 |
|
Hospital Nurse Staffing and Patient Mortality, Nurse Burnout, and Job Dissatisfaction
Linda H. Aiken, PhD,RN;
Sean P. Clarke, PhD,RN;
Douglas M. Sloane, PhD;
Julie Sochalski, PhD,RN;
Jeffrey H. Silber, MD,PhD
JAMA. 2002;288:1987-1993.
ABSTRACT
| |
Context The worsening hospital nurse shortage and recent California legislation mandating minimum hospital patient-to-nurse ratios demand an understanding of how nurse staffing levels affect patient outcomes and nurse retention in hospital practice.
Objective To determine the association between the patient-to-nurse ratio and patient mortality, failure-to-rescue (deaths following complications) among surgical patients, and factors related to nurse retention.
Design, Setting, and Participants Cross-sectional analyses of linked data from 10 184 staff nurses surveyed, 232 342 general, orthopedic, and vascular surgery patients discharged from the hospital between April 1, 1998, and November 30, 1999, and administrative data from 168 nonfederal adult general hospitals in Pennsylvania.
Main Outcome Measures Risk-adjusted patient mortality and failure-to-rescue within 30 days of admission, and nurse-reported job dissatisfaction and job-related burnout.
Results After adjusting for patient and hospital characteristics (size, teaching status, and technology), each additional patient per nurse was associated with a 7% (odds ratio [OR], 1.07; 95% confidence interval [CI], 1.03-1.12) increase in the likelihood of dying within 30 days of admission and a 7% (OR, 1.07; 95% CI, 1.02-1.11) increase in the odds of failure-to-rescue. After adjusting for nurse and hospital characteristics, each additional patient per nurse was associated with a 23% (OR, 1.23; 95% CI, 1.13-1.34) increase in the odds of burnout and a 15% (OR, 1.15; 95% CI, 1.07-1.25) increase in the odds of job dissatisfaction.
Conclusions In hospitals with high patient-to-nurse ratios, surgical patients experience higher risk-adjusted 30-day mortality and failure-to-rescue rates, and nurses are more likely to experience burnout and job dissatisfaction.
INTRODUCTION
The past decade has been a turbulent time for US hospitals and practicing nurses. News media have trumpeted urgent concerns about hospital understaffing and a growing hospital nurse shortage.1-3 Nurses nationwide consistently report that hospital nurse staffing levels are inadequate to provide safe and effective care.4-6 Physicians agree, citing inadequate nurse staffing as a major impediment to the provision of high-quality hospital care.7 The shortage of hospital nurses may be linked to unrealistic nurse workloads.8 Forty percent of hospital nurses have burnout levels that exceed the norms for health care workers.4 Job dissatisfaction among hospital nurses is 4 times greater than the average for all US workers, and 1 in 5 hospital nurses report that they intend to leave their current jobs within a year.4
In 1999, California passed legislation mandating patient-to-nurse ratios for its hospitals, which goes into effect in July 2003. The California legislation was motivated by an increasing hospital nursing shortage and the perception that lower nurse retention in hospital practice was related to burdensome workloads and high levels of job-related burnout and job dissatisfaction. Stakeholder groups advocated widely divergent minimum ratios. On medical and surgical units, recommended ratios ranged from 3 to 10 patients for each nurse.9-11 In early 2002, California's governor announced that hospitals must have at least 1 licensed nurse for every 6 medical and surgical patients by July 2003, a ratio that will move to 1 to 5 when the mandates are fully implemented.12
This study reports on findings from a comprehensive study of 168 hospitals and clarifies the impact of nurse staffing levels on patient outcomes and factors that influence nurse retention.13 Specifically, we examined whether risk-adjusted surgical mortality and rates of failure-to-rescue (deaths in surgical patients who develop serious complications) are lower in hospitals where nurses carry smaller patient loads. In addition, we ascertained the extent to which more favorable patient-to-nurse ratios are associated with lower burnout and higher job satisfaction among registered nurses. We also estimated excess surgical deaths associated with the different nurse staffing ratios vigorously debated in California. Finally, we estimated the impact of nurse staffing levels proposed in California on nurse burnout and dissatisfaction, 2 precursors of turnover.13 Our findings offer insights into how more generous registered nurse staffing might affect patient outcomes and inform current debates in many states regarding the merits of legislative actions to influence staffing levels.
METHODS
Patients, Data Sources, and Variables
Our study combines information about hospital staffing and organization obtained from nurse surveys with patient outcomes derived from hospital discharge abstracts and hospital characteristics drawn from administrative databases.14 The study protocol for linking anonymized nurse data and handling denominalized patient data was approved by the institutional review board of the University of Pennsylvania.
Hospitals
Data were collected on all 210 adult general hospitals in Pennsylvania. Information about hospital characteristics was derived from the 1999 American Hospital Association (AHA) Annual Survey and the 1999 Pennsylvania Department of Health Hospital Survey.15-16 Ultimately, 168 of the 210 acute care hospitals had discharge data for surgical patients in the targeted Diagnosis Related Groups (DRGs) during the study period, as well AHA data, and survey data from 10 or more staff nurses. Six of the excluded hospitals were Veterans Affairs hospitals, which do not report discharge data to the state. Twenty-six hospitals were excluded because their administrative or patient outcomes data could not be matched to our surveys because of missing variables, primarily because they reported their characteristics or patient data as aggregate multihospital entities. In 10 additional small hospitals, the majority of which had fewer than 50 beds, fewer than 10 nurses responded to the survey.
A nurse staffing measure was calculated as the mean patient load across all staff registered nurses who reported having responsibility for at least 1 but fewer than 20 patients on the last shift they worked, regardless of the specialty or shift (day, evening, night) worked. This measure of staffing is superior to those derived from administrative databases, which generally include registered nurse positions that do not involve inpatient acute care at the bedside. Staffing was measured across entire hospitals because there is no evidence that specialty-specific staffing offers advantages in the study of patient outcome17 and to reflect the fact that patients often receive nursing care in multiple specialty areas of a hospital. Direct measurement also avoided problems with missing data common to the AHA's Annual Survey of hospitals, which imputed staffing data in 1999 for 20% of Pennsylvania hospitals.
