Increases in Medicare Spending and Use after Private Equity Acquisition of Retina Practices.

PURPOSE: Private equity (PE) firms increasingly are acquiring physician practices in the United States, particularly within procedural-based specialties such as ophthalmology including retina. To date, the potential impact of ophthalmology practice acquisitions remains unknown. We evaluated the association between PE acquisition and Medicare spending and use for common retina services. DESIGN: Retrospective difference-in-differences analysis using the 20% Medicare fee-for-service claims dataset from January 1, 2015, through December 31, 2019. PARTICIPANTS: Eighty-two practices acquired by PE during the study period and matched control practices. METHODS: We used novel data on PE acquisitions of retina practices linked to the 20% sample Medicare claims data. Retina practices acquired by PE between 2016 and 2019 were matched to up to 3 non-PE (control) practices based on characteristics before acquisition. Private equity-acquired practices were compared with matched control practices through 6 quarters after acquisition using a difference-in-differences event study design. Data analyses were performed between August 2022 and April 2023. MAIN OUTCOME MEASURES: Medicare spending and use of common retina services. RESULTS: Relative to control practices, PE-acquired retina practices increased the use of higher-priced anti-vascular endothelial growth factor (VEGF) agents including aflibercept, which differentially increased by 6.5 injections (95% confidence interval, 0.4-12.5; P = 0.03) per practice-quarter, or 22% from baseline. As a result, Medicare spending on aflibercept differentially increased by $13 028 per practice-quarter, or 21%. No statistically significant differences were found in use or spending for evaluation and management visits or diagnostic imaging. CONCLUSIONS: Private equity acquisition of retina practices are associated with modest increases in the use of higher-priced anti-VEGF drugs like aflibercept, leading to higher Medicare spending. This finding highlights the need to monitor the influence of PE firms' financial incentives over clinician decision-making and the appropriateness of care, which could be swayed by strong economic incentives. FINANCIAL DISCLOSURE(S): Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Private equity in ophthalmology: taking the long view.

PURPOSE OF REVIEW: Private equity's momentum in eye care remains controversial, even as investment continues to hasten the consolidation of ophthalmology and optometry practices. In this review, we discuss the growing implications of private equity activity in ophthalmology, drawing on updated empirical findings from the literature. We also examine recent legal and policy efforts to address private equity investment in healthcare, with implications for ophthalmologists considering sales to private equity. RECENT FINDINGS: Concerns about private equity centres around evidence that some investment entities are not just valuable sources of capital or business expertise, but that they take outright ownership and control of acquired practices in order to drive high returns on investment. Although practices may receive considerable benefits from private equity investment, empirical evidence suggests that private equity's most consistent effect on acquired practices is to increase spending and utilization without commensurate benefits on patient health. Although data on workforce effects are limited, an early study on workforce composition changes in private equity-acquired practices demonstrates that physicians were more likely to enter and exit a given practice than their counterparts in nonacquired practices, suggesting some degree of workforce flux. State and federal oversight of private equity's impact on healthcare may be ramping up in response to these demonstrated changes. SUMMARY: Private equity will continue to broaden their footprint in eye care, necessitating ophthalmologists to take the long view of private equity's net effects. For practices considering a private equity sale, recent policy developments highlight the importance of identifying and vetting a well aligned investment partner, with safeguards to preserve clinical decision-making and physician autonomy.

Consent Practices for Assisted Vaginal Births (AVB) at Two Tertiary Care Hospitals: A Retrospective Review of Physician Documentation.

OBJECTIVE: To determine whether assisted vaginal birth (AVB) consent documentation, a surrogate for in vivo consent, aligns with Canadian practice guidelines at 2 Canadian tertiary-level obstetric centres. METHODS: This was a retrospective review of AVBs (vacuum and forceps) from July 2019 to December 2019 at 2 tertiary-level hospitals with template-based (Site 1) or dictation-based (Site 2) documentation. We extracted, from obstetric and neonatal charts, AVB type, physician and documenter types (resident/fellow/family doctor/generalist obstetrics and gynecology [OBGYN]/maternal-fetal medicine), and consent elements (present/absent) based on a predetermined checklist. Data were summarized and comparisons were made using chi-square test, Fisher exact test, and logistic regression, where appropriate. RESULTS: We identified 551 AVBs (156 forceps, 395 vacuum) with most documentation completed by generalist OBGYNs or residents (333/551, 60.5%). Most vacuum-assisted deliveries documented no specific maternal (366/395, 92.7%) or neonatal (364/395, 92.2%) risks, and 107/156 (68.6%) and 106/156 (67.9%) forceps-assisted deliveries lacked specific documentation of maternal and neonatal risk, respectively. At Site 2, postpartum hemorrhage risk at vacuum-assisted deliveries was more commonly documented (6/90 [6.7%] vs. 2/395 [0.7%], P = 0.002) as was at least 1 neonatal risk and risk of obstetrical anal sphincter injury at forceps-assisted deliveries (50/133 [37.6%] vs. 0/23 [0%], P < 0.001) and (43/133 [32.3%] vs. 0/23 [0%], P = 0.001), respectively. CONCLUSIONS: Opportunity to improve AVB consent documentation exists, warranting quality improvement initiatives.

