Can utilization review criteria be used to determine appropriate pediatric patient placement for a critical care bed expansion?
Children (Health aspects)
Mikhailov, Theresa A.
Kuhn, Evelyn M.
|Publication:||Name: Journal of Healthcare Management Publisher: American College of Healthcare Executives Audience: Trade Format: Magazine/Journal Subject: Business; Health care industry Copyright: COPYRIGHT 2011 American College of Healthcare Executives ISSN: 1096-9012|
|Issue:||Date: Sept-Oct, 2011 Source Volume: 56 Source Issue: 5|
|Topic:||Event Code: 310 Science & research; 200 Management dynamics Computer Subject: Company business management|
|Product:||Product Code: 8060000 Hospitals NAICS Code: 622 Hospitals SIC Code: 8062 General medical & surgical hospitals; 8063 Psychiatric hospitals; 8069 Specialty hospitals exc. psychiatric|
|Organization:||Organization: Society of Critical Care Medicine|
|Geographic:||Geographic Scope: United States Geographic Code: 1USA United States|
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.
Healthcare organizations face the increasing challenge of providing the highest-quality care at a reasonable cost. Across the nation, a rising trend in critical care inpatient days has pushed bed occupancy rates higher, leading to expansion of critical care hospital beds (Odeotola et al. 2005; Randolph et al. 2004). Increasing critical care utilization, concerns about escalating costs, shifting payer mix, and limited reimbursement have prompted operational improvement efforts. Hospitals have focused efficiency efforts on bed utilization, throughput, and surge capacity trends. Additionally, cost-effective staffing models, use of technologies, minimally invasive therapeutic modalities, and optimal care delivery models have been attempted before investing in capacity expansion. Such efforts have contributed to additional bed days within existing capacity. Where facility expansion has been deemed necessary, various models for demand projections have been attempted. Traditional approaches include projections using historical trends of operational measures such as length of stay, scheduled versus unscheduled admissions, clinical specialty service growth, and target occupancy levels. These methods rely on an insufficient number of variables for planning and managing critical care capacities. Other methodologies, such as mathematical modeling, also account for variability and the dynamic nature of the processes in healthcare settings while also allowing for various what-if scenarios (Costa et al. 2003). Tools that help in understanding the effect of acuity factors on length of stay and associated bed needs can complement mathematical modeling. This study focused on using utilization review criteria as a method of evaluating LOC requirements with patient placement in our critical care expansion plan. Evaluation of placement decisions within critical care from previous admissions provided a profile of how critical care patients in our setting transitioned from the highest-acuity critical care requirements to lower-acuity acute levels. Understanding changes in LOC during the patient's critical care stay allowed us to validate critical care bed expansion needs.
Critical care services have been estimated at $555.5 billion, accounting for 13.3 percent of hospital costs (Halpern and Pastores 2010). An analysis of critical care medicine between 2000 and 2005 shows that while overall hospital beds decreased by 4.2 percent, critical care beds increased by 6.5 percent (Halpern and Pastores 2010). As organizations determine bed capacity needs, accurately projecting bed requirements can be a challenge. Depending on methodology, projections may still result in inadequate capacity, possibly causing cancellation of planned surgeries or early transfers out of the intensive care unit. Other projections have also resulted in overcapacity with underutilization of beds and staff. Use of average values has been shown to underestimate bed capacity requirements (Costa et al. 2003). Based on the mathematical modeling, using calculations of 95 percent confidence interval can overestimate bed needs as much as 50 percent (Costa et al. 2003).
Recent advances, such as mathematical modeling and simulation, have helped refine the process for bed capacity projections, incorporating information such as the individual hospital's particular case mix, number of emergency and planned patient admissions, transfer rates, and deferral rates. The simulation model has been useful in determining how calculated bed needs can reduce deferral rates. It also provided the point at which an increase in bed number projections reduced deferral rates but placed the hospital at risk for underutilization of beds. Opportunities to validate simulation models in the critical care setting have been limited; however, Costa and colleagues (2003) point out that the predictions will be valid when the models are built on sound mathematical principles and input rules and data are accurate.
