INNOVATIONS AND ADVANCEMENTS IN IN VITRO FERTILIZATION AND REPRODUCTIVE TECHNOLOGY
02 08 PRESIDENT’S PAGE How Technology Is Revolutionizing Women’s Health HOUSE CALL Cardiovascular Fitness Assessment: A Critical Tool in Preventive Medicine
EVP/CEO LETTER Medical Technology: A Journey from 1990 to 2022
ALLIES Innovations and Advancements in In Vitro Fertilization and Reproductive Technology
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How Technology Is Revolutionizing Women’s Health
Deborah Fuller, MD
Medicine today is evolving as articles, research, and businesses dealing with artificial intelligence and its impact on the future of medicine enter our practice environments. Our current issue of the Dallas Medical Journal will feature other avenues of new technology and more specific areas where artificial intelligence (AI) is already changing the practice of medicine. As an Obstetrician Gynecologist, I wanted to delve into the areas that deal with women’s health.
Artificial intelligence, by definition, refers to a digitalized computer system replicating the processing of the human brain, its intelligent behavior, and critical thinking. With computer technology, these models can potentially improve patient care by speeding up processes and increasing accuracy and efficiency. Already, artificial intelligence has proven its benefits in disease diagnosis and treatment, drug research and development, and health management. AI-based systems have entered various medical fields, especially those with a vital imaging component.
The convergence of technology and healthcare has significantly reshaped the landscape of medical care in recent decades, particularly for women’s health. This transformation spans digital tools, diagnostics, treatments, and disease management, providing women with more personalized, accessible, and effective healthcare solutions. From reproductive health innovations to telemedicine and wearable technology, these advancements have empowered women to take control of their health like never before.
Artificial intelligence application in
gynecology is still early compared to other specialties. However, in cervical cancer screening, AI is strengthening cervical cancer screening and diagnostics. Unfortunately, worldwide, cervical cancer is still highly prevalent, with an incidence of 13.3 cases per 100,000 women. Cervical cancer has a mortality rate of 7.2 deaths per 100,000 women. If detected in its preliminary stages, cervical cancer can potentially be easily treated. In office practice, cervical cancer screening involves HPV testing and cytological examination along with colposcopy. Several authors advocate for the potential of AI-powered cytological examination and colposcopy image analysis, potentially reducing unnecessary biopsies. The development of AI models in cervical cancer diagnosis is also being utilized at the histological level, aiding experienced cytopathologists in improving their negative predictive values.
Additionally, cervical cancer diagnosis can also be guided using MRI. When AI has been compared to experienced radiologists’ evaluation of MRI images, the AI models achieved higher sensitivity and accuracy with similar specificity. AI models can also potentially provide improved prognostic information when compared with current survival analysis models, which is essential for oncologic research in predicting survival for women with cervical cancer. Improved predictions of limited life expectancy in women with recurrent cervical cancer can help healthcare teams provide more personalized decision-making to individualize the level of care provided. Endometrial cancer is the most common gynecological malignancy and has a rising prevalence. Due to
postmenopausal bleeding, endometrial cancer is usually caught in the early stages. Unfortunately, those with advanced-stage disease typically have a poor prognosis. Endometrial biopsies as a screening method have been associated with false negative results. AI algorithms represent tools that can aid with hysteroscopies, histopathological images, and pre-operative MRIs in evaluating the depth of myometrial invasion, thus aiding radiologists rather than replacing them. AI models help predict a diagnosis and, more importantly, help provide significant prognostic information.
Endometriosis is a chronic medical condition, with the extra-uterine growth of endometrial tissue causing pain and, potentially, infertility issues with significant economic and other healthcare burdens on the patient and society. Due to nonspecific symptoms, the initial diagnosis is often complicated. AI algorithms may play an essential role in early disease detection, potentially substituting for diagnostic laparoscopies and thus creating earlier and better disease control.
Ovarian cancer is often initially diagnosed following clinical suspicion with transvaginal ultrasound. Despite advances in chemotherapy, ovarian cancer remains the most lethal gynecologic cancer, mainly because women are diagnosed at advanced stages. AI models that aid in the sensitivity of diagnostic tools could potentially lead to earlier detection of this very lethal malignancy. A computer-aided tool, GynScan, used with transvaginal
ultrasound, has shown 100% classification accuracy, sensitivity, specificity, and positive predictive value in detecting ovarian cancers. Additionally, AI may aid with CT scan and MRI diagnostic accuracy when assessing ovarian masses. AI models could also help distinguish between ovarian cancer types and determine adnexal masses’ malignant or benign nature. Just as AI models can aid with the histopathology of other gynecologic cancers, AI models assist pathologists in assessing ovarian masses. AI is also aiding with predicting the overall survival of ovarian cancer patients, helping with surgical treatment and the use of postoperative chemotherapy, and helping to reduce ovarian cancerrelated mortality.
One of the most significant advancements in women’s healthcare is the rise of digital health applications and telemedicine platforms. These tools have made healthcare more accessible, particularly for women in underserved areas, while reducing the logistical challenges of frequent inperson consultations.
Telemedicine allows patients to consult with healthcare professionals remotely, which has proven particularly beneficial for women. This technology is critical in prenatal and postpartum care areas, where routine checkups can often be conducted via video consultations, reducing the need for travel. For instance, pregnant women in rural areas can watch their pregnancies with fewer in-person visits, receiving specialist advice and guidance remotely. Telemedicine has also played a vital role in expanding access to gynecological consultations, mental health services, and counseling, all essential components of comprehensive women’s healthcare.
Mobile health apps also make waves in women’s health by offering
platforms for tracking menstrual cycles, fertility windows, pregnancy stages, and general well-being. Apps such as Clue, Flo, and Ovia help women monitor their menstrual and reproductive health, offering insights into ovulation patterns, hormone fluctuations, and emotional wellbeing. These apps empower women to understand their bodies better and to seek medical advice when irregular patterns emerge, such as the onset of symptoms indicative of conditions like polycystic ovary syndrome (PCOS) or endometriosis. This kind of proactive monitoring allows for more timely medical intervention, improving outcomes in situations that were once more difficult to diagnose early. Technological advancements in fertility and reproductive health have provided women with more options than ever before. IVF technology has evolved substantially, with advancements in embryo selection techniques, cryopreservation, and hormone therapies. Clinics now use artificial intelligence (AI) to predict the best embryo for implantation, improving the chances of successful pregnancies. Additionally, advances in egg freezing have offered women the ability to preserve their fertility, allowing them to plan families on their timelines, which is particularly valuable for women facing career demands or health conditions that may affect fertility later in life.
Genetic screening technologies have also enhanced reproductive care. Preimplantation genetic diagnosis (PGD) and preimplantation genetic screening (PGS) allow for the screening of embryos for genetic conditions, enabling families to make informed decisions about their reproductive choices. These technologies are precious for women who have a family history of genetic disorders or those undergoing IVF. Fertility wearables, such as basal
body temperature (BBT) monitors and hormone level sensors, provide women with additional tools to track ovulation and hormone cycles in real time. These devices offer insights that were once available only through clinical testing, giving women more immediate control over their fertility planning and reproductive health.
While technology offers immense potential to advance women’s health, it raises ethical and privacy concerns. The collection and use of sensitive health data, particularly in reproductive health apps and wearable devices, pose significant risks if not adequately safeguarded. Breaches in privacy could lead to misuse of personal health information or discrimination based on health data.
Moreover, as AI becomes more integrated into women’s healthcare, there are concerns about biases within the algorithms. If not adequately developed, AI systems may perpetuate gender or racial biases, leading to disparities in healthcare outcomes.
The integration of technology in women’s health will continue to accelerate. However, these advancements must be coupled with stringent data privacy protections and ongoing efforts to eliminate biases in healthcare technologies.
In conclusion, the impact of technology on women’s health is transformative, offering unprecedented opportunities to improve care, enhance diagnostic accuracy, and empower women to manage their health. As these innovations continue to evolve, they hold the potential to close gaps in access and outcomes further, ensuring that women everywhere can benefit from the latest advancements in medical science. DMJ
Medical Technology: A Journey from 1990 to 2022
Jon R. Roth, MS,
CAE
In the past three decades, the landscape of medical technology has undergone a seismic shift, driven by relentless innovation and an everincreasing demand for advanced healthcare solutions. From the early 1990s to 2022, spending on medical technology surged, reflecting both the rapid pace of technological advancements and the growing recognition of their critical role in improving patient outcomes. This editorial delves into the economic trajectory of medical technology spending, exploring the factors that have fueled this growth and the implications for the future of healthcare.
THE EARLY YEARS: 1990S TO EARLY 2000S
The 1990s marked the beginning of a transformative era in medical technology. During this period, the healthcare industry witnessed significant investments in diagnostic imaging, minimally invasive surgical techniques, and the advent of digital health records. The introduction of MRI and CT scans revolutionized diagnostic capabilities, allowing for earlier and more accurate detection of diseases. These innovations, while costly, were quickly adopted by healthcare providers, leading to a steady increase in spending.
By the early 2000s, the integration of information technology into healthcare systems began to take shape. Electronic Health Records (EHRs) started to replace paper-based systems, improving the efficiency and accuracy of patient data management. This shift required substantial investment in IT infrastructure and training, further driving up spending on medical technology.
THE MID-2000S TO 2010S: THE RISE OF PERSONALIZED MEDICINE
The mid-2000s to the 2010s saw the rise of personalized medicine, a paradigm shift that emphasized tailoring medical treatments to individual patient profiles. Advances in genomics and biotechnology played a pivotal role in this transition. The Human Genome Project, completed in 2003, paved
the way for a deeper understanding of genetic factors in health and disease. This knowledge spurred the development of targeted therapies and precision medicine, which, although expensive, promised more effective and personalized treatment options.
