Who should see the patient? on deviations from preferred patient-provider assignments in hospitals.

IF 2.3 3区 医学 Q2 HEALTH POLICY & SERVICES Health Care Management Science Pub Date : 2023-06-01 DOI:10.1007/s10729-022-09628-x
Mariam K Atkinson, Soroush Saghafian
{"title":"Who should see the patient? on deviations from preferred patient-provider assignments in hospitals.","authors":"Mariam K Atkinson,&nbsp;Soroush Saghafian","doi":"10.1007/s10729-022-09628-x","DOIUrl":null,"url":null,"abstract":"<p><p>In various organizations including hospitals, individuals are not forced to follow specific assignments, and thus, deviations from preferred task assignments are common. This is due to the conventional wisdom that professionals should be given the flexibility to deviate from preferred assignments as needed. It is unclear, however, whether and when this conventional wisdom is true. We use evidence on the assignments of generalist and specialists to patients in our partner hospital (a children's hospital), and generate insights into whether and when hospital administrators should disallow such flexibility. We do so by identifying 73 top medical diagnoses and using detailed patient-level electronic medical record (EMR) data of more than 4,700 hospitalizations. In parallel, we conduct a survey of medical experts and utilized it to identify the preferred provider type that should have been assigned to each patient. Using these two sources of data, we examine the consequence of deviations from preferred provider assignments on three sets of performance measures: operational efficiency (measured by length of stay), quality of care (measured by 30-day readmissions and adverse events), and cost (measured by total charges). We find that deviating from preferred assignments is beneficial for task types (patients' diagnosis in our setting) that are either (a) well-defined (improving operational efficiency and costs), or (b) require high contact (improving costs and adverse events, though at the expense of lower operational efficiency). For other task types (e.g., highly complex or resource-intensive tasks), we observe that deviations are either detrimental or yield no tangible benefits, and thus, hospitals should try to eliminate them (e.g., by developing and enforcing assignment guidelines). To understand the causal mechanism behind our results, we make use of mediation analysis and find that utilizing advanced imaging (e.g., MRIs, CT scans, or nuclear radiology) plays an important role in how deviations impact performance outcomes. Our findings also provide evidence for a \"no free lunch\" theorem: while for some task types, deviations are beneficial for certain performance outcomes, they can simultaneously degrade performance in terms of other dimensions. To provide clear recommendations for hospital administrators, we also consider counterfactual scenarios corresponding to imposing the preferred assignments fully or partially, and perform cost-effectiveness analyses. Our results indicate that enforcing the preferred assignments either for all tasks or only for resource-intensive tasks is cost-effective, with the latter being the superior policy. Finally, by comparing deviations during weekdays and weekends, early shifts and late shifts, and high congestion and low congestion periods, our results shed light on some environmental conditions under which deviations occur more in practice.</p>","PeriodicalId":12903,"journal":{"name":"Health Care Management Science","volume":"26 2","pages":"165-199"},"PeriodicalIF":2.3000,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Health Care Management Science","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1007/s10729-022-09628-x","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"HEALTH POLICY & SERVICES","Score":null,"Total":0}
引用次数: 2

Abstract

In various organizations including hospitals, individuals are not forced to follow specific assignments, and thus, deviations from preferred task assignments are common. This is due to the conventional wisdom that professionals should be given the flexibility to deviate from preferred assignments as needed. It is unclear, however, whether and when this conventional wisdom is true. We use evidence on the assignments of generalist and specialists to patients in our partner hospital (a children's hospital), and generate insights into whether and when hospital administrators should disallow such flexibility. We do so by identifying 73 top medical diagnoses and using detailed patient-level electronic medical record (EMR) data of more than 4,700 hospitalizations. In parallel, we conduct a survey of medical experts and utilized it to identify the preferred provider type that should have been assigned to each patient. Using these two sources of data, we examine the consequence of deviations from preferred provider assignments on three sets of performance measures: operational efficiency (measured by length of stay), quality of care (measured by 30-day readmissions and adverse events), and cost (measured by total charges). We find that deviating from preferred assignments is beneficial for task types (patients' diagnosis in our setting) that are either (a) well-defined (improving operational efficiency and costs), or (b) require high contact (improving costs and adverse events, though at the expense of lower operational efficiency). For other task types (e.g., highly complex or resource-intensive tasks), we observe that deviations are either detrimental or yield no tangible benefits, and thus, hospitals should try to eliminate them (e.g., by developing and enforcing assignment guidelines). To understand the causal mechanism behind our results, we make use of mediation analysis and find that utilizing advanced imaging (e.g., MRIs, CT scans, or nuclear radiology) plays an important role in how deviations impact performance outcomes. Our findings also provide evidence for a "no free lunch" theorem: while for some task types, deviations are beneficial for certain performance outcomes, they can simultaneously degrade performance in terms of other dimensions. To provide clear recommendations for hospital administrators, we also consider counterfactual scenarios corresponding to imposing the preferred assignments fully or partially, and perform cost-effectiveness analyses. Our results indicate that enforcing the preferred assignments either for all tasks or only for resource-intensive tasks is cost-effective, with the latter being the superior policy. Finally, by comparing deviations during weekdays and weekends, early shifts and late shifts, and high congestion and low congestion periods, our results shed light on some environmental conditions under which deviations occur more in practice.

