Faculty Bibliography

OBJECTIVE: Recalling a patient to repeat a radiology examination is an adverse and, in certain cases, preventable event. Our objectives were to assess the rate of patient recalls for all imaging performed in the outpatient setting at our institution and to characterize the underlying reasons for the recalls. MATERIALS AND METHODS: We performed a retrospective review of all repeat imaging requests for an inadequate initial imaging study between January 2012 and March 2015. RESULTS: We identified 100 recall requests (mean, 2.6 requests per month), for an overall recall rate of approximately 1 in 8046 ambulatory studies and 1 in 1684 MRI studies. Nearly all recalls (98%) involved adults. A total of 95% of the recalls were for MRI studies. The most common reason for a patient recall request was an incomplete examination, making up 24% of all requests. The other causes were inadequate coverage of the area of interest (22%), protocoling errors (20%), poor imaging quality (15%), additional imaging to clarify a finding (11%), insufficient contrast visualization (7%), and incorrect patient information (1%). CONCLUSION: We found that patient recalls for imaging in the outpatient setting at our institution are not common. When recalls did occur, they were most often related to the acquisition of MR images. Improved technologist education on MRI protocoling and enhanced communication between ordering clinicians and radiologists to clarify the purpose of imaging might reduce the need for repeat ambulatory imaging.

BACKGROUND: Hospital quality improvement efforts absorb substantial time and resources, but many innovations fail to integrate into organizational routines, undermining the potential to sustain the new practices. Despite a well-developed literature on the initial implementation of new practices, we have limited knowledge about the mechanisms by which integration occurs. METHODS: We conducted a qualitative study using a purposive sample of hospitals that participated in the State Action on Avoidable Rehospitalizations (STAAR) initiative, a collaborative to reduce hospital readmissions that encouraged members to adopt new practices. We selected hospitals where risk-standardized readmission rates (RSRR) had improved (n = 7) or deteriorated (n = 3) over the course of the first 2 years of the STAAR initiative (2010-2011 to 2011-2012) and interviewed a range of staff at each site (90 total). We recruited hospitals until reaching theoretical saturation. The constant comparative method was used to conduct coding and identification of key themes. RESULTS: When innovations were successfully integrated, participants consistently reported that a small number of key staff held the innovation in place for as long as a year while more permanent integrating mechanisms began to work. Depending on characteristics of the innovation, one of three categories of integrating mechanisms eventually took over the role of holding new practices in place. Innovations that proved intrinsically rewarding to the staff, by making their jobs easier or more gratifying, became integrated through shifts in attitudes and norms over time. Innovations for which the staff did not perceive benefits to themselves were integrated through revised performance standards if the innovation involved complex tasks and through automation if the innovation involved simple tasks. CONCLUSIONS: Hospitals have an opportunity to promote the integration of new practices by planning for the extended effort required to hold a new practice in place while integration mechanisms take hold. By understanding how integrating mechanisms correspond to innovation characteristics, hospitals may be able to foster integrating mechanisms most likely to work for particular innovations.

BACKGROUND: It is desirable not to include planned readmissions in readmission measures because they represent deliberate, scheduled care. OBJECTIVES: To develop an algorithm to identify planned readmissions, describe its performance characteristics, and identify improvements. DESIGN: Consensus-driven algorithm development and chart review validation study at 7 acute-care hospitals in 2 health systems. PATIENTS: For development, all discharges qualifying for the publicly reported hospital-wide readmission measure. For validation, all qualifying same-hospital readmissions that were characterized by the algorithm as planned, and a random sampling of same-hospital readmissions that were characterized as unplanned. MEASUREMENTS: We calculated weighted sensitivity and specificity, and positive and negative predictive values of the algorithm (version 2.1), compared to gold standard chart review. RESULTS: In consultation with 27 experts, we developed an algorithm that characterizes 7.8% of readmissions as planned. For validation we reviewed 634 readmissions. The weighted sensitivity of the algorithm was 45.1% overall, 50.9% in large teaching centers and 40.2% in smaller community hospitals. The weighted specificity was 95.9%, positive predictive value was 51.6%, and negative predictive value was 94.7%. We identified 4 minor changes to improve algorithm performance. The revised algorithm had a weighted sensitivity 49.8% (57.1% at large hospitals), weighted specificity 96.5%, positive predictive value 58.7%, and negative predictive value 94.5%. Positive predictive value was poor for the 2 most common potentially planned procedures: diagnostic cardiac catheterization (25%) and procedures involving cardiac devices (33%). CONCLUSIONS: An administrative claims-based algorithm to identify planned readmissions is feasible and can facilitate public reporting of primarily unplanned readmissions. Journal of Hospital Medicine 2015. (c) 2015 Society of Hospital Medicine.

