The following is a guest article by Ashay Thakur, VP of Data Strategy at Cedar Gate Technologies

以下是Cedar Gate Technologies数据策略副总裁Ashay Thakur的客串文章

The traditional fee-for-service (FFS) payment model has dominated the healthcare industry’s payment systems for decades. Unfortunately, this focus on quantity over quality has plagued the healthcare industry, leading to high-volume care with little to no accountability for whether it provides value in terms of better patient outcomes.

几十年来，传统的服务费（FFS）支付模式一直主导着医疗保健行业的支付系统。不幸的是，这种对数量而非质量的关注一直困扰着医疗保健行业，导致了大量护理，几乎没有或根本没有责任说明它是否能为更好的患者结果提供价值。

Thankfully, many payers, providers, and employers are moving away from FFS to value-based care (VBC), which prioritizes quality outcomes over volume. .

谢天谢地，许多付款人，提供者和雇主正在从FFS转向基于价值的护理（VBC），它将质量结果优先于数量。。

This transition also holds promise for more cost-effective care delivery. However, the path to successful VBC adoption is not easy, and participants must learn to navigate the nuances and complexities of pursuing a model where the incentives are completely counter to FFS. In VBC, providers are incentivized to keep patients healthy, limiting the need for acute intervention and more care or procedures.

这种转变也有望提供更具成本效益的护理服务。然而，成功采用VBC的道路并不容易，参与者必须学会驾驭追求激励措施完全与FFS背道而驰的模式的细微差别和复杂性。在VBC中，提供者被激励保持患者健康，从而限制了对急性干预和更多护理或程序的需求。

That requires extensive data-sharing capabilities among all healthcare stakeholders to personalize care based on someone’s health history, risks, and unique care needs..

这需要所有医疗保健利益相关者之间广泛的数据共享能力，以根据某人的健康史，风险和独特的护理需求个性化护理。。

Data interoperability is, and has been, a long-standing issue in healthcare. Many payer and provider networks are built on a patchwork of legacy systems that were not designed for value-based care. Much of this technology lacks the capabilities to predict risk, measure outcomes, meet quality goals, and bill for services in new payment models such as prospective bundles or capitation.

数据互操作性一直是医疗保健领域的一个长期问题。许多付款人和提供者网络是建立在传统系统的拼凑上的，这些系统不是为基于价值的护理而设计的。这种技术大多缺乏预测风险、衡量结果、实现质量目标以及在新的支付模式（如预期捆绑或按人头付费）中为服务付费的能力。

The failure to achieve effective data interoperability disrupts provider operations and payer-provider relationships, leading to delayed claim processing, reduced revenue streams, and inefficiencies across the board..

未能实现有效的数据互操作性会破坏提供商运营和付款人与提供商的关系，从而导致索赔处理延迟、收入流减少以及整体效率低下。。

Here’s how these challenges play out for many payers and providers:

以下是这些挑战对许多付款人和提供者的影响：

Struggles to Share Data that Enhances Payer-Provider Collaboration

努力共享数据以增强付款人与提供商的协作

Most payers struggle to implement essential VBC functions, primarily coordinating care with provider partners in their networks. Key obstacles include fragmented data, and limited visibility while working from different datasets – with providers operating out of an EHR while payers mainly use claims data.

大多数付款人努力实现基本的VBC功能，主要是与网络中的提供者合作伙伴协调护理。。

Poor communication and disparate systems only compound these challenges. Addressing these issues requires robust data sharing and interoperability to facilitate more coordinated care and address challenges like health disparities and access. But it must also include capabilities to protect proprietary information and data, so payers don’t worry about compromising their competitive advantage..

糟糕的通信和不同的系统只会加剧这些挑战。解决这些问题需要强大的数据共享和互操作性，以促进更协调的护理，并应对健康差异和获取等挑战。但它还必须包括保护专有信息和数据的功能，以便付款人不必担心损害其竞争优势。。

Grappling with Fragmented Care Management Technologies

应对零散的护理管理技术

The lack of data interoperability among care management systems poses a significant obstacle for payers and providers seeking to implement and collaborate on VBC models. When payers and providers are working from multiple, disconnected solutions, it’s more difficult to create effective care plans that address the needs of a member or patient population while also helping everyone meet value-based care goals.

护理管理系统之间缺乏数据互操作性，这对寻求在VBC模型上实施和合作的付款人和提供者构成了重大障碍。当付款人和提供者使用多种不相关的解决方案时，更难创建有效的护理计划，以满足成员或患者群体的需求，同时帮助每个人实现基于价值的护理目标。

That includes providers trying to collaborate effectively across clinics and facilities to meet patients’ needs and coordinate care. In a recent JAMA survey, just 8% of providers said that they could easily use information from different EHRs..

