Cloud-Based Open-Platform Data Solutions: The Best Way to Meet Today’s Growing Health Data Demands

随着医疗保健从按服务收费模式向基于价值的补偿模式转变，以及对数据驱动改进的需求变得更加迫切，卫生系统发现其当前的数据管理和分析方法跟不上潮流。According to a Gartnerreport, “Because traditionaldata warehouse到2018年底，实践将会过时，数据仓库解决方案架构师必须向广泛的数据分析管理解决方案发展。”传统的内部部署数据仓库缺乏支持当今数据和分析需求的可伸缩性、弹性和分析敏捷性。它们无法满足医疗保健行业对数据存储和分析以及接近实时的见解日益增长的需求。

Organizations must now look beyond traditional on-premise data warehouses toward cloud-based, open-platform data solutions and analytics as a service (AaaS), the provisioning of analytics software and operations through web-delivered technologies.

Cloud-based, open-platform data solutions, such as the Health Catalyst Data Operating System (DOS™), aim to answer healthcare’s growing data needs by combining the features of data warehousing, clinical data repositories, and HIEs in a single, common-sense technology platform. Such open-platform solutions layered on top of existing source systems can support real-time processing and movement of data from point to point, as well as batch-oriented loading and computational analytic processing of that data. This approach allows real-time integration of data from multiple sources and enables the delivery of the right knowledge at the right time in the workflow. The combination of real-time, granular data, domain-specific, and reusable analytics and open-source APIs in an open-platform solution also facilitates the rapid development of new applications. These capabilities give organizations the insights needed to improve care and patient experiences while simultaneously reducing their costs.

With Growing Data Demands, Cloud Hosting for Healthcare Will Become Critical

医疗IT的未来在于云。医疗系统将需要比现在多得多的数据——其中大部分来自于他们的电子病历之外——来引导基于价值的医疗和新的报销形式。他们还需要增加存储容量，以及复杂的分析和机器学习能力，以从这些数据中获得洞见。

Emerging sources of data and greater data demand make cloud-based solutions a critical supplement to on-premises data warehouses, as they serve key needs:

More Patient Data

卫生系统只有相对较少的关于病人的信息——大部分来自于办公室、急诊室和医院的就诊。创新的智能手机应用、家庭监测设备、基因组测序和健康数据的社会决定因素将为患者的整体健康状况增添新的内容。这些数据必须与不断增长的临床和索赔数据集一起进行汇总、标准化和分析，以提供最佳的护理。

Genomic medicine, which enables highly individualized healthcare, is also poised to break into mainstream healthcare and further expand health data. Philadelphia’sGeisinger Health System, for example, announced in 2018 plans to provideDNA sequencingas part of routine medical care to 1,000 patients as a pilot program, then to all 3 million of its patients.Geisingerwill look for mutations in each person’sexome(基因组中已知的功能部分)，约占总基因组的1%。

Progress in genomic medicine will add more patient data to already burgeoning health data stores, and on-premises data warehouses can’t accommodate this additional information. To keep up with the data windfall, health systems will have to supplement on-premises data warehouse capacity with cloud-based solutions. Cloud-based platforms also offer sophisticated analytical and machine learning capabilities, which will help health systems derive actionable insights from the expanding data, a burden that would otherwise fall on already overextended clinicians who lack the required analytics training.

Patient-Driven Demand for Data

As health systems obtain more types of data, consumers want information about their health status and care. Healthcare delivery is not transparent—patients don’t know the details of their doctors’ orders in the hospital, how much care will cost them, or what role comorbidities, such as diabetes, might play in their recovery time. Patients tend to have a hard time obtaining their own health records in a form they can understand and can combine with records from other providers. As consumers demand more information about their health, cloud-based data platforms and AaaS will enable health systems to better deliver that data easily and cost-effectively.

Transitioning to the Cloud: Healthcare Is Making Headway

Though healthcare has been slower to adopt the cloud than other industries (e.g., financial services, hospitality, and energyindustries), health systems are starting to adopt cloud-based solutions at a quicker pace:

• According to a2018 report, health system skepticism about the cloud is dissipating as more cloud vendors offer HIPAA-compliant services with strong security.
• By 2021, Gartner estimates, public cloud vendors will process more than35 percentof healthcare providers’ IT workloads.Cloud computingis becoming the standard for health IT infrastructure with the growth of offerings driving adoption:
  • 基础设施即服务(IaaS)——一种通过互联网提供虚拟化计算资源的云计算形式。
  • 世界杯厄瓜多尔vs塞内加尔波胆预测平台即服务(PaaS)——第三方供应商通过互联网向用户交付硬件和软件。
• According to a recent survey, leading healthcare organizations expect to have85 percentof their applications in the cloud within three years.

