Health Catalyst Editors

Specialty pharmacies serve patients who are managing medical conditions that often require complex therapies. These entities can implement patient engagement technology to improve medication adherence and patient outcomes in the following ways:

1.简化病人开始治疗的流程。
2.提供教育资源和药物坚持提醒。
3.Automate refill and delivery notifications to create staff efficiencies.
4. Increase patient satisfaction, retention, and patient outcomes.

健康问题的社会决定因素可能对健康结果产生严重的负面影响。While these determinants like race and zip code are complex to address, simple text-based outreach can be an effective way to connect disadvantaged people with information, services, and assistance that can help bridge many gaps in care.

Leverage consistent, proactive communication to ensure that patients have accurate, useful information and have the tools they need to stay engaged in their healthcare.

为了在日益严峻的医疗环境中茁壮成长，开展质量改进项目对医疗系统的持续生存比以往任何时候都更加重要。However, health systems need to tackle the right projects at the right time to maximize the impact to their organization.

本文将分享医疗保健中质量改进的临床、财务和操作示例，这些示例可以帮助其他人处理改进项目。Some examples shared include:

• Pharmacist-led Medication Therapy Management (MTM) reduces total cost of care.
• Optimizing sepsis care improves early recognition and outcomes.
•提高准备和改变能力，成功减少临床变异。
• Systematic, data-driven approach lowers length of stay (LOS) and improves care coordination.

Chronic conditions across the United States are prevalent and continue to rise. Managing one or more chronic diseases can be very challenging for patients who may be overwhelmed or confused about their care plan and may not have access to the resources they need. At the same time, care teams are overburdened, making it difficult to provide the support these patients require to stay as healthy as possible. A new approach to chronic condition management leverages technology to enable organizations to scale high-quality care, identify gaps in care, provide personalized support, and monitor patients on an ongoing basis. Such streamlined management will result in better outcomes, reduced costs, and more satisfied patients.

Clinical Trials Day, celebrated every year on May 20, recognizes the clinical research professionals who work tirelessly to improve public health and provide new treatment options to patients.

前美国卫生局局长C. Everett Coop医学博士曾经说过:“药物对不服药的病人不起作用。”然而，病人对处方药物的坚持程度往往达不到最佳水平。例如，根据美国疾病控制与预防中心(Centers of Disease Control & Prevention)的数据，患者约有20%至30%的处方配药不足。不坚持用药的后果可能包括生活质量差、身体限制、住院治疗，甚至更糟，使患者依从性成为优质医疗服务的一个组成部分。致力于让患者按时服药的医疗机构可以借助患者参与技术，主动沟通关于治疗的关键教育，以及不依从性的风险，避免常见的依从性障碍。

先进的数据和分析是为医疗保健开发高效产品的良好基础，但这还不够。Health Catalyst的首席产品官Anne Marie Bickmore解释说，建立世界杯葡萄牙vs加纳即时走地一个产品组合不只是一份产品列表——它始于高质量数据和分析的坚实基础。Bickmore describes four specific guidelines organizations can follow to create better products that drive sustainable improvement:

1.在强大的数据基础上构建产品。
2.以强大的数据基础关注不断变化的医疗保健领域。
3.Take a patient-centric approach.
4. Consider a clinical perspective.

The unforeseen pandemic changed many industries, but according to Sadiqa Mahmood, General Manager and Senior Vice President of Life Sciences Business at Health Catalyst, COVID-19 had a particularly notable effect on the life sciences industry. With the surge in digital solutions, pharmaceutical process changes, and accelerated innovation, the life sciences are experiencing never-before-seen changes. Mahmood suggests that these pandemic-fueled transformations ignited seven positive trends within life sciences that will impact the healthcare industry, pharmaceutical companies, medical technology providers, and health systems:

1.建立伙伴关系。
2.Accelerating digitization.
3.缩短疫苗开发时间。
4. Expanding the use of real-world data.
5. Scaling cloud platforms and securing data.
6. Improving supply chain.
7. Focusing on health equity.

As healthcare increasingly digitizes, organizations must prioritize the patient experience to create a seamless digital health journey. Drivers of patient experience range from the use of empathy in outreach to the technology that delivers information to a patient. Engagement software and strategies optimize patient activation when they follow the following best practices:

1.简化沟通渠道。
2.考虑沟通的时机。
3.优化内容。
4. Determine the cadence.
5. Consider the tone.
6. Leverage data to make improvements.

全球各地的医疗保健组织有一个共同点——希望利用数据改善结果。然而，Health Catalyst的高级副总裁兼全球扩张业务总经理Jeff Selander表示，实现这一目标并不像听起来那么容易。世界杯葡萄牙vs加纳即时走地塞兰德说，为了有效地利用数据来推动整个系统的改进，组织必须使他们的数据民主化或自由。数据民主化始于能够支持强大的数字需求的数字基础设施，如电子患者记录和高效的患者数据分发。一旦基础设施到位，系统必须将数字患者记录连接到其他源系统(例如，患者安全辅助系统和药房)，以获得患者健康的完整情况。牢记这些步骤，卫生系统现在已做好准备，为最广大的人群带来最大的成果。

亚太地区组织对数据、分析和数字化医疗的兴趣日益浓厚，这也带来了挑战。Health Catalyst高级副总裁、亚太地区资深医疗专家Farhana Nakhooda表示，该地区的医疗世界杯葡萄牙vs加纳即时走地系统面临许多障碍，使向数字医疗的过渡变得复杂，包括紧张的金融资产、充足的临床医生以及基于农村和城市地区的差距。However challenging these barriers might be, Nakhooda provides three strategies that systems in APAC and beyond can leverage to accelerate their digital healthcare journey:

1.Promote data and analytics literacy.
2.实现数据治理结构。
3.投资于数据保护和安全。

随着卫生系统越来越重视改善临床表现，它们依靠来自不同来源的临床分析来确定改善的机会。尽管从数据中聚合、组织和获得分析见解的过程很复杂，但Health Catalyst首席临床官、高级副总裁兼临床质量分析总经理Holly Rimmasch解释了为什么这对卫生系统的生存至关重要。世界杯葡萄牙vs加纳即时走地She also takes a deep dive into the following four domains of clinical analytics, showing how healthcare organizations can take their data farther and scale long-lasting clinical improvements:

1.Data acquisition.

2.Clinical analytics usage.

3.Unrealized opportunities of clinical analytics.

4. Patient engagement.

Each health system takes a unique approach to financial management. No matter the organization, Dan Unger, SVP and General Manager of Financial Transformation at Health Catalyst, says that effective financial management should promote better financial decision making across the system. And even though optimal financial management is critical to healthcare operations, organizations struggle to deal with its complexities. With challenges ranging from government-set funding to newly emerging competitors in the market, health systems need to optimize their approach to financial management. Unger suggests healthcare organizations adopt three mental shifts that lead to better financial management:

1.确定一个有远见的财务领导者。

2.与临床和操作领域合作。

3.Implement activity-based costing.

在一个技术创新和非凡互联互通的时代，为什么临床研究要遵循几十年前的模式?临床试验仍然集中在领先的城市医疗中心，患者人数较少，但今天的数据和协作能力可以支持更广泛、更强大的覆盖范围。To that end, the new Health Catalyst Research Offering accelerates and optimizes clinical research in five groundbreaking ways:

1.连接临床研究中的关键角色。
2.提供医疗保健提供者系统、生物制药公司和临床研究组织的网络。
3.Gives access to research-oriented Health Catalyst products and services.
4. Supports clinical study from planning through the active trial.
5. Maintains a national repository of clinical data.

即使是在虚拟世界中，运送3000多名医疗保健领袖和活动家穿越三个国际目的地也是一个不小的壮举。Add world-class data and analytics insight and inspiration from healthcare and beyond to the voyage and you have a three-day journey of a lifetime—otherwise known as theHealthcare Analytics Summit™(HAS) 21Virtual. The 2021 edition of healthcare’s premier analytics summit once again gathered innovators and heroes from around the globe to explore multi-domain analytics as the framework for a winning team approach to healthcare transformation.

Health systems increasingly turn to AI to help all team members make more informed decisions in a shorter time frame. Instead of an artificial-intelligence approach that threatens the critical role healthcare experts play in decision making, organizations should define AI as augmented intelligence. In his first podcast, Dr. Jason Jones, our Chief Analytics and Data Science Officer, explains how augmented intelligence can help health systems accelerate progress toward achieving the Quadruple Aim. The three unique opportunities augmented intelligence offers health systems include the following:

1.Augmented-not人工智能。
2.认为“变更管理。”
3.解决和克服医疗保健方面的差距。

After virtual stops in Singapore and London, HAS 21Virtual与会者可能认为第三天不会超过前两天。然而，当保罗·霍斯特梅尔(Paul Horstmeier)在迪拜大饭店(Grand Dubai Hotel)欢迎全球分析旅行者时，他们都为之惊叹。不愿被奢华的环境所超越，最后一天的议程再次超出预期，包括医学博士艾米·康普顿-菲利普斯(Amy Compton-Phillips)，她监督了美国第一个已知的COVID-19患者的护理，一组强有力的爆发话题，以及特别的医疗分析版《危险边缘》(Jeopardy)。第三天还包括备受期待的#SockofHAS获奖者的公告，HAS积分比赛的结果，受人尊敬的飞轮奖获奖者，以及更多。活动组织者和粉丝们已经兴奋地期待着在2022年9月13-15日再次相聚!

