{"id":5662,"date":"2017-12-02T15:25:49","date_gmt":"2017-12-02T14:25:49","guid":{"rendered":"http:\/\/www.improvementscience.co.uk\/blog\/?p=5662"},"modified":"2017-12-02T15:25:49","modified_gmt":"2017-12-02T14:25:49","slug":"the-strangeness-of-length-of-stay","status":"publish","type":"post","link":"https:\/\/hcse.blog\/?p=5662","title":{"rendered":"The Strangeness of LoS"},"content":{"rendered":"

\"\"<\/a>It had been some time since Bob and Leslie had chatted so an email from the blue was a welcome distraction from a complex data analysis task.<\/p>\n

<Bob>\u00a0<\/em>Hi Leslie, great to hear from you. I was beginning to think you had lost interest in health care improvement-by-design.<\/p>\n

<Leslie><\/em> Hi Bob, not at all.\u00a0 Rather the opposite.\u00a0 I’ve been very busy using everything that I’ve learned so far.\u00a0 It’s applications are endless, but I have hit a problem that I have been unable to solve, and it is driving me nuts!<\/p>\n

<Bob><\/em> OK. That sounds encouraging and interesting.\u00a0 Would you be able to outline this thorny problem and I will help if I can.<\/p>\n

<Leslie><\/em> Thanks Bob.\u00a0 It relates to a big<\/strong> issue that my organisation is stuck with – managing urgent admissions.\u00a0 The problem is that very often there is no bed available, but there is no predictability to that.\u00a0 It feels like a lottery; a quality and safety lottery.\u00a0 The clinicians are clamoring for “more beds<\/em>” but the commissioners are saying “there is no more money<\/em>“.\u00a0 So the focus has turned to reducing length of stay.<\/p>\n

<Bob><\/em>\u00a0OK.\u00a0 A focus on length of stay sounds reasonable.\u00a0 Reducing that can free up enough beds to provide the necessary space-capacity resilience to dramatically improve the service quality.\u00a0 So long as you don’t then close all the “empty” beds to save money, or fall into the trap of believing that 85% average bed occupancy is the “optimum”.<\/p>\n

<Leslie><\/em> Yes, I know.\u00a0 We have explored all of these topics before.\u00a0 That is not the problem.<\/p>\n

<Bob><\/em> OK. What is the problem?<\/p>\n

<Leslie><\/em> The problem is demonstrating objectively that the length-of-stay reduction experiments are having a beneficial impact.\u00a0 The data seems to say they they are, and the senior managers are trumpeting the success, but the people on the ground say they are not. We have hit a stalemate.<\/p>\n

\n

<Bob><\/em> Ah ha!\u00a0 That old chestnut.\u00a0 So, can I first ask what happens to the patients who cannot get a bed urgently?<\/p>\n

<Leslie><\/em> Good question.\u00a0 We have mapped and measured that.\u00a0 What happens is the most urgent admission failures spill over to commercial service providers, who charge a fee-per-case and we have no choice but to pay it.\u00a0 The Director of Finance is going mental!\u00a0 The less urgent admission failures just wait on queue-in-the-community until a bed becomes available.\u00a0 They are the ones who are complaining the most, so the Director of Governance is also going mental.\u00a0 The Director of Operations is caught in the cross-fire and the Chief Executive and Chair are doing their best to calm frayed tempers and to referee the increasingly toxic arguments.<\/p>\n

<Bob><\/em>\u00a0OK.\u00a0 I can see why a “Reduce Length of Stay Initiative” would tick everyone’s Nice If box.\u00a0 So, the data analysts are saying “the length of stay has come down since the Initiative was launched<\/em>” but the teams on the ground are saying “it feels the same to us … the beds are still full and we still cannot admit patients<\/em>“.<\/p>\n

<Leslie><\/em> Yes, that is exactly it.\u00a0 And everyone has come to the conclusion that demand must have increased so it is pointless to attempt to reduce length of stay because when we do that it just sucks in more work.\u00a0 They are feeling increasingly helpless and hopeless.<\/p>\n

<Bob><\/em> OK.\u00a0 Well, the “chronic backlog of unmet need” issue is certainly possible, but your data will show if admissions have gone up.<\/p>\n

<Leslie><\/em> I know, and as far as I can see they have not.<\/p>\n

<Bob><\/em> OK.\u00a0 So I’m guessing that the next explanation is that “the data is wonky<\/em>“.<\/p>\n

<Leslie><\/em> Yup.\u00a0 Spot on.\u00a0 So, to counter that the Information Department has embarked on a massive push on data collection and quality control and they are adamant that the data is complete and clean.<\/p>\n

<Bob><\/em> OK.\u00a0 So what is your diagnosis?<\/p>\n

<Leslie><\/em> I don’t have one, that’s why I emailed you.\u00a0 I’m stuck.<\/p>\n

