Pairing HIE Data with an Analytics Platform: Four Key Improvement Categories

Healthcare organizations need data from multiple sources and multiple organizations to meet population health and value-based payment (VBP) goals. Communitywide networks, such ashealthcare information exchanges(HIEs), are increasingly important in the current healthcare landscape, as they provide the interoperability modern healthcare improvement demands:

• Clinical, pharmaceutical, public health, and quality reporting data.
• Electronic sharing between different information systems (e.g., web services, transmission control protocol/internet protocol [TCP/IP], secure SMTP, DirectTrust, etc.).
• 多种形式的临床交流(如HL7 2。x、HL7 CDA、text、PDF等)。
• Cross mapping from technologies, systems, and formats.

HIE data and technology alone, however, isn’t sufficient for organizations aiming to improve outcomes and lower the cost of care. This report covers the benefits of using HIE data and technology along with an advanced analytics platform and how their combined capabilities meet modern healthcare challenges.

The Case for HIE Data

Large integrated delivery networks (IDNs) that contract for value-based population models require networks of providers and services to deliver holistic care. These networks of affiliated physicians often take the form of clinically integrated networks (CINs) or accountable care organizations (ACOs). Sharing information for a given patient population is a critical factor to the success of the network. Large health systems need data from the EMRs of affiliated physicians who generally work in smaller practices. These IDNs are also commonly missing data from the full continuum of care, (e.g., skilled nursing facilities [SNFs]), which creates real problems during transitions of care. Important clinical information is distributed across different systems, making it difficult to have complete data supporting population health initiatives.

Health systems can extract, transform, and load (ETL) the data from their own EMRs, but they are unable to ETL data from their affiliate physicians’ EMRs. Most often, they rely on claims data as a surrogate for the affiliate EMR data—along with the complications claims data carries (e.g., lag, subpar accuracy, etc.). HIE connections and services bring in clinical data from affiliated systems in real-time to close the gaps that occur with delayed batched claims data.

Bridging community health data with health systems analytics via HIE technology brings healthcare organizations significant benefits:

More Complete Data Builds Confidence Around Risk-Based Contracting

By delivering more complete data, analytic dashboards populated with HIE data can build confidence and close the loop for clinicians taking on risk-based contracts. With a better understanding of how an organization does in performance and clinical quality measures, a health system can align its value-based contracting goals and processes to identify opportunities for improvement while there is still time to react, instead of waiting to process and evaluate claims data in weeks or months. Multiple data sources can validate and improve the accuracy of risk stratification, thanks to more comprehensive patient population information.

Supports Patient Engagement at the Most Impactful and Teachable Moments

通过获取IDN或卫生系统设置之外的大量健康数据，HIE数据允许临床医生在患者最有可能从干预中获益的时候与他们接触。护理协调员和导航员通常是值得信任的患者倡导者，他们利用这些数据使患者充分了解整个护理连续性和整个扩展护理团队(包括snf、探访护士等)的医疗信息。

Gives Organizations Comprehensive Patient Information for Personalized Care

With HIE connections and services, healthcare professionals and patients appropriately access and securely share a patient’s medical information electronically, across the continuum of care (e.g., primary care physicians and specialists, labs, SNFs, visiting nurses, and EDs). By combining this more complete data with analytics, health systems and clinicians can provide safer and more personalized care, thanks to comprehensive access to vital patient data. For example, emergency department staff too often perform triage without a patient’s full history and conditions. A real-time HIE can quickly provide an overview of chronic conditions, medications, allergies, etc. In addition, analytics systems can leverage this insight to stratify risk and identify the most impactable areas for the care team to focus (areas with the greatest return on engagement).

An HIE impacts patients, clinicians, and health systems in several key ways:

• Reduces health-related costs by reducing medical errors and duplicate testing.
• Improves quality andsafetyof patient care by reducing medication and medical errors associated with incomplete data.
• 协调扩展护理团队，促进消费者教育和患者参与自己的医疗保健。
• 通过消除与病人在不同护理环境之间移动相关的不必要的文书工作，提高效率。
• 为护理人员提供更有效的护理和治疗的临床决策支持工具。
• Improves public health reporting and monitoring.
• 促进新兴技术和医疗保健服务的有效部署。
• Provides the backbone of technical infrastructure for leverage by national- and state-level public health initiatives.
• Provides interoperability among individual affiliated physicians and organizational

Even with the above benefits, HIE data alone doesn’t support comprehensive improvement under the population health or value-based demands of healthcare today. To meet these modern goals, healthcare organizations must pair these networks with a fully interoperable analytics platform. An optimal modern healthcare data approach combines HIE data with a cloud-based, interoperable analytics platform (e.g., the Health Catalyst® Data Operating System [DOS™], Figure 1, below).

Four Key Improvement Categories of Pairing HIE Data with an Analytics Platform

While HIEs are rich in clinical data (e.g., records and transactions), they lack depth in the bigger picture of healthcare delivery—elements such as financial costs of care, patient satisfaction and outcomes, and supply chain data. HIE data is often limited to standard-based interfaces and datasets such as theConsolidated-Clinical Document Architecture(C-CDA)Continuity of Care Document(CCD). The CCD, however, was designed for one care provider to communicate with another about patient information (e.g., medications, allergies, and lab values) when transferring that patient from one facility to another—not for the broader goals of VBP and population health.

