Changes in Health Care Workers' Economic Outcomes Following Medicaid Expansion.

Importance: The extent to which changes in health sector finances impact economic outcomes among health care workers, especially lower-income workers, is not well known. Objective: To assess the association between state adoption of the Affordable Care Act's Medicaid expansion-which led to substantial improvements in health care organization finances-and health care workers' annual incomes and benefits, and whether these associations varied across low- and high-wage occupations. Design, Setting, and Participants: Difference-in-differences analysis to assess differential changes in health care workers' economic outcomes before and after Medicaid expansion among workers in 30 states that expanded Medicaid relative to workers in 16 states that did not, by examining US individuals aged 18 through 65 years employed in the health care industry surveyed in the 2010-2019 American Community Surveys. Exposure: Time-varying state-level adoption of Medicaid expansion. Main Outcomes and Measures: Primary outcome was annual earned income; secondary outcomes included receipt of employer-sponsored health insurance, Medicaid, and Supplemental Nutrition Assistance Program benefits. Results: The sample included 1 322 263 health care workers from 2010-2019. Health care workers in expansion states were similar to those in nonexpansion states in age, sex, and educational attainment, but those in expansion states were less likely to identify as non-Hispanic Black. Medicaid expansion was associated with a 2.16% increase in annual incomes (95% CI, 0.66%-3.65%; P = .005). This effect was driven by significant increases in annual incomes among the top 2 highest-earning quintiles (ß coefficient, 2.91%-3.72%), which includes registered nurses, physicians, and executives. Health care workers in lower-earning quintiles did not experience any significant changes. Medicaid expansion was associated with a 3.15 percentage point increase in the likelihood that a health care worker received Medicaid benefits (95% CI, 2.46 to 3.84; P < .001), with the largest increases among the 2 lowest-earning quintiles, which includes health aides, orderlies, and sanitation workers. There were significant decreases in employer-sponsored health insurance and increases in SNAP following Medicaid expansion. Conclusion and Relevance: Medicaid expansion was associated with increases in compensation for health care workers, but only among the highest earners. These findings suggest that improvements in health care sector finances may increase economic inequality among health care workers, with implications for worker health and well-being.

Salve Lucrum: The Existential Threat of Greed in US Health Care.

In this Viewpoint, Donald Berwick explores the pursuit of profit in US health care across sectors­such as pharmaceutical companies, insurers, hospitals, and physician practices­and its harms to patients, and then offers potential solutions.

Fulfilling the Promise of Artificial Intelligence in the Health Sector: Let's Get Real.

OBJECTIVES: This study aimed to showcase the potential and key concerns and risks of artificial intelligence (AI) in the health sector, illustrating its application with current examples, and to provide policy guidance for the development, assessment, and adoption of AI technologies to advance policy objectives. METHODS: Nonsystematic scan and analysis of peer-reviewed and gray literature on AI in the health sector, focusing on key insights for policy and governance. RESULTS: The application of AI in the health sector is currently in the early stages. Most applications have not been scaled beyond the research setting. The use in real-world clinical settings is especially nascent, with more evidence in public health, biomedical research, and "back office" administration. Deploying AI in the health sector carries risks and hazards that must be managed proactively by policy makers. For AI to produce positive health and policy outcomes, 5 key areas for policy are proposed, including health data governance, operationalizing AI principles, flexible regulation, skills among health workers and patients, and strategic public investment. CONCLUSIONS: AI is not a panacea, but a tool to address specific problems. Its successful development and adoption require data governance that ensures high-quality data are available and secure; relevant actors can access technical infrastructure and resources; regulatory frameworks promote trustworthy AI products; and health workers and patients have the information and skills to use AI products and services safely, effectively, and efficiently. All of this requires considerable investment and international collaboration.

A Guide to Industry Payments and an Algorithm for Their Management.

