Lenus eHealth is a fast-growing B2B2C company providing intelligent software solutions to some of the most prominent fitness coaches and other health professionals around the world

Lenus eHealth

Wolfgang Amadeus Mozart, an 18th-century musical prodigy, left an indelible mark on the world with his unparalleled compositions that continue to resonate through the ages. Behind the genius of this prolific composer lies a complex tapestry of life experiences, including struggles with mental health that have sparked debates among scholars and enthusiasts alike. In this exploration, we delve into the life and works of Mozart, seeking to understand the intricate relationship between his mental health and his extraordinary musical legacy. I. Early Life and Prodigy: Mozart's journey began in Salzburg, Austria, on January 27, 1756. A child prodigy, he displayed an exceptional musical talent from a very young age, composing his first symphony at the tender age of eight. His father, Leopold Mozart, recognized and nurtured Wolfgang's gift, touring him across Europe to showcase his extraordinary abilities. The pressure of being a child prodigy undoubtedly shaped Mozart's early experiences, laying the groundwork for the challenges he would face later in life. II. The Strains of Adulthood: As Mozart transitioned into adulthood, the weight of societal expectations and financial pressures began to take a toll on his mental well-being. Despite his undeniable talent, he faced financial difficulties and struggled to secure a stable position. The constant need to prove himself and the uncertainty of his future likely contributed to the stress and anxiety he experienced. III. Personal Loss and Emotional Turmoil: Mozart's personal life was marked by both joy and sorrow. His marriage to Constanze Weber brought moments of happiness, but the death of several of his children in infancy and the financial strains on the family added to his emotional burden. These personal losses and challenges undoubtedly influenced the emotional depth and complexity found in some of his compositions. IV. The Myth of Amadeus and Mental Health: The popular image of Mozart as a carefree and jovial individual, perpetuated by the fictionalized account in Peter Shaffer's play "Amadeus," contrasts sharply with the struggles Mozart faced. The romanticized narrative often overshadows the darker aspects of his life, including his battles with mental health issues. Recent scholarship has sought to dispel the myth and provide a more nuanced understanding of Mozart's psychological landscape. V. Exploring Mozart's Correspondence: A valuable window into Mozart's mental state is provided by his extensive correspondence. Letters to family and friends reveal moments of elation and despair, shedding light on the highs and lows of his emotional life. These letters also provide insights into his creative process, offering a glimpse into the mind of a musical genius grappling with personal and professional challenges. VI. Musical Catharsis: The Marriage of Genius and Madness: Mozart's compositions, while masterpieces of unparalleled beauty, also serve as a mirror to his inner struggles. The intricate interplay of light and dark, joy and sorrow, found in works such as the Requiem and the "Don Giovanni" opera, hints at the emotional turbulence within the composer. Music became Mozart's refuge, a medium through which he could channel his deepest emotions and find solace in the midst of life's tumultuous journey. VII. Legacy and Impact: Despite the challenges he faced, Mozart's legacy endures as a testament to the resilience of the human spirit and the power of artistic expression. His music continues to captivate audiences worldwide, transcending time and space. By unraveling the complexities of his mental health, we gain a richer understanding of the man behind the music and the enduring legacy of Wolfgang Amadeus Mozart. Wolfgang Amadeus Mozart's life and musical legacy offer a fascinating lens through which to explore the intricate relationship between genius and mental health. The interplay of joy and sorrow, triumph and despair, within his compositions and personal experiences, paints a nuanced portrait of a complex individual whose artistry transcended the boundaries of his time. As we delve into the depths of Mozart's psyche, we discover not only the challenges he faced but also the enduring power of his music to touch the human soul. Also published . here

Discovering the Genius of Wolfgang Amadeus Mozart through the Lens of Mental Health

A Mental Model & Framework You Can Use For New Products, Existing Products, and PM Interviews Even In this series… — WTF is a Metric? Part 1: introduction to the basics of metrics, what it is, and why it matters. Part 2: 4 Steps to Defining GREAT Metrics for ANY Product — how to develop a metric from scratch. WTF is Your Metric Doing to Your Customers? Part 3: — how to avoid the unintended consequences of the “one metric that matters.” Metrics matter. Your product’s success is dependent on and defined by metrics. Metrics are a lens into your product’s health and performance. Defining your metrics, therefore, should be as intrinsic to your product development process as defining requirements. In fact, metrics are one of the most frequently asked product manager interview questions: How would you define the success metric for feature X? The Challenge Developing metrics from scratch can be incredibly daunting, precisely because there is so much at stake. Unless you work in an industry where standard metrics are widely accepted (such as in gaming, e-commerce, or retail), metrics are anything but trivial. Many product managers start