Health Workforce Supply Forecasting Model Groups/sunz... · •Regional workforce planning (by...
Transcript of Health Workforce Supply Forecasting Model Groups/sunz... · •Regional workforce planning (by...
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)