Intro & Contents April 2007 Issue

Nosokinetics

Issue 42, April 2007

(c)Authors for content; Peter Millard, Roy Johnston for e-version

(comments to rjtechne at iol dot ie)

Editor's introduction

Back to basics: getting the numbers not too wrong

Researching my plenary talk on "Nosokinetics, getting the numbers right" for the ICICIS conference I realised I was wrong. My eyes opened when searching the web I came across the work of Mahommed ben Musa al-Khwarizmi, who in his 9th Century treatise created algebra - al-jeb wa'l-muqâbal - the transposition and removal of terms of an equation. Then I found the positional number system and looking in the 1911 edition of the Encyclopedia Britannica (which I bought for £10 in 1973) I realised that we could never get the numbers right, we could simply get them "Not too wrong".

In the 12th Century, translations of Khwarizmi book on Arithmetic brought the numerical number systems to the West. (see Box 1). Note that Zero on its own means ‘nothing there' and read on.

Human beings have five fingers on each hand and five toes on each foot. It should surprise no one, therefore, that the Abacuses used to calculate numbers by the Romans, and in China, count in fives and tens. Nor should we be surprised that distance, in the Roman Empire, was measured in yards (the step of a cohort of marching soldiers). But, what may surprise you is that hospital length of stay is counted in nights, not days, probably as a hangover from the occupancy function of the workhouse [1].

What chance is there of getting policy decisions right, if the numbers used to describe bed usage are incomplete [2]. The table (Box 2) shows that three percent of surgical and medical patients are not there, and why the average stay does not describe the distribution of the data. Given the widespread ignorance about measure and measurability, it is hard to see how we can get decision makers to use algebra instead.

In this issue, Louis Gibbs, from Adele Marshall's group, in Belfast, analysing length of stay in minutes in an A&E Department, reveals two streams of patients: those discharged and those admitted, the latter having four phases of care. Also we consider Brijesh Patel's PhD research from Thierry Chaussalet's group, in London, which uses structured equation modelling to create performance maps which show the links and traps in the NHS Performance Indicators.

The computer era brings great benefits to mankind. Thousands of facts can be stored and analysed. Yet the results are only as good as the methods used to create them. Now, at the beginning of the 21st Century, to get the choice between Arithmetic or Algebra into perspective, it is worth returning to the start of the 20th Century and the following quote from the Arithmetic section of the 1911 Encyclopaedia Britannica:

"From the educational point of view, the value of arithmetic has usually been regarded as consisting in the stress it lays on accuracy. … even then accuracy was not found always to harmonize with reality in getting the numbers right. However, ‘the development of physical science has tended to emphasize an exactly opposite aspect, viz. the impossibility, outside a certain limited range of subjects, of ever obtaining absolute accuracy, and the consequent importance of not wasting time in attempting to obtain results beyond a certain degree of accuracy." (Encyclopaedia Britannica 1911)

So be it. If we can't get the numbers right, the 21st challenge is to get the numbers not too wrong.

1. White, R., International Journal of Nursing Studies, 1977. 14: p. 19-27.
2. Harrison, G.W. et al., Health Care Management Science, 2005. 8(4): p. 325-34.

The NHS Plan and performance assessment frameworks

Editor's comment: We congratulate Brijesh Patel from Thierry Chaussalet's Group at the University of Westminster on his successful defence of his PhD submission. Here we consider the practical significance of his research, which reveals the links and traps in the Department of Health NHS Performance Ratings.

What's in the Journals: April 2007.

Lean thinking.

'Patients are not cars' write Ben Tovim and his co-workers from Flinders Medical centre. ‘Providing good clinical care involves compassion and empathy as well as cognitive and organisational skills'. Nevertheless concepts used in manufacturing industry can be helpful in redesigning the pathways of care. In the Australian Health Review (2007, 31(1):10-15) they describe the progress they have made using Lean thinking to redesign care at the Flinders Medical Centre.

Value Streams.

