Setting
The study was carried out as a research project by Mahidol University’s Faculty of Medicine Ramathibodi Hospital, collaborating with Thailand’s Ministry of Public Health. A series of group model building (GMB) sessions were conducted in our workshops held in Bangkok and Nonthaburi, Thailand, during 2017 and 2018.
Study design and participants
The study employed systems thinking and modeling methodology based on the system dynamics approach [8]. We adopted five major phases of the systems thinking and modeling methodology put forth by Maani & Cavana (2007), including 1) problem structuring, 2) casual loop modeling, 3) dynamic modeling, 4) scenario planning and modeling, 5) implementation and organizational learnings. The findings from both our models were presented to high-level executives in the Ministry of Public Health for eliciting comments and feedbacks.
One hundred ten stakeholders, who are policymakers, healthcare administrators, and practitioners from multi- sectors in Thai health systems, participated in a series of our workshop. They were facilitated to co-create a causal model that can explain the mismatches between demands and supplies of the health workforce in Thailand, which progressed from connecting relevant concepts to constructing qualitative causal loop diagrams (CLDs) and quantitative stock and flow diagrams (SFDs).
Group Model Building
Using scripts from system dynamics literature[10, 11], we facilitated the stakeholders by using a structured group model building[7, 12] to engage with relevant stakeholders. First, we discussed and agreed upon the expected outcomes in the next 20 years of the national planning of the health workforce, and drawn the reference mode of such outcomes. Second, we developed a CLD with stakeholders to gain a mutual understanding of what factors caused undesirable consequences, particularly mismatches of supply and demands for the health workforce in Thailand over the decades. Third, we continued working with stakeholders to turn our insights from the CLD into an SFD and simulated the selected health systems outcomes for the next two decades (2018–2037). Lastly, we analyze the consequences of such policy options by simulating them in our system dynamic modeling.
Our GMB sessions produced a CLD that represents a common undersetting among participating stakeholders. The critical variables discussed in the GMB sessions include the population structure of aging society, the unmet health needs of the population, utilization of healthcare services in hospital settings, utilization of healthcare services in non-hospital settings, size of the labor market of hospital care, size of the labor market in non-hospital care, the effectiveness of population health interventions and population’s health literacy. The revised and final CLD contains multiple interacting feedback loops that can be categorized into the four domains: 1) utilization of hospital care; 2) utilization of hospital care; 3) healthcare labor market for hospital care; 4) labor market for non-hospital care; 5) healthcare infrastructure; 6) self-care; and 7) drivers of population health.
As shown in Fig. 1, the balancing and reinforcing loops constitute the dynamic hypotheses of how health system components interact and result in a steady level of unmet health needs and rising demands for utilization of hospital care. Also, increasing demands for the workforce in hospital settings leads to decreasing supplies for the workforce in non-hospital settings, medical errors, rising healthcare expenditures, and an undesirable level of population health status over time.
Model Structure
The dynamic hypotheses, as depicted on CLD, formed a basis for our development of SFD and the structure of the system dynamics model. We constructed three modules to represent our insights from the CLD, which include factors and relationships that can lead to mismatches of supplies and demands for the health workforce in Thailand’s health systems, including 1) population module; 2) healthcare delivery module; 3) Education and labor market module.
1) Population module
Considering how sufficiency of the health workforce can impact the population health status, we considered each person can occupy a health state by the levels of severity of their illness. The status is reflected in Fig. 1 by the stocks: 1) healthy population, 2) population with simple illnesses, and 3) population with complex illnesses. Each health state corresponds to the nature of patient care teams and healthcare models that would be expected to inhibit progression into or regression from more severe health states, as represented by the inflows and outflows. Each person can also progress in terms of aging. Still, we categorized the population to only three groups by ages (0–14, 15–49, 50, and above). It also corresponds to the nature of patient care teams and healthcare models usually needed in that age group. The structure of the population is depicted in Fig. 2.
