Sachin H. Jain, MD, MBA

For years, health care leaders told us that one day, digital health would become a dominant mode of health care delivery. Now, in large part due to the coronavirus disease 2019 pandemic, that day has come. In December 2020, 20% of physician visits were via telehealth—down from 50% in April, but up from 1% before the pandemic.

 As the articles in this issue of the journal demonstrate, further adoption of telehealth will require not just its widespread acceptance as a viable way to deliver care, but also changes in payment models and regulatory frameworks. What I hope does not get left out of this discussion is the imperative to ensure that people on the other side of “the digital divide”—and specifically older adults—don’t get left behind.

The growth in telehealth visits among older adults has largely paralleled that among other age groups. An August 2020 study conducted by the University of Michigan found that telehealth visits among US adults over 50 jumped from 4% in May 2019 to 30% in June 2020.

This situation presents both an opportunity and a dilemma for patients and providers alike. Telehealth and, indeed, a whole suite of virtual tools allow us to provide better, more comprehensive care to our patients in ways that are far more convenient for them. For example, a study by the American Hospital Association found that 3.6 million people forgo medical care each year because of transportation issues including lack of vehicle access.

 For older adults, transportation is the third most commonly cited barrier to accessing health services. For people in this category, remote care could make transportation problems largely irrelevant and provide them with a consistent way to access care.

Unfortunately, according to the Michigan study, just 64% of older Americans said they were “comfortable with video conferencing technologies.” That means that more than one-third of older people face significant challenges when it comes to getting the care they need. This translates to about 40 million Americans who might put off getting care simply because they lack the technological know-how to remotely communicate with their providers.

Aware of these challenges, SCAN Health Plan, the not-for-profit Medicare Advantage organization that I lead, tested a program last year through which we offered zero-cost tech support to some of our members. We helped a 95-year-old man set up a Gmail account. We helped a woman in her mid-70s upload a picture of her injured foot to her primary care physician. Based on the clearly demonstrated need for this program and our success at getting older adults to access care virtually, we expanded the initiative in 2021 to all of our members.

Telehealth and other forms of virtual care are finally here to stay. We spent years waiting for these technologies to go mainstream. Now that they have, let’s make sure we meet our patients where they are and support them as they adapt to these new ways of accessing care.

1. Telehealth adoption tracker. The Chartis Group. Accessed January 5, 2021. https://reports.chartis.com/telehealth_trends_and_implications-aug2020/

2. Telehealth visits have skyrocketed for older adults but concerns, barriers remain. News release. University of Michigan. August 17, 2020. Accessed January 5, 2021. https://news.umich.edu/telehealth-visits-have-skyrocketed-for-older-adults-but-concerns-barriers-remain/

3. Health Research and Educational Trust. Transportation and the role of hospitals. American Hospital Association. November 2017. Accessed January 5, 2021. http://www.hpoe.org/Reports-HPOE/2017/sdoh-transportation-role-of-hospitals.pdf

Articles

From the Editorial Board: Sachin H. Jain, MD, MBA

The scale of the coronavirus disease 2019 pandemic and its disproportionate impact on vulnerable populations has spurred unprecedented focus on and investment in social determinants of health (SDOH). Although the greater focus on social determinants is laudable and necessary, there is a tendency for health care organizations to implement SDOH programs at scale without rigorous evidence of effect, rather than targeting interventions to specific patients and assessing their impact. This broad, and sometimes blind, application of SDOH interventions can be costly and wasteful. We argue for rejecting the “more is better” mindset and specifically targeting patients who truly need and would substantially benefit from SDOH interventions. Matching interventions to the most appropriate patients involves screening for social needs, developing rigorous evidence of effect, and accompanying policy reform.

There is a tendency for health care organizations to implement social determinants of health (SDOH) programs at scale without rigorous evidence of effect, rather than targeting interventions to specific patients and assessing their impact.

Efforts to screen for and address SDOH should receive the same scrutiny as clinical interventions to diagnose and treat disease.

Matching SDOH interventions to the most appropriate patients involves screening for social needs, developing rigorous evidence of effect, and accompanying policy reform.

Long before coronavirus disease 2019 (COVID-19), there was ample evidence that social factors are primary drivers of health outcomes. However, the scale of the pandemic and its disproportionate impact on vulnerable populations have spurred unprecedented focus on and investment in social determinants of health (SDOH).

 Payers such as Tufts Health Plan and Humana have committed funding to addressing social isolation, food insecurity, and transportation access among seniors. At the federal level, HHS provided $1.3 billion to support community health centers in managing patients’ medical and social needs.