Also Ben Tovim and colleagues have used lean thinking to redesign patient flow through the Accident and Emergency service at Flinders Hospital.. A finding of their value stream change resonates with Louis Gibbs data analytical findings of two streams (page 2) -those to be admitted and those not to be admitted. Seeing the needs of admitted patients and non-admitted patients differ, it makes clinical as well as mathematical sense to provide specialist teams to meet their needs. See King, D. L., et al. (2006). Redesigning emergency department patient flows: Application of lean thinking to health care. Emergency Medicine Australasia 18: 391-397.

Overcrowding in Australian Emergency Departments.

Kamini Raj and his coworkers question the value of the National Emergency Overcrowding Study tool. However, their study of the agreement between objective and subjective scores was time limited (three weeks) and no overcrowding occurred, so further research is needed to validate their findings. See Raj, Ket, et al. (2006). National emergency department overcrowding study tool is not useful in an Australian emergency department, Emergency Medicine Australasia 18: 282-288.

Disability increases length of stay.

Take heed. A study of 1942 consecutive emergency admissions to Kent Hospitals, UK, reports the median difference between the observed and predicted HRG length of stay for patients with seven presenting conditions (one or more of stroke, fracture femur, myocardial infarction, acute respiratory infection, chronic obstructive airways and falls) was 1.2 days ( 25th percentile estimate 3.9 days, 75th 10.1 days) for patients with dependency. Hence these patients would have incurred £1.7 million in excess of their HRG based tariff. See Carpenter, I., J. Bobby, et al. (2007). Age Ageing 36(1): 73-78.

Fuzzy Logic and Interval Analysis.

At a conference in Cairo, I learnt, in fuzzy logic an event can occur before or after a specific event. However, in interval logic they can only occur after the event. So a patient can arrive in a clinic before or after their specified time, but they can only be seen by the consultant after he or she has arrived. For a mathematical overview see: Moore, R. Lodwick, W. (2007). Interval analysis and fuzzy set theory, Fuzzy Sets and Systems 135: 5-9.

Optimising hospital bed capacity.

Increased demand and decreasing bed allocations make planning capacity a difficult problem for health care workers. Tackling this problem Akcali, E., M. J. Côté, et al. (2006) develop a mixed integer network flow model with capacity constraints, balancing the budget and waiting times. Different experiments are described. Health Care Management Science 9: 391-404.

Do nurse protocols diminish the capability of nurse practitioners?

A study in the Australian Health Review by Jenny Carryer et al (2007, 31(1): 108-115) caught my eye because it questioned the use of tick box algorithms in clinical protocols. The academic nurse researchers from New Zealand and Australia question the wisdom of training nurse practitioners to take on a clinical supportive role while constraining their decision making by strict protocols. One of the father's of geriatric medicine Prof Ferguson Anderson said ‘No self respecting old person comes into hospital without seven diseases!' I share their concern, often, the best thing to put on an old person's prescription in a line through the drugs that they are taking. No protocol tells you that.

Short term hospital occupancy prediction.

Littig, S. J. and M. W. Isken (2007) develop a modelling approach using three basic flows, arrivals, internal transfers and discharges and four stages inpatient length of stay, total time spent in hospital and nursing unit and the current time. Time series models are used to predict emergency and direct arrivals. Multinomial regression models predict the unit of arrival and forecast three day discharges. The research is practical and a step towards the development of a complex model of flow. A weakness, , is the lack of recognition of Gary Harrison's approach to the problem of modelling bed census data. Health Care Management Science 10(1): 47-66.

We are indebted to IMS MAXIMS plc's sponsorship for enabling the website version to be developed. IMS is a significant supplier to NHS and has an ongoing interest in enhancing the scope of IT support in the NHS, especially in the areas of clinical knowledge and decision support. Developers of systems looking for market opportunities are invited to contact IMS at their UK office, or by e-mail to Paul Cooper (pcooper at imsmaxims dot com). Accessing their web-site http://www.imsmaxims.com will give a feel for the scope of their work.

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