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Volume 56, Number 5
September/October 2011

  • INTERVIEW
    Interview with Brett D. Lee, PhD, FACHE, Senior Vice President of System Clinical Operations, Children's Healthcare of Atlanta
    Stephen J. O'Connor
  • TRENDS
    Portion Control Opportunities: Realy Time Gains for Hospital Patient Throughput
    Alan J. Golding and Shari B. Robbins

  • REFORM
    A Practical Roadmap for the Perilous Journey from a Culture of Entitlement to a Culture of Accountability
    Nathan S. Kaufman
  • ARTICLES
    Can Utilization Review Criteria Be Used to Determine Appropriate Pediatric Patient Placement for a Critical Care Bed Expansion?
    Donna Jamieson, Theresa A. Mikhailov, Kristyn Maletta, Evelyn M. Kuhn, Lauren Giuliani, Jeanne Musolf, Kay Fischer, and Maureen Collins
    Making the CMS Payment Policy for Healthcare-Associated Infections Work: Organizational Factors That Matter
    Timothy Hoff, Christine W. Hartmann, Christina Soerensen, Peter Wroe, Maya Dutta-Linn, and Grace Lee
    Hospital Financial Position and the Adoption of Electronic Health Records
    Gregory O. Ginn, Jay J. Shen, and Charles B. Moseley

Executive Summary
Can Utilization Review Criteria Be Used to Determine Appropriate Pediatric Patient Placement for a Critical Care Bed Expansion?, Donna Jamieson, Theresa A. Mikhailov, Kristyn Maletta, Evelyn M. Kuhn, Lauren Giuliani, Jeanne Musolf, Kay Fischer, and Maureen Collins

The rising trend in critical care utilization has led to the expansion of critical care beds in many hospitals across the country. Traditional models of estimating bed capacity requirements use administrative data such as inpatient admissions, length of stay, and case mix index. The use of such data has been limited in quantifying the complexities of demand variables in critical care bed needs. Mathematical modeling is another method for estimating numbers of beds required. It captures the dynamic changes in the management of critically ill patients that occur when units become full. Depending on data analysis methods used, bed need underestimation or overestimation can occur. In our study, we used utilization review criteria to understand changes in level of care (LOC) during the course of patients' stays and to validate critical care bed expansion needs. Using LOC criteria, we studied the proportion of our intermediate care patients in an acute care unit that met acute, intermediate, or critical care criteria. We also evaluated whether these proportions were related to specific factors such as census ratios, staffing proportions, or severity of illness. Using LOC criteria was helpful in validating our critical care bed projection, which was previously derived from mathematical modeling. The findings also validated our assessment for additional specialty acute care beds.  

Executive Summary
Making the CMS Payment Policy for Healthcare-Associated Infections Work: Organizational Factors That Matter, Timothy Hoff, Christine W. Hartmann, Christina Soerensen, Peter Wroe, Maya Dutta-Linn, and Grace Lee

Healthcare-associated infections (HAIs) are among the most common adverse events in hospitals, and the morbidity and mortality associated with them are significant. In 2008, the Centers for Medicare and Medicaid Services (CMS) implemented a new financial policy that no longer provides payment to hospitals for services related to certain infections not present on admission and deemed preventable. At present, little is known about how this policy is being implemented in hospital settings. One key goal of the policy is for it to serve as a quality improvement driver within hospitals, providing the rationale and motivation for hospitals to engage in greater infection-related surveillance and prevention activities. This article examines the role organizational factors, such as leadership and culture, play in the effectiveness of the CMS policy as a quality improvement (QI) driver within hospital settings. Between late 2009 and early 2010, interviews were conducted with 36 infection preventionists working at a national sample of 36 hospitals. We found preliminary evidence that hospital executive behavior, a proactive infection control (IC) culture, and clinical staff engagement played a favorable role in enhancing the recognition, acceptance, and significance of the CMS policy as a QI driver within hospitals. We also found several other contextual factors that may impede the degree to which the above factors facilitate links between the CMS policy and hospital QI activities.

Executive Summary
Hospital Financial Position and the Adoption of Electronic Health Records, Gregory O. Ginn, Jay J. Shen, and Charles B. Moseley

The objective of this study was to examine the relationship between financial position and adoption of electronic health records (EHRs) in 2,442 acute care hospitals. The study was cross-sectional and utilized a general linear mixed model with the multinomial distribution specification for data analysis. We verified the results by also running a multinomial logistic regression model. To measure our variables, we used data from (1) the 2007 American Hospital Association (AHA) electronic health record implementation survey, (2) the 2006 Centers for Medicare and Medicaid Cost Reports, and (3) the 2006 AHA Annual Survey containing organizational and operational data. Our dependent variable was an ordinal variable with three levels used to indicate the extent of EHR adoption by hospitals. Our independent variables were five financial ratios: (1) net days revenue in accounts receivable, (2) total margin, (3) the equity multiplier, (4) total asset turnover, and (5) the ratio of total payroll to total expenses. For control variables, we used (1) bed size, (2) ownership type, (3) teaching affiliation, (4) system membership, (5) network participation, (6) fulltime equivalent nurses per adjusted average daily census, (7) average daily census per staffed bed, (8) Medicare patients percentage, (9) Medicaid patients percentage, (10) capitation-based reimbursement, and (11) nonconcentrated market. Only liquidity was significant and positively associated with EHR adoption. Asset turnover ratio was significant but, unexpectedly, was negatively associated with EHR adoption. However, many control variables, most notably bed size, showed significant positive associations with EHR adoption. Thus, it seems that hospitals adopt EHRs as a strategic move to better align themselves with their environment.