Autoantigen microarrays reveal myelin basic protein autoantibodies in morphea.

BACKGROUND: Morphea is an autoimmune, sclerosing skin disorder. Despite the recent emphasis on immune dysregulation in morphea, the role of autoantibodies in morphea pathogenesis or utility as biomarkers are poorly defined. METHODS: Autoantigen microarray was used to profile autoantibodies from the serum of participants from the Morphea in Adults and Children (MAC) cohort. Clinical and demographic features of morphea patients with myelin basic protein (MBP) autoantibodies were compared to those without. MBP immunohistochemistry staining was subsequently performed in morphea skin to assess for perineural inflammation in areas of staining. Immunofluorescence staining on mouse brain tissue was also performed using patient sera and mouse anti-myelin basic protein antibody to confirm the presence of MBP antibodies in patient sera. RESULTS: Myelin basic protein autoantibodies were found in greater frequency in morphea (n = 50, 71.4%) compared to systemic sclerosis (n = 2, 6.7%) and healthy controls (n = 7, 20%). Patients with MBP antibodies reported pain at higher frequencies. Morphea skin biopsies, highlighted by immunohistochemistry, demonstrated increased perineural inflammation in areas of MBP expression. Immunofluorescence staining revealed an increased fluorescence signal in myelinated areas of mouse brain tissue (i.e. axons) when incubated with sera from MBP antibody-positive morphea patients compared to sera from MBP antibody-negative morphea patients. Epitope mapping revealed target epitopes for MBP autoantibodies in morphea are distinct from those reported in MS, and included fragments 11-30, 41-60, 51-70, and 91-110. CONCLUSIONS: A molecular classification of morphea based on distinct autoantibody biosignatures may be used to differentially classify morphea. We have identified anti-MBP as a potential antibody associated with morphea due to its increased expression in morphea compared to healthy controls and systemic sclerosis patients.

Mapping Rural and Urban Veterans' Spatial Access to Primary Care Following the MISSION Act.

BACKGROUND: The 2018 MISSION Act sought to improve Veterans' access to primary care by allowing Veterans living more than 30 min from VA care to utilize non-VA clinics. The impact of this legislation may vary for rural compared to urban Veterans. OBJECTIVE: Assess the extent to which the 2018 MISSION Act facilitates spatial access to primary care for Veterans living in rural versus urban Oregon. DESIGN: We identified locations of all VA and non-VA primary care clinics in Oregon then calculated 30-min drive-time catchment areas from census tract centroids to the nearest clinics. We compared measures of spatial access to primary care for Veterans in rural, micropolitan, and urban areas. PARTICIPANTS: American Community Survey data representing Oregon adults. MAIN MEASURES: Two measures of spatial access focusing on the number of clinics (supply), and an access index based on the two-step floating catchment area method (2SFCA) which accounts for number of clinics (supply) and population size (demand). KEY RESULTS: Compared to only 13.0% of rural Veterans, 83.6% of urban Veterans lived within 30 min' drive time of VA primary care. Given the MISSION Act's eligibility criteria, 81.6% of rural Veterans and ~ 97% of urban and micropolitan Veterans had spatial access to primary care. When accounting for both supply and demand, rural areas had significantly higher access scores (p < 0.05) compared to urban areas. CONCLUSIONS: Using MISSION Act guidelines for Veteran access to primary care, rural compared to urban Veterans had less spatial access based on clinic number (supply), but more access when considering clinic number and population size (supply and demand). Geographic Information System (GIS) spatial techniques may help to assess changes in access to care. However, these methods do not incorporate all dimensions of access and work is needed to understand whether utilization and quality of care is improved.

