Healthcare Analytics for Payers: How to Thrive Through Shifting Financial Risk

As the healthcare industry continues to transition to value-based care (VBC) models, financial risk is shifting away from the payers and toward the providers. Payers are looking to alternate payment models (APMs) and other strategies to drive more value to their members.

To succeed in an environment of shared risk, payers must extend theirdata分析和风险管理方面的专业知识。本文解释了这种用于医疗保健支付方分析的扩展方法，以及在风险管理环境中为支付方开发分析路线图的价值。

Making Data an Imperative

The first step in building an analytics strategy is to accept as guiding principle the words of W. Edwards Deming, “Without data you’re just a person with an opinion,” and make data an organizational imperative. The second step is to build a roadmap that leads to a data-driven culture. When payer organizations arrive at the data-driven destination, teams use analytics to identify opportunities and implement solutions to support new strategies, (e.g. enabling their providers to succeed in shared risk). Data drives decisions and actions.

通过遵循分析路线图，支付机构将成熟其对数据的使用和理解，从传统的描述性报告到高级预测分析。This transformation augments human intelligence, and providers and members can directly leverage its benefits:

• Reporting:将数据组织成信息摘要的过程，以监视或描述业务的特定领域是如何执行的。
• Analytics: The process of iteratively exploring aggregated data to discover patterns and extract meaningful insights and building predictive models that augment human intelligence.

A Healthcare Payer Analytics Strategy: Build a Roadmap to Manage Risk

A roadmap will help the payer navigate their own way through a minefield of potential risks associated with these challenges:

• Recruiting and retaining high-quality providers in a highly competitive market.
• 以有限的资源管理越来越多的高风险/高成本成员。
• 有效地应对似乎没完没了的联邦和州立法和支付变化。
• Effectively controlling the rising costs of healthcare services and pharmaceuticals.

The well navigated data-driven journey through the minefield of risks will produce meaningful benefits, including the following six outcomes:

1. Improving satisfaction among provider networks by providing data and analytics services.
2. Increasing the ability to respond to market disruptors by building an agile data operating system.
3. 用具有挑战性的分析来取代重复的手工报告，从而降低员工流失率。
4. 通过展示和捍卫一个计划或战略的成功来改善投资。
5. 展示和沟通有效的管理委托社区资源。
6. Expanding value to members through targeted programs, information, and outreach.

Leveraging Best Practice: The Healthcare Analytics Adoption Model

In the journey towards a data-driven culture, payers may partner with an analytics vendor or go it alone. Either way, an organization should use the Healthcare Analytics Adoption Model (Figure 1) as the context for a tailored analytics roadmap that progresses from a pre-enterprise data operating system to democratized data and, finally, to a data-driven cultures.

The Pre-Enterprise Data Operating System Culture

企业成立前的数据操作系统文化的电子表格孤岛，或分析的口袋，产生冲突的报告和对数据的冲突解释。这就导致了数据所有权的争夺、消费者长长的报告请求队列，以及分析师花费大部分时间寻找和收集数据。重点是获得正确的数据和获得正确的数据(即数据完整性和数据准确性)。

The Democratized Data Culture

民主化的数据文化使常规报告自动化，并利用单一来源的精心策划的数据进行特别分析。治理团队在标准化定义和确定数据获取的优先级方面花费了大量时间。组织开始信任数据，并且随着自助服务应用程序和分析师生成的成员注册的可用性，报告队列显著减少。

The Data-Driven Culture

当支付机构到达数据驱动的目的地时，他们使用分析来识别机会并实施解决方案以支持新战略(例如，使其供应商能够成功地分担风险)。2022卡塔尔世界杯赛程表时间数据驱动决策和行动。重点是利用启发性和预测性分析来加强VBC和人口健康管理。

As organizations mature in the use of data, they move from levels zero to nine (from fragmented point solutions to direct-to-consumer analytics and artificial intelligence) and progress through three key improvement categories:

1. Improving efficiency.
2. 减少变异。
3. Improving health.

Interestingly, as the organization matures its analytics adoption, it can reallocate human resources from lower levels, where they’re spending time on automatable tasks, to higher levels of analytic tasks. This allows its workforce to better apply its expertise (i.e., work at the top of their licenses), which reduces turnover.