Three hospital characteristics were used as control variables: size, teaching status, and technology. Hospitals were grouped into 3 size categories: small ( 100 hospital beds), medium (101-250 hospital beds), and large ( 251 hospital beds). Teaching status was measured by the ratio of resident physicians and fellows to hospital beds, which has been suggested as superior to university affiliations and association memberships as an indicator of the intensity of teaching activity.18 Hospitals with no postgraduate trainees (nonteaching) were contrasted with those that had 1:4 or smaller trainee:bed ratios (minor teaching hospitals) and those with ratios that were higher than 1:4 (major teaching hospitals). Finally, hospitals with facilities for open heart surgery and/or major transplants were classified as high-technology hospitals and contrasted with other hospitals.19
Nurses and Nurse Outcomes
Surveys were mailed in the spring of 1999 to a 50% random sample of registered nurses who were on the Pennsylvania Board of Nursing rolls and resided in the state. The response rate was 52%, which compares favorably with rates seen in other voluntary surveys of health professionals.20 Roughly one third of the nurses who responded worked in hospitals and included the sample of 10 184 nurses described here. No special recruiting methods or inducements were used. Demographic characteristics of the respondents matched the profile for Pennsylvania nurses in the National Sample Survey of Registered Nurses.21 Nurses employed in hospitals were asked to use a list to identify the hospital in which they worked, and then were queried about their demographic characteristics, work history, workload, job satisfaction, and feelings of job-related burnout. Questionnaires were returned by nurses employed at each of the 210 Pennsylvania hospitals providing adult acute care. To obtain reliable hospital-level estimates of nurse staffing (the ratio of patients to nurses in each hospital), attention was restricted to registered nurses holding staff nurse positions involving direct patient care and to hospitals from which at least 10 such nurses returned questionnaires. In 80% of the 168 hospitals in the final sample, 20 or more nurses provided responses to our questionnaire. There were more than 50 nurse respondents from half of the hospitals. We examined 2 nurse job outcomes in relation to staffing: job satisfaction (rated on a 4-point scale from very dissatisfied to very satisfied) and burnout (measured with the Emotional Exhaustion scale of the Maslach Burnout Inventory, a standardized tool).22-23
Patients and Patient Outcomes
Discharge abstracts representing all admissions to nonfederal hospitals in Pennsylvania from 1998 to 1999 were obtained from the Pennsylvania Health Care Cost Containment Council. These discharge abstracts were merged with Pennsylvania vital statistics records to identify patients who died within 30 days of hospital admission to control for timing of discharge as a possible source of variation in hospital outcomes. We examined outcomes for 232 342 patients between the ages of 20 and 85 years who underwent general surgical, orthopedic, or vascular procedures in the 168 hospitals from April 1, 1998, to November 30, 1999. Surgical discharges were selected for study because of the availability of well-validated risk adjustment models.24-29 The number of patients discharged from the study hospitals ranged from 75 to 7746. Only the first hospital admission for any of the DRGs listed in the BOX for any patient during the study period was included in the analyses.
In addition to 30-day mortality, we examined failure-to-rescue (deaths within 30 days of admission among patients who experienced complications).24-29 Complications were identified by scanning discharge abstracts for International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes in the secondary diagnosis and procedure fields that were suggestive of 39 different clinical events. Distinguishing complications from previously existing comorbidities involved the use of rules developed by expert consensus and previous empirical work, as well as examination of discharge records for each patient's hospitalizations 90 days before the surgery of interest for overlap in secondary diagnosis codes.27-29 Examples of complications included aspiration pneumonia and hypotension/shock. Patients who died postoperatively were assumed to have developed a complication even if no complication codes were identified in their discharge abstracts.
| Box. Surgical Patient Diagnosis Related Groups Included in the Analyses of Mortality and Failure-to-Rescue
General Surgery
146-155, 157-162, 164-167, 170, 171, 191-201, 257-268, 285-293, 493, and 494
Orthopedic Surgery
209-211, 213, 216-219, 223-234, 471, 491, and 496-503
Vascular Surgery
110-114, 119, and 120
RETURN TO TEXT |
|
Risk adjustment of mortality and failure-to-rescue for patient characteristics and comorbidities was accomplished by using 133 variables, including age, sex, surgery types, and dummy variables indicating the presence of chronic preexisting health conditions reflected in the ICD-9-CM codes in the discharge abstracts (eg, diabetes mellitus), as well as a series of interaction terms. The final set of control variables was determined by a selection process that paralleled an approach used and reported previously.27-29 The C statistic (area under the receiver operating characteristic curve) for the mortality risk adjustment model was 0.89.30
Data Analysis
Descriptive data show how patients and nurses in our sample were distributed across the various categories of hospitals defined by staffing levels and other characteristics. Logistic regression models were used to estimate the effects of staffing on the nurse outcomes (job dissatisfaction and burnout) and 2 patient outcomes (mortality and failure-to-rescue). We computed the odds of nurses being moderately or very dissatisfied with their current positions and reporting a level of emotional exhaustion (burnout) above published norms for medical workers and of patients experiencing mortality and failure-to-rescue under different levels of registered nurse staffing, before and after control for individual characteristics and hospital variables. For nurse outcomes, we adjusted for sex, years of experience in nursing, education (baccalaureate degree or above vs diploma or associate degree as highest credential in nursing), and nursing specialty. For analyses of patient outcomes, we controlled for the variables in our risk adjustment model, specifically, demographic characteristics of patients, nature of the hospital admission, comorbidities, and relevant interaction terms. For analyses of both patient and nurse outcomes, we adjusted for hospital size, teaching status, and technology.
All logistic regression models were estimated by using Huber-White (robust) procedures to account for the clustering of patients within hospitals and adjust the SEs of the parameter estimates appropriately.31-32 Model calibration was assessed with the Hosmer-Lemeshow statistic.33 We used direct standardization to illustrate the magnitude of the effect of staffing by estimating the difference in the numbers of deaths and episodes of failure-to-rescue under different staffing scenarios. Using all patients in the study and using the final fully-adjusted model, we estimated the probability of death and failure-to-rescue for each patient under various patient-to-nurse ratios (ie, 4, 6, and 8 patients per nurse) with all other patient characteristics unchanged. We then calculated the differences in total deaths under the different scenarios.34 Confidence intervals (CIs) for these direct standardization estimates were derived with the  method described by Agresti.35 All analyses were performed using STATA version 7.0 (STATA Corp, College Station, Tex), and P<.05 was considered statistically significant in all analyses.
