Reducing Hospital Readmissions: A Case for Integrated Analytics

This report is based on a 2018Healthcare Analytics Summitpresentation given by Rhiannon Harms, Executive Director, Strategic Analytics, UnityPoint Health, and Ben Cleveland, Data Scientist, UnityPoint Health, entitled, “Leveraging Predictive Models to Reduce Readmissions.”

To meaningfully reduce readmissions and meetvalue-based care为了实现目标，卫生系统必须在其真正的全系统工作流程中实施分析。在许多组织中，预测模型在护理连续体中是孤立的，它们应用于某些竖井，但没有跨工作流程集成。通过将分析模型完全集成到其工作流程中，如减少再入院策略，并支持其采用，卫生系统可以实现有意义的改进。

Fully integrated analytics deliver real results: A hospital in a regional health system that used an integrated analytics approach improved its risk-adjusted readmissions indexes by 40 percent over three years. The facility surpassed internal system targets in performance and became a top performer in the health system.

This report describes how healthcare organizations, like the example above, can build and adopt predictive models for reducing readmissions and fully integrate these strategies into workflows. Integrated analytics is multidisciplinary effort, requiring input for all individuals connected to the patient’s care (e.g., primary care providers, nurses, care managers, and home health and skilled nursing facility teams), that delivers a common tool for all care teams.

With integrated analytics tools and methods, health systems can achieve four chief goals around reducing hospital readmissions:

1. Improved model performance from current industry standards, such as LACE and HOSPITAL.
2. Prediction of which patients are at risk of being readmitted and dates of highest risk.
3. Near real-time predictions in an automated, easy-to-understand, cross-continuum tool.
4. Recommended actions in the best interest of the patient.

Before Predictive Modeling, Organizations Must Understand Readmissions

在医疗保健组织建立适用的再入院风险预测模型之前，它需要对患者再入院过程有一个全面的了解。了解患者重返医院的关键因素包括护理人员的担忧、患者是否参加了后续预约、药物问题以及患者报告的担忧(例如，直接问患者:“你认为你为什么回来了?”)。

To understand readmissions, the strategy must use patientdatafrom readmission to discharge, including critical touchpoints in the process (Figure 1):

• 病人再次入院。
• Daily readmissions report triggers the workflow.
• Patient undergoes a readmission interview.
• Care team completes a readmissions worksheet.
• Care team holds daily unit huddle.
• 护理小组每天召开再入院会议。
• 护理团队使用新信息创建出院后计划。
• Patient is discharged.

Analytics Team Capabilities Make Predictive Models Actionable

Having a predictive model alone won’t automatically help health systems reduce readmissions. To best leverage an analytics solution, health systems need to know where in the patient’s readmission journey to implement it, as well as have the ability to cut the data to show appropriate views for different teams (e.g., service line or diagnoses group views).

The analytics team delivers essential capabilities to make predictive models actionable:

• Scorecards and dashboards: key performance indicators; standardized, ongoing reports, broadly accessed with self-enabled interpretation.
• Exploratory analysis: abilities to answer unanticipated questions; execute exploratory versus standard reporting and avoid data dumping; highly consultative capabilities.
• Predictive modeling: advanced analytics; statistical methodologies; highly consultative and intensive capabilities.

Tools and Technology for Fully Integrated Readmissions Reduction Analytics

The health system from the example in the introduction used several readmission risk reduction tools to achieve its 40 percent improvement in risk-adjusted readmission indexes over three years:

An Inpatient Readmission Risk Dashboard

Figure 2 shows an inpatient readmission risk dashboard, which displays readmissions risk for all patients in the hospital with a color (from a low in green to a high in red) to indicate daily risk. Care teams can use this specific risk insight to plan appropriate strategies and interventions for each patient.

The Post-Acute Care Team Dashboard

急症后护理团队仪表盘(图3)将患者风险数据转移到出院仪表盘，在风险之上覆盖后续预约。覆盖层突出了病人旅途中的脆弱点。护理团队可以确定风险水平并制定适当的计划(例如，对低风险患者进行少量干预或对高风险患者进行多次随访预约)，并确定安排随访的最佳时间以及患者进行这些预约的可能性。

The triggers in Figure 4 show the different scenarios from the discharge risk assessment.

Strategies for Analytics Adoption

An analytics adoption strategy aims to meet three goals:

1. Which patients do we focus on?
2. What do we do?
3. When do we do it?

When reducing hospital readmissions, the strategy must span the domains of healthcare utilization, diagnosis history, and social determinants of health (Figure 5), as well as visit specifics, including vitals, labs, and medications (Figure 6).

Integrating Analytics for Meaningful Readmissions Reduction

Health systems can achieve significant reduction in readmissions by fully integrating predictive analytics strategies into their workflows. Applicable predictive models will consider the entire patient readmission journey, as well input from the whole care team and the patient; leverage the capabilities of the analytics team, and deliver accessible, easy-to-use tools with meaningful visualizations.

Additional Reading

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

1. How to Apply Machine Learning in Healthcare to Reduce Heart Failure Readmissions
2. Reduce Readmissions with Predictive Analytics and Process Redesign