Utilization and productivity of the Duke physician's associate

The objectives of this study were as follows: (1) Formulate a theoretical model of the typical medical practice as a system for producing medical care, with particular attention to the role of middle-level health workers such as graduates of Duke University's Physician's Associate Program. (2) Develop a methodology for collecting data necessary to estimate the relevant parameters in the model, and to describe current patterns of utilization of Duke PA's. (3) Draw substantive conclusions, insofar as the model and data collection methodology permit, about the actual and potential productivity of Duke PA's.As of October 1971 Duke University had graduated 68 PA's from its Physician's Associate Program. Of these, 34 are employed as assistants in family practice or general medicine in a variety of private and institutional settings. Data were collected on eleven of these 34 PA's. No attempt was made to study the other 34 graduates engaged in clinical specialties, administration, or other functions. Neither was any attempt made to collect data on or draw inferences about the utilization and productivity of graduates of any other program such as the MEDEX Program.⁴     

Strategic sourcing and corporate social responsibility: Aligning a healthcare organization's strategic objectives

Strategic sourcing has long been utilized by organizations to maximize budget efficiency. The process includes a spend analysis, which historically has been performed by identifying the commodities and services purchased that resulted in the greatest spend, and establishing contracts with suppliers for these items in an effort to decrease the overall price through quantity discounts. This process restricts the data used in the spend analysis process to basic transactional information, and has not considered corporate social responsibility objectives as part of the strategic sourcing process. This paper modifies an existing spend analysis process framework, and applies the framework in a case study that uses additional data points to identify opportunities that allow an organization to simultaneously achieve both strategic purchasing and social responsibility objectives. The case study uses healthcare purchasing data from eight Department of Veterans Affairs medical centers. The goal of the model generated using regression analysis in the case study is to determine the buy characteristics that are most likely to generate mandated savings within the medical centers, in conjunction with achieving sustainability goals. The extensions of the regression model were examined to determine how collaborative buyer/supplier relationships can achieve organizational strategic objectives.     

订单管理在汽车制造企业的应用

随着汽车企业的竞争不断加剧,行业利润率降低,制造策略对汽车生产企业显得尤为重要。文章介绍了汽车订单供应线"一体化产量计划"流程,分析了汽车制造业务模型特点,说明订单管理在汽车制造企业中的作用。     

Fairness perceptions and work attitudes revisited: roles of employee specialty,hospital level and ownership

Compensation is widely acknowledged as an important job element in the eyes of employees. Health care is a special industry in which compensation received by employees differs greatly. This study examines empirically the relationships between hospital employees' perceptions of compensation fairness and their work attitudes, taking into account the roles of employee specialty, hospital level and ownership. Using data from 2,938 employees of thirty hospitals in Taiwan, the results indicate that fairness perceptions and work attitudes differ significantly among hospital employees according to their specialties. Hospital level and ownership exert impacts on employees' fairness perceptions, although not on their work attitudes. A positive relationship is observed between fairness perceptions and work attitudes of hospital employees.     

Discovering medical quality of total hip arthroplasty by rough set classifier with imbalanced class

The incidence of THA (total hip arthroplasty) will rise with an aging population and improvements in surgery, a feasible alternative in health care can effectively increase medical quality. The reason of a hip joint replaced is to relieve severe arthritis pain that is limiting your activities. Hip joint replacement is usually done in people age 60 and older. Younger people who have a hip replaced may put extra stress on the artificial hip. This paper uses a serious data screening function by experts to reduce data dimension after data collection from the National Health Insurance database. The proposed model also adopts an imbalanced sampling method to solve class imbalance problem, and utilizes rough set theory to find out core attributes (selected 7 features). Based on the core attributes, the extracted rules can be comprehensive for the rules of medical quality. In verification, THA dataset is taken as case study; the performance of the proposed model is verified and compared with other data-mining methods under various criteria. Furthermore, the performance of the proposed model is identified as winning the listing methods, as well as using hybrid-sampling can increase the far true-positive rate (minority class). The results show that the proposed model is efficient; the performance is superior to the listing methods under the listing criteria. And the generated decision rules and core attributes could find more managerial implication. Moreover, the result can provide stakeholders with useful THA information to help make decision.     

急诊实践教学方法探讨:PBL联合模拟教学法

急诊医学作为一门临床综合学科,其医学的范畴涉及多个专科,具有急迫性、综合性、实践性、复杂性等特点,实践教学难度较大。我院在急诊医学实践教学中,采用以问题为基础的教学法(PBL)联合模拟教学法,显著提高学生的学习主动性,有效地建立急诊临床思维,培养学生实践能力及知识运用能力,促进急诊医学理论知识的深入掌握,提高教学效果。     

信息生命周期管理模型在医疗数据保存中的应用

根据信息生命周期的理念,结合医疗数据的特征,借鉴了同业最佳实践,制定了医院行业的信息生命周期管理模型。通过信息生命周期管理模型,指导并规范医院数据的统一存储,并根据数据的重要性进行数据的分类,不同种类的数据存放到不同级别的存储设备上,这样既能更好地为外部提供服务,又能节省数据的使用成本。     