Day two of the Healthcare Analytics Summit™Virtualagain opened with a warm welcome from “Captain” Paul Horstmeier and his flight crew. Keynote speaker Patrice Harris, MD, MA, FAPA, former President of the American Medical Association, then took the stage to discuss common health equity challenges and solutions. Chris Chen, MD, CEO of ChenMed, followed to share how his organization has redefined value by using a fully capitated economic model. After a quick break, attendees chose between industry outlook sessions focused on health equity and population health. By lunchtime, participants were ready to explore over 30 data-centric projects at the Analytics and AI Showcases and learn about new Health Catalyst products. Attendees wrapped up an action-packed day two with Braindates, one-on-one or group networking sessions to learn more about trending topics of their choice.

A fireside chat with NBA great Steve Kerr, a virtual journey to a premier international destination, a glimpse at robots changing the world, and an investigation into the parallels between motocross and healthcare analytics are just some of the experiences offered during day one of the Healthcare Analytics Summit™ (HAS) 21Virtual. From a digital platform for the second consecutive year, HAS 21Virtualkicked off three days of extraordinary education, inspiration, and entertainment on September 21. With experts from healthcare and beyond, attendees explored the digital trends and best practice experiences driving healthcare success in the new digital era. Additionally, two waves of breakouts offered insights into increasing revenue, decreasing cost, improving quality, and more.

医疗保健机构在大流行期间遭受了财务挫折，现在正在寻找机会弥补失去的收入。卫生系统不应只关注在选择性程序停止数月后提高盈利能力，而应密切审查影响收入周期的医疗保健的其他方面。To take a proactive approach to restore revenue cycle integrity, healthcare leaders should consider six hands-on strategies that promote near- and long-term revenue recovery:

1.准备改变法律。
2.创造积极的远程工作环境。
3.Manage payer policies.
4. Expand telehealth.
5. Set up prior authorization for surgical procedures.
6. Achieve price transparency.

The Healthcare Analytics Summit™ (HAS) 21Virtualfeatures internationally recognized speakers, national and global networking opportunities, and traditional HAS fun—including #Socks of HAS, quiz questions, daily scavenger hunts, prizes, and more. In addition, the 2021 global theme will take attendees on a virtual journey to three international destinations—Singapore, Dubai, and London—while exploring the digital trends and best practice experiences driving healthcare success in the new digital era.

The HAS 21Virtualworld-class speaker line-up includes the following:

1.金州勇士队主教练，两届NBA年度最佳教练，史蒂夫·科尔。
2.AI Rana el Kaliouby，博士，Affectiva联合创始人兼首席执行官，情感和人类感知的先驱和发明者。
3.Chris Chen, MD, CEO of ChenMed, and Brent James, MD, MStat, Clinical Professor, Clinical Excellence Research Center (CERC), Department of Medicine at Stanford University School of Medicine.

COVID-19疫情突出表明，卫生系统必须主动为未来的情况做好准备。组织做好准备的一种方法是使用人工智能(AI)，例如预测分析，来预测临床、运营和财务需求。虽然许多卫生系统拥有预测建模所需的历史和当前数据，但它们往往缺乏启动任何AI项目所需的分析基础和知识，更不用说预测分析之旅了。

Data and analytics technology lay the foundation to support a health system for a successful AI pursuit, including predictive analytics. With the right tools in place, health systems are ready to follow the four-step framework:

1.项目接收和优先级排序。
2.项目开始。
3.Model development.
4. Operationalizing the predictive model.

Hospitals spend over $7.5 million every year on regulatory compliance. Payers, such as CMS, rely on these quality measures to evaluate health system and provider performance and determine reimbursement rates for services rendered. As a result, regulatory performance is critical to the care process and revenue stream. However, many health systems fail to meet these care standards and maximize reimbursement rates because they lack analytic insight into regulatory performance. With a data engine that tracks and submits quality measures data, leaders understand their compliance performance, gaining insight into opportunities to improve patient-centric care and value-based performance. This data-informed approach allows organizations to increase profits through peak regulatory performance and avoid financial penalties associated with underperformance.

随着医疗保健中数据和数据源的增加，组织需要更有效地组织、跟踪数据并向团队成员分发数据。数据治理战略为卫生系统提供了一种标准化方法来管理数据，这是它们最宝贵的资产。有效的数据治理可以帮助领导者最大化他们的数据，促进系统范围内基于数据的决策，并推动可持续的改进。

Healthcare leaders can operationalize data governance in their organizations by considering three key elements of an effective strategy:

1.从数据治理基础开始。
2.确保数据治理策略支持可持续的改进。
3.使数据治理策略与组织优先级保持一致。

With mounting pressures to deliver quality care with fixed resources, data-driven healthcare is pivotal to organizations’ well-being. From operations to the front lines of clinical care, data can drive the best outcome if decision makers have relevant information when they need it. However, many organizations simply use data in one-off situations rather than integrating it into systemwide processes and workflows. To understand what it means to become data driven and take the right steps forward, organizations can apply four key elements:

1.Invest in one source of data truth.
2.应用数据治理策略。
3.促进全系统的数据素养。
4. Implement a cybersecurity framework.

患者参与度的提高会带来更好的临床结果，但医疗机构仍然难以让患者及其家属参与到他们的护理中来。首先，患者对他们的护理和医疗保健能力有不同程度的兴趣，这增加了将每个患者视为护理团队成员的挑战。

无论这些患者参与的障碍有多么困难，企业都可以利用数据来克服它们。通过访问数据，医疗保健领导者和提供商可以确定优化患者参与度的机会。By implementing four data-informed tactics, systems can increase patient engagement and improve health outcomes:

1.实施共同决策干预。
2.促进卫生公平。
3.优先考虑病人的反馈。
4. Provide patient-centered education.

提供精准医疗需要医疗保健从一刀切的方法过渡到个性化的方法。这意味着医疗保健专业人员根据每个患者的个人特征——他们的基因组构成、环境和生活方式——量身定制治疗和预防策略。为了实现这些精确护理目标，研究人员和临床医生必须利用大量不同的真实世界数据。

数据访问和互操作性障碍经常阻碍精准医疗的转型。然而，当前的医疗保健行业趋势增加了研究人员和临床医生更全面地了解医疗条件和患者的机会。这些见解为精准医疗奠定了基础，并为更有效地开发靶向治疗提供了可操作的途径。

Many organizations face high costs and diminishing returns due to unnecessarily high length of stay (LOS) and readmission rates. Elevated LOS and readmission rates can indicate low quality care and also result in costly financial penalties. Therefore, addressing LOS and readmission rates can eliminate avoidable financial consequences, while keeping patients out of the hospital and less likely to develop hospital-acquired infections.

Health systems can leverage analytic insight to reduce unnecessary patient LOS and readmission rates, resulting in lower costs for health systems and better health for patients, by applying three data-driven strategies:

1.实现过程变化。
2.删除排出障碍。
3.Improve care transitions.

儿科护理有独特的挑战，如通过父母或监护人与年轻患者沟通，并与儿童评估疼痛程度。为了克服这些挑战，组织可以依靠操作数据来瞄准儿科改善领域，导致更低的成本和更高的利润率。

Leveraging operational data—instead of focusing solely on pediatric outcomes data—can reveal opportunities for health systems to improve pediatric patient access and, in turn, increase revenue. Organizations can deliver higher quality pediatric care while increasing profits by implementing three data-informed strategies:
1.空间利用率最大化。
2.改善病人的调度。
3.实现虚拟护理。

随着卫生系统继续适应大流行医疗格局，某些挑战仍然存在——包括在微薄的营业利润率上创造收入。收取费用不力是造成收入损失的一个常见原因，而医疗行业的领导者往往无法解决这个问题。由于收费获取是为医院提供的服务获得报酬的过程，糟糕的收费获取流程意味着医院无法为其提供的服务获得全额报酬，从而导致通常无法挽回的收入损失。

Health systems can avoid financial leakage and increase profits by focusing on five problem areas within charge capture practice:
1.紧急服务。
2.手术室服务。
3.药学服务。
4. Supply chain and devices.
5. CDM mapping.

卫生系统依靠有效的收入周期管理来跟踪患者的旅程，处理索赔，并确保组织为其服务收取费用。在当今复杂多变的医疗保健行业，收入周期管理不仅仅是账单和收款，传统的收入周期方法无法满足不断增长的需求。此外，由于COVID-19造成的数量损失，各组织不能错过支付的机会。

The contemporary healthcare landscape requires a comprehensive, standardized, and data-driven revenue cycle process. Health systems that leverage data to support revenue cycle management improve their financial outcomes in three significant ways:
1.减少否认。
2.Increase collections with propensity-to-pay insight.
3.改善discharged-not-final-billed努力。

As health systems experience more pressure to deliver quality care with limited resources during a pandemic, data analysts play a vital role in helping organizations overcome new COVID-19-induced challenges. Data analysts provide direction about the best way to dissect data, identify areas for improvement, and solve complex problems that stand in the way of better healthcare delivery. However, by developing four specific skills, data analysts can optimize their work and help leaders make sound operational, clinical, and financial decisions:
1.以终为始。
2.专注于解决问题。
3.掌握基本能力。
4. Play the data detective.

