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Hackensack Ties Free Tuition To Primary Care Jobs

Hackensack Meridian School of Medicine, based in Nutley, N.J., is aiming to strengthen its primary care physician workforce by addressing the financial burden of medical education. 

Seventy percent of medical students in the class of 2023 graduated with educational debt, according to the Association of American Medical Colleges. Of those, 84% owed more than $100,000, and 54% had more than $200,000. 

To combat this challenge and the growing primary care physician shortage, a handful of universities — including New York University in New York City, Montefiore in New York City and Johns Hopkins in Baltimore — have launched tuition-free programs with donations ranging from $100 million to $1 billion. 

But those efforts have not significantly shifted graduates toward primary care, according to early data. 

"It doesn't appear to really work," Jeffrey Boscamp, MD, president and dean of Hackensack Meridian School of Medicine, told Becker's. "In the end, you come out debt free, and you still do orthopedics or urology or some [other high-paying medical specialty]. It doesn't really drive you toward primary care."

Hackensack Meridian launched its Primary Care Scholars Program in 2024 amid projections that the U.S. Will face a shortage of 87,150 primary care physicians by 2037, according to the Health Resources and Services Administration.

How it works

After being admitted, students may apply to the program by committing to a primary care specialty: pediatrics, family medicine, general internal medicine or geriatrics.

The program offers:

A 50% tuition scholarship during the three-year MD track

A $7,500 relocation stipend

A monthly payment of $2,500 for living expenses

Edison, N.J.-based Hackensack Meridian Health will forgive all tuition, award funds and accrued interest to program participants who complete residency within the system and work there full time for the same number of years their medical school was funded.

The program started with five students in the 2024-25 school year, and doubled to 10 for 2025-26. The medical school plans to expand it to 15 spots next year, Dr. Boscamp said. 

"It's a perfect synergy and a win-win for the school in terms of us always trying to figure out ways that the students' debt burden could be diminished, and for the [hospital] network to get a really finite result out of it that helps them in their strategy," he said.

Inspired by Geisinger

Hackensack's initiative was inspired by Geisinger Commonwealth School of Medicine's Abigail Geisinger Scholars Program, launched in 2019. That program covers tuition and living assistance for students who commit to primary care at the Danville, Pa.-based health system for at least four years after graduation.

What's next

The program is designed to be flexible. Hackensack can expand it to other high-need specialties such as obstetrics or psychiatry, depending on community needs.

Hackensack frames the program as a cost-effective way to grow primary care capacity. 

"It costs a fair amount when you start thinking about medical school cost, plus the stipend," Dr. Boscamp said. "But in the end, if you do the economic analysis, it makes sense, as opposed to buying practices — which can be very expensive and come with physicians who already have full panels. So these are physicians that then come out, [they] have the ability to take on a significant patient load."

Dr. Boscamp said success will take years to measure, but he is optimistic. 

"They went to school with us, they lived in our hospital as a resident for three years, and then they're working for us for three years," he said. "So they're nine years into Hackensack already, in one way or another. We think it's going to be pretty sticky."

Q&A: International Medical Graduates May Help Address Severe PCP ...

December 06, 2022

8 min read

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One of the top challenges facing primary care today is a severe workforce shortage.

By 2034, the Association of American Medical Colleges estimates that there will be a shortage of between 17,800 and 48,000 primary care physicians in the United States. Additionally, recent data from the Health Resources and Services Administration (HRSA) show that, as a whole, the U.S. Has less than half of the PCPs its citizens need. About 99 million people are in one of 8,190 primary care professional shortage areas in the U.S. HRSA estimates that 17,063 primary care practitioners are needed to fill the workforce gap.

According to G. Richard Olds, MD, MACP, the president of St. George's University in Grenada, international medical graduates could help address the primary care workforce shortage. Healio spoke with Olds to learn more about the severity of the shortage and potential solutions.

Healio: How severe is the primary care workforce shortage in the United States?

Olds: There is an overall shortage of doctors in general in the U.S. This has been going on for several decades. Since nearly two-thirds of U.S. Medical school graduates specialize, and we need more than half to go into primary care, the shortage of primary care doctors has been growing disproportionally. COVID has also made this shortage worse since most primary care doctors support themselves in the outpatient area, and during the first year of the pandemic, about 10% of physicians closed their practices permanently. Indeed, by 2034, the Association of American Medical Colleges estimates the U.S. Will face a shortage of up to 124,000 physicians. If in doubt, ask anyone in your area how difficult it is to find a primary care doctor these days. Even if you do find one, the question remains: will you actually see a doctor or a nurse practitioner or a physician assistant?

