Combining Qualitative and Quantitative Analyses to Generate Robust Workforce Intelligence Erin P. Fraher, PhD, MPP and Emmanuel Jo with Andy Knapton, MSc and Mark Holmes, PhD Health Workforce New Zealand Workshop Wellington, New Zealand 1 May 2018 This work is supported with funding from The Physicians Foundation, The University of North Carolina at Chapel Hill and Health Workforce New Zealand.
The Shortage Narrative Prevails This work is supported with funding from The Physicians Foundation, The University of North Carolina at Chapel Hill and Health Workforce New Zealand.
A brief history of workforce projection models Most workforce models: ▪ aim to answer numeric question of too many or too few health professionals ▪ focus on specific professions, not patients’ needs for health care services ▪ model supply of health care services based on professional silos, not teams ▪ make assumptions about future demand and supply based on status quo ▪ not as dynamic as needed to keep pace with health system changes ▪ use quantitative approaches that do not leverage qualitative intelligence This work is supported with funding from The Physicians Foundation, The University of North Carolina at Chapel Hill and Health Workforce New Zealand.
We tried to address these issues by developing a model that uses a “Plasticity Matrix” to map demand to supply Starting question : What health services will patients need? Not how many doctors will we need! Next question : Which types of specialties and professions provide what types of health services in different settings and geographies? This work is supported with funding from The Physicians Foundation, The University of North Carolina at Chapel Hill and Health Workforce New Zealand.
Key plasticity concepts ▪ Scope of services provided by different specialties and professions overlap and are dynamic ▪ Two types of plasticity: – Between plasticity : describes differences in scope of services between specialties and professions – Within plasticity : describes differences in scope of services within same profession or specialty This work is supported with funding from The Physicians Foundation, The University of North Carolina at Chapel Hill and Health Workforce New Zealand.
An example of provider plasticity in all settings in the United States Number of visits for select specialties and types of health care services Circulatory Digestive Endocrine/Immunity Genitourinary Neoplasms Respiratory Cardiology 38,000,000 85,114 1,160,073 248,770 176,393 598,299 Dermatology 120,110 71,224 97,185 17,165 14,004,117 78,427 17,975,183 3,458,440 9,920,149 1,788,739 714,021 6,199,275 Internal Medicine Endocrinology 591,622 154,877 12,114,458 289,956 783,927 74,375 Family Medicine 56,001,735 9,160,169 30,323,947 9,697,999 3,365,688 40,067,469 Gastroenterology 458,052 11,700,000 323,485 319,911 1,056,523 143,921 Other Specialties 19,124,199 19,061,658 16,670,324 55,028,338 42,356,094 53,111,491 Total Visits 132,270,901 43,691,482 70,609,621 67,390,878 62,456,763 100,273,257 This work is supported with funding from The Physicians Foundation, The University of North Carolina at Chapel Hill and Health Workforce New Zealand.
A US example of provider plasticity in all settings Percent of visits for select specialties and types of health care services Circulatory Digestive Endocrine/Immunity Genitourinary Neoplasms Respiratory 29% Cardiology 0% 2% 0% 0% 1% Dermatology 0% 0% 0% 0% 22% 0% How are circulatory visits 14% 8% 14% 3% 1% 6% Internal Medicine Endocrinology 0% 0% 17% 0% 1% 0% currently distributed across specialties? 42% Family Medicine 21% 43% 14% 5% 40% Gastroenterology 0% 27% 0% 0% 2% 0% Other Specialties 14% 44% 24% 82% 68% 53% Total Visits 100% 100% 100% 100% 100% 100% This work is supported with funding from The Physicians Foundation, The University of North Carolina at Chapel Hill and Health Workforce New Zealand.
Some factors affecting plasticity of workforce ▪ Density/availability of ▪ Hospital executives and HR other providers in area decisions about deployment with similar/competing ▪ Professional’s education and scopes of practice training (initial and ongoing) ▪ Funding model and liability ▪ Personal preferences ▪ Model of care and ▪ Patient population referral patterns ▪ Local geography ▪ Regulation This work is supported with funding from The Physicians Foundation, The University of North Carolina at Chapel Hill and Health Workforce New Zealand.
