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Cost of hospital care for older adults with heart failure: medical, pharmaceutical, and nursing costs.

Heart failure, the final common pathway of cardiovascular disease, affects about five million Americans and has been referred to as a global epidemic (Moser and Mann 2002). It is a disabling and costly chronic condition. It is becoming more prevalent as the population ages and survival increases from previously fatal acute cardiac events (Roger et al. 2004). Of the millions living with heart failure, 80 percent are 65 years of age or older.

Those with heart failure incur great economic burden, with costs exceeding those of breast and lung cancer combined (Peacock 2003). In 2004, the estimated direct and indirect cost of heart failure in the United States was $25.8 billion, of which $13.6 billion, or 53 percent, was direct hospital cost (American Heart Association 2004). Heart failure is the most expensive of the Medicare diagnoses in the United States and yet, reimbursement often does not keep up with the mean total hospital charges. Mean total charges were $15,293 per visit in 2000, with the average American hospital losing more than $1,000 per visit (Peacock 2003; Ashish et al. 2004).

Much has been written in the last decade about the positive collective effects on cost and readmission rates by using multidisciplinary disease management approaches, usually involving the continuum of care within and outside the hospital setting (Balinsky and Muennig 2003). Little is known, however, about the unique interventions or contributions to cost of care by specific disciplines; most of the research has been done with measures that come from the large Medicare data sets that describe medical care and treatments and not those of other health care disciplines. This research study analyzes data from an electronic documentation system that includes medical, nursing, and pharmacy treatments to demonstrate the unique contributions of these providers to hospital cost for older adults with heart failure.

STUDY PURPOSE

The purpose of this study was to describe the relationships between (1) patient characteristics, clinical conditions, nursing unit characteristics, and treatments (medical, pharmacy, and nursing) and (2) cost of hospitalization for older patients with heart failure. This exploratory study was guided by an effectiveness model developed by Titler, Dochterman, and Reed (2004) that links patient characteristics, patient conditions, and processes of care, to outcomes of hospital cost. Independent variables are categorized as patient characteristics, clinical conditions, nursing unit characteristics, and treatments (see Table 1). Length of stay was not included as an independent variable as its correlation with hospital cost is, not surprisingly, high (Pearson correlation coefficient was 0.87 in this sample); we wanted to go "beyond" the traditional explanation of hospital cost by number of days in the hospital and rather examine those unit and clinical variables that relate to hospital cost and not have these masked by the length of hospitalization. A brief explanation of each variable is in Table 1 ; a more complete explanation of the measurement of each variable is available in Supplementary Material Appendix A.

METHODS

Data Sources and Sample

Data for this study are part of a study funded by the National Institute for Nursing Research and the Agency for Healthcare Research and Quality that used a retrospective, descriptive design to conduct outcomes research in three older populations (Titler 2000). Descriptive data from the 4-year period July 1998 to June 2002 were accessed from an 843-bed, academic medical center in the Midwest. This study focused on hospital cost of the heart failure population defined by inpatient records with discharge diagnoses classified within the Major Diagnostic Classification (MDC) Diseases and Disorders of the Circulatory System and with a primary or secondary discharge diagnosis of heart failure. The data represent 1,435 hospitalizations of 1,035 adult inpatients, 60 years of age and older. Of the final sample, 43 percent were female, 94 percent white, 58 percent married and 27 percent widowed, and 74 percent retired. The mean age was 74 (SD = 8.9) with a median length of hospitalization of 6.26 days.

Variables used in the analysis for this study originated from four electronic hospital databases: medical record abstract, financial, pharmacy, and nursing. (1) Operational and clinical data were linked at an individual patient level for each hospitalization. Unique patient identifiers were used to build a relational database of selected variables on a separate SQL server (Titler, Dochterman, and Reed 2004). The data obtained from patient records were accepted as reliable based on the reasoning that they are used for important clinical decisions as well as for reimbursement and research. Multiple checks were done to assure that the data in the relational database were identical to those in the patient records.

Dependent Measure: Total Hospital Costs

The dependent variable was total hospital cost. Because actual hospital cost data were unavailable for direct analysis, hospital charges were used to represent financial burden (Ashish et al. 2004). Financial charge data were obtained from the hospital's medical record abstract database for each hospitalization. The charge data were converted to cost data using the cost-to-charge ratios obtained from the Centers for Medicare and Medicaid Services Acute Inpatient Prospective Payment System website (http:/ www.cms.hhs.gov/AcuteInpatientsPPS/). For each hospitalization, the hospital's total charge was multiplied by the overall cost-to-charge ratio for the fiscal year in which the hospitalization occurred. Total hospital costs included costs in the following categories: general services, ICU/special care, pharmacy, laboratory, radiology, operating room, supplies, and other ancillary services.