RESULTS
Characteristics of Hospitals, Nurses, and Patients
Distributions of hospitals with various characteristics, distributions of nurses surveyed, and patients whose outcomes were studied are shown in Table 1. Fifty percent of the hospitals had patient-to-nurse ratios that were 5:1 or lower, and those hospitals discharged 65.9% of the patients in the study and employed 64.4% of the nurses we surveyed. Hospitals with more than 250 beds accounted for a disproportionate share of both patients and nurses (45.5% and 43.4%, respectively). Although high-technology hospitals accounted for only 28.0% of the institutions studied, more than half (55.3%) of the patients discharged and 53.8% of nurses surveyed were from high-technology hospitals. A majority of the patients studied and nurses surveyed were drawn from the 61 hospitals (36.3%) that reported postgraduate medical trainees in 1999.
|
|
|
|
Table 1. Study Hospitals, Surgical Patients Studied, and Nurse Respondents in Hospitals*
|
|
|
As shown in Table 2, 94.1% of the nurses were women and 39.6% held a baccalaureate degree or higher. The mean (SD) work experience in nursing was 13.8 years (9.8). Thirty-one percent of the nurses in the sample worked on medical and surgical general units, while 19.6% and 9.8% worked in intensive care and perioperative settings, respectively. Forty-three percent of the nurses had high burnout scores and a similar proportion were dissatisfied with their current jobs.
|
|
|
|
Table 2. Characteristics of Nurses (N = 10 184) in the Study Hospitals*
|
|
|
Of the 232 342 patients studied, 53 813 (23.2%) experienced a major complication not present on admission and 4535 (2.0%) died within 30 days of admission. The death rate among patients with complications was 8.4%. The surgical case types and clinical characteristics of the patient cohort are shown in Table 3. Slightly more than half of patients (51.2%) were classified in an orthopedic surgery DRG, with the next largest group of patients (36.4%) undergoing digestive tract and hepatobiliary surgeries. Chronic medical conditions, with the exception of hypertension, were relatively uncommon among these patients. Patients who experienced complications and were included in our analyses of failure-to-rescue were similar to the broader group of patients in our mortality analyses with respect to their comorbidities, but orthopedic surgery patients were less prominently represented among patients with complications than in the overall sample.
|
|
|
|
Table 3. Characteristics of the Surgical Patients Included in Analyses of Mortality and Failure-to-Rescue*
|
|
|
Staffing and Job Satisfaction and Burnout
Higher emotional exhaustion and greater job dissatisfaction in nurses were strongly and significantly associated with patient-to-nurse ratios. Table 4 shows odds ratios (ORs) indicating how much more likely nurses in hospitals with higher patient-to-nurse ratios were to exhibit burnout scores above published norms and to be dissatisfied with their jobs. Controlling for nurse and hospital characteristics resulted in a slight increase in these ratios, which in both cases indicated a pronounced effect of staffing. The final adjusted ORs indicated that an increase of 1 patient per nurse to a hospital's staffing level increased burnout and job dissatisfaction by factors of 1.23 (95% CI, 1.13-1.34) and 1.15 (95% CI, 1.07-1.25), respectively, or by 23% and 15%. This implies that nurses in hospitals with 8:1 patient-to-nurse ratios would be 2.29 times as likely as nurses with 4:1 patient-to-nurse ratios to show high emotional exhaustion (ie, 1.23 to the 4th power for 4 additional patients per nurse = 2.29) and 1.75 times as likely to be dissatisfied with their jobs (ie, 1.15 to the 4th power for 4 additional patients per nurse = 1.75). Our data further indicate that, although 43% of nurses who report high burnout and are dissatisfied with their jobs intend to leave their current job within the next 12 months, only 11% of the nurses who are not burned out and who remain satisfied with their jobs intend to leave.
|
|
|
|
Table 4. Patient-to-Nurse Ratios With High Emotional Exhaustion and Job Dissatisfaction Among Staff Nurses and With Patient Mortality and Failure-to-Rescue*
|
|
|
Staffing and Patient Mortality and Failure-to-Rescue
Among the surgical patients studied, there was a pronounced effect of nurse staffing on both mortality and mortality following complications. Table 4 also shows the relationship between nurse staffing and patient mortality and failure-to-rescue (mortality following complications) when other factors were ignored, after patient characteristics were controlled, and after patient characteristics and other hospital characteristics (size, teaching status, and technology) were controlled. Although the ORs reflecting the nurse staffing effect were somewhat diminished by controlling for patient and hospital characteristics, they remained sizable and significant for both mortality and failure-to-rescue (1.07; 95% CI, 1.03-1.12 and 1.07; 95% CI, 1.02-1.11, respectively). An OR of 1.07 implies that the odds of patient mortality increased by 7% for every additional patient in the average nurse's workload in the hospital and that the difference from 4 to 6 and from 4 to 8 patients per nurse would be accompanied by 14% and 31% increases in mortality, respectively (ie, 1.07 to the 2nd power = 1.14 and 1.07 to the 4th power = 1.31).
These effects imply that, all else being equal, substantial decreases in mortality rates could result from increasing registered nurse staffing, especially for patients who develop complications. Direct standardization techniques were used to predict excess deaths in all patients and in patients with complications that would be expected if the patient-to-nurse ratio for all patients in the study were at various levels that figure prominently in the California staffing mandate debates. If the staffing ratio in all hospitals was 6 patients per nurse rather than 4 patients per nurse, we would expect 2.3 (95% CI, 1.1-3.5) additional deaths per 1000 patients and 8.7 (95% CI, 3.9-13.5) additional deaths per 1000 patients with complications. If the staffing ratio in all hospitals was 8 patients per nurse rather than 6 patients per nurse, we would expect 2.6 (95% CI, 1.2-4.0) additional deaths per 1000 patients and 9.5 (95% CI, 3.8-15.2) additional deaths per 1000 patients with complications. Staffing hospitals uniformly at 8 vs 4 patients per nurse would be expected to entail 5.0 (95% CI, 2.4-7.6) excess deaths per 1000 patients and 18.2 (95% CI, 7.7-28.7) excess deaths per 1000 complicated patients. We were unable to estimate excess deaths or failures associated with a ratio of 10 patients per nurse (one of the levels proposed in California) because there were so few hospitals in our sample staffed at that level.
COMMENT
Registered nurses constitute an around-the-clock surveillance system in hospitals for early detection and prompt intervention when patients' conditions deteriorate. The effectiveness of nurse surveillance is influenced by the number of registered nurses available to assess patients on an ongoing basis. Thus, it is not surprising that we found nurse staffing ratios to be important in explaining variation in hospital mortality. Numerous studies have reported an association between more registered nurses and lower hospital mortality, but often as a by-product of analyses focusing directly on some other aspect of hospital resources such as ownership, teaching status, or anesthesiologist direction.19, 27, 36-42 Therefore, a simple search for literature dealing with the relationship between nurse staffing and patient outcomes yields only a fraction of the studies that have relevant findings. The relative inaccessibility of this evidence base might account for the influential Audit Commission in England concluding recently that there is no evidence that more favorable patient-to-nurse ratios result in better patient outcomes.43
Our results suggest that the California hospital nurse staffing legislation represents a credible approach to reducing mortality and increasing nurse retention in hospital practice, if it can be successfully implemented. Moreover, our findings suggest that California officials were wise to reject ratios favored by hospital stakeholder groups of 10 patients to each nurse on medical and surgical general units in favor of more generous staffing requirements of 5 to 6 patients per nurse. Our results do not directly indicate how many nurses are needed to care for patients or whether there is some maximum ratio of patients per nurse above which hospitals should not venture. Our major point is that there are detectable differences in risk-adjusted mortality and failure-to-rescue rates across hospitals with different registered nurse staffing ratios.
