A diuretic protocol increases volume removal and reduces readmissions among hospitalized patients with acute decompensated heart failure.

Despite the widespread use of loop diuretics to treat acute decompensated heart failure (ADHF), robust data supporting their role and optimal dosing strategies are scarce. This analysis aimed to compare clinical outcomes of patients admitted with ADHF who received a diuretic dosing protocol with those who received the usual diuretic therapy. We performed an observational medical records review to compare the use of a nurse-driven diuretic dosing protocol with usual diuretic dosing for patients admitted with ADHF during a 1-year period. Using a propensity scoring model, comparisons were made between groups for total weight loss, length of stay (LOS), 30-day readmissions, in-hospital mortality, 30-day mortality, and acute kidney failure. Sixty-eight of the 596 patients admitted with ADHF during the study period received the diuretic protocol. Protocol use was associated with an additional 2.63-kg weight loss (P=.003) but a trend toward increased LOS compared with patients receiving usual care (P=.097). However, patients receiving the protocol had a significantly lower risk of 30-day readmission (odds ratio, 0.46, 95% confidence interval, 0.22-0.95). Protocol use was not associated with significant differences in kidney failure, inpatient mortality, or 30-day mortality. A diuretic dosing protocol for patients admitted with ADHF improves weight loss and may lower 30-day readmissions, at the cost of potentially increasing LOS.

Comparison of traditional trigger tool to data warehouse based screening for identifying hospital adverse events.

BACKGROUND: Research supports medical record review using screening triggers as the optimal method to detect hospital adverse events (AE), yet the method is labour-intensive. METHOD: This study compared a traditional trigger tool with an enterprise data warehouse (EDW) based screening method to detect AEs. We created 51 automated queries based on 33 traditional triggers from prior research, and then applied them to 250 randomly selected medical patients hospitalised between 1 September 2009 and 31 August 2010. Two physicians each abstracted records from half the patients using a traditional trigger tool and then performed targeted abstractions for patients with positive EDW queries in the complementary half of the sample. A third physician confirmed presence of AEs and assessed preventability and severity. RESULTS: Traditional trigger tool and EDW based screening identified 54 (22%) and 53 (21%) patients with one or more AE. Overall, 140 (56%) patients had one or more positive EDW screens (total 366 positive screens). Of the 137 AEs detected by at least one method, 86 (63%) were detected by a traditional trigger tool, 97 (71%) by EDW based screening and 46 (34%) by both methods. Of the 11 total preventable AEs, 6 (55%) were detected by traditional trigger tool, 7 (64%) by EDW based screening and 2 (18%) by both methods. Of the 43 total serious AEs, 28 (65%) were detected by traditional trigger tool, 29 (67%) by EDW based screening and 14 (33%) by both. CONCLUSIONS: We found relatively poor agreement between traditional trigger tool and EDW based screening with only approximately a third of all AEs detected by both methods. A combination of complementary methods is the optimal approach to detecting AEs among hospitalised patients.

Where did the day go?--a time-motion study of hospitalists.

BACKGROUND: Within the last decade hospitalists have become an integral part of inpatient care in the United States and now care for about half of all Medicare patients requiring hospitalization. However, little data exists describing hospitalist workflow and their activities in daily patient care. OBJECTIVE: To clarify how hospitalists spend their time and how patient volumes affect their workflow. DESIGN: Observers continuously shadowed each of 24 hospitalists for two complete shifts. Observations were recorded using a handheld computer device with customized data collection software. SETTING: Urban, tertiary care, academic medical center. RESULTS: : Hospitalists spent 17% of their time on direct patient contact, and 64% on indirect patient care. For 16% of all time recorded, more than one activity was occurring simultaneously (i.e., multitasking). Professional development, personal time, and travel each accounted for about 6% of their time. Communication and electronic medical record (EMR) use, two components of indirect care, occupied 25% and 34% of recorded time respectively. Hospitalists with above average patient loads spent less time per patient communicating with others and working with the EMR than those hospitalists with below average patient loads, but reported delaying documentation until later in the evening or next day. Patient load did not change the amount of time hospitalists spent with each patient. CONCLUSIONS: Hospitalists spend more time reviewing the EMR and documenting in it, than directly with the patient. Multi-tasking occurred frequently and occupied a significant portion of each shift.

Comparing academic and community-based hospitalists.

In 2006, hospitalist programs were formally introduced at both an academic and community hospital in the same city providing an opportunity to study the similarities and differences in workflows in these two settings. The data were collected using a time-flow methodology allowing the two workflows to be compared quantitatively. The results showed that the hospitalists in the two settings devoted similar proportions of their workday to the task categories studied. Most of the time was spent providing indirect patient care followed by direct patient care, travel, personal, and other. However, after adjusting for patient volumes, the data revealed that academic hospitalists spent significantly more time per patient providing indirect patient care (Academic: 54.7 +/- 11.1 min/patient, Community: 41.9 +/- 9.8 min/patient, p < 0.001). Additionally, we found that nearly half of the hospitalists' time at both settings was spent multitasking. Although we found subtle workflow differences between the academic and community programs, their similarities were more striking as well as greater than their differences. We attribute these small differences to the higher case mix index at the academic program as well greater complexity and additional communication hand-offs inherent to a tertiary academic medical center. It appears that hospitalists, irrespective of their work environment, spend far more time documenting, communicating and coordinating care than they do at the bedside raising the question, is this is a necessary feature of the hospitalist care model or should hospitalists restructure their workflow to improve outcomes?