bottom-up: monitoring and collecting the near-infinite number of data points they can get a hold of. However, an obvious truth quickly begins to surface… …not all data are created equal. Just because you can track everything doesn’t mean you should. because they give you a unique insight into your product’s health. Conversely, there is a lot of subpar data that can cloud your judgement. Some data are more valuable than others A Mental Model for Metrics Let’s step back. Think about your product as a box: You can start to instrument this box and collect all available data, but this can become an aimless approach characterized by constant trial-and-error. In order to build a product your users value, you first need to determine the inputs and outputs of this box. Your inputs are anything your users come to you with: their problems and their resources. Your outputs are the circumstances resulting from the use of your product. These become your . goals But this is not a black box. Most products require the user take within the product to extract value and achieve their . actions goals Quantifying and measuring these actions gives you insights into how (and how many) users are getting value from your product. When you track these actions over time, they become your . metrics Once you start to collect data for your metrics, you can then put them to work towards evaluating your product. The results of your can inform any number of adjustments in your , , or even themselves. evaluations goals actions metrics You can start to see that this very simple model can be applied in any number of ways: Defining success metrics for a new product Determining a success and impact new feature’s Re-vamping metrics for an existing product Fielding about metrics interview questions For each step, here’s a bit more detail on how to do this. The GAME Framework The GAME framework is a . For the remainder of the post, I will refer to a hypothetical “product” but feel free to substitute in “feature” whenever a “product” is discussed. 4-step process that can define metrics for any feature or product Step 1: Articulate Your Goals This is obvious, but you should for your product. Your goals are critical to ensuring that you maintain a purpose-oriented strategic mindset. They also serve as tangible markers for revisiting and validating directional correctness throughout the metrics development process. Placing goal-setting as your first step forces you to be top-down, which is critical for developing metrics. A bottom-up approach is suboptimal because it relies on intuition and can often lead to analysis paralysis. always start by defining your goals To define your goals, ask yourself the following questions: How will my users benefit from my product? What problems do my users want my product to solve for them? How will my users interact with the product? How will my users feel when they use my product? What is my vision for how this product will integrate in my users’ life? User Goals: What are the tactical or strategic business benefits? Increase revenue? Decrease cost? Be more competitive? Enter a new market? What does my business look like if my product is successful? Business Goals: Note that for many of the best products, user and business goals align. These goals are often two sides of the same coin. Once you have this set, you are ready to get in on the action. Step 2: List the Actions That Matter The next step is to define your user actions, or more precisely, all the actions you want your users to take within your product. This should start as a list. Don’t worry about the numbers or whether they are trackable yet. qualitative Here are some example questions you may ask yourself using the common ARM metrics framework. Pick the set of questions that align with your goals: : How will my prospective users hear about my product? What actions do my prospective users have to complete to become a user of my product? What actions do my users have to take to get value from my product? At what point is my product solving a real problem for my users? Acquisition & Activation : What gets my users to come back to my product? What do my users do when they are engaged with and interested in my product? What are the actions that provide my users repeat value? How often does it make sense for my users to take those actions? Daily? Weekly? Monthly? Once in a long while? Retention & Engagement : What form of “payment” does my user purchase with, money or time? What actions do my users take for my product to start charging? If my product is free, what are the reasons my users will opt to pay for it? Do they pay me directly or does someone else pay me when my users take action (e.g. ad/affiliate)? Do they pay me for every action (transaction) or do they pay me periodically (subscription)? Monetization & Revenue You can also use metrics frameworks like to flush out the actions. The key to doing this well is to be : have the broadest coverage, but don’t bother differentiating the minutiae. AARRR comprehensive but not exhaustive For example, if your acquisition user action is “newsletter signup,” don’t worry (yet) about listing each newsletter sign-up interface. Step 3: Define Your Metrics You are now ready to turn each desired user action (qualitative) into a measurable, trackable value (quantitative). Note that if you haven’t already done so, bring engineering and data teams in at this stage to vet your metrics and provide technical guidance on the feasibility of collecting/storing the desired data. Here are some major decisions for how to count each action: : Can the action be directly tracked? Or do you have to use a proxy to measure the action? Direct