Six Challenges to Becoming a Data-Driven Payer Organization

随着医疗保健支付模式从按服务收费转变为基于价值的支付模式，支付者和提供者组织一样，有动力制定人口健康管理(PHM)战略，以降低医疗保健成本。管理人口实际上是管理单个成员在连续保健过程中对服务的使用和结果。为了管理成员的医疗保健旅程，支付者需要数据来适当地将成员分配到一个风险类别，并准确评估医疗保健干预的结果。为了支持这一复杂的、终身的成员管理过程，支付者必须具备支持数据驱动战略的能力和技术基础设施。

多种因素——包括遗传、健康行为(如吸烟和性活动)、社会和经济因素(如就业、教育和收入)以及物理环境(如空气和水质量)——影响成员的健康状况，以及她如何使用保健服务和她在多大程度上参与管理自己的健康。成员可以在各种各样的提供者和网站上接受急性或慢性医疗保健服务。

To weave together health status, engagement, and utilization into a cohesive picture that drives an effective health management strategy, payers must fully leverage data from disparate sources. This article explains the data challenges payers face in adopting and sustaining PHM strategies and how novel approaches to data management can help.

Six Payer Population Health Management Data Challenges

To effectively manage the complexities of a member population for the duration of their enrollment, payers must address six key data challenges:

1. 数据的可用性。
2. 数据访问。
3. 数据聚合。
4. 数据分析。
5. 数据采用。
6. 数据的应用程序。

#1: Data Availability

The amount ofhealthcare data随着卫生系统继续将以数据为中心的电子病历和移动设备纳入工作流程，可用于支持PHM战略的资源继续增长。但是，将相关数据分散到不同的数据源，而不是数据量，会减少支付用户获得数据的机会。这使得确定哪些数据对PHM战略有重大影响，并使这些数据可用成为支付方的一个重要目标。

Even though the acceleration of software solutions and devices fueling the expansion of healthcare data (e.g., EHRs and mobile platforms) appears to be pushing healthcare data into the realm of big data, the data critical data for managing a member’s health will likely be relatively small.

To succeed with a long-term PHM strategy, however, payers must be ready to manage increasingly available data. Later on, this report discusses the Health Catalyst® Data Operating System (DOS™), an open platform data solution designed to support healthcare’s increasing data needs.

#2: Data Access

Technical and legal barriers can make accessing healthcare data challenging:

• The technical challenge: With improvements in data-sharing technology (e.g., asFHIR-based APIs), interoperability between disparate software and systems is improving. These custom solutions, however, are costly and use resources that could be applied to other PHM activities.
• The legal challenge: Payers must follow federalHIPAA共享数据时的隐私法规和其他数据使用协议。随着支付者解决这些障碍并获得更多的数据访问权，他们需要将这些不同系统的数据组合成可用的格式(如仪表板或可视化)。
• 政治上的挑战:随着基于价值的购买方式的转变，过去在支付方和提供商之间共享数据的紧张局面正变得不那么严重。支付者和提供者都旨在共享数据，为优化结果和降低会员成本提供有意义的见解。

To overcome barriers to accessing data, payers must have a clear and compelling reason to challenge the status quo of relying on claims data. New value-based purchasing models that emphasize efficiency and promote optimal health are pushing expanding access to data that can help achieve these goals.

#3: Data Aggregation

Using a Late-Binding™ EDW as found with DOS, payers can combine data from multiple sources into a cohesive framework. Payers today, however, tend to have a legacy data warehouse based on a traditional warehouse technology that creates several barriers to a PHM strategy:

• Legacy EDWs are expensive to maintain.
• 由于数据集成能力有限，它们可能会产生互操作性问题。
• 使用元数据描述源和跟踪数据沿系的数据管理通常是不完整和不一致的，它把重点放在熟悉底层数据上，而不是放在组织的数据文档上。
• Flexibility and scalability in these legacy systems (which is critical for the future demands of PHM) may be limited.

Even with the limitations of legacy EDWs, payers continue to rely on them because the older systems are often prohibitively expensive and time consuming to replace. An alternative to ripping and replacing a legacy EDW is to adopt a dual environment strategy (using both the legacy data warehouse and a new IT model) in which users can incrementally move older data and add new data to a second data warehouse.