During this period, spending on medical technology continued to climb as healthcare providers invested in advanced diagnostic tools and treatments. The adoption of robotic-assisted surgery, for instance, became more widespread, offering greater precision and shorter recovery times for patients. However, these technologies came with high upfront costs and ongoing maintenance expenses, contributing to the overall increase in spending.
THE 2010S TO EARLY 2020S: THE DIGITAL HEALTH REVOLUTION
The 2010s ushered in the digital health revolution, characterized by the proliferation of wearable devices, telemedicine, and mobile health applications. These innovations aimed to enhance patient engagement, improve chronic disease management, and reduce healthcare costs through remote monitoring and virtual consultations. The COVID-19 pandemic accelerated the adoption of telemedicine, highlighting its potential to provide accessible and convenient care.
Investment in digital health technologies surged during this period, driven by both private sector funding and government initiatives. The global digital health market, valued at approximately $106 billion in 2019, was projected to reach $639 billion by 2026.1 This rapid growth was fueled by advancements in artificial intelligence, machine learning, and big data analytics, which enabled more sophisticated and personalized healthcare solutions.
THE IMPACT OF COVID-19
The COVID-19 pandemic had a profound impact on healthcare spending, particularly in the realm of medical technology. The urgent need for diagnostic testing, personal protective equipment (PPE), and ventilators led to unprecedented levels of investment in
medical technology. Governments and private companies alike poured resources into developing and distributing COVID-19 vaccines, which became a critical component of the global response to the pandemic.
In 2020 alone, U.S. healthcare spending increased by 10.6%, driven largely by pandemic-related expenditures.2 This surge in spending underscored the importance of having robust and adaptable medical technology infrastructure in place to respond to public health emergencies.
THE FUTURE OF MEDICAL TECHNOLOGY SPENDING
Looking ahead, the trajectory of medical technology spending is expected to continue its upward trend. Several factors will contribute to this sustained growth:
• Aging Population: As the global population ages, the demand for healthcare services and advanced medical technologies will increase. Older adults are more likely to require medical interventions, driving up spending on diagnostic tools, treatments, and long-term care solutions.
• Chronic Disease Management: The prevalence of chronic diseases such as diabetes, cardiovascular conditions, and cancer is on the rise. Managing these conditions effectively requires ongoing investment in medical technology, including continuous glucose monitors, wearable cardiac devices, and advanced imaging techniques.
• Innovation and Research: Continued investment in research and development will drive the creation of new medical technologies. Breakthroughs in fields such as regenerative medicine, nanotechnology, and biotechnology hold the promise of revolutionizing healthcare, but they also come with significant costs.
• Health Data Analytics: The increasing availability of health data and advancements in analytics will enable more precise and efficient healthcare delivery. However, harnessing the full
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potential of health data requires substantial investment in data infrastructure, cybersecurity, and analytics capabilities.
• Global Health Initiatives: Efforts to improve healthcare access and quality in low- and middle-income countries will drive global spending on medical technology. International collaborations and funding initiatives will play a crucial role in addressing healthcare disparities and promoting health equity.
The economic increase in spending on medical technology from 1990 to 2022 reflects the dynamic and evolving nature of the healthcare industry. Technological advancements have transformed the way we diagnose, treat, and manage diseases, leading to improved patient outcomes and quality of life. While the costs associated with these innovations are substantial, the long-term benefits to society are undeniable.
As we move forward, it is essential to strike a balance between fostering innovation and ensuring the affordability and accessibil-
ity of medical technologies. Policymakers, healthcare providers, and industry stakeholders must work together to create a sustainable and equitable healthcare system that leverages the power of technology to improve health outcomes for all. DMJ
Jon R. Roth, MS, CAE DCMS EVP/CEO
REFERENCES:
1. Statista, “Global total medtech growth per year 2010-2022.”
2. Medical Economics, “U.S. health care spending hits $4.5 trillion in 2022.”
The Nutcracker The Nutcracker
December 8, 2024 2:00 p.m.
HOUSE CALL
Cardiovascular Fitness Assessment: A Critical Tool in Preventive Medicine
by Laura Esterly
In the changing world of preventive health care, measuring heart and lung fitness has become an important way to check overall health. This approach is gaining traction in Dallas and throughout Texas, where health disparities have long been a concern. At its core is a simple but powerful idea: How fit your heart and lungs are isn’t just about being good at sports or the contour of your body, but also a key sign of your overall health. Dr. Tyler Cooper, who leads Cooper Aerobics in Dallas, wants doctors to start regularly testing patients’ fitness levels during checkups.
This approach started back in 1970 when Dr. Tyler Cooper’s father, Dr. Kenneth H. Cooper, called “the father of aerobic exercise,” founded the Cooper Aerobics Center in Dallas. Dr. Cooper’s groundbreaking work with the U.S. Air Force and NASA in the 1960s helped us understand how important exercise is for staying healthy and avoiding illness. His 1968 book, Aerobics, kicked off a fitness trend and began to change how
doctors thought about exercise and health, influencing medical practices across Dallas County and beyond.
THE SCIENCE BEHIND FITNESS TESTING
The importance of fitness assessment was dramatically underscored by a landmark 1989 study published in JAMA, which examined 13,600 individuals and established a clear inverse relationship between physical fitness and mortality risk. This groundbreaking research revealed that even modest improvements in fitness levels could increase life expectancy by an average of six years. Perhaps most striking was the finding that moving from “very poor” to “poor” fitness could reduce mortality risk by 58% – a statistic that continues to influence preventive health approaches at Cooper Aerobics today.
Following in his father’s footsteps, Dr. Tyler Cooper has kept pushing forward in preventive medicine through careful research and practical
use of fitness tests, with the goal of encouraging more physicians to use fitness testing as a tool to improve health and wellbeing for patients.
UNDERSTANDING FITNESS AS A HEALTH PREDICTOR
Heart and lung fitness, usually measured by how much oxygen your body can use during exercise, shows how well your heart, lungs, and metabolism are working together. The CCLS research has consistently demonstrated that fitness level is actually a better predictor of future health than traditional risk factors like high blood pressure, high cholesterol, or smoking – a finding that aligns with and builds upon the 1989 JAMA study’s conclusions about physical inactivity as a major health risk.
Dr. Tyler Cooper emphasizes how important this measurement is: “In my years as a doctor here in Dallas, I’ve come to see fitness as not just a health indicator, but as important as blood pressure or cholesterol levels. The difference is that fitness often has a bigger impact and is easier to improve.”
THE COOPER APPROACH TO FITNESS ASSESSMENT
The Cooper fitness test, developed at Cooper Clinic in Dallas, represents over 50 years of improving how we evaluate fitness. This test uses a standard treadmill exercise to measure how much oxygen your body can use, which helps predict heart health and longevity.
For those who might find a treadmill test daunting, the clinic has also embraced progressive walking programs as an accessible starting point. “Walking is one of the simplest yet most effective forms of exercise,” notes Dr. Cooper. “It requires no special equipment and can be easily integrated into daily routines, making it ideal for patients just beginning their fitness journey.”
RESEARCH-BACKED BENEFITS
The nonprofit research arm of Cooper Aerobics, The Cooper Institute, has produced several studies that show the big impact of fitness on health. Key findings include:
• Reduced Mortality Risk: The 1989 JAMA study and subsequent Cooper Institute research confirm that even small improvements in fitness can dramatically reduce mortality risk.
• Health Care Cost Reduction: Data from the CCLS demonstrates that maintaining higher fitness levels into later life not only increases longevity but also significantly reduces health care costs.
• Disease Prevention: Long-term studies in Dallas have shown that being more fit lowers your chances of getting type 2 diabetes, various cancers, and brain diseases.
IMPLEMENTING FITNESS TESTING IN MEDICAL PRACTICE
For doctors in Dallas thinking about adding fitness testing to their practice, Dr. Cooper recommends:
1. Conducting an initial test to gauge the patient’s starting fitness level
2. Using this information, along with other health factors, to understand overall health risks
3. Creating personalized exercise plans, often starting with progressive walking programs
4. Regular testing to track improvements and adjust health plans
The addition of fitness testing to regular doctor visits would represent a significant shift toward prevention-focused medicine. This approach, backed by decades of research, including the landmark JAMA study and ongoing CCLS findings, offers a powerful tool for addressing health disparities and improving outcomes across Dallas communities.
As the city continues to face challenges with lifestyle-related chronic diseases, this evidence-based approach to fitness assessment and improvement offers a clear path forward for health care providers and patients alike. DMJ
Innovations and Advancements in In Vitro Fertilization and Reproductive Technology
By Brandon Kulwicki, Attorney with Hall, Render, Killian, Heath & Lyman, P.C., and Chandani Patel, Summer Associate with Hall, Render, Killian, Heath & Lyman, P.C.
