Several forms of exercise may be prescribed. Also one or more of the patient devices  110  may be equipped or connected to a carbohydrate-counting database. This enables the HCP  40  to prescribe changes to the carbohydrate count in the patient&#39;s  10  diet. The decrement to the remaining energy surplus value (Remaining_Calories_XS) is tallied in the same manner as for exercise changes. 
     In the example shown, the HCP  40  uses the energy-based dosing screen  630  to change the exercise regimen for the patient  10  by adjusting use of Weight Machine A  635 . The machine&#39;s weight load (WeightMachine_A Weight_Load) is entered in the “load or NA” entry box. The current average value of the reps per week is obtained from the patient device data table  450  ( FIG.  4 E ) and the calibration constant (Calories_per_rep_per_Lb_WeightMachineA) is obtained from the patient device calibration table  440  ( FIG.  4 D ). The change in exercise (Recom_Change-WeightMachine_A_reps) is input by the HCP  40 . The resulting change is usually a decrement to the patient&#39;s remaining excess calories, but just in case, the sign is accounted-for. The resulting change to the remaining energy surplus value (Calories_dRx_WMA) is calculated using the calibration constant as follows: 
       Calories_dRx_WMA=(Recom_Change-WeightMachine_ A _reps)*(Calories-per-rep-per-Lb_WeightMachine_ A )*(WeightMachine_ A _WgtLoad)   (8)
 
     The decremented remaining energy surplus value (Remaining_Calories_XS) incorporating all decrements is converted back to an A1c value after each successive decrement, so that the HCP  40  can see what the predicted A1c will be. The predicted value of A1c is called the energy-adjusted A1c value (Energy_Adjusted_A1c)  611 . The conversion is accomplished by the formulas below: 
     
       
         
           
             
               
                 
                   Carbs_Changed 
                   = 
                   
                     
                       Calories_XS 
                       - 
                       RemainingCalorie_XS 
                     
                     
                       Calories_per 
                       ⁢ 
                       _Carb 
                     
                   
                 
               
               
                 
                   ( 
                   9 
                   ) 
                 
               
             
           
         
       
       
         
           
             
               
                 
                   eBG_Changed 
                   = 
                   
                     Carbs_Changed 
                     
                       HTF 
                       [ 
                       
                         Function 
                         ( 
                         Weight 
                         ) 
                       
                     
                   
                 
               
               
                 
                   ( 
                   10 
                   ) 
                 
               
             
           
         
       
       
         
           
             
               
                 
                   
                     Energy 
                     - 
                     Adjusted_A1 
                     ⁢ 
                        
                     C 
                   
                   = 
                   
                     eA 
                     ⁢ 
                     1 
                     ⁢ 
                     
                       c 
                       [ 
                       
                         FUNCTION 
                         ( 
                         
                           eBG 
                           + 
                           eBG_Changed 
                         
                         ) 
                       
                     
                   
                 
               
               
                 
                   ( 
                   11 
                   ) 
                 
               
             
           
         
       
     
