Project Summary/Abstract Approximately 9 million patients with diabetes (DM) are hospitalized annually and over 30% of inpatients without DM experience high glucose (HG) due to their acute illness. HG increases the risk of infectious and non- infectious complications and death, hospital length of stay (LOS), utilization of hospital resources and overall healthcare costs. While glucose control reduces these risks, controlling HG in the hospital is difficult due to multiple barriers such as recognizing and proactively treating glucose abnormalities, and adequately ordering insulin to treat HG in the hospital. Clinical decision support (CDS) is a system that uses computerized person- specific data in the electronic medical record (EMR) proven to improve hospital care. Among the various modalities, alert-CDS is shown to improve care delivery, providers’ proactivity, and glucose control specifically in intensive care settings of academic institutions. However, alert-CDS has not yet been studied outside of intensive care units (ICU), or in community hospitals where most patients receive care. Furthermore, its impact on patients’ outcomes has not been tested in any setting. The proposed project uses an innovative alert-CDS tool we developed and validated which automatically identifies dysglycemia and inadequacies in insulin administration in the hospital. It alerts clinicians with recommendations to support decision making without superseding their clinical judgement. In our pilot study, we found that this alert-CDS tool reduced recurrent high glucose levels and shortened LOS. Based on this promising preliminary data, in this project we propose to study the impact of our CDS tool on clinical, economic and providers’ performance outcomes among non-intensive care patients both in an academic and a community hospital. We propose to make this resource available intermittently in the EMR every 3 months during 36 months, thus allowing us to compare 18 months of intervention and 18 months of standard care. Based on our pilot study, we expect that a sample size of 12,560 subjects will give us an 80% power of detecting 0.34 days (~ 8 hours) difference in length of stay, the primary endpoint of our study. We propose the following aims: Aim 1) To determine the impact of the alert-CDS over conventional care on the clinical outcomes of non-ICU patients in an academic and a community hospital. Aim 2) To determine the impact of the alert-CDS over conventional care on the economic outcomes of non-ICU patients in an academic and a community hospital. Aim 3) To determine the impact of alert-CDS for inpatient glycemic control on providers’ perspectives, competencies and practice performance between an academic and a community hospital. We hypothesize that the tool will increase providers’ knowledge about dysglycemia allowing them to make better decisions about insulin administration. The anticipated success of our study builds upon a well-established multidisciplinary tea...