Anesthesia Provider Shortage in the United States: A Case Study

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In June 2018, the Department of Veterans Affairs Office of Inspector General released a report detailing the VA Health Administration’s Occupational Staffing Shortages. The report describes the current state of VA Medical Centers across the United States, including self-reported disparities in staffing from the clinical and non-clinical perspectives. In addition, the report proposes to prioritize a standard medical center staffing model at the national level, with variance to compensate for diversity of individual centers at the local level. While staffing needs differed among centers, a central theme emerged throughout the report: there is a severe shortage of anesthesiologists across over 20% of the Veterans Health Administration facilities.

The importance of this statement cannot be further pronounced. As a preeminent model for providing healthcare for those who served for their country, the VA Health System is a critical healthcare institution in the United States. The VA provides not only primary care, but also ancillary healthcare services such as prescription refills, mental health treatment, and minor procedures. In addition, many VA facilities are licensed to perform operative procedures, ranging from basic outpatient procedures to advanced surgeries. In these facilities, which are often located in major urban areas with a high patient volume, anesthesiologists and Certified Registered Nurse Anesthetists (CRNAs) are essential.

Anesthesia  professional societies and Veterans Affairs advocacy groups alike agree that pushing for more anesthesia providers in VA hospitals is essential for providing comprehensive, specialist care. However, the policies surrounding anesthesiology are delineated and historic. In many hospitals and health centers across the United States, CRNAs are considered as highly skilled anesthesia professionals that can effectively manage the entire perioperative patient care episode. Despite the evolution of thought towards provider expertise, the VA has not yet aligned with this trend. In 2016, the VA released a policy change stating that advanced practice registered nurses would be granted full access — however, CRNAs were excluded from this ruling. The nurse subsets that were included in the full practice policy change were as follows: certified nurse practitioners (CNPs), certified nurse-midwives (CNMs), and clinical nurse specialists (CNSs). Upon inquiry as to why CRNAs were not included in the policy change, the VA responded that there was not an unmet need for anesthesiology providers in their hospitals – a discussion point that is in contradiction with the recent occupational staffing reports.

The shortage of anesthesia providers is not a problem isolated to the VA. In 2010, RAND Corporation released a study predicting that the U.S. would experience a shortage of anesthesiologists by 2020 in the magnitude of tens of thousands. National news outlets and the Xenon Health blog have reported previously on the shortage of anesthesia drugs nationwide. Logically, the question arises of a potential solution to such issues. CRNAs can pose as an answer to the question of burgeoning demands for anesthesia providers. As specialized nurses, CRNAs are highly trained to manage anesthesia needs throughout the perioperative cycle. Alongside anesthesiologists, CRNAs can serve as a valuable resource for patients and fellow clinical members of the operative team, guiding care to lead patients toward the best clinical outcomes. Whether in the VA, on a national scale, or hospital-by-hospital, the need for anesthesia providers, inclusive of both anesthesiologists and CRNAs, is evident in the U.S. healthcare system.






Anesthesia for the Parturient with Pre-eclampsia

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Expectant mothers can rejoice in knowing that healthcare advances have eradicated many preventable and rare diseases and improved their comfort and care. Last year, an estimated 4.5 million females in the United States conceived and gave birth. [1] Indeed, it has never been easier for a baby to reach the finish line of their term pregnancy, taking their first breath of our worldly air and completing a loving family. However, the bodily changes that accompany pregnancy are much the same regardless of the era in which we live.  The heart must pump blood for two, and the mother’s circulatory system must adjust to share blood and oxygen to the fetus. In turn, many changes in blood pressure can occur during pregnancy, and monitoring and follow-up for obvious blood pressure abnormalities is indicated over and above routine pre-natal care.

In 3-8% of pregnancies, some women may present with severe hypertension, or very high blood pressure (systolic blood pressure > 160 mmHg) along with significant amounts of protein in the urine. These findings are characteristics of pre-eclampsia, a disorder that typically presents in the third trimester and is due to compromised blood flow from the placenta to the developing baby. While some may report “frothy” urine after using the bathroom, it may also catch the obstetrician’s eye after providing a urine sample during routine prenatal visits. Headaches and edema, which is fluid accumulation in the legs and hands, are non-specific symptoms reported during pregnancy. However, they may be worse in patients with pre-eclampsia. In 10% of patients with poorly managed pre-eclampsia, seizures may occur around the time of labor, requiring continuous monitoring to prevent further harm to the baby. [2]

Anesthesia for pregnant womenAs an anesthesiologist, I am often asked by my pre-eclamptic patients approaching their due date about the available choices for pain control during labor. It is a valid question, considering that either an epidural approach for labor or spinal approach for cesarean section (“C-Section”) will itself affect (i.e. decrease) blood pressure. Other similar options are available for pre-eclamptic patients. If you are a patient with pre-eclampsia, please make sure to discuss the following items with your anesthesiologist ahead of time.