数据湖方法(如Hadoop)是帮助付款人克服遗留数据仓库限制的另一种选择。数据湖可以高效地存储和快速处理海量数据。然而，在Hadoop中构建EDW所需的时间和资源是相当可观的，而通过支持分析和开发分析应用程序的开放平台数据解决方案来补充这一努力，可以更快地为最终用户提供价值。世界杯厄瓜多尔vs塞内加尔波胆预测

#4: Data Analysis

Data analysts need an effective tool to turn raw data into insights that payer leaders and other non-technical data consumers (C-suites, frontline clinicians, etc.) can use. Analysts often use Excel to analyze data. Excel, however, has limitations in identifying the data necessary for a particular analytic project (e.g., insufficient functions) and may not have the capacity for large data sets. To efficiently select and abstract the data, analysts can transform large datasets into visualizations before they are consumed in Excel by following a four-step process:

1. 识别和评估所需数据的质量。
2. 通过研究数据找到有意义的趋势。
3. 解读数据以发现有意义的见解。
4. Present the data in a way that users can take appropriate action.

One of the dangers of multiple analysts using Excel is the fragmentation and modification of the data by the analysts, which makes it difficult to maintain the integrity of the data. The development of data visualizations helps payers avoid these data silos by standardizing the data sources and data presentation in a uniform and consistent fashion.

#5: Data Adoption

Perhaps the most overlooked and underappreciated challenge in using data is a culture and processes that promotes adoption of data analytics throughout the organization. A data-driven environment includes four elements that ensure data is useable and accessible:

1. A commitment from senior leadership.
2. Data governance.
3. Evidence-based best practices.
4. A strategy that ensures the use of data-driven insights to transform internal processes into meaningful outcomes improvement.

Using data governance, payer leadership must drive adoption towards becoming data-driven organizations and incorporating data into decision making. Leadership applies data management tools and processes to support the appropriate security and data accessibility measures, as well evidence-basedprocess improvementpractices that focus on the ultimate goal of applying data to improving processes and outcomes.

#6: Data Application

For payers to succeed in PHM, they need to be able to use data efficiently to improve outcomes to meet core goals such as:

• Identifying and managing high-cost members.
• Negotiating value-based contracts.
• Creating an effective provider incentive program.
• Enhancing quality scores.
• Maximizing provider network performance.

The above goals require risk adjustment models, predictive modeling, social determinants of health data, and a host of payer-specific business rules (e.g., contractual payment provisions or pre-authorization requirements) to enhance the data. Frontline users must have data in a format that aligns with, rather than disrupts, workflows and impacts the desired outcome (e.g., care gap alerts for high-risk members with diabetes).

Managing Healthcare Data’s Complexity with Open Platform Capabilities

An open-platformdata warehousingtool that can support emerging PHM strategies must recognize the immaturity of PHM data analytics, the tremendous growth of data available for PHM, and the exciting potential for applying data in unique ways to improve outcomes. The platform must be flexible to accommodate new data sources and analytic approaches and scalable to meet the expected growth in PHM data.

One example, DOS, can help payers address the data challenges inherent in PHM, and ultimately, lower healthcare costs and improve outcomes for their members. DOS combines the features of data warehousing, clinical data repositories, and health information exchanges (HIEs), giving payers an unprecedented ability to share data and create new opportunities to use data to more effectively manage a member’s health.

在将数据转化为有用的、可操作的见解的过程中，还需要额外的内容，如术语和质量度量。模块化、可伸缩的方法将所有内容打包到库中，可以轻松地集成到现有或新的数据仓库中。然后在多个用例之间共享数据，消除冗余并优化数据流。

To avoid the challenges in adapting legacy EDWs to meet the data acquisition, aggregation, and analysis challenges of PHM and the prohibitive cost of ripping and replacing EDWs, payers might augment the EDW with open-platform technology, such as DOS. Using a data visualization tool, the open architecture of DOS can accommodate data from the existing EDW, or from new data sources, and support ad hoc analysis or analytic application development. DOS also provides a flexible integration point for exchanging data with EMRs or other unique solutions and can prepare data in the legacy EDW for transition to the open platform.

Data and a Data-First Technology Drive Population Health Success for Payers

随着支付方越来越多地依赖PHM战略来改善健康结果、降低成本，并在基于价值的支付中表现出色，一个关键因素将支持他们的成功:数据。然而，成为数据驱动的付费组织的过程充满了数据可用性、访问、聚合、分析、采用和应用方面的显著挑战。为了应对这些挑战，支付者必须采用扩展传统EDW功能的数据管理工具，以充分利用PHM可用数据的不断增长的广度。开放平台数世界杯厄瓜多尔vs塞内加尔波胆预测据仓库，如DOS，将让付款人了解如何改善其成员的健康状况。

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