Publications by authors named "Sachin H Jain"

73 Publications

Impact of Patient-Physician Language Concordance on Healthcare Utilization.

J Gen Intern Med 2021 Aug 3. Epub 2021 Aug 3.

SCAN Group and Health Plan, 3800 Kilroy Airport Way, Long Beach, CA, 90806, USA.

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http://dx.doi.org/10.1007/s11606-021-06998-wDOI Listing
August 2021

The De-adoption of Low-Value Health Care-Reply.

JAMA 2021 03;325(9):888

Humana Inc, Louisville, Kentucky.

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http://dx.doi.org/10.1001/jama.2020.25518DOI Listing
March 2021

Health Costs And Financing: Challenges And Strategies For A New Administration.

Health Aff (Millwood) 2021 02 21;40(2):235-242. Epub 2021 Jan 21.

Gail R. Wilensky is a senior fellow at Project HOPE, in Bethesda, Maryland.

It is likely that 2021 will be a dynamic year for US health care policy. There is pressing need and opportunity for health reform that helps achieve better access, affordability, and equity. In this commentary, which is part of the National Academy of Medicine's Vital Directions for Health and Health Care: Priorities for 2021 initiative, we draw on our collective backgrounds in health financing, delivery, and innovation to offer consensus-based policy recommendations focused on health costs and financing. We organize our recommendations around five policy priorities: expanding insurance coverage, accelerating the transition to value-based care, advancing home-based care, improving the affordability of drugs and other therapeutics, and developing a high-value workforce. Within each priority we provide recommendations for key elected officials and political appointees that could be used as starting points for evidence-based policy making that supports a more effective, efficient, and equitable health system in the US.
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http://dx.doi.org/10.1377/hlthaff.2020.01560DOI Listing
February 2021

The Enduring Importance of Trust in the Leadership of Health Care Organizations.

JAMA 2020 Dec;324(23):2363-2364

Kaiser Permanente Bernard J. Tyson School of Medicine, Pasadena, California.

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http://dx.doi.org/10.1001/jama.2020.18555DOI Listing
December 2020

Implementing a targeted approach to social determinants of health interventions.

Am J Manag Care 2020 12;26(12):502-504

SCAN Group and Health Plan, 3800 Kilroy Airport Way, Long Beach, CA 90806. Email:

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.
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http://dx.doi.org/10.37765/ajmc.2020.88537DOI Listing
December 2020

De-adopting Low-Value Care: Evidence, Eminence, and Economics.

JAMA 2020 Oct;324(16):1603-1604

Humana Inc, Louisville, Kentucky.

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http://dx.doi.org/10.1001/jama.2020.17534DOI Listing
October 2020

The Beneficial Effects Of Medicare Advantage Special Needs Plans For Patients With End-Stage Renal Disease.

Health Aff (Millwood) 2020 09;39(9):1486-1494

Amol S. Navathe is a core investigator at the Corporal Michael J. Cresencz Veterans Affairs Medical Center; an assistant professor in the Department of Medical Ethics and Health Policy, Perelman School of Medicine; and a senior fellow at the Leonard Davis Institute of Health Economics, University of Pennsylvania, all in Philadelphia.

Patients with end-stage renal disease (ESRD) are a vulnerable population with high rates of morbidity, mortality, and acute care use. Medicare Advantage Special Needs Plans (SNPs) are an alternative financing and delivery model designed to improve care and reduce costs for patients with ESRD, but little is known about their impact. We used detailed clinical, demographic, and claims data to identify fee-for-service Medicare beneficiaries who switched to ESRD SNPs offered by a single health plan (SNP enrollees) and similar beneficiaries who remained enrolled in fee-for-service Medicare plans (fee-for-service controls). We then compared three-year mortality and twelve-month utilization rates. Compared with fee-for-service controls, SNP enrollees had lower mortality and lower rates of utilization across the care continuum. These findings suggest that SNPs may be an effective alternative care financing and delivery model for patients with ESRD.
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http://dx.doi.org/10.1377/hlthaff.2019.01793DOI Listing
September 2020

Addressing Patient Bias and Discrimination Against Clinicians of Diverse Backgrounds.

Acad Med 2020 12;95(12S Addressing Harmful Bias and Eliminating Discrimination in Health Professions Learning Environments):S33-S43

S.H. Jain is adjunct professor of medicine, Stanford University School of Medicine, Palo Alto, California, and president and chief executive officer, SCAN Group and Health Plan, Long Beach, California.

