Combining Qualitative and Quantitative Analyses to Generate Robust - - PowerPoint PPT Presentation

This work is supported with funding from The Physicians Foundation, The University of North Carolina at Chapel Hill and Health Workforce New Zealand.

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

• verlap 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

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 Internal Medicine 17,975,183 3,458,440 9,920,149 1,788,739 714,021 6,199,275 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

Number of visits for select specialties and types of health care services

This work is supported with funding from The Physicians Foundation, The University of North Carolina at Chapel Hill and Health Workforce New Zealand.

Circulatory Digestive Endocrine/Immunity Genitourinary Neoplasms Respiratory Cardiology

29%

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%

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%

How are circulatory visits currently distributed across specialties?

A US example of provider plasticity in all settings

Percent of visits for select specialties and types of health care services

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

• ther providers in area

with similar/competing scopes of practice ▪ Funding model and liability ▪ Model of care and referral patterns ▪ Regulation ▪ Hospital executives and HR decisions about deployment ▪ Professional’s education and training (initial and ongoing) ▪ Personal preferences ▪ Patient population ▪ Local geography

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

• f 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

• utpatient 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% 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.

Implications

Shifting care to generalists requires changes in: ▪ number of generalists vs. specialists needed to meet population health needs ▪ training required to practice and continue practicing as generalist ▪ incentives to shift visits from specialists to generalists ▪ referral patterns between specialists and generalists

This work is supported with funding from The Physicians Foundation, The University of North Carolina at Chapel Hill and Health Workforce New Zealand.

Level 2: Plasticity between professions

Policy issue:

Health care home model in NZ- comprehensive primary care delivered by integrated, multi-disciplinary teams

Modeling Scenario:

▪ How much primary care and what services could be shifted between primary care physicians and other professionals? ▪ Can we use plasticity matrix to shift visits between professions to simulate effect of shifting roles?

This work is supported with funding from The Physicians Foundation, The University of North Carolina at Chapel Hill and Health Workforce New Zealand.

Example plasticity matrix for current distribution of visits in primary care

Prevention Diagnosis Treatment Chronic Disease Management Mental Health Care Coordination Health promotion, education Total

# of Visits 151 907 453 605 453 302 151 3,022 % of Visits 5% 30% 15% 20% 15% 10% 5% 100%

Sample distribution of visits seen by primary care physicians

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 shifted visits from general practitioners to other health professionals?

Prevention Diagnosis Treatment Chronic Disease Management Mental Health Care Coordination Health promotion, education Total PC Physician

15 544 91 136 8

794

NPs

30 136 109 151 75 501

PAs

23 91 109 151 75 449

Registered Nurses

15 91 91 91 56 60 30 434

Pharmacists

15 27 18 61 60 181

Dietitians

30 9 18 61 30 148

Social Workers

8 9 30 110 77 38 272

Therapists

15 9 9 12 45 90

Unlicensed providers

48 60 45 153

This work is supported with funding from The Physicians Foundation, The University of North Carolina at Chapel Hill and Health Workforce New Zealand.

Redistribution of tasks frees up GP to take on more visits and concentrate on more complex cases

Prevention Diagnosis Treatment Chronic Disease Management Mental Health Care Coordination Health promotion, education % Total # Total

% Visits Before Shift

5% 30% 15% 20% 15% 10% 5% 100% 3,022

% Visits After Shift

2% 69% 11% 0% 17% 0% 1% 100% 794

Sample distribution of visits seen by primary care physicians before and after shifting tasks to other providers

This work is supported with funding from The Physicians Foundation, The University of North Carolina at Chapel Hill and Health Workforce New Zealand.

Implications

This shift would require:

▪ Risk stratification to determine which health professional needs to see patient for given condition and acuity ▪ Changes in funding ▪ “Right touch regulation” ▪ Changes in how hospitals, HR directors, and practices deploy workforce ▪ Better models of interprofessional education and practice ▪ Mechanisms to address professional turf issues

This work is supported with funding from The Physicians Foundation, The University of North Carolina at Chapel Hill and Health Workforce New Zealand.

Moving from triple to quadruple aim—it’s not just about patient satisfaction

2012 study of providers in patient centered medical homes in US found: ▪ Higher morale ▪ Higher job satisfaction ▪ Reduction in turnover ▪ Risk of burnout

Lewis SE, Nocon RS, Tang H, et al. Patient-Centered Medical Home Characteristics and Staff Morale in Safety Net Clinics. Arch Intern Med. 2012;172(1):23–31. doi:10.1001/archinternmed.2011.580

This work is supported with funding from The Physicians Foundation, The University of North Carolina at Chapel Hill and Health Workforce New Zealand.

