Forecasting demand based on demographic trends is subject to the constant rate fallacy, i.e. trends in morbidity and mortality are changing over time. Understanding these longer term trends is therefore essential to correct planning and commissioning.


Research by HCAF has demonstrated that it is the trend in deaths which is far more important than the ageing population. This relationship arises from the fact that use of primary and secondary care is concentrated in the last six years of life - irrespective of the age at death. In particular hospital beds are used mostly in the last year of life.




   Forecasting & Understanding Demand





Model Description


Outpatient to Inpatient


Admission Rates


Modelling Demand


Admission Rates II


Benchmark Outpatient Attendance


Benchmark Day Case Admissions


Benchmark Elective Overnight Admissions


Benchmark zero day stay emergency admissions


Benchmark overnight emergency admissions


Benchmarking Pitfalls


Acute Intervention Rates


Excess Costs


Intervention Rate Summary












While the ability to forecast demand is important it is the volatility associated with demand that determines the financial risk.


These issues are addressed in the 'Financial Risk' webpage.


Indeed such volatility is a key basis for determining the uncertainty associated with any demand forecast.


















































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Healthcare Analysis & Forecasting

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Understanding demand is central for the planning of healthcare services. Alas future healthcare demand is far more complex than simple population demographics and a deeper understanding of a range of factors is required. For example, the need for hospital beds is strongly influenced by the trends in deaths rather than demography per se (see 'Hospital Beds' folder). This is due to the fact that the bulk of a persons life time acute bed usage occurs in the last year of life.


No forecast stands alone and upper and lower confidence intervals should always be a part of any forecasting exercise.


The evaluation of demand in the NHS has been seriously compromised by counting and coding issues. Studies by HCAF have shown that different sites of the same Trust can count and code in widely different ways. This is especially the case for any zero or same day stay admission (elective or emergency).


The evaluation of what may appear to be excess demand requires adjustment for age, deprivation, ethnicity, students and level of private insurance. HCAF have developed advanced methods to apply these factors and to estimate levels of private insurance for blocks of 300 head of population. The same tools can be used to inform social marketing and to shed light on why intervention rates may differ between GP practices.


The 'Emergency Admissions' folder contains details of studies relating to trends and cycles in emergency admissions. The concepts contained in these papers are equally applicable to understanding trends in elective admissions and outpatient attendances. Indeed, experience shows that such long-term cycles also apply to emergency department and outpatient attendances.


Forecasting Demand Series


British Journal of Healthcare Management (BJHCM)


Jones R (1996) Estimation of annual activity and the use of activity multipliers.

  Health Informatics 2(2): 71-77.

Jones R (2010) Forecasting year-end activity.

  BJHCM 16(5): 248-249  Read Me

Jones R (2010) Forecasting demand to support commissioning.

  BJHCM 16(8): 392-393  Read Me

Jones R (2010) Forecasting emergency department attendances.

  BJHCM 16(10): 495-496  Read Me

Jones R (2011) Death and future healthcare expenditure.

  BJHCM 17(9): 436-437  Read Me

Jones R (2012) Ambulance call-outs and 'disruptive technology'

   BJHCM 18(2): 112-113.  Read Me

Jones R (2012) Forecasting births and midwifery demand.

  Midwifery Magazine 15 (issue 2)  Read Me

Jones R (2012) Are there cycles in outpatient costs?

  BJHCM 18(5): 276-277.  Read Me

Jones R (2012) Age-related changes in A&E attendance.

   BJHCM 18(9): 508-509. Read Me

Jones R (2012) Trends in GP referral: collective jump or infectious push?

   BJHCM 18(9): 488-497. Read Me

Jones R (2012) Increase in GP referral to dermatology: which conditions?

  BJHCM 18(11): 594-596. Read Me

Jones R (2012) Trends in outpatient follow-up rates in England. 

   BJHCM 18(12): 647-655.  Read Me

Jones R (2013) Trends in unscheduled care.

   BJHCM 19(6): 301-302, 304.  Read Me

Jones R (2013) A&E attendance: the tip of a wider trend.

   BJHCM 19(9): 458-459.  Read Me

Jones R (2013) Is the demographic shift the real problem?

   BJHCM 19(10): 509-511.  Read Me

Jones R (2013) Trends in elderly diagnoses: links with multi-morbidity.

    BJHCM 19(11): 553-558.  Read Me

Jones R (2013) The funding dilemma: a lagged cycle in cancer costs.

    BJHCM 19(12): 606-607.  Read Me

Jones R (2014) Forecasting conundrum: a disease time cascade.

   BJHCM 20(2): 90-91.  Read Me

Jones R (2014) What is happening in A&E?

   Journal Paramedic Practice 6(2): 60-62  Read Me

Jones R (2014) Unexpected changes in outpatient first attendance.

   BJHCM 20(3): 142-143.  Read Me

Jones R (2014) Trends in admission for allergy. BJHCM 20(7): 350-351.  Read Me

Jones R (2014) Trends in births and deaths to 2037. BJHCM 20(8): 402-3 Read

Jones R (2014) Trends in emergency admissions per death.

    BJHCM 20(9): 446-447.  Read Me

Jones R (2014) Complex trends in admissions per death. 

    BJHCM 20(11): 541-542.  Read Me

Jones R (2014) Deaths and medical admissions rise in 2012 in Northern Ireland.

    BJHCM 20(11): 543  Read Me

Jones R (2015) Forecasting medical emergency admissions.

    BJHCM 21(2): 98-99.  Read Me

Jones R (2015) Why is it so difficult to accurately forecast medical admissions?

    BJHCM 21(3): in press