The duty to care for all patients is central to the health professions, but what happens when clinicians encounter patients who exhibit biased or discriminatory behaviors? While significant attention has focused on addressing clinician bias toward patients, incidents of patient bias toward clinicians also occur and are difficult to navigate.Clinicians anecdotally describe their experiences with patient bias, prejudice, and discrimination as profoundly painful and degrading. Though this phenomenon has not been rigorously studied, it is not unreasonable to postulate that the moral distress caused by patient bias may ultimately contribute to clinician burnout. Because women and minority clinicians are more likely to be targets of patient bias, this may worsen existing disparities for these groups and increase their risk for burnout. Biased behavior may also affect patient outcomes.Although some degree of ignoring derogatory comments is necessary to maintain professionalism and workflow, clinicians also have the right to a workplace free of mistreatment and abuse. How should clinicians reconcile the expectation to always "put patients first" with their basic right to be treated with dignity and respect? And how can health care organizations develop policies and training to mitigate the effects of these experiences?The authors discuss the ethical dilemmas associated with responding to prejudiced patients and then present a framework for clinicians to use when directly facing or witnessing biased behavior from patients. Finally, they describe strategies to address patient bias at the institutional level.
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http://dx.doi.org/10.1097/ACM.0000000000003682DOI Listing
December 2020

Mobile health clinic model in the COVID-19 pandemic: lessons learned and opportunities for policy changes and innovation.

Int J Equity Health 2020 05 19;19(1):73. Epub 2020 May 19.

Stanford University School of Medicine, Stanford, CA, USA.

Background: Mobile Clinics represent an untapped resource for our healthcare system. The COVID-19 pandemic has exacerbated its limitations. Mobile health clinic programs in the US already play important, albeit under-appreciated roles in the healthcare system. They provide access to healthcare especially for displaced or isolated individuals; they offer versatility in the setting of a damaged or inadequate healthcare infrastructure; and, as a longstanding community-based service delivery model, they fill gaps in the healthcare safety-net, reaching social-economically underserved populations in both urban and rural areas. Despite an increasing body of evidence of the unique value of this highly adaptable model of care, mobile clinics are not widely supported. This has resulted in a missed opportunity to deploy mobile clinics during national emergencies such as the COVID-19 pandemic, as well as using these already existing, and trusted programs to overcome barriers to access that are experienced by under-resourced communities.

Main Text: In March, the Mobile Healthcare Association and Mobile Health Map, a program of Harvard Medical School's Family Van, hosted a webinar of over 300 mobile health providers, sharing their experiences, challenges and best practices of responding to COVID 19. They demonstrated the untapped potential of this sector of the healthcare system in responding to healthcare crises. A Call to Action: The flexibility and adaptability of mobile clinics make them ideal partners in responding to pandemics, such as COVID-19. In this commentary we propose three approaches to support further expansion and integration of mobile health clinics into the healthcare system: First, demonstrate the economic contribution of mobile clinics to the healthcare system. Second, expand the number of mobile clinic programs and integrate them into the healthcare infrastructure and emergency preparedness. Third, expand their use of technology to facilitate this integration.

Conclusions: Understanding the economic and social impact that mobile clinics are having in our communities should provide the evidence to justify policies that will enable expansion and optimal integration of mobile clinics into our healthcare delivery system, and help us address current and future health crises.
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http://dx.doi.org/10.1186/s12939-020-01175-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7236869PMC
May 2020

Impact of complex care management on spending and utilization for high-need, high-cost Medicaid patients.

Am J Manag Care 2020 02 1;26(2):e57-e63. Epub 2020 Feb 1.

Harvard Medical School, 75 Francis St, Boston, MA 02446. Email:

Objectives: 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.

Study Design: 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.

Methods: 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.

Results: 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; P = .036), fewer IP bed days (adjusted difference, -3.46 PMPY; 95% CI, -4.03 to -2.89; P <.001), fewer IP admissions (adjusted difference, -0.32 PMPY; 95% CI, -0.54 to -0.11; P = .014), and fewer specialist visits (adjusted difference, -1.35 PMPY; 95% CI, -1.98 to -0.73; P <.001). There was no significant impact on care center or ED visits.