 On the surface, the bet being made is straightforward and grounded in common sense: If social factors are driving poor health and, in turn, greater health care spending, then investments to address those factors should lead to better outcomes and lower overall health care costs.

Although the greater focus on social determinants is laudable and necessary, optimism should be tempered by our relatively limited understanding of whether and which SDOH interventions actually improve the social circumstances or health of vulnerable patients.

 Interventions can be quite expensive and, if applied to individuals where there is little to no benefit, wasteful.

There is a tendency for health care organizations to implement SDOH programs at scale without rigorous evidence of effect, rather than targeting interventions to specific patients and assessing their impact. We believe that efforts to screen for and address SDOH should receive the same scrutiny as clinical interventions to diagnose and treat disease. Just as we would never prescribe an antibiotic for a patient whose infection could not be appropriately treated by that medicine, we should be careful not to prescribe social interventions without confidence that they will solve the problem at hand.

For example, one might suspect that providing rideshare services to patients in the Medicaid program would reduce no-shows. Yet, a clinical trial conducted at 2 clinics in West Philadelphia, Pennsylvania, showed that patients offered rideshare services missed the same number of appointments as controls. Upon further investigation, researchers found that other factors (eg, caregiver role for family members, stress, pressure to be at work) posed a greater obstacle to some patients than transportation.

 These findings caution against the broad, and sometimes blind, application of SDOH interventions.

Similarly, intensive care management programs for patients with high use of health care services have been tested in select populations to improve outcomes and reduce spending. The “hotspotting” program created by the Camden Coalition of Healthcare Providers is an oft-cited example that uses hospital admissions data to identify “superutilizers” and coordinate outpatient care and social services in the months following discharge. However, a recent randomized controlled trial (RCT) showed that the program did not result in the expected decreases in readmission rates.

 Patients enrolled in the program had diverse medical and social needs, and it is possible that the Camden model’s approach to care management was insufficient for some.

Although COVID-19 should motivate us to shore up investments in SDOH, it is also a pivotal opportunity to rethink our one-size-fits-all approach and curb overuse in our resource-constrained system. This requires rejecting the “more is better” mindset and specifically targeting patients who truly need and would substantially benefit from SDOH interventions. When interventions are broadly applied, the entities that most stand to benefit are the growing for-profit industry of vendors focused on this area, not the patient. Although social services can be used by nearly everyone, organizations must be thoughtful about matching interventions to the most appropriate patients. This involves (1) screening for social needs, (2) developing rigorous evidence of effect, and (3) accompanying policy reform.

Selectively providing social resources to those most in need requires an accurate and reliable method of identifying these individuals. Although most hospitals screen at least some patients for health-related social needs, this screening is often fragmented and ad hoc.

Providers often believe that comprehensive social needs screening is not feasible in a clinic setting, but the success of programs such as WellRx in Albuquerque, New Mexico, proves otherwise. In this pilot, an 11-question instrument was used to screen 3048 patients over a 90-day period. Medical assistants and community health workers connected patients who screened positive for unmet social needs with appropriate services and resources. Although similar programs at other institutions have produced mixed results, the WellRx pilot demonstrates that a consistent and systematic approach to screening is indeed possible.

There is no standard method for social needs screening, and approaches should be tailored to each community and organization. This starts with selecting the right screening tool—although the number and use of screening tools are increasing, few are validated.

 Codesigning screening tools with patients, clinicians, and community stakeholders is a useful strategy; patients can provide valuable insights into the most important questions to ask, clinicians can ensure that screening processes dovetail with their clinical work, and representatives of community-based organizations are privy to the needs and preferences of the communities they serve.

Once individuals with social needs are identified, the next step is matching them to the appropriate interventions. However, the evidence base for SDOH interventions is scant and many questions remain unanswered. Which interventions have the greatest impact on health, and for which patients are these most effective? What are the requisite contextual factors necessary to ensure that an intervention is delivered effectively?

If we expect to make real progress, it is time to start treating SDOH interventions with the same rigor as biomedical interventions. A national survey of health care executives revealed that most do not monitor or evaluate SDOH interventions—and if they are evaluated, most evaluations are uncontrolled pre-post studies or cross-sectional studies without a follow-up period of greater than 1 year.

As a useful model, consider the process of drug development: Compounds are designed and synthesized based on medical need, hypotheses are tested in animal studies, results are analyzed, and compounds are modified accordingly to maximize efficacy and minimize toxicity. This process is iterated before compounds are sent off to clinical trials and ultimately reach the market.