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
谁应该看病人?关于医院中首选患者-提供者分配的偏差。
在包括医院在内的各种组织中,个人并不被迫遵循特定的任务分配,因此,偏离首选任务分配的情况很常见。这是由于传统观念认为,专业人员应该根据需要灵活地偏离首选任务。然而,目前尚不清楚这种传统观点是否正确,以及何时正确。我们利用我们合作医院(一家儿童医院)的通才和专家分配给病人的证据,得出医院管理者是否以及何时应该禁止这种灵活性的见解。为此,我们确定了73种最常见的医疗诊断,并使用了4700多例住院治疗的详细患者电子病历(EMR)数据。同时,我们对医学专家进行调查,并利用它来确定应该分配给每个病人的首选提供者类型。使用这两个数据来源,我们检查了偏离首选提供者分配对三组绩效指标的影响:运营效率(以住院时间衡量),护理质量(以30天再入院和不良事件衡量)和成本(以总费用衡量)。我们发现,偏离首选分配对于任务类型(在我们的设置中患者的诊断)是有益的,这些任务类型要么(a)定义明确(提高操作效率和成本),要么(b)需要高接触(提高成本和不良事件,尽管以降低操作效率为代价)。对于其他任务类型(例如,高度复杂或资源密集型任务),我们观察到偏差要么是有害的,要么不会产生切实的好处,因此,医院应该尝试消除它们(例如,通过制定和执行分配指导方针)。为了理解结果背后的因果机制,我们利用中介分析,发现利用先进的成像技术(如核磁共振、CT扫描或核放射学)在偏差如何影响表现结果方面起着重要作用。我们的发现也为“天下没有免费的午餐”定理提供了证据:虽然对于某些任务类型,偏差对某些性能结果是有益的,但它们同时会在其他方面降低性能。为了给医院管理者提供明确的建议,我们还考虑了与完全或部分实施优先分配相对应的反事实情景,并进行了成本效益分析。我们的研究结果表明,对所有任务或仅对资源密集型任务执行优先分配具有成本效益,后者是更优的策略。最后,通过比较工作日和周末、早班和晚班、高拥堵和低拥堵期间的偏差,我们的研究结果揭示了在实践中偏差更容易发生的一些环境条件。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Health Care Management Science
Health Care Management Science HEALTH POLICY & SERVICES-
CiteScore
7.20
自引率
5.60%
发文量
40
期刊介绍: Health Care Management Science publishes papers dealing with health care delivery, health care management, and health care policy. Papers should have a decision focus and make use of quantitative methods including management science, operations research, analytics, machine learning, and other emerging areas. Articles must clearly articulate the relevance and the realized or potential impact of the work. Applied research will be considered and is of particular interest if there is evidence that it was implemented or informed a decision-making process. Papers describing routine applications of known methods are discouraged. Authors are encouraged to disclose all data and analyses thereof, and to provide computational code when appropriate. Editorial statements for the individual departments are provided below. Health Care Analytics Departmental Editors: Margrét Bjarnadóttir, University of Maryland Nan Kong, Purdue University With the explosion in computing power and available data, we have seen fast changes in the analytics applied in the healthcare space. The Health Care Analytics department welcomes papers applying a broad range of analytical approaches, including those rooted in machine learning, survival analysis, and complex event analysis, that allow healthcare professionals to find opportunities for improvement in health system management, patient engagement, spending, and diagnosis. We especially encourage papers that combine predictive and prescriptive analytics to improve decision making and health care outcomes. The contribution of papers can be across multiple dimensions including new methodology, novel modeling techniques and health care through real-world cohort studies. Papers that are methodologically focused need in addition to show practical relevance. Similarly papers that are application focused should clearly demonstrate improvements over the status quo and available approaches by applying rigorous analytics. Health Care Operations Management Departmental Editors: Nilay Tanik Argon, University of North Carolina at Chapel Hill Bob Batt, University of Wisconsin The department invites high-quality papers on the design, control, and analysis of operations at healthcare systems. We seek papers on classical operations management issues (such as scheduling, routing, queuing, transportation, patient flow, and quality) as well as non-traditional problems driven by everchanging healthcare practice. Empirical, experimental, and analytical (model based) methodologies are all welcome. Papers may draw theory from across disciplines, and should provide insight into improving operations from the perspective of patients, service providers, organizations (municipal/government/industry), and/or society. Health Care Management Science Practice Departmental Editor: Vikram Tiwari, Vanderbilt University Medical Center The department