Healio: How did the shortage get to the point we're at now?

Olds: The overall doctor shortage started in the 1970s when a lot of new medical schools were built. By the 1980s, an article appeared that suggested we would have a doctor surplus based on managed care needs (with primary care doctors serving as gatekeepers, far fewer specialty visits would take place). Clearly, this approach to health management didn't work very well, but the fear we were training too many doctors persisted. Existing medical schools had little or no motivation to increase class size, and regulators made it very difficult to create new schools. As a result, for almost 30 years, no new medical schools were built, and existing schools did not increase their class size. Finally, by 2000 it was becoming clear that, with the U.S. Population growing and aging, we were going to have a significant shortage of doctors. Florida State, in fact, had to sue the U.S. Medical School Accreditors (LCME) to get a new school open in the early 2000s. This was followed by a period of growth of new schools and expansion of existing schools, but that process is very slow, very expensive, and has now slowed down, but the demand keeps rising faster than the supply. For example, I built a new medical school during that period, and it took 7 years, cost $100 million, and only had 50 students in its first class.

Healio: How did the COVID-19 pandemic affect the shortage? And, in turn, how did the shortage affect the pandemic?

Olds: As I noted above, the pandemic closed about 10% of practices closed permanently. In addition, like many other professionals, a lot of doctors have taken early retirement and left the workforce. This was motivated in part by increasing workloads and the increased return on their retirement accounts. As a result, the real shortage is likely a lot worse than pre-pandemic estimates. The severe shortage of doctors clearly made the pandemic worse. Some states required a doctor, in the beginning, to order a COVID PCR test and later to receive antiviral treatment. If you didn't have a primary care doctor, this delayed testing and treatment. Alternatives such as clinics and hospital EDs were overwhelmed, which delayed admissions, testing, and treatment. Home testing has now helped but getting treatment is still an issue. All these factors undoubtedly resulted in excess mortality due to COVID.

Healio: How can international medical graduates help address the PCP workforce shortage?

Olds: For years, foreign-trained doctors have made up about 25% of the physician workforce. About 40% of these doctors are U.S. Citizens who attend schools like St George's University outside the U.S., and 60% are foreign nationals who went to medical school in their own country or another country outside the U.S. Because U.S. Medical school grads are not going into primary care or practicing in rural areas in large numbers, many international medical graduates have filled that gap. About 40% of the primary care doctors in the U.S. Trained outside the states. At my university, 75% of our grads go into primary care fields while 25% specialize.

Healio: Rural areas are particularly impacted by workforce shortages. What efforts are needed to incentivize IMGs to practice in these areas?

Olds: The basic problem in rural America is students from rural areas don't get into U.S. Medical schools proportionally to the 14% of people who live in rural areas. To understand this specific problem, you need to know why doctors practice where they do. About half of the decision is based on where they are from, while 50% of the decision is based on where they finish their residency training. Where they go to medical school has no effect on where doctors practice. U.S. Citizens trained outside the U.S. Are no different; they tend to practice where they came from and where they finished their training. Foreign-born doctors practice where they can get residencies and, later, jobs. As a result, in many rural parts of America, a significant share of doctors are foreign-born. The solution is ultimately to get more students from rural areas into medical school — any medical school — and create more residencies in rural America. Programs that pay doctors extra to practice in these shortage areas or forgive their medical school debt have not been very effective since a student from [New York City] is unlikely to stay in rural America after his/her debt is paid. On the other hand, recruiting students from rural communities and offering them scholarships if they return to practice has been successful. St. George's offers some scholarships on that basis.

Healio: Can you discuss the benefits of having a diversified PCP workforce in the U.S.?

Olds: In general, you want your health care workforce to look like the population they serve. There are many studies that show that Black or Hispanic doctors are more effective in communicating and caring for patients with a similar background. They are also more likely to work in underserved areas where they often come from. One of the less talked about aspects of the current doctor shortage is the fact that almost 80% of U.S. Medical students come from the top two-fifths of economic status. We are largely training the sons and daughters of wealthy Americans. Only about 5% of U.S. Medical students come from rural areas, while 14% of Americans live in rural areas. White and Asian doctors are also overrepresented among these classes. Diversity among medical students creates a better educational environment — students learn about different cultures and patient attitudes about health and disease from their fellow students. It is also helpful, since medicine is a mentoring profession, to have diversity among the faculty to create role models. We often only talk about diversity in terms of ethnicity, but economic diversity, educational background diversity, age diversity, sexual orientation diversity and rural/urban diversity are also important.

Healio: Is there anything else you'd like to add?