Similarly, an evidence-based approach to evaluating SDOH interventions means starting small, observing what does and does not work, and making adjustments as needed. Although the Camden trial results were disappointing, a growing number of RCTs have demonstrated the value of using implementation science to evaluate SDOH interventions. The Individualized Management for Patient-Centered Targets (IMPaCT) program is an excellent example. This community health worker intervention that helps patients with low incomes achieve their chronic disease management goals was first piloted in a primary care clinic in Philadelphia, studied via a single-center RCT, and then expanded to 3 clinics. A multicenter RCT showed that the intervention improved patient-perceived quality of care while reducing hospitalizations.

 SDOH interventions are neither homogenous nor simple, but this is an outstanding example of using rigorous science to evaluate and fine-tune an intervention for a specific population.

These RCTs are also a reminder to widen the aperture on our metrics for success. Of course, organizations need to understand whether patients are using provided services and whether these services are producing their intended effects. However, the Camden and IMPaCT trials illustrate the importance of measuring outcomes beyond health care utilization. Social needs are, by definition, personal, and a combination of quantitative and qualitative metrics is needed to capture the breadth of an intervention’s impact on people’s lives. Moreover, addressing SDOH takes time—organizations must have the patience to realize long-term improvements in health outcomes.

Increasing awareness that social determinants have a profound influence on health care outcomes and costs has motivated a slew of policy changes and recommendations intended to incentivize providers to screen for and address patients’ social needs.

 Although these reforms are a step in the right direction, they can have unintended consequences, such as pressuring organizations into the broad application of SDOH programs.

We recommend that policies incentivize the targeted provision of SDOH interventions to specific communities or individuals most likely to benefit from these services. One way to do this is by holding hospitals and health systems accountable for (1) comprehensive screening to distinguish between patients with and without social needs and (2) rigorous studies examining the effects of interventions on specific subpopulations. For example, federal and state departments of health could require that organizations implement and report data on these components of proposed SDOH interventions to be eligible for funding.

The COVID-19 crisis represents a generational opportunity to reshape our approach to addressing SDOH. It is not enough to continue investing in SDOH because it is “the right thing to do.” At the same time, we are not advocating for depriving patients in need of adequate access to social services. Rather, the reality is that most health systems, especially those in underserved communities, have little bandwidth to meaningfully address social determinants for high-need patients. Pursuing a more targeted approach can ensure that interventions actually bear fruit for individual patients and reduce wastage of limited resources.


 SCAN Group and Health Plan (SHJ), Long Beach, CA; Stanford University School of Medicine (SHJ), Stanford, CA; Harvard Medical School (PC), Boston, MA.

 Dr Jain owns stock in Anthem, Blink Health, Curisium, Cyft, Datavant, Embedded Healthcare, Firefly Health, General Electric, Koneksa Health, Merck, Safeguard Scientifics, Thrive Earlier Detection, Valera Health, Vertex, Vital, and U.S. Healthworks. Ms Chandrashekar reports no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

 Concept and design (PC); drafting of the manuscript (PC); critical revision of the manuscript for important intellectual content (SHJ, PC); provision of patients or study materials (SHJ); and administrative, technical, or logistic support (SHJ).

 Sachin H. Jain, MD, MBA, SCAN Group and Health Plan, 3800 Kilroy Airport Way, Long Beach, CA 90806. Email: Sjain@scanhealthplan.com.

1. Essien UR, Venkataramani A. Data and policy solutions to address racial and ethnic disparities in the COVID-19 pandemic. 

 Health Forum. April 28, 2020. Accessed June 9, 2020. https://jamanetwork.com/channels/health-forum/fullarticle/2765498

2. HHS awards $1.3 billion to health centers in historic U.S. response to COVID-19. News release. HHS; April 8, 2020. Accessed June 18, 2020. https://www.hhs.gov/about/news/2020/04/08/hhs-awards-billion-to-health-centers-in-historic-covid19-response.html

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 May 23, 2019. Accessed June 18, 2020. https://www.healthaffairs.org/do/10.1377/hblog20190520.243444/full/

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7. Lee J, Majerol M, Burke J. Addressing the social determinants of health for Medicare and Medicaid enrollees. Deloitte. February 27, 2019. Accessed June 27, 2020. https://www2.deloitte.com/us/en/insights/industry/health-care/applying-social-determinants-of-health-mcos.html

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We must rethink our one-size-fits-all approach to social determinants of health interventions and instead target patients who truly need and would benefit from these interventions.