vs. Proxy For example, you can measure “clicks” directly, but you may need to proxy “views” with values like scrolling. : Can you group many actions for an overview, then separate out as slices for later analysis? Individual vs. Aggregate For example: total revenue → revenue by product line → revenue by individual product. : Does it matter more for you to measure the overall magnitudes of the action? Or should you track as a comparison using a ratio, whether it be a rate (per time) or a normalizing factor (percentage, per user, etc)? Magnitude vs. Ratio For example, you can measure “total revenue,” or you can measure “revenue per day” and “revenue per payer,” which will give you very different perspectives on the same data. : Can you derive more knowledge from the measure intrinsically? Or do you have to rely on heuristics for the metric to be valuable? Intrinsic vs. Heuristic For example, while “daily active user” (DAU) may be intrinsically useful for B2C apps, a heuristic like “number of paying users who were active in the last 30 days” may be more appropriate for B2B SaaS products that are expected to be used monthly. For most, this is the hardest step in the framework. Some of your decisions are informed by strong intuition, while others are informed by technical constraints or further data analysis. Often you won’t know for certain whether a metric is working until some iterations. At this stage, you can also begin to weed out . Tweak the metrics that can be “gamed,” i.e. artificially inflate or deflate through clever design/engineering or malicious user intent. vanity metrics For example, inflating page views by using pagination or slide shows. Step 4: Evaluate your Metrics The best way to ensure your metrics are providing you the correct insights into your product is to . You simply will not know how a metric behaves until you start collecting data. test and iterate You can check for usefulness by monitoring the metric for any false-positives or false-negatives. For example, if the metric drops, does it signal a real problem in the product? Conversely, if an issue surfaces, is the metric helping you flag this issue? Of course, you would also expect the metric to flag true-positives and true-negatives. The most import evaluation you need to make is the functional usefulness of the metric. Different behavior with your metrics may inform iterations in each of your GAME steps: : Is the data trending in the way you expected? Is the metric stable over time (i.e. not wildly fluctuating)? Is the metric flagging issues with your product and prompting further analysis? Are these the correct data points to collect? If you answer “no” to any of these questions, you want to reconsider how you developed your metrics. Evaluating Metrics : Are these actions reflective of product, user, or business goals? Are your key actions telling the correct story about user behavior? Does your action set cover all emerging user behaviors? Are your actions safe from being “gamed” by your users? If you answer “no” to any of these questions, you want to redefine the set of key actions. Evaluating Actions : Is the metric correlating to business or user success? Are your users happier when your metrics are positive? If you answer “no” to any of these questions, you want to revaluate how your metrics relate to your goals. Evaluating Goals Ask yourself these honest questions and remember that you are looking for your metrics to measure the vital signs that give you an actionable indication of health and performance. Applying GAME: Facebook’s Newsfeed Example 😇 Disclaimer: I have no insider information from Facebook. This section is pure speculation. The Facebook Newsfeed comes up as one of the most common interview questions, particularly because it has the right mix of ubiquity, complexity, and opaqueness. Let’s apply the framework step-by-step: Goals for the Newsfeed can be broken down between user and business goals: : I want to be able to see the latest of what my friends are up to without going to each of their profiles. User Goal : Users are only coming to Facebook when they have a reason to: they are looking to post or they have been messaged/mentioned. We want to improve engagement by creating an evergreen content discovery experience. Business Goal Actions taken on the Newsfeed can be collected into the following qualitative list when considering “engagement” as the primary goal for both user and business: into Facebook to look at Newsfeed Logging to Newsfeed (text, photo/video, photo/video album, live video) Posting Newsfeed items, including watching a video Viewing through the Newsfeed Scrolling on Newsfeed items (content, name/photo of the poster) Clicking or replying to a comment Commenting a Newsfeed item or a comment Liking a Newsfeed item to your own or someone else’s wall (person, page, group), or as a private message Sharing a post (yes, you can … who knew?) Saving look at saved posts later for a Newsfeed item Hiding, unfollowing, reporting, or turning off notifications Notice how the list is relatively : it covers all of the bases but doesn’t try to split hairs between all the different post, click, or comment types. We can also see how product design around creating or changing actions can be a major lever for impacting engagement. comprehensive but not exhaustive Metrics is where this gets interesting. In trying develop the most useful, actionable metric, we can vet them through our various decision points: : Most of the actions above can be tracked directly with clicks for viewing a feed item. For viewing, we may use a proxy to count the number of items viewed, such as “count viewed if 75% of the feed item