{"id":7050,"date":"2019-12-19T11:07:08","date_gmt":"2019-12-19T11:07:08","guid":{"rendered":"https:\/\/www.thinkkidneys.nhs.uk\/kquip\/?p=7050"},"modified":"2020-01-27T14:08:24","modified_gmt":"2020-01-27T14:08:24","slug":"the-evidence-of-quality-improvement","status":"publish","type":"post","link":"https:\/\/www.thinkkidneys.nhs.uk\/kquip\/blog\/the-evidence-of-quality-improvement\/","title":{"rendered":"Quality Improvement here and now \u2013 measurement and beyond"},"content":{"rendered":"\n

Paul Cockwell, KQuIP Co-Chair <\/em><\/p>\n\n\n\n

The major variation in clinical outcomes and patient reported experience between and within renal services is a stark reminder that the system is the major factor in the quality of patient experience and in ensuring that each patient has the opportunity for their best outcome.<\/p>\n\n\n\n

A fundamental goal of quality improvement (QI) is to improve experience and outcomes for patients at a system level. The statement is easy, but the challenges are profound; QI is a heavily squeezed middle of professional activity in healthcare. Rather than embedding in practice it is often seen as a separate activity that requires its \u2018own time\u2019 to develop and deliver. Many clinical staff are not involved in QI in a way that genuinely delivers improvement, and some are not persuaded that involvement in QI is part of their job.<\/p>\n\n\n\n

Let me give you two simple examples of this QI gap<\/strong><\/h4>\n\n\n\n

The\nnational CKD audit showed major shortfalls in albuminuria testing. As\nalbuminuria is the cornerstone of clinical management pathways in CKD, defining\ntreatment choice, blood pressure targets, and referral, huge numbers of\npatients have suboptimal management. And how much QI is going on in this area? Not\na lot, I suspect.<\/p>\n\n\n\n

And what of\nPIVOTAL? a UK based multi-centre trial of high dose iv iron vs reactive dosing.\nThis showed that less patients who received proactive high dose iv iron reached\na primary end point of a composite of death from any cause, nonfatal\nmyocardial infarction, nonfatal stroke, and hospitalization for heart failure\ncompared to patients who received reactive low-dose iv iron. There was a 23%\nlower ESA requirement in the high dose group. How many dialysis unit are using\nhigh dose iv iron almost 1-year after PIVOTAL has been published?<\/p>\n\n\n\n

These are two of many major improvement opportunities. The\nchallenge of albuminuria is defining the best QI approach(es) which may vary by\nlocation and then using it: that is implementation science research and\ngeneralisability. For PIVOTAL it is establishing a simple system for change in a\nclinical service.<\/p>\n\n\n\n

So those of\nus who work directly with patients must accept that we have a major opportunity\nto improve the system and therefore patient experience and outcomes by\ndeveloping our QI skills and embedding them in routine clinical practice. Through\ninvolvement in QI there is also a strong secondary benefit to us as individuals\nthrough developing a broader set of skills that can sustain our careers, using\ntools that we can apply effectively to future problems and new roles. <\/p>\n\n\n\n

We must also state and restate the academic basis for QI<\/strong><\/h4>\n\n\n\n

Eyebrows may rise here, as \u2018academic\u2019 and QI is widely considered an oxymoron, with QI described variably as \u2018common sense\u2019 \u2018boring\u2019 \u2018evidence free\u2019.  Pick an area for improvement, use some fashionable language, set up a project team, and off you go. Baloney words and no methodology. Why evidence seek when there is no basis for evidence?<\/p>\n\n\n\n

But there\nis a strong methodological science that is well established. The challenge is\nthat this science is often not applied. Consequently, QI projects are often not\ncredibly designed to demonstrate impact. Core questions are often not\nincorporated: for example; what is being measured? how is that measurement\nbeing reported? and what are the parameters within that measurement that\ndemonstrate benefit or no benefit?  Failing\nto define measurement a priori and to collect and analyse data with time is the\nrock upon which many QI projects are broken. This is despite the science of\nmeasurement in QI being long established. <\/p>\n\n\n\n

However,\nthe use of measurement in QI is different to the use of measurement in clinical\nand laboratory research. Standard tests for statistics are usually cross\nsectional and will describe uncertainty through a p-value or a Hazard ratio.\nThis may help to describe an experiment or a clinical trial, but it does not\nhelp to describe change with time in a dynamic setting \u2013 which is a requirement\nin QI, where measurement needs to factor for the natural variation in a system\nthat occurs with time. This natural variation also known as regression to\nthe mean<\/em>. This rule states (and I paraphrase) that in a complex\nsystem dependent on many variables, by chance extreme outcomes tend to be\nfollowed by more moderate ones. We all recognise this\u2026\u201d we haven\u2019t had a case\nof anti-GBM disease for 3 years; and then we have had two cases this month\u201d.<\/p>\n\n\n\n

All measurements are made up by two parts: true and random error<\/strong><\/h4>\n\n\n\n