We tested the use of utilization review criteria, InterQual[R] Level of Care/Acute Criteria (Pediatric), to define complexity care requirements and appropriate patient placement (1). A review of the literature suggests that the criteria are useful in assessing patient placement in a variety of settings, such as an adult hospital (Bruce et al. 2002), a Veterans Affairs hospital (Glassman, Lopes, and Witt 1997), and a rehabilitation unit of an adult hospital (Poulos, Eager, and Poulos 2007). Studies of the use of InterQual[R] criteria in a pediatric hospital were not found. Study aims included comparison of physician assessments to assessments based on InterQual[R] criteria (Poulos, Eager, and Poulos 2007), assessment of mix of levels of care of medical patients of a hospital in Canada (Bruce et al. 2002), retrospective analysis of number of subacute patients being cared for in acute care units (Weaver et al. 1998; DeGoster et al. 1997; Glassman, Lopes, and Witt 1997), and prospective assessment of the number of subacute patients being cared for in acute care units (Smith et al. 1996). We chose to study the patient population in a particular acute care unit because it included patients requiring intermediate LOC (see Exhibit 1). In this unit, our patient acuity classification was showing higher acuity trends and higher needs for resources than in previous years. We suspected that an increasing proportion of these patients required more complex medical management and nursing care comparable to intensive care staffing ratios. Therefore, we designated this unit as the study unit.
PURPOSE OF THE STUDY
Improved understanding of factors that affect patient placement on the study unit will improve ability to place the right patient in the right unit with the right resources at the right time. The purpose of this study was threefold: (1) to determine what proportion of patients on the study unit were critical care, intermediate care, and acute care based on InterQual[R] Level of Care/Acute Criteria (Pediatric); (2) to determine what proportion of patients transferred from the pediatric intensive care unit (PICU) to the study unit were critical care, intermediate care, and acute care based on the same criteria; and (3) to determine whether these proportions were related to specific factors, such as census ratios, staffing proportions, or severity of illness.
The study was an institutional review board-approved, retrospective, cross-sectional assessment of patient placement on the study unit applying the 2007 InterQual[R] LOC/Acute Criteria (Pediatric). It was conducted at Children's Hospital of Wisconsin, a 236-bed tertiary pediatric care facility in Milwaukee, Wisconsin.
All patients in the study unit at 1:00 p.m. every fourth day between October 5, 2006, and September 30, 2007, as well as patients transferred from the PICU to the study unit, were included in the study. No patients during the time interval were excluded. Study dates were chosen to represent a typical daily roster and mix of patients and seasonal and weekday variation. Data from 91 study dates representing 2,910 patient days were collected. The total number of cases reviewed was determined using a sample size calculation for the number needed to obtain a 95 percent confidence interval with a width of approximately +/- 2 percent for the percentage of cases classified as intermediate or critical care.
DATA COLLECTION: LEVEL OF CARE
Seven data collectors were trained in InterQual[R] LOC/Acute Criteria (Pediatric). Interrater reliability (IRR) between data collectors was performed using a goal of 90 percent concordance rate; however, after conversations with the company, a peer review process similar to their secondary review process was determined to be more appropriate to the study (McKesson Health Solutions 2007). A patient's LOC was determined by electronic chart review and manual data collection using the established primary review process, with secondary peer review when warranted. (2)
All patients were analyzed based on InterQual's[R] criteria for severity of illness (SI) (3) and intensity of service (IS) (4) on their admission or transfer date closest to study date. On the study date, IS was assessed to ensure appropriateness of admission or transfer LOC. This study-specific procedure deviated slightly from InterQual's[R] process but was applied for the purposes of consistent data collection. We adapted the original LOC classification to categorize medically complex, chronically ill pediatric patients with acute illnesses that we labeled long-term acute care (LTAC). These complex patients met the criteria for IS but not for SI. This adaptation was discussed with the company and recommended for consideration in an upcoming edition.
Unit characteristics data, staffing proportion, and census ratio were analyzed for the study unit, five acute care floors, and the PICU. Staffing proportion was determined by the actual number of registered nurses staffing a particular unit divided by the required number of registered nurses for that unit based on the patient acuity score. Census ratio was determined by the number of beds occupied divided by the number of beds available on that unit.
Paediatric Index of Mortality 2 (PIM2) scores were collected on all patients being admitted or transferred to our study unit. PIM2 is a physiologic-based severity of illness scoring system used to predict mortality for children (Slater, Shann, and Pearson 2003). PIM2 reflects how ill a child is at the time of admission; thus, the data is collected within the first hour following a patient's admission to the unit. Based on the PIM2 score, a severity of illness score in the form of percent risk of mortality is calculated.
