 To illustrate a more comprehensive view of value associated with medicines treating a highly severe illness and to apply these insights to estimate the costs and benefits of 3 treatments for multiple sclerosis (MS): Avonex, Tysabri, and Tecfidera.

 Retrospective study spanning 2002 to 2013. We used economic theory to derive the value of therapy to patients with MS and to individuals who face the risk of contracting MS in the future, under the alternative assumptions that therapies were fully insured or paid for out of pocket.

 Models were parameterized through secondary data analysis and targeted literature review. Estimates of individual value were aggregated to the societal level using therapy-specific treatment prevalence rates. Aggregate consumer value was compared with manufacturer revenue.

 In the baseline model, Avonex, Tysabri, and Tecfidera generated $46.2 billion of total value to consumers, almost one-third of which accrued to those without MS. The total value to consumers was double manufacturer revenue. Results were qualitatively robust to the use of alternate epidemiological and economic parameters. We found that value to the healthy is positively related to disease severity, and that value to both the sick and the healthy are larger when costs are shared via health insurance.

 Theory predicts that treatments for severe disease provide “peace of mind” value to the healthy. Avonex, Tysabri, and Tecfidera have generated significant social value, a large majority of which accrues to consumers. Future economic valuations of medical technology should consider both the potential value to the healthy and the effects of insurance.

	Although many studies have assessed the social value of medical care to the sick, the value to the healthy who may use treatment if they become sick has been largely ignored. We used empirical estimations to parameterize an economic model that describes the value of 3 multiple sclerosis treatments to those who are healthy but face the risk of contracting MS in the future, as well as to the sick. 

		When patients bear the full cost of treatment, the value of the 3 treatments to the sick totals $11.1 billion, while the value to the healthy is $8.9 billion. 

		The value of therapy increases with the severity of the disease being treated. 

		Insurance coverage has a complementary effect on the value of therapy: the total populationwide value of the 3 treatments increases to $46.2 billion when actuarially fair insurance is assumed.

The rising cost of healthcare in recent decades has been accompanied by an increasing interest in quantifying the value of medicine.

 The cost of healthcare—unlike the costs of other goods&mdash;is often borne primarily by healthy consumers who are not currently using it. For example, premiums for private health insurance and taxes for public health insurance schemes are paid by the entire population, not just the patients who happen to be sick at a point in time. This raises a key question: What is the value of healthcare to the sick and to the healthy consumers who are paying for it?

The value of medical care to the sick is readily apparent, whereas the value to the healthy manifests in at least 2 ways. First, healthy individuals value medical technology because it will be available to them if they become sick in the future. The higher the likelihood of becoming sick with a particular disease, the more a healthy person values treatments for that disease. Second, new technology can provide peace of mind in the present, even to those who may never end up using it. The worse the disease, the more a healthy person values this peace of mind.

To illustrate the “peace of mind” value, consider the analogy of a fire extinguisher in one’s home: it provides value should the house catch fire by reducing the damage the fire would cause, and this value increases with the size of the potential loss. Awareness of this potential benefit provides immediate and continuous peace of mind from the protection against fire damage. This value is realized even if a fire never breaks out. Moreover, this peace of mind value increases with the size of the potential loss. A renter with few possessions may worry little about the risk of fire and thus derive few benefits from a fire extinguisher. In contrast, the owner of a home filled with priceless heirlooms might worry more and thus place higher value on the fire extinguisher. The “peace of mind” value is likely to be quantitatively meaningful, because evidence suggests that most consumers dislike risk and value reducing it.

Medical technology provides peace of mind similar to that in the fire extinguisher example. To illustrate, consider a healthy individual today and another in 1990 who are concerned about the prospect of a human immunodeficiency virus (HIV) infection. Both would experience anxiety, but the first individual would be anxious about the risk of complications and the inconvenience of a lifetime of medical treatment; the second individual would be anxious about death. The very real difference between these 2 levels of anxiety contributes to the value that healthy individuals have obtained from modern HIV and AIDS therapies. In other disease areas, the value of a new therapy to a healthy individual can be similarly characterized by decreased anxiety or fear of a diagnosis due to the therapy’s ability to reduce the harm from a disease. This example illustrates that the peace of mind afforded by new medical technologies will be especially valuable for treatments that mitigate the consequences of the most severe diseases.

Multiple sclerosis (MS) provides a useful case study of a severe disease, as it is the leading cause of nontraumatic neurologic disability among young adults.

 In MS, the body’s immune system attacks the central nervous system, creating brain lesions. During relapses, symptoms dramatically worsen and the disease can transition into a stage of progressive disability. MS patients suffer from fatigue and pain, as well as mobility and sensory problems.

 Peak onset occurs between the ages of 20 and 40, often affecting healthy individuals in their prime years of productivity. Thus, MS onset imposes high medical costs and has severe consequences for quality of life and productivity, such as lost income.

MS therapies also highlight the wider debate over the value of new medical technology. Some question the value of innovative drugs that help manage—but do not cure&mdash;debilitating and progressive diseases. Skepticism about the value of such drugs has been fueled by cost-effectiveness studies and a recent United Kingdom risk-sharing scheme.

Our study uses an economic model developed by Lakdawalla, Malani, and Reif (2015)

 to estimate the value of MS therapies to both healthy and sick individuals. We focus on 3 currently available MS therapies, incorporating their specific dates of introduction, magnitudes of health benefit, and prices: Avonex (interferon beta-1a intramuscular, introduced 1996), Tysabri (natalizumab, introduced 2004), and Tecfidera (dimethyl fumarate, introduced 2013).

From an economic perspective, the value of a good is measured as the amount of other consumption that an individual is willing to sacrifice in exchange for it. These trade-offs are conventionally estimated in the framework of a “utility” model that explicitly estimates the value that consumers assign to different goods. A plethora of studies measure the value consumers assign to health relative to other goods,

 and these measurements provide the empirical basis for utility models that estimate the trade-off between health improvements and other consumption. We followed this approach and estimated the value of the MS therapies of interest by constructing an economic model of the trade-off between consumption and health. Following the economic literature, we assumed that the quantity of consumption is determined by the income that remains after medical costs.

 As we describe above, value may accrue not only to those suffering from an illness, but also to those who are currently healthy but still susceptible to future illness. We refer to these constructs as “value to the sick” and “value to the healthy.” Both of these depend, in turn, on how the costs of therapy are incurred. Unlike standard goods, a portion of healthcare is often paid for by nonusers, via insurance. Insurance may increase the value of therapy for both the sick (by replacing direct costs with less costly insurance premiums) and the healthy (by reducing financial risk). Our study thus estimates the value of MS therapy from 4 perspectives: value to the sick and value to the healthy, first under the assumption that costs are fully borne by consumers (without insurance) and then assuming actuarially fair insurance, in which therapy costs are distributed across the entire risk pool (with insurance).

