MHAP study - MHA Phlebotomist Updated: 2023
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Exam Code: MHAP MHA Phlebotomist study June 2023 by Killexams.com team|
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It"s time for Lab Test! The classic game show where you match tubes to tests!
I"m your host Kimi Stry! Ok, players ready to pass your Lab Test? The test is uric
acid; what's the match?
A. SST/Chemistry lab.
B. Lavender/ Chemistry lab.
A blood demo drawn for a uric acid would be drawn in a SST and sent to the
It"s time for Lab Test! The classic game show where you match tubes to tests!
I"m your host Kimi Stry! Ok, players ready to pass your Lab Test? The test is
A1C; what"s the match?
A. Green/chemistry lab.
B. Lavender/chemistry lab.
C. Red / hematology lab.
A blood demo drawn for an A1C (hemoglobin A1C) is drawn in a lavender top
tube and sent to the chemistry laboratory.
79. Which of the following phrases describes the pre-analytical phase of
A. The phase of laboratory testing that involves phlebotomists
B. One of two phases necessary for laboratory testing
C. The phase involving all processes of laboratory testing
D. All of the above
The pre-analytic phase of laboratory testing is the first of the two phases that
cover all processes of laboratory testing. The first phase includes test ordering,
specimen collection, and processing of the samples. The second phase is the
analytic phase, which includes the genuine testing and reporting of results.
Which of the following examples is a pre-analytical variable?
A. Patient did not maintain the designated fasting period for a fasting sample.
B. Light sensitive demo not protected from light.
C. Elevated result not reported immediately
D. A and B
A patient eating during the fasting period necessary for a fasting specimen and not
protecting a light-sensitive specimen from light, are two examples of pre-
analytical variables. Pre-analytical variables account for most laboratory errors.
Blood gas samples, gastrin samples, ammonia samples, and lactic acid samples
are all blood samples that require which special transportation considerations?
A. All must be transported in ice water.
B. All must be transported warmed.
C. All require protection from light.
Some blood analytes break down quickly; transporting the specimen in ice water
slows that process. Other tests that require chilled transportation include renin,
catecholamine, and parathyroid.
82. A physician just called Phil the New Phlebotomy Tech"s supervisor. The
physician was very upset, because he didn"t receive a critical lab result. This is an
example of what kind of variable?
Not informing a physician of laboratory results is an analytical variable.
It"s time for Lab Test! The classic game show where you matching tubes to
tests! I"m your host Kimi Stry! Ok, players ready to pass your Lab Test? The test
is RA; what"s the match?
Blood drawn for an RA would be drawn in red-topped specimen container and
sent to serology.
Labeling a demo at the bedside is an example of which variable control?
Requiring phlebotomists to label specimens at the bedside to avoid specimen mix-
ups and unlabeled specimens is a method of controlling a pre-analytic variable.
85. What is point-of-care testing?
A. Testing that is done to pinpoint an area of patient care
B. Testing that is done in the presence of a patient
C. Testing that can only be done by the patient
D. None of the above
Point-of-care testing (POCT) is testing that is done in the presence of a patient.
POCT can be done by the patient in their own home or by a provider in a care
setting, such as a doctor"s office or public health event.
The preanalytical testing phase includes all of the following EXCEPT:
A. Specimen collection
B. Verifying test results
C. Specimen labeling
D. Specimen processing
The preanalytical testing phase includes specimen collection, specimen labeling,
and specimen processing, but notverifying test results. The preanalytical phase of
specimen testing happens from the time the physician orders the test until the
results are delivered to the physician. Verifying test results belongs to the
analytical phase of testing and involves a review of the acceptability of test
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SAN DIEGO, CA / ACCESSWIRE / May 22, 2023 / From this article you will learn:
Artificial intelligence-the application of digital computing to tasks otherwise associated with human thought, analysis, and judgment-has begun to find a place in every sector of our economy. Medical science is no exception. Around the country, doctors are panic that artificial intelligence has begun to displace them. AI's champions are thrilled: after all, diagnosis is about analyzing data, and that's what AI does best.
They're all wrong.
Between dread and hype lies the truth of AI's value to the medical community, particularly as a diagnostic tool. It's true that AI has increasingly asserted its role in the healthcare field-more than 90% of US hospitals have a strategic plan to integrate AI with their standard modes of practice. AI helps the Mayo Clinic search for biomarkers indicating cancer, facilitates the Cleveland Clinic's effort to mine its pathology specimens for pathology insights, and even predicts which patients at Boston Children's Hospital will miss their appointments.
But recent studies suggest that AI's most productive role is as a complement to human diagnosis, not as its replacement. Researchers at MIT's Computer Science and Artificial Intelligence Lab, for example, found that a carefully calibrated hybrid model involving both AI and human participation resulted in an 8% improvement in the diagnosis of cardiomegaly, exceeding the accuracy of AI or human diagnosis alone.
A new generation of collaborative AI applications positions AI as a tool and a guide for medical personnel without presuming to encroach on the roles of doctors and nurses. Diagu leads this emerging wave with a set of AI tools designed to facilitate quicker, more accurate diagnoses of a wide range of medical conditions. Founded in 2012, Diagu has invested more than ten years of research and development into AI tools that analyze laboratory test results with exceptional sensitivity to a nearly comprehensive range of patient conditions. Its platform promises to benefit labs, patients, and doctors alike:
Implications for diagnostic practice
The means by which AI models are trained have significant implications their use as medical diagnostic tools. A 2022 review of AI in disease diagnosis published in the Journal of Ambient Intelligence and Humanized Computing found that "The most common challenge faced by most of the studies was insufficient data to train the model." Various applications of AI for medical diagnosis reflect this challenge to varying degrees. In some applications-the interpretation of radiological imaging, for example-AI is extremely adept. As the authors of a study in Nature Reviews Cancer put it, "AI excels at recognizing complex patterns in imaging data and can provide a quantitative assessment in an automated fashion." Trained on a sufficiently representative range of images, AI models have outperformed human control groups while delivering results more quickly. We must remember that this blend of speed and accuracy has as much to do with human discretion as with the inherent strengths of AI.
