Measuring technical efficiency in health care organizations

The rising cost of health care has created great interest in developing methods to increase the efficiency of health care organizations. Despite this interest most analyses of prospective payment and other programs designed to control expenditures have examined costs and not efficiency. This article examines a new technique—data envelopment analysis (DEA)—that facilitates the conduct of efficiency studies. The utility of DEA is analyzed by comparing this technique with other methods used to measure efficiency, by discussing the application of DEA in the health care industry and by assessing the validity of results from DEA studies. The article concludes with an assessment of the strengths and weaknesses of DEA and suggestions for refining this technique.     

Design planning to meet goals in health service organizations

The performance of health service organizations is affected by the existence of multiple, often conflicting performance goals, and by various social-psychological processes that characterize the particular structure of the organization. This paper proposes a systematic, management-based methodology for identifying those “best” values for a set of social-psychological processes that enhance achievement of a set of organizational performance goals. The approach is illustrated with data from selected county health departments in North Carolina.     

Using demographic data for ambulatory health care planning: A dental health practice management model

The use of sociodemographic data in planning ambulatory health services is discussed and illustrated. Five global indices are identified as important for establishing contours of need within local community areas: social class, population heterogeneity, resident mobility, family organization, and general stress factors, Knowledge of sociodemographic distributions within a given community can serve as an adjunct for rational decision making in planning and placement of ambulatory health care services. It can also establish a means for evaluating whether extant health services reach their intended targets via comparisons to the social demography of patients receiving care in private practices or public clinics. Such analyses are germane to ambulatory health care practitioners in both the public and private sector.     

医护人员预立医疗照护计划培训需求评估工具的编制

目的 以培训需求分析(training needs analysis,TNA)理论中绩效分析模式为指导框架,编制医护人员预立医疗照护计划(advance care planning,ACP)培训需求评估工具条目池,为下一步培训需求调查工作提供研究基础。方法 先是以文献分析法及资料查阅初步构建ACP培训需求评估工具条目池,再依据Brislin翻译模型对初步构建的英文条目进行翻译及汉化调试,通过德尔菲专家咨询法及预调查对条目进行修订。结果 经文献分析及资料查阅,评估工具内容确定为23个条目,涉及三个主题,分别是基础知识、专科知识及沟通技能。翻译及回译后的量表整体语义评价较高,条目语义等价程度是78.6%~100.0%,量表语义等价程度是82.6%~100.0%。两轮咨询后专家积极性为100%,专家权威程度为0.87,专家咨询协调系数分别是0.271和0.353,各条目重要性均值4.29~5.00,变异系数0.05~0.11。40位进行量表表面效度评价的医护人员对量表各条目问法的认可度为87.5%~100.0%。结论 本研究培训需求评估工具编制过程科学严谨,编制结果良好,为进一步开展培训需求调查工作提供了参考。     

A systems approach to planning large-scale,integrated health care facilities

This paper presents an overview of a systems approach being implemented within the Kaiser-Permanente Medical Care Program (KPMCP) for the planning of large-scale, integrated health care facilities. This approach was motivated by the increasing change and complexity of factors, both external and internal to KPMCP, that now require more systematic treatment and that before were handled by more intuitive approaches. Among others, these factors include dealing with increasing regulation from government agencies and accommodating the active participation of knowledgeable personnel to ensure the continuation of a progressive and innovative system. Described within, along with more general issues, are various computer models, based upon Kaiser concepts and data that have been developed to facilitate the effective allocation of resources.     

Evolution of a computerized support system for health care capacity planning

This paper describes the evolution of a computerized support system for health care capacity planning within the Veterans Administration (VA) health care system. The VA's early attempts at computerization are described, along with the subsequent refinements made necessary by the deficiencies in those early attempts. The evolution of both the planning logic and the software configurations is set forth, along with some plans for future development.     

An analysis of standardized disease classification system for health care planning

This paper deals with the development of the standardized disease classification system, which generates usable statistics for comprehensive health care planning. It is used by ARCH (a regional health planning agency) to obtain a measure of acute hospital utilization and regional health status. A sample of 35,000 patients discharged from 39 acute-care facilities in the St. Louis bistate region was examined and tested. The existing disease classification systems (three versions of ICD and SNDO) were converted into the standardized system. Its application results in a better conceptualization of current health problems. It aids in the formulation and monitoring of health problems.     

A survey of extant organizational and computational setups for deploying predictive models in health systems

ObjectiveArtificial intelligence (AI) and machine learning (ML) enabled healthcare is now feasible for many health systems, yet little is known about effective strategies of system architecture and governance mechanisms for implementation. Our objective was to identify the different computational and organizational setups that early-adopter health systems have utilized to integrate AI/ML clinical decision support (AI-CDS) and scrutinize their trade-offs.Materials and MethodsWe conducted structured interviews with health systems with AI deployment experience about their organizational and computational setups for deploying AI-CDS at point of care.ResultsWe contacted 34 health systems and interviewed 20 healthcare sites (58% response rate). Twelve (60%) sites used the native electronic health record vendor configuration for model development and deployment, making it the most common shared infrastructure. Nine (45%) sites used alternative computational configurations which varied significantly. Organizational configurations for managing AI-CDS were distinguished by how they identified model needs, built and implemented models, and were separable into 3 major types: Decentralized translation (n = 10, 50%), IT Department led (n = 2, 10%), and AI in Healthcare (AIHC) Team (n = 8, 40%).DiscussionNo singular computational configuration enables all current use cases for AI-CDS. Health systems need to consider their desired applications for AI-CDS and whether investment in extending the off-the-shelf infrastructure is needed. Each organizational setup confers trade-offs for health systems planning strategies to implement AI-CDS.ConclusionHealth systems will be able to use this framework to understand strengths and weaknesses of alternative organizational and computational setups when designing their strategy for artificial intelligence.