The percentage of cases with each utilization review criteria classification (and the 95 percent confidence interval) was calculated for each quarter of data and for the entire year. These calculations were also performed for the subgroup of patients transferred from PICU to the study unit.
To determine whether the characteristics of the units described earlier are related to the percentage of patients classified as intermediate care LOC or critical care LOC (versus acute care or other LOC), these percentages were calculated for each study day. The relationship between these percentages and the unit characteristics was investigated using Pearson correlation. The primary analysis examined factors related to the proportion of intermediate care or critical care LOC of the total intermediate care, critical care, and acute care LOC cases.
We found that 17.8 percent of the 2,910 patients on the study unit met critical care LOC criteria, 42.1 percent met intermediate care LOC criteria, and 33.2 percent met acute care LOC criteria (see Exhibit 2). In contrast, 32.7 percent of the 168 patients who were transferred to the study unit from the PICU met critical care LOC criteria on the day of transfer, while 44.6 percent met intermediate care LOC criteria and only 20.8 percent met acute care LOC criteria (see Exhibit 3). Surprisingly, of the 2,910 patients on the study unit, 199 (6.8%) did not meet any of the standard criteria. Of these, 80 (2.7%) patients met discharge LOC criteria, 28 (1%) met our definition of long-term acute care LOC criteria, 11 (0.4%) met observation LOC criteria, 15 (0.5%) met transitional care LOC criteria, and 65 (2.2%) met no criteria. Of the 168 patients who were transferred to the study unit from the PICU only 3 did not meet critical care, intermediate care, or acute care LOC criteria. Of these, 1 (0.6%) met discharge LOC criteria and 2 (1.2%) met transitional care LOC criteria on the day of transfer. Overall, 60 percent of patients on the study unit met either critical care or intermediate care LOC criteria (see Exhibit 2). Excluding patients who met discharge or other LOC criteria (observation, transitional care, long-term acute care, or none), 64 percent of patients on the study unit met either critical care or intermediate care LOC criteria (see Exhibit 2).
Correlation analysis was used to determine factors that were related to the percentage of patients on the study unit that met critical care, intermediate care, and acute care LOC criteria. Census ratios and staffing proportions on the study unit, the PICU, and the acute care floors were considered as possible factors affecting these percentages (see Exhibits 4a and 4b). As the census ratio on the study unit increased, the percentage of patients meeting critical care or intermediate LOC criteria tended to decrease (r = -0.236, p = 0.024). This relationship was true even if the patients who met discharge or other LOC criteria were included. There was no statistically significant relationship between the census ratio on either the PICU or the acute care floors, and the percentage of patients on the study unit meeting critical care or intermediate care LOC criteria regardless of whether patients who met discharge or other LOC criteria were included or not. There was no statistically significant relationship between the staffing proportions on the study unit, the PICU, or the acute care floors and the percentage of patients on the study unit meeting critical care or intermediate care LOC criteria regardless of whether patients who met discharge or other LOC criteria were included.
The median PIM2 risk of mortality was also considered as a possible factor affecting the percentages of patients on the study unit who met critical care or intermediate care LOC criteria (see Exhibits 4a and 4b). There was no statistically significant relationship between the median PIM2 risk of mortality on the study unit and the percentage of patients on the study unit meeting critical care or intermediate care LOC criteria regardless of whether patients who met discharge or other LOC criteria were included. However, the percentage of patients on the study unit who met critical care or intermediate care LOC criteria tended to increase as the number of patients who received PIM2 scores (admissions and transfers) increased (r = 0.329, p = 0.001). This was also the case if patients who met discharge or other LOC criteria were included (r = 0.300, p = 0.004). This appeared to be influenced more by the admissions than by the transfers, as the percentage of patients who met critical care or intermediate care LOC criteria tended to increase as the number of admissions increased (r = 0.280, p = 0.007) but not as the number of transfers increased (r = 0.187, p = 0.075).