Data Analysis

Generalized estimating equations (GEE) were used for analyses to take into account that some patients had more than one hospitalization during the time frame for the study (Liang and Zeger 1986). When the sample includes some patients who have more than one admission during the study's time frame, GEE analysis is recommended over the more usual general linear model (GLM) in order to not over estimate the treatment effects (Liang and Zeger 1986). Analyses were completed using SAS/STAT software, Version 9 of the SAS System for Windows (SAS Institute Inc. 2003). PROC GENMOD was employed for the GEE analysis. A process of building an empirical model was followed to systematically reduce the number of variables used to predict the cost of care and to determine which independent variables made unique contributions to cost, after controlling for other variables. Variables were first tested singularly, using zero-order correlations, for their association with total hospital cost. If the p-value for the zero-order correlation was [less than or equal to] .15, the variable was retained. Significant variables (p [less than or equal to] .15 in zero-order correlations) within each category (e.g., patient characteristics, clinical conditions) (see Table 1) were then tested, controlling for other variables within that category, for a statistically significant relationship to cost, again at the p [less than or equal to] .15 level. The p [less than or equal to] .15 level was chosen to guard against eliminating variables too soon, when they might yet prove to have a statistically significant effect when combined with other independent variables. The final statistical model then tested the significant variables from each category against the variables in the categories appearing before it in the model. When variables from the final category were added, the final model was established, using p [less than or equal to] .05 as the criterion. Score statistics, or the summative p-value and [chi square] for each variable that remained significant at the p [less than or equal to] .05 in the final model, are summarized in Table 2 for each category. Proportional change in cost by variable (odds ratio) and the estimated change in median total cost are represented in Tables 3 and 4.

FINDINGS

The mean total cost of hospitalization was $18,086 (SD $26,736), with a range from $762 to $544,797 and a median total cost of $10,454. Change in median cost is reported in this study due to the wide variability in cost. This wide range may be due in part to several factors. The setting is a large academic referral center and 35 percent of the hospitalizations included invasive diagnostic procedures for heart failure (e.g., cardiac catheterizations and coronary arteriography) while 60 percent included invasive cardiovascular therapeutic procedures (e.g., operating room procedures related to open heart surgery and angioplasty and peripheral vascular surgeries), the latter having the greatest impact on hospital median cost of any single variable included in the model.

The study included 1,435 hospitalizations by 1,075 patients. A total of 183 variables were entered into the analysis with 31 significantly associated with total hospital cost in the final model (Table 2). The mean age of the sample was 72.7 years, consistent with other studies (Munger and Carter 2003). A younger age was significantly associated with greater cost with initial bivariate analysis (p = .005), also consistent with other reports (Wexler et al. 2001), but when other patient characteristics variables were added, this association disappeared. None of the patient characteristics were significantly related to cost in the final model (see Table 2).

Only two clinical conditions remained in the final model: the comorbidity of deficiency anemia and severity of illness (Table 2). Of the 30 comorbid conditions used in the Elixhauser et al. (1998) method, 27 were related to hospital cost at p [less than or equal to] .15 in step one bivariate analysis, but only one, deficiency anemia, remained when all variables were entered sequentially in the final model (Table 2). Deficiency anemia was associated with a 5 percent increase in median cost in the final model with an estimated additional cost of $536 for heart failure patients with this comorbid condition (Table 3). The clinical condition, severity of illness, was significant in step one bivariate analysis and also was significant in the final model (see Table 2). However, there were no statistically significant differences in the final model between the costs of higher levels of severity when compared with minor levels of severity (Table 3).

Two of the four nursing unit characteristics were significant in the final model. The number of units resided on during hospitalization was significantly associated with increased hospital cost when the patient was on 2, 3, or 4 units. Residing on 2 units (p = .0015) added about 10 percent to median cost, or an estimated $1,007; residing on three or 4 units added about 17 percent, or $1,748 to median cost per hospitalization (p = .0001) (Table 3). Interestingly, the cost difference for 5 or more units added only about 3 percent to median cost and was not significant. The variable registered nurse (RN)/patient dip proportion was also significantly associated with increased cost in the final model (p < .0001) (Table 2). The mean RN/patient dip proportion, which represents the largest drop in available RN care during a hospitalization, was 0.4321 with a range of 0.04-0.89. The larger the RN dip proportion, the fewer RN hours available for patient care. For every 0.2 increments in RN dip proportion value, there was a 15.2 percent increase in median cost per hospitalization, or $1,589 (Table 3). The RN to patient ratio, which measures the overall amount of RN hours of care to number of patients, was not significantly related to cost.

Medical, pharmacy, and nursing treatments were each significantly associated with total hospital cost (Tables 2 and 3). The total number of medical procedures was associated with cost (p<.0001); with the addition of each medical procedure, median costs were estimated to increase $623 (see Table 3). Types of medical procedures included both medical and surgical procedures. There were 123 different types of medical procedures performed, for a total of 5,193 medical and surgical procedures for the 1,035 heart failure patients. To distinguish between the cost effects of nonsurgical versus surgical procedures, the different types of procedures were grouped into nine subgroups (see Table 1). Three of these nine groups were significantly associated with cost; two of the three were invasive (Tables 2 and 3). Noninvasive heart failure diagnostic procedures, such as echocardiograms and radioisotope scans, were associated with an increase in median cost of about $800, or 8 percent; Invasive cardiovascular diagnostic procedures (e.g., coronary angiography) were associated with an increased cost of $736, or 7 percent; and invasive cardiovascular therapeutic procedures (e.g., angioplasties, open-heart surgeries) were associated with an increased median hospital cost of $5,294.