In our sample of 168 Pennsylvania hospitals in which the mean patient-to-nurse ratio ranged from 4:1 to 8:1, 4535 of the 232 342 surgical patients with the clinical characteristics we selected died within 30 days of being admitted. Our results imply that had the patient-to-nurse ratio across all Pennsylvania hospitals been 4:1, possibly 4000 of these patients may have died, and had it been 8:1, more than 5000 of them may have died. While this difference of 1000 deaths in Pennsylvania hospitals across the 2 staffing scenarios is approximate, it represents a conservative estimate of preventable deaths attributable to nurse staffing in the state. Our sample of patients represents only about half of all surgical cases in these hospitals, and other patients admitted to these hospitals are at risk of dying and similarly subject to the effects of staffing. Moreover, in California, which has nearly twice as many acute care hospitals and discharges and an overall inpatient mortality rate higher than in our sample in Pennsylvania (2.3% vs 2.0%), it would be reasonable to expect that the difference of 4 fewer patients per nurse might result in 2000 or more preventable deaths throughout a similar period.
Our results further indicate that nurses in hospitals with the highest patient-to-nurse ratios are more than twice as likely to experience job-related burnout and almost twice as likely to be dissatisfied with their jobs compared with nurses in the hospitals with the lowest ratios. This effect of staffing on job satisfaction and burnout suggests that improvements in nurse staffing in California hospitals resulting from the new legislation could be accompanied by declines in nurse turnover. We found that burnout and dissatisfaction predict nurses' intentions to leave their current jobs within a year. Although we do not know how many of the nurses who indicated intentions to leave their jobs actually did so, it seems reasonable to assume that the 4-fold difference in intentions across these 2 groups translated to at least a similar difference in nurse resignations. If recently published estimates of the costs of replacing a hospital medical and surgical general unit and a specialty nurse of $42 000 and $64 000, respectively, are correct, improving staffing may not only save patient lives and decrease nurse turnover but also reduce hospital costs.44
Additional analyses indicate that our conclusions about the effects of staffing and the size of these effects are similar under a variety of specifications. We allowed the effect of nurse staffing to be nonlinear (using a quadratic term) and vary in size across staffing levels (using dummy variables and interaction terms) and found no evidence in this sample of hospitals that additional registered nurse staffing has different effects at differing staffing levels. Limiting our analyses to general and orthopedic surgery patients and eliminating vascular surgery patients (who have higher mortality and complication rates) did not affect our conclusions and effect-size estimates. Also, our findings were not changed by restricting attention to inpatient deaths vs deaths within 30 days of admission. Results were unaffected by restricting analyses to patients who were discharged after our staffing measures were obtained, rather than to the patients who were discharged from 9 months before to 9 months following the nurse surveys that produced our staffing measures. They were also unchanged by restricting the sample of nurses from which we derived our staffing measures to medical and surgical nurses, as opposed to all staff nurses. Finally, they were neither altered by adjusting for patient-to-licensed practical nurse ratios and patient-to-unlicensed assistive personnel ratios (neither of which were related to patient outcomes) nor affected by excluding the hospitals in our sample with smaller numbers of patients or nurses.
One limitation of this study is the potential for response bias, given a 52% response rate. We find no evidence that the nurses in our sample were disproportionately dissatisfied with their work relative to Pennsylvania staff nurses from the National Sample Survey of Registered Nurses (a national probability-based sample survey performed in 2000).21 Furthermore, with respect to demographic characteristics (sex, age, and education) included in both surveys, our sample of nurses also closely resembles those participating in the National Sample Survey of Registered Nurses. We are confident that these results are not specific to this particular sample of nurses. Ultimately, longitudinal data sets will be needed to exclude the possibility that low hospital nurse staffing is the consequence, rather than the cause, of poor patient and nurse outcomes.
Our findings have important implications for 2 pressing issues: patient safety and the hospital nurse shortage. Our results document sizable and significant effects of registered nurse staffing on preventable deaths. The association of nurse staffing levels with the rescue of patients with life-threatening conditions suggests that nurses contribute importantly to surveillance, early detection, and timely interventions that save lives. The benefits of improved registered nurse staffing also extend to the larger numbers of hospitalized patients who are not at high risk for mortality but nevertheless are vulnerable to a wide range of unfavorable outcomes. Improving nurse staffing levels may reduce alarming turnover rates in hospitals by reducing burnout and job dissatisfaction, major precursors of job resignation. When taken together, the impacts of staffing on patient and nurse outcomes suggest that by investing in registered nurse staffing, hospitals may avert both preventable mortality and low nurse retention in hospital practice.
AUTHOR INFORMATION
Author Contributions: Study concept and design: Aiken, Clarke, Sloane, Sochalski, Silber.
Acquisition of data: Aiken, Clarke, Sochalski, Silber.
Analysis and interpretation of data: Aiken, Clarke, Sloane, Silber.
Drafting of the manuscript: Aiken, Clarke, Sloane, Silber.
Critical revision of the manuscript for important intellectual content: Aiken, Clarke, Sloane, Sochalski, Silber.
Statistical expertise: Clarke, Sloane, Silber.
Obtained funding: Aiken, Sloane, Sochalski.
Administrative, technical, or material support: Aiken, Clarke, Sochalski, Silber.
Study supervision: Aiken, Clarke, Silber.
Funding/Support: This study was supported by grant R01 NR04513 from the National Institute of Nursing Research, National Institutes of Health.
Acknowledgment: We thank Paul Allison, PhD, from the University of Pennsylvania for statistical consultation, and Xuemei Zhang, MS, Wei Chen, MS, and Orit Even-Shoshan, MS, from the Center for Outcomes Research at the Children's Hospital of Philadelphia for their assistance.
Corresponding Author and Reprints: Linda H. Aiken, PhD, RN, Center for Health Outcomes and Policy Research, University of Pennsylvania, 420 Guardian Dr, Philadelphia, PA 19104-6096 (e-mail: laiken{at}nursing.upenn.edu).