Speed is easy enough to measure, but assessing diagnostic accuracy requires prior knowledge of the patient's genuine condition-and this in turn demands a final, definitive assessment by a human diagnostician. As the Nature Reviews Cancer article continues, "More accurate and reproducible radiology assessments can then be made when AI is integrated into the clinical workflow as a tool to assist physicians [emphasis added]."
A recent study of AI's ability to analyze lung scans underscores this point. Researchers at the University of South Carolina found that a well-trained AI model was able to identify certain types of lung nodules more accurately than humans, and roughly four times more quickly. The study measured accuracy against the model's ability to identify nodules appearing on lung tissue, which it did with a sensitivity comparable to that characteristic of seasoned radiologists. Before it identified those nodules, the AI model needed to be trained to recognize them. Had the study used an imperfect set of data to train the AI model, its results may well have been significantly different.
Perhaps most strikingly, the study found that when experienced radiologists used AI as a tool for evaluating lung scans, they achieved accurate results with a greater degree of confidence in roughly a quarter of the time they spent on each scan without the assistance of machine learning. These results help describe the vanguard of AI as a diagnostic tool: far from replacing the expertise of human diagnosticians, AI works best as a facilitator. In the University of South Carolina study, AI played a discrete role: to Improve the speed and confidence with which veteran radiologists accurately assessed lung scans. It accomplished this by presenting its own conclusions for the radiologist to review and confirm, greatly narrowing the scope of reasonable possibilities the radiologist was burdened with assessing. The radiologist's role remained essentially the same. AI simply began the process of human analysis at a point further down the road to accurate diagnosis than had previously been possible. This vital interplay of machine learning and human judgment is the future of AI-assisted clinical diagnosis.
Diagu's flagship Diagu Fire system and its GetLabTest solution embrace the fundamental reliance of AI on human discretion. Diagu's platform uses AI to evaluate a patient's full medical history against the knowledge base it has already established by analyzing millions of other, anonymized patient histories. When doctors order lab tests, Diagu facilitates the reporting of test results, supplementing them with its own findings and proposed diagnoses. In the process, it avoids the limitations of first-generation diagnostic AI solutions by focusing on what AI does most reliably: interpreting test results and presenting its conclusions to doctors in the ways that best support quick, accurate diagnoses.
A new generation of diagnostic AI
Diagu represents a significantly different application of AI from those that have become established in medical practice. Most of the examples cited in this paper necessarily reflect AI's first generation as a diagnostic tool. First-generation diagnostic AI solutions typically perform-at greater speed and often with greater accuracy-interpretive functions previously performed by humans. Radiological scans, for instance, benefit greatly from this application of AI, which excels at the sort of pattern-matching necessary to identify and characterize anomalies found in narrowly defined ranges of images. Crucially, this application of AI replaces human analysis on a 1:1 basis: it seeks to render existing workflows more efficient and effective but does not substantially change them.
The emerging generation of diagnostic AI tools promises to optimize the diagnostic process much further, allowing hospitals to establish new, more effective workflows that make full use of both human and machine analysis. Diagu's ability to convey accurate, highly informed diagnostic suggestions along with test results-and to deliver all of this information more quickly than previous workflows allowed-is a good example of diagnostic AI's next wave. While Diagu's approach makes it uniquely appropriate to a broad range of diagnoses and clinical settings, it shares an approach to AI with some other medical technology companies. Freenome, for example, uses computational biology and machine learning to speed the early diagnosis of cancer and to support more efficient and effective treatment planning. PinPoint Data Science offers a similar cancer-screening solution based on multiple blood analytes.
Diagu's GetLabTest service is a unique application of AI in the healthcare industry. While some companies offer similar services, they are more narrowly focused. eConsult Health and Mobile Phlebotomist are healthcare companies operating in several European Union countries and the UK, providing digital triage and remote consultation capabilities, and home-based phlebotomy services, respectively. Both companies are now expanding their reach and seeking partnerships in the United States to bring their services to a new market. It will be interesting to see how they adapt to the US market and what opportunities they discover.
AI as an Interpretive Tool
AI's pattern-matching capabilities make it an especially promising tool for interpreting test data. A study conducted at Massachusetts General Hospital offers a good example of how AI can extend a hospital's existing diagnostic methods. Using low dose computed tomography (LDCT) scans gathered from three different sources, researchers developed an AI model capable of analyzing single LDCT scans in real time and determining the risk of lung cancer among the nonsmoking population. Because the study drew on historic data, researchers were able to confirm that its results were remarkably accurate in determining cancer risk up to six years into the future. Traditional techniques for assessing lung cancer risk require multiple LDCT scans for each patient and arrive at predictions only after analyzing series of scans.
