The impact of web-based and face-to-face simulation education programs on nurses' response to patient deterioration: A multi-site interrupted time series study.

BACKGROUND: Nurses' response to patient deterioration in acute hospital wards is a priority issue. Simulation education programs improve nurses' knowledge and confidence, but the translation into better care is largely unknown for both web based (WB) and face to face (F2F) simulation programs. AIM: To measure the impact of simulation education on nurses' response to patient deterioration in acute medical ward settings, and to compare the impact of WB and F2F versions. DESIGN: An interrupted time series, non-randomised trial across four medical wards in Victoria, Australia. Wards were allocated to either web-based or face-to-face versions of the same simulation program, FIRST2ACT. Interrupted time series measurement for six fortnights both before and after the intervention were used to measure and compare responses to deterioration. Responses to patient deterioration were extracted from medical records and grouped into outcomes for escalation (e.g. initiation of clinical review), assessment and observation (e.g. increased recording of vital signs, conscious state and pain scores) and clinical interventions (e.g. oxygen administration). RESULTS: 126 nurses (89%) participated across the four wards. 946 patient records (506 in the F2F; 440 in the WB group) were included in analyses. There were significant and sustained improvements between pre and post samples in outcomes for escalation (13.0% to 28.8%; p = 0.000) and assessment and observation (conscious state recorded increased from 91.1% to 100%; p = 0.000, and pain score recorded increased from 97.8% to 99.8%; p = 0.000). There were no differences between the web-based and face-to-face groups except in appropriate oxygen application which increased by 7.7% in the F2F group and decreased by 11.8% in the WB group (p = 0.046). CONCLUSIONS: There was a significant improvement in nurses' response to patient deterioration following both versions of simulation, indicating that both have a role to play in supporting nurses' response to patient deterioration.

The FIRST²ACT simulation program improves nursing practice in a rural Australian hospital.

OBJECTIVE: To measure the impact of the Feedback Incorporating Review and Simulation Techniques to Act on Clinical Trends (FIRST(2) ACT) simulation program on nursing observations and practice relevant to patient deterioration in a rural Australian hospital. DESIGN: Interrupted time series analysis. SETTING: A rural Australian hospital. PARTICIPANTS: All registered nurses (Division 1) employed on an acute medical/surgical ward. INTERVENTION: The FIRST(2) ACT simulation program. OUTCOME MEASURES: Appropriate frequency of a range of observations and administration of oxygen therapy. RESULTS: Thirty-four nurses participated (83% of eligible nurses) in the FIRST(2) ACT program, and 258 records were audited before the program and 242 records after. There were statistically significant reductions in less than satisfactory frequency of observations (P = 0.009) and pain score charting (P = 0.003). There was no measurable improvement in the administration of oxygen therapy (P = 0.143), while the incidence of inappropriate nursing practice for other measures both before and after the intervention was too low to warrant analysis. CONCLUSION: FIRST(2) ACT was associated with measurable improvements in nursing practice.

Identifying patient deterioration: using simulation and reflective interviewing to examine decision making skills in a rural hospital.

OBJECTIVES: The study aim was to examine how Registered Nurses identify and respond to deteriorating patients during in-hospital simulation exercises. DESIGN: Mixed methods study using simulated actors. SETTING: A rural hospital in Victoria, Australia. PARTICIPANTS: Thirty-four Registered Nurses each completed two simulation exercises. METHODS: Data were obtained from the following sources: (a) Objective Structured Clinical Examination (OSCE) rating to assess performance of Registered Nurses during two simulation exercises (chest pain and respiratory distress); (b) video footage of the simulation exercises; (c) reflective interview during participants' review of video footage. Qualitative thematic analysis of video and interview data was undertaken. RESULTS: Themes generated from the data were: (1) exhausting autonomous decision-making; (2) misinterpreting the evidence; (3) conditioned response; and (4) missed cues. Assessment steps were more likely to be omitted in the chest pain simulation, for which there was a hospital protocol in place. CONCLUSIONS: Video review revealed additional insights into nurses' decision-making that were not evident from OSCE scoring alone. Feedback during video review was a highly valued component of the simulation exercises.

Mapping patient flow in a regional Australian emergency department: a model driven approach.