Olds: Experts often say that the reason U.S. Medical students don't go into primary care is that we don't pay primary care doctors as much as specialists. Although true and undoubtedly one factor, in England, primary care doctors are paid the same as specialists, and English medical students all want to specialize as well. Often not talked about is the environment in which you train them. Most U.S. Medical schools use large tertiary referral hospitals to train their students, and almost all their faculty are specialists. The same is true in England. This introduces a very strong bias against primary care. If you doubt that, ask your own primary care doctor how often he/she was told by the faculty at his/her own medical school that they are "too smart to go into primary care." We at St. George's have our students do their clinical rotations in teaching hospitals in the U.S. And England, which are mostly large community hospitals. Most of our faculty are primary care doctors. That is one of the reasons why so many of our students go into primary care. The other issue worth mentioning is the idea that the reason U.S. Medical schools mostly take rich white and Asian students is that they are the "most qualified." That is only true if you only look at standardized test scores and GPA. Each year, scores of students in the U.S. Are qualified to get into medical school and would do well based on these metrics, but only 26,000 get in. If we look more broadly at all these qualified students, I think we would have better doctors in the end. This is what we did at UC Riverside. A student who didn't speak English until he/she was 8, never went to college prep or honors-offering schools, had to work through college, and still did well in both standardized tests and GPA, I would suggest, has traveled farther than a student with the same scores coming from affluent suburbs and many life advantages. In the almost 50 years I have practiced medicine, I have never heard a patient say the most important quality in my doctor is that they got an A+ in organic chemistry. I hear all the time that my doctor doesn't communicate with me well, doesn't seem to care about me, or seems eager to get out of the hospital. Give me a few more students who only got an A or B+ in organic chemistry but have good interpersonal skills and are motivated by the right reasons to be a doctor.

References:

AAMC Report Reinforces Mounting Physician Shortage. Https://www.Aamc.Org/news-insights/press-releases/aamc-report-reinforces-mounting-physician-shortage. Published June 11, 2021. Accessed Dec. 5, 2022.

An Updated Look at the Economic Diversity of U.S. Medical Students. Https://www.Aamc.Org/data-reports/analysis-brief/report/updated-look-economic-diversity-us-medical-students. Accessed Dec. 6, 2022.

Duvivier RJ, et al. BMC Family Practice. 2019;doi:10.1186/s12875-019-0933-8.

Educational Commission for Foreign Medical Graduates. Https://www.Ecfmg.Org/news/2022/03/21/match-shows-international-medical-graduates-are-resilient-and-competitive-in-pursuit-of-u-s-graduate-medical-education/#:~:text=The%20match%20rates%20for%20U.S.,increasing%20compared%20to%20last%20year. Accessed Dec. 6, 2022.

Hancock C, et al. Social Science & Medicine. 2009;doi:10.1016/j.Socscimed.2009.08.002.

Health Workforce Shortage Areas. Https://data.Hrsa.Gov/topics/health-workforce/shortage-areas. Accessed Dec. 5, 2022.

Hundreds of St. George's University Students Secure U.S. Residencies in 2022 Match. Https://www.Prnewswire.Com/news-releases/hundreds-of-st-georges-university-students-secure-us-residencies-in-2022-match-301508125.Html#:~:text=Seventy-five%20percent%20of%20St,throughout%20the%20COVID-19%20pandemic. Accessed Dec. 6, 2022.

National Resident Matching Program, Results and Data: 2022 Main Residency Match. Https://www.Nrmp.Org/wp-content/uploads/2022/11/2022-Main-Match-Results-and-Data-Final-Revised.Pdf. Accessed Dec. 6, 2022.

Olds GR, et al. Health Systems and Reform. 2022;doi: 10.1080/23288604.2015.1054548.

Physician Specialty Data Report. Https://www.Aamc.Org/data-reports/workforce/interactive-data/active-physicians-who-are-international-medical-graduates-imgs-specialty-2019. Accessed Dec. 6, 2022.

Report of the Graduate Medical Education National Advisory Committee to the Secretary, Department of Health and Human Services. Https://eric.Ed.Gov/?Id=ED210990. Accessed Dec. 6, 2022.

Rural America at a Glance. Https://www.Ers.Usda.Gov/webdocs/publications/102576/eib-230.Pdf. Accessed Dec. 6, 2022.

Shipman SA, et al. Health Affairs. 2019;doi:10.1377/hlthaff.2019.00924.

U.S. MD-Granting Medical School Applications and Matriculants by School, State of Legal Residence, and Gender, 2022-2023. Https://www.Aamc.Org/media/5976/download?Attachment. Accessed Dec. 6, 2022.