Prior efforts to estimate the value of new medical technologies have typically emphasized only 1 of these 4 perspectives: the value to the sick, without consideration of insurance.

) formally describes the economic model developed by Lakdawalla, Malani, and Reif (2015),

 which we used to measure value from each of these perspectives (utility model—based willingness-to-pay estimation). The value to the sick depends on: 1) health benefits of the therapy for those who are diagnosed with MS, as measured by incremental quality-adjusted life-years (QALYs); 2) the health costs of MS, as measured by QALYs for individuals with and without MS; 3) the costs of therapy; 4) other medical costs with and without therapy; and 5) differences in consumer income, which determine the value of money to a consumer. We measured these both for MS patients receiving best supportive care (BSC) and for MS patients utilizing 1 of the 3 qualified drugs.

The value to the healthy depends on all 5 factors above, along with: 6) the incidence of MS, which measures the risk that healthy people will acquire the disease in any given year; and 7) the degree of consumer risk-aversion, which measures the value of risk-reduction to healthy consumers. In reality, some individuals are not materially at risk for MS, meaning that the population could consist of 3 groups: those with MS, healthy individuals at risk for MS, and healthy individuals not at risk for MS. This third group may never derive benefit from the actual use of the therapies. However, as the causes of MS are still not well understood,

 healthy individuals cannot easily ascertain whether they fall into the second or third groups. Thus, for the purposes of our analysis, we pooled these groups together.

We used parameters in these 7 areas to construct separate economic utility models for each of the 4 perspectives described in Figure 1. Our models assume that the health state and drug utilization choice are constant for an individual within each year. These models are then used to estimate the annual value to consumers of using 1 of our 3 drugs of interest relative to BSC. The incremental value of the 3 drugs is given by the difference in value between using the drug and using BSC.

Finally, we aggregated the different estimates of incremental per-patient value to the societal level using disease prevalence and drug utilization rates. We added up the individual annual values of treatment over the period 2002 to 2013 (the years for which data on the therapies are available) to obtain the aggregate value of the 3 therapies. These aggregate values have been compared with manufacturer revenue to determine the share of value that returns to consumers. Complete details on economic model specification, parameterization, and sensitivity analyses are provided in the eAppendix.

 summarizes the parameters obtained from our literature review and data analysis, which were used to calculate the social value of therapies. As detailed in the eAppendix, our analysis suggests that MS patients earn 37.1% less income than their non-MS counterparts; Avonex users earn 41.4% more income than their MS patient counterparts who are not using disease-modifying therapies (DMTs). Because of data limitations, we were unable to estimate income effects for Tysabri and Tecfidera directly. Instead, we assumed that the effects for those therapies were equal to that of Avonex. This conservative assumption likely understates the income effect of those therapies, because both of those products reduce relapse rates and disability progression more than Avonex does.

An MS diagnosis was also associated with a significant increase in non-DMT medical costs (87.0%), while the use of Avonex and Tysabri reduced annual medical costs by 11.2% and 16.4%, respectively. There were too few cases of Tecfidera usage in the claims data to identify an effect on medical costs (Tecfidera had a sample size of 137 compared with 9272 and 1223 for Avonex and Tysabri, respectively). As a result, we elected to use the Avonex cost offset parameter (—11.2%) for Tecfidera. This is a conservative approach, as Tecfidera was shown to reduce disability progression and relapse frequency more compared with interferons (including Avonex).

As a result of using this conservative estimate, our models likely underestimate the social value of Tecfidera. In the eAppendix, we describe a sensitivity analysis in which this value is set equal to the midpoint of the Avonex and Tysabri estimates (—13.8%).

To complete the economic model, we used established estimates from the literature for the health and risk-aversion parameters. The Table summarizes the baseline and sensitivity values used for the MS epidemiological parameters,

 and the economic parameters for the value of health.

 In addition, the Table displays values used in sensitivity analyses that account for insurance loading and, separately, the cost of drug development borne by manufacturers.

 provides baseline estimates of value for all 3 drugs combined, aggregated over all years from 2002 through 2013. Aggregate value to the sick, when bearing the full cost of therapy, is estimated to be $11.1 billion. When actuarially fair insurance is available—so that the healthy and sick share the cost of treatment&mdash;value to the sick almost triples, to $31.8 billion. Conversely, value to the healthy without insurance is estimated to be $8.9 billion. When insurance is available, value to the healthy rises to $14.4 billion. This increase demonstrates the value of financial risk reduction that is obtained with insurance coverage.

Based on an 80% national average insurance coverage rate, the total value of the 3 therapies is estimated to be $40.9 billion.

 Overall, these results suggest that estimates of the value of medical technologies which ignore either the benefits that accrue to the healthy or the role of health insurance may be biased downward—perhaps severely so.

Impact of Disease Severity on Value to an Individual Insurance Enrollee

Conceptually, the value to the healthy should be higher when considering treatments for more severe diseases. For instance, an effective new treatment for a highly fatal disease provides significantly more peace of mind to the healthy than one for a mild skin condition. Our analysis confirms this intuition by re-estimating the value of 1 therapy (Tysabri) to a healthy individual with insurance, while incrementally varying the assumed severity of MS, holding other factors (including the absolute treatment effect) constant. The results are depicted in 

If MS was not a severe disease, the value of Tysabri to a healthy individual would be small. This is evident on the left side of Figure 3: as the assumed QALY of untreated MS approaches 1, the value of treatment to a healthy individual approaches 0. However, MS is a debilitating disease, with an estimated untreated QALY value of 0.584 for those patients who might be treated by Tysabri.

 At this level, our model estimates the monthly value of Tysabri to a healthy individual to be $6.26. By contrast, we calculate that the actuarially fair per-member-per-month cost of insurance coverage of Tysabri is an order of magnitude smaller—about $0.48. This suggests that individual insurance enrollees gain more value from access to coverage than they lose due to the associated incremental insurance premium.

Significantly, the value of the treatment varies with disease severity, even when clinical effectiveness is held constant. Intuitively, a given improvement in clinical status is worth more to a patient suffering from a more severe disease. Therefore, singular focus on efficacy and/or effectiveness may ignore an important additional determinant of value.

 portrays the relative share of lifetime value accruing to all consumers (both healthy and sick) and manufacturers, aggregated across the 3 therapies. When no insurance is available, an estimated 49% of value accrues to consumers ($20.0 billion—the “population-wide value” previously described), and 51% accrues to manufacturers as revenues ($21.2 billion). Because most individuals in the United States had health insurance during the time period of the study,

 the values under full insurance are empirically relevant. When full insurance is assumed, the share of value accruing to consumers rises to 69% ($46.2 billion), with the remaining 31% accruing to manufacturers. We conservatively assumed that all revenues ($21.2 billion) accrue to the manufacturer as profits. In reality, costs are not 0, and as a result, the true share of value accruing to consumers will be larger than what we have estimated here. In the sensitivity analyses, we provide a revised estimate of the distribution of surplus that incorporates estimates of the opportunity cost of research and development.