2) Healthcare delivery module
In the healthcare delivery module, we displayed the population health demands by health needs as professionally defined[13]. Hence, on the demand side of the healthcare market, each of the health states (healthy population: HP, population with simple illnesses: SP, and population with complex illnesses: CP) creates specific health demands for the health workforce and patient care teams in healthcare models on Fig. 2. The accessibility and utilization of each healthcare model on the population model are also described in Table 1.
Table 1
Effectiveness of utilization of each healthcare model on the population model
Models of Care
|
Users
|
Effects
|
1. Acute care (IPD)
|
• Population with complex illnesses of all ages
|
• Decrease mortality rate
• Increase regression from CP to SP
|
2. Ambulatory care
|
• Population with complex illnesses of all ages
|
• Increase regression from CP to SP
|
3. Emergency care
|
• All population groups
|
• Decrease mortality rate
|
4. Primary care
|
• A healthy population of all ages
• Population with simple illnesses of all ages
• Population with complex illnesses of all ages
|
• Decrease progression from HP to SP
• Decrease progression from SP to CP
• Increase the health-related quality of life (HRQoL) in CP users
|
5. Palliative care
|
• Population with complex illnesses of all ages
|
• No effects on health status
• Positive impacts on quality of life (CP)
|
6. Long-term care
|
• Elderly (population with simple illnesses, population with complex illnesses)
• Disabilities (young & adult)
• Excluding a healthy population of all ages
|
• No effects on health status
• Positive impacts on quality of life (CP)
|
7. Intermediate care
|
• Population with complex illnesses of all age
|
• Increase regression from CP to SP
|
8. Dental care
|
• All population groups
(oral health)
|
• Increase regression from SP to HP
• Increase regression from CP to SP
|
9. Population health
|
• A healthy population of all ages
|
• Decrease incidence via environmental & Behavioral changes
|
The supply side of the healthcare market is determined by the capacity of the health workforce in the patient care teams, which can be categorized into nine types of healthcare teams (or nine care models), including 1) ambulatory care, 2) emergency care, and 3) acute care in hospital settings, as well as 4) primary care, 5) intermediate care (a.k.a. subacute care or transitional care), 6) long-term care, 7) palliative care and end-of-life care (a.k.a. hospice care) and 8) oral healthcare usually organized in non-hospital settings. Besides, a workforce who do population health practices such as community-based disease prevention and health promotion are considered within 9) population health teams.
3) Healthcare education and labor market module
The structure of the health labor market and its relationship with health workforce education and training are shown in Fig. 3. The composition of health professions that forms a typical membership of each healthcare model is shown in Fig. 4. The supply side of the healthcare market is also the demand side of this healthcare labor market. Hence, the demands for hiring the health workforce in each profession are also determined by the capacity of the health workforce in the patient care teams, which can be categorized into nine types of healthcare teams or healthcare models.
Model Parameters
The parameters used in the model are shown in Table 2. These parameters were used in the initial steady state of our model, which represents a dynamic equilibrium and is numerically sensitive to model parameters.
Table 2
Model Parameters used in the simulation model
Model Parameter
|
Unit
|
Population Module
|
healthy population
|
person
|
population with simple illnesses
|
person
|
population with complex illnesses
|
person
|
birth ratio
|
per year
|
death ratio
|
per year
|
progression ratio
|
per year
|
curing effect of care
|
dimensionless
|
death ratio young complex
|
death ratio young simple
|
health-related quality of life (HRQoL)
|
dimensionless
|
treatment duration
|
year
|
incidence rate
|
per year
|
incidence ratio adjustment from access to healthcare
|
dimensionless
|
incidence ratio adjustment from health literacy
|
dimensionless
|
Healthcare Module
|
actual position
|
person
|
demand for healthcare
|
episode/person/year
|
cost per service
|
Baht/episode
|
HRH production cost per head
|
Baht/person
|
labor cost per head
|
Baht/person/per year
|
the targeted number of public health officers
|
person
|
healthcare team
|
team
|
practitioner per service
|
full-time equivalent (FTE)
|
practitioner leaving job ratio
|
per year
|
waiting time before leaving the profession
|
year
|
healthcare team expansion ratio
|
dimensionless
|
time allocation for administrative (not-patient care) work
|
team expansion ratio
|
Healthcare education and labor market module
|
the capacity of HRH training program
|
person/year
|
batch dropout ratio
|
person/batch
|
study period
|
year
|
practitioner (practicing HRH)
|
person
|
workforce pool (not practicing HRH)
|
person
|
To test for policies, we evaluate the policies on four outcomes that concern health workforce planning at the national level. From our GMB process, the sufficiency of the health workforce in Thailand can be seen by 1) population health status, 2) unmet health needs, and 3) health care expenditures.