Author Affiliations: Center for Health Outcomes and Policy Research, School of Nursing (Drs Aiken, Clarke, Sloane, and Sochalski), Leonard Davis Institute of Health Economics (Drs Aiken, Clarke, Sochalski, and Silber), Department of Sociology (Dr Aiken), Population Studies Center (Drs Aiken, Sloane, and Sochalski), and Departments of Pediatrics and Anesthesia, School of Medicine (Dr Silber), University of Pennsylvania, Philadelphia; and Center for Outcomes Research, Children's Hospital of Philadelphia, Philadelphia, Pa (Dr Silber).
REFERENCES
| |
1. Stolberg SG. Patient deaths tied to lack of nurses. New York Times. August 8, 2002:A18.
2. Parker-Pope T. How to lessen impact of nursing shortage on your hospital stay. Wall Street Journal. March 2, 2001:B1.
3. Trafford A. Second opinion: less care for patients. Washington Post. August 20, 2002:HE01.
4. Aiken LH, Clarke SP, Sloane DM, et al. Nurses' reports on hospital care in five countries. Health Aff (Millwood). 2001;20:43-53.
FREE FULL TEXT
5. Henry J. Kaiser Family Foundation. Survey of physicians and nurses. Available at: http://www.kff.org/content/1999/1503. Accessed March 22, 2002.
6. Shindul-Rothschild J, Berry D, Long-Middleton E. Where have all the nurses gone? final results of our Patient Care Survey. Am J Nurs. 1996;96:25-39.
PUBMED
7. Commonwealth Fund. Doctors in five countries see decline in health care quality. Commonwealth Fund Quarterly. 2000;6:1-4.
8. Joint Commission on Accreditation of Healthcare Organizations. Health care at the crossroads: strategies for addressing the evolving nursing crisis, 2002. Available at: http://www.jcaho.org/news+room/news+release+archives/health+care+at+the+crossroads.pdf. Accessed September 17, 2002.
9. Spetz J. What should we expect from California's minimum nurse staffing legislation. J Nurs Adm. 2001;31:132-140.
ISI
| PUBMED
10. Seago JA. The California experiment: alternatives for minimum nurse-to-patient ratios. J Nurs Adm. 2002;32:48-58.
FULL TEXT
|
ISI
| PUBMED
11. Friedman L. Nurse ratios are still too high. San Francisco Chronicle. January 30, 2002:A19.
12. Governor Gray Davis announces proposed nurse-to-patient ratios [press release]. Sacramento, Calif: Office of the Governor; January 22, 2002.
13. Lake ET. Advances in understanding and predicting nurse turnover. Res Sociol Health Care. 1998;15:147-171.
14. Aiken LH, Clarke SP, Sloane DM. Hospital staffing, organizational support, and quality of care: cross-national findings. Int J Qual Health Care. 2002;14:5-13.
FREE FULL TEXT
15. AHA Annual Survey Database. 1999 ed. Chicago, Ill: American Hospital Association; 1999.
16. Commonwealth of Pennsylvania. Hospital Questionnaire: Reporting Period July 1, 1998-June 30, 1999. Harrisburg, Pa: Department of Health, Division of Statistics.
17. Needleman J, Buerhaus PI, Mattke S, Stewart M, Zelevinsky K. Nurse staffing and patient outcomes in hospitals. Available at: http://bhpr.hrsa.gov/nursing/staffstudy.htm. Accessed August 6, 2002.
18. Ayanian JZ, Weissman JS, Chasan-Taber S, Epstein AM. Quality of care for two common illnesses in teaching and nonteaching hospitals. Health Aff (Millwood). 1998;17:194-205.
ABSTRACT
19. Hartz AJ, Krakauer H, Kuhn EM, et al. Hospital characteristics and mortality rates. N Engl J Med. 1989;321:1720-1725.
ABSTRACT
20. Asch DA, Jedrziewski MK, Christakis NA. Response rates to mail surveys published in medical journals. J Clin Epidemiol. 1997;50:1129-1136.
FULL TEXT
|
ISI
| PUBMED
21. The Registered Nurse Population. Rockville, Md: US Dept of Health and Human Services; 1996.
22. Maslach C, Jackson SE. Burnout in health professions: a social psychological analysis. In: Sanders GS, Suls J, eds. Social Psychology of Health and Illness. Hillsdale, NJ: Lawrence Erlbaum Associates; 1982:227-251.
23. Maslach C, Jackson SE. Maslach Burnout Inventory Manual. 2nd ed. Palo Alto, Calif: Consulting Psychologists Press; 1986.
24. Silber JH, Williams SV, Krakauer H, Schwartz JS. Hospital and patient characteristics associated with death after surgery: a study of adverse occurrence and failure to rescue. Med Care. 1992;30:615-629.
ISI
| PUBMED
25. Silber JH, Rosenbaum PR, Ross RN. Comparing the contributions of groups of predictors: which outcomes vary with hospital rather than patient characteristics? J Am Stat Assoc. 1995;90:7-18.
FULL TEXT
|
ISI
26. Silber JH, Rosenbaum PR, Schwartz JS, Ross RN, Williams SV. Evaluation of the complication rate as a measure of quality of care in coronary artery bypass graft surgery. JAMA. 1995;274:317-323.
FREE FULL TEXT
27. Silber JH, Kennedy SK, Even-Shoshan O, et al. Anesthesiologist direction and patient outcomes. Anesthesiology. 2000;93:152-163.
FULL TEXT
|
ISI
| PUBMED
28. Silber JH, Rosenbaum PR, Trudeau ME, et al. Multivariate matching and bias reduction in the surgical outcomes study. Med Care. 2001;39:1048-1064.
FULL TEXT
|
ISI
| PUBMED
29. Silber JH, Kennedy SK, Even-Shoshan O, et al. Anesthesiologist board certification and patient outcomes. Anesthesiology. 2002;96:1044-1052.
FULL TEXT
|
ISI
| PUBMED
30. Hanley JA, McNeil BJ. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology. 1982;143:29-36.
FREE FULL TEXT
31. Huber PJ. The Behavior of Maximum Likelihood Estimates Under Non-standard Conditions: Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability. Berkeley: University of California Press; 1967:221-233.
32. White H. Maximum likelihood estimation of misspecified models. Econometrica. 1982;50:1-25.
FULL TEXT
|
ISI
33. Hosmer DW, Lemeshow S. Applied Logistic Regression. New York, NY: John Wiley & Sons Inc; 1989.
34. Bishop YM, Fienberg SE, Holland PW. Discrete Multivariate Analysis: Theory and Practice. Cambridge, Mass: MIT Press; 1975.
