LYMBis in the Employee Health IT Industry

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According to the American Medical Association, physician burnout rate reached almost 63% in 2021, while professional fulfillment scores plummeted to 22%. This seems like the right time for the medical sector to look for a solution. Whether you already have experience with AI or not, implementing natural language processing in healthcare can take some of the load off your employees’ shoulders and streamline your operations. MarketsandMarkets reports that the global natural language processing in healthcare and life sciences market was valued at $2.2 billion in 2022 and is forecast to reach $7.2 billion by 2027. And this trend will continue as more medical organizations turn to to build custom solutions. NLP service companies So, how is NLP used in healthcare, and how to deploy this technology? Keep reading to find out. What is natural language processing in healthcare? Natural language processing (NLP) is a that uses algorithms to extract meaning from unstructured human language presented either in spoken or written format. NLP requires knowledge of computational linguistics and other machine learning skills. branch of AI Natural language processing is gaining popularity in healthcare as it can analyze large quantities of unstructured medical data, such as doctor notes, medical records, clinical trial reports, and even service reviews that patients post on social media. Research shows that about and not utilized to its full potential. NLP can change that by analyzing data and extracting insights to guide doctors and pharmacists to make more informed decisions. 80% of healthcare data is unstructured Here are some of the main NLP techniques that are useful in healthcare settings: . OCR converts printed and handwritten text to a machine-readable format. It can extract text from images and tables and pass it to other NLP algorithms for further processing. If you want to learn more about the topic, check out our recent . Optical character recognition (OCR) guide on optical character recognition . This approach involves assigning semantic labels based on predefined categories. For instance, it allows doctors to label a patient as “at risk of hospitalization” based on certain keywords in their medical notes. Text classification . This is an information extraction model that can pinpoint entities in text. Physicians can use this technique to extract entities, such as “treatment” and “symptoms,” from a heap of medical data. Named entity recognition . This technique can sort information based on common topics. For example, it can group together all doctors’ notes on patients suffering from a particular disorder. Topic modeling . This NLP method can establish semantic relationships between entities. For instance, it can point out that a particular patient was treated at that hospital under the supervision of that doctor. Relationship extraction NLP use cases in healthcare After learning about what healthcare NLP is and how it works, let’s see how it can contribute to the healthcare field. Clinical documentation management Research shows that physicians for every patient they see. This time could have been spent caring for patients. Instead, it’s wasted on the screen and contributes to doctor burnout. spend 16 minutes on EHRs on average Enhancing clinical documentation through speech recognition Natural language processing in healthcare can help physicians work with electronic health records (EHRs). Doctors can use speech-to-text conversion tools with built-in NLP capabilities to transcribe their notes and enter them into the corresponding patent’s EHR fields. Also, medical staff can query the NLP tools to extract relevant data from EHRs. For instance, Nuance’s solution is a cloud-based speech recognition tool that helps medical personnel document patients’ stories in their EHRs. The company claims that deploying their solution will cut time spent on clinical documentation by half. Concord Hospital deployed Dragon Medical One, and more accurate clinical documentation. Dragon Medical One 75% of employees reported Supporting doctors in decision making NLP solutions can also analyze clinical documents and support physicians in real-time decision making. For instance, after analyzing doctor notes, the system can predict hospital bed demands, which gives hospital staff time to prepare and accommodate incoming patients. Medical coding and billing Medical coding means deriving billable information from clinical notes and transferring it into standardized medical codes. Traditionally, a human coder would perform this task. But manual coding is slow and prone to error, meaning that the provider might not be able to claim and receive the full amount in compensations. NLP-powered computer-assisted coding (CAC) tools can derive information from doctor notes and patient EHRs about different therapies and procedures they encountered, and provide the corresponding insurance codes to solidify claims. One example of CAC natural language processing in healthcare is . The company employs over 150 coding experts and remains up to date with the newest regulations. This solution assists coders by aggregating and analyzing patient documentation, offering auto-suggested tags and comprehensive review and approval tools. 3M 360 Encompass System Clinical trial management Studies reveal that around because they can’t recruit enough participants. Medical NLP can speed up recruitment by scanning patients’ medical data in search of eligible trial candidates. The technology can also help people who want to participate in clinical trials. It can transform eligibility criteria into questions, allowing candidates to filter through many trials fast.Headquartered in Canada, Inspirata built a solution that employs natural language processing in healthcare to . Its NLP engine mines clinical documents and transferred unstructured data into structured insights on patient eligibility. 80% of clinical trials are delayed or terminated facilitate patient recruitment Another successful NLP platform, , can not only help recruit patients but also assist in trial design and site selection. It can parse a variety of sources, such as news feeds, patents, medical literature, and similar trial descriptions. Linguamatics NLP Refer to our to discover how other subdisciplines of AI contribute to this field. article on AI in clinical trials Patient sentiment analysis Applying NLP in healthcare helps medical staff aggregate and analyze customer reviews and opinions from different social media platforms. Natural language processing tools can process thousands of reviews to gauge patients’ attitude towards the care they received. Such tools can identify aspects that frustrate patients, segment them based on frequency, and start implementing improvements for the most recurring negative feedback. For instance, a comment such as “ ” indicates a negative experience with the administrative staff. I waited for 30 minutes on the phone to talk to the receptionist Another way of benefiting from healthcare NLP in sentiment analysis is processing survey results. Instead of relying on social media, healthcare facilities can compose targeted surveys, distribute them among patients, and use NLP solutions to automatically review every response. In a recent study, a team of researchers that can scan survey results to determine how patients feel about their healthcare provider. The team designed a survey with open-ended questions, distributed it on vestibular schwannoma patients, and received 534 responses. The algorithm classified the responses into two groups — positive and negative sentiments. This classification had a 90% overlap with the manual classification produced by human experts. built an NLP solution Check out our blog article if you want to learn more about . how sentiment analysis can boost your customer service Drug discovery To find effective candidate drugs, scientists need to identify the biological origins and understand the disease first. This includes reviewing large amounts of medical literature, patient data, and more. Natural language processing in healthcare can quickly sift through all this information, extract what is relevant, and present it to researchers in a compelling format so that they can learn about similar diseases and how they were treated. For instance, Texas-based offers an AI-powered software that can assist in drug discovery by applying NLP to analyze unstructured data from multiple