Implementing a Targeted Approach to Social Determinants of Health Interventions

 Complex care management programs have emerged as a promising model to better care for high-need, high-cost patients. Despite their widespread use, relatively little is known about the impact of these programs in Medicaid populations. This study evaluated the impact of a complex care management program on spending and utilization for high-need, high-cost Medicaid patients.

 Randomized quality improvement trial conducted at CareMore Health in Memphis, Tennessee. A total of 253 high-need, high-cost Medicaid patients were randomized in a 1:2 ratio to complex care management or usual care.

 Intention-to-treat analysis compared regression-adjusted rates of spending and utilization between patients randomized to the complex care program (n = 71) and those randomized to usual care (n = 127) over the 12 months following randomization. Primary outcomes included total medical expenditures (TME) and inpatient (IP) days. Secondary outcomes included IP admission, emergency department (ED) visits, care center visits, and specialist visits.

 Compared with patients randomized to usual care, patients randomized to complex care management had lower TME (adjusted difference, —$7732 per member per year [PMPY]; 95% CI, –$14,914 to –$550; 

 = .036), fewer IP bed days (adjusted difference, —3.46 PMPY; 95% CI, –4.03 to –2.89; 

 <.001), fewer IP admissions (adjusted difference, —0.32 PMPY; 95% CI, –0.54 to –0.11; 

 = .014), and fewer specialist visits (adjusted difference, —1.35 PMPY; 95% CI, –1.98 to –0.73; 

 <.001). There was no significant impact on care center or ED visits.

 Carefully designed and targeted complex care management programs may be an effective approach to caring for high-need, high-cost Medicaid patients.

A complex care management program for high-need, high-cost Medicaid patients reduced total medical expenditures by 37% and inpatient utilization by 59%. Based on the design of the program, these results suggest that:

Carefully designed and targeted complex care management can be effective among high-need, high-cost Medicaid patients.

Community health workers and other nontraditional healthcare workers can help engage and activate patients, build trust, and better understand and manage the nonmedical drivers of poor health and avoidable spending.

Targeted interventions focused on modifiable risk factors are an effective and efficient approach for reducing unnecessary utilization.

High-need, high-cost patients—those with chronic medical conditions and co-occurring social and behavioral complexity—experience poor health outcomes and high rates of potentially preventable spending.

 Improving care quality and lowering spending for this population have become a focus for policy makers, clinicians, payers, and health systems.

Complex care management programs—broadly defined as efforts to coordinate medical and social services, assist patients and caregivers in managing medical and behavioral health conditions, and address the psychosocial drivers of poor health—have emerged as a promising model to improve care for high-need, high-cost patients.

 In recent years, complex care management programs have been widely adopted by health systems and delivery organizations entering into value-based payment arrangements. A 2018 survey found that 96% of accountable care organizations had implemented complex care management programs for high-need, high-cost patients.

The impact of complex care management has been uneven. Systematic reviews and large-scale program evaluations have not demonstrated a consistent impact on health outcomes, spending, or acute care utilization.

 Some individual programs, however, have led to improvements in patient-reported health and decreases in acute care utilization and total spending.

 Program heterogeneity (eg, practice setting, patient characteristics, care models, staffing) may explain these varied results.

Complex care management programs have traditionally been developed and implemented among Medicare populations.

 Increasingly, these models are being adopted in Medicaid.

 Similar to the Medicare population, high-need, high-cost Medicaid patients have high rates of chronic conditions

 both attributes well suited to complex care management.

 High-need, high-cost Medicaid patients also have high rates of behavioral health disorders and unmet social needs,

 attributes less common among Medicare patients and areas in which the impact of complex care management is uncertain.

Despite the proliferation of complex care management for Medicaid patients, there have been very few rigorous program evaluations, and the overall effect of these programs is unclear. This paper presents the results of a randomized quality improvement trial to evaluate the impact of complex care management on spending and utilization for high-need, high-cost Medicaid patients with complex health and social needs.

This was a randomized quality improvement trial to evaluate the impact of a complex care management program on spending and utilization for high-need, high-cost Medicaid patients. The design and implementation of the program, detailed in the following paragraphs, incorporated attributes of successful complex care management programs in other populations.

 The program was implemented at CareMore Health (CareMore) in Memphis, Tennessee, where CareMore provides comprehensive primary care services for Medicaid patients enrolled in TennCare plans administered by Amerigroup. The program was operated out of a single CareMore care center.