Healthcare consumers are demanding the same level of price transparency for medical care they have in other transactions—particularly as healthcare moves away from a fee-for-service model and patients are responsible for larger portions of their medical bills. Meanwhile, as of January 2021, federal regulation requires health systems to make their service charges publicly available. The healthcare industry, however, hasn’t historically succeeded with consumer-grade price transparency. Organizations must now figure out how to bridge the gap between their costs and patient charges. Doing so requires comprehensive understanding of all the costs behind a service and consumer-friendly explanation of how these expenses translate into prices.

败血症每年至少影响170万美国成年人，使其成为医疗保健机构一个关键的改善机会。然而，事实证明，这种情况给卫生系统带来了问题。常见的挑战包括区分败血症和患者急性疾病以及数据获取。为此，组织必须具备全面、及时的数据和先进的分析能力，以了解其人群中的败血症并监测护理计划。这些工具可以帮助组织识别败血症，早期干预，挽救生命，并随着时间的推移保持改善。

Surviving on thin operating margins underscores the need for all end users at a health system to make decisions based on comprehensive data sets. This data-centered approach to decision making allows team members to take the right course of action the first time and avoid making decisions based on fragmented data that exclude key pieces of information.

To promote data-driven decision making and a data-centric culture, healthcare organizations should increase data access and availability across the institution. With easy access to complete data, end users rely on the same data to make decisions, no matter where they work within the health system.

Two strategies can help organizations integrate and deliver data to end users when they need it:
1.选择适合大多数人需求的基础设施。
2.问正确的问题。

Predictive artificial intelligence (AI) models can help health systems manage population health initiatives by identifying the organization’s most vulnerable patient populations. With these patients identified, organizations can perform outreach and interventions to maximize the quality of patient care and further enhance the AI model's effectiveness.

最成功的模型综合利用了技术、数据和人工干预。然而，组装适当的资源可能具有挑战性。这些障碍包括不共享信息的多种技术解决方案、数百个可能的(通常是完全不同的)2022卡塔尔世界杯赛程表时间数据点，以及适当分配资源和计划正确干预措施的需要。当涉及到人口健康的预测性人工智能时，简单的模型可能利用最强大的预测能力，这允许更知情的风险分层，并确定患者参与的机会。

由于报销费率的变化、COVID-19以及大流行造成护理中断后的管理，卫生系统继续面临财政挑战和负担。在有限资源的情况下薄利经营意味着卫生系统需要采取节约成本的替代措施，以最大限度地利用有限资源。

全面、可靠的数据增加了整个医疗连续体费用的可见性，因此领导者可以利用新的方法来节省资金、产生收入和加速现金流，以保持患者健康和医院的大门敞开。With access to recent data, health systems can focus on three cost-saving strategies:
1.Increase physician engagement.
2.预测支付倾向。
3.实施循证护理标准。

尽管医疗保健领域的患者准入问题一直存在，但COVID-19进一步加剧了准入基础设施的压力。“居家令”、暂时停止亲自上门就诊、交通挑战等导致护理延期或错过。与此同时，大流行时代的变通办法，如向虚拟医疗的转变，推动了更数字化的患者体验。As healthcare consumers and providers increasingly relying on touchless and asynchronous processes, health systems are discovering opportunities to improve patient access and the overall experience.

With the following five steps in a patient access improvement framework, organizations can scale and sustain innovations and lessons learned during the pandemic:
1.Create a patient access task force.
2.评估患者获得服务的障碍。
3.把准入障碍变成机会。
4. Implement an improved patient access plan.
5. Scale and sustain better patient access.

在资金需求、不确定的医疗立法和COVID-19之间摇摆不定，可能会分散医疗领导者对护理患者最重要方面的注意力。在这个动荡的市场中，提供以患者为中心的护理可能具有挑战性，尤其是在传统的医疗保健方法(如亲自探访)被搁置的情况下。这些对日常护理的突然中断凸显了让患者处于护理中心的重要性，无论护理是亲自提供还是虚拟提供。Health systems can manage competing priorities, adjust to pandemic-induced changes, and deliver patient-centered care by focusing on three strategies:
1.改善患者体验。
2.落实有意义措施倡议。
3.将亲身访问转变为虚拟访问。

由于医疗保健支付从按服务收费(FFS)转向基于价值的补偿需要比预期更长的时间，卫生系统必须在现有的基于数量的模式与日益重视价值的模式之间取得平衡。组织处于从数量到价值的不同阶段，并且政策继续发展。对此，该行业的最佳立场是维持FFS收入，同时遵循指导方针和战略，为越来越多的价值做好准备。

Organizations can use four methods to remain agile as they navigate the limbo between volume and value:

1.Understand the first ten years of value-based care and prepare for what’s next.
2.Identify essential strategies for shifting from volume to value.
3.利用医疗保险共享储蓄计划。
4. Use population health management as a path to value.

One of the biggest challenges health systems have faced since the onset of COVID-19 is the disruption to routine care. These care disruptions, such as halted routine checkups and primary care visits, place some patients at a higher risk for adverse outcomes. Health systems can rely on data science, based on past care disruption, to identify vulnerable patients and the short- and long-term effects these care disruptions could have on their health. Data science can also inform the care team which care disruptions to address first. With comprehensive information about care disruption on patients, health systems can apply the right interventions before it’s too late.

当医疗保健领导者做出数据驱动的决策时，他们通常认为自己在数据中看到了相同的东西，并认为他们得出了相同的结论。然而，决策者后来常常发现，他们看待数据的方式不同，并没有得出相同的见解，导致了无效和不可持续的选择。医疗保健领导者可以通过使用统计过程控制(SPC)方法管理不同的数据解释，以找到焦点，避免不同的数据解释，做出更好的决策，并监控变化以实现可持续的未来。By deriving concise insights, SPC separates the signal from the noise, augmenting leaders’ decision-making capabilities.

由于COVID-19在临床、运营和财政方面给卫生系统造成了压力，先进的数据科学能力已成为非常宝贵的大流行资源。各组织利用人工智能(AI)和机器学习(ML)更好地了解COVID-19和其他健康状况、患者群体、运营和财务挑战，并获得更多见解，以支持大流行应对和恢复以及持续的医疗服务。与此同时，改进的数据科学采用指南使AI和ML等能力的实施更容易获得和可操作，允许组织实现有意义的短期改进，并为应急未来做好准备。

COVID-19让数据驱动的医疗有机会在国家和全球舞台上证明其价值。卫生系统、研究人员和决策者利用数据推动从短期应急响应到长期恢复计划的关键决策。

Five areas of pandemic response and recovery stand out for their robust use of data and measurable impact on the course of the outbreak and the individuals and frontline providers at its center:
1.将医院指挥中心扩大到流行病规模
2.Meeting patient surge demands on hospital capacity.
3.控制疾病传播。
4. Fueling global research.
5. Responding to financial strain.

医疗保健流程推动整个卫生系统的活动和结果，从急诊入院和程序到计费和出院。此外，在COVID-19时代的不确定性中，过程质量是护理提供和组织成功的日益重要的驱动因素。考虑到这种广泛的影响范围，过程改进本质上与更好的结果和更低的成本有关。医疗保健流程改进的六项战略说明了战略、技能组合、文化和高级分析在医疗保健持续转型使命中的作用。

随着人工智能(AI)渗透到医疗保健行业，分析领导者必须确保AI保持道德，并对所有患者群体有益。由于没有一个正式的监管机构或管理机构来执行人工智能标准，只有医疗专业人员来维护医疗人工智能中的道德规范。

人工智能在支持疫情应对方面的潜力可以带来巨大的回报。然而，如果医疗专业人员不熟悉人工智能产生的建议的准确性和局限性，确保其合乎道德的实施可能是具有挑战性的。了解数据科学家如何计算算法，他们使用什么数据，以及如何解释这些数据，对于以有意义和合乎道德的方式使用人工智能来改善医疗服务至关重要。By adhering to best practices for healthcare AI, health systems can guard against bias, ensure patient privacy, and maximize efficiencies while assisting humanity.

Many health systems have realized they lack the data and analytics infrastructure to guide a sustainable reactivation plan and recover lost revenue from months of halted procedures due to COVID-19. However, with operational, clinical, and financial data, augmented by analytics tools, leaders have the visibility into hospital and resource capacity to guide a safe, sustainable elective surgery restart plan.

卫生系统复苏之路的第一步是获得可靠的分析，以了解COVID-19对临床、财务和运营结果的全面影响。其次，组织需要数据共享工具，比如数据显示和仪表板，让领导者能够根据一致的数据做出决定，支持组织的重新激活目标。领导者甚至可以通过预测模型和预测程序数量、人员和资源进一步研究数据。

With limited resources compared to large healthcare organizations and fewer personnel to shoulder burdens like COVID-19, medical practices must find ways to deliver better care with less. Delivering quality care, especially in a pandemic, is challenging, but analytics insight can guide effective care delivery methods, especially for smaller practices.