In this study, we determined the proportion of patients on the study unit that met critical care, intermediate care, and acute care based on InterQual[R] LOC/Acute Criteria (Pediatric). As we had anticipated, the proportion of these patients that were more appropriate for an ICU setting than a step-down or intermediate setting was greater than had been previously thought (see Exhibit 2). We also determined the proportion of patients that met these criteria at the time of transfer from the PICU to the study unit. An even greater proportion of transfer patients would have been more appropriately cared for in an ICU setting rather than a step-down or intermediate care setting (see Exhibit 3). We also found a lower proportion of critical care and intermediate care LOG patients on the study unit as the census ratio increased but a higher proportion of patients who met critical care or intermediate care LOC criteria as the number of new patients (admissions to the study unit) increased.
These findings support the need for an expansion of critical care bed capacity. The majority of patients being cared for on the study unit were either critical care or intermediate care LOC, and more than we had anticipated were critical care LOC. Furthermore, the majority of patients being transferred from the PICU to the study unit were either critical care or intermediate care LOC with an even larger proportion than we had anticipated being critical care. Taken together, these findings suggested a shortage of critical care beds in the PICU at this institution.
As the census ratio increased on the study unit, the proportion of critical care and intermediate care LOC patients tended to decrease. If this were also related to a concurrent increased census ratio on the acute care floors, it might suggest inadequate acute care bed capacity in the institution. But this was not the case. Some patients remained on the study unit but no longer met critical or intermediate LOC and were not transferred to general acute care units. This may represent a documentation problem or poor sensitivity of the LOC to the patients' perceived risk or other needs. It might also be related to the absence of an acute care unit with telemetry capability, because a large number of cardiac patients (n = 420 or 14.4%) were cared for in the study unit during the study period. Another possibility for this finding is the inability to identify appropriate providers or skilled resources willing or comfortable with the care of medically complex patients in a general acute care setting. A higher proportion of patients met critical care or intermediate care LOC criteria as the number of new patients (admissions to the study unit) increased. This again supports the suggestion that an expansion of critical care bed capacity was needed at this institution. It also suggests that patients were admitted to the most appropriate inpatient bed available. Interestingly, of the patients being cared for on the study unit, 199 (6.8%) did not meet critical care, intermediate care, or acute care LOC criteria (see Exhibit 2).
The findings suggest the potential usefulness of LOC criteria in refining estimated bed capacity needs and patient placement plans. In most hospitals, case mix index (CMI) is used as an indicator of severity of illness and resource intensity. Some hospitals have used CMI to determine critical care bed needs. Few studies have determined whether a single point of measure such as CMI is effective in predicting critical care bed needs. If CMI alone had been used, it may have caused us to underestimate or overestimate our true bed capacity needs. With the additional use of InterQual[R], the shifts in LOC requirements throughout the patient's stay was helpful in determining how many additional patient days from our study unit population's total patient days were associated with critical care bed needs. For most capacity planning, the added information of actual level of care trends by patient days is not available with the typical analysis of historical volume trends for patient days, admissions, and transfer statistics. The level of care information was used to model different scenarios of range of acuity levels and their implications for total bed requirements. In our planning, all patients that classified as critical care and intermediate care were included in the capacity projections. A subset of the acute care patients was also added due to discussions about clinical risk factors for specific diagnosis. Additionally, the proportion of acute care patients allowed us to identify a trend of specialty acute care resource needs (i.e., beds, type and quantity of monitoring capabilities). Our analysis assisted in estimating bed needs for a subset of our total cardiac patient days that might be transitioned to an acute care unit. We were also able to develop initial projections of monitoring needs and nursing competency needs for cardiac patients for placement in an acute care unit. Because LOC measurements occurred every four days, we were able to trend acuity shifts and associated staffing requirements. While changes in staffing requirements over the patient's stay was not the focus of this study, the data collected allowed us to begin to trend how nursing care requirements shift over the patient's length of stay. The addition of nursing care requirements to future research in this area will be beneficial in adding staffing capacity requirements with any bed addition.
Since its opening, our study unit has been staffed by physicians board certified in pediatric critical care and by nursing and respiratory care staff with intermediate-level critical care competencies. As patient care requirements changed, additional clinical resources were provided to support the shifting patient acuity and complexity care requirements. Our study results led us to reevaluate the duplication of resources in various staffing roles and equipment use, which also helped with critical care bed expansion and patient placement planning.
Given the need for comprehensive methods of measuring demand for critical care services, hospital leaders face the challenge of interpreting and utilizing various methods to assist them in formulating decisions around resource management. Currently, using a variety of measures for capacity projections is necessary to generate and evaluate alternative courses of action and predict future expenditures.