Conclusions: Carefully designed and targeted complex care management programs may be an effective approach to caring for high-need, high-cost Medicaid patients.
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http://dx.doi.org/10.37765/ajmc.2020.42402DOI Listing
February 2020

Homeless Special Needs Plans for People Experiencing Homelessness.

JAMA 2020 Mar;323(10):927-928

Boston Healthcare for the Homeless, Boston, Massachusetts.

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http://dx.doi.org/10.1001/jama.2019.22376DOI Listing
March 2020

Eliminating barriers to virtual care: implementing portable medical licensure.

Am J Manag Care 2020 01;26(1):20-22

CareMore Health System, 12900 Park Plaza Dr, Cerritos, CA 90703. Email:

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.
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http://dx.doi.org/10.37765/ajmc.2020.41223DOI Listing
January 2020

Impact of switching analogue insulin to human insulin in diabetes.

Am J Manag Care 2019 09 1;25(10 Spec No.). Epub 2019 Sep 1.

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September 2019

The sociobehavioral phenotype: applying a precision medicine framework to social determinants of health.

Am J Manag Care 2019 09;25(9):421-423

Division of Health Policy, University of Pennsylvania, 1108 Blockley Hall, 423 Guardian Dr, Philadelphia, PA 19146. Email:

Sociobehavioral phenotypes are actionable risk profiles based on empirically derived social, economic, and behavioral factors that, if applied appropriately, can help healthcare organizations address social determinants of health.
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September 2019

Invisibility and modern medicine.

Authors:
Sachin H Jain

Healthc (Amst) 2019 09 26;7(3):100368. Epub 2019 Jul 26.

CareMore Health & Stanford University School of Medicine, 12900 Park Plaza Drive, Suiite 150, Cerritos, CA, 90703, USA. Electronic address:

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http://dx.doi.org/10.1016/j.hjdsi.2019.100368DOI Listing
September 2019

Implementation of a Health Plan Program for Switching From Analogue to Human Insulin and Glycemic Control Among Medicare Beneficiaries With Type 2 Diabetes.

JAMA 2019 01;321(4):374-384

Program On Regulation, Therapeutics, And Law, Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts.

Importance: Prices for newer analogue insulin products have increased. Lower-cost human insulin may be effective for many patients with type 2 diabetes.

Objective: To evaluate the association between implementation of a health plan-based intervention of switching patients from analogue to human insulin and glycemic control.

Design, Setting, And Participants: A retrospective cohort study using population-level interrupted times series analysis of members participating in a Medicare Advantage and prescription drug plan operating in 4 US states. Participants were prescribed insulin between January 1, 2014, and December 31, 2016 (median follow-up, 729 days). The intervention began in February 2015 and was expanded to the entire health plan system by June 2015.

Exposures: Implementation of a health plan program to switch patients from analogue to human insulin.

Main Outcomes And Measures: The primary outcome was the change in mean hemoglobin A1c (HbA1c) levels estimated over three 12-month periods: preintervention (baseline) in 2014, intervention in 2015, and postintervention in 2016. Secondary outcomes included rates of serious hypoglycemia or hyperglycemia using ICD-9-CM and ICD-10-CM diagnostic codes.

Results: Over 3 years, 14 635 members (mean [SD] age: 72.5 [9.8] years; 51% women; 93% with type 2 diabetes) filled 221 866 insulin prescriptions. The mean HbA1c was 8.46% (95% CI, 8.40%-8.52%) at baseline and decreased at a rate of -0.02% (95% CI, -0.03% to -0.01%; P <.001) per month before the intervention. There was an association between the start of the intervention and an overall HbA1c level increase of 0.14% (95% CI, 0.05%-0.23%; P = .003) and slope change of 0.02% (95% CI, 0.01%-0.03%; P < .001). After the completion of the intervention, there were no significant differences in changes in the level (0.08% [95% CI, -0.01% to 0.17%]) or slope (<0.001% [95% CI, -0.008% to 0.010%]) of mean HbA1c compared with the intervention period (P = .09 and P = 0.81, respectively). For serious hypoglycemic events, there was no significant association between the start of the intervention and a level (2.66/1000 person-years [95% CI, -3.82 to 9.13]; P = .41) or slope change (-0.66/1000 person-years [95% CI, -1.59 to 0.27]; P = .16). The level (1.64/1000 person-years [95% CI, -4.83 to 8.11]; P = .61) and slope (-0.23/1000 person-years [95% CI, -1.17 to 0.70]; P = .61) changes in the postintervention period were not significantly different compared with the intervention period. The baseline rate of serious hyperglycemia was 22.33 per 1000 person-years (95% CI, 12.70-31.97). For the rate of serious hyperglycemic events, there was no significant association between the start of the intervention and a level (4.23/1000 person-years [95% CI, -8.62 to 17.08]; P = .51) or slope (-0.51/1000 person-years [95% CI, -2.37 to 1.34]; P = .58) change.