Participants were drawn from adult Medicaid patients attributed to CareMore primary care physicians (PCPs). Program eligibility criteria were aimed at identifying patients at risk for poor outcomes and unnecessary spending, as well as those most likely to benefit from complex care management. The criteria drew from analyses suggesting that combining predictive models, historical claims, and clinician judgment is the most effective approach to identifying patients for complex care management.

Eligible patients were first required to meet at least 1 of the following criteria: top 5% of total medical expenditures (TME) in the prior 12 months, top 5% of Chronic Illness Intensity Index (CI3) score, or care team member nomination. The CI3 score is a predictive model developed by Amerigroup that uses demographic, clinical, and pharmacy claims data to estimate a patient’s predicted cost in the subsequent 12 months compared with the average Amerigroup Medicaid patient.

Patients meeting 1 of the above criteria were then required to meet at least 1 of the following criteria: 2 or more inpatient (IP) admissions in the prior 12 months, 3 or more emergency department (ED) visits in the prior 12 months, or 2 or more chronic conditions.

Patients less likely to benefit from complex care management because of specific comorbidities (cognitive impairment, severe mental illness without medical comorbidity, active malignancy, pregnancy) or current residence in a long-term care facility were excluded.

Given limited resources, eligible patients were randomized in a 1:2 ratio to complex care management or usual care. Patients not continuously attributed to CareMore over the 12-month period following randomization were considered lost to follow-up. This could occur for several reasons: loss of Medicaid eligibility, enrollment in another TennCare plan, or transfer of care to a non-CareMore PCP.

The program was staffed by a multidisciplinary care team consisting of a community health worker (CHW), a social worker (SW), and a PCP. CHWs and other nontraditional healthcare workers have become increasingly common components of complex care management programs.

 CHWs, drawn from local communities, build longitudinal relationships with patients, provide context-specific activation and engagement, assist with care coordination, and help patients navigate the complexities of health systems and social programs.

 CHW interventions have been shown to improve health outcomes and reduce spending, especially among urban, underserved populations.

With guidance and support from the SW and the PCP, the CHW was responsible for patient outreach, engagement, activation, and accompaniment. The SW was responsible for counseling and brief interventions for patients with behavioral health needs and for coordinating referrals to social service agencies and other medical providers. The PCP was responsible for providing comprehensive care for acute and chronic conditions and for coordinating with specialists and inpatient providers.

Patients randomized to the complex care management program were contacted by the CHW via telephone and invited to enroll in the program. The CHW attempted to contact patients on 3 separate occasions, after which no additional attempts at contact were made.

Program activities were designed around a conceptual model of the drivers of poor outcomes and avoidable spending for high-need, high-cost Medicaid patients. Drawing on existing frameworks,

 these drivers were grouped into 4 intersectional domains: (1) medical complexity and health trajectory, (2) care coordination and engagement, (3) self-management and behavioral health, and (4) environmental and social supports. Within each domain, specific drivers of poor health and high costs were identified. For example, unstable housing was a driver within the environmental and social supports domain, and medication management was a driver within the medical complexity and health trajectory domain (see 

 for a complete list of domains and drivers).

Patients who enrolled in the program were scheduled for a 60-minute in-person intake visit within 7 days of enrollment. During the intake visit, the entire care team (ie, CHW, SW, and PCP) conducted a comprehensive assessment of the patient’s medical, behavioral, and social risk factors. The results of this assessment were used to identify and prioritize the most impactful and impactable drivers of poor outcomes and avoidable spending for each patient. Finally, the patient and the care team codeveloped a tailored care plan that outlined interventions, roles, and responsibilities for addressing each of the prioritized drivers. For example, the SW may be responsible for providing counseling and brief interventions for depression and for coordinating referrals to psychiatry. The PCP may be responsible for rapidly addressing poorly controlled diabetes or congestive heart failure. The CHW may be responsible for contacting patients to identify barriers to adherence and self-management. The patient may be responsible for applying for temporary housing assistance, logging physical activity, or checking in daily with the CHW/PCP to provide blood pressure or blood sugar readings.

After the intake visit, there was frequent, structured follow-up. The CHW called patients at least weekly to assess progress and identify and troubleshoot barriers. The care team met formally every week to review and update care plans. Patients were scheduled for monthly, in-person follow-up visits to review and revise the care plan. Patients admitted to an IP or skilled nursing facility were visited by a member of the care team, if possible. The CHW accompanied patients to specialist, social service, and other appointments as needed.

The program launched on March 1, 2017, and ran until February 28, 2018, at which point patients returned to usual care.