Comprehensive data combined with team members who can turn numbers into real-world information are essential for medical practices to ensure a strong financial, clinical, and operational future. Independent medical practices can rely on four analytics strategies to survive the uncertain healthcare market and plan for a sustainable future:

1.优先访问最新的、全面的数据源。
2.形成数据治理的多学科方法。
3.将数据转化为分析洞察。
4. Invest in analytics infrastructure to support self-service analytics.

COVID-19时代，门诊就诊人数减少了60%，初级保健的经济损失估计超过150亿美元。关闭选择性护理对卫生系统和患者来说在经济上是不可持续的，因为他们仍然需要与大流行无关的护理。虽然虚拟医疗已经成为患者和提供者的一种可行和互利的解决方案，但从面对面到虚拟医疗的转变在后勤和财务上都很复杂。

亲身护理的流程和工作流程不能直接转化为虚拟环境，经济上的成功转变需要深入理解在新常态下驱动患者参与和收入的因素。因此，满足患者需求和财务目标需要强大的企业级分析，深入到提供者层面。

Healthcare financial transformation—improving care delivery while lowering costs—has been an ongoing challenge for health systems in the era of value-based care and an even more prominent concern amid COVID-19.
While better care and reduced expense to organizations and consumers might seem like opposing goals, by understanding the true cost of services and other drivers of expense, organizations can successfully manage costs while maintaining, and even improving, care delivery.
为此，卫生系统可以使用数据和分析驱动的工具和战略来应对财政挑战，包括无补偿护理、延长应收帐款天数、解除非最终账单案件、资源使用效率低下等等。

研究预测，2020年医疗保健行业因COVID-19造成的损失将达到3230亿美元。随着患者数量下降和与大流行相关的费用上升，卫生系统需要一项立即和长期财务复苏的战略。有效的方法将依赖于对大流行如何改变和重塑护理提供模式的深刻而细致的理解。2019冠状病毒病时代最具影响力的变化之一是从面对面办公室探访转向虚拟医疗(如远程医疗)。虽然患者和医疗服务提供者最初转向远程交付，以腾出设施进行COVID-19治疗并减少疾病传播，但虚拟医疗的好处(例如，避免患者前往预约时的时间和资源消耗)使远程医疗成为新的医疗领域中的持久模式。As a result, healthcare financial leaders must fully understand the revenue and reimbursement implications of virtual care.

随着医疗保健数据比以往任何时候都更容易获得，因此有必要将这些数据应用于卫生系统面临的独特挑战，特别是在大流行期间。Even with massive amounts of data, health systems still struggle to move data from spreadsheets to drive change in a clinical setting.

These six methods allow health systems to transform data into real-world analytics, going beyond basic data usage and maximizing actionable insight:

1.创建有效的信息显示。
2.向数据添加上下文。
3.Ensure data processes are sustainable.
4. Certify data quality.
5. Provide systemwide access to data.
6. Refine the approach to knowledge management.

利用真实世界的分析技术推动医疗保健中的数据使用，为卫生系统提供有效的工具，以抗击COVID-19，并在全面、可操作的洞察力的驱动下继续提供优质护理。

Despite pandemic-driven limitations, health systems can still find ways to optimize revenue cycle and generate income. When health systems improve and prioritize patient access through a patient-centered access center, they can improve the revenue cycle performance through decreased referral leakage, better patient trust, and optimum communication across hospital departments.

Rather than relying on traditional revenue cycle improvement tactics, health systems should consider three ways a patient-centered access center can positively impact revenue cycle performance:

1.推进访问。
2.优化资源。
3.利益相关者参与。

由于医疗保健行业继续关注价值，而不是数量，卫生系统面临着向资源有限的大量人口提供高质量的医疗服务的问题。为了实施人口健康举措并取得成果，至关重要的是护理团队应根据可行动的最新数据制定人口健康战略。Health systems can better leverage data within population health and drive long-lasting change by implementing three small changes:

1.增加团队成员对数据的访问。
2.支持广泛的数据利用。
3.Implement one source of data truth.

获得准确、可靠的数据可以在保持成本和改善结果的同时，促进人口健康工作。With actionable analytics providing insight and guiding decisions, population health teams can drive real change within their patient populations.

COVID-19 response and recovery is pushing healthcare to operate at an unprecedented level. To meet these demands and continue to improve outcomes and lower costs, healthcare analytics must perform more actionably and with broader organizational impact than ever. Health systems can follow four strategies to produce high-value analytics to withstand the pandemic and make healthcare better in the long term:

1.减少基准测试。
2.外包监管报告。
3.增强基于风险的分层能力。
4. Run activity-based costing plus at-risk contracting.

The 2020 Healthcare Analytics Summit™ (HAS 20 Virtual) took place for the first time from a remote platform. But, as the 2020 HAS infographic demonstrates, the remote experience delivered on HAS event’s customary high level of engagement and meaningful healthcare insights.
2020年会议的主题是新常态下的分析，向创纪录的观众分享了关于疫情应对和恢复的见解。

在美国，可预防的患者伤害每年导致超过40万例死亡，因此提高安全性是医疗机构的首要任务。为了减少住院患者的风险，卫生系统正在使用患者安全分析和基于触发的监测工具，以更好地了解和识别在其设施中发生的危害类型，并尽早干预。

Six examples of analytics-driven patient safety success cover improvement in the following areas:

1.Wrong-patient order errors.
2.血液管理。
3.艰难梭菌(C. diff)
4. Opioid dependence.
5. Event reporting.
6. Sepsis.

The Healthcare Analytics Summit 20 Virtual (HAS 20Virtual) concluded three days of online programming on Thursday, September 3, 2020. Though COVID-19 forced this year’s event to take place virtually, the geographic dispersal of attendees and presenters didn’t dampen the depth of insights or level of engagement previous summits are known for.
After two days of keynote addresses, breakout presentations, small Braindate gatherings, and project and solution showcase, HAS 20Virtualmaintained its momentum. The conference closed on a powerful note with yet more world-class speakers, groundbreaking innovations, and common theme of the power of analytics and human potential in healthcare’s new normal.

Day two of the Healthcare Analytics Summit 20 Virtual (HAS 20Virtual) included keynote speakers followed by live Q&As, quizzes to earn points for the HAS game, the Analytics Walkabout, Machine Learning Marketplace, and Digital Innovation Showcase.
与会者喜欢主题主题演讲者，如美国食品和药物管理局代理CIO Amy P. Abernethy医学博士，他讨论了数据在应对COVID-19的重要性;Yonatan Adiri，健康的首席执行官。Io，她展示了一项基于智能手机的尿液测试，以提高医疗保健的可及性;and Sampson Davis, MD, emergency medicine physician and New York Times best-selling author, who shared how education saved his life.

For the first time from an online platform, Health Catalyst COO Paul Horstmeier welcomed attendees to the Healthcare Analytics Summit 20 Virtual (HAS 20Virtual), promised a highly interactive online experience that would maintain the breadth and depth of expertise as well as the spirit of innovation of the conference’s in-person iterations.

HAS 20Virtualwill also provide some of the fun and good humor attendees have enjoyed in year’s past–from the Virtual fun run to the friendly competition for the most notable socks–HAS 20Virtual将这些活动搬到了网上。has20第一天的亮点包括来自Eric Topol, MD和Ari Robicsek, MD的主题演讲，以及两个突破的会议浪潮。

公平影响到社会结构，影响到不同种族和族裔患者获得的医疗保健的类型和质量。新冠肺炎疫情凸显了美国医疗服务的不平等，因为疫情对美国黑人社区的影响格外严重。
为了照顾和认识到所有个人的价值，医疗保健必须利用数据和分析来更好地按种族和民族了解患者群体，并确定如何满足其服务不足人群的需求。

As Health Catalyst continues to engage its health system partners in their COVID-19 journeys through virtual client huddles, topics are delving further into restarting ambulatory care and elective procedures. The May 21, 2020, forum explored how organizations are responding to the pandemic and planning for the next phases. Participants explored two vital topics in the COVID-19 era:

•虚拟医疗分析如何支持门诊医疗服务的快速变化。
•分析洞察力如何帮助推动COVID-19金融复苏计划。

卫生系统同样也在努力有效地管理医院的病人流量。通过机器学习和预测模型，卫生系统可以改善整个系统中不同部门的患者流量，比如急诊科。Health systems should focus on three key areas to foster successful data science that will lead to improved hospital patient flow:

关键1。建立一个数据科学团队。
关键2。创建一个ML管道来聚合所有数据源。
Key 3. Form a comprehensive leadership team to govern data.

Improving hospital patient flow through predictive models results in reduced patient wait times, reduced staff overtime, improved patient outcomes, and improved patient and clinician satisfaction.

More than 100 attendees joined the first of a series of Health Catalyst virtual client huddles designed to support client partners and aid collaboration and direct client connections in this time of unprecedented change.
根据2020年4月对Health Catalyst客户的一项调查，72.6世界杯葡萄牙vs加纳即时走地%的客户表示，他们对其他卫生系统的例子、指导和工具有浓厚兴趣。在仅针对客户的会议中，分享了最常见的COVID-19分析项目和一个卫生系统的选择性手术计划。

The health system shared the challenges they faced in understanding the financial impact of halting elective surgeries as well as creating a plan for working through their backlog. They also shared the tools and strategies they are using to aid their financial recovery.