BACKGROUND: The Open Payments database was created to increase transparency of industry payment relationships within medicine. The current literature often examines only 1 year of the database. In this study, the authors use 5 years of data to show trends among industry payments to plastic surgeons from 2014 to 2018. In addition, the authors lay out the basics of conflict-of-interest reporting for the new plastic surgeon. Finally, the authors suggest an algorithm for the responsible management of industry relationships. METHODS: This study analyzed nonresearch payments made to plastic surgeons from January 1, 2014, to December 31, 2018. Descriptive statistics were calculated using R Statistical Software and visualized using Tableau. RESULTS: A total of 304,663 payments totaling $140,889,747 were made to 8148 plastic surgeons; 41 percent ($58.28 million) was paid to 50 plastic surgeons in the form of royalty or license payments. With royalties excluded, average and median payments were $276 and $25. The average yearly total per physician was $2028. Of the 14 payment categories, 95 percent of the total amount paid was attributable payments in one of six categories. Seven hundred thirty companies reported payments to plastic surgeons from 2014 to 2018; 15 companies (2 percent) were responsible for 80 percent ($66.34 million) of the total sum paid. Allergan was responsible for $24.45 million (29.6 percent) of this amount. CONCLUSIONS: Although discussions on the proper management of industry relationships continue to evolve, the data in this study illustrate the importance of managing industry relationships. The simple guidelines suggested create a basis for managing industry relationships in the career of the everyday plastic surgeon.

Industry Payments to Plastic Surgeons, 2013 to 2018: Who's Getting Paid?

BACKGROUND: The Physician Payments Sunshine Act of 2010 mandated that all industry payments to physicians be publicly disclosed. To date, industry support of plastic surgeons has not been longitudinally characterized. The authors seek to evaluate payment trends from 2013 to 2018 and characteristics across plastic surgeon recipients of industry payments. METHODS: The authors cross-referenced those in the 2019 American Society of Plastic Surgeons member database with Centers for Medicare & Medicaid Services Open Payments database physician profile identification number indicating industry funds received within the study period. We categorized surgeons by years since American Board of Plastic Surgery certification, practice region, and academic affiliation. RESULTS: A sum of $89,436,100 (247,614 payments) was received by 3855 plastic surgeons. The top 1 percent of earners (n = 39) by dollar amount received 52 percent of industry dollars to plastic surgeons; of these, nine (23 percent) were academic. Overall, 428 surgeons (11 percent) were academic and received comparable dollar amounts from industry as their nonacademic counterparts. Neither geographic location nor years of experience were independent predictors of payments received. The majority of individual transactions were for food and beverage, whereas the majority of industry dollars were typically for royalties or license. CONCLUSIONS: Over half of all industry dollars transferred went to just 1 percent of American Society of Plastic Surgeons members receiving payments between 2013 and 2018. Considerable heterogeneity exists when accounting for payment subcategories.

Productivity change and its driving forces in Chinese healthcare sector.

Since the last medical reform in 2009, China's public hospitals have been facing the changes in the institutional environment. However, the effects of reforms have not been received enough attention to deliver evidence-based implications. In this paper, we first assess the efficiency of regional public hospitals from 2011 to 2018, employing a proposed method based on an additive indicator and an aggregate directional distance function (DDF). The method applied allows for decomposing total factor productivity (TFP) indicator into three components, including technical efficiency change (TEC), total productivity (TP) and scale efficiency change (SEC). Second, following the efficiency assessment, we carry post-efficiency analysis to identify the determinants of efficiency of the public hospitals. The results show that annual average TFP growth rate is 1.38%, which is driven mainly by TEC. Regional disparities of public hospitals' performance are expanding. Almost 75% of the regions considered show a positive TFP growth. The regression results show that the significant determinants of efficiency of regional public hospitals include the price of and demand for health services.

Workers' compensation claims among private skilled nursing facilities, Ohio, 2001-2012.

INTRODUCTION: Skilled nursing facilities have one of the highest rates of occupational injury and illness among all industries. This study quantifies the burden of occupational injury and illness in this industry using data from a single state-based workers' compensation (WC) system. METHODS: Ohio Bureau of Workers' Compensation claims from 2001 to 2012 were analyzed among privately owned, state-insured skilled nursing facilities and are presented as claim counts and rates per 100 full-time equivalents (FTE). Worker, employer, incident, and injury characteristics were examined among all claims and by medical-only (medical care expenses and/or less than eight days away from work) and lost-time (eight days or more away from work) claim types. RESULTS: There were 56,442 claims in this population of Ohio skilled nursing facilities from 2001 to 2012. Overexertion and bodily reaction, slips, trips, and falls, and contact with objects and equipment accounted for the majority of all WC claims (89%). Overexertion and bodily reaction, and slips, trips, and falls comprised 85% of the 10,793 lost-time claims. The highest injury event/exposure rates for all claims were for overexertion and bodily reaction (3.7 per 100 FTE for all claims), followed by slip, trips, and falls (2.1), and contact with objects and equipment (1.9). CONCLUSION: Understanding the details surrounding injury events and exposures resulting in WC claims can help better align prevention efforts, such as incorporation of safe patient handling policies and lifting aids, improvement in housekeeping practices, and employee training within skilled nursing facilities to prevent worker injury and mitigate related expenses.