Assisted reproductive technologies (ART) is a dynamic field within the world of reproductive care fueled by scientific innovations and research. In particular, advancements in ART have helped scientists and physicians study and address biologic inefficiencies, devise individualized treatment plans, and enable the growth of healthy families.1 Scientists have been able to observe and replicate sperm injection into eggs and study embryonic development to assist both the male and female reproductive systems.2 Perhaps more importantly, these innovations in ART procedures have provided patients with more autonomy and decision-making power alongside their physicians. 3
In the U.S., ART utilization has increased by 50% from 2012 to 2021; in 2021, about 2.3% of all births in the country resulted from ART.4 Texas in particular has one of the highest ART utilization rates in the nation; in 2021, 2% of all births were attributed to ART.5 ART also increasingly allows women in their mid-to-late forties to conceive children; in fact, about 31% of all births resulting from ART in 2023 were to mothers aged 45 or older.6
In vitro fertilization (IVF), which is the most common form of ART, entails the creation of embryos from combining eggs and sperm before either transferring the embryos back into the uterus or freezing them for later use.7 The IVF process runs in cycles.8 The first step of an IVF cycle is ingestion of birth control pills or estrogen, allowing health care providers to manage the number of mature eggs during the egg retrieval process.9 The next step is ovarian stimulation through injectable hormone medicines to encourage all of the eggs in the cycle (as opposed to one or two during natural conception) to mature fully.10 Ovarian response to the medicines are monitored by ultrasounds and blood hormone levels. Eggs are not identifiable by ultrasound, so providers will monitor the size of each ovarian follicle to indicate maturity of the egg contained within it. After ovarian stimulation is egg retrieval; eggs are placed in a petri dish, which is then placed in an incubator.11 This procedure is done 36 hours after the last hormone injection (called the “trigger shot”).12 Once the eggs are retrieved, they are fertilized by intracytoplasmic sperm injection, and, if successful, the fertilized egg becomes an embryo.13 The embryo then develops to the blastocyst stage, which is the stage most suitable for embryo transfer to the uterus.14 Either a fresh embryo is implanted, or, more typically, a frozen embryo is, which is more likely to result in a live birth.15 Part of the transfer process includes taking oral, injectable, vaginal, or transdermal hormones to prepare the uterus for accepting the embryo.16 The transfer occurs via injection into the uterus through a catheter.17 If the IVF cycle is successful and the embryo implants itself on the uterine lining, then pregnancy is confirmed through a blood test.18
CRYOPRESERVATION
One of the processes inherent in IVF is cryopreservation, or the freezing of eggs that have been fertilized by sperm.19 There are two types of cryopreservation techniques – vitrification and slow freezing.20 Vitrification occurs when the provider adds cryoprotective agent (CPA) to embryos, essentially flash freezing the embryo to protect from formation of ice crystals in the cells.21 Slow freezing, which is the less preferred method, entails physicians adding smaller amounts of the CPA and freezing the embryo for over two hours, then removing and storing the embryo in freezer tanks filled with nitrogen.22 After both methods, the
embryos are removed from the liquid nitrogen tanks, thawed to room temperature, soaked to remove the CPA, and then implanted into the uterine lining.23
Cryopreservation allows individuals to create extra embryos so that they can postpone pregnancy and/or preserve fertility, especially in cases where the woman is undergoing cancer treatment or the individual is taking hormones to undergo gender affirmation surgery.24 It also provides options in case of a failed pregnancy.25 Alternatively, the egg donor can donate their unused embryos to others who are struggling to start a family or to researchers to further advance the technology behind assisted reproduction.26 It is important to note here that embryo freezing is different than egg freezing; embryo freezing is post-fertilization, while egg freezing is pre-fertilization; this legal distinction is one of the issues involved with ambiguous legislation on IVF.27
PGT AND GENOMIC MEDICINE
Another technological innovation that works alongside existing IVF technology is preimplantation genetic testing (PGT).28 PGT entails a screening test for genetic abnormalities on embryos created from IVF prior to their implantation in the uterine lining.29 PGT occurs in the blastocyst stage of IVF, where some cells on the outside of the embryo are biopsied and tested for genetic abnormalities. 30 Abnormal genetics is one of the main causes of IVF failure; however, PGT analyzes embryos selected for transfer to make sure they have the correct number of chromosomes. 31 Because women over the age of 37 have a higher risk of abnormal embryo genetics, PGT, along with IVF, enables them to have a healthy pregnancy by allowing their health care provider to select the optimal fertilized egg based on certain genetic markers. 32 PGT also allows couples who are affected by inherited genetic diseases to circumvent transmission to their offspring. 33 By analyzing a patient’s genetic composition to identify individual genetic profiles, health care providers can tailor their treatment plan to enhance effectiveness of fertility treatments and reduce the risk of complications. 34
OVIDUCT-ON-A-CHIP AND EPIGENETICS
“Oviduct-on-a-chip” (OOAC) is a newer, more innovative approach to ART. OOAC functions much like IVF, but it supports more physiological zygote genetic reprogramming than IVF does. 35 This means that OOACs can increasingly identify and investigate factors critical to fertilization and preimplantation development, thereby improving the quality and integrity of IVF zygotes for embryonic and fetal development.
36
OOAC stems from more general “organ-on-a-chip” technologies, which allows scientists to engineer physiological living tissues and organs in precisely controlled environments by stripping the organ to its most basic, self-repeating anatomical elements responsible for specialized organ-specific physiological function. 37 A micro device is then designed to replicate these key features.
38 OOAC also borrows concepts from epigenetics, which is the study of changes in gene expression without altering the underlying DNA sequence, but takes it further by identifying epigenetic markers that can predict a patient’s response to fertility treatments and allowing doctors to customize treatment protocols to optimize success rates.
39
One conventional technique in reproduction sciences is to use tissue explants from human reproductive organs; however, the difficulty in
Manish Gupta, M.D.
sourcing human materials and the high chance of failure of ex vivo models warrant more experimental approaches, specifically ones that can capture the complexity of the human reproductive system.40 Microfabrication techniques such as OOAC allow for in vitro modeling of human reproduction that emulates maternal-fetal interactions during pregnancy, advances ART, and investigates pathophysiological conditions of the reproductive system.41
With regard to ART, application of organ-on-a-chip technologies allows researchers to sort out motile sperm from semen samples through multiple laminar streams and enables perfusion culture of embryos.42 One potential application of this technology is to mimic the inner surface of the oviduct, which is composed of epithelial cells with hormonally regulated motile cilia that work with rhythmic contraction of the oviductal smooth muscle to transport the embryo toward the uterus.43 Additionally, researches and scientists can focus on the early development of the embryo by emulating the secretory cells in the oviductal epithelium that are responsible for producing nutrient-rich fluid that lubricates the epithelial surface that provides nourishment and protection to the embryo.44
ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING
IVF technologies are one of the many ways that artificial intelligence (AI) – the world’s most popular, yet controversial, and ever-evolving technologies – is being used to streamline advancements in scientific fields of study. AI can assist in improving accuracy of embryo selection and predicting treatment outcomes by utilizing advanced algorithms that analyze data from previous IVF cycles to identify patterns and predict the most promising embryos for transfer.45 This application of AI would increase success rates of IVF while reducing time and costs associated with multiple treatment cycles.46 AI also enhances sperm analysis by providing more precise assessments of sperm quality, largely through machine learning processes.47
Even within the field of IVF and reproductive assistance, AI is used in a variety of ways. For example, AI is used in gamete selection to identify early markers of quality by taking into account the follicle size, oocyte morphology, and cytoplasmic characteristics of the woman’s gametes and the morphology, concentration, and motility of the man’s sperm.48 AI could also remove the subjectivity of human assessment of these factors and objectively rank them based on quality.49 However, the most potential in this area is for selection of sperm for intracytoplasmic sperm injection through analysis of swimming patterns, direction of motion, and difference in sperm compartments.50
In terms of IVF treatment regimen, AI can make suggestions based on patient age, gamete quality, medical history, etc. and can be objectively streamlined to avoid variances from clinician to clinician, who may prescribe different treatment regimens based on their unique experiences, training, and in-house practice.51 AI can also data mine existing patient records to discover novel markers that predict pregnancy and live birth.52 Additionally, AI can be used in embryo selection by grading embryos on their ability to reach particular stages of development in a timely manner.53 AI would use routinely generated image and time-lapse videos to objectively and accurately grade and rank embryos, allowing for more standardization in the decision to transfer or freeze.54 It could also accurately identify cell lineages, leading to improved embryo classification and IVF success.55 However, AI’s ability to predict live birth outcome remains at 80%-90% capacity, indicating that the use of AI in ART still has potential for growth.56
STEM CELL THERAPY AND OVARIAN REJUVENATION
Perhaps the most controversial medical innovation to date is the use of stem cell therapy, but its application to ART via ovarian rejuvenation may be perceived more favorably due to its potential to regenerate and repair reproductive tissues. Stems cells are multipotent original cells sourced from early embryonic cells and adult tissues that can divide into various
other cells for repair, development, and regeneration.57 There are five types of stem cell functions – replacement and repair of dead and damaged cells; activation of dormant and suppressing cells, encouraging them to reenter the cell cycle and proliferate through division; paracrine secretion of various enzymes, proteins, and cytokines to promote cell proliferation, inhibit apoptosis of functional cells, and differentiate existing tissue progenitor cells into tissue cells in order to repair damaged tissues and grow new tissues; exertion of immunosuppressive function through cell-cell contact and secretion of soluble factors, inhibiting the proliferation of natural killer cells; and promotion of recovery of inter-cellular signaling.58 For specific organ regeneration applications, tissue-specific stem cells that are found in differentiated organs during the postnatal and adult stages of life play an important role in the repair of organ damage.59
In terms of ART and IVF, female germ stem cells and ovarian stem cells can induce ovarian regeneration and a sustained ovarian function.60 Mitotically active germ cells from human ovaries can be purified and cultured in vitro to form oocytes, showing promise for egg developments into oocyte-like cells in vitro.61 Injection of stem cells from bone marrow can also stimulate ovarian function, restore normal ovarian and hormone levels, and possible allow pregnancy.62
Injection of platelet-rich plasma is becoming popular as nonoperative treatment for various gynecological disorders such as infertility.63 Ovarian rejuvenation can restore ovarian fertility and development when used in conjunction with IVF by enabling the reactivation of the folliculogenesis process and enhancement of the hormone profile, but it has yet to become routine practice until more randomized clinical trials can be conducted.64 While these clinical trials are currently investigating use of stem cells to restore ovarian function in women with premature ovarian failure and repair damaged testicular tissue in men, this science may have the potential to bypass IVF treatment cycles completely, or at minimum, work in conjunction with them.65 Ovarian rejuvenation techniques such as injection of platelet-rich
plasma into the ovaries to stimulate the growth of new follicles may be able to reverse the effects of aging on the ovaries and could expand a woman’s reproductive lifespan, thereby increasing the chances of natural conception.66 DMJ
This article is educational in nature and is not intended as legal advice. Always consult your legal counsel with specific legal matters. If you have any questions or would like additional information about this topic, please contact Brandon Kulwicki at (214) 615-2025 or your primary Hall Render contact.
