Why Process Measures Are Often More Important Than Outcome Measures in Healthcare

Health systems today are scrambling to identify the most important measurements they can use to generate stable, consistent growth. A friend of mine loves to hike and was telling me about a fancy one-legged stool he carries around with him. He explained that he only really needs one leg; packing three-legged stools causes extra weight. While this mentality might work for time spent on the trail, it doesn’t hold up when it comes to measuring healthcare outcomes—I’d choose the balanced stability offered by the three-legged variety any day.

Similarly, healthcare systems shouldn’t rely on the single “leg” of outcomes measures toimprove quality and costs，尽管这些指标很多。此外，我们必须创建一个基于证据的过程度量的坚实基础，以提供更细粒度的数据，然后用平衡度量稳定这些度量，以帮助驱动更好的结果。

3 Types of Measures: Outcome Measures, Balance Measures, and Process Measures

What exactly are evidence-based process measures? First it helps to understand the three types of measures we use in healthcare analytics:

1. Outcome measures:These are the high-level clinical or financial outcomes that concern healthcare organizations. They are the quality and cost targets you are targeting for improvement. These measures are often reported to government and commercial payers. Some examples of metrics foroutcome measuresincludemortality rates,readmissions rates, and surgical site infection rates.
2. Balance measures:These are the metrics a health system must track to ensure an improvement in one area isn’t negatively impacting another area. For example, let’s say length of stay (LOS) in labor and delivery is the outcome metric. The hospital wants to reduce LOS and save money. The balance metric might be patient satisfaction. If mothers feel rushed toward discharge, the outcome there might be a negative impact on patient satisfaction even while improving LOS.
3. Process measures:These measures are the specific steps in a process that lead — either positively or negatively — to a particular outcome metric. For example, let’s say the outcome measure is LOS. A process metric for that outcome might be the amount of time that passes between when the physician ordered the discharge and when the patient was actually discharged. Digging even deeper, you might look at the turnaround time between final take-home medication being ordered and medication delivery to the unit. If it takes the pharmacy three hours to get the necessary medications to the floor — potentially delaying the discharge — you’ve pinpointed a concrete opportunity for healthcare process improvement.

Process Measures Are Important

Process measures are the evidence-based best practices that represent a health system’s efforts to systematize its improvement efforts. To illustrate this, I’ll use a patient injury prevention example. Let’s say your organization needs to reduce the incidence of pressure ulcers commonly known as bed sores. That is your outcome measure. You know your baseline rate, and you want to reduce it, but how are you actually going to drive improvement?

答案很简单:通过实现和跟踪正确的过程指标。本例中的流程度量是指在重症监护病房(ICU)或医疗外科病房中，每个卧床的病人每次都应该执行的步骤。第一个也是最重要的过程测量是使用Braden量表对医院所有适当单位进行压疮风险预测的风险评估。

被确定为“有风险”的病人将接受治疗，以防止褥疮根据您的组织选择的最佳实践协议。例如，你可以建立重新评估、营养、抬抬和重新定位病人、提供特殊的床垫和皮肤护理的协议。重要的是，这个过程中的每一个步骤都可以被跟踪和测量。随着时间的推移，当你建立了足够的样品量，你就可以开始确定哪些过程步骤对于防止负面结果是最重要的。

Determining Root Cause to Solve the Problem

One of the greatest benefits of having this process metric data on hand is the ability to identify what is really causing the problem with pressure ulcers in your organization. I can assure you that the problem does not stem from your people. It stems from your process. In most organizations, however, the system of incident reporting doesn’t recognize this fact. There is a stigma attached to filling out the report: “You failed. You’re a bad nurse because your patient got a bed sore.” Often, many borderline cases go unreported because of the lack of a culture based on a learning environment and too much focus on outcomes metrics.

Really, it is the process that failed, not the person.One of my favorite quotes is from Paul Batalden, MD, and it goes like this: “Every system is perfectly designed to get the results it gets.” If you don’t have a well-designed process in place to prevent pressure ulcers, it should be no surprise if you don’t perform well on that outcome metric.

