issues in measuring and prediting imbalances in … · presentation will focus on the data used for...

ISSUES IN MEASURING AND PREDITING IMBALANCES IN

WORKFORCE NUMBERS AND DISTRIBUTION

MABEL

RESEARCH FORUM

6 May 2016 - Melbourne

Workforce Modelling

9 May, 2016 AFHW - Anaesthesia 1

• HWD carries out supply and demand modelling to predict imbalances in workforce numbers

• Modelling is dependant on different sources of data some of which are self reported.

• A key issue in measuring and predicting imbalances in workforce numbers and distribution is ensuring that data is accurate, reliable, valid and fit for purpose

• A lot of time spent to achieve this and getting the data right is a key issue in workforce modelling

• Presentation will focus on the data used for modelling of the Specialist Medical workforce

Component of Specialist Medical workforce projections

• Supply• NHWDS (stock) - is a combination of the Medical workforce

survey and AHPRA registration data extract. The work producing this dataset was carried out by AIHW but is now produced internally.

• Training (inflow) - Medical college data & Medical Training and Review Panel (MTRP) & IMGS (Migration)

• Demand - (Hospital and Medicare data)• Number of Services/Separations

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• 1. Multiple source of data and supply data quality • 2. Self-reporting of key variables from Medical workforce survey• 3. Definition of Demand for individual Specialist workforces • 4. Geo coding of addresses

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Key issues with Specialist Medical workforce modelling

• Supply: (NHWDS)• Demand: (Hospital and Medicare)• Training: Medical college data• Immigration: Medical college data (IMGS new fellows)

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Multiple sources of data

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NHWDS

WORKFORCE SURVEY

AHPRA EXTRACT

HWD INTERNAL PROCESS

Supply - Internal processes for NHWDS

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MEDICAL SUPPLY

NHWDS

MEDICAL COLLEGE DATA

HWD INTERNAL PROCESS No. 2

Specialist Medical Supply

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MEDICAL DEMAND

ABS POPULATION

MEDICARE

HOSPITAL

Specialist Medical Demand

• A large amount of time is spent processing data although capacity to reduce this over head is being continually developed

• It is important to get the data right• Our team has invested a lot of time and effort getting the data right

and but;• There are still many issues and areas where it can be improved

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Supply data quality issues

• The construction of the NHWDS comprises merging of registration data , demographic data and endorsement data and survey data together so we can have a big picture of each profession. For example, their registration status and practice details.

• There is a lot of invalid input in the survey data. For example, the response of “the country of initial qualification” is entered as “Sydney”.

• A web application was built for use after 1 July 2016 which can correct invalid values by mapping them to valid values.

• Some examples are easily corrected, while others are more difficult to correct, resulting in a loss of useful data. Such as the country to initial qualification, suburb or postcode and the type of qualification obtained.

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Question_ID Invalid Value Valid Value

BIRTH_COUNTRY Saint Vincent and the Grenadines ST VINCENT AND THE GRENADINES

BIRTH_COUNTRY REPUBLIC OF Korea KOREA, REPUBLIC OF (SOUTH)

BIRTH_COUNTRY Eire IRELAND

BIRTH_COUNTRY Palestinian Territory, Occupied ISRAEL

BIRTH_COUNTRY SYRIAN ARAB REPUBLIC SYRIA

BIRTH_COUNTRY IRAN, ISLAMIC REPUBLIC OF IRAN


• Health professionals may incorrectly enter data an example of this is their hours worked.

• For example, the hours total is not the sum of clinical and non-clinical hours.

• To overcome this problem, business rules were developed to clean the data by balancing the hours total, clinical and non-clinical hours.

• In our modelling we look at the number of hours worked in detail so it becomes an important part of data cleaning.

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TOKEN_NUMBER HRS_NONCLINICAL HRS_CLINICAL HRS_TOTAL

Person 1

16 8 90Person 2 8 36 .

Person 3

. 40 .

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• Trainees are self reported in the NHWDS• This dataset contains a rich source of information such as hours

worked, country of birth etc. • This data set is used to generate profiles and describe the workforce • Medical college data is used for training pipelining• However, the college data is usually not as rich as the NHWDS is

terms of demographic information• MTRP historically been used for training pipelining• MTRP and Medical college data is used to develop a training

pipeline to predict the number of new fellows that enter each medical specialty

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Specialist medical workforce training data

Training pipeline/churn

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Movements Per cent Comments

New intake 199 or 22% − Static: is an average of stage 1 trainees since 2006, or− Dynamic where its 22% of total basic trainees per year

Stage 1 to Stage 1 24%


Stage 2 to Stage 2 76% (High % as stage 2 is 2 years)



Stage 3 to New Fellows 49%

Retention rate 95% In future should have different rate for each stage

Through rate 77% If everyone FT and complete in 60 months

49% Actual (incorporates PT, waiting for rotation etc.)

IMGS trainees 203 Static inflow

Partially comparable 15% Of IMGS trainees

Substantially comparable 20% Of IMGS trainees

IMGS new fellow 27% % of IMGS trainees in previous year

Training pipeline projections

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20

40

60

80

100

120

140

160

180

200

2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030

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Self-reporting of key variables

• Self-reported nature of the data has an impact on quality and interpretation.

• For example, if the health professional indicates that they are working in two specialties but fails to report their hours worked in each.

• Professionals may also fail to answer the survey questions regarding their main area of work, we look at the AHPRA registration data and if they have two or fewer registered specialties then their main area of work in imputed according to our business rules.

• There are a range of other business rules which are applied to the NHWDS data before we use it in our modelling

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Total

Not RegisteredRegistered

Employed

Employed in Specialty

Clinicians Administration Teacher/Educator Researcher Other

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Specialist Medical workforce survey classification (Medical Supply internal process)

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Total

Not RegisteredRegistered

Employed

Employed in Specialty

Clinicians Administration Teacher/Educator Researcher Other

• Is the data we used valid and fit for purpose ?• Does the data we use for demand measure what we say it does or

does it include work from other health professionals aside from the group being modelled?

• Does an increase in services correspond to a commensurate increase in required working hours ? Or will improvements in productivity and technology influenced this ?

• Demand can be calculated using Medicare and Hospital data• Trends observed in this data are prone to shocks from new policy

proposals

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Definition of demand for specialist medical workforces

• Modelling of Medical specialties requires use of complex data• There are currently 86 sub-specialties and 23 specialties• We do profiles for all medical specialties as well as trainees and non-

specialists intending to undertake training• Smaller specialties are grouped• Internally we classify medical practitioners for all specialties, trainees

and intentions

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Complexity of the workforce

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Complexity of the workforce

• Geo-coding of addresses is essential for measuring distribution of Medical Specialists

• Different business rules can be applied to a situation where data is omitted or where the input is not clear, for example Box Hill can be in either NSW or Victoria

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Distribution of the Medical workforce

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Distribution of the Medical workforce

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Psychiatry trainees

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