35. Agresti A. Categorical Data Analysis. New York, NY: John Wiley & Sons Inc; 1990.
36. Shortell SM, Hughes EFX. The effects of regulation, competition, and ownership on mortality rates among hospital inpatients. N Engl J Med. 1988;318:1100-1107.
ABSTRACT
37. Institute of Medicine. Nursing Staff in Hospitals and Nursing Homes: Is It Adequate? Washington, DC: National Academy Press; 1996.
38. Aiken LH, Sloane DM, Lake ET, Sochalski J, Weber AL. Organization and outcomes of inpatient AIDS care. Med Care. 1999;37:760-772.
FULL TEXT
|
ISI
| PUBMED
39. Mitchell PH, Shortell SM. Adverse outcomes and variations in organization of care delivery. Med Care. 1997;35(suppl 11):NS19-NS32.
40. Moses LE, Mosteller F. Institutional differences in postoperative death rates. JAMA. 1968;203:492-494.
FREE FULL TEXT
41. Pronovost PJ, Jenckes MW, Dorman T, et al. Organizational characteristics of intensive care units related to outcomes of abdominal aortic surgery. JAMA. 1999;281:1310-1317.
FREE FULL TEXT
42. Needleman J, Buerhaus P, Mattke S, Stewart M, Zelevinsky K. Nurse-staffing levels and the quality of care in hospitals. N Engl J Med. 2002;346:1715-1722.
FREE FULL TEXT
43. Audit Commission. Acute Hospital Portfolio: Review of National Findings: Ward Staffing. London, England: The Audit Commission; 2001:3.
44. Nursing Executive Committee. Reversing the Flight of Talent: Nursing Retention in an Era of Gathering Shortage. Washington, DC: Advisory Board Co; 2000.
 CiteULike  Connotea  Del.icio.us  Digg  Reddit  Technorati
What's this?
RELATED LETTERS
Nursing Burnout and Patient Safety
Deirdre Davis
JAMA. 2003;289(5):549.
EXTRACT
| FULL TEXT
Nursing Burnout and Patient Safety
Elizabeth E. Hand
JAMA. 2003;289(5):549-550.
EXTRACT
| FULL TEXT
Nurses' Working Conditions and the Nursing Shortage
Brian Cromer
JAMA. 2003;289(13):1633.
EXTRACT
| FULL TEXT
RELATED ARTICLE
Meeting the Challenge of Nursing and the Nation's Health
Edward O'Neil and Jean Ann Seago
JAMA. 2002;288(16):2040-2041.
EXTRACT
| FULL TEXT
THIS ARTICLE HAS BEEN CITED BY OTHER ARTICLES
|
Implementation of a Rapid Response Team: A Success Story
Scott and Elliott
Crit Care Nurse 2009;29:66-75.
FULL TEXT
The Nursing Practice Environment and Nurse-Perceived Quality of Geriatric Care in Hospitals
Kim et al.
West J Nurs Res 2009;31:480-495.
ABSTRACT
Measuring Efficiency: The Association Of Hospital Costs And Quality Of Care
Jha et al.
Health Aff (Millwood) 2009;28:897-906.
ABSTRACT
| FULL TEXT
Medication errors in critical care: risk factors, prevention and disclosure
Camire et al.
CMAJ 2009;180:936-943.
FULL TEXT
Factors Associated With Work Stress and Professional Satisfaction in Oncology Staff
Dougherty et al.
AM J HOSP PALLIAT CARE 2009;26:105-111.
ABSTRACT
Rural Nurse Cultural Self-Efficacy and Job Satisfaction
Molinari and Monserud
J Transcult Nurs 2009;20:211-218.
ABSTRACT
The Effect of Variations in Nurse Staffing on Patient Length of Stay in the Acute Care Setting
Tschannen and Kalisch
West J Nurs Res 2009;31:153-170.
ABSTRACT
Can State Databases Be Used to Develop a National, Standardized Hospital Nurse Staffing Database?
Unruh et al.
West J Nurs Res 2009;31:66-88.
ABSTRACT
Toward a New Method for Identifying Facilities and Communities With Critical Shortages of Nurses
Wing et al.
Policy Politics Nursing Practice 2009;10:28-39.
ABSTRACT
Hospital Teaching Intensity, Patient Race, and Surgical Outcomes
Silber et al.
Arch Surg 2009;144:113-120.
ABSTRACT
| FULL TEXT
Intensive Care Units, Communication Between Nurses and Physicians, and Patients' Outcomes
Manojlovich et al.
Am J Crit Care 2009;18:21-30.
ABSTRACT
| FULL TEXT
Effect of Nurse Staffing and Education on the Outcomes of Surgical Patients With Comorbid Serious Mental Illness
Kutney-Lee and Aiken
Psychiatr. Serv. 2008;59:1466-1469.
ABSTRACT
| FULL TEXT
Current and Future State of the US Nursing Workforce
Buerhaus
JAMA 2008;300:2422-2424.
FULL TEXT
Unit Characteristics and Patient Satisfaction: A Multilevel Model
Seago
Policy Politics Nursing Practice 2008;9:230-240.
ABSTRACT
Overcrowding in Hospital Wards as a Predictor of Antidepressant Treatment Among Hospital Staff
Virtanen et al.
Am. J. Psychiatry 2008;165:1482-1486.
ABSTRACT
| FULL TEXT
Patients' Perception of Hospital Care in the United States
Jha et al.
NEJM 2008;359:1921-1931.
ABSTRACT
| FULL TEXT
Leadership Agenda for Change Toward Healthy Work Environments in Acute and Critical Care
Shirey and Fisher
Crit Care Nurse 2008;28:66-78.
FULL TEXT
The use of medical emergency teams in medical and surgical patients: impact of patient, nurse and organisational characteristics
Schmid-Mazzoccoli et al.
Qual Saf Health Care 2008;17:377-381.
ABSTRACT
| FULL TEXT
A Survey of Hospital Quality Improvement Activities
Cohen et al.
Med Care Res Rev 2008;65:571-595.
ABSTRACT
The Current State of Nursing Performance Measurement, Public Reporting, and Value-Based Purchasing
Kurtzman et al.
Policy Politics Nursing Practice 2008;9:181-191.
ABSTRACT
Nurses Working Outside of Nursing: Societal Trend or Workplace Crisis?
Black et al.
Policy Politics Nursing Practice 2008;9:143-157.
ABSTRACT
Nurse Burnout and Patient Safety Outcomes: Nurse Safety Perception Versus Reporting Behavior
Halbesleben et al.
West J Nurs Res 2008;30:560-577.
ABSTRACT
Rationing of nursing care and its relationship to patient outcomes: the Swiss extension of the International Hospital Outcomes Study
Schubert et al.
