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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.
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