1541 Treating Patients with Respect: Assessing the Completion Rate of The ReSPECT Form for Patients Admitted to Hospital

Aim To assess the current completion rate of the Recommended Summary Plan for Emergency Care and Treatment (ReSPECT) form, and to identify ways to improve the completion rate for all inpatients under our care. Method All notes of admitted patients were reviewed for a completed ReSPECT form over a two-week period. Data was collected on whether a form had been completed and its respective fields. This was repeated in three cycles, introducing interventions between each cycle – trainee education and reminders were sent through our WhatsApp messenger group between cycles 1 and 2, and posters implemented between cycles 2 and 3. Results For cycle 1, out of 40 patient notes, 9 (22.5%) had a completed ReSPECT form. This increased to 25 out of 57 patients (43.9%) for cycle 2. Cycle 3 found 1 out of 16 patients (6.7%) had a completed ReSPECT form. Conclusions Our data demonstrated a significant deterioration in our compliance as a department. This could be due to a number of factors ranging from the coronavirus pandemic and staff redeployment to increased workloads, higher patient turnover, and new staff. In any outcome, the poster has proven to be ineffective. Whilst we identified that reminders via electronic message appear to have had an improvement, this has shown to be temporary, and may not be effective in busier times. As such, this audit has revealed a significant need for departmental change and to develop new strategies to improve compliance.

Variation of Daily Care Demand in Swiss General Hospitals: Longitudinal Study on Capacity Utilization, Patient Turnover and Clinical Complexity Levels (Preprint)

BACKGROUND Variations in hospitals’ care demand relies not only on the patient volume but also on the disease severity. Understanding both daily severity and patient volume in hospitals could help to identify hospital pressure zones to improve hospital-capacity planning and policy-making. OBJECTIVE This longitudinal study explored daily care demand dynamics in Swiss general hospitals for 3 measures: (1) capacity utilization, (2) patient turnover, and (3) patient clinical complexity level. METHODS A retrospective population-based analysis was conducted with 1 year of routine data of 1.2 million inpatients from 102 Swiss general hospitals. Capacity utilization was measured as a percentage of the daily maximum number of inpatients. Patient turnover was measured as a percentage of the daily sum of admissions and discharges per hospital. Patient clinical complexity level was measured as the average daily patient disease severity per hospital from the clinical complexity algorithm. RESULTS There was a pronounced variability of care demand in Swiss general hospitals. Among hospitals, the average daily capacity utilization ranged from 57.8% (95% CI 57.3-58.4) to 87.7% (95% CI 87.3-88.0), patient turnover ranged from 22.5% (95% CI 22.1-22.8) to 34.5% (95% CI 34.3-34.7), and the mean patient clinical complexity level ranged from 1.26 (95% CI 1.25-1.27) to 2.06 (95% CI 2.05-2.07). Moreover, both within and between hospitals, all 3 measures varied distinctly between days of the year, between days of the week, between weekdays and weekends, and between seasons. CONCLUSIONS While admissions and discharges drive capacity utilization and patient turnover variation, disease severity of each patient drives patient clinical complexity level. Monitoring—and, if possible, anticipating—daily care demand fluctuations is key to managing hospital pressure zones. This study provides a pathway for identifying patients’ daily exposure to strained hospital systems for a time-varying causal model.

Respectful maternity care for positive birthing experience at Pravara Rural Hospital

Background: Many women in developing countries experience disrespect and abuse during labour and delivery. Respectful maternity care (RMC) is considered as one of the basic reproductive health rights of the women. It is one of the essential components of LaQshya programme of Government of India. The aim of the study was to highlight the important components of the RMC, its implementation and its impact on patient turnover in the maternity unit of Pravara Rural Hospital Loni and review the literature on the subject.Methods: A prospective observational study was conducted for a period of one year from January 2019 to December 2019 at tertiary care hospital. The implementation of RMC was observed and important findings were documented. The patient turnover and cliental satisfaction was noted.Results: It was observed that all components of RMC were strictly followed in maternity unit of Pravara Rural Hospital Loni. The staff and doctors were trained and oriented towards importance of RMC. The patient turnover has increased exponentially year by year. The patient feedback system about the quality of care in labour and delivery ward shows overall satisfaction score of 4.3 on the 5-point Likert scale. There was a surveillance system that supervises and closely monitor the quality of care in labour room in general and RMC in particular.Conclusions: RMC is one of the important components of LaQshya certification process. Respectful maternity care is implemented at Pravara Rural hospital in its true spirit. It has resulted in gaining the faith and trust of the community, which is reflected through exponential rise in the number of deliveries taking place in the hospital.