sources, such as gene cards, PubMed publications, proprietary global research data repositories, clinical trial documentation, and more. After absorbing the available disease-related knowledge, the tool builds an ontology of existing drugs that highlights promising research areas to discover new effective drugs. Lymba Additionally, NLP can analyze social media feeds and patients’ medical records to gauge adverse effects of different drugs. Some tools can even correlate a drug’s side effects to the dosage consumed and the frequency of use. For more exciting applications of you can refer to our blog. AI in drug discovery Challenges of NLP implementation in healthcare Applying natural language processing in healthcare has many benefits. But if you decide to enhance your practice with this technology, here are the challenges that you are likely to encounter. Specific language requirements of the healthcare field Doctors have their specific way of writing clinical notes and other medical documents. This style is typically heavy with jargon, acronyms, and abbreviations, and has limited context. To add to the confusion, a third of the clinical abbreviations present in the Unified Medical Language System Metathesaurus . have several meanings each Even words that are spelled out in full can be confusing. For instance, “discharge” can either mean release from a hospital or bodily fluids, depending on the context. And if you want to extend the application of NLP in healthcare beyond your clinic and use it to analyze patient’s feedback on social media, then you need to familiarize it with the specific abbreviations and emoticons that people use to express their emotions instead of words. If you acquire ready-made general-purpose NLP algorithms, you will need to retrain them to operate in healthcare settings. And even better, familiarize them with the vocabulary used by your doctors. Issues related to human language complexity It’s relatively easy to deploy NLP algorithms to spot terms that are explicitly mentioned and unambiguous. But human language is not always that straightforward. Sometimes, algorithms will need to deal with the following: . Natural language processing models in healthcare have to be able to infer information from the input text. For example, if you want to know whether a patient has social support, there might not be a clear mention of this in their medical notes. However, there might be something like “relative at bedside,” that implies the patient has a form of support. Inferences . A basic keyword-driven NLP will perceive the two statements “wife helps patient with medication” and “patient helps wife with medication” as identical, while in reality the semantics in both cases are very different. Semantics beyond keywords . Clinicians often use negation to indicate absence of medical conditions. For example, a doctor might write “a brain malignancy was ruled out” to document the fact that a malignant brain tumor wasn’t found. Medical NLP algorithms can’t mistake this with the presence of the disease. Negation Decide on how advanced your algorithms need to be based on the target business cases. And if you opt for more basic solutions, be aware of their limitations. Bias and explainability Like any other AI technology, NLP in the medical field can acquire during training on unbalanced datasets or when it continues to learn on the job. Gender bias is one of the most prominent types in natural language processing-based tools. For example, with jobs that require high-level education, such as a doctor, while females are connected to less knowledge-intensive occupations, like a nurse. different types of bias GPT-3 tends to associate males Unfortunately, AI bias is not uncommon in healthcare. For example, an algorithm that was supposed to determine the severity of patients’ illness required for black patients just because, historically, more money was spent on white patients’ needs. significantly downplayed the level of care It can be hard to spot bias when the algorithms are “black box” models that don’t explain how they arrive at their conclusions. One solution to this issue is to deploy . With this technology, healthcare natural language processing tools justify their recommendations, allowing you to verify whether they are biased or not. explainable AI Another way to spot bias is using a curated dataset that scientists designed to detect specific types of bias. This solution is not scalable to large applications but works well with limited usage. In addition to the three NLP-specific challenges presented above, you might encounter general obstacles associated with implementing any AI-powered technology, such as: . Many healthcare organizations still rely on outdated legacy systems that aren’t built to work with AI-driven technology and deal with large amounts of data. Integration with legacy systems . Medical facilities struggle to provide sufficient, high-quality data that faithfully represents the target population. Insufficient training data . Hospitals struggle with the responsibility that comes with using AI, such as who has the final say on selecting the right treatment, and who is responsible if the treatment didn’t work. Ethical considerations and moral hazards For a more detailed explanation, check our article on . top AI implementation challenges Step-by-step implementation of natural language processing in healthcare If you want to use NLP in healthcare, you can follow these steps: . Determine which problems you want to solve with NLP and whether you need advanced language processing capabilities, such as negation and inference. Identify potential use cases . Decide if you want to acquire a ready-made NLP tool or build your own customized product. Off-the-shelf solutions are suited for companies with limited funds and flexible internal processes that can be adapted to accommodate an external product. Consider a custom-built solution if you have rigid workflows, are looking for something scalable, and need to integrate the new tool with legacy systems. In the latter case, you will need to look for a trusted to collaborate with. Build/buy an NLP solution AI healthcare solutions company . Even if you opted for a ready-made solution in the previous step, you will still need to retrain it for optimal performance, as mainstream NLP models are not designed to handle the specifics of the healthcare sector. You will need to that is bias-free and representative of your target population. Prepare the training dataset prepare a training dataset . Test the tool for bias, usability, and how it integrates with the rest of the workflow. Train and validate the model . If you want to build and/or adopt AI models in the medical sector, compliance is of utmost importance. Your tools need to comply with the US Food and Drugs Administration (FDA), the General Data Protection Regulations (GDPR), and any other regulatory bodies in the countries of your operations. You can find more about the topic in our recent article on . Make sure your algorithms are compliant healthcare IT standards . If you and hired a tech vendor, they will design and build the NLP tool with the specifics of your system in mind. They will also help you integrate the solution with your legacy systems. Integrate with your existing systems opted for a custom healthcare solution . The effort doesn’t end at deploying natural language processing in healthcare. You need to audit the NLP models to make sure they are still producing the desired results and update them to reflect any changes in the target use cases. Monitor and adjust the algorithms constantly Concluding thoughts NLP has many applications in healthcare. It can help save time on medical coding, facilitate recruiting participants for clinical trials, keep patients happy through sentiment analysis, and much more. To successfully deploy natural language processing in healthcare, dedicate time to finding a trusted who has experience in the medical field. A tech partner will help you with: machine learning vendor Creating and training custom NLP models that: Are free from bias Fit seamlessly with the rest of your system Tailor to your practice’s vocabulary and have the desired degree of complexity Complying with the privacy requirements of the healthcare sector Supporting you in auditing and updating the algorithms when needed Looking to enhance your medical practice with NLP? ! We will help you build a compliant solution tailored to your internal systems and vocabulary. Drop us a line Also published . here