BACKGROUND: Hospitalized patients with diabetes have experienced a disproportionate reduction in mortality over the past decade. OBJECTIVE: To examine whether this differential decrease affected all patients with diabetes, and to identify explanatory factors. DESIGN: Serial, cross-sectional observational study. SETTING: Academic medical center. PATIENTS: All adult, nonobstetric patients with an inpatient discharge between January 1, 2000 and December 31, 2010. MEASUREMENT: We assessed in-hospital mortality; inpatient glycemic control (percentage of hospital days with glucose below 70, above 299, and between 70 and 179 mg/dL, and standard deviation of glucose measurements), and outpatient glycemic control (hemoglobin A1c). RESULTS: We analyzed 322,938 admissions, including 76,758 (23.8%) with diabetes. Among 54,645 intensive care unit (ICU) admissions, there was a 7.8% relative reduction in the odds of mortality in each successive year for patients with diabetes, adjusted for age, race, payer, length of stay, discharge diagnosis, comorbidities, and service (odds ratio [OR]: 0.923, 95% confidence interval [CI]: 0.906-0.940). This was significantly greater than the 2.6% yearly reduction for those without diabetes (OR: 0.974, 95% CI: 0.963-0.985; P < 0.001 for interaction). In contrast, the greater decrease in mortality among non-ICU patients with diabetes did not reach significance. Results were similar among medical and surgical patients. Among ICU patients with diabetes, the significant decline in mortality persisted after adjustment for inpatient and outpatient glucose control (OR: 0.953, 95% CI: 0.914-0.994). CONCLUSIONS: Patients with diabetes in the ICU have experienced a disproportionate reduction in mortality that is not explained by glucose control. Potential explanations include improved cardiovascular risk management or advances in therapies for diseases commonly affecting patients with diabetes. Journal of Hospital Medicine 2015. (c) 2015 Society of Hospital Medicine.

OBJECTIVE: To characterize the absolute risks for older patients of readmission to hospital and death in the year after hospitalization for heart failure, acute myocardial infarction, or pneumonia. DESIGN: Retrospective cohort study. SETTING: 4767 hospitals caring for Medicare fee for service beneficiaries in the United States, 2008-10. PARTICIPANTS: More than 3 million Medicare fee for service beneficiaries, aged 65 years or more, surviving hospitalization for heart failure, acute myocardial infarction, or pneumonia. MAIN OUTCOME MEASURES: Daily absolute risks of first readmission to hospital and death for one year after discharge. To illustrate risk trajectories, we identified the time required for risks of readmission to hospital and death to decline 50% from maximum values after discharge; the time required for risks to approach plateau periods of minimal day to day change, defined as 95% reductions in daily changes in risk from maximum daily declines after discharge; and the extent to which risks are higher among patients recently discharged from hospital compared with the general elderly population. RESULTS: Within one year of hospital discharge, readmission to hospital and death, respectively, occurred following 67.4% and 35.8% of hospitalizations for heart failure, 49.9% and 25.1% for acute myocardial infarction, and 55.6% and 31.1% for pneumonia. Risk of first readmission had declined 50% by day 38 after hospitalization for heart failure, day 13 after hospitalization for acute myocardial infarction, and day 25 after hospitalization for pneumonia; risk of death declined 50% by day 11, 6, and 10, respectively. Daily change in risk of first readmission to hospital declined 95% by day 45, 38, and 45; daily change in risk of death declined 95% by day 21, 19, and 21. After hospitalization for heart failure, acute myocardial infarction, or pneumonia, the magnitude of the relative risk for hospital admission over the first 90 days was 8, 6, and 6 times greater than that of the general older population; the relative risk of death was 11, 8, and 10 times greater. CONCLUSIONS: Risk declines slowly for older patients after hospitalization for heart failure, acute myocardial infarction, or pneumonia and is increased for months. Specific risk trajectories vary by discharge diagnosis and outcome. Patients should remain vigilant for deterioration in health for an extended time after discharge. Health providers can use knowledge of absolute risks and their changes over time to better align interventions designed to reduce adverse outcomes after discharge with the highest risk periods for patients.