Reorganization of nurse scheduling reduces the risk of healthcare associated infections.
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ABSTRACT:
SUBMITTER: Valdano E
PROVIDER: S-EPMC8016903 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Home health care routing and scheduling is a complex problem that requires many aspects to be considered simultaneously. One of the important aspects is ergonomics. Home health care nurses are at higher risk of work-related health problems such as musculoskeletal disorders and burnout since they are frequently exposed to physical, mental, and environmental ergonomic risks in their jobs. Therefore, it is essential to integrate ergonomic considerations into the construction of daily schedules for home health care nurses to mitigate these health risks. The purpose of this study is to present a mathematical model that incorporate ergonomic risks. We introduce a set of constraints into our model to prevent nurses from encountering excessive workloads. To assess the workload, we propose a subjective assessment method and employ a fuzzy inference system to calculate nurses' perceived workload levels. We applied our model to a several numerical examples to investigate the impact of workload on the nurse daily schedules. We observed that, at a specified workload level, there may be alternative solutions where the number of patients visited is the same. Therefore, we defined an objective function to maximize patient visits while minimizing nurses' workload levels as much as possible. As a result, our model generates solutions that effectively reduce nurse workloads, leading to more balanced schedules. Thus, our study offers a comprehensive approach to home health care scheduling by incorporating ergonomic considerations, ultimately enhancing both patient care and nurse well-being.
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Project description:As antibiotic resistance increases and the rate of antibiotic development slows, it is becoming more urgent to develop novel approaches to prevent and mitigate serious bacterial and fungal infections. Healthcare-associated infections (HAIs), including those caused by Clostridium difficile, Staphylococcus aureus, Pseudomonas aeruginosa, Acinetobacter baumannii, carbapenem-resistant Enterobacteriaceae, and Candida species, are a major cause of morbidity, mortality, and healthcare costs. HAIs are also a key driver of antibiotic use. Vaccines directed toward these pathogens could help prevent a large number of HAIs and associated antibiotic use if administered to targeted populations. Despite numerous scientific and operational challenges, there are vaccine candidates in late-stage clinical development for C. difficile, S. aureus, and P. aeruginosa Basic, preclinical, and early clinical research to develop vaccines for other types of HAIs is also under way. In addition, other prophylactic immune interventions, such as monoclonal antibodies, for several of these pathogens are in advanced development. Here we describe the promise, challenges, and current pipeline of vaccines to prevent HAIs.
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Project description:Nowadays and due to the pandemic of COVID-19, nurses are working under the highest pressure benevolently all over the world. This urgent situation can cause more fatigue for nurses who are responsible for taking care of COVID-19 patients 24 hours a day. Therefore, nurse scheduling should be modified with respect to this new situation. The purpose of the present research is to propose a new mathematical model for Nurse Scheduling Problem (NSP) considering the fatigue factor. To solve the proposed model, a hybrid Genetic Algorithm (GA) has been developed to provide a nurse schedule for all three shifts of a day. To validate the proposed approach, a randomly generated problem has been solved. In addition, to show the applicability of the proposed approach in real situations, the model has been solved for a real case study, a department in one of the hospitals in Esfahan, Iran, where COVID-19 patients are hospitalized. Consequently, a nurse schedule for May has been provided applying the proposed model, and the results approve its superiority in comparison with the manual schedule that is currently used in the department. To the best of our knowledge, it is the first study in which the proposed model takes the fatigue of nurses into account and provides a schedule based on it.