Natural Language Processing in Healthcare: A Path to Adoption

Procesamiento del lenguaje natural en el cuidado de la salud: un camino hacia la adopción

Traitement du langage naturel dans les soins de santé : une voie vers l'adoption

हेल्थकेयर में नेचुरल लैंग्वेज प्रोसेसिंग: ए पाथ टू एडॉप्शन

医療における自然言語処理: 導入への道

Processamento de linguagem natural na área da saúde: um caminho para a adoção

Xử lý ngôn ngữ tự nhiên trong chăm sóc sức khỏe: Con đường dẫn đến việc áp dụng

医疗保健中的自然语言处理：采用的途径

It's the end of the world as we know it, humanity is programmable and our operating system is getting a reboot. The question is when the system is back online what will be in its place? With the recent outbreak of global pandemic COVID-19, the world is being pushed further into digital space, where nation-states, corporations, and a New Commons Movement are engineering economic and social systems in a race to create the new matrices in which we will exist. These next-generation frameworks will forever change the way humans interact, transact, live, and evolve. A Brave New World...or Is It? Over the last decade, blockchain technology, cryptocurrencies, smart contracts, and token economics have been an experiment into the possibility of new political-economic systems that are increasingly decentralized. However, this is just the most recent of many attempts throughout history to democratize the political-economic landscape, and although this time new technologies give us a greater chance at success, there are huge questions about the possibility that people in this world will truly ever be free. These questions teeter without end on the precipice of evolutionary advancement. Since the dawn of man, there has been a battle for power and resources spurred by the hegemonic, colonialist, violent, and extractive tendencies of human nature under the reactionary nature of the reptilian part of our brains. This is best illustrated in the iconic Dawn of Man scene from 2001 Space Odyssey by Stanley Kubrick. Operating from a scarcity mindset, these behaviors have dominated and defined the vast majority of our recorded history. The few exceptions, like a Phoenix rising from the ashes, have seen great human advancements as a direct result of various forms of collapse i.e The Enlightenment after the Bubonic Plague. Whether economic, political, or religious in context, the narratives and myth of pursuit of liberation have common threads, and has been seen many times throughout history. When humans come to face the realities of their own mortality and look extinction in the face, there is a moment of pause - continue the status quo, or evolve. In step with the , which has often been attributed as a factor in by researchers, our generation now is experiencing, in its new awareness and response to a global pandemic, the possibilities to reboot and re-evolve into the next phase of humankind. Psychedelics Renaissance mammalian evolution There is an opportunity once again in collapse and the detritus of a Pandora's box of unforeseen technological repercussions, to reflect on and re-imagine what this next iteration of humanity looks like. Will the reptilian brain prevail ushering in a new Dark Age? Or will the growing Lymbic threads propel us closer to a Regenaissance - a Utopian reality, where an operating system based on abundance leverages technology and resources to provide, at minimum, safety, shelter, basic needs and well being to all life on this planet? When the pandemic dust settles, an indication of the results of the political-economical-evolutionary space race will be unveiled. (Or perhaps we can point to certain indicators now of what that could look like.) But before we delve into the effects, we should look at the causes and impetus for this massive shift in paradigms, or realities, that we are experiencing now. Liberty or Extinction By Jessica Zartler under the DWTFYW Public Licence The system is broken. It has been broken for some time in this iteration of humanity and although we have all known at some level, our malaise has reached a point in space-time where it can no longer be tolerated. The global warning signs of impending frog boil have our instincts screaming. The colonialist consequences of the Reptilian Brain Era, fueled the engine of exponential extraction and destruction during the Industrial Revolution, which happened too quickly for the developing universal consciousness - Lymbic threads - to catch up. However, with the growth of globalization and the memetic nature of shared communication - the new Alexandrian library (the internet) and other technologies creating the hive mind - the speed at which we are destroying ourselves, is being mirrored by the speed at which we are becoming aware of ourselves and the destruction. New feedback mechanisms and the increased velocity of these growing mycelial-like systems of connection and coordination map and mirror a meta-organism which we can now see and measure. (Similar to the expansion in perception humanity experienced when we saw the first pictures of the Earth from space). Now as we are approaching a point known as the singularity, or a kind of karmic pause for those a little more spiritually oriented, we have a choice at this present moment, in relative space-time, that is not fixed or pegged to a yet-future. The actions we take now, in this kind of wormhole or portal, are accelerated and exponential with this dimensional shift in perception from tribal orientation ( ) to meta-organism, from which future fractals will be spun. We have more awareness and visibility into the future than ever before, into the effects