基于计量回归模型的我国医疗服务价格规制效果实证分析

当今中国老百姓"看病贵、看病难"的问题受到了社会各界广泛关注,其中医疗服务市场的核心因素——医疗服务价格直接决定了医疗费用高低和医疗服务利用程度。医疗服务价格规制政策不合理或国家对医疗服务规制不到位是造成目前我国医疗服务市场突出问题的重要原因。本文根据我国基本国情,采用最新的二手数据构建计量回归模型,并在多元回归分析中使用了Prais-Winsten AR(1)迭代法。通过实证的方法证明了医疗服务价格规制是影响医疗费用的重要因素,并得出价格规制出现了内部结构性扭曲的结论。     

Analysis of a mixed model for ordinal data by iterative re-weighted REML

An estimation procedure will be presented for a class of threshold models for ordinal data. These models may include both fixed and random effects with associated components of variance on an underlying scale. The residual error distribution on the underlying scale may be rendered greater flexibility by introducing additional shape parameters, e.g. a kurtosis parameter or parameters to model heterogeneous residual variances as a function of factors and covariates. The estimation procedure is an extension of an iterative re-weighted restricted maximum likelihood procedure, originally developed for generalized linear mixed models. This procedure will be illustrated with a practical problem involving damage to potato tubers and with data from animal breeding and medical research from the literature.     

Aggregate nursing requirement planning in a public health care delivery system.

In this paper, we present an aggregate nursing requirement planning model for inpatient services to provide inputs for preparing yearly budgets in a public health care delivery system. A forecasting system using autoregressive integrated moving average time-series models forms the basis of project demands for nursing hours by medical specialities. These projections along with the institutional constraints and patient care requirements are all incorporated in a linear programming model for assessing needs for permanent staff, overtime pay and contracting temporary help—by medical service, nursing skill level and time period (month). We also expand the model to evaluate the sizing of a pool of float nurses. The model is developed within the framework of routine managerial planning process of the system under study (vis-à vis, the data base and the organizational structure). Historical data are used to estimate input parameters for the model. The staffing needs generated from the model for 1978 are compared with the actual system performance.     

Development of an integrated medical supply information system

The integrated medical supply inventory control system introduced in this study is a hybrid system that is shaped by the nature of medical supply, usage and storage capacity limitations of health care facilities. The system links demand, service provided at the clinic, health care service provider's information, inventory storage data and decision support tools into an integrated information system. ABC analysis method, economic order quantity model, two-bin method and safety stock concept are applied as decision support models to tackle inventory management issues at health care facilities. In the decision support module, each medical item and storage location has been scrutinised to determine the best-fit inventory control policy. The pilot case study demonstrates that the integrated medical supply information system holds several advantages for inventory managers, since it entails benefits of deploying enterprise information systems to manage medical supply and better patient services.     

高职特色专业建设的探索与实践

内涵建设已成为当今高职教育发展的主流,在内涵建设过程中,特色专业建设是重要的组成部分。西安铁路职业技术学院专业特色鲜明,特色专业建设成绩突出,得到了行业及社会认可。分析了该院特色专业建设的基本做法,对"铁道机车车辆"专业进行了特色剖析,并根据学院特色专业建设的成效,提出了高职院校特色专业建设的建议。     

基于CDIO模型的一流专业混合式教学模式研究与实践——以“物流学”为例

针对一流本科专业的学生能力培养目标,结合CDIO教育管理理念,提出了"线上平台和线下课堂"的混合式教学实施方法,并以"物流学"为例阐述具体改革内容。混合式教学促使教学闭环管理过程更为可行和有效,对教师教学水平提升和学科一流专业建设具有重要意义。     

Bayesian analysis of the ordered probit model with endogenous selection

This paper presents a Bayesian analysis of an ordered probit model with endogenous selection. The model can be applied when analyzing ordered outcomes that depend on endogenous covariates that are discrete choice indicators modeled by a multinomial probit model. The model is illustrated by analyzing the effects of different types of medical insurance plans on the level of hospital utilization, allowing for potential endogeneity of insurance status. The estimation is performed using the Markov chain Monte Carlo (MCMC) methods to approximate the posterior distribution of the parameters in the model.     

An application of a spatially distributed queuing model to an ambulance system

An application of a spatially distributed queuing model to an ambulance system is presented. The purpose of this research was to assess the usefulness of a variation of the “hypercube” queuing model developed specifically for modeling an ambulance system. The model was applied to the emergency medical system of Greenville County, South Carolina using historical data. Results indicate that the model provides reasonably accurate estimates of system performance measures when the input parameters can be accurately specified.     

