# MANG 6059 Healthcare Modelling Stella Computer Workshop Exercise 2014 The area of unplanned hospital

MANG 6059 Healthcare Modelling

Stella Computer Workshop Exercise 2014

The area of unplanned hospital admissions of older people due to ill-defined (R-coded) conditions is thought to be one where savings can be made and treatment improved for those concerned. Such patients are later readmitted to hospital as frequently (17% in 30 days) as patients with an “acute” diagnosis. However, the seriousness of their condition, as judged by hospital length of stay and death rate, has been shown to be considerably less than that of acute patients. If such patients can avoid admission to hospital, their quality of life at home can be maintained, and costs to the system can be reduced.

You are asked to build a Systems Dynamics (SD) model using Stella software to show the patient pathway for older patients using unscheduled services to receive healthcare. This model will be used to demonstrate the effect of improving admission avoidance capacity for R-coded patients over the next 20 years, a period for which the older population is predicted to grow substantially. Follow Figure 1 in developing your model and use the following data for Hampshire and the Isle of Wight:

- older population (aged 65 and over), initial value = 239,800
- population growth rate (estimated increase in population aged 65+ per year for next 20 years) = 4.2%
- population mortality rate = 19.5 per 1000
- rate of unscheduled events = 35%
- avoidance rate = 40%
- unscheduled care admission (rate) = 36%
- R-code (diagnosis) rate = 22%
- Acute (non R-code) death rate (in hospital) = 18%
- R-code death rate (in hospital) = 5%

Set the initial value of older population (aged 65 and over) to 239,800. For all other “stocks”, set the initial value to equal the incoming “flow”.

Use the following calculations for “flows”:

- aged 65 = older_population*population__growth_rate
- population deaths = older_population*population__mortality_rate
- contact unscheduled care = older_population*rate_of__unscheduled_events
- arrive hospital = unscheduled_care_contacts*(1-avoidance_rate)
- admit acute ward = hospital__assessment*unscheduled_care_admission
- diagnose acute = acute_wards*(1-Rcode_rate)
- acute hospital deaths = acute__diagnoses*acute__death_rate
- acute returns = acute__diagnoses*(1-acute__death_rate)
- avoid admittance = hospital__assessment*(1- unscheduled_care_admission)
- avoid conveyance = unscheduled_care_contacts*avoidance_rate
- diagnose Rcode = acute_wards*Rcode_rate
- Rcode returns = Rcode__diagnoses*(1-Rcode__death_rate)
- Rcode hospital deaths = Rcode__diagnoses*Rcode__death_rate

Set 1 year as the time increment and 20 years as the run length

Stella Coursework Question – worth 30% of the total mark for this module

1. Use your SD model to show the effects of varying the admission avoidance rate on numbers of patients assessed in hospital and on those entering acute wards, for the next 20 years. For this purpose, you are asked to investigate scenarios with avoidance rates of 40% and 45%. Present your results in a written document, including all relevant graphs and tables. Comment on your results and implications of a policy of increasing avoidance rate.

[10 marks]

Discuss the suitability of this SD model for representing the situation modelled and outline possible improvements. [5 marks]

2. There are several areas in the unscheduled care pathway that involve queueing situations. Choose one of these areas and explain in detail how you would design a hybrid simulation model to explore the effect of the ageing population on the service provided in this area of the pathway. Include queueing systems and resource utilisation in your description. It is NOT necessary to build and run such a hybrid model, but you MAY use suitable software to illustrate your design. Comment on the suitability and difficulties of using such modelling. [15 marks]

Your answers to parts 1 and 2 should be included in your complete Coursework report as Part B. The guideline is 500 words for Part B.

Upload your Stella file onto the MANG6059 Blackboard site. Make sure the filename includes your family name. So for example mine would be “Smith.STM”

The deadline is the same as for the remaining parts of the coursework.

Figure 1: Stella model of unscheduled care pathway for older patients

Honora Smith, March 2014

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