New Zealand’s primary care (GP) patient-centered plasticity matrix GP consultation rate per year (ijkl) i=age group, j=gender, k=ethnicity, l=DHB(Geographic location) Gender DHBN DHB_name agegroup_SU Asian/Indian European/Other Mäori Pacific people Female 011 Northland 00-04 2.2 3.4 2.5 2.4 Female 011 Northland 05-14 1.3 1.7 1.3 1.0 Female 011 Northland 15-24 0.8 2.4 1.6 1.2 Female 011 Northland 25-44 1.8 2.6 2.4 1.7 Female 011 Northland 45-64 1.8 3.2 3.7 2.5 Female 011 Northland 65+ 2.5 5.5 5.6 3.3 Female 123 South Canterbury 00-04 2.3 3.4 1.9 3.1 Female 123 South Canterbury 05-14 1.2 1.5 1.0 1.8 Female 123 South Canterbury 15-24 1.0 3.3 2.0 2.6 Female 123 South Canterbury 25-44 2.0 2.9 2.1 3.8 Female 123 South Canterbury 45-64 1.6 3.6 2.9 5.1 Female 123 South Canterbury 65+ 1.6 5.6 3.3 4.7 Female 082 Whanganui 00-04 2.7 3.4 2.4 3.2 Female 082 Whanganui 05-14 1.3 1.9 1.2 0.9 Female 082 Whanganui 15-24 0.6 3.1 1.9 1.1 Female 082 Whanganui 25-44 1.9 3.6 2.6 1.7 Female 082 Whanganui 45-64 2.8 4.7 4.6 3.3 Female 082 Whanganui 65+ 3.3 7.9 7.1 4.5 This work is supported with funding from The Physicians Foundation, The University of North Carolina at Chapel Hill and Health Workforce New Zealand.
What if we introduce different policies? GP consultation rate per year (ijkl) i=age group, j=gender, k=ethnicity, l=DHB(Geographic location) Gender DHBN DHB_name agegroup_SU Asian/Indian European/Other Mäori Pacific people Female 011 Northland 00-04 2.2 3.4 2.5 2.4 Female 011 Northland 05-14 1.3 1.7 1.3 1.0 Female 011 Northland 15-24 0.8 2.4 1.6 1.2 Female 011 Northland 25-44 1.8 2.6 2.4 1.7 Female 011 Northland 45-64 1.8 3.2 3.7 2.5 Female 011 Northland 65+ 2.5 5.5 5.6 3.3 Female 123 South Canterbury 00-04 2.3 3.4 1.9 3.1 Female 123 South Canterbury 05-14 1.2 1.5 1.0 1.8 Female 123 South Canterbury 15-24 1.0 3.3 what if 2.0-->3 2.6 Female 123 South Canterbury 25-44 2.0 2.9 2.1 3.8 Female 123 South Canterbury 45-64 1.6 3.6 2.9 5.1 Female 123 South Canterbury 65+ 1.6 5.6 what if 3.3-->5 4.7 Female 082 Whanganui 00-04 2.7 3.4 2.4 3.2 Female 082 Whanganui 05-14 1.3 1.9 1.2 0.9 Female 082 Whanganui 15-24 0.6 3.1 1.9 1.1 Female 082 Whanganui 25-44 1.9 3.6 2.6 1.7 Female 082 Whanganui 45-64 2.8 4.7 4.6 3.3 Female 082 Whanganui 65+ 3.3 what if 7.9--> 5 what if 7.1-->5 4.5 This work is supported with funding from The Physicians Foundation, The University of North Carolina at Chapel Hill and Health Workforce New Zealand.
Plasticity matrix can be used to model the dynamic nature of health care system ▪ Plasticity matrix allows us to capture effect on workforce of evolving industry structure that Tom Aretz described ▪ Use plasticity matrix to simulate effect of shifting health care services (and the workforce!): – Between physicians, as care shifts from specialists to generalists – Between professions, as roles change and distribution of care shifts – Between settings, as care shifts from acute, inpatient settings to outpatient settings and patient’s home This work is supported with funding from The Physicians Foundation, The University of North Carolina at Chapel Hill and Health Workforce New Zealand.
Level 1: Plasticity between generalist and specialist physicians Policy issues: ▪ Can we broaden scope of services provided by generalists to include more specialty care? ▪ And can we shift primary care services provided by expensive specialists back to generalists? Modeling Scenario: ▪ Use plasticity matrix to shift visits from specialists to generalists This work is supported with funding from The Physicians Foundation, The University of North Carolina at Chapel Hill and Health Workforce New Zealand.
Plasticity — Providers and Services: A sample matrix for all settings Percent of visits for select specialties and CSAs Circulatory Digestive Endocrine/Immunity Genitourinary Neoplasms Respiratory Cardiology 29% to 10% 0% 2% 0% 0% 1% Dermatology 0% 0% 0% 0% 22% 0% Internal Medicine 14% 8% 14% 3% 1% 6% Endocrinology 0% 0% 17% 0% 1% 0% Family Medicine 42% to 62% 21% 43% 14% 5% 40% Gastroenterology 0% 27% 0% 0% 2% 0% Other Specialties 14% 44% 24% 82% 68% 53% 100% 100% 100% 100% 100% 100% Total Visits This work is supported with funding from The Physicians Foundation, The University of North Carolina at Chapel Hill and Health Workforce New Zealand.
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