Int J Qual Health Care 2008;20:227-237.
ABSTRACT
| FULL TEXT
The Science of Quality Improvement
Fan and Needham
JAMA 2008;300:390-391.
FULL TEXT
Hospital mortality: when failure is not a good measure of success
Shojania and Forster
CMAJ 2008;179:153-157.
FULL TEXT
Impact of Cardiothoracic Resident Turnover on Mortality After Cardiac Surgery: A Dynamic Human Factor
Shuhaiber et al.
Ann. Thorac. Surg. 2008;86:123-131.
ABSTRACT
| FULL TEXT
Defining the Incidence of Cardiorespiratory Instability in Patients in Step-down Units Using an Electronic Integrated Monitoring System
Hravnak et al.
Arch Intern Med 2008;168:1300-1308.
ABSTRACT
| FULL TEXT
Comparison of Change in Quality of Care Between Safety-Net and Non-Safety-Net Hospitals
Werner et al.
JAMA 2008;299:2180-2187.
ABSTRACT
| FULL TEXT
Is What's Good for the Patient Good for the Hospital? Aligning Incentives and the Business Case for Nursing
Needleman
Policy Politics Nursing Practice 2008;9:80-87.
ABSTRACT
Nurses' Moral Sensitivity and Hospital Ethical Climate: a Literature Review
Schluter et al.
Nurs Ethics 2008;15:304-321.
ABSTRACT
Economics of Nursing
Aiken
Policy Politics Nursing Practice 2008;9:73-79.
ABSTRACT
Invited Article: Managing disruptive physician behavior: Impact on staff relationships and patient care
Rosenstein and O'Daniel
Neurology 2008;70:1564-1570.
ABSTRACT
| FULL TEXT
Medication errors involving patient-controlled analgesia
Hicks et al.
Am J Health Syst Pharm 2008;65:429-440.
ABSTRACT
| FULL TEXT
The Rural Nurse Work Environment and Structural Empowerment
Krebs et al.
Policy Politics Nursing Practice 2008;9:28-39.
ABSTRACT
The U.S. Presidential Election and Health Care Workforce Policy
McHugh et al.
Policy Politics Nursing Practice 2008;9:6-14.
ABSTRACT
The influence of teamwork culture on physician and nurse resignation rates in hospitals
Mohr et al.
Health Serv Manage Res 2008;21:23-31.
ABSTRACT
| FULL TEXT
Survival after liver transplantation in the United Kingdom and Ireland compared with the United States
Dawwas et al.
Gut 2007;56:1606-1613.
ABSTRACT
| FULL TEXT
Intensivist physician staffing and the process of care in academic medical centres
Kahn et al.
Qual Saf Health Care 2007;16:329-333.
ABSTRACT
| FULL TEXT
The Role for Leaders of Health Care Organizations in Patient Safety
Clarke et al.
American Journal of Medical Quality 2007;22:311-318.
ABSTRACT
Racial Segregation and Differential Outcomes in Hospital Care
Clarke et al.
West J Nurs Res 2007;29:739-757.
ABSTRACT
Origins of Difficulty in the Nurse-Patient Encounter
Macdonald
Nurs Ethics 2007;14:510-521.
ABSTRACT
Human Becoming and 80/20: An Innovative Professional Development Model for Nurses
Bournes and Ferguson-Pare
Nurs Sci Q 2007;20:237-253.
ABSTRACT
Critical Care Nurses' Work Environments Value of Excellence in Beacon Units and Magnet Organizations
Ulrich et al.
Crit Care Nurse 2007;27:68-77.
FULL TEXT
Factors Influencing the Volume-Outcome Relationship in Gastrectomies: A Population-Based Study
Smith et al.
Ann. Surg. Oncol. 2007;14:1846-1852.
ABSTRACT
| FULL TEXT
The Impact of Health Care Restructuring and Baccalaureate Entry to Practice on Nurses in New Brunswick
Rheaume et al.
Policy Politics Nursing Practice 2007;8:130-139.
ABSTRACT
Impact Of The Nurse Shortage On Hospital Patient Care: Comparative Perspectives
Buerhaus et al.
Health Aff (Millwood) 2007;26:853-862.
ABSTRACT
| FULL TEXT
Burnout Syndrome in Critical Care Nursing Staff
Poncet et al.
Am. J. Respir. Crit. Care Med. 2007;175:698-704.
ABSTRACT
| FULL TEXT
Performance Measurement of Nursing Care: State of the Science and the Current Consensus
Needleman et al.
Med Care Res Rev 2007;64:10S-43S.
ABSTRACT
Nursing Sensitive Databases: Their Existence, Challenges, and Importance
Alexander
Med Care Res Rev 2007;64:44S-63S.
ABSTRACT
Specifying and Standardizing Performance Measures for Use at a National Level: Implications for Nursing-sensitive Care Performance Measures
Riehle et al.
Med Care Res Rev 2007;64:64S-81S.
ABSTRACT
The Impact of Nursing Interventions: Overview of Effective Interventions, Outcomes, Measures, and Priorities for Future Research
Bolton et al.
Med Care Res Rev 2007;64:123S-143S.
ABSTRACT
Advancing the Science in the Measurement of Health Care Quality Influenced by Nurses
Naylor
Med Care Res Rev 2007;64:144S-169S.
ABSTRACT
A 10-Rights Framework for Patient Care Quality and Safety
Wakefield et al.
American Journal of Medical Quality 2007;22:103-111.
ABSTRACT
Extending Work Environment Research Into Home Health Settings
Flynn
West J Nurs Res 2007;29:200-212.
ABSTRACT
Rapid Response Team: Challenges, Solutions, Benefits
Thomas et al.
Crit Care Nurse 2007;27:20-27.
FULL TEXT
Long-Term Care Providers and Their Perceptions of the External Environment: Rural Versus Urban Differences
Weech-Maldonado et al.
Journal of Applied Gerontology 2007;26:78-94.
ABSTRACT
Rapid Response Team Responses--Reply
Winters et al.
JAMA 2007;297:155-155.
FULL TEXT
House Staff Team Workload and Organization Effects on Patient Outcomes in an Academic General Internal Medicine Inpatient Service
Ong et al.
Arch Intern Med 2007;167:47-52.
ABSTRACT
| FULL TEXT
Influence of health, lifestyle, working conditions, and sociodemography on early retirement among nurses: The Danish Nurse Cohort Study
Friis et al.
Scand J Public Health 2007;35:23-30.
ABSTRACT
Relationship Between Medicare's Hospital Compare Performance Measures and Mortality Rates
Werner and Bradlow
JAMA 2006;296:2694-2702.