2022 Survey of America's Physicians. Https://www.Merritthawkins.Com/uploadedFiles/MerrittHawkins_PhysiciansFoundation_COVID19_Survey.Pdf. Accessed Dec. 6, 2022.

Sources/DisclosuresCollapse Disclosures: Olds is the president of St. George's University. He also reports previously serving as the founding dean of the new medical school at UC Riverside, which "was specifically designed to address the severe doctor shortage in Inland Southern California, an area with significant poverty, is rural and largely Hispanic" and "has undoubtedly influenced my perspectives on these issues."

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Value And The Medical Home: Effects Of Transformed Primary Care - AJMC

A patient-centered medical home with intensive case management and a payer partner can significantly improve hospital utilization and may decrease total medical costs for a Medicare population.

Background: The primary care medical home has been promoted to integrate and improve patient care while reducing healthcare spending, but with little formal study of the model or evidence of its efficacy. ProvenHealth Navigator (PHN), an intensive multidimensional medical home model that addresses care delivery and financing, was introduced into 11 different primary care practices. The goals were to improve the quality, efficiency, and patient experience of care.

Objective: To evaluate the ability of a medical home model to improve the efficiency of care for Medicare beneficiaries.

Study Design: Observational study using regression modeling based on preintervention and postintervention data and a propensity-selected control cohort.

Methods: Four years of claims data for Medicare patients at 11 intervention sites and 75 control groups were analyzed to compute hospital admission and readmission rates, and the total cost of care. Regression modeling was used to establish predicted rates and costs in the absence of the intervention. Actual results were compared with predicted results to compute changes attributable to the PHN model.

Results: ProvenHealth Navigator was associated with an 18% (P <.01) cumulative reduction in inpatient admissions and a 36% (P = .02) cumulative reduction in readmissions across the total population over the study period.

Conclusions: Investing in the capabilities of primary care practices to serve as medical homes may increase healthcare value by improving the efficiency of care. This study demonstrates that the PHN model is capable of significantly reducing admissions and readmissions for Medicare Advantage members.

(Am J Manag Care. 2010;16(8):607-614)

ProvenHealth Navigator (PHN), a multidimensional medical home model, was introduced into 11 Geisinger Health System primary care practices with the goal of improving the quality, efficiency, and patient experience of care for Medicare Advantage patients.

This transformed primary care model resulted in significantly fewer hospital admissions (18%) and readmissions (36%) when measured across the entire population.

Total care costs for the entire PHN population decreased 7%, but this decrease did not achieve statistical significance.

Medical home models seeking to change cost trends require a multidimensional transformation of primary care practice with intensive case management and a payer partnership.

Both policy makers and private payers in the United States have begun to recognize that improving care coordination across the fragmented healthcare delivery system is essential to improve the quality and affordability of care. Related efforts include recent Medicare demonstrations examining the impact of external disease management programs and payment reforms that reward integrated care organizations.1,2 An alternative approach—the patient-centered medical home&mdash;involves enhanced primary care practices as the locus of integration and coordination of care. A version of the medical home model was originally described by the American Academy of Pediatrics and has more recently been refined and delineated by a coalition of professional societies through a set of joint principles.3,4

Elements of and antecedents to the current concept of the medical home have been shown to be associated with higher quality of care and patient experience.5-8 Closely related work on Wagner's Chronic Care Model suggests the potential of this approach to improve the quality of care for patients with chronic conditions and prevent costly acute care.9-11 However, empirical evidence is scant about whether efforts to transform practices into medical homes will improve quality or yield healthcare cost savings. Two recent studies demonstrate both the challenges of practicing transformation in primary care and the potential benefits in terms of patient and provider experience as well as preventable acute care utilization.12,13

In this study, we evaluate the impact of a medical home model, Proven-Health Navigator (PHN), introduced for Medicare Advantage enrollees in 11 practices owned by Geisinger Health System (GHS) in Pennsylvania. ProvenHealth Navigator is a new model of care designed to improve the quality, efficiency, and patient experience of care. It functions as a partnership between participating primary care practices and Geisinger Health Plan (GHP). Central to the model is the transfer of population management capabilities, including nurse case managers, from the health plan to the practice sites. This report focuses on the impact of the PHN on hospitalization and healthcare spending compared with a matched set of practices, using 2 years of preintervention and 2 years of postintervention data.

