Allied Health & Select Other Occupations Model Components

Published 2018

In this module:

This module summarizes how HWSM models supply and demand for select allied health occupations and several other occupations (e.g., podiatrists, audiologists). Because of data limitations, discussed later, supply projections could only be made for 11 of the 26 occupations included in this component of the HRSA workforce modeling effort (Exhibit 35).

The research team reached out to professional associations representing various health professions. The team sought to identify the best available data sources, discuss trends affecting workforce supply and demand, and provide the opportunity for feedback on preliminary findings. The information provided in this technical documentation and in HRSA reports does not necessarily reflect the views of the associations that responded. There may not be clear consensus on all assumptions. Individuals from the following associations participated:

  • Academy of Doctors of Audiology (ADA)
  • Academy of Nutrition and Dietetics (AND)
  • American Association for Respiratory Care (AARC)
  • American Chiropractic Association (ACA)
  • American Occupational Therapy Association (AOTA)
  • American Optometric Association (AOA)
  • American Podiatric Medical Association (APMA)
  • American Society of Radiologic Technologists (ASRT)
  • National Registry of EMTs (NREMT)
  • Society of Diagnostic Medical Sonography (SDMS)
  • Society of Nuclear Medicine and Molecular Imaging (SNMMI)

Modeling Supply

For the eleven occupations where there is sufficient data to project future supply, we modeled multiple scenarios. A Status Quo scenario extrapolates current trends in supply determinants:

  • number and characteristics of current and new providers
  • hours worked patterns
  • attrition patterns

Alternative scenarios modeled the sensitivity of future supply to changes in key trends and assumptions. One scenario was a ±10% change in annual numbers of new graduates entering the workforce. Another reflected if attrition patterns were to change such that providers retired up to two years earlier or delayed retirement by up to two years, on average, relative to historical retirement patterns.

Estimating the Base Year Workforce Supply

The primary sources for data on current number and characteristics for the occupations modeled came from pooled 2012-2016 ACS data supplemented with OES data and licensure counts from specialty associations. We pooled multiple years of ACS data to create a larger sample for each state to produce estimates of current supply by age, sex, and race/ethnicity. Counts for number of dieticians came from the Academy of Nutrition and Dietetics. This allows for modeling a more precise category than the SOC category (dieticians and nutritionists) in ACS or OES. State-level counts for the number of podiatrists came from the American Podiatric Medical Association (APMA), which are based on licensure files. Counts for radiation therapists came from the American Registry of Radiologic Technologists’ census from September 2016.

Modeling New Entrants

The primary source for estimating new entrants in each occupation is the 2016 Integrated Postsecondary Education Data System (IPEDS). This is supplemented with data provided by the specialty associations. As described in Section Modeling Supply of Health Professionals II.B, new entrants were added to the workforce via a “synthetic” cohort. The size of the cohort is based on the number and characteristics of recent graduates in each occupation. The number of new entrants for radiation therapists used the American Registry of Radiologic Technologists’ data on radiation therapy exams taken in 2017.

Each new worker is assigned an age, race, and sex that reflected the distributions seen in recent years (Exhibit 36). To estimate the percent female, we used 2012-2016 ACS data on providers age 30-39 to approximate the newer cohort in that occupation. To calculate the age distribution, we compared consecutive years of ACS data. We compared the number of providers of a particular age (e.g., age 29) to the number of providers in the subsequent ACS annual file who were one year older (e.g., age 30). The subsequent ACS file contains information on the workforce from the previous year plus any additions or subtractions to the workforce. In the above example, the difference between the number of providers age 30 and the number of providers age 29 in the previous year reflects the net number of new providers entering the workforce at age 29-30. With this information, we estimated the age distribution of new entrants to the workforce. We used multiple years of data to increase sample size by individual age and occupation.

The number of new entrants and their age-sex distribution were assumed to remain constant during the projection period. For opticians, an additional 1,698 new graduates were added to the number of IPEDS graduates. This estimates the number of additional new opticians that need to join the workforce annually for the optician workforce to grow at rates consistent with OES historical trends. This reflects multiple training paths to become an optician. These paths include on-the-job training or an apprenticeship as an alternative to pursuing an educational certificate or degree.