Therefore, when we make a change in a system, such as increasing theatre capacity for vascular access, the first few measurements after the change should be treated with scepticism. For example, if the scores in the preceding months were on the low end for performance, then a natural variation upwards would have been expected at some stage. We should not extrapolate from small numbers over short periods.<\/p>\n\n\n\n

There is a\nstandard measurement tool for QI that addresses this challenge by providing a\ndynamic measurement that incorporates upper and lower limits for natural\nvariation and therefore can identify a true effect. It is called statistical\nprocess control (SPC): this technique arose from the motor car industry in the\n1920s and was first published on in 1933, 25 years before Kaplan and Meier described\ntheir ubiquitous estimate of survival function. Statistical process control measures\nchange with time to define true changes and can be used with smaller data sets\nto detect changes and trends as early as possible. Repeated measures produce\nvariation, if over time processes are stable then that variation will change\naround a mean level. In SPC we can assess true change and the impact of the\nchanges that effect true change, by changes in the mean level with time.<\/p>\n\n\n\n

The control chart is the key tool of SPC<\/strong><\/h4>\n\n\n\n

It includes a centre line, typically the mean, the upper control line (UCL), and lower control line (LCL). If data falls outside the control lines or displays abnormal patterns, then this indicates special cause variation: this refers to variation causing non-random distribution of events caused by a specific factor (e.g. increase in prevalent fistula rates due to increasing theatre capacity for vascular access capacity). A control chart will often use three standard deviations (SD) for UCL and LCL. At this level the chance of it occurring randomly is low, so you probably need to consider anything outside the control lines as a special variation (true change).<\/p>\n\n\n\n

In addition\nto one point outside the UCL or LCL other markers of true change can be\nused; e.g. two of three successive points more than 2SD from the mean on the\nsame side of the centre line; four of five successive points more than 1SD from\nthe mean on the same side of the centre line; eight successive points on the\nsame side of the centre line; six successive points increasing or decreasing (a\ntrend). The collecting and graphing of data with time is powerful and has the\npotential to engage the clinical team in measurement and interpretation,\nencouraging increased rigour in incorporating measurement into practice.<\/p>\n\n\n\n

A further benefits of control charts is that they do not\nrequire as much data as traditional statistical analysis, which relies on large\naggregated data sets. The UCL and LCL can often be calculated within 30 data\npoints, and then each new data point can be judged for special cause variation\n(statistical significance).<\/p>\n\n\n\n

And from an academic perspective the use of control charts is a rich area; with\nvariations required dependent on distribution of the data, the statistics used,\nand a range of other choices required dependent on the need of the project.<\/p>\n\n\n\n

In QEH Birmingham\nwe have a regular CKD service meeting where we look in detail at data from the\nservice. We introduce change in service through this as it directs us to where\nwe need to improve the service. What is the incidence of uncontrolled starts on\ndialysis? When are patients being supported to prepare for kidney care? What\nabout our AVF waiting times? We use spreadsheet generated data and then show\nthe results as a graph.  We project and\nassess and comment: what does \u2018not as well last month\u2019 \u2018a blip here\u2019 \u2018a bad\nquarter there\u2019 mean? Is this variance or a genuine trend in data? Of course, a\nlightbulb moment, this can be addressed through SCP. An essential tool with a\nstrong academic basis that can be used in clinical practice.<\/p>\n","protected":false},"excerpt":{"rendered":"

Paul Cockwell, KQuIP Co-Chair The major variation in clinical outcomes and patient reported experience between and within renal services is a stark reminder that the system is the major factor in the quality of patient experience and in ensuring that each patient has the opportunity for their best outcome. A fundamental goal of quality improvement (QI) is to improve experience and outcomes for patients at a…<\/p>\n","protected":false},"author":130,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_bbp_topic_count":0,"_bbp_reply_count":0,"_bbp_total_topic_count":0,"_bbp_total_reply_count":0,"_bbp_voice_count":0,"_bbp_anonymous_reply_count":0,"_bbp_topic_count_hidden":0,"_bbp_reply_count_hidden":0,"_bbp_forum_subforum_count":0,"jetpack_post_was_ever_published":false,"_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":true,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"enabled":false},"version":2}},"categories":[1],"tags":[],"class_list":["post-7050","post","type-post","status-publish","format-standard","hentry","category-blog"],"acf":[],"yoast_head":"\nQuality Improvement here and now \u2013 measurement and beyond - The Kidney Quality Improvement Partnership<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.thinkkidneys.nhs.uk\/kquip\/blog\/the-evidence-of-quality-improvement\/\" \/>\n<meta property=\"og:locale\" content=\"en_GB\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Quality Improvement here and now \u2013 measurement and beyond - The Kidney Quality Improvement Partnership\" \/>\n<meta property=\"og:description\" content=\"Paul Cockwell, KQuIP Co-Chair The major variation in clinical outcomes and patient reported experience between and within renal services is a stark reminder that the system is the major factor in the quality of patient experience and in ensuring that each patient has the opportunity for their best outcome. 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