Brandon Kulwicki is an attorney with Hall, Render, Killian, Heath & Lyman, P.C., a national law firm focused exclusively on matters specific to the health care industry. Please visit the Hall Render Blog at http://blogs.hallrender.com/ for more information on topics related to health care law. Special thanks to Chandani Patel, Summer Associate, for her assistance in the preparation of this article.
Join us in celebrating DCMS members who have reached a milestone in their membership with the Society! All of our members are special to us, but each year we take time to highlight those who reach the five-year membership anniversary benchmark. Thank you for your commitment to the Society and for all you do to keep our communities safe and healthy!
Every effort was made to ensure the accuracy of the published membership anniversaries. If there is a mistake, please accept our apologies and send any corrections via email to communications@dallas-cms.org.
75 Years
• Raymond Abrams, MD
70 Years
• William Crane, MD
• James Dimmette, MD
Keller Greenfield, MD
• William Haynes, MD
• Joseph Somer, MD
• Frances Tompkins, MD
• William Woodard, MD
65 Years
• Merlan DeBolt, MD Bruce Fallis, MD
• Gordon Frank, MD
• John Jackson, Jr., MD
• Bassett Kilgore, MD
• Ralph Patman, MD
• Mysore Rao, MD
• Joan Windmiller, MD
60 Years
• William Bennett, MD
• Stanley Boulas, MD
• Curtis Boyd, MD
• Bruce Faust, MD
• Warren Greene, MD
• Ronald Jones, MD
• Donald Klein, MD Joe McIlhaney, Jr., MD
• Ayten Pardue, MD
• George Plum, MD
• David Shelmire, MD
• Wilma Shields, MD
• Saul Sokol, MD
• Robert Souda, MD
• Robert Stone, MD
• David Young, MD
55 Years
• Phil Berry, Jr., MD
• Bobby Black, MD
• John Bourland, Jr., MD
• William Brawley, MD
• J. Hal Brown, MD
• Franklin Casey, MD
• Zaven Chakmakjian, MD
• Donald Cox, MD
• Stanley Feld, MD
• Rhoda Frenkel, MD
• William Head, MD
• Edwin Hitt, MD
• Linda Hughes, MD
• Ridlon Kiphart, MD
• Dennis Landesman, MD
• Hamlet Newsom, MD
• Donald Senter, MD
• Charles Sessions, MD
• David Stager, MD
Donald Vanderpool, MD
• William Weaver, MD
• William West, Jr., MD
• John Whitfill, MD
50 Years
• Gonzalo Aillon, MD
• Robert Barner, MD
• Carlos Botty, MD
• Hoo-Keun Choi, MD
• Zechariah Dameron, III, MD
• William Dammert, MD
• Daniel Dansby, MD
• Abram Eisenstein, MD
• James Fancher, Jr., MD
• Christopher Fetner, MD
Robert Goldberg, MD
• Joseph Goldstein, MD
• Johnny Henry, MD
• Irving Humphrey, III, MD
• Cesar Jimenez, MD
• Wafik Kassab, MD
• Herbert Leiman, MD
• Edward Melmed, MD Allan Naarden, MD
• Thomas Newsome, MD
• Melvin Platt, MD
• Joel Potasznik, MD
• Philip Raskin, MD
• Wyatt Rousseau, MD
• Richard Sachson, MD
• Barry Schwarz, MD
• Robert Steckler, MD
• Gerschon Suster, MD
• Robert Thompson, MD
• Barbara Way, MD
• Lawrence Weprin, MD
• Enrique Zavala-Calderon, MD
45 Years
• Mark Altenau, MD
• Juan W. Arias, MD
• Jeffrey B. Arnstine, MD
• Phillip M. Aronoff, Jr., MD
• Richard H. Ballinger, III, MD
• Terrell B. Benold, MD
• Mark Bernstien, MD
• John Bertrand, MD
• Chester Beyer, Jr., MD
• John Billinghurst, MD
• James P. Blakely, MD
• John Brand, MD
• John Bret, MD
• A. Compton Broders, III, MD
• Robert W. Burns, MD
• Daniel Burstein, MD
• Evangeline T. Cayton, MD
• Jung Il Chi, MD
• Andrew Chubick, MD
• Carlton K. Clarke, MD
• H. David Cook, MD
• Jack M. Cooper, MD Harold Cronson, MD
• Pedro A. Cruz, MD
• Robert Darrow, MD
• James G. Denton, MD
• Richard M. Dickerman, MD
• Larry G. Dossey, MD
• William Downs, MD
• Kathleen Erdman, MD Gary Fish, MD
• Michael Foreman, MD
• David Fosdick, MD
• Andrew Freeman, MD
• Jennifer Freeman, MD
• Pat Fulgham, MD
• Lisa Garner, MD
• James Gill, MD
• Larry C. Gilstrap, III, MD
• Mark Godat, MD
• James Gray, MD
• Billy Griffin, MD
• Robert Haddox, MD
• Robert Hamas, MD
• Paul Hamilton, MD
• Scott Harris, MD
• Glen Heckman, MD
• Robert Henderson, MD
• Richard Hinton, MD
• Seung Hong, MD
• James Hosler, MD
• Liang Hsu, MD
• Martin Hurst, MD
• Charles Iliya, MD
• Robert Jenkins, MD Ronald Kapusta, MD
• Kevin Klein, MD
• Ellen Koerber, MD
• David Korman, MD
• Irwin Korngut, MD
• Charles Levin, MD
• George MacKinnon, MD
• Paul Madeley, MD
• Ivan Mahady, MD
• Saleem Mallick, MD
• Michael Maris, MD
• Peter McKinney, MD
• Brenda Mears, MD
• Allen Meril, MD
• Robert Milstein, MD
• James Moody, MD
• John Moore, MD
• Gerald Moore, MD
• Eugene Morris, MD
• Paul Muncy, MD
• Mark Norris, MD
• Kutsi Onur, MD Donald Osborne, MD
• Gerald Payne, Jr., MD
• Garnett Payseur, MD
• Radie Perry, MD
• Brian Peters, MD
• Anjaneya Puppala, MD
• C. Venkata Ram, MD
• Steven Reeder, MD Mary Rees, MD
• Murray Rice, MD
• James Richards, MD
• Gary Ring, MD
• Alfredo Rodriguez, MD
• David Rosenstock, MD
• David Rozemberg, MD
• Stephen Sakovich, MD
• Sigurd Sandzen, Jr., MD
• Irwin Segal, MD
• Scott Smith, MD
• Bertram Smith, III, MD
• Charles Sparenberg, MD
• William Spencer, MD
• Dan Steinfink, MD
• James Sterling, MD
• William Sutker, MD
• Ernest Swersky, MD
• Asif Syed, MD
• Stephen Tankersley, MD
• Wayne Taylor, Jr., MD
• Van Thi Tran, MD
• Shu Ying Turng, MD
• Thong Tien Vu, MD
• Paul Wade, MD Jack Walters, Jr., MD
• Paul Weatherall, MD
• Richard Wingo, MD
• John Winter, IV, MD
• Leon Wolf, MD
• Timothy Wolff, MD
• Jacob Wolfman, MD
• Mary Woodard, MD
40 Years
• David Ackerman, MD
• Jane Admire, MD
• Lawrence J. Alter, MD
• John Andersen, MD
• William Ardill, MD
• Thaddeus Ashmore, MD
• Kathleen Banks, MD
• Richard Berry, MD
• Preston Blomquist, MD
• John Brooks, MD
• Andrew Brylowski, MD
• Carol Burns, MD
• Nina Cahan, MD
• David Carlson, MD
• Benjamin V. Carnovale, MD William Carpenter, MD
• Eric Chang-Tung, MD
• Janet Collins, MD
• Cristie Columbus, MD
• Brian Cooley, MD
• Daniel E. Cooper, MD
• Shashi K. Dharma, MD
• Arnold Dibella, MD Calvin Dixon, MD
• Paula C. Dobbs-Wiggins, MD
• Peter Dysert, II, MD
• Cara East, MD
• Gene E. Ewing, MD
• Warren Fagadau, MD
• Robert Farkas, MD
• Joshua Fine, MD
• Henry Gelender, MD
• Steven Gellman, MD
• Grant Gilliland, MD
• Fe Q. Gonzaga, MD
• David Gregory, MD
• Tracy Gustafson, MD
• Janet Hale, MD
• Charles Haley, MD
• Robert Haley, MD
• Joseph Hanig, MD
• H. A. Tillmann Hein, MD
• Wesley Herman, MD
• John Herndon, III, MD
• Richard Herrscher, MD
• Patrick Hodges, MD
• Joel Holiner, MD
• Richard Honaker, MD
Philip Huang, MD, MPH
• Paul Hurd, MD
• John Jamison, MD
• Nolan Jenevein, MD
• John Joseph, MD
• Raj Kakarla, MD
• Gary Ketter, MD
• Walter Knight, MD
Philip Korenman, MD
• William Krippner, Jr., MD
• James Lancaster, MD
• Jack Lesch, MD
• Warren Lichliter, MD
• Samuel Lifshitz, MD
• Samuel Listi, MD
• Alipio Mascarenhas, MD
• Cornelius Matwijecky, MD
• Bernie McCaskill, MD
• Conway McDanald, MD
• David Meltzer, MD
• Maisie Miller, MD
• Nalini Naik, MD
• William Norcross, MD
• James Norwood, MD
• Frank Oliver, MD
• Norris Payne, MD
• Michael Pittman, MD
• Jorge Poliak, MD
• Timothy Ponder, MD
• John Porter, MD
• Scott Porter, MD
• John Racanelli, MD
• Susan Rogers, MD
• Richard Rome, MD
• Shelley Rosenbloom, MD
• Julio Rosenstock, MD
• Roni Rothstein, MD