Some occupations such as aides, assistants, or technicians have data limitations that preclude projecting future supply. For occupations that do not require licensure or that have few educational or training requirements, there are insufficient data on the number of people newly entering these occupations. Likewise, occupations with easy entry often have high turnover rates. This creates challenges to obtain reliable supply forecasts.

Modeling Workforce Participation

Labor force participation rates for health professionals were calculated directly for individuals under age 50 using ACS data. The analysis used information on whether the person is active in the workforce. Active is defined as working at least 1 hour per week in their profession. Each simulation year, the model selects some health professionals in the under 50 age group to become inactive and thus count for 0 supply. However, these individuals remain part of the supply model and are eligible to become active again in the following simulation year. For health professionals age 50 and over, we modeled the probability of retiring. The probability increased with age. Attrition patterns for each profession were based on ACS data. These patterns were constructed based on a question asking whether the person is currently employed and whether they were employed last year. Health professionals flagged for attrition drop out of the supply entirely and do not return in later years.

Modeling Hours Worked

We used Ordinary Least Squares regression to model hours worked patterns for each health occupation. The dependent variable is total hours worked in the previous week. Explanatory variables consisted of age group, sex, race, and a year indicator (as the ACS pooled data from 2012-2016).

Estimates and projections consider the changing demographics of the workforce. It also accounts for average hours worked per week differing by age, sex, race, and occupation. Then, the expected number of hours worked by each professional is converted to FTE supply by dividing the total person-hours worked by 40. This creates a uniform standard of 1 FTE as working 40 hours per week regardless of the occupation. It also means that the initial FTE of an occupation can differ from the actual count of persons employed in the occupation.

Modeling State-Level Supply and Migration

Health occupations often have different levels of surplus and shortage throughout the United States. To better estimate this, HWSM includes state-level supply estimates where sufficient data are available. In the ACS and OES, some occupations do not have totals reported in every U.S. state. As such, state-level supply estimates are unavailable for these occupations. Occupations affected include chiropractors, podiatrists, radiation therapists, and audiologists.

For occupations with sufficient state-level data, HWSM models for future movement of health professionals across states. This is accomplished in two steps. First, a logistic regression on ACS data estimates the probability of migrating to any other state for the under 50 population. This regression is a function of age group, sex, race, the current state’s population, and a year indicator. Then, the simulation randomly assigns each professional a probability of moving based on their demographic characteristics. It also assigns those who move a new state based on the destination state distribution observed in ACS data for professionals who changed residence states.

Modeling Demand

Modeling Demand for Health Care Services and Providers provides an overview of the data sources and approach used to model demand for health care services and providers. Here, we provide additional details specific to modeling demand for the health occupations covered in this analysis.

We modeled demand for providers in allied health and selected other occupations under two scenarios:

  1. The Status Quo scenario models what future demand would be if current care use and delivery patterns remained unchanged. It does account for changing demographics and variation across individuals in patterns of seeking health care services.
  2. The evolving care delivery system scenario models trends in the health care system that have the potential to change how care is used and delivered over time.

Status Quo Demand Scenario

The Status Quo scenario starts with the assumption that national supply and demand currently are roughly in equilibrium. It then extrapolates current patterns of care into the future. Predicted probabilities of health care use were applied to project health care service use specific to the settings where these professionals are employed.

Demand for workers in many occupation categories are based on rates of patient-clinician encounters across care delivery settings. These include therapeutic services, rehabilitation services, respiratory care services, and vision and hearing services. Primary data sources include MEPS, NIS, NAMCS, and NHAMCS. Demand for pharmacists and pharmacy technicians and aides is tied to the number of prescriptions written during patient visits to provider offices, out-patient clinics. This comes from ACS industry distribution mapped to employment settings (see appendix, Exhibit A-1). Data on the number of medications prescribed from the 2013-2015 NAMCS and 2007-2011 NHAMCS were used to model the number of prescriptions that an individual would receive. These were aggregated for the entire population. Demand for diagnostic services is based on the overall growth in health care use (e.g., ambulatory visits, inpatient days).

The number of health workers employed in a setting in the base year is assumed to reflect demand for services in that setting. Therefore, projections of future demand for providers were based on the 2016 ratio of providers to services. The distribution of employment across care settings came from the 2012-2016 5-year ACS database. Exhibit A-1 in the Appendix provides detailed data on employment setting, workload and staffing-ratios by provider type.