of our choices via predictive modeling technologies and recent developments of a new space race. see Marshall McLuhan ’s The Gutenberg Galaxy The Cyber-physical Space Race This transitional point in space-time is marked by a new race, but unlike its predecessors, this race is not for external markers of territory or technological supremacy, it is for control over system and incentive design, and thereby the inner recesses by which we can program human behavior and condition responses via economic and social stimuli. When tied with machine learning and algorithmic automation, the protocol and entities that win, could dictate thousands of years of further enslavement or, could map a road to seemingly elusive freedom and abundance (if we make it through the impending environmental calamities). Arguably, the most prominent starting line of the race was when Satoshi Nakamoto succeeded in seeding a new era of programming. While industrial, war-era scarcity-coded systems and bloated bubbles were subsidized by governments’ hyper-inflationary policies and , a new intellectual oligarchy capitalized on Satoshi’s vision, realizing that those with the understanding and power to create new economic dynamics out of that ether could create the rules in the present-future, by manipulating the possibilities and rules of a new operating system based on liquid token economies. the further creation of debt With machine learning, economic manipulation was now decentralized much, in the same way, the printing press decentralized publishing. This time, it is the printing press of money that is fracturing paradigms and unveiling a new wave of unforeseen sociological-evolutionary effects. This technology, which has the exponential ability to move worlds, was unleashed without a tried and tested democratized governance layer, a with enough understanding to act as a watchdog, and other stop-gap measures which could act as check and balance to this new power. fourth estate Operating outside of, or propped up by, nation-states, corporate entities and members of the new intellectual oligarchy have been vying for power. The ICO craze era between 2016-2018 was a bit rushed to claim and swallow large amounts of value in the now “legacy” operating system, to fuel rockets of virtual and corporate-run hegemonies hoping to own the next global economic system, or stake claim to as much of the global network and control over human programming as possible, by literally staking on blockchains in . rigged systems The networks were idealized by Satoshi to be resilient to centralization however, much like with internet 1.0 and the dot com era, not even Satoshi and stewards of decentralized models could foresee how this technology created for liberation once again, could be exploited for deeper enslavement. With Web 2.0, a similar race occurred, with hopes that democratic processes would run free. But in the aftershock of that technological wave, we realized that the tools were being used to weaponize and hijack our bodies, using our very own biological systems, to keep us hooked to the dopamine drips of social media, while harvesting our data and psychometric profiles to extract and shift more and more wealth and resources into these virtual corporate states, and control political outcomes, and by proxy, us ( ). see The Great Hack documentary Now, with Web 3 the stakes are higher as we move into a post-nation-state, cyber-physical governance era. And here, the race to maintain any shred of democracy relies on the underdogs - the success of open source technology, token engineering for public goods, and the emergence of the New Commons Movement. New Commons Mycelial Networks vs. Goliath Screenshot from the documentary Fantastic Fungi showing an illustration of underground mycelial network “Life finds a way”, as so eloquently stated by Jeff Goldblum’s character Dr. Ian Malcolm in Jurassic Park. What many may not know is that Malcolm was a mathematician specializing in chaos theory (apropos for these chaotic times) however, simultaneously emergent with chaos is order; the paradox is the language of universal creation and drawing patterns, whether placebo or not, can create powerful narratives drawing that order into existence. The order is growing like mushrooms - lots of them. Beneath and fueled by the technical, economic, and cultural detritus of the first two iterations of the world wide web (networked neural hive) is an ancient-future intelligence system that nature is regenerating. Enter the protagonist - the New movement. Commons While China’s monolithic project and dystopian nightmare grabs headlines alongside the monopolistic flag of Libra, there is a literal movement taking root and propagating by its own organic, emergent, memetic virality and through the means of ; nature is as nature does - there is balance in The Force. Digital Silk Road Social Credit System Grassroots Economics collective intelligence Rather than pillars, hierarchies, linear engineering and proprietary defensive systems that have developed directives of self-preservation over efficacy, this new movement’s inclusive models of radical open source collaboration, shared stewardship, non-linear routes to emergent solutions and the nascent processes by which organizations form and dissolve for specific purposes, are allowing for rapid growth and meta-programming, seeding a parallel operating system to these monoliths. These decentralized collaborations operate more like organisms, and mimic the natural amalgamation and dispersal patterns found in nature. These processes are not new, but a regenesis. Indigenous cultures around