Unified Modelling Language (UML) models of the patient journey in a regional Australian emergency department (ED) were used to develop an accurate, complete representation of ED processes and drive the collection of comprehensive quantitative and qualitative service delivery and patient treatment data as an evidence base for hospital service planning. The focus was to identify bottle-necks that contribute to over-crowding. Data was collected entirely independently of the routine hospital data collection system. The greatest source of delay in patient flow was the waiting time from a bed request to exit from the ED for hospital admission. It represented 61% of the time that these patients occupied ED cubicles. The physical layout of the triage area was identified as counterproductive to efficient triaging, and the results of investigations were often observed to be available for some time before clinical staff became aware. The use of independent primary data to construct UML models of the patient journey was effective in identifying sources of delay in patient flow, and aspects of ED activity that could be improved. The findings contributed to recent department re-design and informed an initiative to develop a business intelligence system for predicting impending occurrence of access block.

Correlation of MAPT scores with clinical and radiographic assessment of patients awaiting THR/TKR.

BACKGROUND: The Multi-attribute Arthritis Prioritisation Tool (MAPT) score is used as a tool to prioritize referrals to specialist clinics and care given to patients with hip and knee problems. Our pilot study aimed to determine the extent of any relationship between the MAPT scores and the clinician's assessment of severity of disease in terms of surgical waiting list (SWL) categories and radiological assessment. METHODS: This is a retrospective study of patients with symptomatic hip or knee osteoarthritis (OA) that were referred via the orthopaedic waiting list project between January and July 2009 to the Bendigo Health's orthopaedic outpatients clinic and were waitlisted for a total hip replacement (THR)/total knee replacement (TKR). The MAPT score was calculated and the Surgical waitlist Category was obtained from the surgical booking office. The radiographs of all these patients were reviewed and graded independently according to the Kellgren and Lawrence radiographic grading for severity of arthritis. The relationships between MAPT score, SWL category and the Kellgren and Lawrence radiographic grades were examined using graphical methods and Kendall's tau correlation coefficients. RESULTS: There were 62 patients in the study. The Kendall-tau sample correlation coefficient between MAPT score and the radiographic grade is τ(b) = -0.091 (P = 0.330) and between MAPT score and SWL category is τ(b) = 0.007 (P = 0.951). CONCLUSIONS: The sample data suggests that there is no significant relationship between the MAPT score and radiographic severity of OA, or between MAPT score and surgical waitlist category of patients with OA waitlisted for a THR/TKR.

Assessing the impact of streaming in a regional emergency department.

OBJECTIVE: To evaluate the impact of a streaming model, previously validated in metropolitan EDs, on selected performance indicators in a regional ED. METHOD: Multiple linear regression models were applied to monthly time series data from 43 months prior to the intervention and 15 months following the intervention to measure the impact of the streaming model on the following performance indicators: (i) percentage of emergency patients admitted to an inpatient bed within 8 h; (ii) percentage of non-admitted emergency patients with a length of stay of less than 4 h; and (iii) percentage of emergency patients who left without being seen by a doctor or nurse practitioner. SETTING: Bendigo Health ED in regional Victoria. RESULTS: Prior to the introduction of streaming, there was a downward trend in both the percentage of emergency patients admitted to an inpatient bed within 8 h, and the percentage of non-admitted emergency patients with a length of stay of less than 4 h. After the introduction of streaming, these trends were reversed (P = 0.008 and P = 0.004, respectively). There was no statistically significant change in the trend associated with the percentage of emergency patients who left without being seen (P = 0.904). CONCLUSIONS: The implementation of the streaming model had an impact on the two performance indicators associated with length of stay in this regional ED, but did not have a significant impact (positive or negative) on the percentage of patients who did not wait to be seen. These results might interest other EDs in regional hospitals.

Forecasting emergency department presentations.

OBJECTIVE: To forecast the number of patients who will present each month at the emergency department of a hospital in regional Victoria. METHODS: The data on which the forecasts are based are the number of presentations in the emergency department for each month from 2000 to 2005. The statistical forecasting methods used are exponential smoothing and Box-Jenkins methods as implemented in the software package SPSS version 14.0 (SPSS Inc, Chicago, Ill, USA). RESULTS: For the particular time series, of the available models, a simple seasonal exponential smoothing model provides optimal forecasting performance. Forecasts for the first five months in 2006 compare well with the observed attendance data. CONCLUSIONS: Time series analysis is shown to provide a useful, readily available tool for predicting emergency department demand. The approach and lessons from this experience may assist other hospitals and emergency departments to conduct their own analysis to aid planning.