LIMITATIONS AND FUTURE RESEARCH
This study may have been limited by the method in which the 2007 InterQual[R] LOC/Acute Criteria (Pediatric) were applied. The criteria were designed as a concurrent utilization review tool, but in this study the criteria were applied retrospectively to study patients. The information reviewed to determine the appropriate LOC was retrieved from the electronic medical record rather than through concurrent review of the actual chart. Information could not be confirmed by direct communication with providers. Thus, the degree of legibility, accuracy, and completeness of the documentation may have interfered with appropriate LOC being obtained for a given patient. Furthermore, the tool itself appeared inadequate to level all patients residing in the study unit (e.g., the medically complex, chronically ill pediatric patient), prompting our research team to develop our own LOC for these patients. Also, the tool cannot take into account dynamic changes in patient demand versus resource availability. At certain points in time, patient placement may not correspond to physiologic status. Finally, the tool was not designed to address the adequacy of patient placement with respect to bed capacity. Using the tool is time-intensive and may not be cost-effective for determining appropriate patient placement unless it is performed concurrently with daily utilization review determinations.
Further study of the applicability of this tool to the needs of tertiary pediatric institutions is needed. Future studies should focus on those patients who were not able to meet the established LOC criteria. Particular attention should also be given to factors influencing severity of illness, such as the presence of chronic health conditions requiring skilled nursing care and treated at a high LOC in the home under usual "healthy" conditions. This might allow better LOC determination for this small but resource-intensive patient population and thus might allow institutions to develop other patient care areas to meet the needs of these patient populations.
The authors would like to thank everyone who performed data collection or helped with this study, including Beth Ehlert, MSM, RN; Kelly Goetz, RN-BC, CCM, BSBA; Peg Longdin, RN; Jenny Opichka, RN, BSN; Nancy Pavlichek, RN, ADN, BS; Pam Pittner-Tyson, Rig, BSN; Bixiang Ren, MS; Betsey Brown Schmbbe, RN, BSN; Bonnie Stojadinovic, RN, DNP, CPNP-AC-PC; and Robyn Treder, RN-C.
American Academy of Pediatrics Committee on Hospital Care and Section on Critical Care, Society of Critical Care Medicine Pediatric Section Admission Criteria Task Force. 1999. "Guidelines for Developing Admission and Discharge Policies for the Pediatric Intensive Care Unit." Pediatrics 103 (4): 840-42.
Bruce, S., C. DeCoster, I. Trumble-Waddle, and C. Burchill. 2002. "Patients Hospitalized for Medical Conditions in Winnipeg, Canada: Appropriateness and level of Care." Healthcare Management Forum Winter (Suppl): 53-7.
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(1.) Prior to this study, InterQual[c] Level of Care/Acute Criteria (Pediatric) had been recently introduced to our organization by the Case Management Department. This is a commonly used set of criteria by payers for evaluating appropriate healthcare services, severity of illness, and intensity of service criteria.
(2.) For the purposes of this study, a secondary peer review was instituted when a patient's severity of illness (SI) and intensity of service (IS) did not directly align with any of the established LOC criteria, such as some chronically ill, technology-dependent patients. This secondary peer review consisted of two or more experienced data collectors reviewing the patient's presentation and applied therapies and mutually determining the most appropriate LOC.
(3.) "Severity of Illness (SI) criteria consist of objective, clinical indicators of illness, which focus on an individual patient's clinical presentation rather than diagnosis" (McKesson Health Solutions 2007). An example of an SI criteria is "respiratory distress and hemodynamic stability with oxygen saturation < 90% arterial" (McKesson Health Solutions 2007).
(4.) "Intensity of Service (IS) criteria consists of monitoring and therapeutic services, singularly or in combination, that can only be administered at a specific level of care" (McKesson Health Solutions 2007). An example of an IS criteria is "oxygen therapy, continuous [greater than or equal to] 35%" (McKesson Health Solutions 2007).