The primary outcomes were TME and IP bed days. Secondary outcomes included ED visits, IP admissions, care center visits, and specialist visits. The ED visit outcome measure excluded visits that resulted in an IP admission. The care center visit measure included any in-person visit for medical or behavioral health services at one of CareMore’s outpatient facilities. All outcomes were assessed over the 12-month period following randomization (March 1, 2017, to February 28, 2018).

The following covariates were assessed for each patient: age at randomization, gender, CI3 score at randomization, and rates of each outcome measure over the 12 months prior to randomization (March 1, 2016, to February 28, 2017). Spending and utilization rates were calculated from claims data provided by Amerigroup. CI3 scores were provided by Amerigroup. Demographic data were extracted from the CareMore electronic data warehouse.

Baseline characteristics for patients randomized to complex care management and those randomized to usual care were compared using standardized mean differences (SMDs).

 Proposed cutoffs vary, but SMDs greater than 0.1 or 0.25 are generally considered to represent an important difference between groups.

The effect of the program on the primary and secondary outcomes was analyzed through an intention-to-treat approach based on random assignment. Count-based utilization outcomes (IP admissions, IP bed days, ED visits, care center visits, and specialist visits) were modeled with Poisson regression, and the spending outcome (TME) was modeled with ordinary least squares regression. Both adjusted and unadjusted analyses were performed. Adjusted models included age at randomization, gender, CI3 score at randomization, and the corresponding outcome measure over the 12-month period prior to randomization (eg, the model for IP days included IP days from March 1, 2016, to February 28, 2017, as a covariate). Adjusted and unadjusted results were similar. Results from the adjusted models are presented below, with unadjusted results available in the 

 value of .05 was considered to indicate statistical significance. 

 values were adjusted for multiple comparison using the Holm-Bonferroni method.

 All analyses were performed using R version 3.5.1. The evaluation was implemented as a randomized quality improvement trial without institutional review board approval or waiver.

 depicts the flow of patients through the trial. A total of 253 patients met eligibility criteria and were randomized in a 1:2 ratio to complex care management (n = 93) or usual care (n = 160). More than half (56.9%) of the patients randomized to complex care management ultimately enrolled in the program. The most common reason for not enrolling was an inability for the CHW to reach the patient via telephone (37 of 40 patients). A small number of patients were reached but declined to participate in the program (3 of 40 patients). Across both groups, roughly 20% of patients were lost to follow-up in the 12 months after randomization. The intention-to-treat analysis included 71 patients in the complex care management group and 127 patients in the usual care group. Information on appointment-kept rates is provided in the eAppendix.

Baseline rates of spending and utilization were high. The average patient incurred TME of $22,714, spent 6.81 days in an IP facility, and visited the ED 3.30 times during the 12-month period prior to randomization. 

 compares baseline characteristics between patients randomized to complex care management and those randomized to usual care. Compared with patients in the usual care group, patients in the complex care management group were slightly younger (mean age of 43.8 vs 45.9 years), had lower predicted future costs (mean CI3 score of 8.56 vs 9.99), and visited the ED more frequently (3.82 vs 3.01 mean visits).

 compares adjusted rates of utilization and spending between patients randomized to complex care management and those randomized to usual care over the 12 months following randomization. The complex care management program was associated with lower TME (adjusted difference, —$7732 per member per year [PMPY]; 95% CI, –$14,914 to –$550; 

 = .014), and fewer specialist visits (adjusted difference, —1.35 PMPY; 95% CI, –1.98 to 0.73; 

 <.001). There was no significant impact on ED or care center visits.

The eAppendix includes summary vignettes for selected patients enrolled in complex care management. These are intended to supplement the quantitative findings from the primary analysis and offer additional detail on the implementation of complex care planning.

A complex care management program reduced spending and inpatient utilization among high-need, high-cost Medicaid patients. Patients randomized to complex care management had TME that were 37% lower than those randomized to usual care, an absolute reduction of $7732 per patient per year. This spending reduction appeared to be driven primarily by decreases in inpatient utilization—bed days were reduced by 59% and admissions by 44%.

Several potential mechanisms could explain the observed decreases in utilization and spending. Improved management of medical, social, and behavioral risk factors may have prevented acute exacerbations of chronic disease. Patient engagement and activation may have led to improved self-management and adherence. Finally, social and behavioral stabilization may have facilitated safe discharge planning, reducing the need for, or duration of, inpatient admissions.

Complex care management led to a small but significant decrease in specialist visits. Although prior studies of complex care management have not examined specialist use, data from primary care improvement initiatives suggest that enhanced primary care services can reduce downstream specialist utilization.