Even with many health systems moving towards the cloud, there are still concerns about adopting cloud-based solutions. A 2014HIMSS Analyticssurvey found that the major reasonsfor cloud avoidance是安全问题(62%)，可用性和正常运行时间问题(39%)，以及对内部IT运营的持续关注(42%)。然而，自2014年的调查以来，情况发生了很大变化，一些进展解决了这些问题:

• With regard to cyber security,a 2018 KPMG reportobserved, “Moving to the cloud is an opportunity to improve your security profile, as most cloud vendors have more robust cyber-security capabilities than hospitals could build themselves.”
• 随着云基础设施的成熟和更具弹性，对可用性和正常运行时间的担忧在很大程度上得到了缓解。
• Many health systems continue to focus on in-house IT operations, having made substantial investments to develop and maintain their own data warehouses. In spite of these meaningful efforts, many institutions are realizing that their demand for analytics is outpacing their current capabilities. CIOs must choose between investing more in current platforms or recommending a shift to a cloud-based analytics strategy. The good news is that cloud-based data platforms can complement currently deployed analytical tools, providing additional capacity while ensuring a smooth transition to next-generation functionality.

Five Common Challenges with On-Premises Data Warehouses

Whether health systems use the homegrown data warehouses they’ve built over many years to fit their unique needs or use purchased data warehouses from major vendors, traditional data solutions have five key limitations in an increasingly data-driven era:

1. Predicting Future Demand Is Difficult

Most large organizations tend to adopt data-driven decision making unevenly. Capital purchases of data warehouse infrastructure (hardware and software) from traditional analytics vendors (e.g.,SAP,Oracle, orTeradata) require accurate predictions of future demand. Organizations that underestimate the demand may seem unprepared, whereas those that overestimate demand may appear wasteful.

2. Infrastructure Scaling Is Lumpy and Inelastic

When organizations buy their own computes, memory, and storage, they typically do it in fairly large chunks to align with budget cycles. Once they acquire the equipment, it’s theirs until they retire it. This structure makes it difficult to support several common scenarios:

• 软件或系统升级所需的基础设施临时增加。
• Short-term analysis of large datasets for mergers and acquisition modeling, payer contract negotiations, or research.
• In-house development of machine learning models.

3. Security Risk Mitigation Is a Major Investment

Analytics platforms are attractive security targets because they contain a lot of high-value, sensitive data. The more analytics a health system does on its own, the farther it has to stretch its limited cyber security budget. Major software firms may invest more than$1 billionannually in cyber security research and development; by using cloud-based analytics platforms, health systems leverage this investment, versus trying to duplicate it themselves.

4. Data Architectures Limit Flexibility and Are Resource Intensive

Healthcare datasets are diverse in breadth and depth. Cultural adoption of data-driven decision making is creating growth in analytics requests, as are annually updated regulatory and reporting requirements. This progression requires a data architecture that can support two key requirements:

• Reusing logic and calculations.
• Adapting to changing use cases.

The enterprise data model architectural pattern, which has been successful in many other industries, isn’t ideal for healthcare datasets. As analytics demands increase in quantity and variety, analysts end up spending more time trying to extend or adapt the enterprise data model to meet new analytical use cases than they spend on actual analysis. Data lake architectures bypass the rigid enterprise data model and push all logic into the query. This approach eliminates the resource overhead of maintaining an enterprise model, but limits opportunities for reusability.

5. Analytics Expertise Is Misallocated

由于数据分析师经常使用过时的架构，缺乏现代化的工具，他们往往会在基础设施或数据获取问题上花费更多的时间，而不是在更高价值的分析、建模和机器学习工作上。By fixing the structural issues with the analytics platform, analysts will be able to work at the upper end of their skillsets and deliver critical insights.

The Benefits of Analytics as a Service in a Cloud-Based Solution

While organizations can apply a cloud-based data solution to an on-premises IT system to meet the challenges of healthcare’s increasing data demands, solutions are most successful with advanced analytics in the cloud (Aaas). AaaS brings six key benefits to healthcare data work:

1. Aligns Infrastructure Costs with Value Delivered

有了可扩展的基于云的解决方案，医疗系统不需要担心资本2022卡塔尔世界杯赛程表时间投资对其未来需求来说太小或太大或太昂贵。基于云的平台允许医疗系统世界杯厄瓜多尔vs塞内加尔波胆预测按需支付，使其支出与当前需求相匹配。这种实用主义的经济模型(即，向水公司支付用水费用，而不是投资于后院的水处理厂)也使比较特定分析计划的成本与该计划所提供的价值变得更容易。

2. Adds Elasticity

Many IT processes are designed around the assumption that infrastructure is relatively static. Cloud-based infrastructure, however, is elastic, allowing IT leaders to implement more efficient and productive processes.