James E. Shmerling, DHA, FACHE, president and CEO, Children's Hospital Colorado, Aurora, Colorado
Although there has been a downturn in overall construction, the advent of new technology and growing demand have led to increased spending for hospital expansion. Even during the recession, construction spending increased by 7.8 percent to $48 billion. The authors' description of a model for estimating critical care bed capacity using level of care data is therefore very timely. My own organization is in the midst of a $200 million expansion that includes the addition of critical care beds. Our demand analysis was predicated using historical trends, projected specialty growth trends, and anticipated population growth--in other words, what the authors refer to as "traditional models of estimating bed capacity requirements."
I agree with the authors' contention that we need to better understand those factors that affect patient placement. The appropriate placement of patients not only enhances appropriate resource utilization and improves the accuracy of projecting future bed need, it also can affect patient outcomes. Only 27 percent of hospitalized pediatric patients who arrest survive. Only 62 percent of pediatric patients who require acute escalation of care survive. Their appropriate placement is essential to prevent codes outside the ICU and improve patient outcomes.
Conversely, there may be a tendency to overcompensate for this risk by placing noncritical patients in an ICU just to be on the safe side. In Texas, there is a growing debate about the proliferation of newborn intensive care beds. In that state, births are up 18 percent while the number of newborn intensive care beds grew by 84 percent. Whatever the reason, proliferation of critical care beds for reasons other than true patient need results in an inefficient use of expensive and scarce resources and could create a perceived demand for even more critical care beds.
While our organization does not use the InterQual[R] utilization review criteria, we have used Pediatric Early Warning Sign (PEWS) scores to aid in patient placement decision making. An algorithm was drafted internally, guiding patient placement decisions and communications for staff in our emergency department, patient placement department, ICUs, and inpatient units. By using this methodology, we intend to avoid preventable unplanned transfers to the ICU from the inpatient unit within 12 hour of admission. This tool, coupled with utilization review criteria such as that used by the authors, may be even more effective for ensuring appropriate utilization of beds and may provide a more reliable mechanism for projecting future bed needs.
Thankfully most children are healthy and rarely require hospitalization. However, those who do are typically very ill and require the breadth and depth of a hospital that offers the full range of requisite services. Children are not small adults, and the specialized resources required to care for them necessitate vigilant management. The addition of pediatric critical care beds not only carries significant capital costs, but as the authors note, it has significant staffing implications as well. Those costs and the clinical quality and safety implications of appropriate bed placement make the use of data analysis tools such as those described in this study essential.
For more information on the concepts in this article, please contact Ms. Jamieson at email@example.com.
Donna Jamieson, MS, RN, NEA-BC, executive director, patient care, Children's Hospital of Wisconsin; Theresa A. Mihhailov, MD, PhD, associate professor, Department of Pediatrics, Medical College of Wisconsin; Kristyn Maletta, BA, outcomes specialist, Children's Hospital and Health System; Evelyn M. Kuhn, PhD, senior outcomes statistical analyst, Children's Hospital and Health System; Lauren Giuliani, BA, patient care systems and operations improvement manager, Children's Hospital of Wisconsin; Jeanne Musolf, MS, RN, CCM, manager, case management, Children's Hospital of Wisconsin; Kay Fischer, RN, MN, NEA-BC, director, pediatric critical care services, Children's Hospital of Wisconsin; and Maureen Collins, MS, RD, director, outcomes, Children's Hospital and Health System