METHODS

Study Population

We assembled medical claims data for services provided from January 1, 2005, through December 31, 2008,and paid through June 30, 2009, and demographic information for 15,310 members of GHP's Medicare Advantage product. Using these data, we identified all claims for enrollees who were cared for by physician practices that implemented the PHN model and enrollees who were cared for by matched physician practices during the study period. Enrollees who switched physician practices during the study period were excluded from the analyses. Continuous enrollment was not required for inclusion in the study sample; analyses conducted on the continuously enrolled subpopulation yielded qualitatively similar results.

This analysis was approved by the GHS institutional review board.

Study Environment

Located in rural northeastern and central Pennsylvania, GHS is a not-for-profit, integrated healthcare organization comprised of the Geisinger Clinic, which has nearly 800 employed physicians; 2 acute tertiary/quaternary care hospitals; GHP, which serves 190,000 commercial and 38,000 Medicare Advantage members; and numerous other clinical programs and facilities. Geisinger Health Plan also utilizes a network of more than 18,000 non-GHS providers and 80 non-GHS hospitals.

Geisinger Health System has an electronic health record (EHR) implemented systemwide for all ambulatory and inpatient care. This EHR also is used by GHP case managers and patients. These EHR capabilities were operational in all participating practices for several years prior to the launch of the PHN. All Geisinger-owned primary care practices, including the PHN sites, participated in a preexisting, EHR-enabled quality initiative to improve preventive, diabetes, and coronary artery disease care.14

Implementation of ProvenHealth Navigator

The PHN model has 5 functional program components: (1) Patient-Centered Primary Care Team Practice, (2) Integrated Population Management, (3) Micro-delivery Systems, (4) Quality Outcomes Program, and (5) Value Reimbursement System (Table 1). A more detailed description of each component, with a comparison to the National Committee for Quality Assurance (NCQA) Physician Practice Connections and Patient-Centered Medical Home (PPC-PCMH)standards, can be found in the eAppendix at www.Ajmc.Com.

Many of the elements required under the PPC-PCMH standards are provided in the Patient-Centered Primary Care Team Practice component. Access criteria are met through close monitoring of performance on appointment standards (NCQA standard 1). Tracking and registry capabilities for several chronic diseases are embedded into the Primary Care Team Practice component's EHR (NCQA standard 2). Reminders for preventive and chronic disease care are part of the quality improvement initiative described above (NCQA standards 3 and 8). Self-management support has been a central theme in the team-based care approach the practices use; disease and case management were added as part of the PHN model (NCQA standard 4). Electronic prescribing as well as test and referral tracking also are available in the EHR (NCQA standards 5, 6, and 7). Advanced communication capabilities for patients and providers are available through the electronic portals portion of the EHR system (NCQA standard 9).

Although the PHN model was created prior to the publication of NCQA's PPC-PCMH standards, it does address all of the capabilities described in those standards. However, because our goal was to impact the quality, patient experience, and efficiency of care across the full continuum of care, not just in the office of the primary care physician (PCP), we believed that additional components and activities were necessary. The activities included in the 5 components are described further in Table 1. Several are worth noting. First, many of GHP's population management activities were moved to the practice site. Geisinger Health Plan provided case managers for each practice at a ratio of 1 nurse for every 800 Medicare patients to serve as the hub for population-based activities. Second, the model explicitly calls for the PCPs to develop systems of care for their patients when they are seen by other physicians or in other settings. Third, additional financial support was provided by GHP to pay for new services in the PCP office. An example is dedicated phone lines to allow high-risk patients to contact their case managers directly. Fourth, performance reports documenting the quality, utilization, and overall cost-of-care results were provided to the practice. Finally, we added a shared savings incentive model to the GHP reimbursement arrangement. Qualityoutcomes were aligned with preexisting preventive and chronic disease care quality initiatives. Shared savings incentive payments then were based on improvement in bundled metrics for these services and other agreed-upon metrics.

Implementation was focused on the GHP Medicare Advantage population because the high prevalence of chronic illnesses and the resource use of this population provide the best opportunity to demonstrate and evaluate the impact of the interventions. In October 2006 and January 2007, the PHN model was introduced into 2 pilot GHS practice sites selected because of their large GHP Medicare Advantage population and because their locations made them easily accessible for our PHN management team. During 2007 and January of 2008, the PHN model was expanded to include the Medicare Advantage members in 9 additional practices (Table 2). Prior to implementation at each site, all practice staff were trained on the core components of the model.