Our model relies on both epidemiological (eg, incidence rate) and economic (eg, risk aversion) inputs, obtained from the literature and from our original data analysis. Varying these inputs moderately alters the results presented above. For example, assuming the availability of health insurance, estimates of the share of overall value accruing to consumers range from 59% (when the relative value of health is reduced) to 75% (when the treatment prevalence rate is reduced). When using the lower bound for risk aversion (0.15), the share to consumers (assuming insurance coverage) is 62%. These results are presented in the eAppendix (exhibits A12 and A13).

In addition to relying on parameters retrieved from the literature, our model takes as inputs parameters obtained via novel data analysis—specifically the income and medical cost effects of MS (relative to no MS) and of therapy (conditional on MS). These parameters have associated error distributions, and we accounted for these distributions using bootstrap methods. We created 1000 weighted bootstrap samples from the Medical Expenditure Panel Survey and claims datasets and estimated the parameter of interest (population-wide value&mdash;the sum of aggregate value to the sick and aggregate value to the healthy) from each set of regression results. The results are qualitatively robust to the introduction of these error distributions: 95% of the resampled estimates show more aggregate value accruing to consumers than to the manufacturer. The distribution of bootstrapped estimates is presented in the eAppendix (exhibit A14).

At baseline, our model assumes that actuarially fair insurance is available; however, in reality, insurance always involves some loading cost to cover administrative overheads.

 We therefore conducted a sensitivity analysis using an administrative load parameter of 16% (the median of the values reported by Karaca-Mandic et al [2011]).

 Finally, our baseline estimates of manufacturer surplus do not take into account the costs of drug development, and therefore overestimate the percent of surplus accruing to the manufacturer. We calculate the annualized costs of new drug development, based on recent work by DiMasi et al (2016)

 and recalculate the distribution of surplus. When subtracting these costs from manufacturer surplus, the consumer share of surplus increases from 49% to 51% and from 69% to 71% in the cases without and with insurance, respectively.

Severe diseases like MS reduce the health of the sick and inspire fear among the healthy who may be susceptible. Thus, it is important to understand the value that treating such diseases produces for each group. Although some recent economic research has described and estimated this “peace of mind” value to the healthy,

 the concept has not yet been widely presented to the payer or health policy communities. The importance of insurance coverage in expanding the value of medical technology has been similarly neglected.

Our study demonstrates the empirical relevance of value to the healthy in the case of 1 severe illness—MS. When consumers are covered under actuarially fair health insurance, we estimate the aggregate value to the sick of the 3 therapies for MS to be $31.8 billion. Adding value to the healthy (with insurance) leads to a $46.2 billion estimate of population-wide value. The healthy therefore accrue 31.1% of the total consumer value from the 3 therapies. In this scenario, consumers derive 69% of the total value generated by the technology, while the manufacturer retains 31%.

The results of this study also illustrate the unique and complementary relationship between health insurance and medical technology. More generous insurance boosts the value of medical technology, and helps society extract greater value from new innovations. For sick patients, the introduction of actuarially fair health insurance increases the value of therapy to $31.8 billion compared with $11.1 billion when patients bear the full cost of treatment.

Note that the size of the additional value provided by insurance coverage varies depending on the efficiency of insurance. Our baseline model assumes that insurance allocates treatments efficiently. If, on the other hand, insurance leads to overuse or underuse of therapies, then the value of insurance would be lower. By similar logic, if there are other inefficiencies in the market apart from insurance (eg, agency problems that result in physicians failing to maximize the well-being of patients), the value of medical technology would fall in both the insured and uninsured cases. These points represent the more general observation that the value of medical technology is intimately linked to the efficiency of the institutions allocating it to patients.

Our estimates of consumer value and the consumer share of value are conservative in that they do not incorporate all sources of consumer value (eg, alleviated caregiver burden), nor do they consider manufacturer costs of production. Regardless, other severe diseases may display similar patterns, and this analysis may inform value assessments for technologies that treat them.

At the same time, some other severe diseases might also feature known risk factors— asbestosis is an extreme example, which occurs only for individuals occupationally or environmentally exposed to asbestos. In such cases, the healthy can be clearly divided into populations at risk, and populations not at risk. The “at-risk” group derives insurance value, while the “not-at-risk” group cross-subsidizes the value enjoyed by both the sick and the at-risk healthy. This pattern is worth exploring in future research.

Articles

WEB EXCLUSIVE

Availability of multiple sclerosis (MS) therapies provides substantial value to the currently healthy (who may contract MS in the future), particularly when treatment is fully covered by insurance. 

Reconsidering the Economic Value of Multiple Sclerosis Therapies

Although improving the quality and delivery of clinical care is a critical mission for health systems, they are increasingly being tasked with improving the overall health of patients. This new directive is reflected in the growing number of health sector efforts in population health—a concept intertwined with social forces that impact patient care and health outcomes: the social determinants of health. Three policies that have the potential to help health systems intervene on social determinants of health are: 1) the Internal Revenue Service—mandated Community Health Needs Assessment for nonprofit hospitals, 2) value-based payment reform, and 3) CMS’ Accountable Health Communities program. We discuss how these policies fall short of improving the overall health of patients because they ask health systems to play a passive role when it comes to social determinants of health. To mitigate the impact of social determinants, the health sector must lead efforts to address the health-related social needs of patients. A major step forward will involve revising these current policies to support direct, healthcare driven interventions targeting social determinants.

	We propose modifications to 3 policies driving health systems toward intervening on social determinants of health, but fall short because of the passive role required of health systems: 

		The Internal Revenue Service should strengthen the Community Health Needs Assessment program by requiring nonprofit hospitals to address identified needs as part of the standard for nonprofit tax exemption. 

		Value-based payment models should incorporate financial support for at-risk hospitals implementing strategies to address social determinants because of their influence on quality-of-care outcomes. 

		The Accountable Health Communities program by CMS should test and evaluate models that allow health systems to fund social services directly.

In 2005, Seattle’s Downtown Emergency Service Center opened 1811 Eastlake, a supportive housing program for homeless residents with chronic alcohol addiction.