• Jorge Saldivar, MD
• C. M. Schade, MD, PhD
• James Schermerhorn, MD
• Keith Schorn, MD
• Ronnie Shade, MD
• Dhiren Shah, MD
• William Sheldon, Jr., MD
• Cynthia Sherry, MD
• Richard Silver, MD
• Alejandro Singer, MD
• Randlow Smith, Jr., MD
• Terry Sobey, MD
Kathryn Sommerfelt, MD
• Robert Stahlman, II, DO
• Michael Stannard, MD
• Patrice Stephens, MD
• Scott Stephenson, MD
• Richard Suss, MD
• Philip Swanson, MD
• Thomas Swygert, MD John Tenny, MD
• Tich Truong, MD
• Charles Tuen, MD
• Gary Tylock, MD
• Allan Van Horn, MD
• Rocco Vitacca, MD
• Jean Wall, MD
• Worthy Warnack, Jr., MD
• Richard Weiner, MD
• Howard Weiner, MD
• Gordon Whitney, MD
• Weldon Wright, MD
• Lynne Wurts, MD
35 Years
• Edra Abramson, MD
Brent Armstrong, MD
• William Black, MD
• Joanne Blum, MD
• Stephen Boswank, MD
• Jonathan Brough, MD
• E. Sherwood Brown, MD
• Richard Buch, MD
• K. Robin Carder, MD
• Melissa Carry, MD
• Michael Cavenee, MD
• Jorge Cheirif, MD
• Clay Cockerell, MD
• Richard Coker, MD
• Stephen Donica, MD
• Craig Duhon, MD
• Paul Ellis, III, MD
• Michael Fawcett, MD
• Abhimanyu Garg, MD
• Alexandra Gillespie, MD
• Gary Goff, MD
• Phillip Graehl, MD
• Michael Grant, MD
• Michael Gross, MD
• Robert Gross, MD
Thomas Harper, MD
• Jay Harvey, DO
• Patsy Hedges, MD
• Daragh Heitzman, MD
• Miguel Hernandez, III, MD
• Peter Hino, MD
• Nancy Hitzfelder, MD
• Helen Hobbs, MD Robert Holcomb, MD
• Robyn Horsager-Boehrer, MD
• Andrew Jamieson, MD
• Christopher Kampas, MD
• Donald Kennerly, MD
• Karanjit Kooner, MD
• Thomas Lacour, MD
• Stephen Landers, MD
• John LaNoue, Jr., MD
• Jun Lee, MD
• Kirk Lipscomb, MD
• Anthony Macaluso, Jr., MD
• Dee Martinez, MD
• Dan McCoy, MD
• Scott Meril, MD
• William Moore, MD
• Larry Moore, MD
• Lynn Myers, MD
• Reynaldo Perez, MD
• J. Lance Pickard, MD
• David Pillow, Jr., MD
• Paul Gordon Pin, MD
• James Race, MD
• Maryam Rezai, MD
• Luis Robles, MD
Jacob Roffe, MD
• James Sackett, MD
• William Salter, MD
• Leigh Ann Scott, MD
• Kent Skipper, MD
• Lori Stetler, MD
• Laurie Sutor, MD
• Joel Taurog, MD James Thornton, MD
• Erika Trapp, MD
• Sharon Tucker, MD
• Shyla Valentine, MD
• Jaime Vasquez, DO
• Gary Weinstein, MD
• Gunnar West, DO
• Amy Wilson, MD
• Daniel Witheiler, MD
30 Years
• Ronald Angus, Jr., MD
• Carlos Arauz-Pacheco, MD
• Cathleen Bateman, MD
• Robert Berryman, MD
• Frank Birdsell, MD
• Michael Black, MD
• Lawrence Blanchard, MD
• Michael Bolesta, MD
• William Boone, Jr., DO
• C. Bradley Bowman, MD
• Wesley Brady, MD
• Jeffrey Buch, MD
• Don Buford, MD
• Dale Burleson, Jr., MD
• Douglas Cluff, MD
• Carla Cole, DO
• Norma Cump, MD
• Ildiko Edenhoffer, MD
• Philip Eichenholz, MD
• Tabitha Foster, MD
• Keila Garoutte, MD
• Aldo Ghobriel, MD
Dion Graybeal, MD
• Sylvia Hargrave, MD
• John Hillyard, MD
• Stephanie Holman-Ferris, MD
• Michael Holub, MD
• Jodie Hurwitz, MD
• Jane Kao, MD
• Darryl Kawalsky, MD
Jeffrey Kenkel, MD
• Khurshid Khan, MD
• Thomas Kimball, MD
• Sandra Lauriat, MD
• Trang Diem Le, MD
• Robert Leroy, MD
• C. Turner Lewis, III, MD
• Dynal London, MD
• Peter Malouf, DO
• George McAnelly, II, MD
• Kristi McIntyre, MD
• Stanley Pomarantz, MD
• Aruna Potti, MD
• Valen Radimecky, MD
• Ruben Sandoval, MD
• John Schwartz, MD
• Hooman Sedighi, MD
• Jivesh Sharma, MD
• Kristin Smith, MD
• Sonya Sorensen, DO
• Ralph Stein, DO
• Richard Stern, MD
• Larry Taub, MD
• Carolyn Terry, MD
• Mark Thieberg, MD
Marc Tribble, MD
• Anna Tseng, MD
• Travis Van Meter, MD
• Kelly Wimberly, MD
• Allison Wyll, MD
25 Years
• Gordon Aalund, MD Joyce Abraham, MD
• Cesar Albarracin, MD
• Kathryn Ames, MD
• Sami Arslanlar, MD
• Rajesh Atluri, MD
• Jolie Bailey, MD
• Kathleen Bajaj, DO
• Gregory Barnes, MD
• Yousri Barri, MD
• Minal Barve, MD
• Clinton Bell, MD
• Rodney Bowman, MD
• Michelle Brochner, MD
• Julye Carew, MD
• Joseph Chan, MD
• Michael Chapman, MD
• Christopher Cochran, MD
• Cindy Corpier, MD
• Melissa Costner, MD
• Kathryn Dao, MD
• Tuoc Dao, MD
• Early Denison, MD
• Monica Diaz, MD
• Michael Fontes, MD
• Cameron Gerard, MD
• Sidney Gicheru, MD
• Sally Goudreau, MD
• Julia Graves, MD
• Daniel Gravley, MD
• Clinton Haley, MD
• Clint Hamilton, MD
• Andy Hollenshead, MD
• Deanah Jibril, DO George John, MD
• Andrew Kahn, MD
• Amit Khera, MD
• Derek Kieta, MD
• Andrea King, MD
• Michael Kohanski, MD
• David Leifer, MD
• Valerie Liao, MD
• Xercerla Littles, MD
• Jeff Livingston, MD
• Vitaly Margulis, MD
• Ricardo Martinez, MD
• Maureen McGeehan, MD
• John Myers, MD
• Kelley Newcomer, MD
• John Noack, MD
• Carol Norton, MD
• Elizabeth Odstrcil, MD
• Padraig O’Suilleabhain, MD
• Lance Oxford, MD
• Theresa Patton, MD
• Dennis Raymond, MD Robert Rege, MD
• Ylicia Richards, MD
• Jorge Roman-Latorre, MD
• Timothy Rupp, MD
• Paul Saadi, MD
• Lisa Sebastian, MD
• Atul Singhal, MD
• Daniel Sucato, MD, MS Maria Turnage, MD
• J. Calvin Turner, MD
• Javier Vasquez, MD
• Melinda Velez, DO
• Michael Weisberg, MD
• Peter Wells, MD
• Claudia Werner, MD
• Lawrence Whaley, MD Irene Willingham, MD
• Dong-Hi Yoon, MD
20 Years
• Crystal Adams, MD
• Anuradha Agrawal, MD
• Naseer Ahmed, MD
• Farida Ali, MD Kimulique Allen, MD
• David Axmann, MD
• Jonathan Bard, MD
• Shaad Bidiwala, MD
• Patrick Brown, MD
• Travis Browning, MD
• Nicol Bush, MD
• Michelle Caraballo, MD
• Bradley Casolo, MD
• Claire Chu, MD
• Jennifer Coffman, MD
• Fadi Constantine, MD
• Brian Crowhurst, DO
• Donnie Culpepper, MD
• Jennifer Dagen, MD
• Kelley Davis, DO
• Lance Davis, MD
• Jeffrey Elmore, MD
• Samina Fazal, MD
• Neville Fernandes, MD
• Jose Fuentes, MD
• Noor Gajraj, MD
• Javed Gill, MD
• Deepika Gopalakrishnan, MD
• Morris Gottlieb, MD
Seval Gunes, MD
• Michelle Heintges, MD
• Shelby Holt, MD
• Benito Irizarry, MD
• Ginger Isom-Batz, MD
• Harold Jacobson, MD
• Asra Kermani, MD Mohammad Khan, MD
• Carolyn Kollar, DO
• Lisa Kotas, MD
• Ricardo Valdez, MD
• Benjamin Lee, MD
• Jennifer Lerom-Cooper, MD
• Joseph Lovoi, MD
• Sandra Lozano, MD
• Maureen Luby, MD
• Omar Manlapaz, MD
• Sharon Marchand, MD
• Amit Masand, MD
• Lynley McAnalley, MD
• Vincent McColm, MD
• Jennifer McNeill, MD
• Luis Michelsen, MD
• Damien Mitchell, MD
• Allaaddin Mollabashy, MD
• Ravi Mootha, MD
• Amie Napier, MD
• Erin Newman, MD
• Ho Bing Oei, MD
• Gowri Pachigolla, MD
• Atinder Panesar, MD
• Michael Passanante, MD
Shannon Payseur, MD
• Larry Pettit, DO
• Christopher Pierotti, MD
• William Pinson, MD
• Abdul Qavi, MD
• Rashid Rahman, MD
• Shiwali Rai, MD
• Asha Rajashekar, MD
Archana Rao, MD
• Radhika Ravula, MD
• Donza Rogers, MD
• Emily Rosellini, MD
• Melissa Rubenstein, MD
• Kamalanathan Sambandam, MD
• Raghuram Sanga, MD
• Jason Schmidt, MD
• Lori Sedrak, DO
• Gilbert Selkin, MD
• Dan Sepdham, MD
• Anthony Setiawan, MD
• Anupkumar Shetty, MD
• Martin Sigler, MD
• Donald Storey, MD
• Michael Sutker, MD
• Cynthia Swayze-Smith, DO
• Ley Taylor-Jones, DO
• Mika Thomas, MD
• Stephen Timon, MD
• Kavita Trivedi, DO
• Luis Usuga, MD
• Suresh Valloppillil, MD
• Shiiyuh Wang, MD
Robert Wang, MD
• Megan Wood, MD
• Armando Yepes, MD
• Nicole Yost, MD
• Walter Young, MD