Initially, the PHN teams focused on improving the management of the highest risk patients. The GHP-embedded case managers were integrated as part of the practice care team. They were provided with utilization and predictive modeling reports derived from GHP claims data. For the first time, these reports gave the practice teams a systematic way to identify relative risk for their GHP patients. The case managers then met with the highest risk patients to design patient-specific care plans. They also provided close follow-up for patients transitioning from hospital to home.This activity focused on reaching out to the patient within 48 hours of discharge, medication reconciliation, appropriate resources and social supports in the home, and timely follow-up with the patient's PCP. Monthly team meetings that included PCPs, office staff, case managers, and GHP staff were held to evaluate results, discuss practice workflow and care access, and review hospital admissions for missed opportunities.

The case managers also formed partnerships with preferred home health agencies and nursing homes. Outreach and education regarding the PHN strategy were provided to these agencies. Pharmacy management initiatives were developed to improve generic utilization, assist members approaching the Medicare Part D coverage gap, and provide members with acute care protocols for treating exacerbations of chronic conditions.

As progress was made, expanded strategies focused on members at moderate and low risk. Patients with gaps in preventive or chronic care were identified by EHR registries and health plan claims tools. Health plan nurses with training in disease management targeted moderate-risk members with hypertension, coronary artery disease, and diabetes for selfmanagement education; worked with providers to ensure appropriate screenings; and assisted in optimizing medications. Site-based practice staff reached out to low-risk members to coordinate preventive care screenings such as mammograms, colorectal screening, and influenza vaccinations.

Measures of Impact

Because we hypothesized that opportunities to reduce total healthcare spending through this model would relate to the ability of the practice to prevent hospitalizations and readmissions, we constructed monthly series of these events for each patient. Readmissions were defined as all medical—surgical patients admitted to acute care within 30 days from time of discharge for primary admission. Total healthcare spending (plan payment plus copayment) was computed for each member for each month by summing the allowed amount on medical claims. Pharmacy claims were not included in total spending because of variability in prescription drug coverage among members and over time because of the introduction of Medicare Part D in 2006. To protect the confidentiality of GHP payment information, we indexed spending so that the mean for patients in the nonintervention practices in January 2005 was set to $100.

Analytic Approach

We analyzed data at the patient-month level using multivariate linear mixed regression to measure the effect of thePHN intervention on hospital admissions per 1000 members, readmissions per 1000 members, and total nonprescription per member per month medical spending. Because we could access data from each clinic before and after PHN implementation along with concurrent data from non-PHN practices, our chosen approach was to model the expected incremental effect of PHN status on outcomes during the postintervention period, after adjusting for all covariates. The results reported in this article, therefore, represent the effect of PHN within a clinic and not simply a comparison of PHN clinics with non-PHN clinics. Following the approach of Berlin et al,15 we decomposed PHN status into 2 components: the percentage of the study period during which each practice implemented PHN (ie, clinic-exposure association) and whether PHN was in effect during each observation (ie, a time-varying component). This 2-variable approach was necessary because, although our primary interest was the impact of a clinic switching from non-PHN to PHN status, there could be confounding due to the selection of PHN clinics and variability of different clinics' duration of exposure to PHN. The first variable was time invariant and thus could represent the incremental effect of the intervention itself. It could, however, absorb bias from clinic-exposure association as well as biases between clinics selected or not selected for PHN. By contrast, the second variable (ie, a binary indicator of current PHN status centered around the time-invariant component at each site) isolated the actual effect of the PHN intervention on a clinic population, which was the objective of the study. Other covariates in the model were Hierarchical Condition Category (HCC) risk score,16 the year, and interactions between both PHN variables and year (to capture differences in trends within and among the PHN and non-PHN groups) and between PHN variables and HCC score. Age and sex were not included as separate regressors because they were captured in the HCC score. We also included a series of variables for calendar months to capture seasonal variation in utilization and spending.

Regression models were estimated using general estimating equations with exchangeable covariance. Consistent with the underlying nature of the data, we used a log-link function and quasi-Poisson distribution for monthly admissions and readmissions. To analyze monthly spending, we used a standard 2-part model modified to account for a small amount of capitation GHP pays uniformly for all members. The second part of the spending model was specified with a log-link function and normal distribution. These models allowed us to calcunlate the difference between the observed outcomes for active PHN participants and their expected outcomes if the PHN had not been implemented. To adjust for secular trends in the postintervention time period, we utilized data from a group of non-GHS practices that cared for GHP Medicare Advantage enrollees as a control cohort. We considered using either a GHS or non-GHS control cohort, but chose the non-GHS practices for 2 reasons: (1) the larger sample of non-GHS practices available for matching and (2) concerns about spillover effects of PHN on non-PHN practices within GHS. To ensure similarity between the intervention and nonintervention groups, we used propensity score matching to identify a subset of practices that were most similar to the intervention practices in terms of patient population and outcomes in 2005. Because the PHN model was implemented at the practice level, we sought to match the intervention and nonintervention cohorts at this level. We estimated propensity score models predating the adoption of the PHN model by using the following practicelevel variables, all measured for 2005: mean patient age, percentage of male patients, mean HCC score, total per member spending, inpatient admissions, and readmissions. Each practice that adopted the PHN model was matched to 10 practices that did not adopt the PHN model based on the proximity of their estimated propensity score. Only those practices with estimated log odds from the propensity model that were within 0.60 standard deviation of an intervention site were counted as matches. A sensitivity analysis (not shown) with a GHS control cohort yielded results that were within the statistical confidence intervals (CIs) of the results presented here.