 The goal was to reverse the treatment-first paradigm, addressing the debilitating social need (homelessness) before the medical disease (addiction). After a year, societal costs were approximately $40,000 lower for residents enrolled in this Housing First program, with cost savings driven, in part, by healthcare spending reductions. Other social interventions have shown similarly promising effects on health outcomes. In a randomized controlled experiment in 5 large US cities, low-income individuals who received vouchers to move from high-poverty to low-poverty neighborhoods had lower rates of obesity and diabetes.

 These 2 examples are part of the accumulating evidence that supportive interventions directed toward the social and environmental barriers faced by patients—the social determinants of health&mdash;can influence health outcomes and healthcare spending.

Given the mounting evidence, a growing number of policies are beginning to direct the healthcare sector toward playing a more substantial role in addressing these social determinants with a population health lens. New requirements for nonprofit hospitals to conduct a Community Health Needs Assessment (CHNA) create a mechanism for public accountability of tax-exempt healthcare providers. New payment models, such as shared savings programs, make overall health—and thereby, social determinants&mdash;a part of the financial equation for health systems. Lastly, new models being tested by CMS, like the Accountable Health Communities program, explore the impact of bridges created between social services and the healthcare sector.

All of these policy initiatives lead the public and the medical community to believe we have reached a moment in healthcare where population health is part of the healthcare sector’s mission. Indeed, in recent years, healthcare has begun incorporating “population health” in the strategic plans at a growing number of hospitals; however, many population health programs fall short of addressing social determinants.

 Instead, they often limit their focus to disease-specific initiatives or quality and spending goals set by payers. The discordance between the promise of health policies and the current practice of population health creates the following signal: although these policies may drive the healthcare systems toward improving population health, in their current form, they will not sufficiently change our system of delivering care.

Tax-exempt, nonprofit hospitals are required to file a Section H form with the Internal Revenue Service (IRS), detailing how they benefit the community, with a so-called community benefit standard threshold required to receive nonprofit tax exemption. Historically, nonprofit hospitals defined "community benefit" loosely, often reporting charity care or lost revenue due to lower payment rates from public insurance programs rather than direct investments in social services or contributions to organizations addressing the social determinants faced by their patients. In 2009, hospital-led initiatives that focused locally on the health and infrastructure needs of their community constituted just 8% of current community benefit spending, representing less than 1% of total hospital expenditures.

In response, the Affordable Care Act modified the nonprofit tax code to encourage more socially focused initiatives, requiring nonprofit hospitals to conduct a CHNA every 3 years by convening local public health and community leaders. CHNAs have a number of strengths, including defining a community by the geographic area served, rather than patients currently served by a specific hospital, and making needs assessments and implementation plans explicit and public. However, CHNA requirements could go further by requiring certain types of community needs to be assessed, particularly initiatives that are evidence-based, like housing. Moreover, current IRS guidelines do not require hospitals to actually implement these plans to meet the community benefit standard. The CHNA could be strengthened by improving the transparency around the requirements to address those identified needs as part of the standard for nonprofit tax exemption.

New payment models, such as shared savings programs, intentionally or unintentionally insert social determinants into the financial equation facing healthcare systems. These models emphasize outcomes, like 30-day readmissions for the Hospital Readmission Reduction Program, which are significantly impacted not only by the quality of care, but also by the out-of-hospital social needs of patients. Current policy debates have focused on whether to risk-adjust these performance measures with socioeconomic factors to avoid financially penalizing hospitals that disproportionately serve lower-socioeconomic populations.

 We believe this represents a false choice between adjusting away important social differences versus applying financial penalties to address outcomes like readmissions. Alternatively, CMS could reapply funds from penalties to support at-risk hospitals that are implementing strategies to address social determinants that influence quality-of-care outcomes, such as reliable transportation services and improved food security.

The new Accountable Health Communities program by CMS works toward public health engagement by supporting experimental models that strengthen the linkages between the health sector and public health. Although encouraging, funding does not allow hospitals to experiment with providing services or material needs for patients directly. As a result, the scalability of successful models relies heavily on the existence of sustainable, well-resourced services being widely available, which is not the case in many communities.

 In addition to testing models that link the healthcare system to existing social services, there is a severe need to evaluate models that allow health systems to fund these services directly.

The predominant view of healthcare’s role has been providing high-quality medical care and delivering biomedical cures. As a result, health systems have deferred addressing the social and economic needs of patients to public health departments and the government in general. Although improving the quality and delivery of clinical care is important, in order to improve the overall health of the population, the healthcare sector requires a broader strategy that develops and tests direct interventions targeting social determinants. We have highlighted the potential of current policies to integrate social determinants into the business of healthcare; however, these policies fall short because they ask health systems to play a passive role. For meaningful change to occur, the health sector must lead the effort to address the health-related social needs of patients.

The authors would like to thank Dr David Asch for looking at early drafts of this manuscript.

 University of Pennsylvania (JA, KC, DG), Philadelphia, PA.

 The authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article. The University of Pennsylvania owns a nonprofit academic health center.

 Concept and design (JA, KC, DG); drafting of the manuscript (JA, KC, DG); critical revision of the manuscript for important intellectual content (JA, KC, DG); administrative, technical, or logistic support (KC); and supervision (JA).

 Krisda H. Chaiyachati, MD, MPH, The VA Robert Wood Johnson Clinical Scholars Program at the University of Pennsylvania, 423 Guardian Dr, 13th Fl, Blockley Hall, Philadelphia, PA 19104. E-mail: kchai@mail.med.upenn.edu.

1. Larimer ME, Malone DK, Garner MD, et al. Health care and public service use and costs before and after provision of housing for chronically homeless persons with severe alcohol problems. 

. 2009;301(13):1349-1357. doi: 10.1001/jama.2009.414.

2. Ludwig J, Sanbonmatsu L, Gennetian L, et al. Neighborhoods, obesity, and diabetes—a randomized social experiment. 

. 2011;365(16):1509-1519. doi: 10.1056/NEJMsa1103216.

3. Sharfstein JM. The strange journey of population health. 

. 2014;92(4):640-643. doi: 10.1111/1468-0009.12082.

4. Young GJ, Chou CH, Alexander J, Lee SY, Raver E. Provision of community benefits by tax-exempt U.S. hospitals. 

. 2013;368(16):1519-1527. doi: 10.1056/NEJMsa1210239.

5. Committee on Accounting for Socioeconomic Status in Medicare Payment Programs; Board on Population Health and Public Health Practice; Board on Health Care Services; Institute of Medicine; National Academies of Sciences, Engineering, and Medicine. 

Accounting for Social Risk Factors in Medicare Payment: Identifying Social Risk Factors

. Washington, DC: The National Academies Press; 2016.