Using a C-CDA CCD, HIEs can meet about 80 to 90 percent of patient information sharing needs, but they’re not adequate for all population health use cases. Data aggregation in an analytics platform increases the value of data overall by combining HIE with health system data (e.g., patient satisfaction, claims, and cost). For example, as an HIE lacks cost data, by combining HIE and health system resources, an analytics platform can enable community-based cost comparisons.

Pairing HIE data with DOS offers benefits in four key categories that help health systems meet goals under VBP and population health:

1. Workflow

大多数医疗保健分析平台技术在临床工作流程方面存在不足世界杯厄瓜多尔vs塞内加尔波胆预测，因为它不能提供决策或护理点的数据。当患者进入初级保健或专家办公室时，分析平台可能不会立即在护理点产生关键的见解。世界杯厄瓜多尔vs塞内加尔波胆预测例如，如果患者患有糖尿病，没有接受糖化血红蛋白或眼科检查，这些信息可能不会出现在检查室的医护人员面前。通过在正确的时间将正确的患者信息放入工作流程中，具有H世界杯厄瓜多尔vs塞内加尔波胆预测IE数据的可互操作分析平台使洞察变得可行。从患者护理到随访的决策信息，帮助临床医生和护理管理人员通过解决护理方面的差距和更广泛的改善目标来改善预防护理。

Most HIE data is exchanged in real-time, and most analytics platforms don’t support real-time action. Adding HIE technology to an organization’s analytics platform delivers results within moments, directly through standard channels, sending insights (e.g., risk for sepsis and readmissions) into the provider workflow and back to the clinicians via the EMR. HIE technology makes it easier for health systems to get data into the workflow.

在决策时获得正确的数据对于改善患者结果，特别是对患者安全至关重要。例如，在败血症中，快速的数据访问使及时诊断成为可能，这对于获得更好的结果至关重要。HIE还有助于分析系统纳入药物数据，如处方和填写历史，以寻找包括不良药物事件、禁忌症和阿片类药物滥用风险的并发症。

此外，HIE技术的实时数据功能可以通过卫生系统分析平台触发整个护理连续体的及时通知。世界杯厄瓜多尔vs塞内加尔波胆预测订阅的临床医生和护理管理人员可以在患者经历与健康相关的事件时及时收到警报，例如护理警告中的空白。一个组织分析平台集成了决策支持工具，如Health Ca世界杯厄瓜多尔vs塞内加尔波胆预测talyst Leading明智™，提供个性化的观察列表，可配置的可视化，以及可定制的警报和通知。世界杯葡萄牙vs加纳即时走地

2. Machine Learning

HIE increases the amount and reduces the lag of data available to analytics platforms, which provides data scientists a more robust data set with which to train machine learning models. Clinicians can more quickly identify risks and opportunities for intervention as well as predict behaviors and events across entire patient populations.

Predictive analytics can verify patient compliance with medication using prescription fill records to see which medications a clinician has prescribed versus which have been filled. As well, predictive algorithms can identify drug-seeking behavior, using statewide prescription data as well as socioeconomic data from the HIE.

3. Professional Services

将HIE数据与卫生系统分析相结合，可以帮助组织识别出最优秀的临床医生，以实现可持续、高效的CIN。选择正确的临床医生将减少临床护理结果的差异，并围绕临床医生建立更好的结果网络(包括急性和门诊)。HIE还可以帮助临床医生通过了解大量人群的差异，尤其是结合基准、机会分析和优先级分析应用程序(例如Health Catalyst Touchstone™)，成为表现最好的医生。世界杯葡萄牙vs加纳即时走地

在卫生系统分析中使用HIE数据还有助于CIN临床医生从监管报告之外的医疗保健技术中获得真正的价值。增强的决策工具和减轻的临床报告负担带来的价值增加了临床医生加入CIN的动力，并提高了提供者的参与度，因为将HIE和分析相结合减少了生成报告的劳动。

4. Data Governance

HIE environments struggle with data governance, particularly with data quality and ownership, and organizations without good data governance struggle when they introduce an HIE. A commercial analytics platform, and the vendor expertise behind it, helps organizations define and comply with regulations.

Vendors, meanwhile, also benefit from HIE around data governance. Life sciences data, for example, has strict rules around purpose of use, including patient consent and disclosure. HIE data adds patient consent expertise to these health system platforms.

Meeting Today’s Payment and Population Health Goals Requires Both HIE and an Analytics Platform

To meet quality measures under VBP as well as population health goals, healthcare organizations must have the comprehensive patient and community data to power advanced analytics capabilities that only an HIE-and-analytics-platform approach can deliver. Combined, the data and analytics entities provide greater understanding of patient health across the continuum of care and move insights into the workflow at the point of decision making. With real-time communitywide and individual data, clinicians gain a broader context for the patient, allowing more timely, informed, and accurate decisions. This allows for better care and outcomes and builds trust among health systems in taking on risk-based contracts and population health models.

Additional Reading

Would you like to learn more about this topic? Here are some articles we suggest:

1. Five Reasons Why Health Catalyst Acquired Medicity and What It Means for Interoperability, as Explained by Dale Sanders, President of Technology
2. Turning Data from Five Different EHR Vendors into Actionable Insights

---

PowerPoint Slides

你想使用或分享这些概念吗?下载此演示文稿，重点突出。

Click Here to Download the Slides

Pairing HIE Data with an Analytics Platform: Four Key Improvement Categoriesfrom世界杯葡萄牙vs加纳即时走地