Improving Teamwork and Patient Outcomes with Daily Structured Interdisciplinary Bedside Rounds: A Multimethod Evaluation
BACKGROUND: Previous research has shown that interdisciplinary ward rounds have the potential to improve team functioning and patient outcomes.
DESIGN: A convergent parallel multimethod approach to evaluate a hospital interdisciplinary ward round intervention and ward restructure.
SETTING: An acute medical unit in a large tertiary care hospital in regional Australia.
PARTICIPANTS: Thirty-two clinicians and inpatients aged 15 years and above, with acute episode of care, discharged during the year prior and the year of the intervention.
INTERVENTION: A daily structured interdisciplinary bedside round combined with a ward restructure.
MEASUREMENTS: Qualitative measures included contextual factors and measures of change and experiences of clinicians. Quantitative measures included length of stay (LOS), monthly “calls for clinical review,’” and cost of care delivery.
RESULTS: Clinicians reported improved teamwork, communication, and understanding between and within the clinical professions, and between clinicians and patients, after the intervention implementation. There was no statistically significant difference between the intervention and control wards in the change in LOS over time (Wald χ2 = 1.05; degrees of freedom [df] = 1; P = .31), but a statistically significant interaction for cost of stay, with a drop in cost over time, was observed in the intervention group, and an increase was observed in the control wards (Wald χ2 = 6.34; df = 1; P = .012). The medical wards and control wards differed significantly in how the number of monthly “calls for clinical review” changed from prestructured interdisciplinary bedside round (SIBR) to during SIBR (F (1,44) = 12.18; P = .001).
CONCLUSIONS: Multimethod evaluations are necessary to provide insight into the contextual factors that contribute to a successful intervention and improved clinical outcomes.
© 2018 Society of Hospital Medicine
Qualitative Data
Qualitative measures consisted of semistructured interviews. We utilized multiple strategies to recruit interviewees, including a snowball technique, criterion sampling,15 and emergent sampling, so that we could seek the views of both the leadership team responsible for the implementation and “frontline” clinical staff whose daily work was directly affected by it. Everyone who was initially recruited agreed to be interviewed, and additional frontline staff asked to be interviewed once they realized that we were asking about how staff experienced the changes in practice.
The research team developed a semistructured interview guide based on an understanding of the merger of the 2 units as well as an understanding of changes in practice of the rounds (provided in Appendix 1). The questions were pilot tested on a separate unit and revised. Questions were structured into 5 topic areas: planning and implementation of AMU/SIBR model, changes in work practices because of the new model, team functioning, job satisfaction, and perceived impact of the new model on patients and families. All interviews were audio-recorded and transcribed verbatim for analysis.
Quantitative Data
Quantitative data were collected on patient outcome measures: length of stay (LOS), discharge date and time, mode of separation (including death), primary diagnostic category, total hospital stay cost and “clinical response calls,” and patient demographic data (age, gender, and Patient Clinical Complexity Level [PCCL]). The PCCL is a standard measure used in Australian public inpatient facilities and is calculated for each episode of care.16 It measures the cumulative effect of a patient’s complications and/or comorbidities and takes an integer value between 0 (no clinical complexity effect) and 4 (catastrophic clinical complexity effect).
Data regarding LOS, diagnosis (Australian Refined Diagnosis Related Groups [AR-DRG], version 7), discharge date, and mode of separation (including death) were obtained from the New South Wales Ministry of Health’s Health Information Exchange for patients discharged during the year prior to the intervention through 1 year after the implementation of the intervention. The total hospital stay cost for these individuals was obtained from the local Health Service Organizational Performance Management unit. Inclusion criteria were inpatients aged over 15 years experiencing acute episodes of care; patients with a primary diagnostic category of mental diseases and disorders were excluded. LOS was calculated based on ward stay. AMU data were compared with the remaining hospital ward data (the control group). Data on “clinical response calls” per month per ward were also obtained for the 12 months prior to intervention and the 12 months of the intervention.
Analysis
Qualitative Analysis
Qualitative data analysis consisted of a hybrid form of textual analysis, combining inductive and deductive logics.17,18 Initially, 3 researchers (J.P., J.J., and R.C.W.) independently coded the interview data inductively to identify themes. Discrepancies were resolved through discussion until consensus was reached. Then, to further facilitate analysis, the researchers deductively imposed a matrix categorization, consisting of 4 a priori categories: context/conditions, practices/processes, professional interactions, and consequences.19,20 Additional a priori categories were used to sort the themes further in terms of experiences prior to, during, and following implementation of the intervention. To compare changes in those different time periods, we wanted to know what themes were related to implementation and whether those themes continued to be applicable to sustainability of the changes.
Quantitative analysis. Distribution of continuous data was examined by using the one-sample Kolmogorov-Smirnov test. We compared pre-SIBR (baseline) measures using the Student t test for normally distributed data, the Mann-Whitney U z test for nonparametric data (denoted as M-W U z), and χ2 tests for categorical data. Changes in monthly “clinical response calls” between the AMU and the control wards over time were explored by using analysis of variance (ANOVA). Changes in LOS and cost of stay from the year prior to the intervention to the first year of the intervention were analyzed by using generalized linear models, which are a form of linear regression. Factors, or independent variables, included in the models were time period (before or during intervention), ward (AMU or control), an interaction term (time by ward), patient age, gender, primary diagnosis (major diagnostic categories of the AR-DRG version 7.0), and acuity (PCCL). The estimated marginal means for cost of stay for the 12-month period prior to the intervention and for the first 12 months of the intervention were produced. All statistical analyses were performed by using IBM SPSS version 21 (IBM Corp., Armonk, New York) and with alpha set at P < .05.
RESULTS
Qualitative Evaluation of the Intervention
Participants.
Three researchers (RCW, JP, and JJ) conducted in-person, semistructured interviews with 32 clinicians (9 male, 23 female) during a 3-day period. The duration of the interviews ranged from 19 minutes to 68 minutes. Participants consisted of 8 doctors, 18 nurses, 5 allied health professionals, and an administrator. Ten of the participants were involved in the leadership group that drove the planning and implementation of SIBR and the AMU.