EXHIBIT 1 Comparison of Capabilities by Unit Type Critical Care Intermediate Care Acute Care (Pediatric Intensive (Step-Down) Care Unit) Provides definitive Provided to patient Provided on an care for a wide population with a inpatient general range of complex, severity of illness acute pediatric care progressive, rapidly that does not floor. changing, medical, require intensive surgical, and care but does traumatic disorders, require greater often requiring a services than those multidisciplinary provided by routine approach, occurring inpatient general in pediatric pediatric care patients of all (Committee on ages, excluding Hospital Care 1993). premature newborns (American Academy of Pediatrics 1999). Requires invasive May require frequent No invasive monitoring, monitoring of vital monitoring. Less continuous cardio signs and/or nursing frequent vital signs respiratory interventions but nursing assessment/ monitoring, and usually will not intervention. hourly vital signs require invasive RN interventions. monitoring. Twenty-four-hour Attending physician Attending physician intensivist coverage available. oversight. coverage, pharmacist, Ensure resources, respiratory therapy, facilities, and RN staffing levels personnel are of two RNs to one available to provide patient or one RN to care beyond the one to two patients. level of a general pediatric floor and the ability to stabilize a child who becomes critically ill. EXHIBIT 2 Distribution of Patients by Levels of Care 95% Confidence Interval Number Percent Lower Upper 1 = Critical Care 518 17.8% 16.4% 19.2% 2 = Intermediate Care 1226 42.1% 40.3% 43.9% 3 = Acute Care 967 33.2% 31.5% 34.9% 4 = Discharge 80 2.7% 2.2% 3.3% 5 = Other: Observation 11 0.4% 0.2% 0.6% 6 = Other: Transitional Care 15 0.5% 0.3% 0.8% 7 = Other: LTAC 28 1.0% 0.6% 1.3% 8 = Other: None of the above 65 2.2% 1.7% 2.8% Subtotal Other (4-8) 199 6.8% 5.9% 7.8% Total 2910 EXHIBIT 3 Distribution of Transferred Patients at Time of Transfer by Level of Care 95% Confidence Interval 95% Confidence Interval Number Percent Lower Upper 1 = Critical Care 55 32.7% 25.6% 39.8% 2 = Intermediate Care 75 44.6% 37.1% 52.2% 3 = Acute Care 35 20.8% 14.7% 27.0% 4 = Discharge 1 0.6% 0.0% 1.8% 6 = Other: Transitional Care 2 1.2% 0.0% 2.8% Total 168 EXHIBIT 4a Descriptive Characteristics of Inpatient Units Characteristic Number of Standard Study Days Mean Deviation Proportion 91 0.64 0.10 (Critical or intermediate)/ (Critical, intermediate, or acute) Study unit census ratio * 91 0.84 0.10 PICU census ratio * 91 0.84 0.12 General floor census 91 0.67 0.08 ratio Study unit staffing ratio (^) 91 0.96 0.11 PICU staffing ratio (^) 91 0.95 0.10 General floor staffing 91 0.96 0.08 ratio^ Number of patients with 91 5.07 2.16 PIM2 scores AA Non-transfer patients 91 3.22 1.71 Transfer patients 91 1.85 1.40 Mean PIM2 risk of 90 0.013 0.007 mortality (^^) Median PIM2 risk of 90 0.008 0.005 mortality (^^) Characteristic Minimum Maximum Median Proportion 0.44 0.87 0.65 (Critical or intermediate)/ (Critical, intermediate, or acute) Study unit census ratio * 0.53 1.06 0.84 PICU census ratio * 0.53 1.03 0.87 General floor census 0.46 0.84 0.68 ratio Study unit staffing ratio (^) 0.77 1.36 0.94 PICU staffing ratio (^) 0.73 1.28 0.95 General floor staffing 0.78 1.16 0.96 ratio^ Number of patients with 0 11 5 PIM2 scores AA Non-transfer patients 0 8 3 Transfer patients 0 6 2 Mean PIM2 risk of 0.003 0.033 0.012 mortality (^^) Median PIM2 risk of 0.001 0.030 0.008 mortality (^^) * Number of patients being cared for on unit/ number of beds on unit (^) Actual number of RNs in unit/ required number of RNs for unit (^^) PIM2 is a physiologic-based severity of illness scoring system used to predict mortality for children (Slater, Shann, and Pearson 2003). EXHIBIT 4b Correlation of Unit Characteristics with Level of Care Characteristic Correlation p-value Coefficient (+) Study unit census ratio * -0.236 0.024 (++) PICU census ratio * 0.030 0.777 General floor census ratio * 0.163 0.122 Study unit staffing ratio (^) -0.159 0.133 PICU staffing ratio (^) -0.010 0.924 General floor staffing ratio (^) -0.071 0.503 Number of patients with PIM2 0.329 0.001 (++) scores (^^) Non-transfer patients 0.280 0.007 (++) Transfer patients 0.187 0.075 Mean PIM2 risk of mortality (^^) -0.038 0.724 Median PIM2 risk of mortality (^^) -0.173 0.102 (+) Correlation with proportion (critical or intermediate) / (critical, intermediate, or acute) * Number of patients being cared for on unit/number of beds on unit (++) Statistically significant correlation (^) Actual number of RNs in unit/Required number of RNs for unit (^^) PIM2 is a physiologic-based severity of illness scoring system used to predict mortality for children (Slater, Shann, and Pearson 2003).
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