Simplified model of the number of Covid-19 patients in the ICU: update April 6, 2020

ABSTRACTINTRODUCTIONPredicting the number of Covid-19 patients in the Intensive Care Units (ICU) could be useful to avoid the breaking point. We attempted to deduce a formula in order to model the number of the ICU patients in France from the official data and patient turnover in the ICU.METHODSThe Covid-19 ICU patient turnover was calculated using a recurrence relation from the internal data provided by Hospices Civils de Lyon. The number of new Covid-19 cases detected daily was modelized to fit with the last known data in France and extrapolated for the coming days using two scenarios following the existing data in China (best scenario) and Italy (worst scenario). The number of daily admissions in ICU was calculated as the sum of 13.7% of the new Covid-19 cases detected on a given day and 7.8% of the average of the total new Covid-19 cases recorded in the last week. Approximately 39.7% of patients admitted to the ICU were non-intubated with an average ICU length of stay of 4 days. Conversely, 60.3% of patients were intubated and for those who died among them (14.44%) the ICU length of stay was of 4 days for 78.3% of them and of 15 days for 21.7% of them. For the intubated patients that were discharged alive, the ICU length of stay was of 6 days for 44.4% of them and of 20 days for 55.6% of them.RESULTSWe predict a peak of 7072 – 8043 patients for the overall French territory.CONCUSIONDespite a simplified mathematical model, the strength of our study is a narrow possible range of predicted total number of ICU patients.

Longitudinal Study of the Variation in Patient Turnover and Patient-to-Nurse Ratio: Descriptive Analysis of a Swiss University Hospital

Background Variations in patient demand increase the challenge of balancing high-quality nursing skill mixes against budgetary constraints. Developing staffing guidelines that allow high-quality care at minimal cost requires first exploring the dynamic changes in nursing workload over the course of a day. Objective Accordingly, this longitudinal study analyzed nursing care supply and demand in 30-minute increments over a period of 3 years. We assessed 5 care factors: patient count (care demand), nurse count (care supply), the patient-to-nurse ratio for each nurse group, extreme supply-demand mismatches, and patient turnover (ie, number of admissions, discharges, and transfers). Methods Our retrospective analysis of data from the Inselspital University Hospital Bern, Switzerland included all inpatients and nurses working in their units from January 1, 2015 to December 31, 2017. Two data sources were used. The nurse staffing system (tacs) provided information about nurses and all the care they provided to patients, their working time, and admission, discharge, and transfer dates and times. The medical discharge data included patient demographics, further admission and discharge details, and diagnoses. Based on several identifiers, these two data sources were linked. Results Our final dataset included more than 58 million data points for 128,484 patients and 4633 nurses across 70 units. Compared with patient turnover, fluctuations in the number of nurses were less pronounced. The differences mainly coincided with shifts (night, morning, evening). While the percentage of shifts with extreme staffing fluctuations ranged from fewer than 3% (mornings) to 30% (evenings and nights), the percentage within “normal” ranges ranged from fewer than 50% to more than 80%. Patient turnover occurred throughout the measurement period but was lowest at night. Conclusions Based on measurements of patient-to-nurse ratio and patient turnover at 30-minute intervals, our findings indicate that the patient count, which varies considerably throughout the day, is the key driver of changes in the patient-to-nurse ratio. This demand-side variability challenges the supply-side mandate to provide safe and reliable care. Detecting and describing patterns in variability such as these are key to appropriate staffing planning. This descriptive analysis was a first step towards identifying time-related variables to be considered for a predictive nurse staffing model.