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Project description:IntroductionThe number of patients and clinical conditions treated in home healthcare (HHC) is increasing. Care in home settings presents many challenges, including healthcare-associated infections (HAI). Currently, in Belgium, data and guidelines on the topic are lacking.AimTo develop a definition of HAI in HHC and investigate associated risk factors and recommendations for infection prevention and control (IPC).MethodsThe study included three components: a scoping literature review, in-depth interviews with individuals involved in HHC and a two-round Delphi survey to reach consensus among key informants on the previous steps' results.ResultsThe literature review included 47 publications. We conducted 21 in-depth interviews. The Delphi survey's two rounds had 21 and 23 participants, respectively. No standard definition was broadly accepted or known. Evidence on associated risk factors was impacted by methodological limitations and recommendations were inconsistent. Agreement was reached on defining HAI in HHC as any infection specifically linked with providing care that develops in a patient receiving HHC from a professional healthcare worker and occurs ≥ 48 hours after starting HHC. Risk factors were hand hygiene, untrained patients and caregivers, patients' hygiene and presence and management of invasive devices. Agreed recommendations were to adapt and standardise existing IPC guidelines to HHC and to perform a national point prevalence study to measure the burden of HAI in HHC.ConclusionsThis study offers an overview of available evidence and field knowledge of HAI in HHC. It provides a framework for a prevalence study, future monitoring policies and guidelines on IPC in Belgium.
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Project description:Mycolicibacterium neoaurum is a rapidly growing mycobacterium and an emerging cause of human infections. M. neoaurum infections are uncommon but likely underreported, and our understanding of the disease spectrum and optimum management is incomplete. We summarize demographic and clinical characteristics of a case of catheter-related M. neoaurum bacteremia in a child with leukemia and those of 36 previously reported episodes of M. neoaurum infection. Most infections occurred in young to middle-aged adults with serious underlying medical conditions and commonly involved medical devices. Overall, infections were not associated with severe illness or death. In contrast to other mycobacteria species, M. neoaurum was generally susceptible to multiple antimicrobial drugs and responded promptly to treatment, and infections were associated with good outcomes after relatively short therapy duration and device removal. Delays in identification and susceptibility testing were common. We recommend using combination antimicrobial drug therapy and removal of infected devices to eradicate infection.
| S-EPMC10370869 | biostudies-literature
Project description:This article proposes a novel dynamic objective function in a multi-period home health care (HHC) problem, known as the nurse-patient relationship (NPR). The nurse-patient relationship score indicating the trust a patient has for his or her care worker increases when the same people meet regularly and decreases when they are apart. Managing human resources in HHC is a combination of routing and scheduling problems. Due to computational complexity of the HHC problem, a 28-day home health care problem is decomposed into daily subproblems, and solved sequentially with the tabu search. The solutions are then combined to give a solution to the original problem. For problems with less complex constraints, the NPR model can also be solved using exact methods such as CPLEX. For larger scale instances, however, the numerical results show that the NPR model can only be solved in reasonable times using our proposed tabu search approach. The solutions obtained from the NPR models are compared against those from existing models in the literature such as preference and continuity of care. Essentially, the analysis revealed that the proposed NPR models encouraged the search algorithm to assign the same care worker to visit the same patient. In addition, it had a tendency to assign a care worker on consecutive days to each patient, which is one of the key factors in promoting trust between patients and care workers. This leads to the efficacy of monitoring patient's disease progression and treatment.
| S-EPMC9140252 | biostudies-literature
Project description:The emergence of extensively drug-resistant Acinetobacter baumannii (XDRAB) is a serious threat to hospitalized patients. From 2008 to 2010, surveillance detected 25 hospital-acquired infection (HAI) cases caused by XDRAB at a medical center in Taipei. The site of XDRAB infection was bloodstream (n?=?8), urinary tract (n?=?12), lower respiratory tract (n?=?3), surgical site (n?=?1), and cardiovascular (n?=?1). The isolates were resistant to all currently available antibiotics except for colistin. The XDRAB isolates are genetically diverse, shown by pulsed-field gel electrophoresis, but 23 of 25 harbored class 1 integron with a 2.3-kb gene cassette. Most of these isolates carry OXA-23 (n?=?21) and OXA-51-like carbapenemase genes (n?=?25). To identify the risk factors, a case-control study was conducted. The 25 cases were compared with 100 controls randomly selected from hospitalized patients without XDRAB-HAIs, matched by the onset date, ward, and age, at a ratio of 1?4. Prior use of imipenem, meropenem, piperacillin/tazobactam or fourth-generation cephalosporins (adjusted OR: 3.2, 95% CI: 1.03-10.2, P?=?0.04) and >30 days bed-ridden (adjusted OR: 6.0, 95% CI: 1.3-27.6, P?=?0.02) were found to be the independent risk factors for XDRAB-HAIs. These findings highlight that, even in the absence of clonal dissemination, XDRAB can emerge under the selective pressure of broad-spectrum antibiotics and causes subsequent HAIs in compromised hosts. An appropriate response to the XDRAB threat therefore should include a component of prudent use of broad-spectrum antibiotics active against gram-negative bacteria.
