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OBJECTIVE: To propose that automation of the consensus guidelines and mandated targets (CG&MT) in glycemia, hemoglobin A1c, and body weight will facilitate optimal clinical management of patients with diabetes. METHODS: (1) A simplified method for capturing diabetes outcomes at home was devised, (2) relevant portions of the CG&MT were translated into computer code and automated, and (3) algorithms were applied to transform data from self-monitoring of blood glucose into circadian profiles and hemoglobin A1c levels. (4) The resulting procedures were integrated into a USB memory drive for use by health-care providers at the point of care. RESULTS: For input from patients, a simple form is used to capture data on diabetes outcomes, including blood glucose measurements before and after meals and at bedtime, medication, and lifestyle events in a structured fashion. At each encounter with a health-care provider, the patient's data are transferred into the device and become available to assist in identifying deviations from mandated targets, potential risks of hypoglycemia, and necessary prescription changes. Preliminary observations during a 2 1/2-year period from a community support group dedicated to glycemic control on 20 unselected patients (10 with and 10 without use of the device) are summarized. CONCLUSION: With use of the automated information, the health professional is supported at the point of care to achieve better, safer outcomes and practice evidence-based medicine entirely in lockstep with the CG&MT. This automation helps to overcome clinical inertia.

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BACKGROUND: Satisfactory glycemic control, meeting American Diabetes Association recommendations, is often accompanied by unsatisfactory hypoglycemia. The converse is also true. We hypothesize that this diabetes treatment dilemma may be resolved by repeated, objective, prescription checks. To do this, a new, two-part device has been developed. It includes a personal diabetes database for the patient and a built-in diabetes prescription checker for the provider. Its goals are to enhance diabetes education and improve patient care. RESEARCH DESIGN AND METHODS: The device includes a database and supporting software, all contained in a standard USB flash drive. Using the medical prescription, body weight, and recent self-monitored blood glucose (SMBG) data, prescription checks can be done at any time. To demonstrate the device's capabilities, an observational study was performed using data from 11 patients with type 1 diabetes mellitus, on intensified therapy, with a mean glycated hemoglobin A1c <7%, and who all suffered intractable hypoglycemia. Patients had performed SMBG contours on successive days at monthly intervals. Each contour included pre- and postmeal as well as bedtime measurements. The replicated contours were used to predict the patient's glycemic profile each month. Applying a built-in simulator to each profile, changes in the prescription were explored that were consistent with reducing the recalcitrant hypoglycemia. RESULTS: A total of 110 glycemic profiles containing 822 profile points were explored. Of these profile points, 351 (43%) showed risks of hypoglycemia, whereas 385 (47%) fell outside desired ranges. With the simulated changes in the prescription, the predicted risks of hypoglycemia were reduced 2.5-fold with insignificant increases predicted in hemoglobin A1c levels of +0.6 +/- 0.9%. CONCLUSIONS: A novel support tool for diabetes promises to resolve the diabetes treatment dilemma. Supporting the patient, it improves self-management. Supporting the provider, it reviews the medical prescription in light of objective outcomes and formalizes interventions for maximum safety and efficacy.

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BACKGROUND: Satisfactory glycemic control, meeting American Diabetes Association recommendations, is difficult to achieve. Technologically, this is most likely because the circle of care is incomplete. Many have suggested that the introduction of information technology may remedy the situation. However, previous attempts have not succeeded. Recognizing this, we evolved firmware that supports and links both the patient at home and their care providers in the clinic. FIRMWARE DESIGN AND METHODS: The device includes software and a database, all contained in a standard USB flash drive. At home, patients use the database portion of the device (MyDiaBase). It fully complements their diabetes education while capturing pertinent self-management information by tracking self-monitored blood glucose data, body weight, medication dosing, physical activity, diet, lifestyle, and stress. In the clinic, providers use the RxChecker program to perform prescription checks that are based on their patients' outcomes data, thereby effectively closing the circle of care.

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BACKGROUND: Insulin treated diabetic patients often do not adjust their insulin doses. We developed a method to provide a quantitative and qualitative assessment of this behavior. METHODS: Fourteen patients provided logbook pages of their self-monitoring of blood glucose (SMBG) data and insulin doses. We compared the actual decisions of patients in real-life to what they would decide on the same SMBG, as an a posteriori exercise. We also compared these decisions and those proposed by 6 diabetologists on the same sets of data to the recommendations made by HumaLink, an automated insulin dosage system. RESULTS: 1) Patients in real-life modified their insulin doses least often. However, given a chance to make these decisions a posteriori, they modified their insulin doses more often. HumaLink proposed changes even more often, and diabetologists were the most aggressive in changing insulin doses. 2) The decisions proposed by the patients in real-life or a posteriori and by the diabetologists were compared to the recommendations made by HumaLink, using a decisions analysis grid (DAG). For these three groups, full disagreement with HumaLink (patient or physician increases while HumaLink decreases and the opposite) was observed for less than 5% of the cases. 3) By comparison to HumaLink, patient decisions seemed guided by the desire to avoid hypoglycemia. By contrast, decisions by diabetologists seemed often to be guided by the desire to avoid hyperglycemia. CONCLUSION: These methods provide an objective evaluation of insulin dose adjustments by patients with diabetes and may be useful to assess the effectiveness of educational programs.