As with the earlier example from the University of South Carolina, the Massachusetts General study points to a revolutionary breakthrough in diagnostic practice that brings the roles of doctors and AI tools into focus without abrogating the central role of human diagnosticians. This breakthrough begins before a radiologist or doctor receives scans or other test results: AI can make existing technologies more sensitive and more accurate. As we have seen, AI conveys its greatest benefits in diagnostic settings when it enhances the results of traditional tests such as radiological scans and gives diagnosticians a guide to their interpretation. This is especially true of LDCT scans: questions remain as to LDCT's sensitivity, and its false-positive rate has been measured at rates of up to 26.6%. The Massachusetts General study used a highly optimized classification tool, Lung-RADS 1.0, to measure the accuracy of non-AI-guided LDCT scan evaluation; this tool yielded a false-positive rate of 0.10 on a cohort of scans known to be either positive or negative over the study's six-year timeframe, and a FPR of 0.14 on the entire baseline cohort. AI-guided analysis yielded commensurate FPRs of 0.08 and 0.08.
More accurate results encourage timelier and more effective diagnoses. LDCT has already proven itself as the centerpiece of preventive measures for patients who may be at risk for lung cancer, but its reputation for generating false positives gives doctors reason to think carefully about how they act upon its findings. Invasive tests, for example, can greatly Improve a patient's prospects when they confirm true positives, but they disprove false positives at a high cost, both in terms of the time and money required and as negative impacts on the patient's health. Those costs are especially cruel when invasive testing was ordered on the basis of an avoidable false positive. AI's proven ability to Improve the accuracy even of radiological techniques not known for high degrees of sensitivity could truly revolutionize the way in which initial diagnoses are confirmed, getting patients more quickly into more effective courses of treatment.
New advancements in AI facilitate more common forms of medical testing, from bloodwork and urine tests to images of all kinds, and it is in this capacity that the fullest, most reliable application of AI to diagnostic practice is likely to be realized. This view comports with the stated vision of Sumeet Chugh, director of the Division of Artificial Intelligence in Medicine at Cedars Sinai Hospital. In Chugh's words, his team devotes itself to "solving existing gaps [emphasis added] in mechanisms, diagnostics, risk assessment and therapeutics of major human disease conditions." In the view of the researchers most closely in tune with emerging applications of AI, then, the methods and frameworks created and maintained by human doctors still form the foundation of modern medical practice.
Seen as a complement to existing diagnostic practice, AI-enhanced methods can greatly increase the speed and accuracy with which test results are reviewed, interpreted, and used to inform diagnoses. Diagu has made this a defining characteristic of its Diagu Fire lab test analyzer. Working directly with partner hospitals in the UK and Europe, Diagu Fire is initially trained on millions of medical records, including test results and confirmed outcomes. It then pre-processes each patient's full medical history while specimens are away at the testing laboratory. This gives Diagu's AI model a head start toward recommending diagnoses based on the patient's latest round of test results. Drawing on what it has learned from its baseline training, the model identifies patterns in each patient's health history, which allows it in turn to identify ranges of test results as consistent or anomalous with the patient's documented health to date. While the laboratory releases its testing results, Diagu Fire incorporates them into its previous analysis, delivering in near-real time a focused set of diagnostic possibilities and treatment options, complete with the assessed likeliness of each potential diagnosis.
Crucially, Diagu does not seek to replace human discretion altogether. AI can greatly reduce the rate of diagnostic errors, and it is especially adept at identifying and reconciling complex interactions of symptoms, test results, and patients' medical histories. It does so, though, only within the limits of the presumptions that informed the development of its algorithms and the data on which it was trained. Most doctors have found themselves making informed diagnoses on the evidence available to them, only to find that information the patient deemed irrelevant is in fact hugely consequential. The same is true of AI-enhanced diagnostic tools, with one important difference: until they are re-trained on the newly discovered data, they will continue to make the same errors of omission. This further underscores the need to posit AI as a helper, guide, and tool for experienced diagnosticians, and not as a substitute for human oversight of the diagnostic process. AI models never know when they are acting on erroneous or incomplete data, and are capable of presenting their conclusions with an air of definitiveness based solely on the precision with which they analyzed the data available to them. Doctors are, like the rest of us, all too aware of our capacity for human weakness, and their resulting skepticism is an important hedge against AI's limitations.
For that matter, a healthy skepticism toward the grander claims sometimes made for AI can help frame the relationship between doctor and machine in its most effective light. Like any tool, AI is as effective as the person using it. When medical personnel understand the strengths and limitations of the AI tools they consult, they receive the full benefit of those tools. Diagu Fire reflects both the promise of AI diagnostic technology and the need to employ it responsibly by giving doctors suggestions and ranges of possibility, not edicts or mandates. In this context, doctors are able to focus their attentions on the likeliest range of possible diagnoses while still informing their conclusions with the full benefit of their clinical experience. AI also helps diagnosticians see further into the future than older technologies currently allow, and to identify patterns in patients' health histories that argue for preventive measures. For patients, the benefits are at once numerous and obvious: more efficient and responsive care, earlier starts on more targeted treatment plans, and better health outcomes. Hospitals, too, benefit from properly balanced, AI-assisted diagnostic methods. More accurate diagnoses allow for more effective treatment, reducing the costs associated with misdiagnosis.
