Chahed, S.

Chaussalet, T.J.

Dalton, S.

WestminsterResearch

    WestminsterResearch http://www.westminster.ac.uk/research/westminsterresearch   How to predict high dependency cot demand in upcoming days  Sarah Dalton1, 2 Thierry Chaussalet1  1 Faculty of Science and Technology, University of Westminster, UK 2 Colchester Hospital University NHS Foundation Trust, UK   This is an electronic version of a poster presented at the ORAHS 2012 Conference, 15-20 July 2012, Netherlands    The WestminsterResearch online digital archive at the University of Westminster aims to make the research output of the University available to a wider audience.  Copyright and Moral Rights remain with the authors and/or copyright owners. Users are permitted to download and/or print one copy for non-commercial private study or research.  Further distribution and any use of material from within this archive for profit-making enterprises or for commercial gain is strictly forbidden.      Whilst further distribution of specific materials from within this archive is forbidden, you may freely distribute the URL of WestminsterResearch: (http://westminsterresearch.wmin.ac.uk/).  In case of abuse or copyright appearing without permission e-mail repository@westminster.ac.uk How to predict High Dependency Cot Demand in upcoming days Sarah Dalton1,2 and Thierry Chaussalet1 1Heath and Social Care Modelling Group, University of Westminster, UK  2Colchester Hospital  University NHS Foundation Trust, UK 2. System        3. NHS Colchester  Data:  Occupancy and Length of Stay  -  4. Commentary 6. Discussion No doubt shortcomings occur to you.  Just one unit has been studied and where practice and policy change, predicting the future behaviour of the system becomes less reliable.  Nevertheless as stringencies affect our healthcare economies, harnessing existing data efficiently to predict activity, matching resources to demand, reducing hospital length of stay and improving family experience such that the newborn is treated promptly and discharged home safely yet sooner are likely to become  increasingly important.    1. Problem        High Dependency Care in the English local neonatal unit lies at a busy node in the system.  High care encompasses non-invasive respiratory support (CPAP), close observation for drug withdrawal symptoms (NAS) and intravenous feeding (TPN) for gut conditions like necrotising enterocolitis (NEC).  Those repatriated from intensive care centres must be accepted within 24 hours.  One trained neonatal nurse may care for two high dependency babies. We wish to predict demand for high dependency care.  We have knowledge of  range of cot occupancy, length of stay (LOS), age and diagnosis.  Having warning of peaks in demand would be quite useful for timing decisions on elective deliveries as well as repatriations from other hospitals.   0102030405060708 7 6 5 4 3 2 1 0 -1 -2 -3 -4 -5 -6 -7 -8Percentage of days per 365 day year Figure 3: Daily change in cot occupancy Intensive careHigh dependency careSpecial carey = -4E-05x3 + 0.0036x2 - 0.0564x + 0.3045 R² = 0.8756 y = -3E-05x3 + 0.0025x2 - 0.0343x + 0.2426 R² = 0.8736 00.20.40.60.811.20 5 10 15 20 25 30 35 40 45Probability of patient with this condition  leaving high dependency care in next 24 hours Elapsed length of stay (days) Figure 6: Hd – long staying groups CPAPNASPoly.(CPAP)Poly.(NAS)NAS CPAP 00.20.40.60.811.21 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20Probability of moving to high dependency care per patient in next 24 hours Elapsed Intensive care stay (days) Figure 7: IH transition 00.0050.010.0150.020.0250.030.0351 2 3 4 5 6 7 8 9 10 11 12 13 14 15Probability of moving to High Dependency care per patient in next 24 hours Elapsed special care stay (days) Figure 8: SH transition -500501001502002501 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52Number of episodes Length of Stay (Days) Figure 4: Length of Stay by  Intensive, High and Special care episode I LOSH LOSS LOS Table 1    LOS Exponential fit  (Days)     mean         K-S Chi-squared Intensive 2.69 0.3829 1499 High care 5.47 0.1897 1139 Special  care 6.50        poor fit: exploring other fits 5. Claim  Knowing census at each care level, age of patient and reason for which the care is given permit refinement of the prediction of demand for high dependency care.   This model may be simulated.      High dependency cot occupancy, both its distribution and error are shown in Figure (Fig) 2.  Idle time is 14%, whilst cot shortage (>4 required) is 9% of the time.  The daily change in cot occupancy at each care level appears to be normally distributed (Fig 3).  90% of daily changes in high care are by up to one cot.  The goodness of fit of length of stay for the exponential distribution is given (Table 1).  Curves for high and special care have long tails (Fig 4).  Knowing the high dependency treatment or diagnosis helps to group and so further refine the specific length of stay forecast (Fig 5).  Stays for CPAP for chronic lung disease and NAS represent the longest (Fig 6). Knowing the day of age of patients helps with an estimate of whether and when a move to high care occurs.  Longer stays at intensive care (whose treatment contributes to lung disease) guarantee this (Fig 7) whilst longer stays at special care (presumably indicating stability) gravitate against it (Fig 8).   Unfortunately space precludes  our presenting analysis of corrected gestational age.     Now supposing today’s cot count is 1 intensive care and 4 high dependency patients, ie. full, what is the chance of cot shortage in 2 day’s time?  This prediction is made using the equation and the binomial theorem (2009 data) and tested (2010 data) in Fig 9.      We would like to  acknowledge the data-cleaning of the ward clerk Ms Sexton and the words of Professors Millard, Gallivan (UK) and Harrison (US).  Ht = Ho + Ha + IH + SH – Hd  Where Ht and Ho are High Dependency census at time t and time zero; Ha is arrivals direct , IH and SH those arrivals there via intensive and special care  respectively and Hd is those leaving high dependency care, all within the time-spell t over which a forecast is required.  Those patients staying are admissions less discharges. A run of day(s) at one care level we term an episode. Data is drawn from the Standardised Electronic Neonatal Database (SEND) database 2009-11.  85 patients arrived direct to high care giving a mean daily arrival rate of 0.078.   Data is shown next, then explained in the commentary. Figure 2: Probability by number of High care cots occupied 00.050.10.150.20.250.30.351 2 3 4 5 6 7 8probability High dependency cots Figure 9: Comparison of Actual versus forecast for 48 hours of census I=1,H=4,S=? ActualForecast00.20.40.60.811.21 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39Proportion of patients with this condition staying on high dependency care Length of stay (days) Figure 5: Length of High Dependency care episode by condition oxygenobservationTPNTPN?NECCPAPNASCPAP+TPN

How to predict high dependency cot demand in upcoming days

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How to predict high dependency cot demand in upcoming days

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