Occupational exposures to hazardous chemicals and agents among healthcare workers in Bhutan.

BACKGROUND: Occupational exposures to hazardous chemicals among healthcare workers can result in long-term adverse health outcomes. Research on such exposures from low- and middle-income countries is limited. The aim of this study was to estimate the prevalence of exposures to a range of chemicals used in healthcare settings among Bhutanese healthcare workers. METHODS: A cross-sectional study was conducted among healthcare workers (n = 370) working in three hospitals in the western region of Bhutan. Demographic and occupational information was collected, and exposures to asthmagens, carcinogens, ototoxic and other agents were assessed using a web-based tool. The prevalence of exposure to these chemicals was calculated and the circumstances resulting in such exposures were examined. RESULTS: The prevalence of exposure to one or more asthmagen, carcinogen, and ototoxic agent was 98.7%, 28.1%, and 7.6%, respectively; and was 6.2% for anesthetic gases and 2.2% for antineoplastic drugs. The most common exposures were to latex, and cleaning and disinfecting agents in the asthmagens group; formaldehyde in the carcinogens group; and p-xylene among ototoxic agents. The circumstances resulting in exposures were using latex gloves, using bleach and chlorhexidine for cleaning, using formaldehyde as a disinfectant and in the laboratory, and using p-xylene in the laboratory. CONCLUSIONS: The results indicate that a large proportion of Bhutanese healthcare workers are occupationally exposed to chemicals linked to chronic diseases, with exposure prevalence higher than in high-income countries. The study provides information that can be used to formulate policies and to implement control measures to protect healthcare workers.

Forecasting COVID-19 Transmission and Healthcare Capacity in Bali, Indonesia.

OBJECTIVES: In the current early phase of the coronavirus disease 2019 (COVID-19) outbreak, Bali needs to prepare to face the escalation of cases, with a particular focus on the readiness of healthcare services. We simulated the future trajectory of the epidemic under current conditions, projected the impact of policy interventions, and analyzed the implications for healthcare capacity. METHODS: Our study was based on the first month of publicly accessible data on new confirmed daily cases. A susceptible, exposed, infected, recovered (SEIR) model for COVID-19 was employed to compare the current dynamics of the disease with those predicted under various scenarios. RESULTS: The fitted model for the cumulative number of confirmed cases in Bali indicated an effective reproduction number of 1.4. Interventions have decreased the possible maximum number of cases from 71 125 on day 86 to 22 340 on day 119, and have prolonged the doubling time from about 9 days to 21 days. This corresponds to an approximately 30% reduction in transmissions from cases of mild infections. There will be 2780 available hospital beds, and at the peak (on day 132), the number of severe cases is estimated to be roughly 6105. Of these cases, 1831 will need intensive care unit (ICU) beds, whereas the number of currently available ICU beds is roughly 446. CONCLUSIONS: The healthcare system in Bali is in danger of collapse; thus, serious efforts are needed to improve COVID-19 interventions and to prepare the healthcare system in Bali to the greatest extent possible.

Economics of COVID-19: challenges and the way forward for health policy during and after the pandemic.

The emergence of novel coronavirus disease 2019 (COVID-19) pandemic provides unique challenges for health system. While on the one hand, the government has to struggle with the strategies for control of COVID-19, on the other hand, other routine health services also need to be managed. Second, the infrastructure needs to be augmented to meet the potential epidemic surge of cases. Third, economic welfare and household income need to be guaranteed. All of these have complicated the routine ways in which the governments have dealt with various trade-offs to determine the health and public policies. In this paper, we outline key economic principles for the government to consider for policymaking, during, and after the COVID-19 pandemic. The pandemic rightfully places long due attention of policymakers for investing in health sector. The policy entrepreneurs and public health community should not miss this once-in-a-lifetime "policy window" to raise the level of advocacy for appropriate investment in health sector.

Insights From a Temporal Assessment of Increases in US Private Payer Coverage of Tumor Sequencing From 2015 to 2019.