Association of registered nurse and nursing support staffing with inpatient hospital mortality

BackgroundThe association of nursing staffing with patient outcomes has primarily been studied by comparing high to low staffed hospitals, raising concern other factors may account for observed differences. We examine the association of inpatient mortality with patients’ cumulative exposure to shifts with low registered nurse (RN) staffing, low nursing support staffing and high patient turnover.MethodsCumulative counts of exposure to shifts with low staffing and high patient turnover were used as time-varying covariates in survival analysis of data from a three-campus US academic medical centre for 2007–2012. Staffing below 75% of annual median unit staffing for each staff category and shift type was characterised as low. High patient turnover per day was defined as admissions, discharges and transfers 1 SD above unit annual daily averages.ResultsModels included cumulative counts of patient exposure to shifts with low RN staffing, low nursing support staffing, both concurrently and high patient turnover. The HR for exposure to shifts with low RN staffing only was 1.027 (95% CI 1.002 to 1.053, p<0.001), low nursing support only, 1.030 (95% CI 1.017 to 1.042, p<0.001) and shifts with both low, 1.025 (95% CI 1.008 to 1.043, p=0.035). For a model examining cumulative exposure over the second to fifth days of an admission, the HR for exposure to shifts with low RN staffing only was 1.048 (95% CI 0.998 to 1.100, p=0.061), low nursing support only, 1.032 (95% CI 1.008 to 1.057, p<0.01) and for shifts with both low,1.136 (95% CI 1.089 to 1.185, p<0.001). No relationship was observed for high patient turnover and mortality.ConclusionLow RN and nursing support staffing were associated with increased mortality. The results should encourage hospital leadership to assure both adequate RN and nursing support staffing.

Longitudinal Study of the Variation in Patient Turnover and Patient-to-Nurse Ratio: Descriptive Analysis of a Swiss University Hospital (Preprint)

BACKGROUND Variations in patient demand increase the challenge of balancing high-quality nursing skill mixes against budgetary constraints. Developing staffing guidelines that allow high-quality care at minimal cost requires first exploring the dynamic changes in nursing workload over the course of a day. OBJECTIVE Accordingly, this longitudinal study analyzed nursing care supply and demand in 30-minute increments over a period of 3 years. We assessed 5 care factors: patient count (care demand), nurse count (care supply), the patient-to-nurse ratio for each nurse group, extreme supply-demand mismatches, and patient turnover (ie, number of admissions, discharges, and transfers). METHODS Our retrospective analysis of data from the Inselspital University Hospital Bern, Switzerland included all inpatients and nurses working in their units from January 1, 2015 to December 31, 2017. Two data sources were used. The nurse staffing system (tacs) provided information about nurses and all the care they provided to patients, their working time, and admission, discharge, and transfer dates and times. The medical discharge data included patient demographics, further admission and discharge details, and diagnoses. Based on several identifiers, these two data sources were linked. RESULTS Our final dataset included more than 58 million data points for 128,484 patients and 4633 nurses across 70 units. Compared with patient turnover, fluctuations in the number of nurses were less pronounced. The differences mainly coincided with shifts (night, morning, evening). While the percentage of shifts with extreme staffing fluctuations ranged from fewer than 3% (mornings) to 30% (evenings and nights), the percentage within “normal” ranges ranged from fewer than 50% to more than 80%. Patient turnover occurred throughout the measurement period but was lowest at night. CONCLUSIONS Based on measurements of patient-to-nurse ratio and patient turnover at 30-minute intervals, our findings indicate that the patient count, which varies considerably throughout the day, is the key driver of changes in the patient-to-nurse ratio. This demand-side variability challenges the supply-side mandate to provide safe and reliable care. Detecting and describing patterns in variability such as these are key to appropriate staffing planning. This descriptive analysis was a first step towards identifying time-related variables to be considered for a predictive nurse staffing model.