有效、可持续的医疗保健转型取决于为医疗服务提供提供动力的组织运作。业务包括维持卫生系统运行和照顾患者的行政、财务、法律和临床活动。由于手术对提供护理至关重要，具有前瞻性的组织不断努力改善其手术结果。卫生系统可以遵循思想领导，以应对常见的行业挑战——包括减少废物、流程变革中的障碍、有限的医院容量和复杂的项目管理——为其业务改进战略提供依据。

1.Five top insights address the following aspects of healthcare operational outcomes improvement:
2.Quality improvement as a foundational business strategy.
3.使用改进科学来实现真正的改变。
4. Increasing hospital capacity without construction.
5. Leveraging project management techniques.
6. Features of highly effective improvement projects.

对分析的关注导致了“电子病历问题”——医生优先考虑电子病历而不是病人。The Healthcare Analytics Adoption Model (HAAM) walks healthcare organizations through nine levels that lay the framework to fully leverage analytic capabilities to improve patient outcomes:

1级。Enterprise Data Operating System
级别2。Standardized Vocabulary & Patient Registries
Level 3. Automated Internal Reporting
4级。Automated External Reporting
Level 5. Waste and Care Variability Reduction
6级。Population Health Management & Suggestive Analytics
7级。Clinical Risk Intervention & Predictive Analytics
Level 8. Personalized Medicine & Prescriptive Analytics
Level 9. Direct-To-Patient Analytics & Artificial Intelligence

Analytics are crucial to becoming a data-driven organization, but providers and administrators can’t forget about the why behind the data—to improve outcomes. Following the HAAM enables organizations to build a sustainable, analytic platform and empower patients to become data-driven when it comes to their own care.

对分析的关注导致了“电子病历问题”——医生优先考虑电子病历而不是病人。The Healthcare Analytics Adoption Model (HAAM) walks healthcare organizations through nine levels that lay the framework to fully leverage analytic capabilities to improve patient outcomes:

1级。Enterprise Data Operating System
级别2。Standardized Vocabulary & Patient Registries
Level 3. Automated Internal Reporting
4级。Automated External Reporting
Level 5. Waste and Care Variability Reduction
6级。Population Health Management & Suggestive Analytics
7级。Clinical Risk Intervention & Predictive Analytics
Level 8. Personalized Medicine & Prescriptive Analytics
Level 9. Direct-To-Patient Analytics & Artificial Intelligence

Analytics are crucial to becoming a data-driven organization, but providers and administrators can’t forget about the why behind the data—to improve outcomes. Following the HAAM enables organizations to build a sustainable, analytic platform and empower patients to become data-driven when it comes to their own care.

As value-based care (VBC) definitions and goals continue to shift, organizations struggle to create a roadmap for population health management (PHM) and to track associated costs and revenue. However, health systems can move forward with PHM amid the uncertainty by following the best practices of a path to value:

• Begin with Medicare Advantage—a good growth opportunity with low barriers to entry.
• Focus on ambulatory, not acute, care as it delivers more value.
•利用注册表来识别最具影响力的3%到10%的利用率。
• Simplify the physician burden by focusing on reasonable measures.

与航空航天和汽车等行业相比，医疗保健在决策支持创新方面落后。Following the aerospace and automotive arenas, healthcare can learn critical lessons about improving its clinical decision support capabilities to help clinicians make more efficient, data-informed decisions:

1.实现广泛的数字化:医疗保健必须实现有效CDS的资产和操作(患者登记、调度、诊疗、诊断、订单、账单和索赔)的数字化，类似于航空航天实现飞机、空中交通管制、行李处理、票务、维护和制造的数字化。
2.Build data volume and scope: Healthcare must collect socioeconomic, genomic, patient-reported outcomes, claims data, and more to truly understand the patient at the center of the human health data ecosystem.

With leadership alignment, easy access to data, and a roadmap to reach their objectives, health systems can drastically increase revenue and grow market share by applying four principles:

• Key 1. Alignment.
• Key 2. Vehicles.
关键3:五大工具:数据获取、数据敏锐度;finance, vision to execution, and prioritizing outcomes.
• Key 4: Education.

Access to the right data can drive changes that generate $48M in revenue, surpassing the year three market share goals in year two.

由于医疗机构努力为患者提供更好的护理，他们必须有一个有效的临床测量系统来监测他们的进展。首先，在设计临床测量系统时，只有两个潜在的目标:选择测量和改进测量。Understanding the difference between these two aims, as well as the connection between clinical measurement and improvement, is crucial to designing an effective system.

This article walks through the distinct difference between these two aims as well as how to avoid the common pitfalls that come with clinical measurement. It also discusses how to identify and track the right data elements using a seven-step process.

Health system mergers can promise significant savings for participating organizations. Research, however, indicates as much as a tenfold gap between expectation and reality, with systems looking for a savings of 15 percent but more likely to realize savings around 1.5 percent.

推动合并的预期与现实差距是一个复杂的过程，如果没有勤奋的准备和战略，组织很难充分利用成本协同效应。然而，有了正确的框架，卫生系统可以实现流程管理、数据共享和治理结构，使领导层、临床医生和所有利益攸关方围绕合并目标保持一致。

After a tumultuous 2019, healthcare organizations are pivoting to make sense of the latest changes and prepare to face the top 2020 healthcare trends:

•消费主义——卫生系统能否回应消费者对更好获取和价格透明度的要求?
• Financial Performance—With mergers, acquisitions, and private sector companies entering the healthcare arena, how will traditional hospitals and clinics compete?
• Social Issues—How will health organizations respond to the opioid crisis and consider social determinants of health as part of the care process to provide comprehensive treatment?

由于卫生系统在不断变化中挣扎求生，它们必须向前看，积极准备迎接2020年的到来。

Today’s healthcare encounters are too often marked by more clinician screen time than patient-clinician engagement. Increasing regulatory reporting burdens are diverting clinician attention from their true priority—the patient. To put patients back at the center of care, CMS introduced its Meaningful Measures framework in 2017. The initiative identifies the highest priorities for quality measurement and improvement, with the goal of aligning measures with CMS strategic goals, including the following:

1.Empowering patients and clinicians to make decisions about their healthcare.
2.支持创新方法以提高质量、安全性、可及性和可负担性。

卫生系统依靠数据做出明智的决定，但前提是这些数据能得出正确的结论。卫生系统经常使用常用的分析方法得出错误的结论，导致资源浪费和患者病情恶化。It is crucial for data leaders to lay the right data foundation before applying AI, select the best data visualization tool, and prepare to overcome five common roadblocks with AI in healthcare:

1.在诊断分析之前的预测分析只会导致相关性而不是因果关系。
2.变更管理不被认为是过程的一部分。
3.描述作品的错误术语。
4. Trying to Compensate for Low Data Literacy Resulting in Unclear Conclusions.
5. Lack of Agreement on Definitions Causes Confusion.

由于人工智能在医疗保健领域提供了更高的效率和力量，组织仍然需要协作方法、对数据流程的深入理解以及强大的领导力来实现真正的变革。

虽然许多医疗保健组织已经在护理点实现了人工智能(AI)和机器学习(ML)工具，但很少有人成功地将它们应用于高层决策。将人工智能从人工智能扩展到增强智能是一个新的前沿;传统AI专注于提高分析效率，而增强智能则专注于提高医疗领导者的决策能力。

本文讨论了卫生系统应该具备的能力，并提供了两个例子，说明人工智能如何帮助领导人做出最重要的决定。Healthcare leaders’ biggest needs of from AI are the ability to separate signal from noise and make decisions that impact the future.

要在人口健康管理方面取得成功，各组织必须克服各种障碍，包括信息孤岛和资源有限。由于这些挑战的系统性，利益相关方的广泛参与是基于人群的改善的当务之急。

An effective PHM stakeholder engagement strategy incorporates the following:

1.在旅程的一开始就包含尽可能多的涉众。
2.Meets the unique analytics and reporting needs of the organization.
3.Enables users to measure, and therefore manage, PHM outcomes.
4. Provides the real-time analytics value-based care requires.

临床医生推动了许多影响卫生系统质量和成本的决定，它们是成功改善工作的重要组成部分。然而，在当今的医疗保健环境中，临床医生的负担过重是出了名的，让他们参与额外的项目常常是一个巨大的挑战。为了在改善工作中成功地与这些专业人员合作，卫生系统必须制定参与战略，优先考虑临床医生的需求和关切，并利用对临床医生有意义的数据。

Improvement leaders can approach clinician engagement on three levels:

1.Clinician-led当地项目。
2.部门或部门级别的计划。
3.Leadership-level growth and improvement programs.

在医疗保健领域成功利用人工智能(AI)的关键不完全在于预测和规定机器的技术方面，还在于医疗保健组织内部的变革管理。更好地采用人工智能并取得更好的结果，依赖于对变革挑战的承诺、正确的工具和以人为中心的观点。

To succeed in change management and get optimal value from predictive and prescriptive models, clinical and operational leaders must use three perspectives:

1.功能性:这个模型有意义吗?
2.上下文:模型适合工作流吗?
3.操作性:交易的好处和风险是什么?

