Comparison of Computer-based Clinical Decision Support Systems and Content for Diabetes Mellitus

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Introduction

According to UCSF Medical Center, Diabetes“Mellitus has become an epidemic in the United States with about 1 million people over age 20 diagnosed with the condition each year. About 17 million people, or 6 percent of the U.S. population, have diabetes mellitus, a disease in which the body doesn't produce or properly use insulin [1], a hormone produced in the pancreas that converts sugar into energy.” It is determined to be the sixth leading cause of death in the United States. It can also “cause serious health complications such as blindness, kidney failure, nerve damage and the need for lower-extremity amputations. In addition, diabetes is a major risk factor for cardiovascular disease, dramatically increasing the risk for heart disease and stroke.” [1]

Computer-based Clinical Decision Support (CDS) systems have demonstrated improved quality of care and efficiency of workflows. It has been widely utilized across EHRs in the nation. The Health Information Technology for Economic and Clinical Health Act (HITECH) offered incentive payments to providers and hospitals that implemented Meaningful Use by using technology for their Electronic Health record to improve quality and safety.[2] “Health care reform legislation relies on electronic health records and CDS systems to improve the cost and quality of health care in the United States; however, the heterogeneity of CDS content and infrastructure of CDS systems across sites is not well known.” [2] [3]

Objective

The authors of this study “aimed to determine the scope of CDS content in diabetes care at six sites, assess the capabilities of CDS in use at these sites, characterize the scope of CDS infrastructure at these sites, and determine how the sites use CDS beyond individual patient care in order to identify characteristics of CDS systems and content that have been successfully implemented in diabetes care” [2]

Methods

The authors compared CDS systems among six collaborating sites of the Clinical Decision Support Group. They gathered CDS care content from patients with diabetes mellitus then surveyed institutions on the following; characteristics of their site, CDS Infrastructure and capabilities. [2]. Data gathered from their survey were carefully evaluated and analyzed for results.

Results

The results revealed that the approach and characteristics of CDS content characteristics of CDS content differ from each of the six sites. Commonalities found are the following: providing customizability per user/position, applying sophisticated exclusion criteria, and using automation of CDS at the time of decision-making. There were several messages that were actionable recommendations. Most of the sites had monitoring rules (e.g. A1c assessment), but only few sites had rules to diagnose diabetes and or suggest specific treatments. All sites had several CDS rules in place including reminders for eye examinations, influenza/ pneumococcal vaccines, nephropathy screenings, and lipid screenings. These reminders are generally easier to create but with significant use.

Comments

Based on the knowledge that Diabetes is a very common condition, which can lead to very serious complications, this study caught my attention. The authors did a very great job on their methods used and I am hoping that this study will serve as an eye opener and that healthcare organizations will continue to work on designing and implementing more CDS Rules that will focus on diagnosing Diabetes and suggest possible treatments as early as possible and more CDS Rules that will continue to improve patient safety and potentially save several lives.

References

  1. UCSF Medical Center. Conditions and Treatment: Diabetes Mellitus. University of California San Francisco. Accessed from http://www.ucsfhealth.org/conditions/diabetes_mellitus/ 03/25/2015./
  2. 2.0 2.1 2.2 2.3 M. Kantor, A. Wright, et. al. Comparison of Computer-based Clinical Decision Support Systems and Content for Diabetes Mellitus.2011. PMC US National Library of Medicine National Institute of Health. Accessed from http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3631936/ 03/25/2015./
  3. Meaningful Use.http://www.clinfowiki.org/wiki/index.php/Meaningful_use