the world, during the height of their civilizations, flourished for thousands of years under sophisticated ecological-economic systems grounded in biomimicry. Concepts of bioregional, surplus economies, and peaceful commerce, which provided institutionalized generosity, allowed for peaceful trade and relations between First Nations, and rooted resource management, governance, and culture in abundance and reciprocity. ( ) see “Resilience Reciprocity and Ecological Economics” by Dr. Ronald Trosper While monolithic monopolies plant economic monocultures for manipulation and homogenized social systems, these New Commons mycelial networks are planting economic permacultures and engineering for enlightenment. For every monolith, there are thousands of new collaborations like The , , , , , , , , , , , , and more... Commons Stack BlockScience Commons Engine Regen Network Earth Ledger Metagov Aragon P2P Foundation Verus Holochain Radical Exchange Open Collective Seeds These bottom-up neural webs are taking root and anchoring the much-needed distribution and circulation mechanisms to revive dying parts of the meta-organism, in economic, environmental, and human resources. More tangibly, they are providing the counter systems, , and checks and balances to powerful new technologies in Web3 and the Deep Web. governance layers Although these New Commons movements and their open-source systems are under-resourced, underrepresented, and undervalued in the face of the internet Goliaths in our existing systems, the energy they do have is arguably much more powerful. It is the energy that turns tides, lifts mountains, and transforms horizons. If evolution is any indicator, the mushrooms have a fighting chance. Darwin is Rolling in His Grave I just wanted an excuse to use this Darwin meme - and other hopes for the humanity we all wish to see. If you haven’t already, you should search “Darwin Memes” If “survival of the fittest” isn’t the most overused and oversimplified cliche used to justify inequality, I don’t know what is. There has been much ado about Darwin’s words and using them to justify socio-economic policy through a very limited lens. What you don’t hear as much, is that his holistic view of nature included "dependence of one being on another". Collective intelligence research supports that when it comes to problem-solving, open-source collaboration is superior in addressing systemic challenges. This is reflected in tech as well. According to (1998) and JC Herz (2005), intelligence will eventually generate superior outcomes to knowledge generated by proprietary software developed within corporations ( 2008). Investors and markets are already starting to reflect and . Eric S. Raymond open-source Flew predict the impending rise of open source and shift funds To be fair, however, collaboration extremists have also gone a little wild in interpreting Darwin’s words. Competition and collaboration both have their places, but what we should really be looking at in the context of complex systems research and building human ecosystems informed by Mother Nature’s law is an organism's “saturation point”, and the research of evolutionary biologist and futurist, . She is a living systems pioneer who points to the collaboration threshold as the focal point, rather than debating Darwin’s research. Elisabet Sahtouris If there is something else we have learned about evolution, it is that diversity is key for survival. Award-winning geneticist and broadcaster Dr. David Suzuki has written extensively on the topic and say now, more than ever, we must heed nature's lessons. When building human systems amidst the rising tides, the re-localization of operating systems creates fractals of resilience and adapts much more quickly to meet challenges; economic polymorphism is a necessity. The key to ensuring diversity and meeting at the collaboration threshold is our ability to coordinate. Commons systems, growing on the trusses of decentralized networks, are the best hope for humanity. I’m rooting for the underdogs, and so should you. The Dawn of the Regenaissance Nature is the biggest paradox of all. Resilient, powerful, violent, crushing, and tempered over millions of years; it is also tender, vulnerable, pregnable, withered, and endangered. To survive the extinction feedback loop we’ve initiated, we must re-align technology with nature herself, who holds the keyholes to unlock the solutions existing within her codes. As a species, it is time to mature and acknowledge our humble place in the cosmos and our interdependence on each other. By drawing on wisdom from our past and weaving ancient-future technologies, we can harness and scale mechanisms for diversity and collective intelligence by engineering our digital space systems in the patterns of nature, channeling our socio-political-economic system flows into positive feedback loops for the regeneration of our communities and our planet. By learning from the past we can become better futurists, and even better - more conscious present-ists. Narratives are powerful and so are we. Together, let us write the pages of the Regenaissance and the greatest comeback story the world has ever seen. Let us loose the evolutionary shackles of our own creation, and rise again out of the ashes, never to return. Thank you for reading! And a special thank you to Jeff Emmett for his feedback, edits and support, and Camille Corti & Bryan Doreian for their encouragement in the creative process. I would also like to acknowledge the noosphere and  stand in gratitude for all of the researchers who have contributed to the expression of these collective ideas.