The number of different medications used during hospitalization and several specific medications were significantly related to cost (Tables 2 and 3). The addition of any one unique type of medication added $179 to the median costs. Miscellaneous GI (gastrointestinal) medications reduced median costs by over $1,000 per hospitalization. Benzodiazepines were used in 57 percent of hospitalizations at an estimated additional cost of $885 per hospitalization. Nursing interventions included in the study were those used in at least 5 percent of the hospitalizations. The number of different nursing interventions employed during the hospital stay, was statistically significant, and associated with an estimated increased cost of $289 for each additional type of nursing treatment (p< .0001) (Tables 2 and 4).

Ten types of nursing interventions were significantly associated with cost in the final model (Tables 2 and 4). The average number of times the intervention was used in 24 hours (use rate) was reported in quartiles (for interventions used in [greater than or equal to] 95 percent of the hospitalizations) or in thirds (for interventions used in < 95 percent but [greater than or equal to] 5 percent of hospitalizations) (see further explanation in the Supplementary Material Appendix A). Follow-up tests for these 10 were conducted by use rate categories. Six of the 10 interventions were also significant for use rate effect on cost (Table 4): Fluid Management, Pressure Ulcer Care, Oral Health Restoration, Bowel Management, Infection Protection, and Medication Management (Table 4). The Fluid Management intervention, used in 99.7 percent of hospitalizations, was significantly associated with cost (p=.017). When comparing a daily use rate of 6.08 times/24 hours with a daily use rate of 1.19 times/24 hours, the Fluid Management intervention was estimated to save $870 per hospitalization (see Table 4). Performing this nursing intervention two or more times a day is estimated to save $571-$870 per hospitalization. Pressure Ulcer Care was used in 89.3 percent of the hospitalizations but in very small doses. When the average use rate was less than one time per day, the intervention (or lack thereof) appears to increase cost; a use rate of 0.22 is associated with an additional estimated median cost of $1,116 per hospitalization. However, when performed even one time per day (use rate 0.90), the intervention was estimated to save $2,232 per hospitalization (p< .0001). The intervention Oral Health Restoration was used in 83.5 percent of hospitalizations and was estimated to save money when used between 0.8 and 1.75 times per day ($1,016-$1,123). Bowel Management was significantly associated with an estimated $784 reduction in median hospital cost. Infection Protection was used in 73 percent of the hospitalizations and was estimated to reduce cost by more than $1,180 at a use rate of 2.6 times per day. Finally, Medication Management was documented in only 11 percent of the hospitalizations but was estimated to save $2,027 per hospitalization with the top one-third use rate of 1.58 times per day (p< .0001). In the lower one-third use rate, (0.13 times per 24 hours), the "lack" of Medication Management was estimated to add an additional $2,295 to median costs (p<.0001).

GEE analysis does not result in [R.sup.2] statistic, so to estimate the extent of variance in cost explained by the statistical model, a GLM procedure was used (SAS Institute, Inc. 2003). As the GLM procedure requires that the unit of analysis be independent, repeat hospitalizations for the same patient were removed, thereby reducing the sample size to 1,075 patients. The resulting [R.sup.2] was 0.877 indicating that the model accounts for 88 percent of the variance in hospital costs for heart failure patients 60 years of age and older.

DISCUSSION

The average total costs per heart failure hospitalization in this study ($18,086) were higher than the national average for 2000 of $15,293 (AHRQ 2002), perhaps related to the large academic setting and the mixed medical and surgical population. Only one comorbid condition, deficiency anemia, was associated with increases in median heart failure costs in this study. In general, reports about the effect of comorbidity specific to heart failure vary. Previous studies have reported either no impact of multiple comorbidities on hospital costs of heart failure patients (Weintraub et al. 2003), or significant increases in heart failure hospital cost (Zhang, Rathouz, and Chin 2003). Our results support the reported clinical and economic importance of anemia in progressive heart failure (Horwich et al. 2002; Komajda 2004; Nordyke et al. 2004).

Two nursing unit characteristics were significant in this cost model. Given the ongoing debate about minimum levels of nursing staff, it is important to note the findings that staffing below the average (RN/patient dip proportion) increased hospital costs. This variable was created for this study to address the nationwide concern about low staffing levels and to complement the usual measure of overall RN hours of care (see the RN patient ratio in Table 1 and Supplementary Material Appendix A, which was not significantly associated with hospital costs). A 0.2 incremental increase in the RN/patient dip proportion corresponded to a 15 percent increase in cost. The mean value of the RN dip proportion in this study was 0.4321, which would correspond to a 31 percent increase in cost due to staffing variability below a defined average limit. (Note--this variable was calculated using hourly figures for both the number of RNs assigned to deliver care and the number of patients needing care and includes 24 hours for all days of hospitalization; the findings reported here do not address the specific hours when the dips occurred.) Reducing hospital cost by reducing RN staff appears to be a compelling but expensive and potentially dangerous myth (Titler et al. 2005, 2007). Number of units resided on during hospitalization (moving patients from unit to unit during hospitalization) is also associated with increased costs. These costs might be associated with incomplete hand-off communication between units resulting in adverse medical error occurrences that may increase total hospital cost (Kanak et al. in press).