要在当今快速发展的业务环境中取得成功，医疗保健组织必须拥有准确的财务数据。现在，大约50%的CMS支付与价值部分挂钩;医院的营业利润率处于历史最低水平;消费者的需求随着成本的上升而上升。
为了迎接这些新的挑战，卫生系统必须改变其战略，否则就有可能被甩在后面。本文详细介绍了驱动财务转型的运营、组织和财务战略，以及如何获取和利用财务数据、寻找减少浪费的机会等示例。

病人的评论，如“我感到头晕”或“我的胃疼”，可以告诉临床医生个人的健康状况，以及附加的背景信息，包括邮政编码、就业状况、交通工具等。然而，这些关键信息被捕获为免费文本或非结构化数据，使得传统分析无法利用这些信息。

Machine learning tools (e.g., NLP and text mining) help health systems better understand the patient and their circumstances by unlocking valuable insights residing unstructured data:

1.自然语言处理为人类用户分析大量的自然语言数据。
2.文本挖掘通过对大量文本(如词频、单词长度等)的分析来获得价值。

在美国，浪费是一个价值3万亿美元的问题。幸运的是，质量改进理论(W. Edwards Deming)从本质上把高质量的医疗与财务业绩和减少浪费联系在一起。戴明认为，更好的结果可以消除浪费，从而降低成本。

To improve quality and process and ultimately financial performance, an industry must first determine where it falls short of its theoretic potential. Healthcare fails in five critical areas:

1.临床实践中有大量的差异。
2.不适当护理率高。
3.Unacceptable rates of preventable care-associated patient injury and death.
4. A striking inability to “do what we know works.”
5. Huge amounts of waste.

While over 90 percent of organizations in industries worldwide now use cloud computing in their operations, healthcare still lags behind.
As health systems grow their ability to capture data, they still have only a fraction of the data they need to achieve today’s population health and precision medicine goals.

Organizations looking to migrate to more agile cloud-based platforms and leverage data for measurable improvements can learn the fundamentals of this critical transformation in an e-book about the Health Catalyst Data Operating System (DOS™).

What can healthcare learn from Formula One racing?
Health Catalyst医疗事务和生命科学高级副总裁Sadiqa Mahmood博士表示，比赛支持团队利用约30TB的基线数据，创建汽车、赛道和赛车的世界杯葡萄牙vs加纳即时走地数字双胞胎，用于模拟模型，在每场比赛中驱动决策。
Applied in the healthcare setting, a digital twin can help clinicians better understand each patient and their health conditions and circumstances in real time and make comprehensive, informed care decisions.
But for the healthcare digital twin to happen, the industry must move away from data silos and towards a digital learning healthcare ecosystem.

医疗保健正在走向一个个性化医疗的时代，在这个时代，提供者为每个患者定制治疗方案。实现这种量身定制的护理水平意味着数据量、分析和AI能力的新水平，尽管这对医疗保健行业来说是新奇的，但其他行业正在蓬勃发展。在医疗保健朝着分析和人工智能驱动的个性化医疗领域发展的过程中，选择正确的榜样将指导明智的策略并建立最佳实践。

With experience and expertise in these key areas, the military, aerospace, and automotive industries can serve as healthcare’s best examples:

1.The human cognitive processes of complex decision making.
2.其产业的数字化，以其资产的“健康”为关键驱动力。
3.在“大数据”生态系统中运营。

Delivering high-quality, cost-efficient care to specific patient populations within a service line is nearly impossible without a sophisticated costing methodology. Activity-based costing (ABC) provides a nuanced, comprehensive view of cost throughout a patient’s journey and reveals the “true cost” of care—the real cost for each product and service based on its actual consumption—which traditional costing systems don’t provide.

掌握了医疗保健的真实成本，医疗保健负责人就可以更早地确定高危人群——如被诊断为妊娠期糖尿病的孕妇——并更快地实施有效的干预措施(如更严格的监测和更早的筛查)。利用ABC的可操作见解的卫生系统通过在其他服务部门实施相同或类似的过程/临床改进措施进一步受益。

2019年医疗保健分析峰会™(HAS)充满了关于数据民主化、在数字时代提供医疗保健以及分析和人工智能的未来的深刻讨论。
2019年HAS信息图显示，1600名行业领袖参加了会议，其中60%的与会者来自IT/分析师行业，他们讨论了趋势数据主题，通过投票机制与演讲者互动，并利用网络机会分享解决方案和解决问题的方法。2022卡塔尔世界杯赛程表时间

As healthcare organizations today face more security threats than ever, artificial intelligence (AI) combined with human judgment is emerging as the perfect pair to improve healthcare data security.
它们共同推动了一个高度准确的隐私分析模型，该模型允许组织审查患者数据的访问点，并检测系统的EHR何时可能暴露于隐私侵犯、攻击或破坏。
With specific techniques, including supervised and unsupervised machine learning and transparent AI methods, health systems can advance toward more predictive, analytics-based, collaborative privacy analytics infrastructures that safeguard patient privacy.

HAS与会者习惯于对数字健康的未来进行创新和预测。但在HAS 19的最后一天，他们亲眼见到了下一代的转变:少年贾斯汀·阿伦森(Justin Aronson)发表了一个主题演讲，主题是数据民主化将如何使他和他的同龄人解决未来几十年的挑战。
其他的主讲人——谷歌的Marianne轻度，前拜耳首席数据官Jessica Federer和Beth Israel Deaconess System的CIO博士John halamka贡献了他们对医疗保健下一个时代的愿景，在20个分组会议中，演讲者分享了将推动数字化转型的经验、过程和技术。

2019年医疗保健分析峰会(HAS 19 -医疗保健分析峰会19)的第一个完整的一天的主题演讲来自托马斯杰斐逊大学首席执行官Steve Klesko博士、畅销书作家Daniel Pink、前新泽西州总检察长Anne Milgram和MDLIVE医疗集团总裁Lyle Berkowitz博士。两波分组会议讲述了来自全国各地组织的成功故事，以及他们通过进一步数字化实现转型的历程。

下一代医疗保健转型领导者认为，数据民主化对于未来的改善至关重要。
High school student Justin Aronson explains how he leverages open-source health laboratory data to build a tool that improves the clinical interpretation of sequenced genetic variants.
Aronson的云数据集成和可视化系统Variant Explorer运行在基因组和表型数据上，这些数据可以在公共档案馆ClinVar上轻松访问。他说，大规模数据民主化是解决当前和未来医疗保健问题的关键。

在过去的一个世纪里，一系列的革命推动了美国医疗体系的发展，使医疗质量和结果的各个方面都有了显著的改善。Although healthcare organizations have focused on moving towards value-based care for decades, the data shows that the shift is indeed taking place and fee-for-service models are declining.

医疗保险共享储蓄计划(MSSP)的新变化将有助于推动这一变化，因为MSSP的修订要求aco更早地承担更多的财务风险。This article covers the following topics:

1.这是导致今天的挑战的重要历史时刻。
2.为什么金融需求驱动我们经济模式中的文化变革。MSSP帮助医疗保健组织取得财务成功的方法。
4. How to utilize data to develop better healthcare delivery systems.

在过去的一个世纪里，一系列的革命推动了美国医疗体系的发展，使医疗质量和结果的各个方面都有了显著的改善。Although healthcare organizations have focused on moving towards value-based care for decades, the data shows that the shift is indeed taking place and fee-for-service models are declining.

医疗保险共享储蓄计划(MSSP)的新变化将有助于推动这一变化，因为MSSP的修订要求aco更早地承担更多的财务风险。This article covers the following topics:
1.这是导致今天的挑战的重要历史时刻。
2.为什么金融需求推动我们经济模式的文化变革。
3.MSSP帮助医疗保健组织取得财务成功的方法。
4. How to utilize data to develop better healthcare delivery systems.

The Health Catalyst Population Builder: Stratification Module allows healthcare organizations to identify the right patient populations in order to deliver the right care at the right time.
该解决方案提供了一个无缝的流程，可以使用预定义的、容易定制的人群作为构建模块，从多个来源(EMR、索赔和临床)对人群进行分层。
有了对其管理的患者的全面了解，各组织就可以沿着其护理连续体绘制人口图，并自信地将适当人口过渡到人口健康干预措施。

Introducing the Health Catalyst Population Health Foundations solution, which draws on integrated claims and clinical data, and provides essential, extensible tools and machine-learning capabilities for optimizing results in value-based risk arrangements.
Accompanying solution services ensure that the strategic value of data is maximized to improve performance in risk contracts—and provide side-by-side subject matter expert partnership for establishing short- and long-term goals for population health management.

