Reducing Hospital Onset Clostridioides Difficile Infections Decreases Costs by $855K

Community Health Network, a hospital system in Indiana, discovered that its hospital-acquired C. diff infection (HA-CDI) rate was higher than the national benchmark. The organization knew it needed to decrease infection rates, but without timely, meaningful data, leaders couldn’t identify the right areas to focus improvement efforts. With the use of a high-level, robust analytics system that allowed better access to data, team members were able to determine where to focus their efforts.

COMMUNITY HEALTH NETWORK AIMS TO REDUCE HOSPITAL ACQUIRED C. DIFF INFECTIONS

Hospital-acquired infections are a significant patient safety concern for healthcare organizations. In the U.S., an estimated 223,900 hospitalized patients develop Clostridioides difficile (C. diff) infection, a bacterial infection associated with antibiotic treatment that can cause life-threatening diarrhea, resulting in 12,800 deaths annually.¹

Ranked among the nation’s most integrated healthcare systems, Indianapolis-based Community Health Network (CHNw) is Central Indiana’s leader in providing convenient access to exceptional healthcare services.

INACCESSIBLE, UNTIMELY DATA: A ROADBLOCK TO REDUCING INFECTION RATES

CHNw recognized its HA-CDI rate was higher than the national benchmark and sought to reduce it. Individual hospitals had deployed siloed improvement activities that aimed to reduce HA-CDI. Without cohesive standard processes that addressed the root cause, those efforts were largely ineffective.

CHNw知道它需要改进，但组织缺乏及时的、有意义的数据，这将允许它充分探索业绩和识别改进的机会。

While the data the organization needed for improvement was available in the EMR, it was incredibly difficult to access. Furthermore, meaningful data could only be obtained through time-consuming manual processes at an individual hospital level, impeding the ability to understand network-wide performance. Without an efficient mechanism for drilling down to root causes, CHNw could not implement impactful solutions to mitigate HA-CDIs.

CHNw needed a comprehensive, data-driven approach to reduce HA-CDI rates, decrease costs, and improve patient safety.

ACCESS TO ACTIONABLE DATA DECREASES HOSPITAL ACQUIRED C. DIFF INFECTION RATES

CHNw uses a consistent, systematic improvement methodology to drive and accelerate improvement efforts. The health system convened an interdisciplinary network improvement team to apply its improvement methodology to decrease HA-CDI.

The improvement team leveraged the Health Catalyst^®Data Operating System (DOS™) platform and a robust suite of analytics applications, including the C. diff Analytics Accelerator, to understand organizational performance and drill into root causes.

Using the C. diff Analytics Accelerator, CHNw was able to quickly and easily visualize C. diff performance data, including HA-CDI rate per 10,000 patient days, the reported number of HA-CDI cases, the number of positive cases, and days since the last HA-CDI (see Figure 1).

CHNw used the analytics accelerator to drill into detailed unit and patient-level detail, identifying three common opportunities that drove the improvement strategy:

1. Improving knowledge about CDI: Infection prevention registered nurses developed and provided education for clinicians describing clinical symptoms that necessitated appropriate laboratory testing and patient isolation to prevent further spread of CDI to other patients, including algorithms to help guide testing and treatment decisions.
2. Ensuring early detection and isolation: During the admission process, clinicians receive decision support, which uses information documented in the EMR to inform clinicians if the patient has met criteria for risk of CDI. The decision support also contains the key evaluation criteria for CDI risk, indicates that lab testing should be ordered, and guides clinicians to place an isolation order, and to place the patient in isolation.
3. 准确的CDI和样本:CDI检测订单还包含临床决策支持，描述患者症状，以指示何时需要检测，何时不需要。当临床医生重写决策支持时，他们需要输入偏离最佳实践的原因描述。当下达了CDI检测命令时，注册护士会收到一个提醒，即该患者已接到CDI检测命令，应将其隔离，直到确认结果为阴性。CDI测试命令包括必需的隔离命令。

CHNw进一步利用分析加速器，定义来自标准组织流程的偏差，并在分析加速器中可视化偏差数据。CHNw was able to quickly identify the following:

• 需要CDI检测但尚未接受检测的患者。
• Patients who had a lab order entered for CDI testing but were not yet in isolation.
• The number of lab orders per 10,000 patient days.
• The average time to complete each step of the order workflow.
• Test usage data and positive rates.

The discrete data, including the patient-level detail required to effectively review the deviation from standard practice, are visualized in the analytics accelerator, enabling rapid review of deviations, eliminating the burdensome manual chart review process, and expediting follow-up with individual clinicians to discuss and understand the reasons for the deviation (see Figure 2).

CHNw利用偏差数据来识别模式和趋势，以及与这些趋势相关的根本原因，从而实现持续改进。

RESULTS

Using this comprehensive data-driven methodology, CHNw substantially reduced HA-CDI rates, improving patient safety and quality while also reducing the cost of care, in one year:

• 一年内节省了85.5万美元，每1万患者日住院发病CDI率相对降低了31.8%，避免了33个ha -CDI。
• 41.1 percent relative reduction in the number of labs per 10,000 patient days that did not meet the criteria for testing—nearly 1,000 fewer unnecessary lab orders.

“The C. diff Analytics Accelerator enabled us to make a huge impact on our patient’s lives, to improve quality, and to reduce our costs by more than $850K. We’re applying our learning to similar patient safety events, improving safety and financial performance across our network.”

– Patrick McGill, MD, EVP, Chief Analytics Officer

WHAT’S NEXT

有了早期诊断和隔离CDI症状患者的标准工作流程，CHNw计划进一步加强整个网络的抗生素管理策略，并将其工作扩展到门诊护理领域，以进一步降低CDI发病率。该组织还在调查其清洁产品的做法，以防止患者流动率高的地区，如成像和急诊部门的交叉污染。

REFERENCES

1. Centers for Disease Control and Prevention. (2015). Healthcare-associated infections.Clostridioides difficile infection.