The types of medications significantly related to cost in this study were not the most frequently used cardiac medications (e.g., diuretics, [beta] blockers, etc.) (Stroupe et al. 2004). In contrast, many of the medication categories associated with increased costs (Table 3) are less common in typical heart failure patients. The eyes, ears, nose, throat (EENT) drugs, and EENT antibiotics administered in 64 and 35 percent of the hospitalizations, respectively, are likely related to surgical patients. By contrast, one frequently used medication inversely related to cost is GI medication. This impressive reduction ($1,015) may reflect a subgroup of heart failure patients in which the diagnostic and treatment effects of GI medications assisted in ruling out cardiac etiologies of chest pain and may have resulted in earlier discharge for otherwise stable heart failure patients. Benzodiazepines (such as Versed, Lorazepam, and Valium), usually contraindicated in the elderly due to side effects of delirium and falls, were used in 57 percent of the hospitalizations. This drug category may represent prediagnostic or preprocedural sedation. However, in this elderly population, it would be of interest to follow-up the choice of this drug during acute heart failure hospitalizations in future studies to determine appropriateness and potential alternatives to use. With use in 57 percent of an estimated one million primary heart failure hospitalizations per year, a benzodiazepine would be prescribed during 570,000 hospitalizations. At an additional cost of $900 per hospitalization, the inappropriate administration of benzodiazepines may represent an avoidable cost.

The finding of almost 100 percent use of the Fluid Management nursing intervention is not only biologically plausible in medical and nursing management of the heart failure patient, but the number one priority in acute heart failure management. When this critical thinking and skill-based nursing intervention is done even twice a day, the cost reduction is again compelling and consistent with the expected biologic benefit to the patient. Conversely, it is concerning that the intervention, Medication Management, is only used in 11 percent of hospitalizations and at the lowest use rate, 0.13 times per day. This "lack of medication management" is associated with a projected 22 percent increase in cost, or $2,303 in this study. However, when this primarily critical thinking intervention is performed only one and a half times per day (use rate 1.58) it is associated with a 19 percent reduction in cost per hospitalization, or a savings of $2,027. Extrapolating once again to one million heart failure acute care admissions per year in the United States, the potential cost impact of improving quality nursing care is enormous and literally worth the attention of nurses, nurse leaders, administrators, boards, and public policy makers.

SUMMARY

This exploratory study used electronic data from one academic medical center, which limits the generalizability of findings, and thus, it needs to be replicated using multiple hospitals of varying size, type, and geographic distribution. A limitation is that the outcome variable does not address quality but only cost of care and making decisions about resource allocation based on cost without considering quality of care is shortsighted (Institute of Medicine 2001). Thirdly, data used in this study were obtained from electronic data sources developed for other purposes and may not reflect all care delivered. Despite these limitations, this study illustrates the feasibility and importance of including processes of care variables, particularly nursing treatments, in cost and analyses. The exploratory study also begins to address the importance of empirically demonstrating what nurses do (nursing interventions) and nurse staffing in analytic cost models.

The methodological techniques used herein can be used to explicate contributions of processes of care for other outcomes such as patient safety, complications, and patient satisfaction. Most of the variables that impact cost are interventions (medical, pharmacy, and nursing), validating the overall model used to guide this research. That is, cost of care should be related to services (interventions) delivered and not patient demographics. In this research, it was demonstrated that hospital cost was chiefly related to services. The results indicate that the total number of medical, pharmacy, and nursing treatments add to the cost of care and suggest that medical procedures and new medications be carefully considered for a possible therapeutic effect before they are added to the treatment plan. A number of nursing interventions, notably those related to risk management or prevention of complications (e.g., Fall Prevention, Infection Protection, and Medication Management) did not increase hospital cost. More research is needed to study the impact of nursing interventions on hospital cost as well as on clinical outcomes.

The importance of having adequate RN staffing was demonstrated in the study with staffing below the unit average associated with increased cost. The reasons for these findings are not included in the study but likely relate to nurses ability to prevent, recognize, and provide early treatment for complications (as per the interventions listed in the above paragraph) thereby assisting in recovery time and reducing cost.

Overall, the findings of this study indicate the importance of conducting research in health care that includes the interventions of multiple providers. This is one of the first studies to our knowledge that includes nursing unit and nursing intervention variables as predictors of hospital cost. The study was possible because the hospital used a standardized classification (e.g., the Nursing Interventions Classification [NIC] [Dochterman and Bulechek 2004]), to document nursing interventions and collected nurse staffing data frequently on each patient unit.

ACKNOWLEDGMENTS

This study was supported by the National Institute of Nursing Research and the Agency for Health care Research and Quality (PI: Titler. R01 NR005331). The authors would like to acknowledge Kim Jordan for her superb assistance in preparing this manuscript for publication.

Disclosures: None.

Disclaimers: None.

REFERENCES

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Munger, M. A., and O. Carter. 2003. "Epidemiology and Practice Patterns of Acute Decompensated Heart Failure." American Journal of Health System Pharmacy 60 (suppl 4): 53-6.

Nordyke, R.J., J.J. Kim, G. A. Goldberg, R. Vendiola, D. Batra, M. McCamish, and J. W. Thomasson. 2004. "Impact of Anemia on Hospitalization Time, Charges, and Mortality in Patients with Heart Failure." Value in Health: Journal of the International Society for Pharmacoeconomics and Outcomes Research 7 (4): 464-71.

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Roger, V. L., S. A. Weston, M. M. Redfield, J. P. Hellermann-Homan, J. Killian, B. P. Yawn, and S.J. Jacobsen. 2004. "Trends in Heart Failure Incidence and Survival in a Community-Based Population." Journal of the American Medical Association 292 (3): 344-50. SAS Institute Inc. 2003. SAS/STAT User's Guide (Version 9). Cary, NC: SAS Institute.