Assess the Current Capabilities

Before adopting and executing an analytics roadmap, a payer organization should complete an analytics readiness assessment. The assessment identifies where an organization falls along the analytics adoption model—where it stands compared to industry standards—and ensures organizational expectations are in line with organizational capabilities.

分析合作伙伴应该提供分析准备评估。Health Catalyst, for example, offers an analytics readiness assessment with three components:

1. Organization (readiness, demand, and structure): This component evaluates an organization’s ability to use data to support its business objectives in five major areas (Figure 2):
   • Leadership culture and governance.
   • 金融对齐。
   • Analytics.
   • Best Practice.
   • Adoption.

组织自我评估以关键数据消费者和/或消费者群体确定得分背后的细节;这些数据为上述领域的一对一访谈提供了信息。

2. Technology(工具、数据源和工作流):该组件评估利用数据和分析的力量和机会，以满足围绕技术、分析、报告和分析工作流的业务目标，使用最佳实践模型作为比较指南。获取、分析、组织、标准化和交付过程中的每一步都被映射并与行业最佳实践进行比较。
3. Staff skills(technical, analytics, and contextual): This component establishes a baseline to help identify areas of staff skills around eight areas of expertise within three contexts (Figure 3) and across the continuum of analytics complexity (e.g., determining the correct skills sets for analytics performance, given current and anticipated future analysis demands):
   1. Health care data and operations.
   2. Analysis.
   3. 数据查询。
   4. Visualization.
   5. Data management.
   6. Process improvement.
   7. Data modeling.
   8. Statistical methods.

Tailor Your Route: Prioritize Waypoints and Allocate Resources

The results of the analytics readiness assessment (Figure 4) give the organization a readiness score (low, medium, or high), recommendations for improving analytics readiness, and the starting point for a customized analytics roadmap for that organization in the context of the Healthcare Analytics Adoption Model.

The readiness assessment also generates a list of short-term and long-term recommendations in three categories to tailor the route on an organization’s analytics roadmap:

Category	Sample Recommendations (suggested waypoints to build into a roadmap)
People	组织分析——考虑最佳实践中心和范围模型。 Educate analysts on data management and data architecture.
Process	同时扩展数据访问和数据治理。 Evaluate/adapt the Healthcare Analytics Adoption Model for the individual analytics journey.
Technology	Compare current thedata warehouse堆叠数据操作系统的最佳实践。 消除多个重叠和过时的数据可视化工具。

Analytics Readiness Brings Immediate and Escalating Benefits

The good news is that benefits begin accruing as soon as the organization executes the readiness assessment. Value builds through each phase of analytics maturity:

1. Reduce expense/automation:为了在一个以价值为基础的环境中获得成功和生存，支付人必须继续完善他们的分析能力。但是，他们必须在维护核心服务的同时进行这种转换。自动化常规的内部和外部报告服务使它们更高效，同时减少资源密集。
2. Increase revenue/innovation:As an analytics environment lessens reporting burdens, it frees up payer resources to bring on new programs (e.g., moving towards VBC). With greater analytics capability, payers can see patterns and relationships in the data to identify and implement innovative opportunities for improvement.
3. Increase skills/staff retention:People with analytics skills have many competitive career opportunities today. For payer organizations to retain a solid workforce, they must offer their staff a clear analytics vision, analytics tools, and analytics techniques for solving healthcare challenges that are on par with other industries.

Analytics Are a Growing Payer Imperative

The analytics readiness assessment aims to help organizations determine if their analytics capacity is adequate and appropriate for succeeding in tomorrow’s healthcare environment. VBC models will mature as the inevitable context for addressing the rising costs of healthcare, and payers that mature their analytics capabilities in sync with the changes in the market will thrive.

Additional Reading

你想了解更多关于这个话题吗?Here are some articles we suggest:

1. Customer Journey Analytics: Cracking the Patient Engagement Challenge for Payers
2. Six Challenges to Becoming a Data-Driven Payer Organization

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