这包括提供者试图在诊所和设施之间进行有效合作，以满足患者的需求并协调护理。在最近的JAMA调查中，只有8%的提供商表示他们可以轻松使用来自不同EHR的信息。。

Maintaining Security and Protecting Patient Data

维护安全并保护患者数据

Technological alignment is a frequent challenge for payers, driven by the complexity and volume of medical data needed for operations. Organizations must be able to efficiently manage a diverse tech stack to ensure interoperability. Misalignment can lead to increased risk of HIPAA violations or data breaches, operational inefficiencies, and care delays..

由于操作所需的医疗数据的复杂性和数量，技术一致性是付款人经常面临的挑战。组织必须能够有效地管理多样化的技术堆栈，以确保互操作性。未对齐可能导致HIPAA违规或数据泄露风险增加，运营效率低下和护理延迟。。

The Need for Technological Alignment

技术调整的必要性

Technological alignment is essential to overcome these data interoperability challenges. Next-generation enterprise data management systems are designed to offer all healthcare stakeholders – including payers, providers, and patients – access to accurate, enriched data that supports the entire care journey.

技术协调对于克服这些数据互操作性挑战至关重要。下一代企业数据管理系统旨在为所有医疗保健利益相关者（包括付款人、提供者和患者）提供准确、丰富的数据，以支持整个护理过程。

They go beyond simply storing data, enabling users to capture information from disparate sources, then cleansing, normalizing, and homogenizing the information in a centralized data lake. .

它们不仅仅是简单地存储数据，使用户能够从不同的来源捕获信息，然后在一个集中的数据池中清理、规范化和同质化信息。。

These systems leverage advanced technologies such as AI and machine learning to aggregate, enrich, and present data to support informed decision-making. These advanced enterprise data management systems can then feed clean data into analytics, care, and payment applications to help users predict and stratify risk in member populations, optimize employer benefits plans, build high-performing provider networks, and target populations for appropriate clinical interventions..

这些系统利用人工智能和机器学习等先进技术来汇总、丰富和呈现数据，以支持明智的决策。然后，这些先进的企业数据管理系统可以将干净的数据提供给分析、护理和支付应用程序，以帮助用户预测和分层成员人群的风险，优化雇主福利计划，建立高性能的提供者网络，并针对适当的临床干预人群。。

The benefits of enterprise data management extend beyond improved data interoperability. These systems also empower healthcare organizations to meet regulatory requirements, enhance reporting capabilities, and support performance in any VBC model. By simplifying tech stacks and removing barriers to collaboration, enterprise data management systems pave the way for more effective AI-driven decision support and predictive analytics..

企业数据管理的好处超出了改进的数据互操作性。这些系统还使医疗保健组织能够满足法规要求，增强报告能力，并支持任何VBC模型的性能。通过简化技术堆栈和消除协作障碍，企业数据管理系统为更有效的人工智能驱动的决策支持和预测分析铺平了道路。。

A Path Forward for Data Management

数据管理的前进之路

We live and work in a time when data is the key to unlocking better care. By embracing the next generation of enterprise data management systems, healthcare organizations can overcome the persistent challenges of fragmented data and inefficient communication. They offer a sustainable solution that guarantees providers and payers can make informed decisions and collaborate effectively.

我们生活和工作的时代，数据是解锁更好护理的关键。通过采用下一代企业数据管理系统，医疗保健组织可以克服数据碎片化和通信效率低下的持续挑战。他们提供了一个可持续的解决方案，保证提供者和付款人能够做出明智的决定并有效合作。

This improves patient outcomes and operational efficiency and supports the successful adoption of VBC models..

这提高了患者的治疗效果和操作效率，并支持VBC模型的成功采用。。

About Ashay Thakur

关于Ashay Thakur

Ashay Thakur is the VP of Data Strategy at Cedar Gate Technologies. He oversees strategic development and governance of the organization’s data foundation, driving innovation to enhance scalability, quality, and excellence across Cedar Gate’s end-to-end value-based care platform.

Ashay Thakur是Cedar Gate Technologies的数据战略副总裁。他监督组织数据基础的战略发展和治理，推动创新，以提高Cedar Gate端到端基于价值的护理平台的可扩展性、质量和卓越性。

TagsAshay Thakur Care Management Cedar Gate Technologies Data Interoperability Data Management Fee for Service FFS patient data Value Based Care VBC

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