RESULTS

Because of overlap between sites, the propensity score model yielded a total of 75 distinct non-Geisinger practices within the common support region that could be matchedwith 1 or more of the 11 intervention sites (data not shown). Data from each of these sites were weighted to appropriately reflect the number of intervention sites with which they were matched. At baseline (2005), there were no statistically significant differences in sex or HCC scores of patients treated in the 11 PHN sites compared with the propensity score—matched non-GHS practices (Table 3). Patients treated in the intervention sites were approximately 6 months younger on average than those treated in comparison sites (P <.001), but because all results were regression adjusted, any potential bias associated with this age difference should have been eliminated. Average monthly admissions and readmissions per 1000 patients also were similar between the 2 groups (P = .24 and P =.74, respectively). Average spending per member per month was approximately 4% higher in the intervention cohort than in the comparison cohort (P = .04).

Table 4 and the Figure present the cumulative percent differences in actual admissions, readmissions, and total spending for PHN members versus the expected outcomes if PHN had not been implemented. As described previously, these expected outcomes were calculated by setting the time-dependent PHN indicator variable to zero in each multivariable regression model while keeping all other covariates constant. Outcomes were expressed as estimated effects with bootstrapped 95% CIs and as P values. The PHN model wa associated with a total cumulative reduction of 56 admissions per 1000 members per year (18%; 95% CI, −30% to −5%; P <.01). The PHN model also was associated with a

cumulative effect of 21 fewer readmissions per 1000 members per year (−36%; 95% CI, −55% to −3%; P = .02). Finally, the regression model estimated that the PHN model reduced cumulative total spending by 7%, but this difference did not reach significance (95% CI, −18% to 5%; P = .21). Results were qualitatively similar if all non-GHS clinic sites, rather than the propensity-matched comparison group, were used as a control cohort (data not shown).

DISCUSSION

Introduction of a medical home care delivery model was associated with a significant reduction in hospital admissions and readmissions for a population of Medicare Advantage enrollees. Our findings present a contrast to the recently published results of the Medicare Health Support demonstration, a set of parallel, randomized controlled trials of traditional disease management delivered by third parties to disease-specific populations.2 Despite targeting sicker individuals, participating programs had little effect on healthcare utilization or spending. We hypothesize that the comparative success of the PHN model was partly due to its ability to leverage existing physician—patient and interprovider relationships to fundamentally change the way care is delivered rather than work outside the system to improve care.

Because the PHN model is a complex intervention, it is difficult to ascertain which elements are responsible for specific improvements in care. Our findings, coupled with qualitative observations, however, highlight the importance of placing nurse case managers directly into the practices and arming them with data and analytical capabilities. With timely information on emergency department (ED) and inpatient use, case managers are able to manage transitions of care to ensure early postdischarge follow-up and medication reconciliation. In addition, proactive identification of at-risk individuals provides an opportunity to use patient-specific action plans to implement timely interventions for acute exacerbations of chronic illnesses.

Our lack of findings on per member per month spending likely was due at least in part to the sample size and duration of the study, coupled with the skewed distribution of the healthcare spending data. We note that based on its own actuarial analysis, GHP found that the PHN practices did generate savings and triggered incentive payments under the quality-based shared savings incentive system.

Based on our understanding of the initial focus of PHN practices and the policy importance of understanding the impact on resource use, we focused the analyses on hospital utilization and total costs. In future work, it will be important to examine a broader scope of process and outcome measures, including ED visits, clinical quality measures, physician and nurse professional satisfaction, and patient experience. Tracking this larger set of outcomes will not only allow us to better answer questions of sustainability but also permit refinement of the model.