The Benefits of Emerging AI Technologies
When used to its greatest potential advantage, as with Diagu's solution, AI can convey a wide range of significant benefits to healthcare systems, hospitals, doctors, and patients. Some of these benefits are somewhat mundane: As cited in an article in Forbes on February 22, 2023, "From a financial perspective, wider adoption of AI could lead to savings of 5% to 10% in US healthcare spending-roughly $200 billion to $360 billion annually in 2019 dollars - within the next five years, according to a paper recently published by the National Bureau of Economic Research. . AI solutions are capable of streamlining intra-hospital communications, producing more efficient staff rotation schedules, facilitating patient intake, and automating much of the billing and collections process. As the backbone of natural language processing solutions, AI also holds great promise as a potential translator and facilitator of information within and across medical facilities. In the process, such applications of AI also promise to make medical care more efficient, responsive, and accurate.
The cost of healthcare services goes a considerable way toward determining patient satisfaction, especially when more efficient, lower-priced care is also more effective. AI diagnostic tools like Diagu Fire reduce the risk of diagnostic errors while supporting preventive health measures based on evidence unavailable until now to doctors and physicians. The result is a clearer and shorter path to better patient outcomes.
AI also holds the promise of more portable healthcare, further extending its benefits-especially to people in underserved areas of the country and to overworked hospital staff. Diagu's GetLabTest system offers a glimpse of how AI might support an extended healthcare system. GLT allows patients to purchase single diagnostic tests or themed packages geared toward addressing specific health concerns or goals. When blood samples are needed, patients can visit a local clinic or arrange for a licensed phlebotomist to draw a demo right at home. Diagu's AI models analyze the results as soon as they are ready, and immediately direct patients to video conferences with medical certified appropriate to their diagnoses.
This approach removes many of the practical obstacles facing people across the country in light of hospital closures and consolidations. Telemedicine extends healthcare to people in regions underserved by medical facilities, and GetLabTest builds on that promise by shortening the distance among testing, diagnosis, and treatment. Like much of what AI seeks to improve, the Get Lab Test model could be replicated without the support of machine learning, but far more slowly and inefficiently. The speed with which medical conditions are diagnosed and treated is only one factor affecting health outcomes, but it can be a deciding one.
Improving healthcare access is an important public health goal. So is the kind of long-range forecasting made possible by AI. Systems like Diagu's are capable of spotting emerging public health crises more quickly and accurately than traditional methods have allowed. As we have seen, AI can derive accurate conclusions from smaller groups of test data than some traditional diagnostic methods. It can also identify emerging health issues farther in advance than older techniques. Each of these advantages facilitates earlier detection and treatment of both chronic and emerging public health concerns, giving researchers, doctors, administrators, and public officials as much time as possible to respond to looming threats.
AI and the Role of Doctors
Like any technology, AI's role is largely what its overseers-both technical personnel and hospital administrators-choose it to be.
On one hand, AI is able to deliver consistent, objective, evidence-based diagnoses based on volumes of data too vast for human practitioners to consider. Properly trained and implemented, AI can support real-time diagnoses of conditions that would otherwise take hours or even days to identify.
But these advantages only translate into improved patient outcomes when they support the judgment of qualified medical personnel and free doctors and nurses to provide the types of care that AI cannot. Not all patients are able to effectively communicate each of their symptoms when they first visit a clinic or hospital; an automated diagnostic tool will remain oblivious to the subtle hints such patients may send as to their real condition. A doctor, especially one who has been able to trade some time previously spent on administrative tasks for more time with patients, is far better positioned to gather all the information such patients intend to convey.
Again, Diagu's approach offers a compelling vision of AI's ability to facilitate established medical practice. Using HL7's FHIR API, Diagu allows doctors to order tests, receive results from testing laboratories, and relay test results directly to doctors along with Diagu's AI-generated predictive analysis. Patients who wish to begin the process themselves can use GetLabTest. After the lab processes their samples, patients receive SMS notifications and a PDF summarizing their test results. They also receive suggestions as to which certified can best help them respond to any issues raised by their tests. Appropriate levels of information are made available to patients and doctors. For their part, doctors who meet with GetLabTest patients receive full documentation of each test's results, along with Diagu's AI-generated predictive analysis of the results, and a provisional diagnosis of the patient's condition.
Diagu currently partners with hospitals, clinics and laboratories in the UK, Poland and other European countries. As part of its expansion, Diagu is actively seeking partnerships with laboratories and hospitals in the United States to train its AI models on anonymized datasets and pilot the use of its services such as Diagu Fire and GetLabTest. In addition, Diagu is working to integrate third-party products and platforms such as Workbeep and Gigbeep, which facilitate the work of phlebotomists in the UK. It will be interesting to see how Diagu adapts to the US market and what opportunities they discover in this new territory.
Early attempts to apply AI in hospitals and clinics were greeted with a combination of grand expectations and nervous apprehension. As it happens, each of those responses appears to have represented something of an overreaction. With time, the field has matured somewhat, and a truly revolutionary role for AI technology has emerged. Far from supplanting either doctors or existing testing methods, AI diagnostic tools have proven capable of significantly improving the performance of both. Most importantly, the advances made possible by AI Improve patient outcomes by facilitating quicker, more accurate diagnoses and better-informed, more responsive treatment plans.
The new generation of collaborative AI is poised to unlock AI's true promise and make good on some of its loftiest expectations. By combining the pattern-matching prowess of machine learning with the responsiveness and nuance only a doctor can provide, collaborative AI diagnosis represents a chance to see farther into patients' futures than ever before. As we saw in the example from Massachusetts General, AI can diagnose some conditions just as accurately as human experts, more quickly and, crucially, with less testing data from a given patient. As companies like Diagu work to make lab testing easier, more accessible, and more efficient, AI diagnostic tools will increasingly be able to predict a patient's future health concerns beyond the power of human doctors to do so. Doctors, for their part, will be able to develop courses of treatment that we cannot currently imagine.
