Health Workforce SupplyForecasting Model

Emmanuel JoManager, Analytics

Health Workforce New ZealandMinistry of Health

About Health Workforce New Zealand

• Health Workforce New Zealand leads and supports the training and development of the health and disability workforce.

• HWNZ has contracts for the provision of training with district health boards (DHBs), tertiary education providers and other health provider organisations. These are the specification requirements and eligibility criteria for HWNZ-funded programmes in:

Medical , Nursing, Dental, Allied Health, Mental Health, Disability Support Services, Midwifery, etc

Example of age distribution of a specialist workforce

Source: 2014 RNZCGP Workforce Survey Report

Missing

Identifying patterns

age, scope, ethnicity, geographic specific patterns (exit, entry working hours etc)

Big Data: extremely large data sets that may be analysed computationally to reveal patterns, trends, and associations, especially relating to human behaviour and interactions

Source: http://grigory.us/big-data-class.html

Forecasting

https://positivepsychologyprogram.com/affective-forecasting/

TWOStreams

For each scope and any geographical sub group

New / Re-Entering

Practitioners

Age

20 * * * * *Age

31

Age

32

Age

33

Age

34

Age

35Age

36

Age

37

Age

38Age

39 * * * * *Age

75+

ExitExit

ExitExit

Age

20 * * * * *Age

31

Age

32

Age

33

Age

34

Age

35Age

36

Age

37

Age

38Age

39 * * * * *Age

75+

New entry / Re-entry

Exit

Exit

Exit Exit

Re-Entry

Existing Practitioners

Conceptual diagram of medical workforce supply

Tools used ?

Formulas used in the model

For the pre-existing specialists at k year(s) can be calculated by

for i = 20 − 74, Oi,j,k = Oi−1,j,k−1 × 1 − Li−1,j,k−1

for i = 75 + , Oi,j,k = Oi−1,j,k−1 × 1 − Li−1,j,k−1 + Oi,j,k−1 × 1 − Li,j,k−1

Let i = age 20,21,22,… , 75 + Let j = Anaesthesia, Cardiothoracic Surgery, Clinical Genetics,… . . , Vascular surgery Let k = 0 base year , 1,2,3,4,5,6,7,8,9,10 Let Oi,j,k = Number of pre-existing specialists for i age, j specialty, after k year s

Let Ei,j,k = Number of entered specialists remain after k year(s)

Let 1 Full Time Equivalent (FTE) = 40 Hours per week Let Ri,j,k = Ratio of FTE per Head Count for specialists for i age, and j specialty, at k year

Let Li,j,k = Exit rate for specialists for i age, and j specialty at k year

Let Gj,k = Entry volume for j specialty at k year

Let Ai,j,k = Entry age distribution for i age, and j specialty at k year

Let Hi,j,k = Expected number of specialists in Head Count (HC) for i, and j after k year(s)

Let Fi,j,k = Expected number of specialists in Full Time Equivalent (FTE) for i, and j after k year(s)

Expected number of specialists in Head Count (HC) for i, and j after k year(s) can be calculated by

Hi,j,k = Oi,j,k + Ei,j,k

Expected number of specialists in Full Time Equivalent (FTE) for i, and j after k year(s) can be calculated by

Fi,j,k = Hi,j,k × Ri,j,k

Total number of specialists for j specialty after k year(s) in HC can be calculated by

Hi,j,k

75+

i=20

Total number of specialists for j specialty after k year(s) in FTE can be calculated by

Fi,j,k

75+

i=20

Number of new and re-entered specialists remain after k year(s) can be calculated by

for i = 20 − 74, Ei,j,k = Gj,k−1 × Ai,j,k−1 + Ei−1,j,k−1 × 1 − Li−1,j,k−1

for i = 75+, Ei,j,k = Gj,k−1 × Ai,j,k−1 + Ei−1,j,k−1 × 1 − Li−1,j,k−1 +

+ Ei,j,k−1 × 1 − Li,j,k−1

Reflecting future workforce characteristics

• Variable exit rates for each age, scope, and year have been used to reflect generational characteristics of the workforce

• Variable Full Time Equivalent(FTE) per Head Count (HC) ratio is used for each age, scope, and future year have been used to reflect different patterns of working over different parts of the lifespan, generational characteristics.

• New/re-entering practitioners includes new practitioners from training, immigrations to New Zealand, and re-entering practitioners after having a break.

General Practitioners in NZ

General Practitioners in NZ

General Practitioners in NZ

General Practitioners in NZ

General Practitioners in NZ

General Practitioners in NZ

General Practitioners in NZ

General Practitioners in NZ

General Practitioners in NZ

General Practitioners in NZ

General Practitioners in NZ

General Practitioners per 100,000 60+ age population(235 entries per year)

General Practitioners per 100,000 60+ age population (300 new entries per year)

Conclusions

The model has been, and is very useful for:

• Providing evidence for prioritization of training funding for most regulated workforces(Medical specialists, Nurses, Midwives, Psychologists, etc)

• Exploring consequences of changing workforce policies (example: delaying retirement age, training numbers, FTE/HC ratios in future years, how age distribution of a workforce will change)

• Planning training volumes in collaboration with Medical Colleges

• Regional workforce planning (by building regional model using the same concept)

• Identifying future health workforce capacity to inform government health initiatives(example: National Bowel Screening Programmes)