How to Build a Healthcare Analytics Team and Solve Strategic Problems

本报告基于2018年医疗保健分析峰会上由堪萨斯大学健康中心精益推广副总裁David M. Wild MD和业务架构与分析总监Chris Harper MBAi、MPM、业务架构与分析总监所做的演讲，题为“我们如何开发高级分析团队来解决我们的战略问题”。

Health systems have a wealth ofdataavailable, but often struggle with leveraging that data to solve strategic problems. A healthcare analytics team—made up of the right people with the right skills—can go a long way toward addressing organizational challenges and improving patient care. These analysts use advanced analytics to produce actionable insights, creating positive impacts throughout the organization.

This article will walk through the steps needed to put an effective applied analytics team in place.

The Value of a Healthcare Analytics Team

Healthcare organizations often struggle with the time-value curve of data. The time it takes between a clinical event and being able to collect and use data to make and measure an improvement is far too long. Health systems can shorten the time-value curve of analytics with an applied healthcare analytics team. These trained analysts, working with the right tools, can get to actionable data faster, providing value inimproved outcomes、运营效率和底线。

组织机构也面临数据商的问题。现在有大量的数据可用，但是数据资产、数据使用信息和具有数据技能的人员通常分散在组织中，并且没有战略上的一致。

虽然医疗保健组织通常可以在没有专门的分析团队的情况下收集所需的数据，但这个过程缓慢、低效且难以管理。一个专注的团队，在关键利益相关者的支持下，可以专注于更快地收集准确的数据，并将这些数据转化为驱动结果的见解。

Conduct a Current State Assessment

Labor in any organization is a finite resource, and because data analytics can be time intensive, there is a limit to the data employees and consultants can process and report. In many healthcare organizations, much of data analysts’ time is spent managing reports, leaving little time for analysis and consulting. As required reporting volume grows, time for analysis shrinks, further compounding the problem. To identify solutions, organizations must understand the needs and limitations of processes in use. A current state assessment can reveal this information.

Conducting a current state assessment is vital for health systems challenged by collecting and using data effectively. An assessment highlights gaps in data analysis and provides insights into duplicative or inefficient processes. It might include looking at who collects and reports data, what tools they are using, and where the data comes from.

Identify Solutions

The next step is to identify solutions to challenges uncovered during the current state assessment. For example, if the organizational goal is to become more efficient and effective at gathering and reporting data, leaders must find a way to change the productivity curve. How can they decrease the time it takes to report data while increasing the time available to analyze the data for actionable insights?

To answer this question, leaders should map out requirements for a viable solution. This might look like the following list of requirements:

• Accommodates increasing report demand with an analytic platform approach.
• Provides more value-add activities, such as data analysis and consulting with stakeholders.
• 引入了一个可扩展的报告平台，该平台使用集中的、明确定义的标准化数世界杯厄瓜多尔vs塞内加尔波胆预测据元素，同时支持业务单元报告自主权。
• Speeds the delivery of more dynamic information to decision makers.
• Allows key performance indicators (KPIs) and detailed information to flow all the way to the desktop level of the organization.
• Increases investment in data analysis and consulting with stakeholders to add value.
• 转向可扩展的报表平台，该平台使用集中的、明确定义的标准化数据世界杯厄瓜多尔vs塞内加尔波胆预测元素，并支持业务单元的自主权。
• Delivers more dynamic information to decision makers faster.

图1显示了报告开发占用了分析师的大部分时间，这使得分析和咨询的时间所剩无几。因为劳动力是有限的，随着报告数量的增加，对分析师时间的未满足需求也会增加。自动化报表会改变生产力曲线。如果分析师能够显著地减少开发报告的时间，他们就可以为组织增加更多的价值，做他们受训做的事情——分析数据并从分析中提供见解。

Once the organization’s challenges and potential solutions are mapped out, it’s time to get buy-in about how to get from the current state to a future, desired state. To do this, organizations must look at both people and tools and invest in team members to prepare them for the task.

在构建医疗保健分析团队时，经常讨论的一个问题是是否要采用中心化的方法。相反，从文化的角度决定最好的方法并更多地考虑团队的技术架构可能会更好地为组织服务。这可能包括查看团队需要哪些技能，然后决定如何组织这些人。技能评估提供数据，说明目前的工作人员是否已经或能够迅速接受必要的技能培训，以产生可操作的知识。不是每个团队成员都必须拥有每一项技能，但作为一个整体，团队必须拥有几项技能才能成功。

Below is a sample list of foundational skills to include in a skills assessment:

• 基本技能(通用)。
  • Technical skills, such as visualization basic application, SSIS, SSRS, visualization application, EMR radiology, and EMR reporting.
  • 业务/临床技能，如EMR数据库建模、EMR前端工作流和EMR构建/工作流。
  • 分析技能，如法规和合规性(HIPPA/ICD-10)、临床需求收集和医疗保健质量指标。
• Foundational skills (specialty).
  • 临床术语标准化。
  • 数据管理的发展。
  • Data modeling.
  • Data mining.
  • Data visualization development.
  • 先进的统计建模。
  • 统计分析。

An analytics team must have many of these foundational skills and know how to apply them in order to provide efficient and effective data analysis. Once the team is up and running, the organization might later decide to add additional specialty skills to the team, either through training, hiring, or contracting.