This is a golden age for diagnostic medicine. Diagu invites you to meet that emerging opportunity with a suite of remarkable yet eminently practical tools for doctors and patients. We build solutions that allow the best of machine learning and human expertise-including yours. Schedule a demonstration of Diagu Fire or GetLabTest and see for yourself what the future holds in store for all of us.
The Future of AI Diagnostics is Here. Discover What it Means for You.
As AI health solutions continue to shape the future of healthcare, it is more important than ever to stay informed and engaged in the latest developments in the field. Diagu is ready to keep you up to date on the technologies and methods that shape our industry. Sign up today for regular updates on the latest news and analysis surrounding digital health solutions, and to learn how Diagu is poised to revolutionize diagnostic practice. When you do, a dedicated member of our team will personally reach out to you to discuss emerging solutions that are changing the way we diagnose disease and prepare treatment plans. Don't miss out on this opportunity to be a part of the digital health revolution-register now on https://getlabtest.com/.
SOURCE: Diagu sp. z o.o.
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SPRINGFIELD â Springfield Technical Community College (STCC) will offer two healthcare courses, emergency medical technician (EMT) and phlebotomy certification, starting in June.
The EMT course, which begins Tuesday, June 6, is open to anyone 18 and older, while the phlebotomy course, starting Tuesday, June 20, is for people working in approved healthcare worker roles.
The EMT course, which runs June 6 through Aug. 19, covers all medical concepts and techniques used to provide emergency care in pre-hospital settings. The program teaches skills such as cardiopulmonary resuscitation and automated external defibrillation, among other EMT skills. On-campus labs are Tuesday from 8 a.m. to noon.
The class is a mixture of online and in-person work. For more information and to register, visit stcc.io/emt.
The phlebotomy for healthcare workers class, open to anyone licensed in various medical fields, runs June 20 through July 11. Class hours are 9 a.m. to 2:30 p.m., and labs for the course will be held in-person on the STCC campus.
The non-credit course, offered through the Workforce Development Center at STCC, prepares students to take the exam to become a certified phlebotomist in Massachusetts. The class is designed to teach workers in certified healthcare positions to draw blood for diagnostic procedures.
Anyone interested in taking the class should have a current healthcare certification in at least one of the following healthcare roles (those with certification in other healthcare fields not listed may be eligible):
certified nursing assistant (CNA), emergency medical technician (EMT), patient care technician (PCT), certified medical assistant (CMA), licensed practical nurse (LPN), certified electrocardiogram technician (CET), or certified dental assistant (CDA).
Certified phlebotomists can work in emergency rooms, clinics, doctorâs offices, and bloodmobiles, among other healthcare venues. The fee for the class includes the National Healthcare Assoc. Exam.
To enroll online and learn more about this course, visit www.stcc.edu/wdc/healthcare and click the âphlebotomy certification for healthcare workersâ link.
For more information, contact the Workforce Development Center at (413) 755-4225 or [emailÂ protected].
The application cycle for the fall 2023 program is now open.
Among the many post-bac programs offered around the country, and even at Drexel University, the Master of Science in Biomedical Studies (MBS) program stands out with its unique blend of undergraduate and graduate coursework and MCAT preparation. Located on Drexel University's Center City Campus in Philadelphia, Pa., this full-time, two-year special master's program is designed for students with a lower undergraduate science GPA. It offers a unique opportunity to enhance students' credentials to become competitive applicants for medical or other health professional schools. The structured, carefully balanced biomedical studies curriculum evolved from our former one-year Medical Science Preparatory and second-year Master of Science in Biological Science programs.
What does the Biomedical Studies program offer?
Stepwise academic challenge
In Year 1, advanced undergraduate science courses in chemistry and physics help boost students' science competencies and undergraduate science GPA; graduate coursework supports implementing study and time-management skills essential to succeed in the rigorous second-year coursework.
In Year 2, students immerse themselves in coursework equivalent to the first year of medical school, taught by the same College of Medicine faculty members who teach Drexel medical students. Students' academic success will demonstrate their readiness for medical and other health professional schools and, if certain criteria are met, guarantees an interview with Drexel's MD program.
Extensive preparation for the MCAT
Both undergraduate and relevant graduate coursework (including biochemistry and psychology) are designed to provide the background tested in the various sections of the revised MCAT. MBS also includes a dedicated on-campus, year-long MCAT preparation course offered by Princeton Review, to further enhance the coursework and instill strong test-taking skills.
Building a strong application portfolio
The Biomedical Studies program focus extends beyond academics to support the growth of a well-rounded applicant by providing opportunities for community service and summer research. Our team-based holistic advisement approach helps students fine-tune all steps of the medical school application process.
Biomedical Studies students also benefit from our linkage and affiliation opportunities. Students who take the full Biomedical Studies curriculum and meet specific GPA and MCAT criteria are guaranteed an interview with Drexel University College of Medicine's MD program. Additional linkage and affiliation agreements exist with Touro College of Osteopathic Medicine (New York) and Edward Via College of Osteopathic Medicine (VCOM).
Please visit the curriculum page for more information
Admission to the MD Program
A number of academically excellent pre-medical and pre-health program graduate students are interviewed each year for admission to Drexel University College of Medicine's MD program. The MD program enrolls a strong cohort of students from our pre-med and pre-health programs. For the past several years, pre-med and pre-health graduates comprised about ten percent of the entering MD class.