Conclusions And Relevance: Among Medicare beneficiaries with type 2 diabetes, implementation of a health plan program that involved switching patients from analogue to human insulin was associated with a small increase in population-level HbA1c.
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http://dx.doi.org/10.1001/jama.2018.21364DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6439763PMC
January 2019

Applying Machine Learning Algorithms to Segment High-Cost Patient Populations.

J Gen Intern Med 2019 02 12;34(2):211-217. Epub 2018 Dec 12.

Department of Medical Ethics and Health Policy, University of Pennsylvania Perelman School of Medicine, 1108 Blockley Hall, Philadelphia, PA, 19104, USA.

Background: Efforts to improve the value of care for high-cost patients may benefit from care management strategies targeted at clinically distinct subgroups of patients.

Objective: To evaluate the performance of three different machine learning algorithms for identifying subgroups of high-cost patients.

Design: We applied three different clustering algorithms-connectivity-based clustering using agglomerative hierarchical clustering, centroid-based clustering with the k-medoids algorithm, and density-based clustering with the OPTICS algorithm-to a clinical and administrative dataset. We then examined the extent to which each algorithm identified subgroups of patients that were (1) clinically distinct and (2) associated with meaningful differences in relevant utilization metrics.

Participants: Patients enrolled in a national Medicare Advantage plan, categorized in the top decile of spending (n = 6154).

Main Measures: Post hoc discriminative models comparing the importance of variables for distinguishing observations in one cluster from the rest. Variance in utilization and spending measures.

Key Results: Connectivity-based, centroid-based, and density-based clustering identified eight, five, and ten subgroups of high-cost patients, respectively. Post hoc discriminative models indicated that density-based clustering subgroups were the most clinically distinct. The variance of utilization and spending measures was the greatest among the subgroups identified through density-based clustering.

Conclusions: Machine learning algorithms can be used to segment a high-cost patient population into subgroups of patients that are clinically distinct and associated with meaningful differences in utilization and spending measures. For these purposes, density-based clustering with the OPTICS algorithm outperformed connectivity-based and centroid-based clustering algorithms.
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http://dx.doi.org/10.1007/s11606-018-4760-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6374273PMC
February 2019

Subgroups of High-Cost Medicare Advantage Patients: an Observational Study.

J Gen Intern Med 2019 02 3;34(2):218-225. Epub 2018 Dec 3.

Department of Medical Ethics and Health Policy, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.

Background: There is a growing focus on improving the quality and value of health care delivery for high-cost patients. Compared to fee-for-service Medicare, less is known about the clinical composition of high-cost Medicare Advantage populations.

Objective: To describe a high-cost Medicare Advantage population and identify clinically and operationally significant subgroups of patients.

Design: We used a density-based clustering algorithm to group high-cost patients (top 10% of spending) according to 161 distinct demographic, clinical, and claims-based variables. We then examined rates of utilization, spending, and mortality among subgroups.

Participants: Sixty-one thousand five hundred forty-six Medicare Advantage beneficiaries.

Main Measures: Spending, utilization, and mortality.

Key Results: High-cost patients (n = 6154) accounted for 55% of total spending. High-cost patients were more likely to be younger, male, and have higher rates of comorbid illnesses. We identified ten subgroups of high-cost patients: acute exacerbations of chronic disease (mixed); end-stage renal disease (ESRD); recurrent gastrointestinal bleed (GIB); orthopedic trauma (trauma); vascular disease (vascular); surgical infections and other complications (complications); cirrhosis with hepatitis C (liver); ESRD with increased medical and behavioral comorbidity (ESRD+); cancer with high-cost imaging and radiation therapy (oncology); and neurologic disorders (neurologic). The average number of inpatient days ranged from 3.25 (oncology) to 26.09 (trauma). Preventable spending (as a percentage of total spending) ranged from 0.8% (oncology) to 9.5% (complications) and the percentage of spending attributable to prescription medications ranged from 7.9% (trauma and oncology) to 77.0% (liver). The percentage of patients who were persistently high-cost ranged from 11.8% (trauma) to 100.0% (ESRD+). One-year mortality ranged from 0.0% (liver) to 25.8% (ESRD+).