Evolving Care Delivery Scenario

The evolving care delivery scenario builds on the Status Quo scenario that models changes in demand due to changing demographics. This scenario is described in Section III.D. In this section, we provide additional detail on trends and factors that could affect demand for the allied health occupations modeled.

Achieving the modeled population health outcomes on a national basis would require:

  • increased levels of counseling by dietitians and nutritionists
  • increased access to and adherence to medications
    • this could increase demand or pharmacy services

Weight loss and other improvements in patient health would reduce demand for some types of pharmaceuticals. These would prevent or delay the onset of chronic disease and other adverse health outcomes.

A second component of this scenario centers on providing a continuum of care across care delivery settings and coordinating multidisciplinary care. The goals of this principle are to:

  • efficiently and effectively shift care from higher cost to lower cost settings
  • shift care from higher cost to lower cost providers
  • avoid unnecessary care

There is a growing body of literature on this topic. A limitation of this literature is whether findings from specific interventions or populations can be generalized to a broader population. It is also uncertain how to translate published findings into a scenario that can be modeled.1

Likewise, different types of interventions can have overlapping outcomes. It is unclear whether the impacts from multiple interventions are additive or complementary. For this scenario, we modeled a 5% reduction in hospital inpatient and ED utilization gradually through 2025. We also modeled a corresponding 5% reduction in the health workforce that supports patient care in these settings. We think the 5% decline assumption is conservative, and the potential impact is larger. Over the past decade, there have been substantial declines in per capita use of hospital-based services. This occurred as national attention focused on preventive care to reduce avoidable hospital care and to shift care from hospitals to appropriate ambulatory settings. The magnitude of this 5% reduction assumption is supported by a growing body of literature. This literature explores how different types of interventions and care delivery models can change patient health care needs and care utilization patterns. Examples include the following:

  • Reduced risk of hospitalization and rehospitalization: Participation in a PCMH team-based intervention reduced rehospitalization rates from 18.8% to 7.7%.2  Another study reports that PCMH reduced hospitalizations for PCMH-targeted conditions by 13.9% versus a 3.8% reduction in hospitalizations for other conditions.3  Project RED (Re-Engineered Discharge), BOOST (Better Outcomes for Older adults through Safe Transitions), and other interventions have used team-based care to treat patients discharged from hospitals with the goal of reducing rehospitalization. Many of these interventions used nurse practitioners, nurses, social workers, physician assistants, and other health workers. They ensured patients and their families receive appropriate counseling and follow-up care to reduce rehospitalization risk. AHRQ reports that RED reduced 30-day all cause rehospitalization by 2 percentage points (or about 11%, dropping from 18.6% to 16.6% readmission rate).4  Similarly, BOOST appears to reduce hospital readmission rates by about 2 percentage points (or about 13.6% among implementation hospitals).5 Much of the literature on reducing avoidable hospitalization is disease-specific (e.g., cardiology, pulmonology, diabetes, asthma, cancer, and behavioral health).6 7 8 9
  • Reduced emergency department use by redirecting avoidable ED visits to appropriate primary care and community-based settings: Estimates of avoidable ED visits vary by definition of “avoidable” and by patient population. One study estimated that between 13.7% and 27.1% of non-emergent ED patients could have been managed at an urgent care center or retail clinic.10  Other studies consider some emergent visits avoidable (e.g., stroke, myocardial infarction) if appropriate preventive care could have prevented the incident. A study by Truven Health Analytics suggests that 71% of ED use is potentially avoidable. Avoidable in this study is that the care could be appropriately treated elsewhere, the medical condition necessitating the visit could have been treated to prevent the event from occurring, or the care is unnecessary.11  A study by Kaiser Permanente Northern California examined low-risk patients. It found that if an ED physician made a brief phone follow-up or mailed information about alternative service options, it reduced subsequent ED utilization. Specifically, patients’ subsequent (6-month) ED utilization were reduced by 22% for patients age 65 years or older and by 27% for patients under age 65.12  One study reports that transition to PCMH status is associated with 5–8% reductions in ED utilization among chronically ill patients. The largest reductions were in ED visits among patients with diabetes and hypertension. There was no change in ED utilization among patients without chronic disease.13  Another study reports that PCMH reduced ED visits for PCMH-targeted conditions by 17.2% versus a 3.1% reduction in ED visits for other conditions.3

Activities to divert care from costly hospital settings might simply change where the care is provided but not necessarily reduce overall demand for health workers.

