The Digitization of Healthcare: Why the Right Approach Matters and Five Steps to Get There

This report is based on a Dale Sander’s webinar, “Raising the Digital Trajectory of Healthcare,” presented August 15, 2018.

With ever-evolving technology and analytics capabilities, healthcare could be on the brink of adata-driven transformation. However, while many other industries are capturing the vast potential of artificial intelligence (AI) to revolutionize their work, healthcare hasn’t yet reached the level of digitization to allow it to leverage these next-generation tools and capabilities. To capitalize on the opportunities of this advanced analytics era, healthcare must raise its digital trajectory.

Global management consulting firm McKinsey ranks industries based on theirDigital Quotient(DQ), which it derives from a cross product of three areas: data assets, data skills, data utilization. The healthcare sector’s DQ comes in at below almost all other industries, leaving a lot of room increase digitization. There’s a specific way, however, in which healthcare must approach digitization that’s mindful of the human side of healthcare decision making while prioritizing data and the technical infrastructure for a true data- analytics-driven industry.

Increasing Digitization for More Humanity Around Healthcare

The right way to increase digitization will also increase the sense of humanity around healthcare to better address patient and clinician needs. Healthcare may have largely ignored the human, or softer side, of a digital strategy. Clinicians today feel the brunt of a less human-centered digital environment and suffer from the emotional burdens of burnout, including the highest rate ofsuicideof any profession. Healthcare’s current data-driven strategy is ineffective, and even harmful to the industry it’s meant to serve. It’s nonhuman-centered approach is robbing clinicians of their sense of mastery, autonomy, and purpose and holding them to performance measures that don’t adequately represent the quality of their work.

A 2018articledocumented physician burnout, attributing much of it to the EHR era and the poor way the U.S. healthcare system has implemented a data-driven strategy. In a survey, the Medical Group Management Association (MGMA) identified that 84 percent of physicians are now participating in the Merit-based Incentive Payment System (MIPS). Of them, 82 percent consider the Medicare Access and CHIP Reauthorization Act (MACRA) Quality Payment Program very or extremely burdensome, and 73 percent said MIPS does not support their practice’s clinical quality priorities. These data points indicate how far healthcare’s data-driven strategy has alienated physicians, as compulsory measures that have little to do with the quality of the outcomes or patient relationships are increasingly burdensome.

Healthcare Must Fill Big Data Gaps

在医疗保健中生成的大多数数据是关于医疗保健的管理开销(例如，索赔数据)，而不是关于患者当前的健康和福祉状态。平均而言，医疗机构每年从患者那里收集三次数据，一年中还有362天没有跟踪健康信息。为了优化诊断和治疗，预测健康风险，并制定长期护理计划，临床医生需要完整的患者数据，这些数据可以提供日常健康和环境的全面图景，以及急性护理环境之外的医疗保健情况(例如，初级保健提供者和专家)。

At best, EHRs hold only 8 percent of the data full healthcare digitization and advanced analytics require. Only 20 percent of the factors affecting health outcomes fall inside a traditional healthcare delivery system, as most of what affects health outcomes falls outside of the four walls of healthcare delivery (e.g., social determinants of health). This model also leaves no data on healthy patients, as they have no clinical encounters, who represent the ideal artificial intelligence training set. This leaves no data to train AI algorithms about how to achieve more healthy patients, not treat sick patients.

Innovations Will Put Patients at the Center of the Data Ecosystem

虽然医疗保健的数字化战略在很大程度上忽视了患者整体健康状况的数字化，但未来几年将有创新技术进入市场，这些技术将捕捉更多的日常健康数据。健康追踪创新，比如个人可以随时佩戴的生物传感器，最终将为患者提供比卫生系统更多的健康数据和人工智能驱动的见解，使患者在决策过程中更有能力。

For example, John Rogers, the founder and executive director of the Center for Bio-Integrated Electronics at Northwestern University, is producing bio-integrated electronics in the form of microns-thin, one-inch squared, skin-pliable sensors. The tiny wafers have a Bluetooth antenna, a CPU, physiologic monitors, and a wireless power system. Some professional sports teams are already starting to wear these during competition. Rogers and team aim to eventually print the sensors on skin as a dissolvable tattoo.

In the current healthcare data ecosystem, patients are embedded within a largely disintegrated healthcare delivery system, allowing for problems with handoffs between the bubbles surrounding the patients. These different bubbles are generating very little data about the patient, and, as noted earlier, the data is mostly administrative versus telemetry about the patient’s health. A data ecosystem with the patient at the center shares biosensor-enabled patient-generated data back to the healthcare system. The process constantly updates and uploads data to a cloud-based AI platform, where algorithms diagnose the patient’s condition, calculate composite and specific health risk scores, and recommend options for treatment or maintaining health.

