Background
Methodology
Variables | Assumptions | Data source | ||
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Supply side | ||||
Inflow | Education | • No. of State-funded quotas 2012–2013 | Constant at the 2012–2013 level (‘as is’ scenario) | MIUR |
Stocks | Regional public NHS | • Headcounts of physicians by sex, age and specialization declared | Appropriate at 2011 levels for all stocks | Regional databases |
Private (no. 1,021) | Ad hoc survey | |||
Conventioned (GPs, district paediatrics) | According to recommended population ratios | Normative | ||
Outflows | Regional public NHS | • Sex-, age- and specialization-specific exit rates | Leaving the regional NHS due to retirement, shift to the private sector and move to other regions before retirement. Observed exit rates in 2001–2011 by cause apply each year | Regional databases of 2001–2011 observations |
Private sector and self-employed personnel | • Age-specific exit rates | Females leave at 67 and males at 70 | Normative | |
Demand side | ||||
Population | Demographic projections | • Sex, 5-year band population projections to 2030 | Central scenario | Regional statistics bureau |
Service utilization | Outpatient activities (ASA) plus hospital discharges (SDO) by specialization provided to patients between 2002 and 2011 | • Patients’ sex, 5-year bands, consumption rates by specialization | 2002–2011 outpatient and inpatient utilization rate trend line extrapolation and projection to 2021. Expected regional age/sex cohorts will consume more or less of each specialization service and a different mix of outpatient visits and hospital discharges | Regional databases (ASA, SDO) |
Hospital beds | Public hospital beds by specialization at 2011 | • No. of public hospital beds and optimal staffing standards per specialization | Physician-to-hospital bed standards define the future requirement of specialists | National guidelines |
Supply side assumptions and data
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Public sector (Regional NHS)
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Private hospital sector (AIOP)
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Self-employed HRH contracted by the Regional NHS: ambulatory specialists, general practitioners, district paediatricians.
Demand side drivers and assumptions
Population demographic projections to 2030: Demographic change is undoubtedly relevant in predicting future service utilization, although it is not the only one. Migration flows, fertility rates and ageing will affect future service demand and, consequently, staffing requirements. In the demand model, we included the regional population projections, sex-age specific to 2030, developed by the regional statistics bureau in 2011 [30]. Physician-to-target-population ratios are assumed to be appropriate at baseline (2011) and do not vary over the projection horizon. Some specializations, such as Gynaecology and Obstetrics, Geriatrics, Infant Neuropsychiatry and Paediatrics, are clearly bound to specific population segments.Ambulatory visits and hospital discharges by discipline (service utilization driver): Given the lack of well-defined physician-to-epidemiological-condition ratios, healthcare pathways’ staffing requirements can be defined as the ratio between the volume of activities and the type of specialist involved. We retrieved information on hospital discharges and outpatient services (diagnostic appointments, treatment and rehabilitation) provided to the population between 2002 and 2011 and used them to proxy a physician requirement model [31]. Service utilization data for public and private inpatient and outpatient appointments are recorded by two regional databases: hospital discharge records (SDO) and ambulatory specialist consultations (ASA). Most ASA and SDO records can be attributed to a specific discipline, providing a valuable indicator of specialization-specific resource utilization by the population. As a starting point, we analysed how service utilization by the resident population (5-year age/sex-specific bands) has changed for inpatient and outpatient activities in the last decade. In the chosen period, a general reduction of public hospitalization occurred; a drop of inpatient services was recorded for males and females in the 70–74 age band (-22.5% and -21.9%, respectively), while a reverse trend occurred in the 0–5 age band (+69.4% and +111.1%, respectively) for surgical specialties. A decrease in inpatient activities was compensated by an increase in outpatient activities, particularly significant for the Medical area. By observing a decade of in/outpatient service utilization, we extrapolated specific trend lines for each ‘discipline-sex-age’ combination up to 2030. These trend lines are projected until 2021 and then a binding factor is considered until 2030. In this way, the model ought to account for increasing de-hospitalization of some procedures and for latent shifts in productivity, due to technological changes, which we were not able to define systematically for all 43 profiles. In the self-employment and private sectors, doctors are likely to attend a clinic only a few hours a week and to carry out consultations in their surgeries for which no registry exists. Therefore, medical HRH working outside the public system are linked only to population projections in our model. Combining the population projections and the service utilization trend lines for 2011–2030, we obtain the expected volume of in/outpatient activities, which hence return the expected number of specialists required to provide them.Hospital beds: The number of beds assigned to a given specialization is a ‘chokepoint’, a structural constraint that is commonly used to estimate the optimal staffing levels. This indicator can be considered appropriate when most of the activities involving a specific specialization are related to hospitalization; this is particularly true only for Medical specializations where peri-operative activities are a key aspect and for some Surgical specializations. However, we included this driver because the public spending review of the Italian NHS explicitly stresses the reduction and conversion of public beds. Hospital beds assigned to each discipline can therefore be considered as a constraint to which the future number of public medical specialists can be bound. The bed-driven scenario exploits the staffing standards developed by national experts to be applied to Regions that run a healthcare deficit [32]. In these guidelines, physician-to-bed ratios vary between 0.24 head per bed for low-complexity specializations to 1 for intensive care.