The process of training machine learning algorithms requires a large data set and many iterations of feature engineering (in which a data scientist applies domain knowledge to a data set to select appropriate inputs for a machine learning model). Once the model has been developed, those computing resources are no longer required. The elasticity of resources offered by cloud computing are ideally suited to the wide variations in intensity of machine learning workloads.

许多测试或培训环境在营业时间之外是不使用的。使用基于云的服务，组织可以在不使用这些环境时关闭它们，并根据需要请求资源和构建环境。与静态的内部基础设施相比，随着时间的推移，两者都可以产生有意义的节省。

By turning off the servers in a development or training environment, users save the cost of running the servers. For example, if an organization needs a new environment but will only need it for a few months, in the cloud, it can add a new environment in minutes and only pay for the environment while it’s using it.

3. Makes Resources More Available

The worldwide footprint of cloud infrastructure available from Microsoft, Amazon, and Google makes it more economical to deploy high availability solutions. Instead of leasing space in a secondary data center and filling it with equipment that’s rarely used, organizations can configure a minimal footprint in one or more places, then access the rest of the infrastructure only when it’s needed. This cloud-native approach to high availability offers a broader range of both technical and economic options.

4. Enables Machine Learning for More Accurate, Faster Insights

Health systems increasingly rely onmachine learning to identify opportunitiesfor improvement. More sophisticated forms of machine learning require a broader range of data (e.g., claims data and information from multiple EHRs). All of this data can be manipulated in a data lake, an open reservoir for the vast amount of data inherent with healthcare that reduces the time and resources required to map data. Manipulating data in the data lake generally requires a cloud-based database framework, such asHadoop.

With cloud hosting for healthcare, organizations can use machine learning to supply valuable, timely analytics insights to their clinicians and financial managers. For example, health systems need ways to predict sepsis and intervene before it becomes life threatening. Managing sepsis involves many data points and a variety of clinical protocols; to reduce sepsis mortality, health systems must be able to analyze vast amounts of data with machine learning tools. Skilled data analysts can do this effectively in the cloud with access to all of the health system’s data, as well as external sources.

5. Access to Specialized Resources

专业的分析技能在某些情况下是有用的，但卫生系统通常没有足够的持续需求来证明全职资源是合理的。例如，分析师可能需要特别复杂的地理空间可视化，这超出了他或她当前的技能来实现。AaaS供应商可以为那些不需要或不能证明这些内部角色的客户提供这些专业知识，以及广泛的专业知识。

6. Supports Outsourcing Analytics

在过去的几十年里，医疗保健组织已经转向外包主要业务功能。为了顺应这一趋势，卫生系统可以向AaaS供应商寻求满足其技术和人员需求。这些科技公司通常可以招聘并留住具有分析和数据科学专业知识的人才，这些人才拥有高需求的工作技能。卫生系统的首席信息官应该期望他们的服务提供商在合同中承诺高水平的绩效，并通过分析举措将支付与节约挂钩。

The Cloud Is the Platform of the Future

With the growing demand for analytics and data storage capacity,healthcare datawarehousing must become cloud-based, and health systems must host more of their analytics work in the cloud. The cloud is the only way health systems can afford the infrastructure and the IT talent to manage the requirements of value-based care, the upsurge of new kinds of data, and consumers’ increasing demand for healthcare information. Because of the strict limit to the machine learning tools health systems can apply on premises, most organizations are best served by placing their data and applications in the cloud and using AaaS platforms.

随着云服务的发展，传统的数据仓库结构必须改变。基于规则的交易系统需要几个月的时间来重新编程，即使是简单的报告，也不能提供临床医生提高护理质量和降低成本所需的实时见解。为了提供专门的报告，快速聚合和规范化来自许多不同来源的数据，并促进新应用程序的开发，组织可以在现有数据源上构建一个开放平台、基于云的解决方案。世界杯厄瓜多尔vs塞内加尔波胆预测When a health system operates this open-platform solution in a public cloud with AaaS, it’s poised for success.

Additional Reading

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

1. The Key to Transitioning from Fee-for-Service to Value-Based Reimbursement
2. Machine Learning in Healthcare: What C-Suite Executives Must Know to Use it Effectively in Their Organizations
3. Precision Medicine: Four Trends Make It Possible
4. Healthcare Analytics Platform: DOS Delivers the 7 Essential Components

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