By tracking process measures, you can pinpoint the root cause of the system’s failure. You might find that you have a shortage of pressure-redistributing mattresses in the supply room, or that you didn’t do a risk assessment for every patient who arrived on the unit, or that the right skincare products aren’t accessible on the floor when needed. Whatever the problem, you can then assess it and fix it.

What you’re trying to do is move from a craftsmanship mentality to a system of production. And process metrics are the way that you do that. They’re the checklists to systematically guarantee that the right care will be delivered to every patient, every time.

Using Process Measures and Reducing Variation

过程措施提高质量和成本，使组织减少了护理交付的变化量。当您在护理过程中潜在的变化点建立过程指标时，您可以监测和减少不适当的变化。价值流图是一个很好的工具来概述护理过程，并确定可以测量的潜在变化点。

价值流图概述了向患者传递价值的过程中的步骤。这些步骤中的每一个都可能有一个对测量过程一致性有用的过程度量。通过测量这些步骤，您可以发现变化点。例如，您可能会发现过程中的一个步骤需要一些单位1小时完成，而其他单位需要5小时。也可能有些单位需要2000美元，有些单位需要8000美元。这些变化的实例提供了探索数据的机会，以理解变化存在的原因。这样一来，就有可能将流程标准化，从而使所有患者无论去哪个科室、哪家医院或哪家诊所，都能以尽可能低的成本持续获得最高质量的护理。

Why You Need an Enterprise Data Warehouse to Track Process Metrics

那么，为什么没有更多的医疗机构拥有这类系统呢?答案是，他们没有处理结果、过程和平衡指标的基础设施。如果他们只有资源来跟踪其中的一个，他们会选择结果指标，因为这些指标必须提交给CMS。

组织很难跟踪所有类型的度量的原因是他们的分析方法过于依赖手工工作。当您没有合适的技术基础设施来自动提取和分发数据时，您最终不得不手动完成所有工作。您可能能够成功地使用手工方法来跟踪一个甚至两个流程的改进，但是一旦您遇到第三个、第四个或第五个流程，手工工作就变得不可持续了。如果没有持续的测量，一旦你进入下一个过程，你就无法维持在一个过程中获得的收益。

That’s where an enterprise data warehouse (EDW) with a robust and flexible analytics architecture comes in. An EDW forms the foundation for healthcare analytics by bringing all of a health system’s data into a single source of organizational truth. This makes it possible to eliminate the manual process of gathering data and instead, delivers the automation you need to track a wide variety of outcome, process, and balance measures simultaneously. With an EDW, analysts can then focus their time on discovering patterns in the data that will lead to understanding, insight, and ultimately action. But without an EDW, it will be very difficult for analysts to provide reliable and repeatable reports and in-depth analyses of areas that will reveal the best opportunities for improving outcomes.

The Health Catalyst Data Operating System (DOS™) Helps Healthcare Organizations Move Beyond the Data Warehouse

Traditional data warehousing, which solved some of the data integration issues facing healthcare organizations, is no longer good enough. AsGartnerreported, traditional data warehousing will be outdated and replaced by new architectures by the end of 2018. And current applications are no longer sufficient to manage these burgeoning healthcare issues. The technology is now available to change the digital trajectory of healthcare.

The Health Catalyst Data Operating System (DOS™) is a breakthrough engineering approach that combines the features of data warehousing, clinical data repositories, and health information exchanges in a single, common-sense technology platform.

DOS offers the ideal type of analytics platform for healthcare because of its flexibility. DOS is a vendor-agnostic digital backbone for healthcare. The future of healthcare will be centered around the broad and more effective use of data from any source. Clinical and financial decision support at the point of care is almost nonexistent in healthcare, restricted to a few pioneering organizations that can afford the engineering and informatics staff to implement and maintain it. With DOS, this kind of decision support is affordable and effective, raising the value of existing electronic health records and making new software applications possible.

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