Stroupe, K. T., E. Y. Teal, M. Weiner, I. Gradus-Pizlo, C. Brater, and M. D. Murray. 2004. "Health Care and Medication Costs and Use among Older Adults with Heart Failure." American Journal of Medicine 116: 443-50.

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Titler, M. G.,J. Dochterman, D. M. Picone, L. Everett, X-J. Xie, M. Kanak, and Q. Fei. 2005. "Cost of Hospital Care for Elderly at Risk of Falling." Nursing Economic$ 23 (6): 290-306.

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Wexler, D.J., J. Chen, G. L. Smith, M.J. Radford, S. Yaari, W. D. Bradford, and H. M. Krumholz. 2001. "Predictors of Costs of Caring for Elderly Patients Discharged with Heart Failure." American Heart Journal 142 (2): 350-7.

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SUPPLEMENTARY MATERIAL

The following supplementary material for this article is available online: Appendix A. Expanded Definitions of Independent and Intervening Variables.

NOTE

(1.) Approval for the research was obtained from the University's Institutional Review Board.

Address correspondence to Marita G. Titler, Ph.D., R.N., F.A.A.N., Senior Assistant Director, University of Iowa Hospitals and Clinics, Director, Research, Quality and Outcomes Management, Department of Nursing Services and Patient Care, University of Iowa Hospitals and Clinics, 200 Hawkins Drive, RM T100 GH. Gwenneth A. Jensen, R.N., M.N., C.N.S., Doctoral Student, is with the University of Iowa College of Nursing. Joanne McCloskey Dochterman, Ph.D., R.N., F.A.A.N., Professor Emeritus, is with the University of Iowa College of Nursing, Swisher, IA. Xian-Jin Xie, Ph.D., Assistant Professor of Biostatistics, is with the Center for Biostatistics and Clinical Science & Simmons Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX. Mary Kanak, R.N., Ph.D., Graduate Research Assistant, is with the University of Iowa College of Nursing, Iowa City, IA. David Reed, Ph.D., Statistician, is with the Office for Nursing Research, and the Interventions and Outcomes Project, University of Iowa College of Nursing, Iowa City, IA. Leah L. Shever, R.N., Ph.D.(c), Advanced Practice Nurse, Research, Quality and Outcomes Management, is with the Department of Nursing Services and Patient Care, University of Iowa Hospitals and Clinics, Iowa City, IA.
Table 1: Independent and Intervening Variables

Concepts Variables Definitions/Measures

Patient Age Age in years on day admitted
 to hospital
 characteristics Gender Male or female
 Ethnicity Caucasian and all others
 Marital status Married; separated, divorced
 or single; widowed
 Religion Catholic, Protestant, other
 faiths, no preference/none
 designated
Clinical Medical diagnoses Six groups of primary medical
 conditions diagnoses classified using
 the Clinical Classification
 Software (www.ahrq.gov/
 data/hcup.ccs.htm): heart
 failure without
 hypertension, acute
 myocardial infarction,
 other cardiac conditions,
 conduction disorders,
 peripheral vascular
 disease, and noncardiac
 circulatory diseases
 Comorbidity 30 comorbidities that
 existed before the
 patient's hospitalization
 and not related to the
 principal reason for
 hospitalization measured
 by the Elixhauser et al.
 (1998) method
 Severity of illness The extent of physiological
 decompensation or organ
 system loss of function as
 assigned by the APR-DRG
 system (3M Health
 Information Systems 1993):
 1 = minor, 2 = moderate,
 3 = moderate, 4 = severe

Nursing unit Number of units 1 = 1 unit, 2 = 2 units,
 characteristics resided on during 3 = 3 and 4 units, 4 = 5+
 hospitalization units
 Percent of time in Percent of time during
 intensive care hospitalization spent in
 unit intensive care
 Average RN/patient Average ratio of RNs to
 ratio during patients assigned to
 hospitalization quartiles: 76-100% (best
 staffing), ,51-75%,
 26-50%, 1-25% (worse
 staffing)
 RN/patient dip The largest drop in RN care
 proportion for a specific
 hospitalization, range of
 0-1 with incremental
 increases of 0.2 or 201%
Treatments Number of The number of procedures
 different (0-12) prescribed by
 medical physicians during the
 procedures hospitalization
 Type of medical 123 procedures classified
 procedure using the Clinical
 Classification Software
 (www.ahrq.gov/data/
 hcup.ccs.htm) grouped into
 nine categories: invasive
 and noninvasive
 cardiovascular diagnostic
 procedures, invasive and
 noninvasive
 noncardiovascular
 diagnostic procedures,
 invasive and noninvasive
 cardiovascular therapeutic
 procedures, invasive and
 noninvasive
 noncardiovascular
 therapeutic procedures;
 and blood product
 therapeutic procedures
 (0 = No, the treatment was
 not received, 1 = Yes, the
 treatment was received at
 least once)
 Total number of The number of unique
 different medications coded by the
 medications American Hospital
 Formulary Service
 classification (McEvoy
 2000) administered during
 the hospitalization
 Type of medication Those medications (coded as
 0 = not administered, 1 =
 at least one drug from the
 class or subclass was
 administered) used in 2%
 or more of the population
 (N = 74).
 Total number of The number of unique nursing
 different nursing interventions as coded by
 interventions the Nursing Interventions
 Classification (Dochterman
 and Bulechek 2004),
 administered during the
 hospitalization
 Type of nursing Each intervention used in 5%
 interventions or more of the
 hospitalizations (N = 53)
 was assigned to one of
 three categories
 determined by the percent
 of hospitalizations the
 intervention was used and
 its use rate (average
 number of times the
 intervention was used per
 day): (1) used in 95% or
 greater of the
 hospitalizations, the use
 rates were divided in
 quartiles; (2) used in >
 5% but < 95%, the use
 rates were divided into
 thirds; (3) used in 5% or
 less, coded as a
 dichotomous variable

