Online Program

Relationship between computerized provider order entry and clinical decision support functionalities and risk-adjusted inpatient mortality rates

Monday, November 4, 2013

Deshia Leonhirth, MBA, South Carolina Rural Health Research Center, University of South Carolina, Columbia, SC
Janice C. Probst, PhD, University of South Carolina, South Carolina Rural Health Research Center, Columbia, SC
Kevin Bennett, PhD, Family & Preventive Medicine, University of South Carolina School of Medicine, Columbia, SC
James W. Hardin, PhD, Department of Biostatistics, University of South Carolina, Arnold School of Public Health, Columbia, SC
Shawn Stinson, M.D., Palmetto Health, Columbia, SC
Medha V. Vyavaharkar, PhD, MPH, MD, South Carolina Rural Health Research Center, Department of Health Services Policy and Management, Arnold School of Public Health, University of South Carolina., Columbia, SC
OBJECTIVE: Examine the relationship between Computerized Provider Order Entry (CPOE) and Clinical Decision Support (CDS) pre and post adoption and risk-adjusted inpatient mortality rates for abdominal aortic aneurysm (AAA) repair, coronary artery bypass grafting (CABG), and percutaneous coronary intervention (PCI). METHODS: A pretest-posttest design using data from 2009-2010 from the Nationwide Inpatient Sample (NIS) linked the American Hospital Association Hospital Electronic Health Record (EHR) Adoption Survey. Study population includes individuals who underwent AAA repair, CABG, or PCI. These inpatient surgical procedures were selected from the Agency for Healthcare Research and Quality's (AHRQ) Inpatient Quality Indicators (IQIs). Through preliminary analysis of the 2009 data the population will include 4,531,706 discharge records. The treatment will include implementation of meaningful use (MU) identified functionalities for CPOE and CDS across all clinical units. The dependent variable is the in-hospital mortality for individuals who underwent at least one of the procedures of interest. Patient-level covariates include age, sex, race, and risk of mortality subclass. Hospital-level covariates include bedsize, location, ownership, and teaching status. Mortality rates will be risk-adjusted and only be representative of estimates with a relative standard error less than 30 percent. Risk-adjusted mortality rates will be tested using pairwise t-tests. Analyses will be weighted to reflect the complex sampling design of the NIS and the clustering of patients within individual hospitals. RESULTS: This original dissertation research will be completed prior to the APHA annual meeting in October 2013. DISCUSSION: We hypothesize implementation of CPOE and CDS functionalities following MU guidelines will be related to lower risk-adjusted inpatient mortality rates for three of AHRQ's IQI's. It is important to understand the relationship of adoption of MU identified functionalities and patient outcomes. Regardless of whether a relationship is detected, the results will provide policy makers with insight on the relationship between EHRs and patient outcomes.

Learning Areas:

Communication and informatics
Public health or related public policy

Learning Objectives:
Discuss the relationship between meaningful use functionality implementation and patient outcomes.

Keyword(s): Outcomes Research, Technology

Presenting author's disclosure statement:

Qualified on the content I am responsible for because: I am Doctoral Candidate at the University of South Carolina in the Arnold School of Public Health in the department for Health Services Policy and Management. I work as a Research Assistant at the South Carolina Rural Health Research Center where I have gained experience in health services research.
Any relevant financial relationships? No

I agree to comply with the American Public Health Association Conflict of Interest and Commercial Support Guidelines, and to disclose to the participants any off-label or experimental uses of a commercial product or service discussed in my presentation.