A computerized method for helping to ensure that selection of a choice is relevant to achieving a therapy goal of a patient with diabetes, the method comprising: specifying via an input device (36, 40) of a computer one or more data collection protocols contained in a data collection protocols module that address specific diagnosis and therapy needs of the patient with diabetes; selecting via the input device (36, 40) of the computer a patient model available in a patient models module that models mathematically one or more aspects of human physiology defined in differential equations; performing analysis on the selected patient model via running sub-modules of the patient models module on the computer which determines parameters of the selected patient model by employing data fitting techniques; performing model verification of the determined parameters via a model validation module running on the computer which ensures that the selected patient model has captured appropriate dynamics that address the specific diagnosis and therapy needs of the patient; and characterized by, entering into the computer patient specific data collected by an external device per the specified one or more data collection protocols in which the computer checks the data quality of the collected patient-specific data with a data quality procedure in the form of a mathematical module that examines the quality of the collected data in terms of its performance and/or statistical properties and provides on a monitor of the computer a series of therapy recommendations for the patient each with a relevance rating that is based on the effect selection of such a therapy recommendation based on the collected patient-specific data quality will have on generating changes on the patient's therapy, applying the patient-specific data to the selected patient model to generate therapy parameters from the model after selection of one of the therapy recommendations, and performing analysis by performing in silico simulations to challenge critical cases for therapy robustness and assessing the stability of the solution via an analyses module running on the computer using the applied patient-specific data and the selected patient model which provides on the monitor of the computer at least one recommended patient-specific therapy based on therapy parameters comprising basal rate, insulin sensitivity and carbohydrate to insulin ratio generated from the selected model for approval. specifying via an input device (36, 40) of a computer one or more data collection protocols contained in a data collection protocols module that address specific diagnosis and therapy needs of the patient with diabetes; selecting via the input device (36, 40) of the computer a patient model available in a patient models module that models mathematically one or more aspects of human physiology defined in differential equations; performing analysis on the selected patient model via running sub-modules of the patient models module on the computer which determines parameters of the selected patient model by employing data fitting techniques; performing model verification of the determined parameters via a model validation module running on the computer which ensures that the selected patient model has captured appropriate dynamics that address the specific diagnosis and therapy needs of the patient; and characterized by, entering into the computer patient specific data collected by an external device per the specified one or more data collection protocols in which the computer checks the data quality of the collected patient-specific data with a data quality procedure in the form of a mathematical module that examines the quality of the collected data in terms of its performance and/or statistical properties and provides on a monitor of the computer a series of therapy recommendations for the patient each with a relevance rating that is based on the effect selection of such a therapy recommendation based on the collected patient-specific data quality will have on generating changes on the patient's therapy, applying the patient-specific data to the selected patient model to generate therapy parameters from the model after selection of one of the therapy recommendations, and performing analysis by performing in silico simulations to challenge critical cases for therapy robustness and assessing the stability of the solution via an analyses module running on the computer using the applied patient-specific data and the selected patient model which provides on the monitor of the computer at least one recommended patient-specific therapy based on therapy parameters comprising basal rate, insulin sensitivity and carbohydrate to insulin ratio generated from the selected model for approval.