Extended Real-World Data: The Life Science Industry’s Number One Asset

The life science industry (pharmaceutical, medical device, biotech, digital therapeutics companies, and other innovators) has invested significantly indata-具体来说，扩展的真实世界数据(RWD)/真实世界证据(RWE)。More importantly, the industry has realized that focusing onpopulation healthmanagement (PHM) andoutcomes improvementis its guiding principle and top goal, and data is one part of how it will achieve that goal.

除了商品化索赔数据资产之外，以患者为中心的数据正在成为药物开发管道中的重要工具。它从发现、新适应症、临床开发、试验设计和测量结果(如副作用)到确定谁正在使用一种已获批准的药物以及为什么使用该药物，并确定药物补偿的价值-有效性。As data has become a staple decision driver at most life science companies, organizations are increasingly aware of the need to bridge the divide between these two data imperatives:

• Leveraging the fully digital experiences (e.g., patient-facing applications) and the existing clinical, provider setting.
• Working with large healthcare systems and their providers and patients, with a thorough understanding of clinical operations, to drive meaningful change.

本报告解释了扩展RWD和RWE对生命科学行业的重要性，以及如何与合适的医疗转型公司合作，以访问改善药物开发流程所需的以患者为中心的数据。

Leveraging Data to Overcome the Inefficiencies of Drug Development

药物开发过程需要8到15年，耗资高达110亿美元，依赖于昂贵且往往效率低下的临床试验过程，以及昂贵而稀疏的数据(通常只有声称的数据)，无法提供患者健康的全面性。For example, claims data will show that a patient fills a prescription but gives no insight into outcomes, side effects, etc.

生命科学公司可以通过与医疗转型公司合作，获取和利用扩展的RWD和RWE，以更好地了解使用其药物的人群及其结果，从而改善这一过程并节省成本。As the entire system (regulators, payers, manufacturers, and providers) aligns around outcomes, the following knowledge areas will enable a fair, outcomes-driven healthcare system:

• Real-world patient care (e.g., care variability between and within health systems).
• Measuring outcomes in real time across a diversity of provider types, shapes, sizes, and geographies.
• Understanding the clinical processes that drive those outcomes.

Healthcare today has a crucial opportunity, as, for the first time, key industry players are aligning on the same key goals. Regulatory, cost, and reimbursement pressures are driving the urgency to deliver the right treatment to the right patient, as measured by real-world outcomes and monitoring. This means that manufacturers, payers, and providers all benefit from solving similar challenges:

• 我如何识别从“X”治疗中风险较低、获益最高的患者?
• 我如何在PHM水平上管理我的患者群体，以推动临床和经济结果之间的总体平衡?
• 如何预测、识别、最小化、监测和衡量药品安全问题?
• 我如何确保高标准的治疗坚持、患者教育、支持和随访，以最大限度地发挥结果潜力?

While RWD (mostly claims and some EHR data) has shown more useful potential for life sciences in the past few years, its impact has often been limited to specific settings, disease areas, geographies, payers, etc. The coming years will likely see a substantial impact across most therapeutics, both for their development, launch, and post-launch activities.

The FDA Leads the Way: RWD for Regulatory Approvals

The21st Century Cures Actwas signed into law in 2016, boosting the value of RWD and RWE for theFDA还有生命科学产业。《治愈法案》旨在加快医疗产品的开发和创新，并更加关注莱茵集团和rwd驱动的决策。根据国会的说法，RWE是来自临床试验(例如，随机试验和观察性研究)以外的关于药物使用和潜在益处或风险的数据。国会将RWD描述为关于患者健康状况和/或护理提供的数据，这些数据通常从电子病历、索赔和账单、患者生成的数据等方面收集。Both RWE and RWD are growing in volume and depth with the increasing use of computers, mobile devices, and wearables and gaining utility as advanced analytics capabilities (e.g.,AI and machine learning) enable more personalized and actionable insights.