     While the example above adjusts the weight load for Weight Machine A  635  for adjusting the exercise regimen for the patient  10 , other exercise regiments may not require changes in load. When the HCP  40  is satisfied with the results shown in energy-based dosage screen  630 , he/she exits the screen  630  and proceeds with the patient&#39;s update process. The screen and status of the parameters remain as-is, so that the HCP  40  can return to the screen, if desired  630 . The latest calculated energy-adjusted A1c value (Energy_Adjusted_A1c)  611  is used by the dosage step  760  of the ADM selection process  700  for determining/calculating the dosage for each of the recommended ADMs  810  so that the energy-based A1c adjustments are accounted for in the dose calculations. For each recommended ADM  810 , the dosage step  760  further compares a sum of a current dose value (Current_Dose) and a starting dose value (Start_Dose) with a maximum allowable dose (Max_Dose). The Current_Dose may be obtained from the current medications table  430  ( FIG.  4 C ) and the Start_Dose and the Max_Dose may be obtained from the ADM table  510  ( FIG.  5 A ). 
     If an ADM is included in the current medications table  430 , the energy-adjusted A1c value  611  is greater than the target A1c value, and the sum of the current dosage value and the start dosage value for the ADM is greater than the maximum dosage value, then the system recommends the current dosage value for the ADM and provides a prompt (i.e. note) to maintain the current dosage value of the ADM and to add another ADM. If the sum of the current dosage value and the start dosage value is less than or equal to the maximum dosage value and if the dosage notes are null, then the recommended dosage value is the sum of the current dosage value and the start dosage value. However, if the dosage notes are not null, such as when special dosing instructions are identified for an ADM, then the system  100  provides a prompt (i.e. note) for the HTC to consult manufacturer dosing instructions for all ADMs, except for metformin. In the case of metformin, the system  100  recommends maintaining the current dosage value and adding another ADM. In cases where the ADM is listed in the current medications table  430  and the energy-adjusted A1c value is less than or equal to the target A1c value, the system  100  recommends the current dosage for the ADM, and provides a prompt (i.e. note) recommending no change in dosage. 
     Once the ADM selection process  700  determines the recommended dosage values for each recommended ADM during the dosage step  760 , the process executes a cost step  770  to calculate a total cost of the suggested recommended therapy based on the cost per dose and the total dosage values recommended for each recommended ADM  810 . Thus, the cost step  770  may determine a cost for each recommended ADM  810  by multiplying the cost per dose times the total dosage value recommended and then sum the costs of all the recommended ADMs  810  to determine the total cost of the suggested recommended therapy. Thereafter, the ADM selection process  700  executes a selection screen step  780  for generating an ADM selection screen  640  ( FIG.  6 D ) based on the total cost of the suggested recommended therapy calculated during the cost step  770 . 
     Referring to  FIG.  6 D , the ADM selection screen  640  graphically displays a representation of the ADM selection table  800  on the display  116 ,  146 . In the example shown, the ADM selection screen  640  includes energy-based treatment information  642  and a listing  644  of the recommended ADMs  810 ,  810   a - c . In the example shown, the listing  644  includes a first recommended ADM  810   a  of Jardiance (empagliflozon), a second recommended ADM  810   b  of Invokana (canegliflozin), and a third recommended ADM  810   c  of Lantus (glargine U-100). Each recommended ADM  810  of the listing  644  on the screen  640  includes an associated recommended dosage value  646 , ADM notes  647  (i.e. side-effects, dosages, adverse interactions), and a fitness level  648  indicating how well a particular ADM matches the patient  10 . The HCP  40  may edit the ADM selection screen  640  to make changes to the recommended ADMs. For example, the HCP may adjust one or more of the recommend dosage values  646 . The ADM selection screen  640  may also include a button  649  for opening the ADM selection table  800 . Thus, the HCP  40  may select the button  649  to access the ADM selection table  800  when the HCP  40  wants to view and/or select an ADM that was not included in the recommended ADMs on the ADM selection screen  640 . 
     Once the HCP  40  is satisfied with the recommended ADMs, the HCP  40  may save the recommended therapy regimen. Referring back to  FIG.  7   , the ADM selection process  700  executes a transmission step  790  to transmit the recommended therapy regimen to the patient  10 . The process  700  may transmit the recommended therapy regimen to the patient  10  via at least one of a text message (SMS), electronic mail, a pre-recorded telephone message, a printed report, a web-based application, or by a downloadable application, for example. The dosing controller  160  may route the recommended therapy regimen to one or more of the patient devices  110 . Using the recommended therapy regimen, the smart pill bottle  123   c  containing one of the recommended ADMs  810  may alert the patient  10  when the regimen specifies it is time for the patient  10  to administer the ADM  810 . For instance, the bottle  123   c  may include a display that presents the appropriate dosage for the patient  10  to administer. The bottle  123   c  may also unlock when it is time for the patient  10  to administer the ADM  810 . Similarly, when the recommended ADM  810  includes insulin (e.g., basal insulin such as Lantus), the dosing controller  160  may send a recommended dosage to the pen  123   b  that causes the pen  123   b  to automatically dial in a number of units associated with the recommended dosage and administer the recommended dosage to the patient  10 . 