Application of Shrinkage Techniques in Logistic Regression Analysis: A Case Study

Logistic regression analysis may well be used to develop a predictive model for a dichotomous medical outcome, such as short-term mortality. When the data set is small compared to the number of covariables studied, shrinkage techniques may improve predictions. We compared the performance of three variants of shrinkage techniques: 1) a linear shrinkage factor, which shrinks all coefficients with the same factor; 2) penalized maximum likelihood (or ridge regression), where a penalty factor is added to the likelihood function such that coefficients are shrunk individually according to the variance of each covariable; 3) the Lasso, which shrinks some coefficients to zero by setting a constraint on the sum of the absolute values of the coefficients of standardized covariables.
Logistic regression models were constructed to predict 30-day mortality after acute myocardial infarction. Small data sets were created from a large randomized controlled trial, half of which provided independent validation data. We found that all three shrinkage techniques improved the calibration of predictions compared to the standard maximum likelihood estimates. This study illustrates that shrinkage is a valuable tool to overcome some of the problems of overfitting in medical data.     

The insight-value-perception (iVP) model for user-centered design

Through an inductive, multi-case analysis of eight medical device firms, this paper maps user-centered design and development practices to product performance outcomes. First, the data highlight that designing products under variable use conditions enabled firms to optimize products for widespread use and to increase the predictability of product outcomes. Second, the study shows that product adoption relied on maximizing benefits for multiple product stakeholders with often-conflicting needs, while minimizing required changes in end user behavior. Third, the paper highlights that managing perceptions toward product use through performance data positively influenced product adoption among the cases studied to a greater degree than did market demand or regulatory clearance. Fourth, rival explanations for outcomes involving limited user involvement, indicate that competitive advantages were achieved through maintaining dominant financial and intellectual property positions. From these findings, the insight-value-perception (iVP) model for user-centered design was constructed. This model links design and development practices to outcomes, from a combination of consumer-oriented, technical, and financial perspectives. Although rooted in the experiences of device developers within early stage medical device companies, the iVP Model may be used as a theoretical framework to examine a range of complex systems involving an embedded network of product users and stakeholders.     

MULTIPLE EVENT INCIDENCE AND DURATION ANALYSIS FOR CREDIT DATA INCORPORATING NON‐STOCHASTIC LOAN MATURITY

Applications of duration analysis in economics and finance exclusively employ methods for events of stochastic duration. In application to credit data, previous research incorrectly treats the time to predetermined maturity events as censored stochastic event times. The medical literature has binary parametric ‘cure rate’ models that deal with populations that never experienced the modelled event. We propose and develop a multinomial parametric incidence and duration model, incorporating such populations. In the class of cure rate models, this is the first fully parametric multinomial model and is the first framework to accommodate an event with predetermined duration. The methodology is applied to unsecured personal loan credit data provided by one of Australia's largest financial services organizations. This framework is shown to be more flexible and predictive through a simulation and empirical study that reveals: simulation results of estimated parameters with a large reduction in bias; superior forecasting of duration; explanatory variables can act in different directions upon incidence and duration; and variables exist that are statistically significant in explaining only incidence or duration. Copyright © 2013 John Wiley & Sons, Ltd.     

Modeling emergency medical response to a mass casualty incident using agent based simulation

Emergency managers have to develop plans for responding to disasters within their jurisdiction. This includes coordinating multiple independent agencies participating in the response. While much of this is currently done by use of intuition and expert judgment, models can be used to test assumptions and examine the impact of policies and resource levels. The autonomous nature of responders as well as the rapidly changing information during a disaster suggests that agent based models can be especially suited for examining policy questions. In this work, we built an agent based model of a given urban area to simulate the emergency medical response to a mass casualty incident (MCI) in that area. The model was constructed from publicly available geographic information system and data regarding available response resources (such as ambulances, EMS personnel and hospital beds). Three different agent types are defined to model heterogeneous entities in the system. By simulating various response policies, the model can inform emergency responders on the requirements and response protocols for disaster response and build intuition and understanding in advance of facing actual incidents that are rare in the day-to-day operating experiences.     

Sustainable inventory management in blood banks considering health equity using a combined metaheuristic-based robust fuzzy stochastic programming

This study examines the challenges of healthcare systems toward sustainable inventory management of blood products. In this regard, three main goals are pursued; First, promoting social equity in providing medical services to various patients and reducing the risk to lives. Second, the optimal management of medical products in a way that minimizes economic costs. Third, optimal management of biological waste due to inventory corruption and reduction of greenhouse gas emissions caused by transportation, to the least environmental pollution. To achieve these purposes, a practical demand-driven multi-objective inventory model is presented by utilizing hybrid policies in an uncertain environment. In the proposed model, the demands are in two types, including elective and non-elective. These demands are classified according to their medical urgency, substitution allowance, and product freshness. A hybrid robust fuzzy stochastic programming approach is applied to capture real-world uncertainties. The proposed model is implemented for blood platelet. The solution is obtained using a combined metaheuristic technique established on genetic algorithms and simulated annealing according to global and local search paradigms. To create a proper perspective for decision-makers, sensitivity analysis is performed. Besides, the performance of the model is proved by the realization. The results show that the performance of the proposed RFSP model is better than the Nominal model. Also, it performs well in minimizing the overall system costs and environmental degradation, besides reducing shortage and wastage. It is also effective for taking steps toward equity in health and suggests a proper strategy for dealing with emergencies.