6. Levi J, Segal LM, St. Laurent R, Lang A. Investing in America’s health: a state-by-state look at public health funding and key health facts. Robert Wood Johnson Foundation website. http://www.rwjf.org/content/dam/farm/reports/reports/2012/rwjf72503. Published March 2012. Accessed October 2016.

To improve overall health, and not just healthcare, health systems require policies that fund and promote direct interventions targeting social determinants.

Health Systems Tackling Social Determinants of Health: Promises, Pitfalls, and Opportunities of Current Policies

 The objective of this study was to determine if severe mental illness and/or a history of substance use in mothers of babies of a healthy weight was associated with infant mortality.

 This was a cross-sectional observational study using CareSource historical billed Medicaid Managed Care plan (MMC) claims in Ohio.

 CareSource is Ohio’s largest MMCP, serving approximately 1.2 million Medicaid consumers. Claims from 89,159 babies of a healthy weight (≥2500 grams) and their mothers were selected from the CareSource Ohio MMCP population from January 2011 through December 2014. The mental health and substance abuse status of the mother was identified from claim history. A logistic regression model was used to estimate the odds ratio for infant mortality based on the presence or absence of maternal severe mental illness (MSMI) or maternal substance abuse (MSU).

 The logistic regression model fit showed that the odds of infant mortality for infants born weighing  2500 grams or more was significantly higher when the mother was treated either for MSMI (χ

 = .006) at any time before or after delivery.

 Findings indicate that to address infant mortality, a focus on only babies born premature or low birth weight will result in missing a notable segment of the population that requires attention. Mothers who have babies with a healthy weight of at least 2500 grams, but who are diagnosed with either MSMI or MSU, need at least equal attention if inroads are to be made in reducing infant mortality.

To address infant mortality, a focus only on babies born prematurely or with a low birth weight will be missing a notable segment of the population that requires attention. 

Babies born with a healthy weight of 2500 grams but whose mothers are  diagnosed with either severe mental illness or substance use need at least equal attention to premature or low birth weight babies if inroads are to be made in reducing infant mortality. 

Managed care strategies should include an integration of mental and physical health to maximize impact.

In 2012, Ohio ranked 47th nationally for infant mortality (7.56 per 1000 live births)

—a rate significantly higher than the overall US infant mortality rate (6.05 per 1000 live births) and the Healthy People 2020 goal (6.0 per 1000 live births).

  State and local entities in Ohio, like the Ohio Department of Medicaid and Office of Mental Health and Addiction Services (MHA), as well as other human services agencies, have joined forces to address this issue.

 These organizations have been working in conjunction with Medicaid Managed Care plans (MMCP) within Ohio to emphasize the need to better coordinate care for pregnant mothers from preconception through the baby’s first year of life. Because CareSource is the largest MMCP in Ohio, covering approximately 60% of enrollees, the nonprofit provider took this as a call to action and focused analytic efforts to better understand details behind infant mortality.

The published causes of infant mortality in Ohio are identical to those seen at the national level: premature birth or low birth weight (LBW), serious birth defects, sudden infant death syndrome, maternal complications during pregnancy, and injuries/trauma.

 Pregnant mothers are at an increased risk for these outcomes when they engage in high-risk behaviors, such as smoking, drug use, and alcohol consumption—all of which are more prevalent among low-income, underserved individuals, like those covered by Medicaid.

 Given that 40% of births in Ohio occur among Medicaid beneficiaries, this population is the focus of many efforts to reduce infant mortality.

 Traditionally, these efforts have focused on prenatal care, with a goal of reducing the number of  LBW infants. These babies, weighing less than 2500 grams, have a significantly higher mortality rate than those over 2500 grams (5.5 pounds) at birth.

In 2010, the infant mortality rate for infants less than 2500 grams was 50.98 per 1000 live births, while the rate for infants born at or over 2500 grams was 2.13 per 1000.

 In the CDC report referenced for these statistics, the authors also noted that 91.8% of all US born infants weighed at least 2500 grams.

 According to unpublished CareSource data from a 2015 report, LBW infants, covered by CareSource accounted for approximately 60.7% of infant deaths (48.3% of infant births) from January 1, 2011, through December 31, 2014, while the remainder of deaths (39.3%) represent infants who were reported as healthy weight, or at least 2500 grams. Although the mortality rate is significantly lower for infants of a healthy weight, even a relatively low rate represents a significant number. The overall volume of healthy-weight infants resulting in infant mortality and a lack of literature in this area led to exploratory research for this study.

One factor that may explain some of the deaths among infants born at a healthy weight is the presence of maternal severe mental illness (MSMI) or maternal substance use (MSU). The link between MSU during pregnancy and infants born at a low birth weight, pre-term, and/or with physical deformities has been well established in the literature.

 However, the relationship between MSMI and infant mortality has not been extensively studied beyond the impact of postpartum depression on the health of the infant, and little has been done to evaluate the influence of MSU on infants born at a healthy weight. Any mental or physical health problems experienced by the mother may be further exacerbated by the inherent lack of communication or coordination of care between certified Community Mental Health Centers (CMHCs) and MMCPs. In Ohio Medicaid, mental health services are currently “carved out” to CMHCs and are fully paid for through Medicaid fee-for-service arrangements—unlike other health services, which are covered and paid for through the MMCPs. The CMHC may or may not be aware of medical issues, such as early pregnancy or the birth of an infant, and the MMCP has little knowledge of the mental health or substance abuse treatment received by enrollees, as there are no reporting requirements between the programs. This benefit design is quite common in MMCPs and results in fragmented care that may increase the risk of mortality in otherwise healthy infants.

Given that 86.5% of Medicaid enrollees in Ohio participate in an MMCP,

 understanding the relationship between MSMI and MSU status in the context of infant mortality may help optimize future program design and inform strategic policy decisions. Thus, the purpose of the present study was to address recommendations from the Ohio Infant Mortality Taskforce and explore possible associations between MSMI and MSU to mortality for infants of a healthy birth weight included within the Ohio Medicaid population.

 These analyses utilized information gathered by the Ohio Infant Mortality Task Force and incorporated claims data from CareSource’s Medicaid population, focusing on mortality rates among infants born weighing at least 2500 grams. 

