• Reducing readmissions: Nurses to the rescue?

    Below are some quotes from Hospital Nursing and 30-Day Readmissions Among Medicare Patients With Heart Failure, Acute Myocardial Infarction, and Pneumonia, by Matthew McHugh and Chenjuan Ma (Medical Care, 2013). The study is based on a cross-sectional survey of registered nurses in California, New Jersey, and Pennsylvania in 2005-2006, supplemented Medicare and AHA data.

    • [H]ospitals organized as better places for nurses to work—those that value nurses’ autonomy, excel in frontline manager supervisory ability, invest in staff development, support good nurse-physician relations, have high proportions of educated staff, and staff for manageable workloads—empower nurses to provide high-quality care resulting in better patient outcomes.[9] The majority of evidence shows that hospitals with these features have better patient outcomes.[10–16]
    • Nurses’ round-the-clock presence at decisive moments allows them to prepare patients and families for discharge throughout the hospitalization. This preparation and teaching supports seamless transitions to other settings. Bedside nurses also act as sentinels—identifying early warning signs and addressing complications and adverse events in the acute care setting that increase patients’ risk of readmission.[19,20] Nurses are the frontline for providing many of the core processes of care aimed at preventing readmissions—knowledge assessment, patient education, discharge preparation, and care-coordination. These processes, however, can be disrupted when nurses have little autonomy, poor interdisciplinary relationships, minimal managerial support, overwhelming workload, inadequate resources, and poor integration throughout the institution’s decision-making structure.
    • Models included covariates characterizing structural and descriptive attributes of hospitals that may be associated with quality of care outcomes.[30–33] Size was defined by the number of staffed hospital beds within the facility. Teaching status was categorized as none (no residents or fellows), minor [between 0.01 and 0.25 resident/fellow-to-bed ratio, inclusive], and major (resident/fellow-to-bed ratio > 0.25). High-technology hospitals had open-heart surgery capabilities, organ transplant capabilities, or both. Ownership was defined as not-for-profit or for-profit. We used dummy variables to indicate the category based on population size of the hospital’s geographic location. The volume of cases was measured by taking the average of the total number of cases for the hospital by condition for years 2005–2006.[34] We created a hospital-level variable categorizing volume into quartiles. We also linked Medicare cost report data to calculate a measure of total operating margin—the ratio of a hospital’s total revenues related to direct patient care and total operating expenses.
    • We estimated robust logistic regression models separately for each condition to determine the relationship between the work environment, patient-to- nurse ratios, proportion of BSN-educated nurses, and the risk-adjusted odds of readmission. The key predictor variables—nurse work environment, nurse staffing, and nurse education—were hospital-level measures.
    • Each additional patient per nurse in the average nurse’s workload was associated with a 7% higher odds of readmission for heart failure [odds ratio (OR) = 1.07; confidence interval CI, 1.05–1.09], 6% for pneumonia patients (OR = 1.06; CI, 1.03–1.09), and 9% for myocardial infarction patients (OR = 1.09; CI, 1.05–1.13). Care in a hospital with a good versus poor work environment was associated with odds of readmission that were 7% lower for heart failure (OR = 0.93; CI, 0.89–0.97), 6% lower for myocardial infarction (OR = 0.94; CI, 0.88–0.98), and 10% lower for pneumonia (OR = 0.90; CI, 0.85–0.96) patients.
    • The clinical significance of the effects of staffing and work environment on readmission could be considerable. On the basis of our estimates, the average difference in heart failure readmission rates between hospitals with poor versus good work environments is 1.4%, which, based on Hospital Compare data, nearly equals the SD in the readmission rate for these patients (1.9%). If a hospital with a poor work environment could improve to a good environment, we would expect its readmission rate to decline from roughly the 84th to 50th percentile or the 50th to 16th percentile in this distribution of hospitals. A hospital that could change its work environment from poor to good and reduce nurse workloads from 6 to 4 patients per nurse would, all else being equal, see their readmission rates reduced from 25% to 21%.


    • The hypothesis that patient-to-nurse ratios and work environment could influence hospital readmissions is eminently plausible.
    • The results are supportive of this hypothesis.
    • However, there are important limitations.
      • It is an analysis of three states. Half the data were from California. This threatens generalizability to the rest of the U.S.
      • There were no controls for other factors of quality that might be correlated with both readmissions and staffing ratios or work environment. It is possible that the variables of focus are soaking up other aspects of quality. If so, one cannot really expect the degree of decreases in readmission rates implied just from changing those variables alone.
      • One can almost always level the forgoing critique. It’s impossible to control for everything. So, one broad-brush way to address it is to use hospital-level fixed effects, letting hospitals serve as their own controls. That would answer the question, how much do changes in nurse staffing and work environment within a hospital affect changes in readmission rates? However this approach is not possible with cross-sectional or very short panel data. Since the data are cross-sectional here, the authors are stuck. (I’m not sure I’d buy a random effects specification, though it might be informative if it caused the results to change substantially.) Bottom line: some sensitivity testing with some other measures of quality would be good to see. (It would be important to find aspects of quality not themselves driven by nurses.) My confidence in the results would increase if the effect of the variables of interest did not change much, even though we could not rule out some important unobserved quality.
    • It’s good to see some researchers pushing on hospital-level modifiable factors and relating them to readmissions. The premise of penalizing hospitals based on readmissions is that such modifiable factors exist.


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