Medication reconciliation: comparing a customized medication history form to a standard medication form in a specialty clinic (CAMPII 2).

BACKGROUND: Medication history forms completed by patients are an essential part of the medication reconciliation process. OBJECTIVE: In a crossover prospective study, investigators compared the accuracy and acceptability of a "fill-in-the blank" medication history form (USUAL) to a customized form (CUSTOM) that contained a checklist of the 44 most frequently prescribed diabetes clinic medications. METHODS: The content of both forms was compared to a "gold-standard" medication list compiled by a clinical pharmacist who conducted a medication history and reviewed pharmacy profiles and medical chart. Subject preference and time to complete the forms were also determined. Accurate was defined as complete and correct (name, dose, and frequency) relative to the gold standard. RESULTS: A total of 77 subjects completed both forms. Complete list accuracy was poor; there was no difference in the accuracy between CUSTOM (6.5%) and USUAL (9.1%) (odds ratio [OR], 0.33; P = 0.62). Out of a total of 648 medications, subjects accurately listed 43.7% of medications on CUSTOM and 45.5% on USUAL (OR, 0.88; P = 0.41). The 44 medications on the checklist were more than twice as likely to be accurately reported using CUSTOM than with USUAL (OR, 2.1; P = 0.0002). More subjects preferred CUSTOM (65.7%) compared with USUAL (32.8%, P = 0.007). CONCLUSION: Medication self-report is very poor, and few subjects created an accurate list on either form. Subjects were more likely to report the drugs on the checklist using CUSTOM than when they used USUAL; however, there was no difference in the overall accuracy between CUSTOM and USUAL.

Use of a uniform treatment algorithm abolishes racial disparities in glycemic control.

PURPOSE: The purpose of this study is to compare glycemic control between blacks and whites in a setting where patient and provider behavior is assessed, and where a uniform treatment algorithm is used to guide care. METHODS: This observational cohort study was conducted in 3542 patients (3324 blacks, 218 whites) with type 2 diabetes with first and 1-year follow-up visits to a municipal diabetes clinic; a subset had 2-year follow-up. Patient adherence and provider management were determined. The primary endpoint was A1c. RESULTS: At presentation, A1c was higher in blacks than whites (8.9% vs 8.3%; P < .001), even after adjusting for demographic and clinical characteristics. During 1 year of follow-up, patient adherence to scheduled visits and medications was comparable in both groups, and providers intensified medications with comparable frequency and amount. After 1 year, A1c differences decreased but remained significant (7.7% vs 7.3%; P = .029), even in multivariable analysis (P = .003). However, after 2 years, A1c differences were no longer observed by univariate (7.6% vs 7.5%; P = .51) or multi-variable analysis (P = .18). CONCLUSIONS: Blacks have higher A1c than whites at presentation, but differences narrow after 1 year and disappear after 2 years of care in a setting where patient and provider behavior are comparable and that emphasizes uniform intensification of therapy. Presumably, racial disparities at presentation reflected prior inequalities in management. Use of uniform care algorithms nationwide should help to reduce disparities in diabetes outcomes.

Age, BMI, and race are less important than random plasma glucose in identifying risk of glucose intolerance: the Screening for Impaired Glucose Tolerance Study (SIGT 5).

OBJECTIVE: Age, BMI, and race/ethnicity are used in National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) and American Diabetes Association (ADA) guidelines to prompt screening for pre-diabetes and diabetes, but cutoffs have not been evaluated rigorously. RESEARCH DESIGN AND METHODS: Random plasma glucose (RPG) was measured and 75-g oral glucose tolerance tests were performed in 1,139 individuals without known diabetes. Screening performance was assessed by logistic regression and area under the receiver operating characteristic curve (AROC). RESULTS: NIDDK/ADA indicators age >45 years and BMI >25 kg/m(2) provided significant detection of both diabetes and dysglycemia (both AROCs 0.63), but screening was better with continuous-variable models of age, BMI, and race and better still with models of age, BMI, race, sex, and family history (AROC 0.78 and 0.72). However, screening was even better with RPG alone (AROCs 0.81 and 0.72). RPG >125 mg/dl could be used to prompt further evaluation with an OGTT. CONCLUSIONS: Use of age, BMI, and race/ethnicity in guidelines for screening to detect diabetes and pre-diabetes may be less important than evaluation of RPG. RPG should be investigated further as a convenient, inexpensive screen with good predictive utility.

Increasing patient empowerment and improving diabetes care by utilizing a computer-based patient "roadmap".

The 20.8 million Americans with diabetes are at risk of amputation, kidney failure, blindness, and death which could be decreased if glucose control were better. Patients need motivation and empowerment to perform the daily management of diabetes. We are using informatics to help them organize their questions for providers and to generate "road maps" of their progress and future directions of care.

An informatics model for clinical research--screening for impaired glucose tolerance study.

A major challenge for large scale, multi-site research studies is the time-sensitive tracking of numerous subjects, samples and results through multiple encounters and processes, by many team members in several different locations. This may be compounded by collaborations with other studies to share samples, data collection and subjects. The Screening for Impaired Glucose Tolerance (SIGT) study has met this challenge by developing a secure, informatics system with web and client server interfaces.

An informatics-supported intervention improves diabetes control in a primary care setting.

Although research has shown that proper management of diabetes can improve outcomes, glucose control is worsening. This partly reflects the failure of providers to intensify diabetes therapy when indicated, termed clinical inertia. Our intervention used (a) decision support reminders which provided patient specific recommendations for management at each visit, and (b) computer generated provider specific feedback on performance. This intervention improved the frequency with which providers intensified the therapy and improved glycemic control.