ABSTRACT
| FULL TEXT
Quality of Care for the Treatment of Acute Medical Conditions in US Hospitals
Landon et al.
Arch Intern Med 2006;166:2511-2517.
ABSTRACT
| FULL TEXT
Changing the Work Environment in Intensive Care Units to Achieve Patient-Focused Care: The Time Has Come
McCauley and Irwin
Am J Crit Care 2006;15:541-548.
ABSTRACT
| FULL TEXT
Changing the Work Environment in ICUs to Achieve Patient-Focused Care: The Time Has Come.
McCauley and Irwin
Chest 2006;130:1571-1578.
ABSTRACT
| FULL TEXT
Rapid Response Teams--Walk, Don't Run
Winters et al.
JAMA 2006;296:1645-1647.
FULL TEXT
Critical Care Nurses' Work Environments: A Baseline Status Report
Ulrich et al.
Crit Care Nurse 2006;26:46-57.
FULL TEXT
Unit Type Differences in RN Workgroup Job Satisfaction
Boyle et al.
West J Nurs Res 2006;28:622-640.
ABSTRACT
Opportunities and obstacles for good work in nursing.
Miller
Nurs Ethics 2006;13:471-487.
ABSTRACT
Directions for the development of nursing knowledge.
Sullivan-Marx
Policy Politics Nursing Practice 2006;7:164-168.
ABSTRACT
The impact of nurse turnover on patient, nurse, and system outcomes: a pilot study and focus for a multicenter international study.
O'Brien-Pallas et al.
Policy Politics Nursing Practice 2006;7:169-179.
ABSTRACT
The road less traveled: nursing advocacy at the policy level.
Spenceley et al.
Policy Politics Nursing Practice 2006;7:180-194.
ABSTRACT
Impact of Staffing on Bloodstream Infections in the Neonatal Intensive Care Unit
Cimiotti et al.
Arch Pediatr Adolesc Med 2006;160:832-836.
ABSTRACT
| FULL TEXT
Do we need more doctors?
Bloor et al.
JRSM 2006;99:281-287.
FULL TEXT
Impact of nursing overtime on nurse-sensitive patient outcomes in new york hospitals, 1995-2000.
Berney and Needleman
Policy Politics Nursing Practice 2006;7:87-100.
ABSTRACT
Short-term mortality in hip fracture patients admitted during weekends and holidays
Foss and Kehlet
Br J Anaesth 2006;96:450-454.
ABSTRACT
| FULL TEXT
More nursing, fewer deaths
Clarke and Aiken
Qual Saf Health Care 2006;15:2-3.
FULL TEXT
Impact of nursing on hospital patient mortality: a focused review and related policy implications
Tourangeau et al.
Qual Saf Health Care 2006;15:4-8.
ABSTRACT
| FULL TEXT
Nurse-to-Patient Ratios: What Do We Know?
Greenberg
Policy Politics Nursing Practice 2006;7:14-16.
The Use of Contract Licensed Nursing Staff in U.S. Nursing Homes
Bourbonniere et al.
Med Care Res Rev 2006;63:88-109.
ABSTRACT
Nurse Staffing In Hospitals: Is There A Business Case For Quality?
Needleman et al.
Health Aff (Millwood) 2006;25:204-211.
ABSTRACT
| FULL TEXT
Measuring the Nursing Workforce: Clarifying the Definitions
Reinier et al.
Med Care Res Rev 2005;62:741-755.
ABSTRACT
Mortality Among Very Low-Birthweight Infants in Hospitals Serving Minority Populations
Morales et al.
Am. J. Public Health 2005;95:2206-2212.
ABSTRACT
| FULL TEXT
Nursing Structure, Processes, and Patient Outcomes in Army Medical Centers
Kee et al.
West J Nurs Res 2005;27:1040-1058.
ABSTRACT
Early Evidence on California Staffing Ratios Should be Interpreted With Caution
Dunton and Schumann
Policy Politics Nursing Practice 2005;6:354-357.
Quality of Work Life of Independent vs Employed Family Physicians in Wisconsin: A WReN Study
Beasley et al.
Ann Fam Med 2005;3:500-506.
ABSTRACT
| FULL TEXT
Effect of Cuts in Medicare Reimbursement on Process and Outcome of Care for Acute Myocardial Infarction Patients
Volpp et al.
Circulation 2005;112:2268-2275.
ABSTRACT
| FULL TEXT
Evidence-Based Therapies and Mortality in Patients Hospitalized in December with Acute Myocardial Infarction
Meine et al.
ANN INTERN MED 2005;143:481-485.
ABSTRACT
| FULL TEXT
Factors That Influence Staffing of Outpatient Substance Abuse Treatment Programs
Lemak and Alexander
Psychiatr. Serv. 2005;56:934-939.
ABSTRACT
| FULL TEXT
Improving the ICU: Part 1
Garland
Chest 2005;127:2151-2164.
ABSTRACT
| FULL TEXT
AACN Standards for Establishing and Sustaining Healthy Work Environments: A Journey to Excellence
The American Association of Critical-Care Nurses
Am J Crit Care 2005;14:187-197.
FULL TEXT
Interdisciplinary Collaboration: Essential for Improved Wound Care Outcomes and Wound Prevention in Home Care
Dailey
Home Health Care Management Practice 2005;17:213-221.
ABSTRACT
Factors Influencing In-hospital Mortality in Community-Acquired Pneumonia: A Prospective Study of Patients Not Initially Admitted to the ICU
Marrie and Wu
Chest 2005;127:1260-1270.
ABSTRACT
| FULL TEXT
A National Survey of Pediatric Critical Care Resources in the United States
Odetola et al.
Pediatrics 2005;115:e382-e386.
ABSTRACT
| FULL TEXT
Role of Computerized Physician Order Entry Systems in Facilitating Medication Errors
Koppel et al.
JAMA 2005;293:1197-1203.
ABSTRACT
| FULL TEXT
First, Do No Harm
Dracup and Bryan-Brown
Am J Crit Care 2005;14:99-101.
FULL TEXT
Fumbled Handoffs: One Dropped Ball after Another
Gandhi
ANN INTERN MED 2005;142:352-358.
ABSTRACT
| FULL TEXT
Workforce Issues in Rural Areas: A Focus on Policy Equity
Ricketts
Am. J. Public Health 2005;95:42-48.
ABSTRACT
| FULL TEXT
Resident Burnout
Thomas
JAMA 2004;292:2880-2889.
ABSTRACT
| FULL TEXT
Nuclear Medicine Technologist Job Satisfaction
Knight
J. Nucl. Med. Technol. 2004;32:220-228.
FULL TEXT
|
