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Generalizable items and modular structure for computerised physician staffing calculation on intensive care units.

ABSTRACT: Intensive care medicine remains one of the most cost-driving areas within hospitals with high personnel costs. Under the scope of limited budgets and reimbursement, realistic needs are essential to justify personnel staffing. Unfortunately, all existing staffing models are top-down calculations with a high variability in results. We present a workload-oriented model, integrating quality of care, efficiency of processes, legal, educational, controlling, local, organisational and economic aspects. In our model, the physician's workload solely related to the intensive care unit depends on three tasks: Patient-oriented tasks, divided in basic tasks (performed in every patient) and additional tasks (necessary in patients with specific diagnostic and therapeutic requirements depending on their specific illness, only), and non patient-oriented tasks. All three tasks have to be taken into account for calculating the required number of physicians. The calculation tool further allows to determine minimal personnel staffing, distribution of calculated personnel demand regarding type of employee due to working hours per year, shift work or standby duty. This model was introduced and described first by the German Board of Anesthesiologists and the German Society of Anesthesiology and Intensive Care Medicine in 2008 and since has been implemented and updated 2012 in Germany. The modular, flexible nature of the Excel-based calculation tool should allow adaption to the respective legal and organizational demands of different countries. After 8 years of experience with this calculation, we report the generalizable key aspects which may help physicians all around the world to justify realistic workload-oriented personnel staffing needs.

SUBMITTER: Weiss M

PROVIDER: S-EPMC5547429 | biostudies-other | 2017 Aug

REPOSITORIES: biostudies-other

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Project description:BackgroundIntensivists play an essential role in improving the outcomes of critically ill patients in intensive care units (ICUs). The transition of ICU physician staffing from low-intensity ICUs (elective intensivist or no intensivist consultation) to high-intensity ICUs (mandatory intensivist consultation or a closed ICU) improves clinical outcomes. However, whether a transition from high-intensity to low-intensity ICU staffing affects ICU outcomes and quality of care remains unknown.MethodsA retrospective observational study was conducted to examine the impact of high- versus low-intensity staffing models on all-cause mortality in a suburban secondary community hospital with 400 general beds and 8 ICU beds. The ICU was switched from a high-intensity staffing model (high-former period) to low-intensity staffing in July 2019 (low-mid period) and then back to high-intensity staffing in March 2020 (high-latter period). Patients admitted from the emergency department, general ward, or operating room after emergency surgery were enrolled in these three periods and compared, balancing the predicted mortality and covariates of the patients. The primary outcome was all-cause mortality analyzed using hazard ratios (HRs) from Cox proportional hazards regression. An interrupted time-series analysis (ITSA) was also conducted to evaluate the effects of events (level change) and time.ResultsThere were 962 eligible admissions, of which 251, 213, and 498 occurred in the high-former, low-mid, and high-latter periods, respectively. In the matched group (n = 600), the all-cause mortality rate comparing the high-former period with the low-mid period showed an HR of 0.88 [95% confidence interval (CI), 0.56, 1.39; p = 0.58] and that comparing the high-latter period with the low-mid period showed an HR of 0.84 [95% CI, 0.54, 1.30; p = 0.43]. The result for comparison between the three periods was p = 0.80. ITSA showed level changes of 4.05% [95% CI, -13.1, 21.2; p = 0.63] when ICU staffing changed from the high-former to the low-mid period and 1.35% [95% CI, -13.8, 16.5; p = 0.86] when ICU staffing changed from the low-mid to the high-latter period.ConclusionThere was no statistically significant difference in all-cause mortality among the three ICU staffing periods. This study suggests that low-intensity ICU staffing might not worsen clinical outcomes in the ICU in a medium-sized community hospital. Multiple factors, including the presence of an intensivist, other medical staff, and practical guidelines, influence the prognosis of critically ill patients.

Project description:BackgroundIncreasing numbers of intensive care units (ICUs) are adopting the practice of nighttime intensivist staffing despite the lack of experimental evidence of its effectiveness.MethodsWe conducted a 1-year randomized trial in an academic medical ICU of the effects of nighttime staffing with in-hospital intensivists (intervention) as compared with nighttime coverage by daytime intensivists who were available for consultation by telephone (control). We randomly assigned blocks of 7 consecutive nights to the intervention or the control strategy. The primary outcome was patients' length of stay in the ICU. Secondary outcomes were patients' length of stay in the hospital, ICU and in-hospital mortality, discharge disposition, and rates of readmission to the ICU. For length-of-stay outcomes, we performed time-to-event analyses, with data censored at the time of a patient's death or transfer to another ICU.ResultsA total of 1598 patients were included in the analyses. The median Acute Physiology and Chronic Health Evaluation (APACHE) III score (in which scores range from 0 to 299, with higher scores indicating more severe illness) was 67 (interquartile range, 47 to 91), the median length of stay in the ICU was 52.7 hours (interquartile range, 29.0 to 113.4), and mortality in the ICU was 18%. Patients who were admitted on intervention days were exposed to nighttime intensivists on more nights than were patients admitted on control days (median, 100% of nights [interquartile range, 67 to 100] vs. median, 0% [interquartile range, 0 to 33]; P<0.001). Nonetheless, intensivist staffing on the night of admission did not have a significant effect on the length of stay in the ICU (rate ratio for the time to ICU discharge, 0.98; 95% confidence interval [CI], 0.88 to 1.09; P=0.72), ICU mortality (relative risk, 1.07; 95% CI, 0.90 to 1.28), or any other end point. Analyses restricted to patients who were admitted at night showed similar results, as did sensitivity analyses that used different definitions of exposure and outcome.ConclusionsIn an academic medical ICU in the United States, nighttime in-hospital intensivist staffing did not improve patient outcomes. (Funded by University of Pennsylvania Health System and others; ClinicalTrials.gov number, NCT01434823.).

Project description:ObjectiveTo describe the organisation, staffing patterns and resources available in critical care units in Kenya. The secondary objective was to explore variations between units in the public and private sectors.Materials and methodsAn online cross-sectional survey was used to collect data on organisational characteristics (model of care, type of unit, quality- related activities, use of electronic medical records and participation in the national ICU registry), staffing and available resources for monitoring, ventilation and general critical care.ResultsThe survey included 60 of 75 identified units (80% response rate), with 43% (n = 23) located in government facilities. A total of 598 critical care beds were reported with a median of 6 beds (interquartile range [IQR] 5-11) per unit, with 26% beds (n = 157) being non functional. The proportion of ICU beds to total hospital beds was 3.8% (IQR 1.9-10.4). Most of the units (80%, n = 48) were mixed/general units with an open model of care (60%, n = 36). Consultants-in-charge were mainly anesthesiologists (69%, n = 37). The nurse-to-bed ratio was predominantly 1:2 with half of the nurses formally trained in critical care. Most units (83%, n = 47) had a dedicated ventilator for each bed, however 63% (n = 39) lacked high flow nasal therapy. While basic multiparametric monitoring was ubiquitous, invasive blood pressure measurement capacity was low (3% of beds, IQR 0-81%), and capnography moderate (31% of beds, IQR 0-77%). Blood gas analysers were widely available (93%, n = 56), with 80% reported as functional. Differences between the public and private sector were narrow.ConclusionThis study shows an established critical care network in Kenya, in terms of staffing density, availability of basic monitoring and ventilation resources. The public and private sector are equally represented albeit with modest differences. Potential areas for improvement include training, use of invasive blood pressure and functionality of blood gas analysers.

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Generalizable items and modular structure for computerised physician staffing calculation on intensive care units.

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