Meet Our Faculty
Meet Tameka Jackson-Leung, DHSc
Tameka Jackson-Leung, DHSc, teaches Diversity in Healthcare and serves as a co-instructor for the Community Dimensions of Medicine course. These courses are offered via the Master of Science in Biomedical Studies program, and the Master of Science in Interdisciplinary Health Sciences program, within the Division of Pre-medical and Pre-health Programs of the Graduate School of Biomedical Sciences and Professional Studies. Read more.
Meet Our Students & Alumni
"The unique aspect of the MBS program is that it does not focus solely on improving one aspect of oneâs medical school application, such as grades. The program is comprehensive and designed to Improve all aspects of oneâs application, and it also provides application support." Read Caroline's Story
"I don't think I would be in medical school if it weren't for the Biomedical Studies program. It not only helped me with my GPA, but I significantly increased my MCAT score too. Additionally, the faculty helped me with figuring out where to apply, gave me advice on my essay and prepared me for interviews." Read Brian's Story
In The Media
Erica Riddick, MS Biomedical Studies '20, MD '24, wrote about "Health Disparities During COVID-19" in the winter 2021 issue of Pulse. Read more (see page 3).
Media Watch: Drexel Students 3-D Print a Helping Hand for Young Violinist
Drexel graduate students create device to help young musician
Itâs no secret that medical school admissions are extremely competitive. At the University of New Haven, we take a unique and progressive approach to preparing our students to attend medical school through the pre-medical studies designation.
At the University of New Haven, the pre-medical studies designation is not an academic major or stand-alone program. It must be declared in addition to an academic major via the Health Professions Advising Center.
The pre-medical studies designation is open to all University of New Haven students, regardless of academic major.
As a pre-med student at the University of New Haven youâll be provided with countless experiential opportunities and the resources to ensure that youâre the most competitive medical school candidate possible. As the University of New Haven we provide:
Admission into medical school is very competitive. But if you have the drive and work ethic, we can support you with preparation in order to successfully gain admission to the medical school of your choice.
We Prepare Our Students for Success
Our students go on to great careers in medicine, pharmacy, physician assisting and other health professions. Here is a list of selected universities they have attended in the last 10 years.
Penn State School of Medicine
Physician Assistant Studies
Concordia University-Wisconsin (Doctor of Pharmacy)
Ross University School of Veterinary Medicine (Doctor of Veterinary Medicine)
Other Health Programs
Boston College (MS in Nursing)
If you've spent the last week binging "Queen Charlotte: A Bridgerton Story," you're likely well-versed in the complicated (and steamy!) love between Queen Charlotte and King George III. But this high-society "Bridgerton" prequel unravels a lot more than romance, power, and gossip. It tells the tale of class and marriage, while also exploring the tormenting illness of King George III, which at least some experts believe to be porphyria.
That's right: while "Queen Charlotte" is a fictionalized retelling of the story of Charlotte and George, the genuine King George III did have medical issues, and at one point they were suspected to be caused by porphyria, an extremely rare genetic disease that Cleveland Clinic says is estimated to affect fewer than 200,000 people in the United States. (But it can be asymptomatic, so some people with it may be undiagnosed.) The disorder is usually inherited from a parent and is caused by high levels of a naturally occurring chemical in the body known as porphyrins. This leads to negative symptoms primarily affecting the skin and nervous system.
At this point, however, the idea that King George III suffered from porphyria is pretty widely contested. The diagnosis first came up in the 1960s, when two psychiatrists claimed that his medical records showed that he had the condition. But over the years, other research indicated that in King George III's case, porphyria was a misdiagnosis, and he may instead have been dealing with bipolar disorder, reports the journal Clinical Medicine. But even so, the show has reignited interest in porphyria. Here's everything you need to know about the liver disorder, including its symptoms, how it's diagnosed, and whether it's treatable.
What Is Porphyria?
Porphyria describes a group of eight rare genetic disorders that affect the skin and nervous system as a result of a buildup of natural chemicals called porphyrins, according to Cleveland Clinic. Porphyrins are naturally found in the body and are needed to make heme, which is a molecule necessary to make hemoglobin (a protein found in red blood cells). If the body has too many porphyrins it can be detrimental, the Mayo Clinic notes.
There are also two main types of porphyrias. Acute porphyrias start rapidly and mainly affect the nervous system, while cutaneous porphyrias (such as porphyria cutanea tarda, the most common type of all porphyrias) mainly attack the skin. A few types of porphyrias can affect both the nervous system and skin, but it's less common.
What Are the Symptoms of Porphyria?
The symptoms of porphyria depend on the individual and type, but symptoms can range from mild to severe, per Cleveland Clinic.
Those living with cutaneous types of porphyria (which primarily affect the skin) often have an oversensitivity to sunlight and experience itching, blisters or abrasions on the skin, scarring, fragile skin, and extreme swelling or irritation when exposed to the sun.
Acute porphyrias typically cause a sudden onset of symptoms that usually last hours, days, or weeks, according to Cleveland Clinic. Common symptoms include pain in the abdomen, chest, arms, legs, or back, urinary retention (the inability to empty your bladder completely), constipation, nausea, vomiting, muscle weakness, seizures, confusion, and hallucinations.
Some porphyria patients, whether acute or cutaneous, may also have a reddish-purple or brown colored urine due to the presence of excess porphyrins.
How Is Porphyria Treated?
Porphyria can be difficult to diagnose since many symptoms resemble other diseases and disorders, but it's typically diagnosed through blood, urine, and stool samples, or a DNA genetic test, according to the American Porphyria Foundation.
