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Capella University Data Validation in Health Care Question

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Book: Strome, Trevor L., and Trevor L. Strome. Healthcare Analytics for Quality and Performance Improvement, John Wiley & Sons, Incorporated, 2013. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/capella/detail.action?docID=1443907. Created from capella on 2022-02-09 23:45:54. Developing and Using Effective Indicators What’s measured improves. —Peter F. Drucker Healthcare organizations (HCOs) have more data available to them than ever before. Raw data is rarely useful, however, for healthcare quality and performance improvement. To begin with, there is now often too much data generated through all the activities and systems within healthcare to use effectively. Indicators provide convenient performance snapshots of processes, financial measures, and outcomes critical to the quality and performance goals of the HCO. This chapter will discuss the importance of indicators in quality and performance improvement, and how to create or choose indicators that are most effective for the requirements of your HCO. Measures, Metrics, and Indicators There is a saying that “you can’t improve what you can’t measure.” While this may not be strictly true— I have seen HCO undergo tremendous improvement via the foresight and vision of remarkable leaders— bringing about change in healthcare requires measurement of processes and workflows and effective representation of those measurements. As a result of the increasing volumes of available data and the abundance of analysis tools, many different reports, dashboards, and other information requests are being generated for decision making. Even though HCOs are experiencing a proliferation of dashboards and other information tools, many are still struggling to improve their quality, performance, HOW TO MAKE MEASURES MORE USEFUL There is some thinking that the development of metrics and indicators is the sole domain of the business or QI teams. It is vital that analytics teams are aware of how to develop effective indicators, however, because it is they who bring indicators to life. They need to know not only how to analyze data but also how to put that data into context. When asked for metrics and measures, analytics teams should know that the analysis is only part of the solution; every indicator should be presented with appropriate ranges and targets. If this information is not available for inclusion with the indictor on a dashboard, report, or other analytical application, analytics teams should approach the requestor of the information for that context. Without that context, the information gets buried in just another report that does not assist the HCO in making decisions or achieving its quality goals. and competitiveness. It is clear, then, that having data, producing more reports, and developing more dashboards is not the only answer. Rather than simply collecting more data, healthcare leaders need information grouped and summarized in logical ways that let them know how their organization is performing. The usual starting point is to define measures, metrics, and indicators that are representative parameters for examining the performance of the organization. These three terms are commonly (but incorrectly) used interchangeably. Although there is by no means universal agreement as to the exact definition of the terms, the definitions below are sufficient to convey how the terms differ in meaning, and how those differences relate to the measurement of healthcare. Tip Rather than simply collecting more data, healthcare leaders need information grouped and summarized in logical ways that let them know how their organization is performing. Measure. The term “measure” (when used as a noun) in healthcare typically refers to a quantitative value representing some aspect of patient care, and may (or may not) be linked to specific performance and QI initiatives. Typically, measures have not been processed (except for perhaps being grouped in some logical manner) and may include variables such as time (such as hours waiting), counts (such as patients), and other similar data. Since almost any quantitative value can be considered a measure, I like to consider measures as the raw data that forms the basis for further analysis. Figure 7.1 illustrates a measure— simply the number of patients who have been triaged in the emergency department over a seven-day period. This information is “nice to know,” in that it provides some context as to the busyness of the emergency department over that time period; however, it doesn’t provide any additional information about the performance of the department. Metric. A metric is some aspect of healthcare quality or performance to which a quantitative value is attributed for purposes of monitoring and evaluation. I consider metrics to be measures with more focus and purpose. Metrics typically specify a given point of time or a time period. Metrics can be situational (for example, they may be relevant only for a special purpose or project), but can also measure performance longitudinally, as long as the metric is relevant to some aspect of quality or performance that the HCO needs to monitor. Examples of metrics used in healthcare improvement include time (such as length of stay), number of patients seen by a physician per shift, number of medication errors, and other important descriptors of quality and performance. Figure 7.2 illustrates a metric, in this case the percentage of patients whose triage scores were overridden by the triage nurse from what was suggested by the computer’s triage algorithm. I would consider this a metric, because it ties directly to a process within the department (the triage of patients) and it relates to quality (too frequently overriding triage scores may present a clinical and legal risk, and may suggest that the computerized triage algorithms need adjusting). The purpose of monitoring this metric is to minimize clinical and legal risk and to ensure clinical quality. In this case, we can see that March 1 and 2 had higher override rates than the other seven days, but the chart tells us little else. A few things are missing from this metric that would make it really useful: some indication of what a good (or acceptable) override rate is— the target— and how current performance measures up against previous (or baseline) performance. Without this additional context, it is difficult to know if any corrective action is necessary, and if so, what action to take. Indicator. A metric without context may be insufficient for making decisions— it is merely “a number,” and having too many metrics may actually contribute to information overload and impede decision making. Indictors, then, are metrics that are more useful for driving business decisions, because indicators have context assigned to them. See Figure 7.3 for the graph of a sample indicator. Some of the most important pieces of contextual information that separates an indicator from a metric is having an acceptable range and target assigned to the indicator, which is necessary to ■ ■ Identify whether current performance is “good” or “bad,” Determine how far away performance is from reaching its performance target, and Tell whether performance is trending toward meeting the target (or staying within target range) or if it is trending away from the target (or trending toward becoming out of the target range). The triage override rate metric becomes a true indicator and trigger for action once we add the baseline performance (so that we can compare current performance over past performance, in this case, over the last 30 days) and the target (which is what would be considered an acceptable rate of triage override). With the two new pieces of information, we can see in Figure 7.3 that, over all, performance over the last seven days was better than the baseline for five out of seven days, and that performance was within the target range for four out of seven days. It is possible that March 2 and 7 are outside the target range due to random variation, but March 1 appears to stand out. This could, for example, trigger the nurse manager to see who was triaging that day— perhaps one or more of the triage nurses is inexperienced and needs a refresher on the triage tool. Note how important the target information is: if the acceptable range for override rates was 20 percent (not 10 percent), then likely no corrective action would need to be taken at all.
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