 In this study, specialist utilization could have been decreased by increased engagement with primary care, better management of chronic medical conditions in the primary care setting, or better communication and coordination between the PCP and specialists. Because there were no additional financial or nonfinancial incentives to reduce utilization in the complex care management group, it is less likely that the reduction in specialist utilization was due to restricted access.

Complex care management for high-need, high-cost Medicaid patients significantly reduced total medical expenditures and inpatient utilization in a randomized quality improvement trial.

Impact of Complex Care Management on Spending and Utilization for High-Need, High-Cost Medicaid Patients

Telemedicine offers a promising solution to the growing physician shortage, but state-based medical licensing poses a significant barrier to the widespread adoption of telemedicine services. We thus recommend a mutual recognition scheme whereby states honor each other’s medical licenses. Successfully implementing mutual recognition requires policy, technological, and administrative changes, including a federal mandate for states to participate in mutual recognition, consistent standards for using and regulating telemedicine, a mechanism to enable interstate data sharing, financial support for states, and a “state of principal license” requirement for physicians. Reforming the United States’ outdated system of state-based medical licensure can help meet patient demand for virtual care services and improve access to care in rural and medically underserved areas.

The United States’ antiquated system of state-based medical licensure creates unnecessary hurdles that reduce the potential of telemedicine to address the growing physician shortage and improve access to care in rural and medically underserved areas.

Mutual recognition, a scheme whereby states honor each other’s medical licenses, is a comprehensive potential solution to enhance license portability and eliminate barriers to virtual care.

Implementing mutual recognition necessitates policy, technological, and administrative reforms to protect patient safety, prioritize the financial well-being of states, and streamline physician reimbursement.

By 2030, the United States could see a shortage of nearly 120,000 physicians.

 Telemedicine can alleviate the impacts of this shortage by helping physicians make use of unused time and see additional patients, allowing patients to access a larger pool of physicians and connecting specialists to hospitals in rural and medically underserved areas.

 Although telemedicine is growing in use and acceptance, state licensing laws create false geographic barriers and pose a significant challenge to widespread adoption.

In the United States, state medical boards regulate physician licensing, creating a patchwork of inconsistent state licensure laws. With few exceptions, physicians must acquire and maintain a license for each state in which they practice medicine. This antiquated system of state-based medical licensure, originally enacted in the 19th century to reduce medical malpractice and protect patient safety, has profound implications for the promise of telemedicine to increase access to care for vulnerable populations and mitigate the impacts of the national physician shortage.

 In this commentary, we argue for moving away from state-based medical licensure and describe policy, technological, and administrative changes necessary for moving toward portable medical licensure.

Physicians must be licensed in each state where current and future patients are located, so physicians practicing telemedicine across state borders may be responsible for obtaining and staying compliant with up to 51 different state practices of medicine at any given time. There are some exceptions—for example, 10 states issue special purpose licenses for physicians who wish to come to their state for a limited time, scope, and purpose, such as to demonstrate a new technique or to educate medical students.

 However, these exceptions are few and far between.

The multistate licensure process is long and expensive; some states require physicians to pay annual license renewal fees, complete additional coursework, submit required documentation, and participate in interviews. Even after physicians complete these requirements, state medical boards can take several months to process licensing applications.

 Along with imposing substantial direct costs on physicians, state-specific licensing reduces competition that could lower healthcare prices, limits opportunities for physicians to gain experience by seeing more patients, and exacerbates health disparities.

 The impact of restricting telemedicine falls hardest on poor patients, the uninsured, and those who rely on state Medicaid programs, many of whom lack access to reliable transportation and cannot travel across state lines to see specialists.

Over the past decade, the Federation of State Medical Boards (FSMB), a national nonprofit organization that represents 70 state medical and osteopathic boards, has advanced several proposals to enhance license portability and reduce regulatory barriers to telemedicine. These include the Uniform Application and the Federation Credentials Verification Service, which are both web-based applications that eliminate the need for physicians to reenter identifying information and credentials when applying for multiple licenses. Most recently, the FSMB instituted the Interstate Medical Licensure Compact, an agreement to expedite the medical licensure process among member states. Physicians in good standing can freely practice in member states as long as they possess a “full and unrestricted” license in their state of principal license (SPL). To date, only 24 states have joined the compact.

These proposals are a step in the right direction but far from the solution. They simply streamline—not eliminate&mdash;the process of applying for multiple medical licenses. Additionally, they do not reduce the cost to doctors of maintaining multiple medical licenses, estimated at $300 million each year.