In 2018, the FDA published theFramework for FDA’s Real-World Evidence Program, which further details the usefulness of RWD in trials for new therapies. The emphasis on RWD is leading to a new approach that includes pragmatic trials (e.g., trials where the control arm is based on RWD from the standard of care) and synthetic cohorts (generating historical controls from historically accumulated trial controls, and/or simulating them on current RWD cohorts). Because pragmatic trials are poised to slash costs and reduce timelines drastically, life science companies that adopt them early will differentiate themselves in the market.

Key RWD/RWE challenges are emerging around access to health systems and patients as different organizations compete to enroll patients in traditional studies, as well as innovative studies that leverage data-driven approaches. It’s not sufficient for life science companies to leverage data; they must also create clear value for providers and patients, ensuring that innovations in clinical development help health systems achieve certain goals:

• Develop population health and clinical operations strategies that align clinical trials with standards of care.
• Choose trials that are truly of value to the patient and to the health system that manages them.

Leveraging Broader and Deeper Real-World Data to Improve Outcomes and Avoid Waste in the Clinical Development Process

For much of the past decades, inefficiencies in clinical drug development amounted not only to money the pharmaceutical industry spent (from research to launch) but also in poorer overall outcomes for patients, who experienced a rigid, synthetic clinical trial environment (i.e., being put on a placebo arm for the sake of the trial design rather than the sake of the patient or excluded from promising trials due to the complexity of trial designs). A real-world approach marries clinical development with the realities of healthcare:

• Comorbidities.
• Complexity.
• The need to measure real-world outcomes (versus synthetic outcomes within a sanitized trial setting).

Critical Value Across the Life Science Pipeline

By leveraging extended RWD/RWE, life science companies gain critical value across their pipeline (Figure 1), allowing them to:

• Save money.
• Accelerate clinical trials time and time to market.
• 创造一个更有洞察力的数字营销策略。
• Improve matching of patients with drugs.

Figure 1: Data uses across the life science pipeline

Offerings have started to grow around certain therapeutic areas (e.g., oncology EHRs), andhealthcare analyticsvendors are now expanding offerings to meet the demand for integrated data from many sources (e.g., labs, consumer behavior, and more [Figure 2]) that capture the breadth of patient health.

Figure 2: Life science companies are interested in a variety of data

As Clinical Trials Evolve, Extended RWD/RWE Becomes Paramount

生命科学行业历来把最大的赌注押在高度消毒的临床试验上，因为监管部门的批准等同于补偿。随着该领域的发展，开发人员了解到，合成试验设置不能反映药物在现实世界中的表现。当药物被商业化并可用于更广泛的人群时，其安全性和有效性可能会与选定的试验人群不同。

有限的人群和临床试验的控制性引发了广泛的RWD/RWE运动，通过临床试验设置之外的数据来了解患者的行为和真实世界的表现。随着监管机构和支付人的适应，开发在现实环境中安全有效的治疗方法的压力越来越大。

Much of the initial evidence, however, was confined to large, but shallow, claims-derived datasets. These datasets present many challenges in the clinical trials setting:

• Conversion of claims into specific visits and a clear history of the patient (coding challenges).
• 理解数据集的完整性/不完整性(数据质量挑战)。
• 提供商和服务位置的分类(来源追踪)。
• Selecting the most useful measures of utilization and expenditures (outcomes metrics).

A few deeper, EHR-derived datasets emerged, especially for some diseases (e.g., cancer) from specialized companies or from specific regions, payers, etc. Most of these datasets, however, are not broad and deep enough and can often only be partially linked together.

Looking Beyond the EHR for Broad and Deep Data

Driving outcomes improvement requires integration of data across sources, but with much of RWD to date focused on claims and with limited EHR data, the life science industry lacks the breadth and depth to leverage RWD in drug development. As Figure 3 explains, and the experience of healthcare transformation companies confirms, only 8 percent of the needed data resides in the EHR. Having large claims data and some EHR data is only scratching the surface of true outcomes measurement and transformation work for both population health and personalized approaches.