Reboot: A New Operating System for Humanity 

This paper is under CC BY-SA 4.0 DEED license. available on arxiv Authors: (1) Juan Mera Men´endez; (2) Martin Bartlett. Table of Links Abstract and Introduction Initial Application Performance Tests Best Practices and Techniques Combinations Discussion Related Work Conclusion and References VIII. CONCLUSION Performance is an aspect that should always be taken into account when creating Lambda functions, especially if you plan to combine them with Java. Once the performance improvements offered by these approaches for using Java with AWS Lambda have been quantified. It is evident that Java can be used competitively with AWS Lambda without the need to resort to runtimes like Node.js or Python (which usually report better performance). Having tested various combinations of proposed strategies and demonstrated that the performance is similar to what can be achieved with the use of GraalVM, we recommend avoiding its usage. We consider it essential to profile the Java functions to select the optimal memory configuration and using AWS SDK v2. From there, optionally, the next step is to the rest of the techniques to achieve one of the proposed combinations. Additionally, following the general best practices such as initializing connections outside the handler or reduce the bundle size, among others, also contribute to optimizing Java functions. The optimization strategies we propose are not the only ones available, there are many other techniques that can similarly yield excellent results. It is important to highlight that the process of optimizing a function is linear in terms of difficulty. In other words, it’s relatively easy to obtain a significant improvement, but if you want to squeeze out the maximum performance, the process requires trial and error and greater effort. It’s also important to highlight that the percentage of improvement offered by each approach depends on the specific use case in which they are applied. This percentage can vary, either increasing or decreasing. REFERENCES [1] H. Puripunpinyo and M. Samadzadeh, “Effect of optimizing java deployment artifacts on aws lambda,” in 2017 IEEE Conference on Computer Communications Workshops (INFOCOMWKSHPS), 2017, pp. 438–443. DOI: 10.1109/INFCOMW.2017.8116416. [2] D. Bardsley, L. Ryan, and J. Howard, “Serverless performance and optimization strategies,” in 2018 IEEE International Conference on Smart Cloud (SmartCloud), 2018, pp. 19–26. DOI: 10.1109/SmartCloud.2018.00012. [3] D. Jackson and G. Clynch, “An investigation of the impact of language runtime on the performance and cost of serverless functions,” in 2018 IEEE/ACM International Conference on Utility and Cloud Computing Companion (UCC Companion), 2018, pp. 154–160. DOI: 10.1109/UCC-Companion.2018.00050. [4] O. Hosseini Mehrshad Sahragard, “Aws lambda language performance,” 2019. [Online]. Available: https://gupea.ub.gu.se/handle/2077/62454. [5] A. L. Dowd, “Performance of java in function-as-a-service computing,” University of Texas Libraries, 2020. [Online]. Available: https://repositories.lib.utexas.edu/handle/2152/84357. [6] S. O’Toole, “Aws lambda java tutorial: Reducing cold starts,” 2020. [Online]. Available: https://www.capitalone.com/tech/cloud/aws-lambda-java-tutorial-reduce-cold-starts/v=1694091771423. [7] D. Quaresma, D. Fireman, and T. E. Pereira, “Controlling garbage collection and request admission to improve performance of faas applications,” in 2020 IEEE 32nd International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD), 2020, pp. 175–182. DOI: 10.1109/SBAC-PAD49847.2020.00033. [8] M. Sipek, D. Muharemagi´c, B. Mihaljevi´c, and ˇ A. Radovan, “Enhancing performance of cloudbased software applications with graalvm and quarkus,” in 2020 43rd International Convention on Information, Communication and Electronic Technology (MIPRO), 2020, pp. 1746–1751. DOI: 10.23919/MIPRO48935.2020.9245290. [9] Q. I. S. Hassan B. Hassan Saman A. Barakat, “Survey on serverless computing,” Journal of Cloud Computing, 2021. [10] C. D. Ron Mosquera Bryan Alexander y Villegas Armijos, “Mejora del rendimiento de aplicaciones serverless basadas en la nube con graalvm y quarkus,” 2022. [11] StackOverflow, “Developer survey 2022,” 2022. [Online]. Available: https://survey.stackoverflow.co/2022/. [12] Q. Wu and L. K. John, “Performance of java in function-as-a-service computing,” in 2022 IEEE/ACM 15th International Conference on Utility and Cloud Computing (UCC), 2022, pp. 261– 266. DOI: 10.1109/UCC56403.2022.00047. [13] Datdog, “The state of serverless,” datadog, 2023. [Online]. Available: https://www.datadoghq.com/state-of-serverless/. 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Performance Best Practices Using Java and AWS Lambda: Conclusion and References

This paper is under CC BY-SA 4.0 DEED license. available on arxiv Authors: (1) Juan Mera Men´endez; (2) Martin Bartlett. Table of Links Abstract and Introduction Initial Application Performance Tests Best Practices and Techniques Combinations Discussion Related Work Conclusion and References VII. RELATED WORK As far as we know, and up to this date, there aren’t too many articles seeking to enhance the Java approach with AWS Lambda in the same manner as we do. Nonetheless, there are some similar studies. In [21] Buliani addresses part of the performance issue and proposes and applies a series of techniques incrementally, analyzing the overall improvement step by step. [12] investigates the performance impact of cold start and containerization for Java-based FaaS functions and compares it to an alternative using Python. [2] takes a similar approach, suggesting a variety of ways to enhance the performance of AWS Lambda but without integrating it with Java. In [5] Dowd investigates the overhead and cold start for functions written in Java, attempting to determine the extent to which the findings depend on the programming language. There are also other studies that have a certain relationship with ours, either because they explore some of the techniques we propose or because they are related with the performance of AWS Lambda. [3], [4] analyze the performance of the various managed runtimes provided by AWS for executing code in different languages on Lambda functions without optimizing it. In [1] Hussachai et al demonstrates how optimizing the Java artifact results in superior performance during the execution of the Lambda function. In [7] evaluate the use of a garbage collector control strategy concerning performance improvement, that is an interesting strategy that we didn’t take in considerationin our study. Related with GraalVM, [8], [10] incorporate this tool with the aim of leveraging the advantage it offers in terms of performance.

