Population Health Analytics Enables Rapid Identification of Super-Utilizers

Half of the $3.5 trillion spent on healthcare annually in the U.S. can be attributed to five percent of the population, who are often ideal candidates for risk-stratified care management. This process gives a health risk status to patients and then uses this status to inform and improve care.

为了降低不必要的成本，医院姐妹卫生系统(HSHS)和医生临床整合网络(PCIN)需要一种方法来自动对可能受益于护理管理服务的患者进行风险分层，并消除其护理管理者在识别高危患者方面所做的繁重的手工工作。为了有效、高效和准确地对其护理管理进行风险分层，并确定哪些患者将受益于额外的护理管理干预措施，HSHS和PCIN利用了一个人群健康分析平台——结合来自多个来源的信息，生成自动的风险分层患者名单。世界杯厄瓜多尔vs塞内加尔波胆预测

RISK-STRATIFIED CARE MANAGEMENT FOR IMPROVING OUTCOMES

In the U.S., five percent of the population accounts for half of the $3.5 trillion in annual healthcare spending.¹These “super-utilizers” are often ideal candidates for risk-stratified care management, which assigns a health risk status to a patient and then uses that health risk status to direct and improve care.²The ability to identify, stratify, and manage high-risk patients is critical for organizations working to change cost structure and outcomes.

作为一个多机构医疗保健系统，HSHS在伊利诺斯州和威斯康星州的14个社区为260多万患者提供服务，包括15家医院、数十家社区医疗中心和诊所、近2300名医生合作伙伴和14600多名同事。PCIN是一个医生领导的组织，有超过1800家供应商服务于伊利诺伊州和威斯康星州。HSHS和PCIN共同努力，通过其护理整合战略(与医生合作伙伴密切合作，提供高质量的、以患者为中心的护理)来改善和提高护理质量，并降低总体护理成本。

MANUAL PROCESSES IMPEDES RISK-STRATIFICATION SUCCESS

The organization had already been using risk-stratified care management to ensure that its resources were appropriately deployed to those patients who would most benefit from care management interventions. However, the analytics tool it used from a previous vendor contained risk algorithms that were not visible to PCIN and hidden away in a “black box.” It was difficult to understand which variables contributed to the patient being identified as high risk.

PCIN停止使用分析工具后，护理管理人员手动将多达1200名患者的数据输入到该组织的患者接收工具中，为每个患者创建一个唯一的标识符。护理管理人员将审查现有的索赔数据，然后物理地输入患者的人口统计数据，为每个患者填写多达10个数据字段。这些是耗时的过程，并没有产生PCIN所需的风险分层数据，以最大限度地发挥其护理管理干预的影响。此外，通过多个数据源跟踪患者被证明是非常困难的。

为了成功地降低不必要的成本，PCIN需要一种方法来自动化风险分层，消除人工操作。任何自动化都应该为组织提供对生成风险评分的因素的可见性，并且需要灵活，允许PCIN随着时间的推移改变和提高风险评分。

CONSOLIDATED DATA AND AUTOMATED PROCESSES OPTIMIZE RISK STRATIFICATION

To effectively, efficiently, and accurately risk stratify its care management and identify patients who would benefit from additional care management interventions, PCIN turned to the Health Catalyst^®Data Operating System (DOS™) platform and a robust suite of analytics applications, including the Health Catalyst Pop Analyzer™: Stratify Expert Data Collection™.

DOS consolidates multiple sources of data, including from within the EMR, and claims data from multiple payers. The platform links and tracks patients across the various sources of information, automatically assigning one unique identifier to each patient.

Using the Stratify Data Collection, PCIN has increased flexibility that other vendor solutions have not provided. The organization can define and create its own risk-stratification models, plus use custom algorithms that are visible, to identify, stratify, and target high-risk patients for specific PCIN care management programs. It is easy for users to see and understand the data used to generate the risk scores.

The organization elected to use the analytics application to develop custom algorithms that identify high-risk patients who could benefit from care management services. Each day, a risk-stratified patient list is generated for care managers to review. The list includes patients that may be appropriate for one or more of its care management programs:

• Chronic disease program patient list:Patients on this list have chronic diseases like diabetes, chronic obstructive pulmonary disease, asthma, heart failure, hypertension, and hyperlipidemia with a hierarchical conditional category coding score greater than 35.
• Transitional care program patient list:This list includes patients who have recently been admitted (inpatient or observation status) to the hospital within the past three days, and/or were discharged from the hospital within the past three days.
• High emergency department (ED) utilization list:Patients are included if they experience three or more ED visits within the past ninety days or six or more ED visits within the past year, both triggered when the most recent visit occurred within the past seven days. Initially designed to identify patients who had three or more ED visits within the last year, too many patients were included, some of whom had not required services during the last six months. Learning from the data, PCIN refined the risk-prediction model and narrowed its focus to those patients it needed to identify—patients with an ED visit within the last seven days and excluding ED visits where the patient was transferred to another acute care hospital.

Rather than spending valuable time reviewing claims data to identify patients that may be appropriate for care management services, care managers arrive at work each day with a list of patients already populated in their work queue. They can then review the list and manage the intake of appropriate patients. Leaders, care managers, and users now can easily adjust the risk scores to identify rising risk patients and evaluate populations of interest.

Care managers are able, for the first time, to apply more than one risk model to patients, assisting them in identifying patients who have chronic diseases and high ED utilization, or another combination. They are then able to determine if it is appropriate to enroll the patient in more than one care management program.

除了改进的、自动化的风险分层外，PCIN现在还可以获取执行综合项目评估所需的数据。在此之前，该组织无法访问其理事会所需的丰富数据。护理管理项目负责人现在能够轻松地确定其团队参与的患者数量，并评估干预对患者结果的影响。

RESULTS

该组织现在可以访问按风险分层的患者名单，使其能够与适当的患者接触，以降低成本和改善结果。PCIN has achieved a:

• 风险分层的患者识别工作流程的效率提高了100%。
  • Previously, the care management team would spend hours weekly consolidating data from multiple sources, creating patient lists, and then formatting the lists/reports so the information could be used effectively for patient intake.
  • PCIN的护理经理不需要花费大量的时间来创建一个可能从护理管理服务中受益的患者名单，而是可以将这些时间重新用于患者护理，更好地为患者提供服务，以改善结果。
• Reduction in burdensome manual data entry and automation of risk predictions, substantially increasing the number of patients PCIN can identify and evaluate for its care management programs. In just one day, the analytics application identified more high-risk patients than had been identified in the previous year and a half.
• Full understanding of the data used to create the risk-stratified list and can easily adjust the algorithm to better meet changing patient or program needs.

“我们不再花时间手工创建病人名单。令人兴奋的是，我们的团队来了，我们的名单已经填满，并准备接收患者!这简化了我们的工作，使我们能够高效地工作。”

Tricia Hannig, RN, BSN
Director of Quality Improvement
Physician Clinical Integration Network, HSHS ACO

WHAT’S NEXT

The organization will continue using the analytics application to risk stratify its patients. Next, PCIN plans to evaluate how to integrate machine learning into the risk algorithms to further refine predictions and improve the accuracy of its risk-prediction models.

REFERENCES

1. Centers for Medicare & Medicaid. (2018).National health expenditure data – Historical.
2. American Academy of Family Physicians. (n.d.).医疗之家的主要功能-护理管理。