Accountable Care Organizations (ACOs) and clinically integrated networks (CINs) are two types of organizations working to address the problem of rising costs. As ACOs and CINs continue to evolve, organizations moving into value-based care (VBC) face an ever-changing landscape.
这篇文章着眼于ACO和CIN模型的演变，今天ACO使用什么新工具来促进成功，以及从在其他付费模式中取得成功的组织吸取的教训。它还探讨了医疗专家所认为的替代支付模式的未来，以及为满足这些不断变化的需求而开发的能力。

在当今充满挑战的环境中，医疗保健领导者必须寻求机会，通过改善财务业绩和报销来提高收入。一些常见的策略包括减少未偿付票据持有账户的数量，减少A/R天数，以及管理已解除的非最终账单(DNFB)案例。

This article tackles, the following topics:

• Common reasons accounts remain unbilled.
•发现改进的机会。
• Using data analytics and process improvement to achieve financial goals.
• Creating lasting improvements.

目前，在医疗保健领域，尤其是在医疗编码领域，一个热门的话题是层次条件分类(HCC)风险调整模型，以及准确的编码如何影响医疗机构的报销。

随着近三分之一的医疗保险受益人加入了医疗保险优势计划，医疗机构比以往任何时候都更重要的是关注这种模式，确保医生正确地进行诊断，以确保公平的补偿。本文将介绍风险调整模型的基础知识，为什么编码准确性如此重要，以及跨学科工作组应采取的五个行动项目。They include:

1.Having an accurate problem list.
2.Ensuring patients are seen in each calendar year.
3.改进决策支持和EMR优化。
4. Widespread education and communication.
5. Tracking performance and identifying opportunities.

While healthcare data is an ever-growing resource, thanks to broader EHR adoption and new sources (e.g., patient-generated data), many health systems aren’t currently leveraging this information cache to its full potential. Analysts can’t extract and analyze a significant portion of healthcare data (e.g., follow-up appointments, vitals, charges, orders, encounters, and symptoms) because it’s in an unstructured, or text, form, which is bigger and more complex than structured data.

自然语言处理(NLP)利用人工智能(AI)从大约80%以文本形式存在的健康数据中提取和分析有意义的见解，从而挖掘非结构化数据的潜力。虽然NLP仍然是一种不断发展的能力，但它在帮助组织从数据中获得更多信息方面显示出了希望。

机会分析使用数据来确定潜在的改善措施，并量化这些措施的价值——包括病人护理的好处和经济影响。这一过程是发现无根据和昂贵的临床变异的有效方法，进而制定减少变异的策略，改善结果和节约成本。将机会分析过程标准化，使其可重复，并优先考虑可操作的机会。

Quarterly opportunity analysis should follow four steps:

• Kicking off the analysis by getting analysts together to do preliminary analysis and brainstorm.
• Engaging with clinicians to identify opportunities and, in the process, get clinician buy in.
•深入研究建议的机会，优先考虑那些能带来最大好处的机会。
• Presenting findings to the decision makers.

Bobbi Brown, MBA, and Stephen Grossbart, PhD have analyzed the biggest changes in the healthcare industry and 2018 and forecasted the trends to watch for in 2019. This report, based on their January 2019, covers the biggest 2019 healthcare trends, including the following:

•医疗保健业务，包括新的市场进入者、商业模式和战略转变，以保持竞争力。
• Increased consumer demand for more transparency
•对所有人群进行持续的质量和成本控制监控。
• CMS proposals to push ACOs into two-sided risk models.
•更少的过程衡量，但更多的质量结果审查供应商。

客户旅程分析使用机器学习和大数据来跟踪和分析客户何时以及通过什么渠道与组织互动，目的是影响行为(例如，零售客户的购买行为)。同样，医疗机构希望影响与健康相关的行为，如按规定服药和不吸烟，以改善结果和降低护理成本。在与分析服务提供商的合作中，一家支付机构正在利用医疗保健消费者的客户旅程分析来确定患者外展的最佳机会和渠道。通过这种分析驱动的参与度策略，付费用户发现了一个机会，可以显著提高患者的参与度——预计整体参与度将从18%提高到31%。

卫生系统拥有大量数据，但往往难以利用这些数据及时解决战略问题。医疗保健分析团队由拥有正确工具和技能的正确人员组成，可以帮助解决这些挑战。本文介绍了组织建立有效的分析团队所需要采取的步骤。

These include the following:

• Recognizing the need for change.
• Demonstrating the value of an analytics team.
• Conducting a current state assessment.
•识别解决方案。2022卡塔尔世界杯赛程表时间
•实施分阶段的方法。
•制定路线图。
•进行推销。
•将路线图付诸行动。

The article also includes the foundation skills to look for when putting together the team and tips on how best to organize.

Health systems are challenged by the need to keep patients and employees satisfied and engaged. This can be especially difficult for organizations in flux, growing, merging, and changing. And as leaders of these organizations know, poor patient satisfaction ratings lead to reduced reimbursements, which affect the bottom line.

为了应对这一挑战并提高患者满意度，卫生系统领导人正在利用技术，如舍入软件，支持有效沟通，推动文化变革，提高护理人员和患者的满意度，并鼓励参与。
Embedding rounding technology into current processes makes rounding better and easier. The correlation between effective, efficient rounding and high patient satisfaction scores is clear. Rounding can and does increase engagement and satisfaction, which in turn leads to higher reimbursement potential. Learn how health system leaders can move from talking about rounding technology to incorporating it into daily workflow.

卫生系统试图衡量越来越多的临床指标，但这些指标往往忽略了什么对患者重要。患者报告结果(PROs)是增强患者能力和帮助定义良好结果的缺失环节。本文将介绍患者报告的结果度量(PROMs)如何帮助识别最佳实践并推动系统范围内的质量改进。PROMs can help health systems do the following:

•作为适当性和效率的指南。
• Lead to better shared decision-making.
• Demonstrate value and transparency

本文还讨论了在“还有一件事”的文化中PROMs对提供者的影响，以及有效实现的技巧。

患者安全是医疗机构最关心的问题。幸运的是，卫生IT可以帮助领导和一线临床医生不断努力改善患者护理。这本电子书包括十篇概述技术和病人护理交叉的文章，突出组织如何实施病人安全的最佳实践。

随着人们越来越重视以价值为基础的医疗，问责医疗组织(ACOs)将继续存在。在ACO中，医疗保健提供者和医院为了通过向Medicare患者提供高质量的协调医疗保健来降低成本和提高患者满意度的共同目标走到一起。

然而，许多助理医生缺乏指导，在理解如何使用数据来改善护理方面存在困难。实施稳健的数据分析系统，以自动化数据收集和分析过程，并将数据与ACO质量报告措施对齐。

The article walks through four keys to effectively implementing technology for ACO success:

1.使用分析平台构建数据存储库。世界杯厄瓜多尔vs塞内加尔波胆预测
2.将数据带到护理点。
3.Analyze claims data, identify outliers, including successes and failures.
4. Combine clinical claims, and quality data to identify opportunities for improvement.

Many health systems have a hospital capacity problem as demand for patient beds rises. When the supply of usable patient beds can’t meet demand, the negative impact on patients and staff can be significant.

Hospitals can solve capacity problems with four key concepts:

1.Using data, start with the problem and the ideal solution.
2.确保分析团队与整个组织的团队合作，包括领导层。
3.让领导花时间与运营团队一起了解工作流程。
4. Focus on the impact, not the tool.

今天的医疗保健正处于巨大的变革之中。The opportunities for improvement are great if healthcare systems can do the following:

• Reduce clinical variation.
•减少不适当护理以及与护理相关的病人受伤和死亡的比率。
• Follow accepted best care practices.
•消除浪费。

这篇文章涵盖了医疗保健系统中不同类型的浪费，减少浪费的方法，围绕减少浪费机会的财政调整，以及减少临床差异的重要性。医疗保健系统的核心驱动力必须是提高临床质量。通过适当的临床管理，通过废物管理，更好的护理往往是更便宜的护理。

由于医疗系统面临削减成本的压力，但仍需提供高质量的护理，他们将需要从整个护理连续体中寻找答案，减少护理中的差异，并关注新兴技术。本文将介绍如何评估新兴医疗保健技术的安全性和有效性，以及如何使用健壮的数据分析平台确定影响重大的改进项目的优先级。世界杯厄瓜多尔vs塞内加尔波胆预测Topics covered include:

•识别创新变化的重要性。
• Ways to improve outcomes and decrease costs.
• The value of an analytics platform.
•能够产生创新火花的可靠信息。
• Identifying and evaluating emerging healthcare technology.
•了解使用哪些数据。
•在评估新兴医疗保健技术时，功效和效果之间的差异。

卫生系统继续将减少再次入院列为优先事项，作为其基于价值的支付和人口健康战略的一部分。但是那些没有将分析完全集成到重新接纳减少工作流程中的组织很难达到改进的目标。By embedding predictive models across the continuum of care, versus isolated them in episodes of care, health systems can leverage analytics for meaningful improvement.

将预测模型集成到重新入院减少工作流程的组织已经实现了多达40%的风险调整重新入院指数的减少。有效的分析集成策略使用多学科开发方法来满足患者整个护理团队的需求，并为患者医疗保健过程中的所有参与者提供通用工具。

Overcrowding in the emergency department has been associated with increased inpatient mortality, increased length of stay, and increased costs for admitted patients. ED wait times and patients who leave without seeing a qualified medical provider are indicators of overcrowding. A data-driven system approach is needed to address these problems and redesign the delivery of emergency care.