医疗保健数据资产路线图

Figure 1 shows a recommendation for a data assets roadmap for healthcare to guide data acquisition and data government strategies to truly understand the patient at the center of healthcare. Much of healthcare is stuck in the lower left quadrant of this diagram (healthcare encounter and claims data).

随着数据在其他领域(例如，社会经济和基因组学数据)变得更加可用，以及医疗保健准备好使用这些数据的资源，行业将扩大其数据的范围，并在工作流决策中有效地利用这些数据。指南可以帮助卫生系统了解实现数字化需要完成哪些工作并做好哪些准备。

Five Must-Haves for Healthcare Digitization the Right Way

To digitize in the right, sustainable way, health systems must follow these five guidelines:

1. Achieve and Maintain Clinician and Patient Engagement

Healthcare must ensure that its data-driven and digital strategies are adding to clinicians’ sense of mastery, autonomy, and purpose. Data must support their passion for their skill and sense of mission, rather than making them feel constantly monitored. These same principles must eventually apply to patients, as data-driven and digital strategies need to help patients feel like they’re mastering their own health and are autonomous.

Patient and clinician engagement require sensitivity to the human receiving the message, allowing people to face the truth without feeling threatened or over-measured. This is the human side of the data-driven strategy in healthcare, and, for successful digitization, it’s just as critical, or more so, than technology and advanced analytics.

2.Adopt a Modern Commercial Digital Platform Versus a Homegrown Solution

医疗保健需要一个现代化的、能够处理数字化的数据集成平台。世界杯厄瓜多尔vs塞内加尔波胆预测在一个领域内管理数据仍然非常困难，尤其是像医疗保健这样的复杂领域。在数据层工作的应用程序开发人员必须自己处理数据。还没有人对数据进行预处理，使数据层易于访问和利用。然而，在数据操作系统(例如，健康催化剂®数据操作系统[DOS™️])中，数据世界杯葡萄牙vs加纳即时走地是堆栈中的最后一层，使应用程序开发人员更容易利用复杂的软件。

A modern digital platform has seven attributes:

1. 可重用的临床和业务逻辑。注册中心、值集和其他数据逻辑位于原始数据之上，可以通过开放api访问、重用和更新，从而支持第三方应用程序开发。
2. 提供分析和工作流应用程序的单一数据流。通过平台，从数据源一直到数据表达的近实时数据流支持数据的事务级交换或分析处理。世界杯厄瓜多尔vs塞内加尔波胆预测
3. 结构化和非结构化数据集成。该平台集世界杯厄瓜多尔vs塞内加尔波胆预测成了相同环境下的文本、图像和离散结构化数据。
4. Closed-loop capability. The methods for expressing the knowledge in the platform, include delivering that knowledge at the point of decision making (e.g., back into the workflow of source systems, such as an EHR).
5. Microservices架构。除了抽象的数据逻辑，开放微服务api还用于平台操作，如授权、身份管理、数据管道管理和DevOps遥测。世界杯厄瓜多尔vs塞内加尔波胆预测这些微服务还支持在平台上构建第三方应用程序和持续交付软件更新(相对于定期的重大更新)。世界杯厄瓜多尔vs塞内加尔波胆预测
6. AI /机器学习。该平台原世界杯厄瓜多尔vs塞内加尔波胆预测生运行AI和机器学习模型，并能够快速开发和利用ML模型。
7. 不可知论者数据湖。该平台可世界杯厄瓜多尔vs塞内加尔波胆预测以部署在任何医疗保健数据湖的顶部。逻辑的可重用形式必须支持不同的计算引擎(如SQL、Spark SQL、SQL on Hadoop等)。

Figure 2 shows an example of a modern data operating system architecture (Health Catalyst’s DOS). The diagram shows curated data content in the upper-third of the diagram; this contains the intermediate data models that have comprehensive and persistent agreement about logic, making it easier for application developers in the upper-right (the DOS marketplace) to develop apps and take advantage of all the infrastructure underneath it.

由于人工智能算法是商品，医疗保健部门必须理解在人工智能领域实现机器学习所需基础设施的重要性。尽管公共云使得平台基础设施可访问且价格合理，但选择自建而非商业解决方案世界杯厄瓜多尔vs塞内加尔波胆预测的组织可能缺乏高级分析所需的可伸缩性和能力。2022卡塔尔世界杯赛程表时间

3. Digitize the Assets (the Patients) and the Processes

Any industry requires digitization of the assets it’s trying to manage and optimize, followed by digitization of the production system for managing those assets. Using the aircraft/airline industry as an example, airplanes are the digital assets. The processes that the industry has digitized—including air traffic control, baggage handling, ticketing, and maintenance—shows the ability to manage both the people as well as the aircraft.