There is no cure or way to prevent porphyria, but there are treatments and symptom management techniques, which depend on the type and severity of symptoms. For those with cutaneous porphyrias, avoiding sunlight is key and doctors may recommend eliminating substances that often trigger symptoms, such as alcohol. Some people may also benefit from frequent blood draws, known as therapeutic phlebotomy, which reduces the amount of iron in the liver and can minimize symptoms.
Treatment for acute porphyrias typically involves heme and/or glucose infusions through an IV, which decrease the number of porphyrins produced in the liver, according to Cleveland Clinic. For more severe cases, treatment may also include blood transfusions, surgery to remove the spleen, or liver and bone marrow transplants.
Some studies also suggest that maintaining a balanced diet and reducing carbohydrate intake can minimize symptoms. Smoking should also be completely avoided.
Famous People With Porphyria
The exact number of people who have porphyria is unknown, but some research suggests that one in every 20,000 people may have some type of the condition. And while it's not always confirmed that historical figures were officially diagnosed porphyria, a case (aside from King George III) has been linked to Prince William of Gloucester, a cousin of Queen Elizabeth II.
Mary, Queen of Scots, and her father, James V of Scotland, are also believed to have had acute porphyria since they were ancestors of King George III and inherited the disease.
Some research also suggests Vincent van Gogh had acute porphyria since it accounts for all signs and symptoms of his underlying physical and mental illnesses.
Fact checked by Nick Blackmer
When the pollen count is through the roof and the mold spores are flying, itâs easy to surmise that allergies are the culprit behind your itchy eyes and runny nose. But finding out exactly whatâs causing your seasonal or food-related distress can be a bit harder. With the growing popularity of at-home testing kits, there are more ways than ever to get to the bottom of your allergy mysteryâbut are they accurate?
All allergy tests are looking for the same thingâincreased levels of immunoglobulin E (IgE) in response to an allergen. IgE is a protein created by mast cells when they come into contact with an allergen. When those cells react, the body responds with symptoms like itchiness, congestion or inflammation.
While both at home tests and in office tests aim to detect the same protein, they do so in different ways and with different levels of information available.
We asked two allergists to weigh in on at home testing kits versus getting tested in a doctorâs office. Neeta Odgen, MD, medical advisor for Curex, and Shuba Iyengar, MD, co-founder and chief medical officer of Allermi, weighed in on the pros and cons of in-person testing versus at-home test kits.Â
The verdict: At-home testing is a good starting place, but nothing is as accurate and sensitive as the testing provided in a physicianâs office.
Some allergy tests look for another protein, immunoglobulin G (IgG). These tests are unable to properly diagnose true allergies and should be avoided, according to the American Academy of Allergy, Asthma, and Immunology.
At-Home Allergy Testing
Companies like Test My Allergy, Everlywell, and Nectar offer testing thatâs accessible to anyone, although most tests stipulate that they should be used by people over 18. Tests can be ordered online and range in price from $60â$500, depending on the number of allergens that they test for. Many kits test for both environmental and food allergies.
The method is simple: A simple finger prick yields enough blood to place on a card that is then mailed to a lab for analysis. A few days or weeks later, users should receive results, generally via email or a patient portal, that tell what allergens they react to the most based on their immunoglobulin E (IgE) levels.
At-home tests are typically not covered by insurance, although they may be eligible for HSA/FSA spending. Many companies will also offer a superbill that patients can use to submit to their physicians for approval and reimbursement.
Are At-Home Tests Accurate?
Serum IgE tests are considered generally accurate, although without the added benefit of a full medical history, they may be more useful as a way to rule out allergens rather than positively identify them, according to a 2021 study.Â
At-home tests offer easy access and fairly expedient results, but they may only be a first step to getting treatment. Some tests offer access to virtual care with a physician who can prescribe medications to help deal with the symptoms. Others offer only data with little analysis.
Iyengar said that if a patient brought in test results from an at-home test, she would use that information as a starting point but would likely order further sensitive testing to pinpoint allergens more specifically.
For some patients, though, at-home serum IgE testing is ideal. Iyengar said that patients with extreme eczema may benefit, since a clear patch of skin for testing may be hard to come by. Patients who are taking allergy medication to deal with severe symptoms like hives are also good candidates, because the medications donât interfere with serum tests. In-office scratch testing requires patients to be medication free for a certain period of time before testing.
Related: Best At-Home Allergy Tests
In-Office Scratch Testing
Scratch testing is the most common form of allergy testing, and the most reliable and specific, according to Ogden. This method is also available to anyone, but may be less accessible due to the availability of appointments.
âThe scratch test is taking FDA-approved solutions, touching your skin slightly, and abrading it to assess for a reactive response that we call wheal and flare,â Ogden said. âThe big difference is that in a doctorâs office, you get the results immediately. Within 15 minutes of the test, itâs being read and we can find out what youâre allergic to. A blood test takes longer.â
Allergy testing in a doctorâs office can range in price depending on your insurance, and sometimes appointments can be tough to come by. In some situations, itâs worth exploring a third option, known as concierge testing. In this situation, a testing company, such as Curex, will send a phlebotomist to your home if youâre in an eligible area. The phlebotomist will draw blood and have it tested using the same serum IgE testing. Once results are in, the patient will have a consultation with a provider about which treatment plan is right for them.
Related: The Difference Between Blood Tests and Skin Tests for Allergies
How Accurate Is Scratch Testing for Allergies?
Not only is scratch testing faster, Iyengar says that it reveals more information than mail-in blood tests.