\n

<Bob><\/em>\u00a0OK.\u00a0 We need a diagnosis, and that means we need to take a “history” and “examine” the process.\u00a0 Can you tell me the outline of the RLoS Initiative.<\/p>\n

<Leslie><\/em> We knew that we would need a baseline to measure from so we got the historical admission and discharge data and plotted a Diagnostic Vitals Chart\u00ae.\u00a0 I have learned something from my HCSE training!\u00a0 Then we planned the implementation of a visual feedback tool that would show ward staff which patients were delayed so that they could focus on “unblocking” the bottlenecks.\u00a0 We then planned to measure the impact of the intervention for three months, and then we planned to compare the average length of stay before and after the RLoS Intervention with a big enough data set to give us an accurate estimate of the averages.\u00a0 The data showed a very obvious improvement, a highly statistically significant one.<\/p>\n

<Bob><\/em> OK.\u00a0 It sounds like you have avoided the usual trap of just relying on subjective feedback, and now have a different problem because your objective and subjective feedback are in disagreement.<\/p>\n

<Leslie><\/em> Yes.\u00a0 And I have to say, getting stuck like this has rather dented my confidence.<\/p>\n

<Bob><\/em> Fear not Leslie.\u00a0 I said this is an “old chestnut” and I can say with 100% confidence that you already have what you need in your T4 kit bag?<\/p>\n

<Leslie><\/em>Tee-Four?<\/p>\n

<Bob><\/em> Sorry, a new abbreviation. It stands for “theory, techniques, tools and training<\/em>“.<\/p>\n

<Leslie><\/em>\u00a0Phew!\u00a0 That is very reassuring to hear, but it does not tell me what to do next.<\/p>\n

<Bob><\/em>\u00a0You are an engineer now Leslie, so you need to don the hard-hat of Improvement-by-Design.\u00a0 Start with your Needs Analysis.<\/p>\n

\n

<Leslie><\/em> OK.\u00a0 I need a trustworthy tool that will tell me if the planned intervention has has a significant impact on length of stay, for better or worse or not at all.\u00a0 And I need it to tell me that quickly so I can decide what to do next.<\/p>\n

<Bob><\/em> Good.\u00a0 Now list all the things that you currently have that you feel you can trust.<\/p>\n

<Leslie><\/em> I do actually trust that the Information team collect, store, verify and clean the raw data – they are really passionate about it.\u00a0 And I do trust that the front line teams are giving accurate subjective feedback – I work with them and they are just as passionate.\u00a0 And I do trust the systems engineering “T4” kit bag – it has proven itself again-and-again.<\/p>\n

<Bob><\/em> Good, and I say that because you have everything you need to solve this, and it sounds like the data analysis part of the process is a good place to focus.<\/p>\n

<Leslie><\/em>\u00a0That was my conclusion too.\u00a0 And I have looked at the process, and I can’t see a flaw. It is driving me nuts!<\/p>\n

<Bob><\/em>\u00a0OK.\u00a0 Let us take a different tack.\u00a0 Have you thought about designing the tool you need from scratch?<\/p>\n

<Leslie><\/em> No. I’ve been using the ones I already have, and assume that I must be using them incorrectly, but I can’t see where I’m going wrong.<\/p>\n

<Bob><\/em> Ah!\u00a0 Then, I think it would be a good idea to run each of your tools through a verification test and check that they are fit-4-purpose in this specific context.<\/p>\n

<Leslie><\/em> OK. That sounds like something I haven’t covered before.<\/p>\n

<Bob><\/em> I know.\u00a0 Designing verification test-rigs is part of the Level 2 training.\u00a0 I think you have demonstrated that you are ready to take the next step up the HCSE learning curve.<\/p>\n

<Leslie><\/em> Do you mean I can learn how to design and build my own tools?\u00a0 Special tools for specific tasks?<\/p>\n

<Bob><\/em> Yup.\u00a0 All the techniques and tools that you are using now had to be specified, designed, built, verified, and validated. That is why you can trust them to be fit-4-purpose.<\/p>\n

<Leslie><\/em> Wooohooo! I knew it was a good idea to give you a call.\u00a0 Let’s get started.<\/p>\n

\n

[Postscript] And Leslie, together with the other stakeholders, went on to design the tool that they needed and to use the available data to dissolve the stalemate.\u00a0 And once everyone was on the same page again they were able to work collaboratively to resolve the flow problems, and to improve the safety, flow, quality and affordability of their service.\u00a0 Oh, and to know for sure that they had improved it.<\/em><\/p>\n","protected":false},"excerpt":{"rendered":"

It had been some time since Bob and Leslie had chatted so an email from the blue was a welcome distraction from a complex data analysis task. <Bob>\u00a0Hi Leslie, great to hear from you. I was beginning to think you had lost interest in health care improvement-by-design. <Leslie> Hi Bob, not at all.\u00a0 Rather the … <\/p>\n

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