Customer Journey Analytics: Cracking the Patient Engagement Challenge for Payers

This report is based on a 2018Healthcare Analytics Summit由EY Analytics首席执行官Christer A. Johnson和信诺信息管理与分析客户分析主管Alexander (Alex) Marano主持的演讲，题为“使用机器学习和大数据驱动患者参与和更好的健康结果”。

There are moments in every patient journey in which a well-informed, well-timed intervention (e.g., phone call, personal visit, etc.) can effectively engage patients and positively influence their health-related behavior. Random engagement, however, is far less effective than analytics-driven strategic engagement. To know when to reach out to and which patients to focus on, payers need an approach that leverages machine learning and big data.

其他行业已经使用分析驱动的粘性策略，或客户旅程分析，来分析客户互动、人口统计和生活方式事件的实时数据，并使用这些洞察来影响客户行为(例如，零售行业的购买行为)。医疗保健行业现在正努力利用类似的方法来有效地接触医疗保健消费者，以帮助他们避免危险的健康行为，从而采取改善他们的健康和降低医疗保健成本的行为。本报告描述了一家全球分析服务提供商和一家大型支付机构之间的协作，以在医疗保健消费者中利用客户旅程分析。

Patient Engagement Solutions Target Avoidable Risky Behaviors

患者参与解决方案通过针对有负面健康后果2022卡塔尔世界杯赛程表时间但在很大程度上是可以避免的行为来支持改善结果和降低成本。这些行为对患有慢性和多种健康疾病的患者有重大影响，他们属于最昂贵的人群。

Considering the following examples of unacceptable high rates of avoidable risky behaviors, improving patient engagement must become an industry priority:

• Twenty-four percent患有糖尿病的成年人不知道自己得了糖尿病。
• About13 million有高血压的成年人不知道自己有高血压。
• One in fivedeathsis caused by cigarette smoking.

Addressing the above avoidable health concerns above and their like presents a sizeable healthcare improvement opportunity. But to impact these statistics and the patients who comprise them, health services providers must understand behavioral patterns and factors most linked to engagement.

Patient Journey Analytics for Strategic Engagement

Customer journey analytics follows patient behavior to identify patterns most likely associated with engagement. Analysis may show behaviors and characteristics linked with likelihood to engage and key times to reach out to those patients. For example, the period following a specialist visit can be an engagement opportunity as patients tend to seek information (e.g., internet searches) at this time. If a case manager reaches out to the patient after the specialist appointment, she may have the answers the patient is looking for.

To leverage journey analytics to improve patient engagement, payers create the journey data, follow a framework for and execute the four phases of journey analytics, and operationalize the analytics.

Creating Patient Journey Data

患者旅程数据的创建(图1)从一个包含事件、总体标准、结果、步骤、属性和相应SQL逻辑的参考库开始。从这个库中可以得到病人属性、时序行程和行程步骤属性。These datasets allow for ongoing and future applications:

• 持续:机会评估、自动化洞察引擎、个体路径分析以及用于感知/响应/预测/参与的规则引擎。
• 未来:模拟分析，以模拟卫生服务干预措施的可能影响，以及自动数据挖掘，以识别新的机会。

The Patient Journey Analysis Framework

The framework for patient journey analysis (Figure 2) asks questions about the patient journey in three key categories:

1. Understanding value:
   • 将客户从不希望的结果转变为希望的结果的机会的总价值是多少?
2. Understanding negative outcomes:
   • What kind of customers are having negative outcomes?
   • What kind of providers may be influencing negative outcomes?
3. Understanding the relationship between journeys and positive outcomes:
   • Which are the most frequent journeys?
   • Which journeys lead to the most positive outcomes?

The framework also considers patient attributes (e.g., demographics, conditions, height, weight, and risk score) and provider attributes (e.g., network status, treatment rules, operating hours, and percentage correct treatment) and patterns (“steps”) most associated with engagement or lack of engagement (“event”). Steps may include internet searches, in-network primary care physician visits, in-network specialist visits, care gaps, ED visits, and more. These insights can help payers improve the timing, channel, and content they use to engage members with chronic and complex conditions in coaching that lowers medical costs and improves healthcare outcomes.

Journey analytics also helps payers identify the most impactable candidates for engagement using likelihood-to-engage score leverages. When the payer mentioned in this report compared using the likelihood-to-engage score with a traditional approach to outreach, predicted overall engagement increased from 18 percent to 31 percent.

支付者以前基于索赔数据制定审计策略。作为一种分层的数据方法，按时间顺序排列的客户旅程数据湖(图3)结合了索赔和EMR数据、负面事件、身体和语言信号以及数字信号。支付者可以将数据应用到治疗过程中，以发现干预措施将增加患者选择更好路径的可能性的时刻(例如，遵守随访预约或按规定服药)。这些见解确定了路径，有助于在时机成熟时发出警报，采取行动改善结果，降低成本。Actions include the following:

• Predict future events and behaviors.
• 识别暗示预期行为改变的信号。
• Prescribe interventions to shape the right outcomes.

The Four Phases of Patient Journey Analysis

Patient journey analysis occurs in four phases. Each phase yields detailed journey data for patients identified for case management, a list of the most important journey steps by case management category, and potential engagement and engagement lift by case management category (e.g., oncology or OBGYN):

1. Data collection and journey data creation
   • Identify selected patients for case management.
   • Query multiple sources to design a time-sequenced journey dataset.
2. Journey steps importance test
   • Calculate importance of and rank each journey step.
   • Use above results to select the most important journey steps.
3. Rules selection
   • Modify journeys to include only the most important journey steps.
   • 确定最普遍的规则(旅程步骤的组合)。
   • Calculate score for each rule as total engaged/total outreached.
   • 创建规则，使某些规则优先于其他规则。
4. Results
   • Use the selected rules to score patients by case management category.
   • Combine scores at case management level to determine overall engagement rate. (The payer who implemented journey analytics identified the opportunity for a 30 percent increase in engagement [Figure 4]).

Operationalizing Patient Journey Analytics

To be effective, payers must operationalize customer journey analytics. The key differentiators between basic analytics and operationalized analytics is that basic analytics only analyzes and reports (informs), whereas operationalized analytics, anticipates, engages, senses, and responds (the middle orange box in Figure 5). With the added capabilities of operationalized analytics, payers can better sense opportunity, engage in real time, and personalize these interventions over time.

Operationalized journey analytics have significant benefits over basic analytics:

• 利用所有可用的大数据(而不是只关注可能的数据)。
• Identifies drivers of outcomes (versus tests hypotheses).
• Run hundreds of tests simultaneously (versus limited manual evaluation).
• Learns and modifies actions continuously (versus retrospective annual turning).

Anticipate, Sense, and Respond

Healthcare organizations can’t help patients if they can’t engage them effectively, and in an increasingly complex health landscape, better patient outreach is an imperative. Engagement improves significantly with an analytics-driven strategy that identifies whom to engage, when, and how. With patient journey analytics, payers and other healthcare organizations can influence better health behaviors to improve outcomes, customer satisfaction, and lower healthcare costs.

Additional Reading

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

1. Six Challenges to Becoming a Data-Driven Payer Organization
2. The Secret to Patient Compliance: An Application of The Four Tendencies Framework

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