Concepts Variables Data Sources

Patient Age Medical Record Abstract

 characteristics Gender Medical Record Abstract
 Ethnicity Medical Record Abstract
 Marital status Medical Record Abstract
 Religion Medical Record Abstract
Clinical Medical diagnoses Medical Record Abstract
 conditions
 Comorbidity Medical Record Abstract,
 ICD-9 codes
 Severity of illness Medical Record Abstract;
 Uniform Hospital
 Discharge Data Set
Nursing unit Number of units Nursing Information System
 characteristics resided on during
 hospitalization
 Percent of time in Nursing Information System
 intensive care
 unit
 Average RN/patient Nursing Information System
 ratio during
 hospitalization
 RN/patient dip Nursing Information System
 proportion
Treatments Number of Medical Record Abstract,
 different ICD-9-CM codes
 medical
 procedures
 Type of medical Medical Record Abstract,
 procedure ICD-9-CM codes
 Total number of Pharmacy Repository
 different
 medications
 Type of medication Pharmacy Repository
 Total number of Nursing Information System
 different nursing
 interventions
 Type of nursing Nursing Information System
 interventions

RN, registered nurse; ICD-9-CM, International Classification of
Diseases 9th Revision Clinical Modification.

Table 2: Results of Analysis for Heart Failure Cost

 Variables Significant in Final
Original Variables Entered Model ([chi square] and p-value)

Patient characteristics None significant
 Age
 Gender
 Ethnicity
 Marital status
 Religion
 Occupation
Clinical conditions
 Primary diagnosis None significant
 Heart failure without
 hypertension
 Acute myocardial infarction
 Other cardiac conditions
 Conduction disorders
 Peripheral vascular disease
 Noncardiac circulatory
 diseases
 Comorbidities Only one significant
 30 analyzed according to Deficiency anemia: [chi square] =
 Elixhauser method 3.87, p = .0491
 Severity of illness Significant
 Severe IV (19.4%)
 Major III (.51.4%) Severity of illness: [chi square]
 Moderate II (26.8%) = 10.11, p = .0177
 Mild I (2.4%)
Nursing unit characteristics Only two significant
 Number of units resided on Number of units resided in during
 during hospitalization hospitalization: [chi square] =
 24.85, p [less than or equal
 to] .0001
 Percentage of time in intensive
 care unit (ICU)
 Average RN/patient ratio RN/patient dip proportion:
 [chi square] = 37.83, p [less
 than or equal to] .0001
 RN/patient dip proportion
Treatments
 Medical
 Total number of medical Total number of medical
 procedures procedures: [chi square] =
 37.83, p < .0001 = 79.52,
 P<.0001
 Nine types of medical Three of nine groups significant
 procedures Noninvasive heart failure
 diagnostic: [chi square] =
 13.29, p = .0003
 Invasive heart failure diagnostic:
 [chi square] = 7.19, p =.0073
 Invasive cardiovascular therapy:
 [chi square] = 108.32, p<.0001
 Pharmacy
 Total number of different Total number of different
 medications medications: [chi square] =
 29.30, p [less than or equal
 to] .0001
 74 different medications used 12 medications, p [less than or
 equal to] .05
Nursing
 Total number of different Total number of different
 interventions interventions: [chi square to]
 = 39.05, P<.0001
 53 different interventions used 10 interventions, p [less than or
 equal to] .05

Table 3: Cost of Significant Clinical Conditions, Nursing Unit
Characteristics, Medical and Pharmacy Treatments * in the Final Cost
Model for Heart Failure: (N= 1,435, Median Hospital Cost = $10,454)

 Estimate

Clinical conditions
 Comorbidity
 Deficiency anemia 0.0500
 Severity of illness % of sample
 Severe 19.4 -0.0318
 Major 51.4 -0.0062
 Moderate 26.8 -0.0840
 Minor 2.4 0.0000
Nursing unit characteristics
 RN/patient dip proportion 0.7076

 Number of units resided on % of sample
 during hospitalization
 5 or more 36.5 0.0255
 3 or 4 33.2 0.1546
 2 19.0 0.0920
 1 11.0 0.0000
Multidisciplinary treatments
 Medical
 Total number of medical 0.0579
 procedures
 Procedure type 1% of sample
 Noninvasive heart failure 54.0 0.0735
 diagnostic
 Invasive heart failure 35.0 0.0681
 diagnostic
 Invasive cardiovascular 60.0 0.4097
 therapeutic
 Pharmacy
 Total number of different 0.0170
 medications
 Individual medications % of sample
 Benzodiazepines 57.2 0.0813
 Misc. EENT drugs 64.3 0.1573
 EENT antibiotics 35.1 0.1167
 Antiemetics 18.8 0.0542
 Miscellaneous GI 55.5 -0.1021
 Cephalosporins 28.1 0.1410
 Potassium sparing diuretics 10.5 0.1015
 Skin antibiotics 8.2 0.1681
 Skin antifungals 8.1 0.1188
 Irrigating solutions 5.7 0.1796
 Vitamin K activity 4.9 0.1055
 Miscellaneous CNS agents 3.3 0.2346