The first outcome is the overall population health status represented by the percentage of a healthy population in the country, which indicates an adequate health workforce in the effective healthcare models for the demands of population health. Another population health outcome is the health-related quality of life (HRQoL) of the Thai population, which captures the degree and effectiveness of long-term care and palliative care necessary for aging, disabled, and terminal stage patients who cannot be converted to a healthy state. The second outcome is unmet health needs, which reflect limited access to necessary care for their health status. An inadequate health workforce does not only compromise population health status, but can also create long-waiting time, congested patients at healthcare facilities, and equitable access to necessary care. The third outcome is the healthcare expenditure, which is the primary concern of the government and partially address the cost-effectiveness of policy interventions from the societal perspective.
Policy Experimentation
We ran our system dynamics simulations under four scenarios in three main model parameters were changed (i.e., service gap, out-of-pocket cost, and the number of doctors) to conduct policy experimentation and illustrate the potential impacts of each policy in the next 20 years (2017–2038) under the following scenarios:
-
Scenario I Business-As-Usual (BAU): All key policy variables were kept constant. Under this scenario, all model inputs, including the effectiveness of the available health workforce actively working in all healthcare models in Thailand, was assumed to be equal and remain unchanged over the simulation time.
-
Scenario II Decentralizing primary care (Policy#1): The health workforce planning takes into the account of decentralization of primary care units from the Ministry of Public Health (MoPH) of the central government to the ownership of local governments, and also limiting new recruitments of physicians into the public facilities of from the year 2027 on.
-
Scenario III expansion of public financing and modernizing primary care (Policy#2): The health workforce planning takes into the account of expanding the public funding to care delivery by the private sector and also the modernization and digitalization of MoPH primary care units.
-
Scenario IV Major reforms of care delivery models (Policy#3): The health workforce planning considers the significant reforms of all care delivery models by MoPH healthcare facilities. This scenario mainly shifts the focus from only filling the health workforce in hospitals care to produce a significant proportion of the health workforce that is better qualified for working in non-hospital settings.
Model validation
The model is validated using unit consistency test, structural validity test, and behavioral replication test[14]. To test for unit consistency, we used the unit test function in the Stella Architect software. We focused on two dimensions. First, the unit of each variable must have the meaning and consistent with the description of that variable. The second dimension is that the unit must be consistent throughout the model. After testing for the unit consistency, the unit of all variables represents the real meaning of those variables. Besides, Stella software shows no unit error, which indicates that the unit is consistent throughout the model. Therefore, the model passes the unit consistency test.
For the structural validity test, we tested the model by showing the model to the group of experts who works in the healthcare industry, research relating to healthcare service, and the government agencies who manage healthcare security and healthcare services. The experts agree that the structure of the model reflects the actual situation. Therefore, the model passes the structural validity test.
Lastly, we did a behavioral replication test. The reference model was drawn using multiple data, including the number of Thai populations by ages and their reported health state from the National Statistics Office’ Health and Welfare Survey 2007, 2009, 2011, 2013, and 2015. Also, we have the data of oral health state from the oral health survey 2000, 2007, and 2013. The number of the health workforce in Thailand by each type of care model was obtained by the research’s primary survey doing December 2017 and January 2018.
The simulation result in the model can trace the actual number of Thai populations receiving care. Therefore, the model passes the behavior replication test.