     Referring to  FIG.  9   , a method  900  of selecting a diabetes treatment regimen includes obtaining  902 , at data processing hardware  112 ,  132 ,  142 , prescribing drug information and published guidelines for each of a plurality of Anti-Diabetes Medications (ADMs)  810  available for managing glucose levels. The ADMs may be used to manage glucose levels in outpatients having Type 2 Diabetes or for those who are at risk of developing Diabetes. The method  900  includes the data processing hardware  112 ,  132 ,  142  receiving  904  patient information associated with a patient  10  seeking selection and dosing of one or more of the available ADMs  810 . 
     For each available ADM, the method  900  includes the data processing hardware  112 ,  132 ,  142  determining  906  an adverse demerit value  812   a , an instruction demerit value  812   b , and a guideline demerit value  812   c  based on the patient information and the prescribing drug information  196  and published guidelines  198  for the corresponding ADM  810 , and determining  908  a total demerit value  812   e  by summing the adverse demerit value  812   a , the instruction demerit value  812   b , and the guideline demerit value  812   c . The method  900  also includes the data processing hardware  112 ,  132 ,  142  ordering  910  the total demerit values  812   e  for the available ADMs  810  from lowest to highest and selecting a predetermined number of recommended ADMs associated with the lowest total demerit values  812   e.    
     The method  900  also includes the data processing hardware  112 ,  132 ,  142  determining  912  a recommended dosage for each recommended ADM  810  and transmitting a therapy regimen to a patient device associated with the patient, the therapy regimen including the recommended ADMs  810  and the recommended dosage for each recommended ADM  810 . 
     Various implementations of the systems and techniques described here can be realized in digital electronic circuitry, integrated circuitry, specially designed ASICs (application specific integrated circuits), computer hardware, firmware, software, and/or combinations thereof. These various implementations can include implementation in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device. 
     These computer programs (also known as programs, software, software applications or code) include machine instructions for a programmable processor and can be implemented in a high-level procedural and/or object-oriented programming language, and/or in assembly/machine language. As used herein, the terms “machine-readable medium” and “computer-readable medium” refer to any computer program product, apparatus and/or device (e.g., magnetic discs, optical disks, memory, Programmable Logic Devices (PLDs)) used to provide machine instructions and/or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal. The term “machine-readable signal” refers to any signal used to provide machine instructions and/or data to a programmable processor. 
     Implementations of the subject matter and the functional operations described in this specification can be implemented in digital electronic circuitry, or in computer software, firmware, or hardware, including the structures disclosed in this specification and their structural equivalents, or in combinations of one or more of them. Moreover, subject matter described in this specification can be implemented as one or more computer program products, i.e., one or more modules of computer program instructions encoded on a computer readable medium for execution by, or to control the operation of, data processing apparatus. The computer readable medium can be a machine-readable storage device, a machine-readable storage substrate, a memory device, a composition of matter affecting a machine-readable propagated signal, or a combination of one or more of them. The terms “data processing apparatus”, “computing device” and “computing processor” encompass all apparatus, devices, and machines for processing data, including by way of example a programmable processor, a computer, or multiple processors or computers. The apparatus can include, in addition to hardware, code that creates an execution environment for the computer program in question, e.g., code that constitutes processor firmware, a protocol stack, a database management system, an operating system, or a combination of one or more of them. A propagated signal is an artificially generated signal, e.g., a machine-generated electrical, optical, or electromagnetic signal that is generated to encode information for transmission to suitable receiver apparatus. 
     A computer program (also known as an application, program, software, software application, script, or code) can be written in any form of programming language, including compiled or interpreted languages, and it can be deployed in any form, including as a stand-alone program or as a module, component, subroutine, or other unit suitable for use in a computing environment. A computer program does not necessarily correspond to a file in a file system. A program can be stored in a portion of a file that holds other programs or data (e.g., one or more scripts stored in a markup language document), in a single file dedicated to the program in question, or in multiple coordinated files (e.g., files that store one or more modules, sub programs, or portions of code). A computer program can be deployed to be executed on one computer or on multiple computers that are located at one site or distributed across multiple sites and interconnected by a communication network. 