 As a more comprehensive solution, we recommend a mutual recognition scheme whereby states honor each other’s medical licenses. This model has been successfully adopted in Europe and Australia and by the Veterans Health Administration, US military, and US Public Health Service.

 Furthermore, because standards for medical education apply nationwide and physician training requirements are set by federal agencies such as HHS, mutual recognition is warranted.

Proponents of the status quo argue that mutual recognition compromises patient safety, reduces revenues from state licensing fees, and complicates physician reimbursement. To address these challenges, mutual recognition should be accompanied by (1) a federal mandate, (2) consistent standards for using and regulating telemedicine services, (3) increased data sharing among states, (4) financial support for states, and (5) a requirement for physicians to select an SPL.

To reduce barriers to interstate medical practice, some states have attempted unilateral action. For example, in 2016, the Florida House of Representatives passed a bill with a provision allowing physicians licensed in other states to offer telemedicine services in Florida. However, the Florida Senate eliminated the provision. To avoid similar situations, Congress can require states to participate in mutual recognition. In fact, legal research suggests that federal action to promote interstate telemedicine is justified based on the Commerce Clause of the US Constitution, which states that Congress has the power “to regulate commerce…among the several states.”

 Another benefit of instituting a federal mandate is a consistent set of definitions needed to support mutual recognition (eg, SPL).

Standards for Using and Regulating Telemedicine Services

Each state currently defines the “practice of medicine” differently, making it difficult to discern what constitutes an acceptable telemedicine consultation in any given state. Standards of practice, conduct, and behavior during telemedicine consultations—including requirements related to physician credentialing, patient education, and physician supervision of other healthcare professionals&mdash;vary widely among states.

 Thus, the shift to mutual recognition must be accompanied by efforts to establish consistent standards for using and regulating telemedicine services. These standards should be defined at the federal level.

State medical boards are tasked with the responsibility of monitoring and disciplining physicians licensed in their state. However, due to gaps in information sharing among states, nearly one-third of physicians disciplined in one state are able to practice elsewhere without limitations, repercussions, or public disclosure. This is especially dangerous in a mutual recognition scheme in which out-of-state physicians routinely see and treat patients with little oversight.

For mutual recognition to promote patient safety, information on malpractice, medical errors, and license cancellation or suspension must be shared among states, made publicly available to patients, and used to enforce disciplinary actions across state borders. States should use resources like the National Practitioner Data Bank, established in 1986 as a central data repository for malpractice payments and state disciplinary actions, to conduct rigorous background checks before physicians participate in telemedicine consultations and deliver care across state borders.

Given the administrative and technological costs of implementing mutual recognition and dismantling the existing state-based medical licensure system, HHS could provide states with incentive payments for adopting mutual recognition agreements and eliminating state-specific licensing and renewal fees. Additionally, HHS could offer grant funding to enhance interstate data sharing systems and other functions that promote mutual recognition. While financial support for states is an important first step, mutual recognition can reduce healthcare costs in the long run as telemedicine services increase access to care for rural and underserved populations.

One challenge of mutual recognition is attributing physicians to any given state. This can complicate physician reimbursement for telemedicine services and reduce the effectiveness of state healthcare programs dependent on physician participation. A possible solution is requiring physicians to select a single SPL for questions regarding reimbursement and attribution. This requirement already exists for physicians participating in the Interstate Medical Licensure Compact. Because physicians would fall under the jurisdiction of their SPL’s medical board, the SPL could hold primary responsibility for collating information from interstate data-sharing systems and enforcing disciplinary action against errant physicians.

The environment of medicine is changing. More than three-fourths of patients want access to virtual care services, and patients across all age groups express a desire to use telemedicine to gain easier, more immediate access to physicians.

 To meet growing patient demand and solve our country’s critical physician shortage, we can and must reform our outdated state-based licensing system.

 Harvard Medical School (PC), Boston, MA; CareMore Health System (SHJ), Cerritos, CA; Stanford University School of Medicine (SHJ), Stanford, CA.

 Dr Jain is an employee of CareMore and Aspire Health, which are both multistate provider entities. Ms Chandrashekar reports no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

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

 Sachin H. Jain, MD, MBA, CareMore Health System, 12900 Park Plaza Dr, Cerritos, CA 90703. Email: Sachin.Jain@caremore.com.

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In this commentary, the authors argue for moving away from state-based medical licensure and describe policy, technological, and administrative changes necessary for implementing portable medical licensure.

Sachin H. Jain, MD, MBA

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