 Proportional
 Change in Cost
 p ([dagger])

Clinical conditions
 Comorbidity
 Deficiency anemia .0483 1.051
 Severity of illness Overall p =.01 77
 Severe .6300 0.969
 Major .9187 0.994
 Moderate .1699 0.919
 Minor
Nursing unit characteristics
 RN/patient dip proportion <.0001 1.152 per .2
 increments
 Number of units resided on Overall p -.0001
 during hospitalization
 5 or more .5977 1.026
 3 or 4 .0001 1.167
 2 .0015 1.096
 1
Multidisciplinary treatments
 Medical
 Total number of medical <.0001 1.060
 procedures
 Procedure type
 Noninvasive heart failure .0002 1.076
 diagnostic
 Invasive heart failure .0067 1.070
 diagnostic
 Invasive cardiovascular <.0001 1.506
 therapeutic
 Pharmacy
 Total number of different <.0001 1.017
 medications
 Individual medications
 Benzodiazepines .0032 1.085
 Misc. EENT drugs .0001 1.170
 EENT antibiotics <.0001 1.124
 Antiemetics .0382 1.056
 Miscellaneous GI .0018 0.903
 Cephalosporins .0004 1.151
 Potassium sparing diuretics .0022 1.107
 Skin antibiotics <.0001 1.183
 Skin antifungals .0012 1.126
 Irrigating solutions .0002 1.197
 Vitamin K activity .0148 1.111
 Miscellaneous CNS agents .0036 1.264

 Change in Median Cost ([double
 dagger]) in Dollars per
 Hospitalization

Clinical conditions
 Comorbidity
 Deficiency anemia $536.00
 Severity of illness
 Severe -$327.22
 Major -$64.62
 Moderate -$842.29
 Minor --
Nursing unit characteristics
 RN/patient dip proportion $1,589.30

 Number of units resided on
 during hospitalization
 5 or more $270.01
 3 or 4 $1,747.86
 2 $1,007.43
 1 --
Multidisciplinary treatments
 Medical
 Total number of medical $623.17
 procedures
 Procedure type
 Noninvasive heart failure $797.33
 diagnostic
 Invasive heart failure $736.74
 diagnostic
 Invasive cardiovascular $5,293.69
 therapeutic
 Pharmacy
 Total number of different $179.24
 medications
 Individual medications
 Benzodiazepines $885.44
 Misc. EENT drugs $1,780.85
 EENT antibiotics $1,294.06
 Antiemetics $582.26
 Miscellaneous GI -$1,014.70
 Cephalosporins $1,583.04
 Potassium sparing diuretics $1,116.83
 Skin antibiotics $1,913.71
 Skin antifungals $1,318.75
 Irrigating solutions $2,056.76
 Vitamin K activity $1,163.56
 Miscellaneous CNS agents $2,764.14

* See Table 4 for nursing interventions.

([dagger]) The change in the log of hospital cost per unit change for
the corresponding predictor.

([double dagger]) The ratio (change in the log of hospital cost per
unit change for the corresponding predictor) minus one multiplied by
the median hospital cost. EENT, eyes, ears, nose, throat; GI,
gastrointestinal.

Table 4: Cost of Significant Nursing Interventions for Heart Failure
by Use Rate (N = 1,435, Median Hospital Cost = $10,4,54)

 Use Rate/
 Use Levels 24 Hours *

Total number of different nursing Count of
 interventions interventions
Type of nursing intervention
 (% receiving at least once during
 hospitalization)
Fluid Management (99.7%) Highest 25% 6.08
 Promotion of fluid balance and Next highest 25% 3.31
 prevention of complications Next lowest 25% 2.10
 resulting from abnormal or Lowest 25% 1.19
 undesired fluid levels. ([double
 dagger])
Routine Care (96.7%) Highest 25% 7.71
 Provision of care to the adult Next highest 25% 7.02
 patient newly admitted to an Next lowest 25% 5.98
 inpatient setting, such as call Lowest 25% 2.63
 light within reach, asking about
 sleep, giving AM care,
 observing
IV Therapy (94.5%) Top third 6.50
 Administration and monitoring of Middle third 3.02
 intravenous fluids and Lower third 1.32
 medications No use 0
Pressure Ulcer Care (89.3%) Top third 0.90
 Facilitation of healing in pressure Middle third 0.38
 ulcers Lower third 0.22
 No use 0
Discharge Planning (84.0%) Top third 1.23
 Preparation for moving a patient Middle third 0.91
 from one level of care to another Lower third 0.52
 within or outside the current No use 0
 health care agency
Oral Health Restoration (83.5%) Top third 2.16
 Promotion of healing for a patient Middle third 1.75
 who has an oral mucosa or Lower third 0.81
 dental lesion No use 0
Fall Prevention: adult (83.3%) Top third 3.89
 Instituting special precautions with Middle third 1.87
 patients at risk from falling Lower third 0.19
 No use 0
Bowel Management (76.9%) Top third 2.88
 Establishment and maintenance of Middle third 1.56
 a regular pattern of bowel Lower third 0.46
 elimination No use 0
Infection Protection (73.2%) Top third 3.08
 Prevention and early detection of Middle third 2.56
 infection in a patient at risk Lower third 1.03
 No use 0
Medication Management (10.8%) Top third 1.58
 Facilitation of safe and effective Middle third 0.40
 use of prescription and over-the- Lower third 0.13
 counter drugs No use 0