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Scenario 1: population-driven demand
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Scenario 2: inpatient- and outpatient-driven demand
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Scenario 3: hospital core (bed standards) and increasing outpatient consultation demand.
Results
Occupational and training gaps: three demand scenarios vs. ‘as is’ national residency training
Area | Selected specialization | Stock at 2011
a | Demand % increase at 2030 | Training gaps in 2030 w.r.t. ‘as is’ national grants | ||||
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Scenario 1 | Scenario 2 | Scenario 3 | Scenario 1 | Scenario 2 | Scenario 3 | |||
Surgical | General surgery | 580 | 12.0 | 12.6 | 8.8 | 49 | 46 | 68 |
Gynaecology and obstetrics (Figure 1a) | 540 | 12.0 | 8.5 | 6.1 | 19 | 38 | 51 | |
Neurosurgery | 93 | 12.0 | 18.7 | 53.1 | -34 | -40 | -45 | |
Ophthalmology | 322 | 12.0 | 18.3 | 17.4 | -76 | -96 | -93 | |
Orthopaedic and trauma | 623 | 12.0 | 15.4 | 7.8 | 81 | 60 | 107 | |
Otorinolaringoiatry | 201 | 12.0 | 19.3 | 12.3 | 1 | -14 | 0 | |
Urology | 191 | 12.0 | 21.5 | 7.1 | 3 | -15 | 13 | |
Cardiac surgery | 44 | 12.0 | 13.8 | 6.9 | 53 | 53 | 56 | |
Medical | Geriatrics | 237 | 20.3 | 31.2 | 16.4 | 46 | 20 | 55 |
Internal medicine (Figure 1b) | 997 | 12.0 | 15.4 | 9.9 | -344 | -378 | -323 | |
Emergency medicine | 595 | 12.0 | 10.6 | 10.0 | -260 | -252 | -248 | |
Infant neuropsychiatry | 171 | 17.5 | 36.7 | 37.9 | 2 | -31 | -33 | |
Psychiatry | 590 | 11.1 | 4.5 | 3.6 | -63 | -25 | -19 | |
Gastroenterology | 159 | 12.0 | 34.3 | 25.9 | 31 | -5 | 9 | |
Cardiology | 553 | 12.0 | 40.0 | 15.6 | 101 | -54 | 81 | |
Respiratory diseases | 163 | 12.0 | 27.4 | 17.9 | 85 | 60 | 75 | |
Nephrology (Figure 1c) | 145 | 12.0 | 45.9 | 28.1 | 129 | 80 | 106 | |
Rheumatology | 36 | 12.0 | 34.8 | 34.3 | 64 | 56 | 56 | |
Services | Anatomopathology | 130 | 12.0 | 15.4 | 0.0 | 58 | 54 | 74 |
Radio-diagnostics | 729 | 12.0 | 32.4 | 32.3 | 148 | -1 | 1 | |
Radiotherapy | 68 | 12.0 | 41.3 | 40.6 | 115 | 95 | 95 | |
Anaesthesiology (Figure 1d) | 1,009 | 12.0 | 35.2 | 22.2 | 171 | -63 | 69 | |
Physical and rehabilitation medicine | 262 | 12.0 | 2.4 | 1.7 | 24 | 49 | 51 |
An optimal allocation model for regional funded residency grants
Area | Number of grants (2012–2024) | ||
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Scenario 1 | Scenario 2 | Scenario 3 | |
Surgical | 73 | 71 | 98 |
Medical | 239 | 277 | 237 |
Services | 36 | 0 | 13 |
av. | 348 | 348 | 348 |
Area | Grant requirement (2012–2024) | ‘as is’ scenario | ∆% w.r.t. ‘as-is’ scenario | ||||
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Scenario 1 | Scenario 2 | Scenario 3 | Scenario 1 | Scenario 2 | Scenario 2 | ||
Surgical | 1,425 | 1,519 | 1,364 | 1,617 | -11.9 | -6.1 | -15.6 |
Medical | 3,340 | 3,814 | 3,398 | 2,743 | 21.8 | 39.0 | 23.9 |
Services | 1,782 | 1,567 | 1,871 | 1,998 | -10.8 | -21.6 | -6.4 |
Tot. | 6,547 | 6,900 | 6,633 | 6,358 | 3.0 | 8.5 | 4.3 |
∆% w.r.t. 6,706a allocations | -2.4 | 2.9 | -1.1 |
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