An endocrinologist-supported intervention aimed at providers improves diabetes management in a primary care site: improving primary care of African Americans with diabetes (IPCAAD) 7.

OBJECTIVE: Management of diabetes is frequently suboptimal in primary care settings, where providers often fail to intensify therapy when glucose levels are high, a problem known as clinical inertia. We asked whether interventions targeting clinical inertia can improve outcomes. RESEARCH DESIGN AND METHODS: A controlled trial over a 3-year period was conducted in a municipal hospital primary care clinic in a large academic medical center. We studied all patients (4,138) with type 2 diabetes who were seen in continuity clinics by 345 internal medicine residents and were randomized to be control subjects or to receive one of three interventions. Instead of consultative advice, the interventions were hard copy computerized reminders that provided patient-specific recommendations for management at the time of each patient's visit, individual face-to-face feedback on performance for 5 min every 2 weeks, or both. RESULTS: Over an average patient follow-up of 15 months within the intervention site, improvements in and final HbA1c (A1C) with feedback + reminders (deltaA1C 0.6%, final A1C 7.46%) were significantly better than control (deltaA1C 0.2%, final A1C 7.84%, P < 0.02); changes were smaller with feedback only and reminders only (P = NS vs. control). Trends were similar but not significant with systolic blood pressure (sBP) and LDL cholesterol. Multivariable analysis showed that the feedback intervention independently facilitated attainment of American Diabetes Association goals for both A1C and sBP. Over a 2-year period, overall glycemic control improved in the intervention site but did not change in other primary care sites (final A1C 7.5 vs. 8.2%, P < 0.001). CONCLUSIONS: Feedback on performance aimed at overcoming clinical inertia and given to internal medicine resident primary care providers improves glycemic control. Partnering generalists with diabetes specialists may be important to enhance diabetes management in other primary care settings.

The Intelligent Dosing System: application for insulin therapy and diabetes management.

Diabetes mellitus is an increasing public health problem. Insulin is an essential tool in the management of hyperglycemia, but methods of dose adjustment are purely empirical. The Intelligent Dosing System (IDS, Dimensional Dosing Systems, Inc., Wexford, PA) is a software suite that incorporates patient-specific, dose-response data in a mathematical model and then calculates the new dose of the medication needed to achieve the next desired therapeutic goal. We discuss the application of the IDS in insulin management. The IDS concept and the initial modeling used to construct an insulin doser are reviewed first. Additional data are then provided on the use of the IDS for titrating insulin therapy in a clinical setting. Finally, recent modifications in the IDS software and future applications of this technology for insulin dosing and diabetes management are discussed.

Management of insulin therapy in urban diabetes patients is facilitated by use of an intelligent dosing system.

The Intelligent Dosing System (IDS, Dimensional Dosing Systems, Inc., Wexford, PA) is a software suite that incorporates patient-specific, dose-response data in a mathematical model, and then calculates the new dose of agent needed to achieve the next desired therapeutic goal. We evaluated use of the IDS for titrating insulin therapy. The IDS was placed on handheld platforms and provided to practitioners to use in adjusting total daily insulin dose. Fasting glucose, random glucose, and hemoglobin A1c were used as markers against which insulin could be adjusted. Values of markers expected at the next follow-up visit, as predicted by the model, were compared with levels actually observed. For 264 patients, 334 paired visits were analyzed. Average age was 54 years, diabetes' duration was 10 years, and body mass index was 33.2 kg/m(2); 57% were female, 88% were African American, and 92% had type 2 diabetes. The correlation between IDS suggested and actual prescribed total daily dose was high (r = 0.99), suggesting good acceptability of the IDS by practitioners. Significant decreases in fasting glucose, random glucose, and hemoglobin A1c levels were seen (all P < 0.0001). No significant difference between average expected and observed follow-up fasting glucose values was found (145 vs. 149 mg/dL, P = 0.42), and correlation was high (r = 0.79). Mean observed random glucose value at follow-up was comparable to the IDS predicted level (167 vs. 168 mg/dL, P = 0.97), and correlation was high (r = 0.73). Observed follow-up hemoglobin A1c was higher than the value expected (7.9% vs. 7.4%, P < 0.0055), but correlation was good (r = 0.70). These analyses suggest the IDS is a useful adjunct for decisions regarding insulin therapy even when using a variety of markers of glucose control, and can be used by practitioners to assist in attainment of glycemic goals.

Inpatient to outpatient transfer of care in urban patients with diabetes: patterns and determinants of immediate postdischarge follow-up.

BACKGROUND: A key opportunity for continuing diabetes care is to assure outpatient follow-up after hospitalization. To delineate patterns and factors associated with having an ambulatory care visit, we examined immediate postdischarge follow-up among a cohort of urban, hospitalized patients with diabetes mellitus. METHODS: Retrospective study of 658 inpatients of a municipal hospital. Primary data sources were inpatient surveys and electronic records. RESULTS: Patients were stratified into outpatient follow-up (69%), acute care follow-up (15%), and those with no follow-up (16%); differences between groups were detected for age (P =.02), percentage discharged with insulin (P =.03), and percentage receiving a full discount for care (P<.001). Among patients with a postdischarge visit, 43% were seen in our specialty diabetes clinic, and 26% in a primary care site. Adjusted analyses showed any follow-up visit significantly decreased with having to pay for care. The odds of coming to the Diabetes Clinic increased if patients were discharged with insulin, had new-onset diabetes, or had a direct referral. CONCLUSIONS: In this patient cohort, most individuals accomplished a postdischarge visit, but a substantial percentage had an acute care visit or no documented follow-up. New efforts need to be devised to track patients after discharge to assure care is achieved, especially in this patient population particularly vulnerable to diabetes.