An ethical analysis of clinical triage protocols and decision-making frameworks: what do the principles of justice, freedom, and a disability rights approach demand of us?

BACKGROUND: The expectation of pandemic-induced severe resource shortages has prompted authorities to draft and update frameworks to guide clinical decision-making and patient triage. While these documents differ in scope, they share a utilitarian focus on the maximization of benefit. This utilitarian view necessarily marginalizes certain groups, in particular individuals with increased medical needs. MAIN BODY: Here, we posit that engagement with the disability critique demands that we broaden our understandings of justice and fairness in clinical decision-making and patient triage. We propose the capabilities theory, which recognizes that justice requires a range of positive capabilities/freedoms conducive to the achievement of meaningful life goals, as a means to do so. Informed by a disability rights critique of the clinical response to the pandemic, we offer direction for the construction of future clinical triage protocols which will avoid ableist biases by incorporating a broader apprehension of what it means to be human. CONCLUSION: The clinical pandemic response, codified across triage protocols, should embrace a form of justice which incorporates a vision of pluralistic human capabilities and a valuing of positive freedoms.

Engaging Consumers in Medicaid Program Design: Strategies from the States.

Policy Points As Medicaid programs grow in scale and complexity, greater consumer input may guide successful program design, but little is known about the extent to which state agencies are engaging consumers in the design and implementation of programs and policies. Through 50 semistructured interviews with Medicaid leaders in 14 states, we found significant variation in consumer engagement approaches, with many common facilitators, including leadership commitment, flexible strategies for recruiting and supporting consumer participation, and robust community partnerships. We provide early evidence on how state Medicaid agencies are integrating consumers' experiences and perspectives into their program design and governance. CONTEXT: Consumer engagement early in the process of health care policymaking may improve the effectiveness of program planning and implementation, promote patient-centric care, enhance beneficiary protections, and offer opportunities to improve service delivery. As Medicaid programs grow in scale and complexity, greater consumer input may guide successful program design, but little is known about the extent to which state agencies are currently engaging consumers in the design and implementation of programs and policies, and how this is being done. METHODS: We conducted semistructured interviews with 50 Medicaid program leaders across 14 states, employing a stratified purposive sampling method to select state Medicaid programs based on US census region, rurality, Medicaid enrollment size, total population, ACA expansion status, and Medicaid managed care penetration. Interview data were audio-recorded, professionally transcribed, and underwent iterative coding with content and thematic analyses. FINDINGS: First, we found variation in consumer engagement approaches, ranging from limited and largely symbolic interactions to longer-term deliberative bodies, with some states tailoring their federally mandated standing committees to engage consumers. Second, most states were motivated by pragmatic considerations, such as identifying and overcoming implementation challenges for agency programs. Third, states reported several common facilitators of successful consumer engagement efforts, including leadership commitment, flexible strategies for recruiting and supporting consumers' participation, and robust community partnerships. All states faced barriers to authentic and sustained engagement. CONCLUSIONS: Sharing best practices across states could help strengthen programs' engagement efforts, identify opportunities for program improvement reflecting community needs, and increase participation among a population that has traditionally lacked a political voice.

Morphea patients with mucocutaneous involvement: A cross-sectional study from the Morphea in Adults and Children (MAC) cohort.

BACKGROUND: Demographic and clinical findings of patients with mucocutaneous morphea have not been well characterized, to our knowledge. OBJECTIVE: To determine the demographic and clinical characteristics of morphea patients with mucocutaneous lesions who were enrolled in the Morphea in Adults and Children cohort. METHODS: Cross-sectional study of 735 patients in the Morphea in Adults and Children cohort from 2007 to 2018. RESULTS: A total of 4.6% of linear morphea patients had oral involvement versus 2.4% among the entire cohort, whereas 10.3% of generalized morphea patients had genital involvement versus 3.7% among the entire cohort. Patients with genital lesions were older at disease onset than those with oral morphea (57 versus 11.5 years; P < .001) and had more frequent extragenital lichen sclerosus et atrophicus (59.2% versus 5.6%; P = .004). LIMITATIONS: Selection bias and limited number of affected subjects. CONCLUSION: Oral morphea lesions predominate in younger patients with facial linear morphea, whereas genital lesions predominate in postmenopausal women with overlying extragenital lichen sclerosus et atrophicus.

Developing an Automatic System for Classifying Chatter About Health Services on Twitter: Case Study for Medicaid.