Performance Best Practices Using Java and AWS Lambda: Related Work

This paper is under CC BY-SA 4.0 DEED license. available on arxiv Authors: (1) Juan Mera Men´endez; (2) Martin Bartlett. Table of Links Abstract and Introduction Initial Application Performance Tests Best Practices and Techniques Combinations Discussion Related Work Conclusion and References VI. DISCUSSION In Figure 4 and Figure 5, you can see a summary of the cold start improvement and warm Lambdas, respectively. Next, we will analyze each approach and the possibilities they offer. Starting with an appropriate configuration for each function, in our opinion, is the minimum optimization that any function should have, both to improve performance and to balance cost. In addition to offering a significant improvement, it is perfectly compatible with all the other techniques mentioned in this article. Continuing with the use of Snapstart, this technique focuses on mitigating cold starts. In our experience, this technique could not produce as significant an improvement as to justify the incompatibilities it generates, including the lack of support for the arm64 architecture or custom runtimes, along with other important features such as the capability to use EFS or to attach the lambda to a VPC. The improvement offered by using the Arm64 architecture seems to depend on the specific use case in which it is employed. For example, it can be particularly beneficial for compute-intensive applications such as high-performance computing. This could explain why the improvement in our case is not too noticeable. Nevertheless, its use is highly recommended as it can be combined with other approaches discussed, and its adoption is on the rise. The major drawback is the incompatibility with some third-party technologies or dependencies related to this architecture. The use of AWS’s SDK v2 for Java applications is also practically mandatory, as long as it supports all the services and the majority of the dependencies integrated into the function. Thanks to its improvements and modular approach, this second version of the SDK significantly increases performance. Moreover, it is perfectly mixable with the rest of the techniques without adding notable limitations. Combined with version 1, if any unsupported features are needed, they can be utilized as required. As expected, leveraging the benefits of ahead-of-time compilation generates a significant performance boost both in cold starts and when functions are already warm. However, the main drawback is the complexity that this approach adds. It’s common to encounter runtime issues that may require the developer to deal with low-level aspects, and any modifications can become challenging. In principle, it is perfectly combinable with the rest of the techniques that support custom runtimes. The possibilities offered by the JAVA TOOL OPTIONS environment variable are quite extensive, allowing for significant Java VM configuration. In our case, we only tested tiered compilation, but there are other possibilities such as configuring garbage collector behavior [7]. In any case, it’s worth trying to use this variable and integrating it into Java functions whenever the JVM is used because it doesn’t impose any limitations, and the improvement is significant. It’s also important to highlight that investing efforts in refactoring the function’s code and applying the details mentioned in subsection IV-G can achieve an even greater performance improvement than the techniques discussed. This work is entirely dependent on the use case and requires trial and error as well as skill. Regarding the simultaneous application of techniques, for our combinations, we recommend the use of either of the two, so you should choose the one that provides the most benefit, either in terms of performance or greater adaptability to the specific use case.

Performance Best Practices Using Java and AWS Lambda: Discussion

This paper is under CC BY-SA 4.0 DEED license. available on arxiv Authors: (1) Juan Mera Men´endez; (2) Martin Bartlett. Table of Links Abstract and Introduction Initial Application Performance Tests Best Practices and Techniques Combinations Discussion Related Work Conclusion and References V. COMBINATIONS After defining the proposed techniques and quantifying the effects of their application, it becomes essential to attempt to combine them with the intention of detecting synergies, incompatibilities, and understanding the improvements that can be achieved by applying several of these. With this intention, and due to the unavailability of Snapstart in combination with the Arm64 architecture, the following combinations are proposed: • Best memory configuration + SDK v2 + JAVA TOOL OPTIONS environment variable + Snapstart • Best memory configuration + SDK v2 + JAVA TOOL OPTIONS environment variable + Arm64 architecture Two new prototypes have been created, optimized with the previous optimization combinations, and their performance has been measured. The results are compared with each other and independently with the GraalVM prototype. In Figure 3, you can observe the percentage of improvement compared to the performance offered by the initial system, both for the cold start of functions and for the overall performance.