 Proportional
 Change in Cost Overall p

Total number of different nursing 1.027 <.0001
 interventions
Type of nursing intervention
 (% receiving at least once during
 hospitalization)
Fluid Management (99.7%) 0.917 .0176
 Promotion of fluid balance and 0.945
 prevention of complications 0.926
 resulting from abnormal or
 undesired fluid levels. ([double
 dagger])
Routine Care (96.7%) 0.925 .0108
 Provision of care to the adult 0.998
 patient newly admitted to an 0.920
 inpatient setting, such as call
 light within reach, asking about
 sleep, giving AM care,
 observing
IV Therapy (94.5%) 1.084 .0108
 Administration and monitoring of 0.997
 intravenous fluids and 1.052
 medications
Pressure Ulcer Care (89.3%) 0.786 <.0001
 Facilitation of healing in pressure 1.037
 ulcers 1.107

Discharge Planning (84.0%) 0.959 .0301
 Preparation for moving a patient 1.016
 from one level of care to another 0.944
 within or outside the current
 health care agency
Oral Health Restoration (83.5%) 1.019 <.0001
 Promotion of healing for a patient 0.893
 who has an oral mucosa or 0.903
 dental lesion
Fall Prevention: adult (83.3%) 1.002 .0001
 Instituting special precautions with 0.940
 patients at risk from falling 1.059

Bowel Management (76.9%) 0.960 .0040
 Establishment and maintenance of 0.925
 a regular pattern of bowel 1.023
 elimination
Infection Protection (73.2%) 0.939 .0038
 Prevention and early detection of 0.887
 infection in a patient at risk 0.943

Medication Management (10.8%) 0.806 <.0001
 Facilitation of safe and effective 1.007
 use of prescription and over-the- 1.220
 counter drugs

 Change in Median
 Cost ([dagger])
 P for use in Dollars per
 levels Hospitalization

Total number of different nursing $289.33
 interventions
Type of nursing intervention
 (% receiving at least once during
 hospitalization)
Fluid Management (99.7%) .0036 -$870.12
 Promotion of fluid balance and .0678 -$571.33
 prevention of complications .0046 -$772.83
 resulting from abnormal or --
 undesired fluid levels. ([double
 dagger])
Routine Care (96.7%) .1151 -$781.54
 Provision of care to the adult .9671 -$20.89
 patient newly admitted to an .0919 -$831.71
 inpatient setting, such as call --
 light within reach, asking about
 sleep, giving AM care,
 observing
IV Therapy (94.5%) .1687 $867.31
 Administration and monitoring of .9569 -$30.27
 intravenous fluids and .3400 $548.10
 medications --
Pressure Ulcer Care (89.3%) <.0001 -$2,232.30
 Facilitation of healing in pressure .4767 $390.80
 ulcers .0285 $1,115.67
 --
Discharge Planning (84.0%) .2645 -$424.97
 Preparation for moving a patient .6562 $165.43
 from one level of care to another .1039 -$581.20
 within or outside the current --
 health care agency
Oral Health Restoration (83.5%) .6029 $199.47
 Promotion of healing for a patient .0017 -$1,122.63
 who has an oral mucosa or .0058 -$1,015.65
 dental lesion --
Fall Prevention: adult (83.3%) .9675 $15.69
 Instituting special precautions with .0718 -$631.43
 patients at risk from falling .1000 $612.10
 --
Bowel Management (76.9%) .2290 -$418.95
 Establishment and maintenance of .0171 -$784.44
 a regular pattern of bowel .4215 $241.10
 elimination --
Infection Protection (73.2%) .0693 -$636.34
 Prevention and early detection of .0002 -$1,180.31
 infection in a patient at risk .0658 -$594.03
 --
Medication Management (10.8%) <.0001 -$2,026.69
 Facilitation of safe and effective .8803 $74.49
 use of prescription and over-the- .0001 $2,295.47
 counter drugs --

* The change in the log of hospital cost per unit change for the
corresponding predictor.

([dagger]) The ratio (change in the log of hospital cost per unit
change for the corresponding predictor) minus one multiplied by the
median hospital cost.

([double dagger]) Definitions from Dochterman and Bulechek (2004).
Nursing Interventions Classification (NIC), 4th edition, Mosby Inc.

Bold face indicates statistically significant P values (% [less than
or equal to] .05).
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Title Annotation:Hospital Use and Costs
Author:Titler, Marita G.; Jensen, Gwenneth A.; Dochterman, Joanne McCloskey; Xie, Xian-Jin; Kanak, Mary; Re
Publication:Health Services Research
Article Type:Report
Geographic Code:1USA
Date:Apr 1, 2008
Words:7635
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