BACKGROUND: The wide adoption of social media in daily life renders it a rich and effective resource for conducting near real-time assessments of consumers' perceptions of health services. However, its use in these assessments can be challenging because of the vast amount of data and the diversity of content in social media chatter. OBJECTIVE: This study aims to develop and evaluate an automatic system involving natural language processing and machine learning to automatically characterize user-posted Twitter data about health services using Medicaid, the single largest source of health coverage in the United States, as an example. METHODS: We collected data from Twitter in two ways: via the public streaming application programming interface using Medicaid-related keywords (Corpus 1) and by using the website's search option for tweets mentioning agency-specific handles (Corpus 2). We manually labeled a sample of tweets in 5 predetermined categories or other and artificially increased the number of training posts from specific low-frequency categories. Using the manually labeled data, we trained and evaluated several supervised learning algorithms, including support vector machine, random forest (RF), naïve Bayes, shallow neural network (NN), k-nearest neighbor, bidirectional long short-term memory, and bidirectional encoder representations from transformers (BERT). We then applied the best-performing classifier to the collected tweets for postclassification analyses to assess the utility of our methods. RESULTS: We manually annotated 11,379 tweets (Corpus 1: 9179; Corpus 2: 2200) and used 7930 (69.7%) for training, 1449 (12.7%) for validation, and 2000 (17.6%) for testing. A classifier based on BERT obtained the highest accuracies (81.7%, Corpus 1; 80.7%, Corpus 2) and F1 scores on consumer feedback (0.58, Corpus 1; 0.90, Corpus 2), outperforming the second best classifiers in terms of accuracy (74.6%, RF on Corpus 1; 69.4%, RF on Corpus 2) and F1 score on consumer feedback (0.44, NN on Corpus 1; 0.82, RF on Corpus 2). Postclassification analyses revealed differing intercorpora distributions of tweet categories, with political (400778/628411, 63.78%) and consumer feedback (15073/27337, 55.14%) tweets being the most frequent for Corpus 1 and Corpus 2, respectively. CONCLUSIONS: The broad and variable content of Medicaid-related tweets necessitates automatic categorization to identify topic-relevant posts. Our proposed system presents a feasible solution for automatic categorization and can be deployed and generalized for health service programs other than Medicaid. Annotated data and methods are available for future studies.

Health Policy Perspective: Medicaid and State Politics Beyond COVID.

The COVID-19 pandemic is poised to drastically alter the Medicaid program. While state Medicaid programs are currently expanding coverage policies and enrollment to address acute public health needs, states will soon face significant budget shortfalls. These impending changes may renew partisan debates about restrictive policies like work requirements, which generally require beneficiaries to verify their participation in certain activities-such as employment, job search, or training programs-in order to receive or retain coverage. We argue that restrictive Medicaid policies are driven, to a great extent, by political party affiliation, highlighting the outsized role of partisanship in Medicaid policy adoption. To combat these dynamics, additional efforts are needed to improve community-informed decision-making, strengthen evaluation approaches to tie evidence to policymaking, and boost participation in and understanding of the political processes that affect policy change.

Access to Primary, Mental Health, and Specialty Care: a Comparison of Medicaid and Commercially Insured Populations in Oregon.

OBJECTIVE: To describe how access to primary and specialty care differs for Medicaid patients relative to commercially insured patients, and how these differences vary across rural and urban counties, using comprehensive claims data from Oregon. DESIGN: Cross-sectional study of risk-adjusted access rates for two types of primary care providers (physicians; nurse practitioners (NPs) and physician assistants (PAs)); four types of mental health providers (psychiatrists, psychologists, advanced practice NPs or PAs specializing in mental health care, behavioral specialists); and four physician specialties (obstetrics and gynecology, general surgery, gastroenterology, dermatology). PARTICIPANTS: 420,947 Medicaid and 638,980 commercially insured adults in Oregon, October 2014-September 2015. OUTCOME: Presence of any visit with each provider type, risk-adjusted for sex, age, and health conditions. RESULTS: Relative to commercially insured individuals, Medicaid enrollees had lower rates of access to primary care physicians (- 11.82%; CI - 12.01 to - 11.63%) and to some specialists (e.g., obstetrics and gynecology, dermatology), but had equivalent or higher rates of access to NPs and PAs providing primary care (4.33%; CI 4.15 to 4.52%) and a variety of mental health providers (including psychiatrists, NPs and PAs, and other behavioral specialists). Across all providers, the largest gaps in Medicaid-commercial access rates were observed in rural counties. The Medicaid-commercial patient mix was evenly distributed across primary care physicians, suggesting that access for Medicaid patients was not limited to a small subset of primary care providers. CONCLUSIONS: This cross-sectional study found lower rates of access to primary care physicians for Medicaid enrollees, but Medicaid-commercial differences in access rates were not present across all provider types and displayed substantial variability across counties. Policies that address rural-urban differences as well as Medicaid-commercial differences-such as expansions of telemedicine or changes in the workforce mix-may have the largest impact on improving access to care across a wide range of populations.