This retrospective, cohort study included a de-identified sample of 121,925 infants born alive from January 1, 2011, through December 31, 2014. Inclusion criteria stipulated that the baby and mother be continuously enrolled in CareSource, an Ohio-based MMC, for 12 months or until the date of the infant’s death. Infant mortality was defined as any baby who died within 365 days of birth (DOB). Approximately 27% of the available samples (n = 32,766) were excluded based on the following criteria: no recorded birth weight, n = 6285 (5.1%); unknown mortality status, n = 15,475 (12.7%); and infants born weighing less than 2500 grams, n = 11,006 (9%). Mortality status would be unknown if no information was received from the mother, hospital, or state Medicaid entity; mortality status, as well as birth weight, might also be missing if the baby was not eligible for Medicaid coverage until birth (no delivery claim received). Additionally, gestational age was excluded from the analysis due to approximately 44% of the sample missing this information or issues with inconsistency across facility claims data. The data flow is presented in Figure 1.

Mothers’ MSU status was determined by examining the claims submitted to CareSource. More specifically, this MMCP utilizes assignment of the Expanded Diagnostic Cluster (EDC) of substance use as defined by the Johns Hopkins Adjusted Clinical Groups System (ACG).

 The indicator for MSU was flagged “yes” if the mother was treated for a diagnosis that was identified as a part of an EDC for Substance Use any time after the date of the estimated last menstrual period through 1 year post delivery. MSMI issues were identified using similar methodology: if the mother was treated for a diagnosis that was identified as part of an EDC that included psychosis, bipolar, or personality disorders, and the diagnosis occurred any time after the date of the estimated last menstrual period through 1 year post delivery, the indicator for MSMI was flagged as “yes.” The distributions of mothers’ MSU and MSMI were similar between the full sample and those excluded from analyses.

Given the lack of normality and homoscedasticity of variables, as well as the binary nature of the dependent variable (infant mortality), a logistic regression model was fit to assess the odds ratio of infant mortality given 2 diagnostic flags: 1) MSU or MSMI status and 2) maternal race. A stepwise method was used to fit the best model, which included the 3 maternal variables and all possible interactions of the 3 as independent variables. The best fit model was assessed using the Hosmer and Lemeshow Goodness of Fit Test.

 SAS version 9.4 (SAS Institute Inc, Cary, North Carolina) was used for all analyses.

This study was conducted using de-identified claims data. No human subjects review by an internal review board was required because the finding will be used internally at CareSource for quality improvement of health programs for CareSource beneficiaries. Also, the receipt and analysis of de-identified data does not constitute research involving human subjects.

The 2500 grams or more birth weight cohort represented 73.1% of the live births recorded in the state of Ohio during the target time period. Thirty-nine percent of the infant deaths recorded during this time occurred within this segment of the population. Of the 89,159 infants included in the study 59% were white, 30.5% black, 3.5% other, and 7% were not provided; 12.1% had mothers with MSU, 5.6% had mothers with MSMI, and 2.1% had mothers with both MSU and MSMI (Table).

The logistic regression model fit showed that the odds of infant mortality for those born at healthy weights were significantly higher when the mother was treated either for MSMI (χ

 = .006). The interaction of MSMI and MSU on infant mortality was not a significant factor in the stepwise logistic regression (χ

 >.05). The odds ratio estimate for MSU was 1.689 (95% confidence interval [CI], 1.167-2.445), indicating the likelihood of infant mortality is 69% higher when MSU is present. Similarly, the likelihood of infant mortality for mothers with MSMI is 73% higher than it is for those without it, with an odds ratio estimate of 1.726 (95% CI, 1.066-2.795). Interaction with maternal race was not significant (χ

 >.05 for both). The goodness of fit test showed no evidence of lack of fit 

 = .505) for the selected model. Visual representations of these results are provided in Figure 2.

The majority of studies addressing infant mortality include maternal physical health, but not maternal mental health. Studies also address the poor health status of the baby at birth (ie, prematurity, birth defects), but very little research focuses on healthy-weight babies.

 This study explored the possible association of elevated odds of infant mortality of healthy-weight babies as impacted by MSMI or MSU. The increased odds of infant mortality associated with MSMI and prevalence of MSU combine to create new opportunities for focused efforts to reduce infant mortality outside of what is traditionally known.

While beneficiaries tend to face unique challenges related to health disparities and access to healthcare, behavioral health carve-out in Ohio creates an additional challenge of fragmented care.

  For pregnant mothers with MSMI or MSU, this can compound current barriers that negatively impact care coordination needed and potentially increase the risk of infant mortality to babies of a healthy weight. In an effort to bridge this gap, CareSource expanded their High Risk Case Management program to engage all women with a severe mental health illness or substance use diagnoses who have delivered a baby of a healthy weight in the year immediately following delivery. Ongoing evaluations will be performed to assess the impact of CareSource’s approach to addressing the fragmentation of care. 

A retrospective cohort study does not allow any temporal conclusions to be made. The study shows that there are increased odds of infant mortality when the mother is diagnosed with severe mental illness or substance use, but no cause-and-effect inference can be made from the available data. Prior health information and behavioral health treatment is limited to what is available during the time of eligibility or sent by CMHCs after claims have been processed. Additionally, no relationships could be established with this data using socioeconomic status (ie, poverty, marital status, and educational attainment), since demographic data is limited to information provided by the state at the time of enrollment.

 Analyses were exploratory, conducted using CareSource Ohio MMCP claims only. Generalizability to other locations and payer mixes may or may not be appropriate. Further research is needed to assess socioeconomic status and timing of MSMI/MSU events in relation to infant mortality status. 

Limiting the focus of infant mortality prevention to only babies born premature or low birth weight eliminates a large portion of potentially preventable deaths. Prevention efforts must also include social support and mental health/substance use treatment for mothers of infants born at a healthy weight and diagnosed with MMSI/MSU which could lead to better health outcomes for the mother and baby.

The authors would like to acknowledge the contributions of Erin Brigham, MPH, Beth Hartzler, PhD, and Catherine Meade, BA.

 School of Health and Rehabilitation Sciences (SEW), The Ohio State University, Columbus, OH; CareSource (RWG), Dayton, OH; Front Edge Analytics, LLC (RWG), Dayton, OH.

 CareSource funded the study and supplied the data.

 Dr White received payment from CareSource for involvement in the preparation of this manuscript. At the time this work was completed, Mr Gladden was an employee of CareSource, which funded the study. Mr Gladden is currently the CEO of Front Edge Analytics, LLC.

 Concept and design (RWG, SEW); acquisition of data (RWG, SEW); analysis and interpretation of data (RWG, SEW); drafting of the manuscript (RWG, SEW); critical revision of the manuscript for important intellectual content (RWG, SEW); statistical analysis (SEW); obtaining funding (SEW); and supervision (RWG).

 Susan E. White, PhD, RHIA, CHDA, Associate Professor — Clinical, School of Health and Rehabilitation Sciences, The Ohio State University, 543 West 10th Ave, Columbus, OH 43210. E-mail: white.2@osu.edu.

