High-Value Data and Analytics: Bringing Critical Healthcare Insights into Focus

The simple truth in a complex healthcare landscape is that betterdataandanalytics让决策者更清楚地了解他们的环境。Just as a higher-resolution photograph shows greater detail and brings background images into focus, high-value data and analytics clarifies information, helping healthcare leaders recognize looming threats andimprovementopportunities while they have time to act.

高价值数据和分析反映了许多领域(例如，临床、收入、成本、住院和门诊等)的使用，以创建一个组织福祉的全面图景。如果医疗保健领导者有能力和基础设施将高价值数据和分析资产无缝地、重复地和自动地集成到每个决策中，他们就可以优化其分析投资的价值，或关注更多细节。

Why Do Healthcare Organizations Need High-Value Data and Analytics?

Healthcare leaders aiming to power better decisions across the enterprise start to optimize their data and analytics investments by considering a beginning healthcare success framework (Figure 1) and identifying opportunities and roadblocks to progress.

如图1所示，一个由数据和分析支持的医疗保健业务框架包含三个机会驱动因素:收入、成本和质量。该框架确保组织始终关注这三个领域，使他们能够识别高价值的机会和相关的挑战。随着这些领域在整个企业中相互连接，同时处理它们对于确定新的成功途径至关重要。深入了解每个领域和挑战的细节——高分辨率——需要高价值的数据和分析，以便敏锐地关注成功的所有障碍。

How Do High-Value Data and Analytics Put Critical Healthcare Insights in Focus?

High-value data and analytics are requisite capabilities for multidomain healthcare success, as they put critical insights in focus in a complex, crowded information landscape:

High-Value Data

After its people (i.e., delivery teams), high-value data is a healthcare organization’s most mission-critical asset for success. This class of data requires ingesting and processing a growing volume of sources to support revenue, cost, and quality use cases and ensuring data is integrated, reusable, expert informed, and transparent.

High-Value Analytics

An organization integrates high-value analytics into standard, easy-to-use analytic andbusiness intelligencetools. High-value analytics are essential to enabling augmented answers to a broader set of business-critical issues and optimizing the analytic capabilities across the organization. This disseminating of analytics is known as productized artificial intelligence (AI).

Completing the Picture: The Modern Data and Analytics Platform

While high-value data and analytics are critical, putting any asset to work in favor of business goals require technology—in this case, a modern data and analytics platform. A modern, enterprisewide data and analytics platform (e.g., the Health CatalystData Operating System (DOS™)) is foundational to data and analytic success and productized AI.

A winning platform will be the following:

• Opento support a broad variety of standardized (e.g., regulatory measures), custom use cases, and business objectives as data demands shift.
• Modern, flexible, and scalableto support high-growth and high-value data and analytics needs.
• 医疗具体to support the complexities of the healthcare industry (e.g., standard connectors to healthcare data system, terminology, etc.).
• Confidence- and trustworthyto earn team member and leadership buy-in and ongoing use.

A modern data and analytics platform with the above attributes will substantially scale an organization’s analytics productivity, setting the stage for healthcare leaders to view their data engines as “data factories” that continuously develops comprehensive, clear images of the organization’s status.

How a Data Factory Mentality Operationalizes High-Value Data and Analytics in the Workflow

A data-factory mentality evokes images of efficiency, scalability, quality. Like an industrial factory, the data factory performs operations, logistics, and processing—in this case to develop consistent, reliable, high-resolution views of organizational data:

Operations: Data Ops

Data factory processes start with data ops, which integrates high-resolutiondata quality在生产过程的每一步。数据运维使用来自现代数据工程的工具，包括版本控制和部署流程，并进行持续监控，以对影响数据产品的更改做出反应。

Logistics: Data Orchestration

数据编排就像工厂中的物流系统。现代平台数据编世界杯厄瓜多尔vs塞内加尔波胆预测排能力确保原始数据图像或材料在高效和自动化的过程中以正确的顺序、在正确的时间到达正确的位置。

Processing: Data Processing Tools

数据处理包括获取原始数据材料，对其进行处理，并将其提炼成最终产品—高分辨率或详细的数据视图。Process tools can include bulk or streaming data ingestion tools, highly scalable databases, and advanced analytics tools that enablemachine learningand natural language processing to refine to final data product.

The data-factory perspective overrides the common request-based approach to the data engine, in which data teams produce reports when requested, overwhelming the system, and delaying data for decision makers. Instead, the factory produces high-quality data and analytics efficiently, enablingself serviceand empowering team members with reusable data and analytics to drive decisions.

Bringing Healthcare Challenges and Opportunities into Focus

Low-quality data and analytics are like a low-resolution photo that doesn’t depict all images clearly—information might exist about an emerging problem (e.g., potential patient safety events or operational or financial problems), but subpar data and analytics blurs the early warning signs; once threats are close enough to detect, decision makers often don’t have time to make real-time (less retrospective) risk mitigation. In healthcare data and analytics, progressing from that blurry, low-resolution image to the crisp, high-resolution alternative requires high-value data and analytics and a modern platform, all operationalized with a data-factory mentality for efficiency, scalability, quality.

Additional Reading

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

• Cloud-Based Open-Platform Data Solutions: The Best Way to Meet Today’s Growing Health Data Demands
• How to Build a Healthcare Data Quality Coalition to Optimize Decision Making
• Healthcare Data Warehouse Models Explained
• The Six Biggest Problems With Homegrown Healthcare Analytics Platform
• Healthcare Analytics Platform: DOS Delivers the 7 Essential Components