  • Shift care from higher-cost to lower-cost providers: A strategy that has been pursued to address both the issue of rising costs and workforce shortages is task performance substitution. This is widely documented in the literature for physician assistants (PAs) and advanced practice nurses (APNs) performing tasks that historically were carried out by physicians.14  Among the occupations covered in this report, shifting care among occupations pertains to:
    • occupational therapists (OT)
    • OT assistants
    • OT aides
    • physical therapists (PT)
    • PT assistants
    • PT aides
    • pharmacists
    • pharmacy technicians
    • pharmacy aides
    • dietitians
    • nutritionists
    • dietetic technicians

Some studies have tried to determine the impacts that substitutions have had on costs and outcomes in allied health occupations. The evidence base in the US is not particularly robust, and the majority of studies being carried out in Australia.

Providing pharmacy services in a community setting has been noted to improve patient outcomes and decrease health care costs. The expansion of these services is often challenged by time constraints for dedicated patient care or workload burdens.15 16 17 18 19  The SafeMed program seeks to improve transitions of care by using lower-cost health workers, particularly certified pharmacy technicians, as community health workers.20  These workers provide medication management services to high-utilizer patients with multiple chronic conditions by serving as pharmacist extenders. This program achieved positive trends in outcomes such as increasing home visit completion rates within three days of discharge from 46.9% to 68.5%. It also coordinated with pharmacists to increase outpatient comprehensive medication review, rising from a monthly average of 1.9 in 2013 to 7.9 in 2014.

The study suggests that using these lower-cost workers in these roles can prove beneficial. Further exploration of the impact on clinical outcomes and health care use is still required. There is insufficient information for modeling demand scenarios around substitutability.

A third component of this scenario is increased use of value-based insurance design (VBID). This seeks to increase use of high-value, under-utilized services by removing access barriers (usually by lowering the cost of such care). It also tries to decrease use of low-value, over-utilized services by raising access barriers (usually by raising the cost of such care). A 2014 Aon Hewitt survey found that only about 25% of U.S. employers are currently using or adding VBID for medical and pharmacy plans. Within 3-5 years, 59% of employers plan to add VBID for medical plans and 57% plan to add VBID for pharmacy plans.21  For scenario modeling, we assume that VBID coverage will gradually increase to 100% coverage by 2025.21

VBID could potentially affect demand for a wide range of health workers. Based on the published literature, the impact of VBID on health workforce demand is likely to be small. VBID simultaneously increases demand for some services and pharmaceuticals while decreasing demand for other services and pharmaceuticals. This mitigates the workforce demand impact. The following highlights allied health and select other occupations where VBID could affect demand for services.

  1. Pharmacy occupations: Numerous studies suggest that VBID increases overall medication adherence. This, in turn, could increase demand for pharmacists, pharmacy technicians and pharmacy aides. Improved adherence is associated with greater use of medications as prescribed by a health provider, though a variety of metrics have been used to measure adherence. A review of 20 studies on VBID for pharmaceuticals suggested average increased medication adherence of 3.4% after one year.22  Few studies explore the impact beyond one year. A study of medication refill records for 74,748 individuals covering 8 drug classes over 2 years found that medication adherence increased by 1.4% to 3.2% (midpoint 2.3%) at one year. It increased to 2.1% to 5.2% (midpoint 3.7%) within two years.23  For modeling, we assume that VBID will increase pharmaceutical demand by 3.7% among patients participating in VBID pharmacy plans. This will increase demand for pharmacy-related occupations by this level.
  2. Diagnostic services occupations: Certain diagnostic services are provided by medical and clinical laboratory technologists and technicians. Diagnostic imaging services are provided by diagnostic medical sonographers, nuclear medicine technologists, and radiologic technologists. The literature suggests that VBID does cause marginal changes in utilization of diagnostic services to discourage low value services and encourage high value services. The net impact on demand for diagnostic services occupations is likely to be small.24 25
  3. Physical therapy (PT): A retrospective analysis of medical claims studied the impact of a Geisinger Health Plan initiative aimed at patients with back pain. Geisinger preauthorized patients to receive a “PT bundle” of up to five PT visits for a single one-time copay.26  Among patients with back pain, PT visits increased by 74% while ED visits declined by 11% over the subsequent 17 months.26  Gellhorn’s 2012 study evaluated the impact of early PT visits for acute lower back pain (LBP).27  Patients receiving PT within 30 days after their initial primary care physician visit for LBP had a lower risk of subsequent surgery or epidural steroid injection, compared to patients receiving PT after 90 days.27  In addition, the use of frequent office visits is significantly lower among early PT patients versus those who received PT late. A second study by Fritz also evaluated the effect of early PT visits with similar results.28  Patients who visited a PT provider within 14 days of primary care consultation showed lower subsequent health care utilization. This included imaging, surgery, spine injections, and opioid medications. There is limited information to inform a scenario for workforce demand modeling. The net effect of VBID is likely to increase demand for PT occupations, but the size of this increase is uncertain.