For healthcare to understand its assets (patients), it needs to make patients more digital. Healthcare has digitized registration, scheduling, encounters, diagnosis, orders, billings, and claims. Healthcare must now digitize beyond the clinical encounter to capture the whole picture of patient health and healthcare optimization.

4. Understand the Importance of Data to Drive AI Insights

Healthcare aspires towards making health optimization recommendations for patients that are informed not only by the latest clinical trials but also by local and regional data about similar patients and their real-world health outcomes over time. From a data perspective, this requires outcomes in cost data, predictive analytics, machine learning, social determinants of health, and recommendation engines.

为了取得成功，人工智能需要该领域内数据的广度和深度。这不仅仅是指一个组织拥有的记录数量，而是指关于这些患者的事实数量。为了完善这个数字生态系统，医疗保健需要收集更多关于这些患者的信息。

AI-enabled registries can provide more accurate patient information than rules-based registries. A Swedishstudyevaluated the ability of k-means clustering (a popular unsupervised algorithm) and hierarchical clustering (clusters with predetermined ordering from top to bottom) in AI to identify previously unidentified sub-groups of patients with diabetes. Current diabetes definitions tend to use a rules-based registry defined according to ICD codes (e.g., a lab test). Going forward, however, these registries will be more defined by the patterns that data show, not in the rules that registry users apply to and impose on the data. Increasing the density of patient data with more digitization will further enable these advanced analytics insights.

Figure 3 represents the analogy between a human brain and AI pattern recognition. In this context, a human is looking at a crowd of people and using the retina as the data collection system for feature extraction. As this individual is looking at people, she’s starting to extract features about the people—their height, weight, skin color, hair color, age, etc. When the observer immediately starts thinking about number of women in the crowd, the number of men in the crowd, the number of red heads, the number of blonds, brunettes, etc., and passes through this loop repeatedly with different data, the faster and better she becomes at feature extraction and classifying people by whatever it is that she’s looking for.

Using a neural network framework, AI mimics the human pattern recognition and classification process (e.g., telling the viewer she sees people). Generative adversarial networks (GANs) mimic the opposite human process; they describe what people look like. These GANs can start producing images of people and images of data in general. From an AI perspective, GANs might produce the ability to generate training sets like never before.

人工智能概念在医疗保健领域已经存在了一段时间，但随着医疗保健获得更多数据和人工智能技术的改进，它们的应用将发生变化。A 2018 journalarticleabout the real-time use of AI for the identification of small polyps during a colonoscopy followed clinicians using AI-enabled scopes during the diagnostic process. The study found 94 percent accuracy in the detection of small polyps. That’s significant, as these polyps are hard to identify with the human eye.

5. Prioritize Data Volume over AI Model Sophistication

There’s a long-existing debate about data volume versus AI model sophistication—whether a complex AI model can overcome a lack of data volume and data features. There was a school of thought that sophisticated models could overcome a lack of data. Apaper, however, from a team at Google revealed that simple models with a lot of data trump more elaborate models based on less data. The findings suggest that as AI models increasingly become a commodity, the data will make the difference. To achieve personalized, precision healthcare, the industry needs to invest in the accumulation of better data about patients. AI model sophistication on its own is not going to overcome the limitations of poor data and inadequate data size.

The Digital Trajectory of Healthcare Starts and Continues with the People

医疗保健数字化战略的基本目标必须是提高临床医生和患者的掌握能力、自主性和目的。首先从人类的角度来看，医疗行业还必须了解获得更多患者数据的紧迫性，能够全面管理和利用这些信息的现代数据平台可以更全面地讲述关于患者健康的故事。世界杯厄瓜多尔vs塞内加尔波胆预测为了以正确的人员和数据为中心提高数字轨迹，组织可以遵循数字化指导方针，包括吸引临床医生和患者，采用现代数字平台，实现整个患者的数字化，理解数据对AI的重要性，以及不断增长的数据量。世界杯厄瓜多尔vs塞内加尔波胆预测

Additional Reading

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

1. Cloud-Based Open-Platform Data Solutions: The Best Way to Meet Today’s Growing Health Data Demands
2. Healthcare Analytics Platform: DOS Delivers the 7 Essential Components
3. The Homegrown Versus Commercial Digital Health Platform: Scalability and Other Reasons to Go with a Commercial Solution
4. Unleashing the Data to Sustain Spine Service Line Improvements

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