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数字病理学能带来成本节约吗?一家大型综合医疗保健机构实施数字病理学的财务预测。

Can digital pathology result in cost savings? A financial projection for digital pathology implementation at a large integrated health care organization.

作者信息

Ho Jonhan, Ahlers Stefan M, Stratman Curtis, Aridor Orly, Pantanowitz Liron, Fine Jeffrey L, Kuzmishin John A, Montalto Michael C, Parwani Anil V

机构信息

Department of Dermatology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA.

International and Commercial Services Division, UPMC, Pittsburgh, PA, USA.

出版信息

J Pathol Inform. 2014 Aug 28;5(1):33. doi: 10.4103/2153-3539.139714. eCollection 2014.

Abstract

BACKGROUND

Digital pathology offers potential improvements in workflow and interpretive accuracy. Although currently digital pathology is commonly used for research and education, its clinical use has been limited to niche applications such as frozen sections and remote second opinion consultations. This is mainly due to regulatory hurdles, but also to a dearth of data supporting a positive economic cost-benefit. Large scale adoption of digital pathology and the integration of digital slides into the routine anatomic/surgical pathology "slide less" clinical workflow will occur only if digital pathology will offer a quantifiable benefit, which could come in the form of more efficient and/or higher quality care.

AIM

As a large academic-based health care organization expecting to adopt digital pathology for primary diagnosis upon its regulatory approval, our institution estimated potential operational cost savings offered by the implementation of an enterprise-wide digital pathology system (DPS).

METHODS

Projected cost savings were calculated for the first 5 years following implementation of a DPS based on operational data collected from the pathology department. Projected savings were based on two factors: (1) Productivity and lab consolidation savings; and (2) avoided treatment costs due to improvements in the accuracy of cancer diagnoses among nonsubspecialty pathologists. Detailed analyses of incremental treatment costs due to interpretive errors, resulting in either a false positive or false negative diagnosis, was performed for melanoma and breast cancer and extrapolated to 10 other common cancers.

RESULTS

When phased in over 5-years, total cost savings based on anticipated improvements in pathology productivity and histology lab consolidation were estimated at $12.4 million for an institution with 219,000 annual accessions. The main contributing factors to these savings were gains in pathologist clinical full-time equivalent capacity impacted by improved pathologist productivity and workload distribution. Expanding the current localized specialty sign-out model to an enterprise-wide shared general/subspecialist sign-out model could potentially reduce costs of incorrect treatment by $5.4 million. These calculations were based on annual over and under treatment costs for breast cancer and melanoma estimated to be approximately $26,000 and $11,000/case, respectively, and extrapolated to $21,500/case for other cancer types.

CONCLUSIONS

The projected 5-year total cost savings for our large academic-based health care organization upon fully implementing a DPS was approximately $18 million. If the costs of digital pathology acquisition and implementation do not exceed this value, the return on investment becomes attractive to hospital administrators. Furthermore, improved patient outcome enabled by this technology strengthens the argument supporting adoption of an enterprise-wide DPS.

摘要

背景

数字病理学有望改善工作流程并提高诊断准确性。尽管目前数字病理学常用于研究和教育,但其临床应用仅限于小众领域,如冰冻切片和远程二次诊断会诊。这主要是由于监管障碍,也因为缺乏支持积极经济成本效益的数据。只有当数字病理学能带来可量化的益处,如更高效和/或更高质量的医疗服务时,数字病理学才能被大规模采用,并将数字切片整合到常规解剖/外科病理学“无切片”临床工作流程中。

目的

作为一家大型学术型医疗保健机构,预计在获得监管批准后将采用数字病理学进行初步诊断,我们的机构估算了实施企业级数字病理学系统(DPS)可能带来的运营成本节省。

方法

根据从病理科收集的运营数据,计算实施DPS后前5年预计节省的成本。预计节省基于两个因素:(1)生产力提高和实验室整合节省的成本;(2)由于非专科病理学家癌症诊断准确性提高而避免的治疗成本。对黑色素瘤和乳腺癌因解释错误导致的增量治疗成本进行了详细分析,包括假阳性或假阴性诊断,并推算到其他10种常见癌症。

结果

对于一家年病例量为219,000例的机构,如果在5年内逐步实施,基于病理学生产力预期提高和组织学实验室整合,预计总成本节省为1240万美元。这些节省的主要因素是病理学家临床全职等效能力的提升,这受到病理学家生产力提高和工作量分配的影响。将当前局部的专科签出模式扩展到企业级共享的普通/专科签出模式可能会使错误治疗成本降低540万美元。这些计算基于乳腺癌和黑色素瘤每年的过度治疗和治疗不足成本,估计分别约为每例26,000美元和11,000美元,并推算到其他癌症类型为每例21,500美元。

结论

对于我们这家大型学术型医疗保健机构,全面实施DPS预计5年总成本节省约为1800万美元。如果数字病理学的购置和实施成本不超过这个值,投资回报率对医院管理人员来说将很有吸引力。此外,这项技术带来的患者预后改善强化了支持采用企业级DPS的理由。

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