We reviewed the literature on other trends in care delivery as applied to the allied health and other occupations modeled. The following are select findings from this review. There is little information in the literature to inform scenario parameters for demand modeling.

  • Bundled payments: We reviewed literature on bundled payments to ascertain whether there were changes in services utilization or demand for providers. An evaluation of Medicare claims from 2013-2015 found no statistically significant changes in length of stay, ED use, or readmission after hospital discharge.29
  • Non-pharmacological alternatives to pain management: Chiropractic care may be an alternative to traditional medicine-based therapies to relieve pain associated with some medical conditions. This comes from studies sponsored by AHRQ.30 31  The drive to find non-pharmacological alternatives to pain management in part reflects efforts to combat the epidemic of opioid addiction. These efforts, however, predate the opioid crisis.32 33  A report from the National Academies of Science, Engineering and Medicine concluded that the burden of pain on human lives, finances, and social consequences should make this far-reaching issue a national priority.34  A report summarizing a 2014 meeting of the Association of Chiropractic Colleges Educational Conference examined how chiropractic care contributed to the changing health care landscape. The report emphasized how well-suited chiropractic services are for the PCMH and ACO models.35  Chiropractic care can be cost-prohibitive because many insurance companies do not cover these services. Some states Medicaid programs are considering increased coverage for chiropractic care for adults. This would tie into national efforts to combat the opioid crisis. National efforts to use non-pharmacological alternatives for pain relief could increase demand for chiropractors. The magnitude of such an increase is unknown.
  • Clinical decision support/health information exchange: Information systems or electronic health records that provide clinical decision support could reduce low-value care or prevent redundant or duplicative testing. Clinical decision support systems also could potentially encourage greater use of high-value care and screening or diagnostic procedures. A systematic review of interventions aimed at reducing use of low-value health services documents interventions outcomes. The studies, however, focus on specific services or pharmaceuticals viewed as low value. This provides limited use to inform a scenario modeling the workforce demand implications.36  Likewise, a health information exchange involves sharing electronic information on lab results, clinical summaries and medication. The goals are to increase efficiency in care delivery, improve patient outcomes, and reduce service use and medical costs. A systematic review of published studies found little evidence that health information exchanges provide value towards achieving these goals.37
  • Technology: The use of technology in the evolution of the healthcare system has some implications for health workforce demand. Three-dimensional visualization technology has the potential to reduce time required for surgical procedures thus improving efficiency. This could reduce demand for the number of health workers needed to support care delivery.38  Digital health technologies have the potential to improve how care is delivered. Examples include trackable pill technologies to improve patient medication adherence39 , track health outcomes40 , and provide health coaching.41  Some technologies could increase demand for certain types of health workers. The productivity gains could also decrease demand for certain types of providers. The net effect on demand for scenario modeling is uncertain.

Exhibit 37 summarizes the parameters and assumptions used to develop the evolving care delivery system scenario. There is an abundance of published studies on care delivery trends and policies influencing how care is used, delivered, and financed. There is also a paucity of information on how this will affect overall utilization of health care services and demand for the occupations modeled in this report.

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