• Richard Zemenick, DO
• Julius Zsohar, MD
15 Years
• Ashwani Agarwal, MD
• Kartik Agusala, MD
• Alison Airall-Ryan, MD
• John Alexander, Jr., MD
• Lisa Alvarez, MD
• Howard Anderson, Jr., MD
• Ali Ashraf, MD
• Gail Aznavorian-Bentley, MD
• T. Joshua Baker, MD
• Sarah Baker, MD
• Vikas Bhushan, MD
• Christina Bourland, MD
• Anthony Boyer, MD
• Kendall Carll, MD
• Alexander Cho, MD
• Henry Choi, MD
• Sooyeon Choi, MD
• Renuka Chowdhury, MD
• Zachary Compton, MD
• Laura Craigmiles, DO
• William Crampton, MD
• Karl Csaky, MD
• Thomas Dalton, MD
Harold Delasalas, MD
• Mauricio Delgado-Ayala, MD
• Seemal Desai, MD
• Devyani Deshpande, MD
• David Di Giovanni, MD
• Leah Dill, DO
• Jean Dymott, MD
• Jason Edwards, MD
Saleemah Fahmi, MD
• Hattie Feetham, MD
• David Fetzer, MD
• Ivan Figueroa, MD
• Korie Flippo, MD
• Jesus Flores, MD
• Jeri Foshee, MD
• John Garman, DO
• Michael Gilly, MD
• Satish Goel, MD
• Kaela Gordon, MD
• Cori Grantham, MD
• Amy Gruber, MD
• Puneet Gupta, MD
• Mohammed Hamzeh, MD
• Muhammad Haq, MD
• Heidi Harms, MD
• Jon Harris, MD
• Thomas Heffernan, MD
• Michael Hennessy, MD
• John Holland, Jr., MD
• Christopher Hughes, MD
• Michael Hunte, MD
• Rodney Infante, MD
• Siddharth Jain, MD
Adam Jaster, MD
• Jodi Jones, MD, FACEP
• Radhika Kainthla, MD
• Thomas Kerr, MD
• Harry Kim, MD
• Alexander Kirk, MD
• Jeffery Kirlangitis, MD
• John Krause, MD Robert Lance, MD
• Andrew Lester, MD
• Ravina Linenfelser, DO
• Jaswanth Madisetty, MD
• Matthew Mahowald, MD
• Stewart Master, MD
• G. Philip Matthews, MD
• I-Fan Theodore Mau, MD Christopher McLeod, DO
• John Medlin, DO
• Amit Mehta, MD
• Austin Miller, MD
• Mahshid Moein, MD
• Justin Moreland, DO
• John Nguyen, MD
• Lucas Njo, MD
• Bradley Oetman, MD
• Federico Osorio, MD
• Andrew Paulson, MD
• Ashleigh Payne, MD
• Edward Pearson, MD
• Patricia Petroff, MD
• Linda Pham, MD
• Neelema Pinnapureddy, DO
• Daniel Podolsky, MD
• Dan Pong, MD
• Lisa Pruett, MD
• Tania Purkayastha, MD
• David Rahn, MD
• Ryan Randles, MD
• David Reading, MD
• Lee Reagor, MD
• LoriJean Reed, MD
• Juan Rendon, MD
• Tana Roberts, MD
• Erin Roe, MD, MBA
• Anand Rohatgi, MD
• Matthew Rowley, MD
• Rex Russell, MD
• John Sadler, MD Jeffrey Sandate, MD
• Manohar Saraf, MD
• Stephanie Saxton-Daniels, MD
• Joseph Scales, MD
• F. David Schneider, MD, MSPH
• Anna Schroeder, MD
• Michael Sea, MD Kristin Sears, DO
• Nehal Shah, MD
• Kanwar Singh, MD
• Erica Snook, MD
• Varun Sondhi, MD
• William Sory, MD
• Ann Spangler, MD
• Laura Staub, MD
• Stephanie Stephens, MD
• Timothy Swift, MD
• Carlos Taboada, MD
• Joseph Taiwo, MD
• Jimmie Thompson, MD
• Anil Tibrewal, MD
• Lee Tisdale, DO
• Mai Thi Tran, MD
• Kien Trung Tran, MD
• Gene Voskuhl, MD
• Richard Wagner, DO
• William Waldrop, MD
• Rachel Walsh, MD
• Yili Wang, MD
• Winnie Wang, MD
• Kenneth White, MD
• Melissa Whitworth, MD Karl Winters, MD
• Scott Witherspoon, MD
• Amy Woods, MD
• Tiffany Woodus, MD
• Paul Youn, MD
• Peter Zechner, MD, PhD
• Jacob Zide, MD
• Jeff Zsohar, MD
10 Years
• Asha Abraham, DO
• Annie Abraham, MD
• Ashley Agan, MD
• Anshul Agarwal, MD, PhD
• Shakil Ahmed, MD
• Regan Allen, MD
• Puneet Bajaj, MD
• Maria Bano, MD
• Ronnie Barakat, MD
• Bettina Barr, MD
• David Bashover, MD
• Daniel Beck, MD
• Elena Berry, MD
• Kanu Birdi, MD
• Alex Black, MD
• Ryan Blalock, MD
• Jeffrey Brekke, MD
• Sabrina Browne, MD
• Scott Burdette, MD
• John Burris, MD
• Maude Carmel, MD
• Adam Cary, DO Jeffrey Chambliss, MD
• Zubair Chao, MD
• Yusuf Chauhan, MD
• Shital Chavda, MD
• Shailendra Chavda, MD
• Jeffrey Chen, MD
• Shirish Chennaiahgari, MD
• Eric Chiang, MD
Jennifer Chu, MD
• Steven Clark, MD
• Seth Cohen, DO
• Kayla Colvill, MD
• Frances Compton, MD
• Ryan Constantine, MD
• Brandon Cornelius, MD
• Trenton Custis, MD
Daniel Da Costa, MD
• Lori Dao, MD
• Roberto De La Cruz, MD
• Louis DeGironemo, MD
• Brianne Dentel, MD
• Lathmany Dorfman, MD
• Delaney Dowd, MD
• Matthew Emanuel, MD
• Joshua Emmett, MD
• Jenson Erapuram, MD
• Matthew Feldman, MD
• Lauren Fine, MD
• Jeannine Foster, MD
• Neha Gaddam, MD
• Cassidy Gafford, MD
• Lilliam Cortes, MD
• Sarita Gayle, MD
• Monisha Gidvani, MD
• Allison Green, MD
• David Grinsfelder, MD
• Uma Gunasekaran, MD
• Rachel Gunderson, MD
• Vikas Gupta, MD
• Rebecca Haines, MD
• Jeremy Hall, MD Rachael Haverland, MD
• Jesse Hernandez, MD
• Chelsea Hinkle, MD
• Donald Hohman, Jr., MD
• Anita Holtz, MD
• Syed Hussaini, MD
• Tam Huynh, MD
• Andrew Indresano, MD
• Tiffany Jackson, MD
• Daniel Jackson, MD
• Paresh Jaini, DO
• Aisha Shenawi Jamal, MD
• Riham Jamaleddine, MD
• Michael Jauregui, MD
• Faraz Jeelani, MD
• Haneol Jeong, MD
• Leslie Johnson, MD
• Romaine Johnson, MD
• Margarita Johnston, MD
• Suresh Kachhdiya, MD
• Hemangi Kale, MD
• David Kang, MD, DDS, MS, FACS
• Brian Katan, MD
• Prashant Kedia, MD
• Carla McStay, MD
• Saadat Ali Khan, MD
• Gaurav Khatri, MD
• Ronnie Khoury, MD
• Tae Kim, MD
• John Kim, MD
• Sivaramya Kollipara, MD
Jonathan Koning, MD
• Daniel Koshy, MD
• Mary Ratner, MD
• Alexis Kropf, MD
• Matthew Lawrence, MD
• Byung Lee, MD
• Brandon Lee, MD
• Mary Liu, MD
Ann Lopez, DO
• Kevin Luu, MD
• Cecilia Gaffaney, MD
• James Malter, MD
• Christine Mansfield, MD
• Richard Markus, MD
• Hal Martin, DO
• Claire Mauvais, MD
• Maranatha Mclean, MD
• Aalap Mehta, DO
• Alyssa Mercadel, MD
• Nathaniel Milburn, MD
• Ian Miller, MD
• Jennifer Misenhimer, MD
• Angela Moemeka, MD
• Roberto Monge, DO
• Steven Montalvo, MD
• Amanda Motomochi, MD
• Sara Mucowski, MD
• Elisa Muniz, MD
• Sina Najafi, DO
• Vijay Nama, MD
• Anjali Nambiar, MD
• Jessica Nelson, MD
• Melissa Nelson, MD
Elizabeth Newsom, MD
• Fallon Ngo, DO
• Gabriella Nguyen, MD
• Peter Nguyen, MD
• Nicholas Norris, MD
• Gerald Nystrom, MD
• Jack O’Brien, MD
• John O’Connor, MD
Monisha Parikh, MD
• Jason Park, MD
• Herschel Patel, MD
• Rebecca Pedersen, MD
• M. Leslie Pfeiffer, MD
• An Pham, MD
• Diana Pham, MD
• Gerson Pineda, MD
• Patricio Polanco, MD
• Matthew Porembka, MD
• Elizabeth Portillo, MD
• Stephen Pratt, MD
• Michael Preston, MD
• Angela Price, MD
• Claudio Ramaciotti, MD
• Chandana Ravikumar, DO
• Vance Redfield, MD
• Dietrich Riepen, MD
• Richard Rissman, MD
• Macym Rizvi, MD
• Syeda Rizvi, MD
• Courtney Roberts, MD
• Nicole Rogers, MD
• Jia Romito, MD
• James Ryan, MD
• Ravindra Sarode, MD
• Aimee Schimizzi, MD
• Michael Sebert, MD
• Pierre Semrani, MD
• Anirban Sensarma, MD
• Matthew Sherman, MD
• Christine Shiang, MD, PhD Bill Shumate, Jr., MD
• Genine Siciliano, MD
• Chenelle Slepicka, MD
• Sudha
Somasundaravelayudham, MD
• Kim-Anh Song, MD
• Robert Spicer, MD
Dianne Srinilta, MD
• Divya Srivastava, MD
• Britton Staheli, MD
• Shelby S. Stribling, MD
• Ashish Sureka, MD, MPH
• Mustafa Suterwala, MD
• Gaurav Synghal, MD
• Anumeha Tandon, MD
• Aanchal Taneja, MD
• Stephanie Teotia, MD
• James Theisen, MD
• Philip Tolley, MD
• Phuong Tran, MD
• Madhuri Tunuguntla, MD
• Jonathan A. Verma, MD
• David Vier, MD
• Rachel Villegas, DO
• Jessica Voit, MD
• Patricia Vories, MD
• Khoan Vu, MD
• Babu Welch, MD
• Katharine White, MD