Figure 3: Only 8 percent of required data resides in the EHR

To fully understand the patient experience, life science needs to access the remaining 92 percent of data that resides in other systems. Specialty EHRs (e.g., oncology and cardiology) fill in some commercialized data gaps but miss a lot of the data that drives population health (e.g., cost, patient satisfaction, lab results, etc.). Extended RWD/RWE requires broader sources to truly understand patients; life science companies can access this knowledge by partnering with an established healthcare transformation company. Health Catalyst, for example, has more than 200 data sources integrated within its systems. As data sources expand into patient-reported information (e.g., from patient-facing apps), integrated vendor analytics will become more critical to round out extended RWD (Figure 4).

Figure 4: Extended RWD

To produce extended RWD, a healthcare transformation company must have five key capabilities:

1. Ability to measure real world outcomes.
2. Direct and maintain trusted relationship with providers across the continuum of care, versus one specialty area (e.g., oncology).
3. Ability to bridge the gap between actionable insight and real-world action, (e.g., interventions at population health level and patient-level).
4. Access to the breadth and depth of knowledge across data, analytics, and clinical operations to tackle problems and identify actionable solutions.
5. Interdisciplinary teams to achieve measurable success with data science (e.g., subject matter experts [SMEs], data engineers, and analysts), ready-to-use technological tools and platforms and, crucially, the clinical operations experience and insight that allows true healthcare transformation and meaningful change by operating at the level ofgovernance, culture, andprocess improvement.

From Insights to Real-World Action

RWD和RWE只是推动结果的开始;生命科学公司需要有能力根据数据采取行动，将其转化为惠及患者的提供者级别的行动(例如，运行临床研究、患者教育/参与项目、坚持项目、安全项目等)。与正确的医疗改革组织合作，通过连接拥有多个医疗系统和患者的公司，以及确定假设是否可行和改善的数据，帮助生命科学实现现实世界的行动。例如，一家生命科学公司可以使用浅层数据来预测药物反应、发现模式并形成见解，但只有当它将药物部署到现实世界的医院环境中时，它才能了解实际的现实世界影响。

Together, the life science industry and the right healthcare transformation companies can drive change, monitor, and measure drug performance. This completes the full circle of real-world action (Figure 5), from opportunity to action, with a solution that achieves five key goals:

1. Leverages extended RWD from trusted provider networks.
2. 利用咨询服务、专业服务、数据科学、分析、中小型企业、临床运营和PHM专业知识，形成可扩展和可操作的莱茵集团。
3. 确定提供方与患者进行测试的实际行动(利用中小型企业，例如医院运营专业知识)。
4. Deploys and measures performance and refines the real-world action deployment in collaboration with providers.
5. Scales successful real-world actions across provider networks in collaboration with providers.

Figure 5: The full circle of real-world action

Two Benefits of Healthcare Transformation Companies Committed to Outcomes Improvements

By partnering with the right healthcare transformation company, life science companies gain two key capabilities:

1. Core Capabilities—Because an effective healthcare transformation company is focused around outcomes improvement, it brings essential capabilities to the life science drug pipeline. The three core capabilities of best practice, analytics, and adoption that support outcomes improvement can also serve as an effective framework in drug development.
2. Strategic Consulting and Professional Services-战略咨询和专业服务(例如，中小企业、运营专业知识、分析/数据工程技术技能和治理)拥有独特的数据，可以快速确定解决方案，利用数据吸收和可视化技术，并与深入的运营专业知识相辅相成，以将驱动成功的人为因素和流程融入环境中。2022卡塔尔世界杯赛程表时间通过可靠的供应商网络，生命科学公司可以合并数据和专业技能，并完善解决方案，直到它可以跨供应商运行和扩展。

Powered by Extended Data, the Next Wave of Healthcare Transformation Serves All Key Industry Players

While the life science industry is accustomed to utilizing data for certain insights, its next step is to scale those insights into actions—similar to how regulators and payers have realized the value of extended RWD for key decisions concerning regulatory approvals and reimbursements. By partnering with organizations committed to healthcare transformation and leveraging extended RWD, real world insights, trusted provider networks, PHM approaches, and the definitions and real-time measurements of real-world outcomes, life science companies can achieve meaningful outcomes-driven approaches to the development, regulation, launch, reimbursement, and monitoring of new therapies.

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