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BACKGROUND: The promise of the Diabetes Control and Complications Trial (DCCT) has yet to be realized in clinical practice. Notwithstanding intensive education and intensified therapy, there is a distinct lack of a suitable alternative to the intensive decision support that was also provided in the DCCT. Recently, a novel glucose predicting engine has been developed and validated. Use of its predictions in decision support in respect to medication dosing, diet, exercise, and stress promises to empower patients to achieve better diabetes control while reducing hypoglycemia and preventing body weight gain. A graphical user interface (GUI) suitable for these purposes is here described. METHODS: The kernel of the GUI is a registry database located on a server accessible to both patients and their providers. The patient-GUI includes the resources of the glucose predicting engine and user-friendly, intuitive means to enter body weight and all home-monitored blood glucose levels. In response, means to modify medication dosages (dosing decision support) and modify planned diet and physical activity (lifestyle decision support) are afforded the user. Each action is animated so that the patient can visually see the impact of his or her changes on predicted glucose outcomes and the pending risks of hypoglycemia. RESULTS: A staged sequence of screens supports the self-management tasks, including selection of the current meal period, the entry of data, and documentation. The GUI returns current medications and presents up-down buttons for adjusting dosages, for changing carbohydrates, for changing exercise, and for predicting the effects of stress. For each adjustment, the impact on medications or predicted glycemia outcomes is animated. CONCLUSIONS: A new GUI that incorporates a novel glucose predicting engine is intended for all insulin-treated patients with diabetes. It may help patients and their providers to realize better glycemic control and thereby achieve the promise of the DCCT.

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BACKGROUND: Glycemic control is fundamental to the management of diabetes and maintenance of health. Popular measures of performance in glycemic control include A1c and self-monitoring of blood glucose (SMBG). As measures of performance, A1c has perspective, but it fails to recognize hypoglycemia, while SMBG lacking overall perspective finds use mainly by patients to simply evaluate their glycemic status and current response to therapy. An additional, preferably visual, measure of performance in diabetes management in general and glycemic control in particular is needed. METHODS: To form a visual measure of performance, a graphical method of analysis from the statistician's toolbox (known as the lag plot) was adapted. It can utilize SMBG data sets from any source, including memory meters and registry databases in call centers. Data are retrieved, processed, formatted, and then plotted on a PC screen or printer. The resulting lag plots visually characterize the performance of glucose control achieved over periods (selectable by the user) from days to months. Supporting numerical statistics provide rigorous outcome measures that correlate with glycated hemoglobin. RESULTS: Clinical use of the lag plot is illustrated in seven case studies spanning the range from no diabetes, through glucose intolerance, early-onset type 2 diabetes mellitus, type 1 diabetes, intensified therapy, pump therapy, and finally islet cell transplantation. Visual comparisons before and after action/referral show impacts of interventions, incidences of hypoglycemia, and changes in the polyglycemia of unstable diabetes. Statistical significance of observed changes are quantified. CONCLUSIONS: The simple lag plot can empower patients and their providers to identify problems in glycemic control, seek proactive action, adopt beneficial strategies, evaluate outcomes, and, most importantly, rule out interventions with no benefit.

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From inception, the electronic patient record has raised issues of data protection and patient confidentiality. These privacy issues have become more complicated with the introduction of electronic links to patient information held in databases sited on local and wide area networks. The first purpose of this paper is to review, from the provider's perspective, the issues surrounding patient confidentiality, data security, and consequential provider liabilities. The second is to propose possible immediate strategies and long-term solutions. Clinical procedures in diabetes practice create patient data from confidential information. This information is owned by the patient, received by the provider, enriched by a professional interpretation, and merged with other data into health records. Ownership, privacy, accountability, and responsibility issues are raised. Consequential data security and patient privacy are easily met by storage in a locked box or file cabinet. Conversion of such records into digital data in databases on local and wide area networks markedly increases the provider's exposure to liabilities. Current methods for securing remote data exist. These involve user authentication and secure transmission, but remote data storage is far less secure than a locked box. New tools for the secure storage of patient data are outlined. These involve encryption and decryption by the provider alone. A suite of computer protocols is presented that can restore security equivalent to a "locked box" and thus reduce liabilities for the provider. Providers should protect the privacy of their patients by encrypting all data that are stored in remote repositories. The tools to do this are urgently needed. A standardized digital protocol for verifying user identities, preserving patient confidentiality, and controlling data security by encryption will fully mitigate provider liabilities. Standardization and economies of scale promise future cost containment.

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