is in the device viewport.” Direct vs. Proxy except : Because our primary goal is measuring engagement, we care about our users engaging with the Newsfeed in general more than any specific action, so we will define a metric with aggregate measures. With that said, some actions (like posting) are more valuable to us, so we may choose to group them differently. For example, we can aggregate Newsfeed actions into the first bucket, actions into a second bucket, and actions (inclusive of links, comments, share, blocks) into a third. Individual vs. Aggregate posting viewing interacting : Looking at the magnitude of Newsfeed action is relatively meaningless since any given user can generate a great number of actions. It is more useful if we consider either actions-per-day or actions-per-user (or both, i.e. actions-per-user-per-day). Magnitudes vs. Ratios : Unfortunately, there’s no easy measure to determine whether someone is “engaged” or not. Let’s say a analysis shows that people who OR OR are 2x more likely to come back to Facebook the next day. We can use this data to develop a heuristic around an engagement metric, whereby if a user crosses any of the three thresholds above, they are considered “engaged.” Intrinsic vs. Heuristic hypothetical post at least 1 feed item view at least 20 feed items interact with at least 3 feed items Pulling this all together, our metric for the Facebook Newsfeed will look something like this…: …where “engaged user” is a that’s developed by both actions and comparing it as a to “total users” on a daily basis. heuristic aggregating direct and proxy ratio Evaluations will come in the form of validating many of the assumptions we made above by putting your newly defined metric to work. For example: : We may find that the definition around “engaged user” is overly permissive and that 90% of our users are regularly considered “engaged,” which doesn’t leave much room for insight or improvement. We dig deeper to find that of the 90%, a majority became engaged through the “view at least 20 feed items” criteria, so we adjust that threshold up to 50 to create a higher bar for an engaged user. Honing Heuristics : We may find that people who take actions against negative posts (hiding, unfollowing, reporting, or turning off notifications) actually correlates inversely to engagement. From this, we can decide to pull these actions out of our “interacting” bucket (bucket 3) and develop a separate metric to monitor negative Newsfeed behavior. Disaggregating Actions : We may find that we’ve done such a great job on engaging users with the Newsfeed that engagement is no longer the primary goal. Instead, we may decide to shift the goal to “monetization,” where we develop ad-related features into the Newsfeed. While engagement may not be the primary goal anymore, we want to continue monitoring the engagement metric to ensure the new monetization features do not have an adverse impact on engagement. Shifting Goals A Case for Defining Your Metrics Before You Need Them In a “move fast and break things” world, it is tempting to concern yourself with metrics only after the product has launched. However, having metrics defined before you start development can translate to huge benefits because: Metrics help you maintain focus Metrics set a for team to rally around common goal Metrics hold you (the PM) to your users and business accountable Metrics enable more during the critical launch period, including triage and prioritization objective decision-making Flying blind at any stage of your product’s lifecycle will substantially decrease your ability to learn. After all, you can’t improve what you don’t measure. Having the right data up front may be difference between success and failure for a ! startup I hope this has been a useful breakdown on how to develop metrics. Please leave comments if you have any thoughts or ideas about how to improve this post, or was able leverage the tools here to improve your metrics! For more Product or general business writing, please follow me on Medium or browse my other writing: @vincelawco. I can also be found at my website , on answering product-related questions, or at the community if you want to chat! Management http://vincelaw.co Quora PMHQ Slack

4 Steps to Defining GREAT Metrics for ANY Product

4 pasos para definir GRANDES métricas para CUALQUIER producto

For a long time we thought that our French system was the best in the world. And it was. But, it’s been years that we have observed an increase in costs in our healthcare system, for individuals, while the user experience has not really changed or improved ( worldwide, ranking us #11 in Europe). healthcare Bloomberg ranking France #8 Europe Health Consumer Index Figures are alarming: in France, (yet, it’s below the 18%-level in the US), €250bn every year, and it has been in constant augmentation for the last 50 years. This growth comes with an even larger for our beloved Sécurité Sociale. 