seeks research from academicians and practitioners that highlights Management Science based solutions directly relevant to the practice of healthcare. Relevance is judged by the impact on practice, as well as the degree to which researchers engaged with practitioners in understanding the problem context and in developing the solution. Validity, that is, the extent to which the results presented do or would apply in practice is a key evaluation criterion. In addition to meeting the journal’s standards of originality and substantial contribution to knowledge creation, research that can be replicated in other organizations is encouraged. Papers describing unsuccessful applied research projects may be considered if there are generalizable learning points addressing why the project was unsuccessful. Health Care Productivity Analysis Departmental Editor: Jonas Schreyögg, University of Hamburg The department invites papers with rigorous methods and significant impact for policy and practice. Papers typically apply theory and techniques to measuring productivity in health care organizations and systems. The journal welcomes state-of-the-art parametric as well as non-parametric techniques such as data envelopment analysis, stochastic frontier analysis or partial frontier analysis. The contribution of papers can be manifold including new methodology, novel combination of existing methods or application of existing methods to new contexts. Empirical papers should produce results generalizable beyond a selected set of health care organizations. All papers should include a section on implications for management or policy to enhance productivity. Public Health Policy and Medical Decision Making Departmental Editors: Ebru Bish, University of Alabama Julie L. Higle, University of Southern California The department invites high quality papers that use data-driven methods to address important problems that arise in public health policy and medical decision-making domains. We welcome submissions that develop and apply mathematical and computational models in support of data-driven and model-based analyses for these problems. The Public Health Policy and Medical Decision-Making Department is particularly interested in papers that: Study high-impact problems involving health policy, treatment planning and design, and clinical applications; Develop original data-driven models, including those that integrate disease modeling with screening and/or treatment guidelines; Use model-based analyses as decision making-tools to identify optimal solutions, insights, recommendations. Articles must clearly articulate the relevance of the work to decision and/or policy makers and the potential impact on patients and/or society. Papers will include articulated contributions within the methodological domain, which may include modeling, analytical, or computational methodologies. Emerging Topics Departmental Editor: Alec Morton, University of Strathclyde Emerging Topics will handle papers which use innovative quantitative methods to shed light on frontier issues in healthcare management and policy. Such papers may deal with analytic challenges arising from novel health technologies or new organizational forms. Papers falling under this department may also deal with the analysis of new forms of data which are increasingly captured as health systems become more and more digitized.
期刊最新文献
Assessing the performance of Portuguese public hospitals before and during COVID-19 outbreak, with optimistic and pessimistic benchmarking approaches. A reinforcement learning approach for the online dynamic home health care scheduling problem. Evaluating machine learning model bias and racial disparities in non-small cell lung cancer using SEER registry data. Forecasting to support EMS tactical planning: what is important and what is not. Health care management science for underserved populations.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1