  1. Hydration – Your anesthesiologist will mention that you will need extra fluids prior to receiving an epidural or spinal. Hydration is decreased in pre-eclamptic patients, and you will need a “bolus” or continuous infusion of fluid through an IV ahead of time.
  2. Blood profile – A nurse will draw a sample of your blood to send off to the lab to check the levels of electrolytes, blood cells, and platelets (specialized cells that help to form a clot and stop bleeding). This level is low and can further decrease around the time of labor, thereby increasing the risk of bleeding with epidural procedures and complicating anesthetic management. Levels and function of our clotting system are also measured during this study. Your doctor may order for extra platelets or other related products if your levels are too low.
  3. Seizure prevention – Your obstetrician may administer magnesium around the time of labor to prevent seizures. This requires a planned hospitalization during the routine monitoring of magnesium levels.
  4. Blood pressure control – Pre-eclamptic patients are given medications to control their blood pressure while maintaining blood flow via the placenta. A beta-blocker (labetalol) and smooth muscle relaxant (hydralazine) have been thoroughly studied. However, your obstetrician and healthcare team monitoring your blood pressure can provide you with individualized treatment and monitoring plans after a diagnosis has been established.

Because of these considerations, many pre-eclamptic patients will receive their labor care, and even pre-natal care, at a hospital or center specializing in patients with similar hypertensive or other pregnancy-related disorders.



  1. Curtin SC, Abma JC, Ventura SJ, Henshaw SK. Pregnancy rates for U.S. women continue to drop. NCHS data brief, no 136. Hyattsville, MD: National Center for Health Statistics. 2013.
  2. Ronsmans C, Graham WJ on behalf of the Lancet Maternal Survival Series steering group, “Maternal mortality; who, when, where and why.” The Lancet, Maternal Survival, September 2006.


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Machine Learning in Healthcare

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By Haroon Chaudhry MD

President and CEO, Xenon Health

The expansion of electronic health care records across hospitals and hospital systems, other healthcare institutions and physician offices has created a substantially large set of data that can be used to streamline healthcare delivery and improve patient outcomes. Machine learning involves computer pattern recognition of data sets and utilization of previous computations to produce dependable decisions. In other words, the computer learns without being programmed to perform specific tasks. Machine learning can be leveraged in a variety of settings in healthcare to assist or improve decision making.

Natural language processing, or NLP, is the manipulation of human language, including text, by software. Deep learning applications attempt to simulate neuronal activity of the human brain and recognize digital representations of images, sound and other information. Deep learning is being utilized to mine semantic interactions of radiologic images and reports from PACS, or picture archiving and communication systems. One study utilized natural language processing to mine 200,000 images and match them with their descriptions in automated fashion. Semantic diagnostic knowledge can be derived by mapping patterns between radiologists’ text reports and their related images. The objective would be to extract and associate radiologic images with clinically semantic labels via interleaved text/image data mining and deep learning on PACS databases. Due to the enormous collection of radiology images and reports stored in hospital PACS, this type of initiative has significant implications for future diagnostic protocols, potentially setting the stage for an automated system of quality control in diagnostic imaging interpretation.

In healthcare, computational risk stratification involves many technical challenges. However, the goal of using automated mining to improve clinical outcomes persists. Nosocomial infections are infections acquired during hospitalization. Statistically, one in twenty-five patients in US acute care facilities get such infections. Some studies have attempted to derive data-driven models that can assist in formulating effective prevention strategies. Such models are based on thousands of time-varying and time-invariant variables. There are complex temporal dependencies among the many variables leading to significant challenges in constructing effective models. However, advances in computational power and the significantly expanding volume of health record data is allowing researchers to construct more accurate predictive models.

Machine LearningEHR phenotyping uses patient data to identify features that may represent computational clinical phenotypes. Such phenotypes can be used to identify high risk patients and improve the prediction of morbidity and mortality. These computable phenotypes are similar in nature to decision trees. Sparse tensor factorization of multimodal patient data, transformed into count data, generates concise sets of sparse factors that are recognizable by medical professionals. Patients can be treated as weighted composites of such factors.

Deep topic modeling involves modeling data characterized by vectors of word counts. Examples of deep topic models include HDP, or hierarchical DP and nested HDP (nHDP). In DP based models, the general mechanism is parameterized by distributions over topics with each topic characterized by distribution over words. In non DP-based models, modules are parameterized by a deep hierarchy of binary units. The DFPA model characterizes documents based on distributions over topics and words while utilizing a deep architecture based on binary units.

Personalized Medicine, also known as precision medicine is an integrative analysis of heterogeneous data from a patient’s history to enhance care. The objective is to utilize clinical markers that are collected when a patient first presents and then longitudinally during follow-up encounters. The goal for ML in precision medicine is to use latent factors that influence disease expression as opposed to standard regression models that utilize observed characteristics alone to explain variability. Using such a prognostic tool that integrates various kinds of longitudinal data can assist in identifying patients at greatest risk of disease progression and to take an appropriate course of action. Challenges in both the traditional standard regression model and the latent factor model include the fact that data collected during routine clinical visits can be inconsistent. In addition, predicting disease patterns can be difficult regardless of the model due to unknown patient factors such as genetic mutations.

Batch reinforcement learning methods are being used to aid in medical decision making. Batch RL is a reinforced learning setting where a set of transitions sampled from the system is fixed. The goal of the learning system is then to derive an optimal policy solution out of this sample batch.

In medicine, human decision making is based on a variety of competing objectives that include not just effectiveness of the treatment, but also factors such as potential side effects and cost. As ML algorithmic studies continue, the objective is to optimize the various factors in the treatment decision making process to enhance patient outcomes and deliver value to the healthcare system.

Responsible Opioid Prescribing

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The opioid epidemic has been widely covered in the media and has drawn the attention of lawmakers and the medical community. Physicians, physician assistants, nurse practitioners, and other medical providers responsible for prescribing opiate medications are often the subject of scrutiny as to their prescribing practices. In some studies, taking opioids for a