• Cherese Wiley, MD
• Stephen Wilkins, Jr., MD
• Taylor Wolfe, MD
• John Wu, MD
• Min Zhang, MD
• Suyue (Mike) Zhang, MD
• Nikola Zivaljevic, MD
5 Years
• Shehetaj Abdurrahim, MD
• Maya Adams, MD Ammar Adenwalla, MD
• Folashade Afolabi, MD
• Priyanka Agarwal, MD
• Raafae Agha, MD
• Sarosh Ahmed, MD
• Sarah Ahmed, MD
• Shahbaz Ahmed, MD
• Venkatesh Aiyagari, MD Mica Alex, MD
• Daniel Alfson, MD
• Sameer Allahabadi, MD
• Hope Allen, MD
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Legal Uncertainty in IVF: Key Concerns for Physicians in the Current Political Climate
By Brandon Kulwicki, Attorney with Hall, Render, Killian, Heath & Lyman, P.C., and Chandani Patel, Summer Associate with Hall, Render, Killian, Heath & Lyman, P.C.
Q: Given the current political landscape, what are some of the risks and concerns involved for physicians providing assisted reproductive care services such as IVF?
A: The following are some factors that physicians may consider when administering assisted reproductive technology (ART) treatments such as IVF.
AMBIGUITY OF LEGAL STATUS
State law varies in either exempting IVF from personhood classifications that hinge on whether the ovum has been fertilized or transplanted, or by creating intentionally vague legislation regarding personhood status and associated rights.1 Some states have explicitly exempted IVF from personhood-friendly restrictions on abortion care, while other states define the status of an embryo but stop short at guaranteeing IVF rights.2 These ambiguous personhood classifications create multiple risks of liabilities for individuals and their physicians seeking to undergo and administer IVF and other ART procedures. 3 Additionally, in states where the legal categorization of embryos as either people or property remains unclear, the line between destruction of embryos and disposal of medical waste becomes increasingly blurred.4
RESTRICTIONS ON GENETIC TESTING5
Physicians who seek to optimize fetal birth outcomes through genetic testing may have to decide between retaining their medical license or advocating for their patient’s healthy pregnancy. The risk of facing civil and criminal liability from technical and logistical decision making would likely create a chilling effect on clinicians regarding customized administration of ART procedures such as IVF, even where those procedures are explicitly legalized. Patients seeking IVF care who decide against embryo transfer due to a chromosomal abnormality that would likely result in neonatal death may be subject to wrongful death liability as well.
INEFFICIENT HEALTH CARE6
Another issue stemming from legislative play on embryonic personhood is the ability of physicians to harvest a maximal number of embryos per IVF treatment cycle. Each cycle requires the fertilization of eggs with the goal of obtaining as many viable eggs as possible; however, with a potential ban on the freezing of backup embryos, physicians may have to cap the number eggs harvested per cycle, resulting in medical waste inefficiencies and more expensive treatments. This will also affect the patient by requiring more treatment cycles, thereby increasing their costs and increasing exposure to risks from more procedures and fertility drugs.
INHIBITING INNOVATION7
If physicians lose the ability to personalize IVF treatment for each patient’s needs, then advancements in ART and other reproductive technologies may be stifled. Restrictive legislation on ART, compounded with existing U.S. policies that “impede scientific progress in reproductive biology and women’s health,” such as the prohibition against the use of federal funds for human embryo research, can
inhibit efforts of preventing pregnancy loss through scientific understanding of early embryonic development that would support the development of healthy offspring from IVF. Funding, support from the medical community, and peer review from health care institutions such as the NIH are critical in helping scientists and providers pinpoint and address the current shortcomings in reproductive technologies.
TAKEAWAYS
In Texas, the concept of embryonic personhood is gaining traction even where explicit IVF protections are lacking.8 In 2023, the Texas Court of Appeals categorized embryos as property, not people.9 However, consistent with the state’s abortion ban and pro-life stance, lawmakers and public figures like Ted Cruz and Greg Abbott have expressed support of personhood protections, with the latter caveating that increased scrutiny of these protections is warranted by unique factual circumstances.10 Additionally, there are currently no restrictions on genetic testing for IVF; however, Texas physicians will need to keep themselves apprised of the varying IVF bill proposals in order to safely and confidently render reproductive care while avoiding legal liability. DMJ
This article is educational in nature and is not intended as legal advice. Always consult your legal counsel with specific legal matters. If you have any questions or would like additional information about this topic, please contact Brandon Kulwicki at (214) 615-2025. Special thanks to Chandani Patel, Summer Associate, for her assistance in the preparation of this article.
Enhancing Health Care Communication With Large Language Models— The Role, Challenges, and Future Directions
By Charumathi Raghu Subramanian, MD; Daniel A. Yang, MD; Raman Khanna, MD
Large language models (LLMs), a subset of artificial intelligence (AI) models trained on massive data sets that can synthesize and generate human-like text, have attracted tremendous interest in health care.1 Researchers and health care professionals are actively exploring ways to integrate the use of LLMs for clinical administration tasks, clinical note generation, diagnostic support, and other activities. In addition to the Generative Pre-trained Transformer (GPT), there are other general purpose models, such as bidirectional encoder representations from transformers, and pathways language model, that have fine-tuned variants for medical and biomedical domains. There are also models trained exclusively on clinical text corpora, such as GatorTron and NYUTron, that have shown early promise in projecting hospital length of stay, in-hospital mortality, and hospital readmissions.2
Effective patient communication is critical in health care, and given the significant role that written text plays in this context, textbased AI models present numerous possibilities for enhancing patient communication. 3 Organizational health literacy is the degree to which health care organizations implement strategies to make it easier for patients to understand health information, navigate the health care system, and manage their health.4 Central to achieving organizational health literacy is the enhancement of patient-oriented written communication. Studies have demonstrated that improving the readability of such communications is positively associated with patient outcomes,5 and LLMs could be a potential tool here.