11.6% of our GDP goes to healthcare budget deficit And while costs are increasing, satisfaction towards the system is decreasing for every stakeholder including doctors, hospitals, patients… Where does that come from and what can we do? Difference between user and patient First things first, we, the people, are not to be considered as patients anymore. We are users of our . Users when we are patients, users when we assess our risks, but also users when we are healthy. healthcare Semantically, patients are considered an object on which doctors identify symptoms that need to be treated. On the other hand, the user is an active player of his healthcare: he is increasingly the center of attention. The needle will move from physicians monitoring health indicators from time to time to ourselves collecting them in order to share the proper summaries to experts. Indeed, today we are only monitored when something goes wrong, a comparative should analyse “healthy vs. not healthy” and the continuity in-between. medicine Apple Watch Health (source: Hello Heart) We live a crucial moment for this user “empowerment”: (Apple is hiring tons of health experts), by Google, … We will be able to follow our biometrics and our health evolution. In parallel, patient communities are created ( , ) around their diseases. The p̷a̷t̷i̷e̷n̷t̷ user, is the new actor of his health. Apple Watch blood monitoring weight analysis SmartPatients PatientsLikeMe Medicine digitisation The excellent book from Robert Wachter: The Digital Doctor This new healthcare user needs tools to work with his ecosystem of doctors, hospitals, private clinics, insurances… Here comes medicine digitalisation, which has been a slow, painful trend over the last years. This digitising phase took place recently particularly in the US and the UK, with the introduction of the Electronic Health Record (EHR). All information about a patient is supposed to be gathered at one point. If today , hospitals still do not communicate with one another, , and user interfaces are still very poor. Few doctors consider this transition as a success. Mainly, because they have been designed for the ones paying: hospitals who want to optimize their billing and who have beenheavily incentivized by the government to invest in EHRs. more than 70% of American hospitals have an EHR doctors still do not agree on what level of information they should share with patients Dossier Médical Personnel In France, the “Dossier Médical Personnel”, our equivalent of EHR, has been promoted by the government with over . And we haven’t seen any proper private initiatives yet. €500m investment for less than 400.000 subscribers. It has turned into a fiasco What should we see in an EHR? On a user-based approach, I believe it should be the combination of our medical interactions with the healthcare system (doctors you’ve seen and when, which drugs have been prescribed, what have been the diagnoses, lab results, etc…) combined with standard information from wearables and sensors that monitor our health. Google ontact lens to measure glucose in tears. (Credit: New York Times, Google, via Agence France-Presse — Getty Images) Connected devices (wearables, quantified self apps, sensors…) are huge collectors of health data and information. Google is right now working on several sensors to monitor pulse, , with its new brand (formerly Google Life Sciences)… A lot of start-ups such as with its HealthPatch, continuous blood pressure monitoring for , and are very active. Nevertheless, these data will be almost worthless for the end-user if we do not edit the right data-management to analyse information. , and the next big step is to switch from displaying data to generating valuable insight from data. sugar levels Verily Vital Connect Quanttus clinical trials some others Quantified-self does not mean qualified-self Alphabet introducing Verily their new branding for Google Life Sciences We have to move forward a complete EHR system step by step. It does not have to be fully complete and universal from scratch. It is a matter of daily implementation. Doctors tasks will be replaced or completed With this huge amount of information coming from everywhere, the development of new technologies, doctors’ job is to evolve along with the interaction with users. And we hope for the best. I really believe that doctors will remain at the center of our healthcare system for a long time. It is their scope and the way they will be assisted that will change. Da Vinci robotic surgery in action Robotics has started to enter in healthcare mainly with system developed by Intuitive Surgical, that has performed more than 3 million surgeries worldwide. It is still expensive as an investment, every operation requires the purchase of another specific pack, but there is a trend towards more and more robotisation and tools to assist surgeries. Here in France, we have which has not reached the same market penetration yet.New generation of surgery robots is under development with the objective to be autonomous, and to conduct routine surgeries in less time, with less human errors, and ultimately cheaper than standard ones. Will surgery become a commodity accessible to everybody? The question is still open and requires a lot of development. Da Vinci robotic Robot Rosa from Medtech If we can help the hand, we can also help the brain. I spoke in a about Artificial Intelligence (and the difficulty to define it), but we see a trend in machine-learning / artificial intelligence to help doctors take better decisions. is somehow trying to lead the way with the Watson tools. The aim is to distribute them to every single hospital. They have signed deals with (work on rare pediatric diseases), (prediction of health deterioration), Medtronic, or (understanding scientific papers and create a health coach), (providing access to data from apps) in order to support doctors in their decisions on diagnoses and treatments. Deep learning has also proven very useful in image recognition, with companies such as which raised another . previous article IBM Watson Boston Children’s Hospital CVS Health Johnson & Johnson Apple Enlitic $10m for radiology image recognition I believe that AI will enable better diagnosis and personalized medical recommendations, help to scale information and distribute it. It will be the support tool for the new doctors to access, manage and treat data. It follows the same trend that consumer knowledge which has switched from memory to Google. Personalised medicine & prevention This evolution and the huge amont of data we are able to analyse are to be completed by new data-streams and more focus on prevention and healthcare personalisation. Among this, DNA sequencing and analysis is a fascinating topic. The history of genome sequencing is fantastic, beginning with a between public and private initiatives (aka ) to sequence in 2001 the first complete human genome. And since then, cost and speed have improved three times faster than Moore’s law. $100m in 2001 to c.