Level 3: Plasticity between settings: shifting care (and workforce) from inpatient to outpatient settings

Inpatient Outpatient Total Visits Cardiology

8% 92% 42,209,010

Dermatology

0% 100% 33,812,260

Internal Medicine

1% 99% 13,515,224

Endocrinology

1% 99% 210,909,100

Family Medicine

11% 89% 20,848,212

Gastroenterology

2% 98% 136,161,597

Other Specialties

3% 97% 543,919,067

Total Visits

3% 97% 1,001,374,470

Current distribution of visits between inpatient and outpatient settings

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 could shift half of visits from inpatient to outpatient

Inpatient Outpatient Total Visits Cardiology

4% 96% 42,209,010

Dermatology

0% 100% 33,812,260

Internal Medicine

1% 99% 13,515,224

Endocrinology

0% 100% 210,909,100

Family Medicine

5% 95% 20,848,212

Gastroenterology

1% 99% 136,161,597

Other Specialties

2% 98% 543,919,067

Total Visits

1% 99% 1,001,374,470

Distribution of visits after shifting from inpatient to outpatient settings

This work is supported with funding from The Physicians Foundation, The University of North Carolina at Chapel Hill and Health Workforce New Zealand.

Example: Shift in colonoscopies in the US

▪ Eberth et al (2018) identify major shifts in professionals performing colonoscopies in South Carolina ▪ Proportion of internists and family medicine physicians performing colonoscopies increased significantly, particularly in rural areas where supply of gastroenterologists has declined ▪ Also found significant shift in screening to outpatient settings

Eberth JM, Thibault A, Caldwell R, Josey MJ, Qiang B, Pena E, LaFrance D, Berger FG. A statewide program providing colorectal cancer screening to the uninsured of South Carolina. Cancer. 2018 May;124(9)1912-1920. doi: 10.1002/cncr.31250.

This work is supported with funding from The Physicians Foundation, The University of North Carolina at Chapel Hill and Health Workforce New Zealand.

Technology key to new models of care

▪ Can we make plasticity matrix three dimensional to reflect the intersection of labor, technology and capital? ▪ Tom Aretz notes that to be useful, technology “has to replace something, not be additive”. Some examples:

– EHRs for risk stratification – Wireless monitoring to track blood pressure, pulse, weight, oxygen – Patients given tablets to communicate with primary care team

This work is supported with funding from The Physicians Foundation, The University of North Carolina at Chapel Hill and Health Workforce New Zealand.

Example: Kaiser delivering 52% of visits virtually

Eric Wicklund. Kaiser CEO: Telehealth Outpaced In-Person Visits Last Year. mHealth Intelligence. October 11, 2016. https://mhealthintelligence.com/news/kaiser-ceo-telehealth-outpaced-in-person-visits-last-year.

Care Anywhere strategy In 2016, 52% of Kaiser’s 110 million visits were done via smartphone, videoconferencing, kiosks, and other technology tools.

This work is supported with funding from The Physicians Foundation, The University of North Carolina at Chapel Hill and Health Workforce New Zealand.

Future directions for plasticity matrix

▪ Effect of capital: redesigning work spaces to allow better communication between providers, patient flow, lean principles etc. ▪ Shift of care and workforce toward prevention and public health ▪ Modeling plasticity between health and social care workforce ▪ Adding patient to plasticity matrix

This work is supported with funding from The Physicians Foundation, The University of North Carolina at Chapel Hill and Health Workforce New Zealand.

Challenges, and opportunities, of taking plasticity approach versus traditional, silo-based approach

▪ Can we shift the narrative away from physician shortages? ▪ Do we have the data needed? How do we get it? ▪ Do we, as workforce planners, have the right skills? Do we need more qualitative approaches? ▪ How can we engage clinicians and employers to help develop plasticity matrices where we don’t have data? ▪ Will we face resistance from professions? Policy makers?

This work is supported with funding from The Physicians Foundation, The University of North Carolina at Chapel Hill and Health Workforce New Zealand.

Questions? Want to know more?

Erin Fraher

+1 919-966-5012 erin_fraher@unc.edu

Director Program on Health Workforce Research and Policy http://www.healthworkforce.unc.edu

Emmanuel Jo

+64 21 246 4406 emmanuel_jo@moh.govt.nz

Manager Analytics and Modelling Health Workforce Zealand Ministry of Health https://www.health.govt.nz/

• ur-work/health-workforce