In this context, the study by Zaretsky and team6 addresses an important task: using an LLM, GPT-4 (OpenAI), to transform hospital discharge summaries into a format that is readable for patients. Zaretsky et al6 reviewed existing summaries and patient preferences to determine which elements should be included or excluded in the new format, processed the original summaries to retain only the specific patient-relevant elements (e.g., removing billing codes), and inputted the processed summary into the LLM with instructions, modified through prompt engineering, to produce a concise, one1page, patient-readable summary. These
revised summaries were then assessed for their readability and understandability coupled with the balancing metrics of accuracy and completeness. Zaretsky et al6 found that the LLM-generated summaries were shorter, more readable, and more understandable than the original discharge summaries. More than half of discharge summaries (54 of 100 summaries [54%]) were transformed successfully into a patient- readable format with top ratings in accuracy,; yet 18 summaries were flagged for potential safety risks. Furthermore, among the 46 reviews flagged as less accurate, 24 (52%) were due to omissions and 4 (9%) were due to hallucinations.6 These findings improve on readability scores and rates of hallucinations found in other patient education studies using LLMs.7,8
Given the principle of primum non nocere,
the potential safety risk that Zaretsky et al6 found is concerning. It is perhaps understandable that omissions occurred, given strict word restrictions to fit the discharge summary into a single page. It is conceivable that a longer discharge summary could have reduced rates of omission. While not evaluated in this study,6 it is also possible that human clinicians might also omit key clinical elements in a brief discharge summary.
More concerning, despite their relative rarity, was the presence of hallucinations, particularly given the high confidence with which they were presented.6 While the impact of LLM-generated hallucinations in health care remains unclear, hallucinations that are patient facing are more concerning than those facing clinicians, who may be able to better identify and correct them. Inaccura-
cies noted in this study by Zaretsky et al,6 such as mentioning nonexistent infections or chest pain, not only pose safety risks but also threaten the trust between patients and health care practitioners.
In addition to these safety concerns, the practical challenges of implementing such a tool in routine clinical practice must be considered. Manual processing of the discharge summaries to remove unnecessary elements is unlikely to be feasible on a busy clinical service, although there are technical solutions that could automate some or all of this work. More substantively, Zaretsky et al6 underwent an extensive six6-week process of prompt engineering, and it is unclear the extent to which their work could be reusable directly or whether it would require additional reengineering at other institutions with different practice patterns and disease burdens.
With that said, Zaretsky and colleagues6 should be commended for this early study, which highlights a novel use case of LLMs in health care to generate net new text to enhance patient education. Other studies have assessed LLM performance in replacing writing that clinicians would need to otherwise generate (e.g., clinical notes, claim denial appeal letters); the study by Zaretsky et al6 evaluates LLM performance for writing that clinicians would not usually, and perhaps rarely, have time to write for their patients. Using technology to create new products rather than just replace existing ones is an important step in realizing the gains of new digital technologies.9 As Zaretsky et al6 find, LLMs may not be ready for widespread unsupervised use to generate patient- facing discharge summaries, given real safety risks and formidable, if solvable, technological and workflow barriers, but perhaps in the near future, with better safety profiles, more automated inputs and outputs, and strict clinician oversight, they may become important tools in enhancing health care communication. DMJ
ARTICLE INFORMATION:
Published: March 11, 2024. doi:10.1001/jamanetworkopen.2024.0347
Corresponding Author: Charumathi Raghu Subramanian, MD, Division of Clinical Informatics and Digital Transformation, University of California, San Francisco, 10 Koret way, San Francisco, CA 94143 (charumathi. raghusubramanian@ucsf.edu).
Author Affiliations: Division of Clinical Informatics and Digital Transformation, University of California, San Francisco (Raghu Subramanian, Khanna); Kaiser Permanente, Oakland, California (Yang).
Conflict of Interest Disclosures: Dr. Khanna reported having a patent for CareWeb (with royalties paid from Baxter) outside the submitted work. No other disclosures were reported.
REFERENCES:
1. Bommasani R, Hudson DA, Adeli E, et al. On the Opportunities and Risks of Foundation Models. arXiv. Preprint posted online July 12, 2022. http://arxiv.org/abs/2108.07258
2. Jiang LY, Liu XC, Nejatian NP, et al. Health system-scale language models are all-purpose prediction engines. Nature. 2023;619(7969):357-362. doi:10.1038/s41586-023-06160-y
3. Clusmann J, Kolbinger FR, Muti HS, et al. The future landscape of large language models in medicine. Commun Med (Lond). 2023;3(1):141. doi:10.1038/s43856-023-00370-1
4. Brega AG, Hamer MK, Albright K, et al. Organizational health literacy: quality improvement measures with expert consensus. Health Lit Res Pract. 2019;3(2):e127-e146. doi:10.3928/2474830720190503-01
5. Kaper MS, Sixsmith J, Reijneveld SA, de Winter AF. Outcomes and critical factors for successful implementation of organizational health literacy interventions: a scoping review. Int J Environ Res Public Health. 2021;18 (22):11906. doi:10.3390/ ijerph182211906
6. Zaretsky J, Kim JM, Baskharoun S, et al. Generative artificial intelligence to transform inpatient discharge summaries to patient-friendly language and format. JAMA Netw Open. 2024;7(3):e240357. doi:10.1001/ jamanetworkopen.2024.0357
7. Hung YC, Chaker SC, Sigel M, Saad M, Slater ED. Comparison of patient education materials generated by chat generative pre-trained transformer versus experts: an innovative way to increase readability of patient education materials. Ann Plast Surg. 2023;91(4):409-412. doi:10.1097/SAP.0000000000003634
8. Moons P, Van Bulck L. Using ChatGPT and Google Bard to improve the readability of written patient information: A proofof-concept. Eur J Cardiovasc Nurs. 2023;zvad087. doi:10.1093/ eurjcn/zvad087
9. Brynjolfsson E, McAfee A. The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies. W. W. Norton & Co; 2016.
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Helping Texans get the healthcare they need includes tackling barriers to medication access. That’s why GoodRx is partnering with the Dallas County Medical Society to help make prescriptions more affordable. Together, we can help increase access and adherence for patients, improving health outcomes.
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With GoodRx, anyone—whether they’re insured or not—can save on generic and brand-name drugs. In the last year, approximately 50% of the 100 most purchased prescriptions filled
using GoodRx were cheaper than the average commercial insurance state copays, based on industry data. Thanks to healthcare professionals like you, we helped Dallas residents save $550 million dollars on medications last year, with the average GoodRx user saving approximately $72 per prescription, based on our internal data.
GoodRx prescription savings cards are accepted at major pharmacies in Dallas and nationwide. Visit our website tailored for healthcare professionals, GoodRx for HCPs, to compare prices at local pharmacies and order a free GoodRx savings kit for your office.
To learn more about how GoodRx can help you help your patients, visit: goodrx.com/hcp.
Equality Health is a value-based care enabler that leverages the proven capabilities of value-based payment models to transform healthcare for diverse and often marginalized populations. From predictive modeling to advanced care-tracking tools, utilizing Equality Health’s proprietary software, participating PCPs can streamline value-based administration and stay one step ahead of a patient’s journey.
Equality Health provides solutions that address the challenges of transitioning to and working in value-based care, so providers are able to concentrate on what matters most: patient health. Our Medicaid-first care model reduces administrative burden, streamlines processes, provides in-person support and offers additional financial opportunities.
Equality Health’s technology platform gets directly to the root of the multiple payer portal problem by providing one portal for multiple plans. CareEmpower® enables practices to monitor, track, and manage preventive care all in one place.
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Equality Health provides providers and their practices with an inperson care team that helps to optimize workflows to allow practices to more easily identify and initiate care for high-risk patients and close care gaps. Every element of support that we provide empowers practices to function more efficiently and more effectively, with improved patient outcomes the ultimate goal.
Equality Health partners with over 3,200 PCPs and 700,000 lives across Arizona, Texas, Tennessee, Louisiana and Virginia. Our members engage with holistic and personalized programs delivered through the lens of social and cultural needs. Equality Health is revolutionizing how care is delivered by establishing critical linkages with payers, providers, members, and community resources.
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We Help Members Improve Their Health Insurance Coverage and Control Costs Health Insurance Open Enrollment
Hundreds of medical groups and thousands of physicians, their dependents and employees across Texas have improved their health insurance coverage and controlled costs by working with TMA Insurance Trust. We helped them secure more comprehensive coverage or find a more budgetfriendly option. Many times, it’s both. How? We are group specialists and have uncovered special options available only during Open Enrollment that help owners and independents access group PPO insurance for their employees, or just for themselves - even when they do not have employees. And this is all backed by a higher level of service and care our dedicated members deserve.
Here are some of the ways we help owners and independents get better coverage:
• Practice owners with group coverage for their practice: There are opportunities to help control the cost to your practice and be able to offer your employees health insurance – with a mix of group PPO and HMO plans.
• Practice owners with staff on their own health coverage: A spouse’s plan or that of another provider) you may be able to get group PPO coverage just for yourself and your family.
• Partners with no W-2 employees: You may be eligible for group coverage only for yourself. You’ll need to provide partnership documentation and the company’s SS4 or recent K-1 (Form 1065).
• Physicians who own a business with their spouse, or their spouse is a W-2 employee: You may qualify for group coverage even without partnership documentation.
• Establish a Health Savings Account (HSA): If you select a high deductible health plan to manage costs better, you can open an HSA. These accounts offer tax advantages, spending flexibility and ongoing saving and investment opportunities.
• Starting your own practice: TMA Insurance Trust can help you set up a customized group plan that aligns with your practice’s needs.
To improve your health insurance situation, contact a TMA Insurance Trust health insurance advisor. They can be contacted at 800-880-8181, Monday to Friday 8:00 to 5:00 CST, or by visiting us online at tmait.org
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