$1,000 today. Not so bad. race Craig Venter Evolution of DNA sequencing costs from $100m to less than $1,000 in 15 years (source: National Human Genome Research Institute) Several initiatives are around to understand better our genome. Not in France of course. National initiatives: UK project with the NHS support, whole country sequencing in , the US and Obama pushing for a with the “Precision Medicine Initiative”, . But also private initiatives such as (Craig Venter again), which mission is to accumulate the largest genomics data set ever with 1 million full human genomes, microbiomes, … 100,000 genomes Iceland $215m sequencing project China announcing even larger numbers Human Longevity Inc. Ling Min Jian; lab technician at the Beijing Genomics Institute (BGI), the world’s largest genome sequencing center. Collecting enormous amount of data is a great challenge that we are addressing now worldwide. But understanding or generating insight from it is even harder. It is like trying to find a bug in a program by looking at the 0s and 1s. You have to get really good eyes and computer power. That’s also why it’s one of the big topic of interest for the GAFA. The excellent of , founder of health website Doctissimo and DNA sequencing DNAVision, are a great read on the topic. articles Laurent Alexandre Illumina’s gene sequencer (Credit: Illumina) Nevertheless, sequencing is changing specific parts of personalised medicine. The one I want to focus on is cancer sequencing. By sequencing blood samples, we detect cancer earlier, we can select and test treatments based on your cancer genetics, and thus we can dramatically reduce costs and risks. Two companies recently raised $100m each in the field: and through Bill Gates, Jeff Bezos, and . Of course, we are still at the beginning and industrial scaling will raise new questions, but it’s out there. Guardant Health GRAIL initiative Illumina Once we can analyse your genome, microbiome, environmental interaction we could be able to target treatments, anticipate diseases… We already see that machine-learning analysis of demographics and somehow epigenetics data (in the broad sense) gives us great assessment of individual risks in order to be guided in the healthcare system. I see two steps in the progress: first a better understanding of our risks to develop diseases based on an overall appreciation of who we are and that will leads us to meet the right doctors at the right time. My hope is that soon enough these doctors will have the best tools to personalise the way they help us to be healthy. All these technologies, for what? Towards new players to serve users As I said before, we are now to be considered as users. And as users, we take informed decisions on what we should buy. One of the central players in the healthcare system is health insurance and sadly, health insurance is one of the last industries which has done nothing to make things accessible: we don’t know what we pay for, what the level of service is, if there is any service at all... In the US, statistics say that . This market status in the US is well documented in this . Metrics seems to be similar in France even if less documented. only one out of seven Americans understands the basic components of a health-insurance plan (Journal of Health Economics) great series of articles The complexity arises from several factors: a multi-stakeholder market (I am not the one choosing how I treat my symptoms, it is the doctor who makes the choice), a complex billing process (shared between doctor, Sécurité Sociale, Complémentaire Santé, reste à charge, ticket modérateur…), no user-centric focus. Indeed, even if you wanted to interact more with your health insurance, the user experience would be really poor today. We don’t understand benefits, websites are far away from what we see in other fields, and it looks like an insurer’s objective to make his customer service difficult to reach. It obviously creates strange dynamics and misalignment of interests. The interactions with your insurance are just about transaction, confrontation . “how can I get paid back as fast as possible?” Nevertheless, I believe that we are today at the edge of a new beginning where product design and technologies can help us to create a user-centric insurance where everything is about giving the best experience. In the US, some start-ups with very interesting metrics of adoption have paved the way: in New York, or . Oscar Collective Health Clover Health In France, we can improve the understanding of what we pay for, how we can navigate in this complex and sometime unfriendly healthcare system. It means building trust between players (the patient, the doctor, the insurer) and going towards more active and personalized prevention. A great health insurance should help us do that. It should be the platform to reach all the technologies and services we have started to analyse in this article, always thinking of what is best for its users. It is a dramatic switch from what exists today, but it can be done with the right focus and technologies.

