Anesthesia management Archives - Page 3 of 20 - Xenon Health

To recognize Children’s Eye Health and Safety Month, The American Association of Nurse Anesthetists (AANA) has proposed an extensive list of guidelines for the administration of anesthesia for ophthalmologic procedures on children.^[1] In this document, Certified Registered Nurse Anesthetists (CRNAs) are emphasized as primary practitioners of pediatric anesthesia, combining in-depth technical knowledge with a holistic approach to healing. This document exemplifies the increasing focus on pediatric anesthesia, as noted by anesthesiologists, anesthesia management companies, and CRNAs alike. From a historical standpoint, the use of anesthesia in children has typically been limited to specialists such as pediatric anesthesiologists, who undergo specific training via fellowship in order to satisfy stringent requirements for the practice. Furthermore, the use of pediatric anesthesia has also been limited to specific procedures, recognizing the increased potency of anesthesia on younger patients. Many anesthesiologists are aware of the case of Caleb Sears, a 6-year old boy who passed away soon after administration of general anesthesia in a dental setting. While Caleb’s case has been used to debate the medical ethics of pediatric anesthesia, it is also a sharp reminder of the dangers that come from not consistently updating medical approaches to anesthesia. In response to advocacy stirred up by the tragic case of Caleb, an increased focus has been placed on pediatric anesthesia in the medical community. Therefore, anesthesiologists, CRNAs, and medical researchers are approaching pediatric anesthesia with a fresh lens. Recent developments have thus concluded that a modern approach to pediatric anesthesia combines optimized team management with updated dosage recommendations, facilitating a safe and effective administration of anesthesia to young people.

The optimization of medical team management is essential in the administration of pediatric anesthesia. The American Academy of Pediatrics recommends against a “single-operator model”, in which one individual is tasked with both the sedation and the surgery itself, an event frequent in dental and oral surgery settings.^[2] Instead, it is now fully recommended that there be one separate individual who can administer anesthesia, monitor vital signs, provide PALS (Pediatric Advanced Life Support) if necessary, and step in to assist the primary surgeon in case of emergency. This role can be taken by an anesthesiologist, but can also be fulfilled by a CRNA or trained midlevel medical practitioner. CRNAs with additional training or rotations in pediatrics can often have deep knowledge of the topic, and are viewed as valuable additions to a medical team working with children.

Furthermore, specific dosage requirements are in the process of review in order to ensure that administration of anesthesia to children is safe and effective. The Food and Drug Administration had previously warned that using anesthesia on children aged 3 and younger can produce developmental problems if administered at high intensity or for a sustained period of time. In response, the American Academy of Pediatrics conducted a epidemiological study of the topic, aiming to investigate the proposed conclusion in a large population-based setting. In controlled trials using humans (as opposed to primates or other model organisms), the AAP found that a short, one-time use of anesthesia in young children provoked no developmental issues.^[3] This conclusion was then supported by many medical academies and professional associations, including the Society for Pediatric Anesthesia, the International Anesthesia Research Society, and the American Society for Anesthesiologists. In sum, with the proper dosage, administered by a correctly trained medical practitioner, the use of anesthesia on young children does not result in adverse developmental consequences. Researchers will continue to focus on the issue of pediatric anesthesia, to ensure that there are detailed dosage instructions for each drug utilized and thus a proper course of action for CRNAs and anesthesiologists.

Anesthesia is an invaluable tool for smoother surgeries. A specialized team model, in combination with specific and up-to-date dosage recommendations, can ensure that anesthesia remains a viable and safe option for all, including those under pediatric care.

^[1]http://www.aana.com/newsandjournal/News/Pages/080117-What-Parents-Should-Know-About-Their-Child%27s-Vision.aspx

^[2] http://www.aappublications.org/news/2017/07/28/Sedation072817

^[3]http://www.aappublications.org/news/2017/01/10/Anesthesia011017

Sharps injuries are an overrepresented problem in the peri- and intraoperative setting. Anesthesia providers and perioperative nursing staff are at particular risk for sustaining needle stick injuries, as their roles involve frequent administration of medications and placement of lines, often under urgent or stressful circumstances. Surgical personnel are also at risk given the use and necessary passing of sharp instruments between team members intraoperatively.

The Association of Perioperative Registered Nurses (AORM) regularly publishes guidelines and evidence-based recommendations to promote sharps safety and reduce the number of needle stick injuries in the perioperative setting.