Conclusions: We identified clinically distinct subgroups of patients within a heterogeneous high-cost Medicare Advantage population using cluster analysis. These subgroups, defined by condition-specific profiles and illness trajectories, had markedly different patterns of utilization, spending, and mortality, holding important implications for clinical strategy.
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http://dx.doi.org/10.1007/s11606-018-4759-1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6374249PMC
February 2019

Improving High-Risk Patient Care through Chronic Disease Prevention and Management.

J Law Med Ethics 2018 Sep;46(3):773-775

Pooja Chandrashekar, A.B., is a Fullbright Fellow studying social policy in India. Sachin H. Jain, M.D., M.B.A., is an Adjunct Professor at the Stanford University School of Medicine and president of CareMore Health.

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http://dx.doi.org/10.1177/1073110518804240DOI Listing
September 2018

The health care innovation bubble.

Authors:
Sachin H Jain

Healthc (Amst) 2017 Dec 8;5(4):231-232. Epub 2017 Sep 8.

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http://dx.doi.org/10.1016/j.hjdsi.2017.08.002DOI Listing
December 2017

Getting Real about Health Care Costs - A Broader Approach to Cost Stewardship in Medical Education.

N Engl J Med 2017 Mar;376(10):913-915

From Brigham and Women's Hospital, Boston (R.B.P.); and Stanford University, Stanford (A.M., S.H.J.), and CareMore Health System, Cerritos (S.H.J.) - both in California.

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http://dx.doi.org/10.1056/NEJMp1612517DOI Listing
March 2017

The residency-MBA program: A novel approach to training physician leaders.

Healthc (Amst) 2016 Sep 29;4(3):142-4. Epub 2015 Oct 29.

Harvard Business School, Boston, MA, United States; Brigham and Women's Hospital, Boston, MA, United States. Electronic address:

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http://dx.doi.org/10.1016/j.hjdsi.2015.10.009DOI Listing
September 2016

Nonemergency Medical Transportation: Delivering Care in the Era of Lyft and Uber.

JAMA 2016 Sep;316(9):921-2

CareMore Health System/Anthem Inc, Cerritos, California4Stanford University School of Medicine, Palo Alto, California.

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http://dx.doi.org/10.1001/jama.2016.9970DOI Listing
September 2016

When doctors go to business school: career shoices of physician-MBAs.

Am J Manag Care 2016 06 1;22(6):e196-8. Epub 2016 Jun 1.

CareMore Health System, 12900 Park Plaza Dr, Ste 150, Cerritos, CA 90703. E-mail:

There has been substantial growth in the number of physicians pursing Master of Business Administration (MBA) degrees over the past decade, but there is continuing debate over the utility of these programs and the career outcomes of their graduates. The authors analyzed the clinical and professional activities of a large cohort of physician-MBAs by gathering information on 206 physician graduates from the Harvard Business School MBA program who obtained their degrees between 1941 and 2014. Key outcome measures that were examined include medical specialty, current professional activity, and clinical practice. Chi square tests were used to assess the correlations in the data. Among the careers that were tracked (n = 195), there was significant heterogeneity in current primary employment. The most common sectors were clinical (27.7%), investment banking/finance (27.0%), hospital/provider administration (11.7%), biotech/device/pharmaceutical (10.9%), and entrepreneurship (9.5%). Overall, 84% of physician-MBAs entered residency; approximately half (49.3%) remained clinically active in some capacity and only one-fourth (27.7%) reported clinical medicine as their primary professional role. Among those who pursued residency training, the most common specialties were internal medicine (39.3%), emergency medicine (10.4%), orthopedic surgery (9.2%), and general surgery (8.6%). Physician-MBAs trained in internal medicine were significantly more likely to remain clinically active (63.8% vs 42.4%; P = .01). Clinical activity and primary employment in a clinical role decreased after degree conferment. After completing their education, a majority of physician-MBAs divert their primary professional focus away from clinical activity. These findings reveal new insights into the career outcomes of physician-MBAs.
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June 2016

Remembering the strength of weak ties.

Am J Manag Care 2016 Mar;22(3):202-3

CareMore Health System, 12900 Park Plaza Dr, Ste 150, Cerritos, CA 90703. E-mail:

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March 2016

Race-Conscious Professionalism and African American Representation in Academic Medicine.