Characteristics of Twitter Use by State Medicaid Programs in the United States: Machine Learning Approach.

BACKGROUND: Twitter is a potentially valuable tool for public health officials and state Medicaid programs in the United States, which provide public health insurance to 72 million Americans. OBJECTIVE: We aim to characterize how Medicaid agencies and managed care organization (MCO) health plans are using Twitter to communicate with the public. METHODS: Using Twitter's public application programming interface, we collected 158,714 public posts ("tweets") from active Twitter profiles of state Medicaid agencies and MCOs, spanning March 2014 through June 2019. Manual content analyses identified 5 broad categories of content, and these coded tweets were used to train supervised machine learning algorithms to classify all collected posts. RESULTS: We identified 15 state Medicaid agencies and 81 Medicaid MCOs on Twitter. The mean number of followers was 1784, the mean number of those followed was 542, and the mean number of posts was 2476. Approximately 39% of tweets came from just 10 accounts. Of all posts, 39.8% (63,168/158,714) were classified as general public health education and outreach; 23.5% (n=37,298) were about specific Medicaid policies, programs, services, or events; 18.4% (n=29,203) were organizational promotion of staff and activities; and 11.6% (n=18,411) contained general news and news links. Only 4.5% (n=7142) of posts were responses to specific questions, concerns, or complaints from the public. CONCLUSIONS: Twitter has the potential to enhance community building, beneficiary engagement, and public health outreach, but appears to be underutilized by the Medicaid program.

State-Based Marketplaces Outperform Federally-Facilitated Marketplaces.

In response to regulatory changes at the federal level, states that run their own marketplaces have taken steps to stabilize their individual markets. In this comparison of state-based and federally-facilitated marketplaces from 2016-2018, we find that SBMs had slower premium increases (43% vs. 75%), and fewer carrier exits, than FFMs. The total population participating in FFMs declined by 10%, while the enrolled population in SBMs remained largely stable, increasing by 2%. We find that the performance of the ACA marketplaces varies by state and appears to cluster around marketplace types.

Imbalanced target prediction with pattern discovery on clinical data repositories.

BACKGROUND: Clinical data repositories (CDR) have great potential to improve outcome prediction and risk modeling. However, most clinical studies require careful study design, dedicated data collection efforts, and sophisticated modeling techniques before a hypothesis can be tested. We aim to bridge this gap, so that clinical domain users can perform first-hand prediction on existing repository data without complicated handling, and obtain insightful patterns of imbalanced targets for a formal study before it is conducted. We specifically target for interpretability for domain users where the model can be conveniently explained and applied in clinical practice. METHODS: We propose an interpretable pattern model which is noise (missing) tolerant for practice data. To address the challenge of imbalanced targets of interest in clinical research, e.g., deaths less than a few percent, the geometric mean of sensitivity and specificity (G-mean) optimization criterion is employed, with which a simple but effective heuristic algorithm is developed. RESULTS: We compared pattern discovery to clinically interpretable methods on two retrospective clinical datasets. They contain 14.9% deaths in 1 year in the thoracic dataset and 9.1% deaths in the cardiac dataset, respectively. In spite of the imbalance challenge shown on other methods, pattern discovery consistently shows competitive cross-validated prediction performance. Compared to logistic regression, Naïve Bayes, and decision tree, pattern discovery achieves statistically significant (p-values < 0.01, Wilcoxon signed rank test) favorable averaged testing G-means and F1-scores (harmonic mean of precision and sensitivity). Without requiring sophisticated technical processing of data and tweaking, the prediction performance of pattern discovery is consistently comparable to the best achievable performance. CONCLUSIONS: Pattern discovery has demonstrated to be robust and valuable for target prediction on existing clinical data repositories with imbalance and noise. The prediction results and interpretable patterns can provide insights in an agile and inexpensive way for the potential formal studies.