Having effective tools is also imperative. Not all data analysis software is created equal, so it’s important to have the right technology. Technology needs vary from organization to organization, but ideally organizations invest in a comprehensive solution with a variety of tools that will help speed the variety of tasks analysts spend the bulk of their time doing. An organization might spend a billion dollars on technology and building out a pristine data analytic platform, but this does nothing if the organization doesn’t have the right people to drive insights, and ultimately improvements. Neither people nor tools alone will do the job. Technology is vital to improving efficiency and accuracy of data reporting, and so is having the right people on board.

Implement a Phased Approach to Develop the Team

Developing an effective healthcare analytics team takes forethought and planning. Many organizations use a phased approach when building a team of analysts who are ready and able to solve the most pressing strategic problems. This might include the following three phases:

• Phase 1: Foundational capacity– This is the operational reporting layer. During this phase, organizations should invest in developing core capabilities around retro-perspective business intelligence (BI) and reporting and visualization tools.
• Phase 2: Advanced capabilities– Phase two involves investing in statistical and forecasting tools to answer strategic questions.
• Phase 3: Predictive capability– This involves usingpredictive analytic统计工具，以及用于预测、监测和报告结果的电子病历。

Build a Roadmap

A roadmap is a guiding document that clearly lays out where an organization is and where it’s going, along with the concrete steps to get there. Part of building a roadmap involves introspection and organizational analysis. To do this, organizations need to take a three-step approach:

• 发现真相。这个问题识别任务是任何过程改进计划的第一步。如果一个组织不了解挑战是什么，找到并实施有效的解决方案将是困难的。2022卡塔尔世界杯赛程表时间对于组织来说，寻找真相可能是困难的，因为“真相”是模糊的，并且与个人的观点有关。但是能够在这一点上达成共识的组织将会成功。
• Tell the truth.第二步是向领导展示发现，以获得潜在解决方案的认可。2022卡塔尔世界杯赛程表时间这需要外交手腕和敏感性，需要明确的目标感。
• 面对真相。The third step is to understand the challenges. It can take humility and being open to the notion that change is necessary to benefit patients. It requires a community rather than an individual focus.

The Pitch – How to Get Leadership Buy In

Getting leadership buy-in to invest in an applied healthcare analytics team requires a clear, concise message. Data is an asset. That’s a given in any healthcare organization. Most health system leaders will agree that analyzing data is critical—and that data analysis is a supporting function of strategy. So, the key to getting leadership on board to invest in an analytics team may require illustrating how to turn data analysis into a function of competitive advantage rather than just a supporting service.

This is challenging, however, if leadership regularly makes decisions based on the available data or even gut intuition. To meet that challenge, the project leader should make this pitch: The benefit of a data analytics team is that analysts will take data and, as quickly as possible, develop information from that data. They will then share what they’ve learned with operational leads who can translate that information into actionable insights. It’s these insights that have an immediate, measurable impact. The pitch should also highlight the competitive advantage of a dedicated healthcare analytics team. If the analytics team can do this work better and faster than the competition, what was once believed to be a support service can become a competitive advantage.

Put the Roadmap into Action

Once there is leadership buy-in, the next step is to put the roadmap into action. There are three general steps to building an analytics team:

1. Ask the right questions to solve the right problems.

Organizations that take time to ensure the team is asking the right questions to solve the right problems tend to have better results. Zeroing in on what those questions are takes a clear understanding of operations and outcomes. Knowing the biggest challenges and identifying solutions moves the roadmap from theory into action.

2. 利用现有资源建立团队。

有限的资源会让组建一个分析团队变得很有挑战性，但这是可以做到的。使用可用的资源，而不是获得额外的预算批准，可以使构建团队的过程更有效。而且，任何在不增加支出的情况下增加价值的团队或过程更有可能获得认可。

3. Find space for the team to operate.

Finding space for the analytics team to operate can also be challenging. If office space isn’t available, look for alternative locations to set up computer stations.

When these goals have been accomplished, the healthcare analytics team can start doing what they were created to do—analyzing data to create actionable insights that can lead to improved processes, care, and outcomes.

From Actionable Insights to Improved Patient Care

数据就是力量，为了释放这种力量，医疗保健组织需要训练有素的分析师、适合这项工作的正确工具，并仔细考虑成功的必要步骤。第一步是想清楚。当所有的基础工作都完成后，真正的、可衡量的改变就会发生。下一步是制定计划，以组织的独特文化和情况为指导。每个组织都是不同的，所以不存在建立和利用分析团队的“正确”方法。一旦利益相关者同意首先解决哪些战略问题，并理解应用分析团队将如何推动变化，团队就可以开始工作了。最后，重要的是要记住为什么。

深思熟虑地建立一个路线图并遵循它，可以让应用分析团队——以及数据——发光发亮。一个合格的，敬业的分析团队可以提供可操作的知识，告知质量举措，将积极影响患者护理和患者体验。

Additional Reading

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

1. Reducing Hospital Readmissions: A Case for Integrated Analytics
2. What Healthcare Analysts Can Learn About Data Analytics From the World of Surfing
3. How to Turn Data Analysts into Data Scientists
4. 6 Essential Data Analyst Skills for Your Healthcare Organization
5. The Number One Skill for a Healthcare Data Analyst

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