[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-1-en-105":3,"doc-seo-565368-105":53,"doc-detail-565368-en":126},{"code":4,"msg":5,"data":6},0,"success",[7,14,19,24,29,34,39,44,49],{"id":8,"doc_module":9,"doc_module_name":10,"category_name":11,"show_sort_weight":12,"slug":13},11,1,"Template","Presentations",90,"presentations",{"id":15,"doc_module":9,"doc_module_name":10,"category_name":16,"show_sort_weight":17,"slug":18},12,"Resumes",80,"resumes",{"id":20,"doc_module":9,"doc_module_name":10,"category_name":21,"show_sort_weight":22,"slug":23},14,"Invoices",70,"invoices",{"id":25,"doc_module":9,"doc_module_name":10,"category_name":26,"show_sort_weight":27,"slug":28},15,"Posters",60,"posters",{"id":30,"doc_module":9,"doc_module_name":10,"category_name":31,"show_sort_weight":32,"slug":33},16,"Social Media",50,"social-media",{"id":35,"doc_module":9,"doc_module_name":10,"category_name":36,"show_sort_weight":37,"slug":38},17,"Forms",40,"forms",{"id":40,"doc_module":9,"doc_module_name":10,"category_name":41,"show_sort_weight":42,"slug":43},18,"Letters",30,"letters",{"id":45,"doc_module":9,"doc_module_name":10,"category_name":46,"show_sort_weight":47,"slug":48},21,"Paper Templates",5,"papers-templates",{"id":50,"doc_module":9,"doc_module_name":10,"category_name":51,"show_sort_weight":4,"slug":52},158,"General","general-158",{"code":4,"msg":54,"data":55},"ok",{"site_id":56,"language":57,"slug":58,"title":59,"keywords":60,"description":61,"schema_data":62,"social_meta":119,"head_meta":121,"extra_data":123,"updated_unix":125},105,"en","approach-to-run-charts-his-data-and-measurement","Approach to Run Charts (HIS) - Data and Measurement","","Approach to Run Charts (HIS) explains how Healthcare Improvement Scotland applies run chart theory to improvement projects by constructing, using, and interpreting time-based data. It provides practical guidance on displaying monthly or weekly data, choosing sample sizes, setting baseline medians, and extending them to enable reliable detection of change. The guide details interpretation rules for non-random variation, including shifts, trends, run counts, and astronomical points, plus how to rephase run charts when performance levels change.",{"@graph":63,"@context":118},[64,80,101],{"@type":65,"itemListElement":66},"BreadcrumbList",[67,71,74,77],{"item":68,"name":69,"@type":70,"position":9},"https://docshare.wps.com","Home","ListItem",{"item":72,"name":10,"@type":70,"position":73},"https://docshare.wps.com/template/",2,{"item":75,"name":11,"@type":70,"position":76},"https://docshare.wps.com/template/presentations/",3,{"item":78,"name":59,"@type":70,"position":79},"https://docshare.wps.com/template/approach-to-run-charts-his-data-and-measurement/565368/",4,{"url":78,"name":59,"@type":81,"image":82,"author":87,"headline":59,"publisher":90,"fileFormat":93,"inLanguage":57,"description":61,"dateModified":94,"datePublished":95,"encodingFormat":93,"isAccessibleForFree":96,"interactionStatistic":97},"DigitalDocument",{"url":83,"@type":84,"width":85,"height":86},"https://docshare.wps.com/thumbnails/approach-to-run-charts-his-data-and-measurement/565368.png","ImageObject",442,249,{"name":88,"@type":89},"Lucas Martin","Person",{"url":68,"name":91,"@type":92},"DocShare","Organization","application/pdf","2026-10-20","2026-10-13",true,{"@type":98,"interactionType":99,"userInteractionCount":47},"InteractionCounter",{"@type":100},"ViewAction",{"@type":102,"mainEntity":103},"FAQPage",[104,110,114],{"name":105,"@type":106,"acceptedAnswer":107},"What is a run chart and why is it used in quality improvement?","Question",{"text":108,"@type":109},"A run chart is a simple analytical tool used to understand changes in data over time. In quality improvement, it helps identify non-random variation and understand change in a system or process.","Answer",{"name":111,"@type":106,"acceptedAnswer":112},"Which rules help highlight non-random variation on a run chart?",{"text":113,"@type":109},"Healthcare Improvement Scotland uses four common rules: a shift, a trend, too many or too few runs, and astronomical points. These are based on published definitions from Perla et al.",{"name":115,"@type":106,"acceptedAnswer":116},"How are baseline medians set and extended in run chart analysis?",{"text":117,"@type":109},"Baselines use a defined number of data points before improvement activity (e.g., 12 monthly outcomes, 10 weekly process measures, or six monthly process measures). Ideally, baselines exclude periods showing non-random variation, and extended baselines are carried forward with a visually distinct extended median (e.g., a dotted line).","https://schema.org",{"og:url":78,"og:type":120,"og:title":59,"og:site_name":91,"og:description":61},"article",{"robots":122,"canonical":78},"index,follow",{"doc_id":124,"site_id":56},565368,1792382637,{"code":4,"msg":5,"data":127},{"doc_id":124,"user_id":128,"nickname":88,"user_avatar":129,"doc_module":9,"category_id":8,"category_name":11,"doc_title":59,"doc_description":61,"doc_content":130,"file_id":131,"file_url":132,"file_type":133,"file_size":134,"view_count":47,"is_deleted":4,"is_public":9,"is_downloadable":9,"audit_status":9,"page_count":135,"language":136,"language_code":57,"site_id":56,"html_lang":57,"table_of_contents":137,"faqs":138,"seo_title":139,"seo_description":61,"update_tm":140,"read_time":76},8796095360427,"https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d","Data and Measurement:  \nHow we put run chart theory into practice for improvement projects  \nJune 2017  \nA run chart is a simple analytical tool that helps us understand changes in data over time. We describe here, within parameters typical of a quality improvement project , how Healthcare Improvement Scotland constructs, uses and interprets run charts to understand change in a system or process.  \nThe purpose of this guide is to provide some simple, practical advice on how we deal with data in run charts.  \nFor an introduction to run charts and rules for interpretation, we recommend the paper published by Perla, Provost and Murray, The Run Chart: a Simple Analytical Tool for Learning from Variation in Healthcare Processes1.  \nAlthough, their paper provides clear guidance on how to construct a basic run chart and detect a signal of non-random variation, the next steps you would take in your analysis are not necessarily covered. This is largely due to the subjectivity of some of these decisions.  \nWe hope this guide on our approach will help with your journey of data interpretation. Provost & Murray also go into more detail in The Health Care Data Guide: Learning from Data for Improvement2.  \nData sets for quality improvement projects take many shapes and sizes; we cannot provide generic guidance that will suit  \nevery eventuality, but for the purpose of this guide, we will work within a basic set of data criteria:  \n● data are displayed monthly at a minimum, weekly process data are preferable  \n● if using a sample, sample size is 20 or more for each data point, and  \n● time-between run charts are used when more than half the data points are at the extreme values on the scale (for example 0 or 100 on a percentage scale) .  \nWe think that the approach taken will suit most applications, but if your data set falls out with the parameters above then some more thought may be required.  \nInternally, the Data & Measurement Advisors in the Data Measurement & Business Intelligence Team can provide this support. However, if you are working outside Healthcare Improvement Scotland, you are also welcome to access this support (see end of guide for contact details) .  \nInterpreting a run chart  \nThere are four common rules for highlighting non-random variation in run charts: a shift, a trend, too many or too few runs, and astronomical points. There are slightly different published definitions for these, but in Healthcare Improvement Scotland we use the following definitions taken from Perla et al 20101.  \nShift  \nA shift is six or more consecutive points all above or all below the median. Values that fall on the median do not add to, nordo they break, a shift. Skip all values that fall on the median and continue counting.  \nTrend  \nA trend is five or more consecutive points all going up or all going down. If the value of two or more consecutive points is the same, only count the first point and ignore the repeating values; like values do not make or break a trend.  \nToo many or too few runs  \nA run is a series of points on one side of the median. A reference table (see Perla et al 20101) is used to determine if there are too many or too few runs which can suggest non-random variation.  \nAstronomical points  \nAn astronomical point is one which is obviously very different from the other values. This is not a mathematical assessment but rather one of subject matter knowledge and, therefore, must be made in partnership with someone who is working directly on the measure of interest and is aware of what may cause or influence such a value.  \nSetting medians  \nText books and training courses often refer to a data series with a median applied across the whole data set. In quality improvement projects within the parameters set out earlier, we recommend the use of a baseline median, from which variation is then detected.  \nBaseline medians  \nWhere enough data are available before any improvement activity, we would generally use at least 12 data point","cbCaid3rxYxj1tlf","https://ap.wps.com/l/cbCaid3rxYxj1tlf","pdf",672830,8,"English","# Data and Measurement\n## Interpreting a run chart\n## Setting medians\n## Baselines and applying rules\n## Rephasing run charts\n### What is rephasing?\n### When to rephase","[{\"question\":\"What is a run chart and why is it used in quality improvement?\",\"answer\":\"A run chart is a simple analytical tool used to understand changes in data over time. In quality improvement, it helps identify non-random variation and understand change in a system or process.\"},{\"question\":\"Which rules help highlight non-random variation on a run chart?\",\"answer\":\"Healthcare Improvement Scotland uses four common rules: a shift, a trend, too many or too few runs, and astronomical points. These are based on published definitions from Perla et al.\"},{\"question\":\"How are baseline medians set and extended in run chart analysis?\",\"answer\":\"Baselines use a defined number of data points before improvement activity (e.g., 12 monthly outcomes, 10 weekly process measures, or six monthly process measures). Ideally, baselines exclude periods showing non-random variation, and extended baselines are carried forward with a visually distinct extended median (e.g., a dotted line).\"}]","Approach to Run Charts (HIS) - Data and Measurement | PDF",1791852569]