These recommendations include implementing the use of safety-engineered devices. For scalpels, options include retractable scalpel blades, shielded or sheathed scalpel blades, and scalpel blade removal devices. Safe tissue closure devices include tissue staplers and adhesives in lieu of suturing. A systemic review of 14 randomized controlled trials found no significant difference between sutures and adhesives in regard to infection, patient and user satisfaction, and cost; however sutures were better in minimizing wound dehiscence and were faster to use.

Whenever possible, needleless systems should be used for collecting blood or bodily fluids after initial access establishment. Administering medications should be done without needles whenever possible (e.g. using IV ports that don’t require puncture). When needles are required, they should have safety engineered features. These include sliding sheaths that cover needles after use, hinged needle guards, sliding needle guards, and retractable needles. Safe practices include not recapping needles, or if recapping is necessary and a safe needle device is unavailable, using a one-handed scooping technique.

Using needleless or blunt entry devices to withdraw contents from multi-dose vials is another recommendation put forward. When opening glass ampules, using a disposable or reusable ampule breaker (which could be as simple as a 4×4 gauze) can decrease injuries.

Using a puncture-resistant sharps containment device is important in sharps disposal after use. In the operating room, a neutral zone should be implemented during passing of sharp instruments – i.e. the instruments are put down and picked up rather than passed hand to hand. A no-touch technique should be used when handling sharps to reduce manual handling – i.e. not manipulating suture needles with hands while loading or repositioning, using blunt instrument holders instead.

In addition to implementing strategies to prevent sharps injuries, health care facilities should also have a plan for post-exposure care that is familiar and readily available to their workers.

Sharps safety is an important concern in the perioperative setting, and it is paramount that both providers and facilities be aware of strategies to reduce the incidence of sharps-related injuries.

References

Dumville J.C., Coulthard P., Worthington H.V., et al: Tissue adhesives for closure of surgical incisions. Cochrane Database Syst Rev 2014; 11: pp. CD004287

Spruce, L. Back to Basics: Sharps Safety. AORN J. 2016 Jul;104(1):30-6. doi: 10.1016/j.aorn.2016.04.016. Sharps Safety in the Perioperative Setting

Klaus Schwab of the World Economic Forum calls it the Fourth Industrial Revolution; artificial intelligence (AI) and machine learning (ML) are poised to penetrate every industry, discipline, and economy, and health care is no different. A survey of healthcare executives by AI market research firm TechEmergence revealed that 50% of respondents believe that ML will be ubiquitous in healthcare by 2025.[i] As soon as next year, ML technology will be incorporated in 30% of health systems as tools assisting physicians in making personalized treatment decisions.[ii]

Though ML represents a disruptive force in the economy, particularly in the organization of human resources, understanding the technology and its limitations is crucial for healthcare stakeholders to direct its advancement for the benefit of patient care.

What is Artificial Intelligence and Machine Learning?

The field of AI is concerned with creating computer systems that are able to perform tasks normally requiring human intelligence, including but not limited to: visual perception; speech recognition; decision-making; and translation between languages. ML describes one approach to AI that uses algorithms beyond condition- or rule-based structure and “learns” from data to improve its performance. ML is concerned with creating algorithms that give computers the ability to learn from data and make predictions or decisions without being explicitly programmed. Major software and hardware advances that have enabled ML include parallel computing, the computing of simultaneous processes using multiple CPU cores, and the cloud, which allows for unprecedented access to large amounts of patient data (necessary for the training of some ML algorithms) and provides the computational capacity necessary for the development of ML models. One example of ML models include neural networks that are meant to conceptually imitate the structural cognitive pathways of the human brain).

ML in Healthcare:

The adoption of ML in healthcare is driven largely by big data (i.e. electronic health records, EHR) and personalized devices, effectively engaging ML development in a positive feedback loop. As medical futurist Bertalan Mesko, PhD, states, medical data is expected to reach 44 zettabytes by 2020, and the sheer task of managing that data will be best achieved by the implementation of ML tools.[iii] ML relies on big data, which will necessitate advances in ML to extract cogent conclusions from big data.

There are applications of AI in anesthesiology as well, and it isn’t unreasonable to imagine a system, through the cognitive processing of patient data, probabilistically and/or algorithmically making efficient and accurate decisions in “monitoring the depth of anesthesia, determining the amount of anesthetic gas to administer, somatosensory evoked potential monitoring, classifying patients, and mitral valve analysis to coding and billing”.[iv] By enabling data-driven physician and consumer decision-making, ML may play a key role in lowering healthcare costs.