âWhen we look at these tests, we look at two parameters. One is sensitivity and the other is specificity. And specificity is the ability for a test to be able to pick up on an allergy if youâre truly allergic to it,â Iyengar said. âSpecificity is the ability of a test to be able to confidently say this person is not allergic to something. And when you look at the sensitivity of skin testing, it is much higher than the sensitivity of a blood test.â
Iyengar said that a thorough medical history can also add helpful context when it comes to diagnosing and prescribing the right medicines to help relieve allergies quickly. And a big plus for scratch tests: Youâre already seeing an allergist, so a treatment plan can be formulated quickly.
What This Means For You
Allergy testing can be pricey, but finding your sensitivities can help you finally find relief. If you have access to an allergist, you will likely get fast, accurate results with the option to have medicine prescribed immediately. While this is the ideal situation, there are circumstances where a mail-in test makes more sense. Just make sure that whatever test you choose uses an accepted testing method and certified lab for the best results.
May 9 (UPI) -- A new Australian study published Tuesday appears to support past findings that medical cannabis treatment improves the quality of life among patients with a wide range of health conditions.
The results of the study, led by Thomas R. Arkell, of the Center for Human Psychopharmacology at Swinburne University of Technology in Hawthorn, Australia, were posted on JAMA Open Network.
The findings came from a case series of 3,148 patients, many of whom showed significant improvements over all eight conditions listed on a short health survey.
The survey assessed health-related quality of life issues when subjects started treatment with medical cannabis, and it found that improvements were largely sustained over time.
"This study suggests a favorable association between medical cannabis treatment and quality of life among patients with a diverse range of conditions," the authors said in the study. "However, clinical evidence for cannabinoid efficacy remains limited, and further high-quality trials are required.
Although the study suggested a positive correlation with the use of medical cannabis, authors said they could not rule out the possibility that adverse events may have been caused in whole or part by the disease state and concomitant medications.
"The relatively high incidence of adverse events still affirms the need for caution with THC prescribing and careful identification of patients with contraindications," the authors said.
They said that 53.6% of the participants were female,30.2% were employed and the group had a mean age of 55.9. Most of the participants (68.6%) reported chronic non-cancer pain was the most common indication for treatment, followed by cancer pain (6.0%), insomnia (4.8%) and anxiety (4.2%).
Most of the cannabis prescriptions were for orally administered products, including oils and capsules.
"CBD-dominant products were associated with [the] largest improvements on the role-physical domain, while THC-dominant products were associated with largest improvements on the physical functioning domain," the authors said.
The authors noted, though, that the study was limited by the use of a retrospective case series design without a control group, which restricted what conclusions could be drawn around treatment efficacy.
"Given the ongoing increase in medical cannabis prescribing, other clinics should strongly consider implementing a similarly rigorous clinical data collection protocol in order to monitor clinical safety and patient-reported outcomes associated with medical cannabis use," the authors said.
PERRYSBURG â Hope Carico is looking for a career where she can help people.
âI wasnât ever really sure what I wanted to do but I knew I wanted to help people so I thought the medical field would be the best place for me,â she said.
The North Baltimore High School senior chose to attend Penta Career Center to study medical technology.
The first thing I think of about Penta is opportunities, Carico said.
âI came here because I knew Penta would give me a great head start in my life.â
Carico originally wanted to be an oncologist, but she doesnât want to watch all of her patients die.
Her goal is to become a radiologist.
With radiology, Carico wonât get attached to patients yet still be able help them.
âAnd I really like bones.â
Bones are straight forward, they never let you down and theyâre very simple, Carico said.
âBut thereâs a lot of stepping stones along the way.â
Her plans include attending Kent State Universityâs regional campus in Salem as a radiology technician and earning an associate degree.
That program is not offered on the main KSU campus, she said.
Medical school would be next, she said.
âThatâs the goal. As of right now I want a doctor in radiology, but things could change.â
Carico came to Penta as part of its Sophomore Exploratory program.
She said she tried small animal care, criminal justice, culinary and cosmetology. She said her mom and dad thought she was going to be an engineer.
âNo,â Carico said with a laugh. âI donât like office jobs. I like to be moving and talking and socializing.â
As a radiology technician, she will be taking X-rays, and working to prevent radiology exposure, she said.
Carico has been a member of HOSA for two years and competed in medical innovations her first year and won first place in regional competition. This year, she competed in medical nursing assistant and earned third place.
HOSA is a global student-led organization recognized by the U.S. Department of Education and the Department of Health and Human Services. Itâs mission is to empower future health professionals to become leaders in the global health community, through education, collaboration, and experience.
She has earned her state tested nursing assistant (STNA) certification.
Carico said although she has the certification, she does not want to be a nursing assistant.
She also is working toward her phlebotomy technician certification through an after-school Adult Education program.
âAfter I get five more pokes and two more capillaries and take my test, I will be a registered phlebotomist.â
Pokes are vena punctures and capillaries are a blood sugar test, she explained.
âItâs not about being correct,â Carico said when asked how many times sheâs missed a vein on the first try. âItâs about being confident in your skills. The less I think about it the better my pokes are.â
If she wasnât taking phlebotomy, she would be a nursing assistant in college.
âBut I think that by doing phlebotomy it will help me be more successful. Itâs less stressful.â
Carico is a member of Pentaâs National Technical Honor Society, participates in SkillsUSA and is a Penta student ambassador. She is a member of the National Honor Society at North Baltimore.
She is the daughter of Carmen and Pete Shepard.
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