These conclusions are tempered by several study limitations. First, our measure of medical spending excluded the cost of prescription drugs. There are concerns that improved care coordination may increase the cost of prescription drugs, thereby decreasing or eliminating medical services savings.17 However, a separate analysis of the changes in drug expense over time for both groups of practices demonstrated no differential impact or erosion of savings in the PHN sites. Second, the PHN model is situated in an integrated payer—provider environment (ie, the payer and provider are part of the same corporate entity) with long-standing use of an ambulatory EHR, in a Medicare population with high baseline spending and relatively little patient turnover. These factors almost surely contributed to PHN's success and may therefore limit generalizability to other settings. The PHN model, however, has subsequently been introduced into non-GHS practices. Moreover, implementation experience to date suggests that the key components of the PHN model are on-site case management, the use of population data, and the shared savings incentives, all of which could be implemented outside of an integrated delivery model. Third, as described above, while the PHN model is aligned with the NCQA PPC-PCMH standards, it also includes other components. In particular, PHNimplementation likely differs from many other medical home efforts that may not include robust case management programs, attention to care delivered outside of the PCP office, shared savings reimbursement, or direct health plan support. Although advocates of the medical home will see these findings as general support for that model, attention should be paid to the specific elements of the intervention and the limits to generalizability noted above. Finally, although every effort was made to account for secular trends and confounding factors, our study was observational in nature and the usual caveats with regard to causal inference apply.

The strengths of the study design also are significant and include the fact that we were able to analyze healthcare spending and utilization for an entire population before and after the intervention alongside a well-matched comparison group. This approach allowed for relatively robust causal inference while minimizing the potential confounding effects of regression to the mean and survivorship biases.18

Cost control in the US healthcare system has long been an elusive goal. In the current economic downturn, the need for evidence-based policies to meet this objective is more urgent than ever. To the extent that these results can be generalized, PHN offers an appealing model to improve value through prevention of hospital admissions and readmissions by transforming the delivery of primary care. Continued experimentation with models based on these principles in a range of practice settings and patient populations will be critical for policy development.

Acknowledgments

Dr Rosenthal acknowledges financial support from the Commonwealth Fund. Data were provided through the Actuarial Services Department of Geisinger Health Plan. The authors thank Richard Bitting, BBA, and Albert Wolstein, MBA. Analysis was provided by staff at the Henry Hood Center for Health Research.

Author Affiliations: From Geisinger Health System (RJG, JT, DED, JG, SBP, FJB, TRG, KMW, BHH, RAP, GDS), Danville, PA; Harvard School of Public Health (MBR), Boston, MA; University of Pennsylvania (JAR), Philadelphia, PA; Humana, Inc (RG); Louisville, KY.

Funding Source: There was no external funding for this report.

Author Disclosures: Drs Gilfillan, Davis, Graham, Pierdon, Bloom, Graf, Hamory, Paulus, and Steele and Ms Tomcavage and Ms Weikel are employees of the Geisinger Health System, and to the extent the ProvenHealth Navigator intervention exerted a positive effect on the total cost of care for this population, Geisinger's financial performance may have been improved. The intervention also may have contributed to improvement in Geisinger's quality metrics. As employees, these authors have an incentive compensation arrangement that is impacted by Geisinger's financial and quality results. Dr Bloom also reports serving on Merck's Speaker's Bureau on the Patient-Centered Medical Home. Dr Goldman reports having worked for Geisinger during the implementation of the ProvenHealth Navigator. Drs Rosenthal and Roy report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

Authorship Information: Concept and design (RJG, JT, MBR, DED, JG, SBP, RG, KMW, GDS); acquisition of data (RJG, JT, DED, FJB, KMW); analysis and interpretation of data (RJG, JT, MBR, JG, JAR, TRG, RG, KMW, RAP, GDS); drafting of the manuscript (RJG, JT, MBR, DED, JG, JAR, FJB, BHH, RAP, GDS); critical revision of the manuscript for important intellectual content (RJG, MBR, JG, JAR, SBP, FJB, TRG, RG, BHH, RAP, GDS); statistical analysis (MBR, JG, JAR, RG); provision of study materials or patients (JT, TRG), administrative, technical, or logistic support (RJG, JT, SBP, FJB, TRG); and supervision (RJG, SBP, RG, RAP, GDS).

Address correspondence to: Richard J. Gilfillan, MD, 2811 N St NW, Washington, DC 20007. E-mail: rjgilfil@ptd.Net.

1. Government Accountability Office. Medicare Physician Payment: Care Coordination Programs Used in Demonstration Show Promise, but Wider Use of Payment Approach May Be Limited. Washington, DC: GAO; February 2008. GAO-08-65.

2. Peikes D, Chen A, Schore J, Brown R. Effects of care coordination on hospitalization, quality of care,