1. Infant mortality. State of Ohio Department of Health website. http://www.odh.ohio.gov/~/media/ODH/ASSETS/Files/data%20statistics/maternal%20and%20child%20health/wh_infantmortalityrate.ashx. Published 2013. Accessed March 20, 2016.

2. Maternal, infant, and child health. Office of Disease Prevention and Health Promotion website. https://www.healthypeople.gov/2020/topics-objectives/topic/maternal-infant-and-child-health. Accessed March 20, 2016.

3. MacDorman MF, Gregory EC. Fetal and perinatal mortality: United States, 2013. 

4. Bada HS, Das A, Bauer CR, et. Low birth weight and preterm births: etiologic fraction attributable to prenatal drug exposure. 

5. Improving maternal mental health. World Health Organization website. http://www.who.int/mental_health/prevention/suicide/Perinatal_depression_mmh_final.pdf. Published 2008. Accessed March 20, 2016.

6. Center for Disease Control and Prevention. Grand Rounds: public health approaches to reducing U.S. infant mortality. 

7. Mathews TJ, MacDorman MF. Infant mortality statistics from the 2010 period linked birth/infant death data set. 

8. Lester BM, Andreozzi L, Appiah L. Substance use during pregnancy: time for policy to catch up with research. 

. 2004;1(1):5. doi: 10.1186/1477-7517-1-5.

9. Meyer MC, Johnston AM, Crocker AM, Heil SH. Methadone and buprenorphine for opioid dependence during pregnancy: a retrospective cohort study. 

. 2015;9(2):81-86. doi: 10.1097/ADM.0000000000000092.

10. Frohna JG, Lantz PM, Pollack H. Maternal substance abuse and infant health: policy options across the life course. 

11. Clifford S, Thom J; Best Practices in Schizophrenia Treatment Center, Department of Psychiatry, Northeastern Ohio Universities Colleges of Medicine and Pharmacy; Ohio Coordinating Center for Integrated Care. Implementation guide for integrating behavioral health and primary care in Ohio. Northeastern Ohio Universities Colleges of Medicine and Pharmacy website. http://www.neomed.edu/academics/bestcenter/implementation-guide-for-integrating-behavioral-health-and-primary-care-in-ohio-aug..pdf. Published July 2010. Accessed March 22, 2016.

12. Medicaid managed health care enrollment reports: 2015 monthly enrollment. Ohio Department of Medicaid website. http://medicaid.ohio.gov/RESOURCES/ReportsandResearch/MedicaidManagedCarePlanEnrollmentReports.aspx#1465537-2015-monthly-enrollment. Accessed March 20, 2016.

13. Infant Mortality Task Force. Preventing infant mortality in Ohio: task force report. Ohio Department of Health website. http://www.odh.ohio.gov/~/media/ODH/ASSETS/Files/cfhs/child%20and%20family%20health%20services/infant%20mortality%20task%20force/infantmortalityexecutivesummary.ashx. Published November 2009. Accessed March 20, 2016.

14. The Johns Hopkins ACG System. The Johns Hopkins University website. http://acg.jhsph.org/index.php?option=com_content&view=article&id=46&Itemid=366. Accessed July 27, 2015.

. New York, NY: John Wiley & Sons, Inc; 2000.

16. Paradise J, Garfield R. What is Medicaid’s impact on access to care, health outcomes, and quality of care? Setting the record straight on evidence. Kaiser Family Foundation website. http://kff.org/report-section/what-is-medicaids-impact-on-access-to-care-health-outcomes-and-quality-of-care-setting-the-record-straight-on-the-evidence-issue-brief/. Published April 2, 2013. Accessed March 22, 2016.&ensp;

To address infant mortality, focusing only on babies who were born prematurely or with a low birth weight will be missing an attention-worthy segment of the population.

Maternal Mental Health and Infant Mortality for Healthy-Weight Infants

 To understand the clinical roles in which internal medicine (IM) subspecialists engage, especially those involving ongoing patient management.

 Measures of physician clinical roles were based on survey responses collected from 8020 mid-career IM subspecialists who registered for the American Board of Internal Medicine maintenance of certification program (86% registration/response rate) between 2009 and 2013.

 Each subspecialist reported their percentage of clinical time in 5 clinical roles: primary, principal, longitudinal consultative, medical consultative, and procedural care. We characterized an IM subspecialist’s clinical role focus as those roles that composed a majority of their clinical time.

 Most IM subspecialists reported spending a majority of their time performing 1 (65%) or 2 (31%) clinical roles. Most (54%) reported a clinical role focused on ongoing patient care management roles, including principal care (eg, total responsibility for a specific condition, 23%), longitudinal consultative care (eg, shared care, 21%); or a mixed clinical role focus composed of both principal and longitudinal consultative care (8%). We also found that physicians focused on ongoing patient care management roles represent a significant percentage of physicians within most IM subspecialties (ranging from 19% to 88% across subspecialties).

 A subspecialist’s clinical role focus is an important practice characteristic, and many subspecialists perceive themselves as playing a significant role in care management. These findings suggest there are opportunities to incorporate subspecialists into newer payment and care delivery reforms; they also bring to light reasons that training and certification programs should consider the different clinical role foci subspecialists adopt.

	Internal medicine subspecialists’ predominant clinical role is an important practice characteristic, and most subspecialists play a significant role in ongoing patient management: 

		Many subspecialists report a clinical role focused on either principal care or longitudinal consultative care. 

		These data suggest there are opportunities to incorporate subspecialists into payment and care delivery reforms. 

		Physician training and certification programs should consider the clinical roles in which subspecialists engage when designing their programs.

Understanding the clinical roles that internal medicine (IM) subspecialists play is critically important in terms of both how we train and certify subspecialists, as well as how they are integrated into new models of care.

 In general, a subspecialist’s clinical roles can be divided into those that include some degree of ongoing patient management responsibility versus providing consultative services on behalf of the managing physician.

 The subspecialist's consultative roles are either cognitive (eg, providing diagnostic or management advice) or procedural (eg, evaluation for and/or performing a procedure on behalf of the managing physician) in nature.

 The subspecialist’s patient management clinical roles can include operating as their patient’s primary care provider or principal care provider, or in a longitudinal consultative (eg, shared care) role. The primary care provider role includes assuming responsibility for accessibility, continuity, coordination, and comprehensive care for all patient concerns.

 The principal care provider role includes assuming responsibility for the ongoing management of a patient’s specific condition inside the domain of their specialty (eg, a rheumatologist managing care for a patient with lupus).

