scholarly journals Variation of daily care demand in Swiss general hospitals: a longitudinal study on capacity utilization, patient turnover and clinical complexity levels (Preprint)

10.2196/27163 ◽  
2021 ◽  
Author(s):  
Narayan Sharma ◽  
René Schwendimann ◽  
Olga Endrich ◽  
Dietmar Ausserhofer ◽  
Michael Simon
Keyword(s):  
Longitudinal Study ◽  
Capacity Utilization ◽  
General Hospitals ◽  
Daily Care ◽  
Clinical Complexity ◽  
Patient Turnover

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

2021 ◽  
Author(s):  
Narayan Sharma ◽  
René Schwendimann ◽  
Olga Endrich ◽  
Dietmar Ausserhofer ◽  
Michael Simon
Keyword(s):  
Longitudinal Study ◽  
Disease Severity ◽  
Capacity Utilization ◽  
Patient Volume ◽  
General Hospitals ◽  
Complexity Level ◽  
Daily Care ◽  
Clinical Complexity ◽  
Demand Fluctuations ◽  
Patient Turnover

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.

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

2019 ◽  
Author(s):  
Sarah N Musy ◽  
Olga Endrich ◽  
Alexander B Leichtle ◽  
Peter Griffiths ◽  
Christos T Nakas ◽  
...  
Keyword(s):  
Longitudinal Study ◽  
Descriptive Analysis ◽  
Nurse Staffing ◽  
Data Sources ◽  
University Hospital ◽  
Nursing Workload ◽  
High Quality ◽  
Discharge Data ◽  
Normal Ranges ◽  
Patient Turnover

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.

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

10.2196/15554 ◽  
2020 ◽  
Vol 22 (4) ◽  
pp. e15554
Author(s):  
Sarah N Musy ◽  
Olga Endrich ◽  
Alexander B Leichtle ◽  
Peter Griffiths ◽  
Christos T Nakas ◽  
...  
Keyword(s):  
Longitudinal Study ◽  
Descriptive Analysis ◽  
Nurse Staffing ◽  
Data Sources ◽  
University Hospital ◽  
Nursing Workload ◽  
High Quality ◽  
Discharge Data ◽  
Normal Ranges ◽  
Patient Turnover

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.

School Achievement and Adult Qualifications among Adoptees: A Longitudinal Study

1998 ◽  
Vol 39 (5) ◽  
pp. 669-685 ◽  
Author(s):  
Barbara Maughan ◽  
Stephan Collishaw ◽  
Andrew Pickles
Keyword(s):  
Longitudinal Study ◽  
School Achievement

A Longitudinal Posthumous Study of Letter Writing in a Late Elderly Case

2015 ◽  
Vol 20 (1) ◽  
pp. 22-33
Author(s):  
Angel Ball ◽  
Jean Neils-Strunjas ◽  
Kate Krival
Keyword(s):  
Longitudinal Study ◽  
Elderly Woman ◽  
Semantic Content ◽  
Cognitive Status ◽  
Letter Writing ◽  
Linguistic Performance ◽  
Errors Analysis ◽  
Normal Status

This study is a posthumous longitudinal study of consecutive letters written by an elderly woman from age 89 to 93. Findings reveal a consistent linguistic performance during the first 3 years, supporting “normal” status for late elderly writing. She produced clearly written cursive form, intact semantic content, and minimal spelling and stroke errors. A decline in writing was observed in the last 6–9 months of the study and an analysis revealed production of clausal fragmentation, decreasing semantic clarity, and a higher frequency of spelling, semantic, and stroke errors. Analysis of writing samples can be a valuable tool in documenting a change in cognitive status differentiated from normal late aging.

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