Performance Best Practices Using Java and AWS Lambda: Combinations

This paper is under CC BY-SA 4.0 DEED license. available on arxiv Authors: (1) Juan Mera Men´endez; (2) Martin Bartlett. Table of Links Abstract and Introduction Initial Application Performance Tests Best Practices and Techniques Combinations Discussion Related Work Conclusion and References IV. BEST PRACTICES AND TECHNIQUES Below are the proposed techniques and approaches to reduce the cold start of Java functions and improve their overall performance. Along with their measurements in terms of performance tests. The proposed practices will be compared in the VI.Discussion section. A. Better suited configuration Increasing the allocated memory for a function is probably the most well-known way to reduce cold starts and also the simplest. The allocation of memory to a function is related to the amount of CPU assigned to that function. A greater amount of CPU generally makes the initialization of the function faster. However, it’s not always the case that more memory equals better performance. It’s also necessary to consider that higher memory allocation will result in higher costs, but if the computing time is reduced due to increased performance, the overall cost will decrease as well. To properly balance the memory configuration of a Lambda function, the Lambda Power Tuner solution [19] can be of great help to profile the function, displaying graphs such as the one shown in Figure 2 for Catalog lambda of our solution. In our case, chose the right memory value for the functions configuration suppose an improvement of about 70% in terms of cold start going from an average of 16307 ms to 4867 ms. In addition to a noteworthy 46% when the functions are warmed passing from an average of 512 ms to 216 ms as can be seen in Table II. For the rest of the versions that do not have an appropriate memory configuration, the default value of 512 MB is used. B. Snapstart Snapstart [16] is a feature offered by AWS to mitigate the cold start of Java functions. It involves initializing the function at the moment a version of it is created. Lambda takes a snapshot of the initialized lambda’s runtime environment, encrypts and caches it. When initializing new functions, it does so from the snapshot rather than starting from scratch, thus improving performance. A notable drawback of this feature is that it does not support many other features offered by AWS Lambda, such as custom runtimes, the arm64 architecture, Amazon EFS, and other managed runtimes that are not Java 11 or Java 17. In this case, enable Snapstart on our functions allow an enhancement of 16% at the cold start (going from an average of 16307 ms to 13736 ms) and an improvement of 21% attending requests when the functions are warmed making a difference of over 100 ms in each request answered (Table III). C. Arm64 architecture The instruction set architecture within AWS Lambda, either x86 64 or arm64, determines the processor that the function will use. AWS’s official documentation suggests that the arm64 architecture [18] with its Graviton processor can offer significantly better performance than the x86 64 architecture. Regarding our solution, the impact of using arm64 on the latencies was positive, getting over a 14% of improvement on cold starts and a 13% when the lambdas are warmed. Reducing the average latency by over 2000 ms and approximately 70 ms, respectively. The warmed lambda functions data can be seen in Table IV. D. AWS SDK v2 for Java The AWS SDK for Java 2.x [15] is the second version of the SDK provided by AWS for Java. This version is designed with a modular approach, allowing you to include only the service-specific JARs you need, reducing the size of your artifacts. That’s a key point of reducing the cold start, loading less number of classes when initialize the function. Furthermore, it boasts several other performance improvements such as asynchronous nature, connection management, concurrency support, resource management, and so forth. Due to that, in terms of performance its use is highly recommended. For us, migrate the lambda functions from sdk v1 to sdk v2 suppose an enhancement of about 40% in both cold start and warmed functions. On first going from 16307 ms to 9926 ms and on second one passing from 512 ms to 298 ms of latency as is visible in Table V. E. GraalVM GraalVM [20] is a platform that enables ahead-of-time compilation of Java code, leveraging the Native Image tool to produce a standalone binary. As a result, this binary offers significantly better performance compared to traditional Java code that runs using the JVM and is compiled at runtime. The binary is produced for the OS of the environment the code is built. It’s important to note that AWS Lambda is only compatible with GraalVM through the use of custom runtimes. Other disadvantage is that the ahead-of-time compilation sometimes would be tricky to make it work properly adding even the necessity of dealing with low level issues caused by AOT compilation. For us, create a native image of our code and uses it generates a great improvement of 83% on cold start, going form 16307 ms to 2800 ms. When the lambdas are warmed, it goes from and average of 512 ms to 231 ms which means a 55% of enhancement, visible in Table VI. F. Environment variable: JAVA TOOL OPTIONS AWS Lambda provides some customization options [17] for the managed Java runtime, which can enhance overall function performance. This is achieved through the use of the JAVA TOOL OPTIONS environment variable, which allows access to JVM features such as tiered compilation or garbage collector behavior among others. In this case we use only tiered compilation, fixing the level to 1, enabling the C1 compiler which means optimized code for fast start-up time. The exact value for the mentioned key is ”-XX:+TieredCompilation - XX:TieredStopAtLevel=1”. Java 17 managed runtime is default set to level 1 for AWS Lambda, while in Java 11, you need to set it manually. Level two can be used for an overall better performance, not only focus on the start time. In our test system, enabling that option translates to a 40% improvement in cold start times and a 31% improvement in response times for the system with warm Lambdas (Table VII). G. Others In addition to the previous techniques and approaches we propose and have tested, there are many other best practices [6], [14], [21]. Whether they are more dependent on the use case or more general, below, we mention some of them: • Reduce the size of the bundle to enhance the initialization phase. As less classes to load as fast the function will initialize. • Initialize the connections of third party out of the handler and same the static resources on /tmp to reuse it from one execution to another. • A framework, if needed, should be as lighter as possible. Due to that we suggest replacing spring with other smaller frameworks like Vert.x • Try to avoid reflection, that feature makes some JVM optimizations impossible to be done as it works with dynamic types. • Initialize all necessary dependencies and classes during initialization time. These approaches, combined with some of the previous ones we have tested, for sure will greatly reduce the impact of cold starts and increase the performance of Java functions by a really significant percentage.

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