 This is differentiated from a longitudinal consultative care role, wherein a subspecialist shares responsibility for managing a patient’s condition with the patient’s primary care provider (eg, a primary care physician and a cardiologist dividing and/or sharing cardiac care responsibilities for a patient with heart failure).

Although the consultative roles for subspecialists are well recognized, there is evidence suggesting that subspecialists commonly deliver ongoing routine care.

 However, the specific patient management clinical roles in which they engage while providing this care is not well understood, and many data collected on the subject are more than a decade old. Given the increasing emphasis placed on improving care quality by better integrating and coordinating care across the multitude of providers with whom each patient comes into contact, it is important that we better understand the role, or roles, in which the subspecialist is engaged when providing this care.

To get a better understanding of the frequency with which IM subspecialists engage in these different clinical roles, we examined recent clinical activity reports by mid-career internists who subspecialized after their general internal medicine training. We also explored whether subspecialists vary the clinical roles in which they engage or adopt a singular focused role, and examined the degree to which physicians within a subspecialty adopt similar clinical roles. Notably, IM subspecialists represent about 15% of all physicians (and about 25% of nongeneralists) board certified over the past decade by the American Board of Medical Specialties.

We used data drawn from a survey completed during the enrollment process, and then updated every 18 months or more, for the 10-year Maintenance of Certification (MOC) program of the American Board of Internal Medicine (ABIM). This survey was initially developed by ABIM staff members to inform MOC policies and programs. This development team consisted of physician and nonphysician staff members, and the questions were tested with non-ABIM physicians to ensure clarity and consistency in their interpretation. We used data from subspecialists initially certified between 2000 and 2003, 86% of whom subsequently registered for MOC and completed the survey between 2010 and 2013 (14% nonresponse rate). After additional exclusions (detailed in 

), our final sample included 8020 clinically active IM subspecialists (90% of professional time in patient care) (see eAppendix Table A [

]). Although the data used were collected by the ABIM, the design and conduct of the study; management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication, were all conducted by the authors independently of the ABIM. Physicians who enroll in an ABIM certification program enter into a business associates agreement that permits the ABIM use of their de-identified data at an aggregate level for research purposes.

 All data were de-identified prior to analysis.

The survey included a question about time spent in different clinical roles (eAppendix [section 1]). Specifically, we examined the percentage of clinical time that respondents reported engaging in the following roles: 1) primary care, 2) principal care, 3) longitudinal consultative care, 4) medical consultative care, and 5) interventional or diagnostic procedural care. Clinical time delivering hospital or intensive care unit care was excluded.

Among these 5 roles, we classified 3 (primary, principal or longitudinal consultative care) as patient management roles because each involves the physician being continually responsible for some aspect of their patient’s care over time. This is distinct from medical consultative or procedural care roles, in which the physician is providing care (eg, evaluations and/or procedures) on behalf of the referring physician, who remains responsible for ongoing patient management.

The primary distinction between the 3 patient management roles is the scope of care for which the subspecialist is responsible. For example, the primary care provider role is one in which a physician assumes responsibility for all patient needs and generally serves as the medical home and first contact for new problems. In contrast, principal care is what occurs when a patient has 1 dominant medical condition that is chronic and is cared for almost exclusively by the subspecialist. This patient would also likely have a primary care physician who would see them for preventive care and acute problems unrelated to the major (ie, principal) condition. For example, a patient with severe lupus might receive principal care from a rheumatologist regarding medication and monitoring of their condition. Longitudinal medical consultative care is distinct from principal care because the patient may continue to see the subspecialist for select components of their care, but the primary care physician (or other healthcare provider) shares in the responsibility for caring for the patient’s primary condition. For example, a patient with diabetes may see their primary care physicians for some of their diabetes care (eg, routine glycated hemoglobin monitoring) and visit their endocrinologist only for certain aspects of care, (eg, medication adjustments).

We examined IM subspecialists’ engagement in clinical roles in 2 ways. First, we examined the average percentage of time physicians were engaged in the 5 different clinical roles. However, we observed a large degree of heterogeneity in mean time spent in different roles between physicians within a subspecialty because most physicians (96%) reported spending a majority of their clinical time in only 1 or 2 clinical roles.

To characterize this variation in clinical role foci across physicians, we also examined how frequently subspecialists reported spending a majority of their clinical time in 1 or 2 of the different clinical roles (which we refer to as their clinical role focus). In particular, when subspecialists reported a majority of their time was spent in 1 clinical role, we classified them as having a singular clinical role focus (eg, reporting 70% of time in principal care becomes having a principal care focus). For subspecialists for whom a single role did not compose a majority of their time, we classified them as having a mixed clinical role focus if the sum of their 2 largest clinical roles composed a majority of their time (eg, 40% procedural and 30% medical consultative care becomes procedural plus medical consultative care focused). We also grouped subspecialties as procedural or nonprocedural (eAppendix [section 2]).

 All analyses were conducted using SAS version 9.3 (SAS Institute, Cary, North Carolina).

Average Percentage of Time in Different Clinical Roles

Overall, IM subspecialists reported 64% (standard deviation [SD] = 31) of their clinical time on average to be in clinical roles that involve patient management, mostly in principal care (mean = 29%) or longitudinal consultative care (mean = 30%; see eAppendix Tables B and C for median and mean clinical time by subspecialty). Physicians with nonprocedural subspecialties reported more time in patient management roles (mean = 79%; SD = 27%) than physicians with procedural subspecialties (mean = 51%; SD = 29%). That said, the time in clinical roles varied considerably among individual physicians within a subspecialty as indicated by the coefficient of variation often exceeding 100% (eAppendix Table C). This suggests that viewing measures of central tendency for these subspecialty groups, such as the mean or median time in the different clinical roles, may mask the typical clinical activity of many of these physicians.

Number of Subspecialists Grouped by Clinical Role Focus

Most internal medicine subspecialists report the majority of their clinical time is spent in principal care or longitudinal consultative care patient management roles.

November 2016

Reconsidering the Economic Value of Multiple Sclerosis Therapies

Health Systems Tackling Social Determinants of Health: Promises, Pitfalls, and Opportunities of Current Policies

Maternal Mental Health and Infant Mortality for Healthy-Weight Infants

The Role of Internal Medicine Subspecialists in Patient Care Management

Medical Home Transformation and Breast Cancer Screening

A New Chapter in Health Reform

Enhancing Patient and Family Engagement Through Meaningful Use Stage 3: Opportunities and Barriers to Implementation

Referrals and the PCMH: How Well Do We Know Our Neighborhood?

Does Medicare Advantage Enrollment Affect Home Healthcare Use?

Prescribers' Perceptions of Medication Discontinuation: Survey Instrument Development and Validation

Integrated Care Organizations: Medicare Financing for Care at Home