The Effect of the Vermont Diabetes Information System on Inpatient and Emergency Department Use: Results from a Randomized Trial

Abstract

Objective-To describe the effect of the Vermont Diabetes Information System (VDIS) on hospital and emergency room use Data Source-Statewide discharge databaseStudy Design-Randomized controlled trial of a decision support system for 7,412 adults with diabetes and their 64 primary care providers.Data Collection/ Data Extraction-Charges and dates for hospital admissions and emergency room care in Vermont during an average of 32 months of observation. Data from New York hospitals were not available.Principal Findings-Patients randomized to VDIS were admitted to the hospital less often than control subjects (0.17 admissions vs. 0.20; P=0.01) and generated lower hospital charges ($3,113 vs. $3,480; P=0.019). VDIS patients also had lower emergency room utilization (0.27 visits vs. 0.36; P<0.0001) and charges ($304 vs. $414; P<0.0001). The intervention was particularly effective in men and in older subjects.Conclusions-In spite of data limitations that tended to reduce the apparent effect of the system; this randomized, controlled trial showed that VDIS reduces hospitalization and emergency room utilization and expenses. © 2010 Elsevier Inc. All rights reserved.Correspondence to: Shamima Khan. Conflict of interest: Drs. MacLean and Littenberg, along with the University of Vermont, hold equity in Vermedx, Inc. which distributes the Vermedx® Diabetes Information System, which is based on the work described in this manuscript. The authors comply with all conflict of interest policies of the National Institutes of Health and of the University of Vermont.Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. VDIS uses the Chronic Care Model as an organizing framework with daily data feeds from otherwise independent laboratories, automatic test interpretation using algorithms based on consensus guidelines, use of fax and mail to report to providers and patients not easily reached by electronic networks, and report formats that are accessible and useful to patients and providers. The primary function of the system is to collect pertinent clinical information (hemoglobin A1C, cholesterol, serum creatinine, and urine protein results) and generate five types of reports: flow sheets to providers with accurate and timely laboratory results, reminders of overdue laboratory tests to providers, overdue reminders to patients, alerts to patients with elevated test results, and summary population reports for providers regarding their diabetes roster.4 -5The intervention has been described in detail elsewhere, along with a prospective, clusterrandomized clinical trial in which 7,412 patients and ...

Computerized Decision Support Systems in Primary Care for Type 2 Diabetes Patients Only Improve Patients' Outcomes when Combined with Feedback on Performance and Case Management: A Systematic Review

“…Three studies only showed improvements in the process of care. Improvements were found in yearly HbA1c testing, retinal exams, and the composite of three tests, as well as in laboratory monitoring, 18 low-density lipoprotein testing, and the composite of all Cleveringa et al, [15][16][17] Phillips et al 14 and Ziemer et al, 6 and MacLean et al 19 and Khan et al 18 represent the same respective study with different outcome parameters. a Value in randomized column is 1 if patients were randomized or when cluster randomization with the generalized estimating equation (GEE) was used.…”

“…13 The study from Phillips et al 14 and Ziemer et al 6 regarded the same study population, with different outcome measures. This also applies to the three studies of Cleveringa et al [15][16][17] and to the studies from Khan et al 18 and MacLean et al 19 Therefore eventually 20 RCTs were included. Ninety-five articles were excluded because of different reasons, for example, review article (n = 14), no RCT (n = 22), no CDSS used in the intervention (n = 26), glucose monitoring system (n = 10), or diabetes self-management program (n = 11) (Fig.…”

The Effects of Computerized Clinical Decision Support Systems on Laboratory Test Ordering: A Systematic Review

“…Economic Outcomes.-Three studies reported economic outcomes, namely Bates et al, 25 Smith et al, 21 and Khan et al 29 Bates et al 25 reported the possible charge savings when implementing a CCDSS that reminds a physician upon ordering a new test that the same test had already been ordered within certain test-specific intervals. They calculated that the 24% decrease in test ordering correlated to a $35 000 annual savings for the clinic.…”

“…46,47 Thirteen studies (57%) evaluated CCDSSs in primary care,* 5 (22%) in hospital outpatient ambulatory care, 26,31,33,36,44 and 5 (22%) in hospital inpatient care. 22,25,32,34 Intervention.-Fifteen different CCDSSs were studied, of which 6 (40%) were focused solely on laboratory testing, † 5 (33%) also included other reminders such as treatment options, [27][28][29]31,39,[41][42][43] and 4 (27%) were not stand-alone systems, but were developed by an EHR software vendor. ‡ Of the CCDSSs focused solely on laboratory testing, 3 (50%) were targeted at regulating blood clotting in patients using anticoagulants.…”

“…[20][21][22][24][25][26][27][28][29][30][31][32][33][34][35][36][37] Eight studies (35%) were conducted in Europe. 23,[38][39][40][41][42][43][44][45] One study (4%) was conducted in Europe and Australia.…”