这篇文章探讨了急诊护理中常见的问题，并探讨了如何开始一段成功的改善质量的旅程，以转变急诊科的护理交付。including an exploration of the following topics:

• A four-step approach to redesigning the delivery of emergency care.
•了解ED的表现。
• Revising High-Impact Workflows.
•修改人员配备模式。
•设定领导期望。
• Improving the Patient Experience.

健康社会决定因素(SDOH)数据捕捉了医疗服务系统以外对患者健康的影响。传统的健康数据(例如，来自医疗保健的数据)只讲述了患者和人群健康故事的一部分。为了解健康影响的全部范围(例如，从环境到关系和就业状况)，各组织需要患者日常生活的数据。由于基于价值的支付日益迫使卫生系统提高质量和降低成本，在今天，SDOH数据的紧迫性特别强。Without fuller insight into patient health (what happens beyond healthcare encounters) organizations can’t align with community services to help patients meet needs of daily living—prerequisites for maintaining good health.

Standardizing SDOH data into healthcare workflows, however, requires an informed strategy. Health systems will benefit by following a standardization protocol that includes relevant and comprehensive domains, engages patients, enables broader understanding of patient health, integrates with organizational EHRs, and is easy for clinicians to follow.

Current quality measures are expensive and time consuming to report, and they don’t necessarily improve care. Many health systems are looking for better ways to measure the quality of their care, and they are using data analytics to achieve this goal. Data analytics can be helpful with quality improvement. There are four key considerations to evaluate quality measures:

1.Organizations must develop measures that are more clinically relevant and better represent the care provided.
2.临床医生的认可是至关重要的。Without it, quality improvement initiatives are less likely to succeed.
3.围绕改进工作的工具和工作上的投资必须增加。Tools should include data analytics.
4. Measure improvement must translate to improvement in the care being measured.

当采取了正确的措施来推动医疗保健的改善时，患者护理和结果可以而且确实会改善。

虽然许多行业都在利用数字化转型来加快生产率和质量，但医疗行业是数字化程度最低的行业之一。医疗保健数据在很大程度上是不完整的，不能完全代表患者的健康状况，不能充分支持诊断和治疗、风险预测和长期医疗保健计划。但是，即使医疗保健数字化的紧迫性显而易见，行业也必须提高这一轨迹的敏感性，以应对对临床医生和患者的影响。正确的数字战略不仅将旨在获得关于患者健康的更全面信息，而且还将利用数据增强相关人员的权能和参与。

Health systems can follow five guidelines to digitize in a sustainable, impactful way:

1.Achieve and maintain clinician and patient engagement.
2.Adopt a modern commercial digital platform.
3.将资产(病人)和流程数字化。
4. Understand the importance of data to drive AI insights.
5. Prioritize data volume.

Drs. Allen Frankel and Michael Leonard have developed a framework for creating high-reliability organizations in healthcare. This report, based on their 2018 webinar, covers the components and factors of this frame work, including:

• Leadership
• Transparency
• Reliability
• Improvement and Measurement
• Continuous Learning
• Negotiation
• Teamwork and Communication
• Accountability
• Psychological Safety

Twenty years after Intermountain Healthcare launched its enterprise data warehouse in 1998, industry leaders are looking at what they did right, what they’d do differently, and what the future holds for healthcare data and analytics.
While early successes (such as a hiring framework of social, domain, and technical skills; lightweight data governance; and late-binding architecture) continue to hold their value, advanced analytics and technology and innovation in diagnosis and treatment are reshaping the capabilities of and demands on the healthcare data warehouse.
Present-day and future healthcare IT leaders will have to revisit approaches to data warehousing people, processes, and technology to understand how they can improve, continue to adapt, and fully leverage emerging opportunities.

数据无处不在。但是，如果没有从数据中提取意义并将见解转化为行动的计划，数据就无法影响结果。从数据中产生价值需要工作，但这是可以做到的。为了创建引人注目的数据洞察，促进行动，卫生系统可以遵循可操作的医疗数据分析的三个指导原则，并聘用具备7项重要技能的分析师。

Three principles form the foundation for actionable healthcare data analytics:

1.平衡投资。
2.雇佣通才而不是专才。
3.开发一个高度一致且松散耦合的团队。

Based on a 2018 Healthcare Analytics Summit presentation, this report details the four phases necessary for successful healthcare data governance:

1.Elevate a vision and agenda that align with organizational priorities.
2.Establish an organizational structure to fulfill the data governance mandate.
3.按优先级执行数据治理项目，分配人员和资源，并严格专注于工作。
4. Extend data governance investments and efforts through established practices.

Each step must follow the core principles of stakeholder engagement, shared understanding, alignment, and focus. Effective healthcare data governance is not a one-time event and requires ongoing and iterative efforts.

Every day, healthcare professionals face the challenge of determining how to get patients to make good healthcare decisions and follow recommendations. The Four Tendencies framework, developed byThe New York Times畅销书作家格雷琴·鲁宾(Gretchen Rubin)可以让这个任务变得更容易，并通过揭示每个人对期望的反应来提高病人的依从性。By asking this question, healthcare practitioners can gain exciting insights into how patients respond to expectations to in order to help them achieve their goals.

This report covers the following:

1.四种倾向的概览。
2.了解这些倾向如何影响医疗保健环境中的行为。
3.与不同倾向的病人和同事一起工作的实用技巧。

医疗保健组织的生存依赖于应用精益原则。采用精益原则的组织可以在提高护理质量的同时减少浪费。通过在日常护理中应用严格的临床数据测量方法，医疗保健系统确定最佳实践协议，并将其纳入临床工作流程。Data from these best practices are applied through continuous-learning loop that enables teams across the organization to update and improve protocols–ultimately reducing waste, lowering costs, and improving access to care.

This executive report based on a presentation by Dr. Brent James at a regional medical center, covers the following:

1.精益医疗原则如何帮助提高医疗质量。
2.医疗保健组织创建持续学习循环所需采取的步骤。
3.精益方法如何通过消除浪费和提高净运营利润率和ROI来创造财务杠杆。

2018年医疗保健分析峰会的统计数据在这张有趣的信息图中展示。A few of those statistics show our commitment to put on an educational, valuable summit:

• 1300 attendees from 419 organizations
• 97% overall satisfaction rating
• 98% likely to recommend to a friend

在盐湖城举行的2018年医疗保健分析峰会的最后一天，我们继续获得了该峰会5年历史上评分最高的主题阵容。
Dr. Penny Wheeler shared some important tips about improvement.
三位数字创新者展示了将永远改变医疗保健的令人兴奋的技术和方法，金·古德塞尔向我们展示了为什么她是唯一的第一人——数据授权的、未来的基因组患者。

The first full-day of the 2018 Healthcare Analytics Summit (HAS 18) featured keynotes from Marc Randolph (Co-Founder, Netflix), Dr. Brent James, Dr. Daniel Kraft, Dr. Toby Cosgrove, Dr. Jill Hoggard Green, and Dr. Robert Wachter. Two waves of breakout sessions covered success stories from organizations all over the nations, complete with countless lessons learned.

2018年对医疗专业人员的一项调查显示，缺乏有效的技术正在阻碍患者安全的进展。尽管大多数医疗保健组织声称安全是首要任务，但在对患者安全结果产生重大影响方面仍面临严峻挑战。

调查受访者表示，无效的信息技术以及相关的对可能的危害事件缺乏实时预警是改善患者安全的最大障碍。They cited a number of key obstacles:

1.Lack of resources.
2.Organization structure.
3.缺乏对安全措施的补偿。
4. Changes in patient population.

这项针对400多名医疗专业人员的调查解决了许多医院领导提出的一个大问题:为什么尽管我们做出了努力，但我们没有看到患者安全的改善?

Why did Health Catalyst acquire Medicity? Dale Sanders, President of Technology, shares five reasons and what it means for interoperability:

1.Medicity has several petabytes of valuable data content.
2.Medicity’s data governance expertise.
3.Medicity’s 7 x 24 real-time cloud operations expertise.
4. Medicity’s expertise in real-time EHR integration.
5. Medicity’s presence and expertise in the loosely affiliated, community ambulatory care management space.

Looking for a way to share his extensive experience with data warehousing in healthcare, in 2002 Dale Sanders wrote what many consider to be the “EDW Bible.”
它是一个具有指导原则的文档，如果遵循它，将推动数据仓库的价值和利用率。We’ve made that report available now.

Health Catalyst President of Technology, Dale Sanders, gives straightforward answers to tough questions about the future of AI in healthcare.
他首先驳斥了一个普遍的观点:由于采用了电子病历，我们在医疗保健领域充斥着有价值的数据。The truth involves a need for deeper data about a patient.

医疗保健行业的合并和收购可能涉及许多emr和其他IT系统。有时候，领导者觉得他们必须撕毁和替换这些系统，才能完全整合组织。然而，戴尔·桑德斯认为，这并不是答案。
This report, based upon his July 2017 webinar, outlines the importance of a data-first strategy and introduces the Health Catalyst^®数据操作系统平台。世界杯厄瓜多尔vs塞内加尔波胆预测DOS可以在促进IT战略方面发挥关键作用，以适应日益增长的医疗保健并购环境。