OBJECTIVES: To examine the temporal trajectory of insurance coverage for next-generation tumor sequencing (sequencing) by private US payers, describe the characteristics of coverage adopters and nonadopters, and explore adoption trends relative to the Centers for Medicare and Medicaid Services' National Coverage Determination (CMS NCD) for sequencing. METHODS: We identified payers with positive coverage (adopters) or negative coverage (nonadopters) of sequencing on or before April 1, 2019, and abstracted their characteristics including size, membership in the BlueCross BlueShield Association, and whether they used a third-party policy. Using descriptive statistics, payer characteristics were compared between adopters and nonadopters and between pre-NCD and post-NCD adopters. An adoption timeline was constructed. RESULTS: Sixty-nine payers had a sequencing policy. Positive coverage started November 30, 2015, with 1 payer and increased to 33 (48%) as of April 1, 2019. Adopters were less likely to be BlueCross BlueShield members (P < .05) and more likely to use a third-party policy (P < .001). Fifty-eight percent of adopters were small payers. Among adopters, 52% initiated coverage pre-NCD over a 25-month period and 48% post-NCD over 17 months. CONCLUSIONS: We found an increase, but continued variability, in coverage over 3.5 years. Temporal analyses revealed important trends: the possible contribution of the CMS NCD to a faster pace of coverage adoption, the interdependence in coverage timing among BlueCross BlueShield members, the impact of using a third-party policy on coverage timing, and the importance of small payers in early adoption. Our study is a step toward systematic temporal research of coverage for precision medicine, which will inform policy and affordability assessments.

Advanced Data Analytics for Clinical Research Part I: What are the Tools?

The concept of Big Data is changing the way that clinical research can be performed. Cardiothoracic surgeons need to understand the dynamic digital transformation taking place in the healthcare industry. In the last decade, technological advances and Big Data analytics have become powerful tools for businesses. In healthcare, rapid expansion of Big Data infrastructure has occurred in parallel with attempts to reduce cost and improve outcomes. Many hospitals around the country are augmenting traditional relational databases with Big Data infrastructure. Advanced data capture and categorization tools such as natural language processing and optical character recognition are being developed for clinical and research use, while Internet of Things in the form of wearable technology serves as an additional source of data usable for research. As cardiothoracic surgeons seek ways to innovate, novel approaches to data acquisition and analysis enable a more rigorous level of investigatory efforts.

Advanced Data Analytics for Clinical Research Part II: Application to Cardiothoracic Surgery.

In the first part of this series, we introduced the tools of Big Data, including Not Only Standard Query Language data warehouse, natural language processing (NLP), optical character recognition (OCR), and Internet of Things (IoT). There are nuances to the utilization of these analytics tools, which must be well understood by clinicians seeking to take advantage of these innovative research strategies. One must recognize technical challenges to NLP, such as unintended search outcomes and variability in the expression of human written texts. Other caveats include dealing written texts in image formats, which may ultimately be handled with transformation to text format by OCR, though this technology is still under development. IoT is beginning to be used in cardiac monitoring, medication adherence alerts, lifestyle monitoring, and saving traditional labs from equipment failure catastrophes. These technologies will become more prevalent in the future research landscape, and cardiothoracic surgeons should understand the advantages of these technologies to propel our research to the next level. Experience and understanding of technology are needed in building a robust NLP search result, and effective communication with the data management team is a crucial step in successful utilization of these technologies. In this second installment of the series, we provide examples of published investigations utilizing the advanced analytic tools introduced in Part I. We will explain our processes in developing the research question, barriers to achieving the research goals using traditional research methods, tools used to overcome the barriers, and the research findings.

Hidden big data analytics issues in the healthcare industry.

The goal of the study was to identify big data analysis issues that can impact empirical research in the healthcare industry. To accomplish that the author analyzed big data related keywords from a literature review of peer reviewed journal articles published since 2011. Topics, methods and techniques were summarized along with strengths and weaknesses. A panel of subject matter experts was interviewed to validate the intermediate results and synthesize the key problems that would likely impact researchers conducting quantitative big data analysis in healthcare studies. The systems thinking action research method was applied to identify and describe the hidden issues. The findings were similar to the extant literature but three hidden fatal issues were detected. Methodical and statistical control solutions were proposed to overcome the three fatal healthcare big data analysis issues.