However, there are important questions to consider with ML, as there are with any major technological advance.

Issue #1: Ethics in Practice

If a machine makes a mistake, who bears the ultimate responsibility? From an ethical and legal standpoint, it is substantially more difficult to hold an algorithm accountable for a “wrong decision”. This is because ML models are not explicitly programmed to make decisions based on certain conditions but instead have been developed to learn from data in order to devise treatment pathways optimizing patient outcomes.  For a health care professional non-conversant in ML or computer science, the workings of such algorithms can feel opaque and inscrutable. In a clinical setting, a lack of understanding of the “thought process” behind an ML model may engender mistrust between technology and the people they were designed to help. Doug Marcey, vice president of technology for Plexus Technology Group, along with other healthcare consultants, indicates that the best way to approach ML use in patient care while avoiding these concerns is to keep the physician in the driver’s seat: “If you ask a doctor why they prescribed a drug, they can explain their thought process. A lot of ML models are just statistical. You can’t ask the model why it made a diagnosis or recommendation. That’s why, at least in the short term, we’re not going to replace people. We need someone at the helm who’s going to take responsibility.”

Issue #2: Infrastructural Readiness

ML algorithms are only as good as the data they are acting on, and electronic medical records still face barriers such as a high degree of heterogeneity of quality and network systems. Rural healthcare systems, which are traditionally slow to adopt EHR will also find it difficult to leverage ML tools.

Given that the penetration of ML in healthcare is all but inevitable, health care professionals should prepare by asking their practice, hospital, or management organization for the tools and expertise necessary to improving the quality of their data reporting.

Issue #3: Industry Resistance to Implementation:

In 2016, GE pulled Sedasys, its automated anesthesiology machine from the market, citing a lack of demand by hospitals and physicians. Resistance by health practitioners stemmed from a belief that Sedasys was the first of many disruptive innovations that will phase out human providers in favor of automated systems. However, such attitudes may ultimately prove counterproductive and ill-founded, at least in the short-term. “I think machine learning will become integrated into anesthesia, but will never replace the physician,” says Jody Locke, ABC vice president for anesthesia and pain practice management. “The big issue in anesthesia—and the big potential for machine learning in anesthesia—is risk stratification and identifying the potential for never events.” The vast majority of ML platforms, including IBM’s Watson Health are intended to serve as auxiliary tools to help physicians make decisions regarding patient care, or to perform tasks under the supervision of physicians. In order for this relationship to be productive and successful, physicians must embrace this technological shift and bring their perspective to bear on the utility of these ML platforms.

[i]https://www.techemergence.com/machine-learning-in-healthcare-executive-consensus/?utm_source=ABCeAlert&utm_campaign=ArtificialIntelligence&utm_content=6-12-17

[ii] https://www.forbes.com/forbes/welcome/?toURL=https://www.forbes.com/sites/jenniferhicks/2017/05/16/see-how-artificial-intelligence-can-improve-medical-diagnosis-and-healthcare/&refURL=&referrer=&utm_source=ABCeAlert&utm_campaign=ArtificialIntelligence&utm_content=6-12-17#273dcd716223

[iii]http://medicalfuturist.com/artificial-intelligence-will-redesign-healthcare/?utm_source=ABCeAlert&utm_campaign=ArtificialIntelligence&utm_content=6-12-17

[iv]http://www.anesthesiallc.com/publications/anesthesia-industry-ealerts/1037-ai-and-machine-learning-in-healthcare-and-anesthesia

The Better Care Reconciliation Act of 2017 (BCRA), like its House counterpart the American Health Care Act (AHCA), contains several components that rely on stratification of health risk in the insurance markets to lower premium costs for young and healthy Americans. Despite the publicity surrounding the bill, a surance market.

The BCRA largely retains ACA regulations for the individual and small business insurance marketplace (i.e. strict regulation of medical underwriting and no exclusions of insurance based on pre-existing conditions), but includes a key measure for small businesses and self-employed individuals to opt into the large group insurance market, which is far less regulated.

These new health care plans are called “small business health plans” (SBHPs). Like the large group market, SBHPs are exempt from individual marketplace mandates to cover enumerated essential benefits and allow for medical underwriting in formulating premium rates.[i]  The Department of Labor certifies whether a business qualifies as a small business. Ostensibly, it will be an organization with fewer than 50 employees, though the bill is not specific. With scanter coverage, SBHP premiums will initially be relatively low compared to plans on the individual marketplace.