| S-EPMC3897568 | biostudies-literature
Project description:BackgroundCoronavirus disease 2019 (COVID-19) pandemic disrupted adherences to healthcare-associated infection (HAI) prevention protocols. Herein, we studied the characteristics of all HAIs occurring in critically ill COVID-19 patients.MethodsA retrospective, single-center cohort of critical COVID-19 patients during 2021. Microbiological samples were collected if HAI was suspected. We analyzed all factors that could potentially induce HAI, using septic shock and mortality as endpoints.ResultsSixty-four among 161 included patients (39.7%) presented a total of 117 HAIs with an incidence density of 69.2 per 1,000 hospitalization days. Compared to the prior COVID-19 period (2013-2019), the identification of HAI increased in 2021. HAIs were classified into ventilator-associated pneumonia (VAP; n=38), bloodstream infection (n=32), urinary tract infection (n=24), catheter-related infection (n=12), and fungal infection (n=11). All HAIs occurred significantly earlier in the post-COVID-19 period (VAP: 6 vs. 10 days, P=0.045, in 2017 and 2021). Acinetobacter baumannii (39.5%) and Klebsiella pneumoniae (27%) were the most commonly isolated pathogens that exhibited a multidrug-resistant (MDR) profile, observed in 89% and 64.5%, respectively. The HAI factors were laboratory abnormalities (odds ratio [OR], 6.4; 95% confidence interval [CI], 2.3-26.0), cumulative steroid dose (OR, 1.9; 95% CI, 1.3-4.0), and invasive procedures (OR, 20.7; 95% CI, 5.3-64.0). HAI was an independent factor of mortality (OR, 8.5; P=0.004).ConclusionsDuring the COVID-19 era, the incidence of HAIs increased and MDR isolates remained frequent. A severe biological inflammatory syndrome, invasive devices, and elevated cumulative steroid dosages were related to HAIs. HAI was a significant death factor.
| S-EPMC10718505 | biostudies-literature
Project description:ObjectivesA nurse duty roster is usually prepared monthly in a hospital ward. It is common for nurses to make duty shift requests prior to scheduling. A ward manager normally spends more than a working day to manually prepare and subsequently to optimally adjust the schedule upon staff requests and hospital policies. This study aimed to develop an automatic nurse roster scheduling system with the use of open-source operational research tools by taking into account the hospital standards and the constraints from nurses.MethodsArtificial intelligence and end user tools operational research tools were used to develop the code for the nurse duty roster scheduling system. To compare with previous research on various heuristics in employee scheduling, the current system was developed on open architecture and adopted with real shift duty requirements in a hospital ward.ResultsThe schedule can be generated within 1 min under both hard and soft constraint optimization. All hard constraints are fulfilled and most nurse soft constraints could be met. Compared with those schedules prepared manually, the computer-generated schedules were more optimally adjusted as real time interaction among nurses and management personnel. The generated schedules were flexible to cope with daily and hourly duty changes by redeploying ward staff in order to maintain safe staffing levels.ConclusionsAn economical but yet highly efficient and user friendly solution to nurse roster scheduling system has been developed and adopted using open-source operational research methodology. The open-source platform is found to perform satisfactorily in scheduling application. The system can be implemented to different wards in hospitals and be regularly updated with new hospital polices and nurse manpower by hospital information personnel with training in artificial intelligence.
| S-EPMC9305000 | biostudies-literature
Project description:The challenge of healthcare-associated infections is compounded by the higher incidence of resistant organisms and the decreasing utility of antimicrobial agents. Historic and current vaccines have already contributed to reductions in healthcare-associated infections, and future vaccines have the potential to reduce these infections further. Through examples of bacterial and viral vaccines, this review will attempt to chart the way forward.
| S-EPMC5933536 | biostudies-literature