Implementing a Hospitalist Program in a Critical Access Hospital

Purpose

The hospitalist model of inpatient care has rapidly expanded, but little is known about hospitalist care in critical access hospitals (CAHs). We aimed to determine the impact of a hospitalist model of care on staff satisfaction, patient volumes, patient satisfaction, length of stay, and care quality in a CAH.

Methods

We initiated a hybrid rotating hospitalist program in September 2008 at Winneshiek Medical Center (Decorah, Iowa), a 25‐bed rural CAH. We reviewed patient volumes, Centers for Medicare and Medicaid Services core quality measures, acute length of stay, and staff satisfaction for primary care—hospitalist physicians and inpatient and clinic nurses. Patient volume and length of stay were compared with CAH data reported by the Iowa Hospital Association.

Findings

Patient volumes (acute, skilled, and observation) increased by 15% compared with a 17% decrease for statewide CAHs. Length of stay decreased from 2.88 to 2.75 days and remained lower than the average stay for Iowa CAHs (3.05 days). In the year after implementation, we observed no deterioration in core quality measures (range, 93%‐100%) or patient satisfaction (86th percentile). Inpatient nurse satisfaction and primary care‐hospitalist satisfaction improved. Early clinic nurse skepticism showed improved satisfaction at the 5‐year review.

Conclusions

Hospitalist care contributed to ongoing delivery of high‐quality care and satisfactory patient experiences while supporting the mission of a CAH in rural Iowa. Implementation required careful consideration of its effects on the outpatient practice. Broader implementation of this model in CAHs may be warranted.     

亚专业化新型排班管理在内分泌代谢科病房中的应用

目的探讨亚专业化新型排班管理在内分泌代谢科病房中的应用及效果。方法划分亚专业小组,根据亚专业分组实施新型排班,并比较实施前后护理质量、患者满意度和护士工作满意度。结果实施亚专业化新型排班管理后,护理质量、患者满意度、护士工作满意度均提高(P<0.01或P<0.05)。结论亚专业化新型排班管理能够深化优质护理服务内涵,保障护理质量,是一种有效的排班管理模式。     

改进的混沌蜂群算法在流水线调度中的应用

针对批量流水线调度问题,提出了一种改进的人工蜂群算法优化最大完成时间。该算法用NEH方法产生初始解,利用混沌的遍历性生成新的邻域解。为了跳出局部最优,使用最优解插入扰动替换一些连续若干步不能改进的解以提高算法的全局搜索能力,仿真实验结果验证了算法的可行性。     

Agents endowed with uncertainty management behaviors to solve a multiskill healthcare task scheduling

Health organizations are complex to manage due to their dynamic processes and distributed hospital organization. It is therefore necessary for healthcare institutions to focus on this issue to deal with patients’ requirements. We aim in this paper to develop and implement a management decision support system (DSS) that can help physicians to better manage their organization and anticipate the feature of overcrowding. Our objective is to optimize the Pediatric Emergency Department (PED) functioning characterized by stochastic arrivals of patients leading to its services overload. Human resources allocation presents additional complexity related to their different levels of skills and uncertain availability dates. So, we propose a new approach for multi-healthcare task scheduling based on a dynamic multi-agent system. Decisions about assignment and scheduling are the result of a cooperation and negotiation between agents with different behaviors. We therefore define the actors involved in the agents’ coalition to manage uncertainties related to the scheduling problem and we detail their behaviors. Agents have the same goal, which is to enhance care quality and minimize long waiting times while respecting degrees of emergency. Different visits to the PED services and regular meetings with the medical staff allowed us to model the PED architecture and identify the characteristics and different roles of the healthcare providers and the diverse aspects of the PED activities. Our approach is integrated in a DSS for the management of the Regional University Hospital Center (RUHC) of Lille (France). Our survey is included in the French National Research Agency (ANR) project HOST (Hôpital: Optimisation, Simulation et évitement des Tensions (ANR-11-TecSan-010: http://host.ec-lille.fr/wp-content/themes/twentyeleven/docsANR/R0/HOST-WP0.pdf)).     

Basing drug scheduling decisions on scientific ranking of harmfulness: false promise from false premises

In recent years a number of studies have attempted to rank drugs by a single measure of harmfulness as the basis for decisions about scheduling and classification. These efforts are fundamentally flawed, both conceptually and methodologically. The effort to provide a single measure masks the variety of non-comparable dimensions that are relevant, the fact that benefits are ignored for most, but not all, drugs and that the harms of a drug are not invariant to the policy regime chosen. Methodologically, the most prominent recent effort ignores drug interactions and mixes aggregate and individual harms inappropriately. Instead we suggest that multiple dimensions of harm need to be displayed to inform human judgments of what drugs should be scheduled. Harm is not usefully reducible to a single dimension, and even perfect rankings would not constitute a 'sufficient statistic' for determining scheduling decisions.     

对护士APN排班方式满意度的调查

目的了解护士对APN排班方式的满意度,初步探讨APN排班方式对护士工作及其个人和家庭的影响。方法采用自行设计的护士排班满意度调查问卷以不记名自填式问卷调查法,调查2个科室共51名执业护士对APN排班方式的满意度。经测定,问卷的Cronbach’S＆系数为0．724。结果已婚、本科学历的护士对APN排班方式满意度分别为69．57％、88．89％,高于未婚、专科学历的39．29％和47．62％,差异有统计学意义（x2=4．647,5．098;P均〈0．05）。护士认为APN排班方式使护士之间合作性增加,护患交流机会增多,业余可利用时间增加,夜班心理压力减轻。结论护士对APN排班的满意度较高,较之传统的排班方式,APN排班考虑了轮班工作对护士自身健康、家庭生活及业余学习的影响,更加人件化、科举化。     

平行机上单位加工时间加权总完工时间排序问题的反问题

在给定工序下, 排序问题的反问题研究目标是对于预先给定的加工任务, 要求确定加工时间或者工件权重的最小调整值, 使得给定的工件排序最优。 本文研究了平行机上单位加工时间的加权总完工时间排序问题的反问题, 即对于给定的加工工序, 在不同范数下, 通过最小限度调整工件的权值, 实现给定加工工序最优, 同时满足调整权值后,目标函数值不超过原来的值。     

量子进化算法在柔性作业车间调度问题中的应用

针对柔性作业车间调度完工时间最小化问题,提出了一种基于量子计算的量子进化算法。根据柔性作业车间调度问题的特点,设计出基于工序编码和基于机器编码的量子编码及解码方法。引入动态旋转角策略和跳跃基因算子,并通过实例验证了算法的有效性。     

我院静脉用药调配中心“4+2”排班模式的创新实践与体会

目的:建立适合静脉用药调配中心(PIVAS)工作实际需要的排班模式。方法:依据我院PIVAS的人员数量、操作台数量、日工作量,划分工作岗位及任务,逐步建立与完善"4+2"排班模式(即6d为1个周期,工作4d、休息2d),并与传统的7d工作制的排班模式比较。结果与结论:与传统工作模式比较,"4+2"排班模式更简单、明确,规律性强,易于灵活掌握,符合实际工作需要,更利于人性化管理。