Unfortunately, SBHPs hardly make for a sustainable health expenditure solution. SBHPs encourages adverse selection, which could destabilize small business insurance markets and individual insurance markets alike.  Theoretically, a small business employer or self-employed individual (in the 14 states that allow self-employed individuals to purchase coverage as a “small group of one”[ii]) could opt for a SBHP when they are relatively healthy and then revert back to a more comprehensive individual marketplace plan once a covered group member becomes sick or injured. The result thereby drives up premiums in the individual marketplace, since the SBHPs are self-selected to exclude low-risk individuals from sharing risk in the larger individual market. There are no regulations in the current draft of the BCRA that addresses these problematic shifts between SBHPs and individual market plans.

Given the general trend for consolidation among health care professionals, there are an increasing number of non-hospital aligned practices, including management service organizations or single-specialty groups, that qualify as small businesses. According to the most recent AMA Physician Practice Benchmark Survey, the majority of physicians work in small practices, with 57.8% working in practices of 10 or fewer physicians. [iii] Furthermore, less than 20% of anesthesiologists count the hospital as being their primary employer. The SBHP rule makes it more likely that small physician groups can purchase health insurance with worse coverage but at lower premiums to lower overhead costs, but due to the imbalance in pooled health risks in the SBHP market, these groups must be prepared for higher premiums for better coverage should they need it. The Ted Cruz Amendment, added to the BCRA draft on July 14^th, would likely exacerbate this problem by further loosening medical underwriting regulations.

[i]http://www.kff.org/health-reform/issue-brief/association-health-plans-for-small-groups-and-self-employed-individuals-under-the-better-care-reconciliation-act/

[iii]https://www.ama-assn.org/sites/default/files/media-browser/public/health-policy/PRP-2016-physician-benchmark-survey.pdf

Recent statistical reports indicate that the medical field is experiencing a shortage of both primary care and specialty physicians, increasing the likelihood of a widespread serious physician shortage in the United States.  A physician shortage, as calculated by the AAMC, is a calculation of the gap between physician supply and demand. According to the Association of American Medical Colleges’ Center for Workforce Studies, the physician shortage is expected to approach 104,900 physicians by 2030. By physician category, the AAMC predicts a shortage of between 7,300 and 41,300 for primary care physicians, as compared to a shortage of between 33,500 and 61,800 for non-primary physicians.^[1] The latter category includes physicians categorized under specialty services, under which anesthesiology is housed. Note that the AAMC projections are variable to account for a variety of elastic conditions implicit in physician supply and demand calculation. The lower end of the spectrum provides estimates for an optimistic calculation, referencing several policy approaches to ameliorate the physician shortage. However, the higher end of the range represents a final sum based on inaction on the part of healthcare providers, anesthesia management services, and medical educators alike.

A multipronged approach is necessary to avoid a serious physician shortage in 2030, particularly in specialized fields such as anesthesia services. In sum, a multipronged approach may be comprised of the intersection between innovative medical technologies, modernized medical education policies, and optimized collaborative delivery practices. Innovative medical technologies will provide an incentive for physicians to enter a specialized field by minimizing time spent on administrative services. For example, many novel medical database applications streamline the documentation process, allowing physicians to focus on diagnostic and therapeutic work above clerical tasks. In addition, a push for modernized medical education policies will encourage students to pursue medical careers, specifically in anesthesiology and other specialty fields. These policies may be advocated via support for post-medical school training at the federal level; an increased emphasis on medical specialization in the medical school curriculum; or other such policy levers that add incentives for medical students to specialize and thus reduce the projected physician shortage. Finally, the optimization of collaborative delivery practices will minimize physician shortage by effectively applying assets towards medical cases. In practice, this will require maximizing the use of Certified Registered Nurse Anesthetists (CRNAs) in anesthesiology practices, as well as a reconfiguration of other key members of the medical team. In this way, anesthesiologists will be supported throughout the duration of each medical cases, ensuring the longevity of physicians in the field.

Taken together, the above multipronged approach is one potential mechanism to address serious physician shortage. However, anesthesiologists, anesthesia management companies, and medical educators must share knowledge to develop further techniques to address a physician shortage in anesthesia, thus minimizing the threat of a serious and impactful shortage in the United States.

^[1] https://aamc-black.global.ssl.fastly.net/production/media/filer_public/73/32/7332e443-2302-4daa-a56e-6937a43646ea/2017_workforce_projections_key_findings.pdf