     The processes and logic flows described in this specification can be performed by one or more programmable processors executing one or more computer programs to perform functions by operating on input data and generating output. The processes and logic flows can also be performed by, and apparatus can also be implemented as, special purpose logic circuitry, e.g., an FPGA (field programmable gate array) or an ASIC (application specific integrated circuit). 
     Processors suitable for the execution of a computer program include, by way of example, both general and special purpose microprocessors, and any one or more processors of any kind of digital computer. Generally, a processor will receive instructions and data from a read only memory or a random access memory or both. The essential elements of a computer are a processor for performing instructions and one or more memory devices for storing instructions and data. Generally, a computer will also include, or be operatively coupled to receive data from or transfer data to, or both, one or more mass storage devices for storing data, e.g., magnetic, magneto optical disks, or optical disks. However, a computer need not have such devices. Moreover, a computer can be embedded in another device, e.g., a mobile telephone, a personal digital assistant (PDA), a mobile audio player, a Global Positioning System (GPS) receiver, to name just a few. Computer readable media suitable for storing computer program instructions and data include all forms of non-volatile memory, media and memory devices, including by way of example semiconductor memory devices, e.g., EPROM, EEPROM, and flash memory devices; magnetic disks, e.g., internal hard disks or removable disks; magneto optical disks; and CD ROM and DVD-ROM disks. The processor and the memory can be supplemented by, or incorporated in, special purpose logic circuitry. 
     To provide for interaction with a user, one or more aspects of the disclosure can be implemented on a computer having a display device, e.g., a CRT (cathode ray tube), LCD (liquid crystal display) monitor, or touch screen for displaying information to the user and optionally a keyboard and a pointing device, e.g., a mouse or a trackball, by which the user can provide input to the computer. Other kinds of devices can be used to provide interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback, e.g., visual feedback, auditory feedback, or tactile feedback; and input from the user can be received in any form, including acoustic, speech, or tactile input. In addition, a computer can interact with a user by sending documents to and receiving documents from a device that is used by the user; for example, by sending web pages to a web browser on a user&#39;s client device in response to requests received from the web browser. 
     One or more aspects of the disclosure can be implemented in a computing system that includes a backend component, e.g., as a data server, or that includes a middleware component, e.g., an application server, or that includes a frontend component, e.g., a client computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the subject matter described in this specification, or any combination of one or more such backend, middleware, or frontend components. The components of the system can be interconnected by any form or medium of digital data communication, e.g., a communication network. Examples of communication networks include a local area network (“LAN”) and a wide area network (“WAN”), an inter-network (e.g., the Internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks). 
     The computing system can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. In some implementations, a server transmits data (e.g., an HTML page) to a client device (e.g., for purposes of displaying data to and receiving user input from a user interacting with the client device). Data generated at the client device (e.g., a result of the user interaction) can be received from the client device at the server. 
     While this specification contains many specifics, these should not be construed as limitations on the scope of the disclosure or of what may be claimed, but rather as descriptions of features specific to particular implementations of the disclosure. Certain features that are described in this specification in the context of separate implementations can also be implemented in combination in a single implementation. Conversely, various features that are described in the context of a single implementation can also be implemented in multiple implementations separately or in any suitable sub-combination. Moreover, although features may be described above as acting in certain combinations and even initially claimed as such, one or more features from a claimed combination can in some cases be excised from the combination, and the claimed combination may be directed to a sub-combination or variation of a sub-combination. 
     Similarly, while operations are depicted in the drawings in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed, to achieve desirable results. In certain circumstances, multi-tasking and parallel processing may be advantageous. Moreover, the separation of various system components in the embodiments described above should not be understood as requiring such separation in all embodiments, and it should be understood that the described program components and systems can generally be integrated together in a single software product or packaged into multiple software products. 
     A number of implementations have been described. Nevertheless, it will be understood that various modifications may be made without departing from the spirit and scope of the disclosure. Accordingly, other implementations are within the scope of the following claims. For example, the actions recited in the claims can be performed in a different order and still achieve desirable results.