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A Michael Albisser

Publications and source records attributed to A Michael Albisser.

4 recordsLinked to original sources
PubMed2005

A graphical user interface for diabetes management that integrates glucose prediction and decision support.

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.

Blood Glucose↗
PubMed2005

How good is your glucose control?

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.

Adult↗
PubMed2003

Patient confidentiality, data security, and provider liabilities in diabetes management.

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.

Confidentiality↗