Acad Med 2016 07;91(7):913-5

B.W. Powers is an MD/MBA candidate, Harvard Medical School and Harvard Business School, Boston, Massachusetts. A.A. White is professor of medical education and orthopaedic surgery, Harvard Medical School, Boston, Massachusetts. N.E. Oriol is dean of students and associate professor of anesthesia, Harvard Medical School, Boston, Massachusetts. S.H. Jain is chief medical officer, CareMore Health System, Cerritos, California.

African Americans remain substantially less likely than other physicians to hold academic appointments. The roots of these disparities stem from different extrinsic and intrinsic forces that guide career development. Efforts to ameliorate African American underrepresentation in academic medicine have traditionally focused on modifying structural and extrinsic barriers through undergraduate and graduate outreach, diversity and inclusion initiatives at medical schools, and faculty development programs. Although essential, these initiatives fail to confront the unique intrinsic forces that shape career development. America's ignoble history of violence, racism, and exclusion exposes African American physicians to distinct personal pressures and motivations that shape professional development and career goals. This article explores these intrinsic pressures with a focus on their historical roots; reviews evidence of their effect on physician development; and considers the implications of these trends for improving African American representation in academic medicine. The paradigm of "race-conscious professionalism" is used to understand the dual obligation encountered by many minority physicians not only to pursue excellence in their field but also to leverage their professional stature to improve the well-being of their communities. Intrinsic motivations introduced by race-conscious professionalism complicate efforts to increase the representation of minorities in academic medicine. For many African American physicians, a desire to have their work focused on the community will be at odds with traditional paths to professional advancement. Specific policy options are discussed that would leverage race-conscious professionalism as a draw to a career in academic medicine, rather than a force that diverts commitment elsewhere.
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http://dx.doi.org/10.1097/ACM.0000000000001074DOI Listing
July 2016

Delivery Models for High-Risk Older Patients: Back to the Future?

JAMA 2016 Jan;315(1):23-4

CareMore Health System/Anthem Inc, Cerritos, California4Stanford University School of Medicine, Palo Alto, California.

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http://dx.doi.org/10.1001/jama.2015.17029DOI Listing
January 2016

Characterizing Sleep Issues Using Twitter.

J Med Internet Res 2015 Jun 8;17(6):e140. Epub 2015 Jun 8.

Boston Children's Hospital, Harvard Medical School, Boston, MA, United States.

Background: Sleep issues such as insomnia affect over 50 million Americans and can lead to serious health problems, including depression and obesity, and can increase risk of injury. Social media platforms such as Twitter offer exciting potential for their use in studying and identifying both diseases and social phenomenon.

Objective: Our aim was to determine whether social media can be used as a method to conduct research focusing on sleep issues.

Methods: Twitter posts were collected and curated to determine whether a user exhibited signs of sleep issues based on the presence of several keywords in tweets such as insomnia, "can't sleep", Ambien, and others. Users whose tweets contain any of the keywords were designated as having self-identified sleep issues (sleep group). Users who did not have self-identified sleep issues (non-sleep group) were selected from tweets that did not contain pre-defined words or phrases used as a proxy for sleep issues.

Results: User data such as number of tweets, friends, followers, and location were collected, as well as the time and date of tweets. Additionally, the sentiment of each tweet and average sentiment of each user were determined to investigate differences between non-sleep and sleep groups. It was found that sleep group users were significantly less active on Twitter (P=.04), had fewer friends (P<.001), and fewer followers (P<.001) compared to others, after adjusting for the length of time each user's account has been active. Sleep group users were more active during typical sleeping hours than others, which may suggest they were having difficulty sleeping. Sleep group users also had significantly lower sentiment in their tweets (P<.001), indicating a possible relationship between sleep and pyschosocial issues.

Conclusions: We have demonstrated a novel method for studying sleep issues that allows for fast, cost-effective, and customizable data to be gathered.
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http://dx.doi.org/10.2196/jmir.4476DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4526927PMC
June 2015

The digital phenotype.

Nat Biotechnol 2015 May;33(5):462-3

1] Harvard Medical School, Boston, Massachusetts, USA [2] Children's Hospital Boston, Boston, Massachusetts, USA.

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http://dx.doi.org/10.1038/nbt.3223DOI Listing
May 2015
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