Boosting French Healthcare

Managing applications often comes up as one of the biggest concerns for businesses; How can it work smoothly? How do we ? How do we maintain best practices with constantly evolving infrastructure? In this article, we run through the best approach for operational excellence looking at , , and and best practices. at scale monitor so many resources serverless monitoring strategy serverless alerting strategy security compliance The Serverless Challenge In a containerized application, there would be anywhere between 5-20 servers with business logic within them. To monitor this, an agent would simply be attached to oversee the entire system and . The , as we know. At scale, there can be thousands of resources using tens of different services making monitoring and alerting much more complex. alert of any issues serverless model is quite different A typical serverless application would look like this: Challenge 1: The , particularly if we consider that for everyone resource there are at least five pieces of related data. Think logs, metrics, traces, and configuration data. volume of data from a serverless application grows exponentially Challenge 2: As . While we may know of a few points of failure within our own infrastructure, . For this reason, it’s best to view each resource as a potential source of failure that must be monitored. infrastructure grows, so does the variety of failures it’s near impossible to know them all The Solution Observability. Different from visibility, observability is the measure of the internal states of a system inferred by the knowledge of its external output. Using this method requires no interference and instead puts data output to work, helping to visualize and understand the health and performance of the serverless application. For success, it’s a case of breaking the traditional silos of data and sorting and organizing the mass volume into workable, relevant, and actionable insights. Serverless Application Monitoring Strategy A how your application is running, gathering insights, and discovering opportunities for performance and cost optimization. strategy for serverless monitoring is key to understanding First, let’s look at the primary goals of monitoring: Reduce time to discovery for customer-affecting incidents. Increased confidence in building quickly and iterating products with minimal risk. Enabling developers to focus on customers, not operations. To learn from mistakes, and to pre-empt more issues. Next, we need to conduct some risk mapping (SLAs) making clear what parameters are acceptable. What are the application requirements for , and These three elements are intertwined and dependent on each other, so it’s important to make this clear early on. What is the acceptable time to discover failures? After discovery, what is the acceptable time to fix an error or failure? Speed Uptime Cost? Finally, state clearly the . This may be completely obvious but it’s important to have these referrals any time it goes off track. requirements of having monitoring in place For the infrastructure to ingest, navigate and interrogate the data. To detect failures as quickly as possible. To debug the system quickly and understand the issue efficiently. Statements and requirements now made, next is the best approach to navigating and monitoring data. It’s important to democratise away from silos for true interrogation and useful analysis. This means breaking down traditional barriers between logs, metrics and tracing data. Having a unified view through correlating metric events from log events, and having cross region and cross account visibility provides better context within the bigger picture of the application. It’s also important to have the ability to look at data differently making the data really work for you and your needs. This might look like various reports, a dashboard or search and query functions. Arguably, however, the most important quality for serverless monitoring is elasticity. We need the architecture to automatically digest the changing data from constantly changing resources, without the overhead of importing or configuring the data to make it readable. This is how Dashbird makes full use of elastic data ingestion. While big picture views are important, so are specific level views. Account and Microservices Level : to detect major problems and fix these as early as possible. Goal Through this specific lens, we can for overall application health, the most concerning areas as well as cost and activity metrics. understand trends Resource Level : to understand the specific resource’s health, performance, costs, and associated problems. Goal Given this is a place where developers spend most of their time, it’s to include in your strategy. Looking at a Lambda resource, for example, there are multiple areas to analyze: invocations, errors, cost, cold starts, and memory usage. We need to be able to drill down into anomalies, and past and present errors in order to improve and better align with best practices. Here is how Dashbird does this: one of the most important views Execution Level : to understand the problem in detail. Goal It’s at the execution level that we are able to like duration, memory usage, and start and end times for issues and optimization. Going deeper though, we can look at the profile of the execution; requests to other resources, how long it took, and its level of success. We can also detect and here. source full activity details retries cold starts It’s with excellent monitoring, we can defend against known and unknown failures. Serverless Application Alerting Strategy For true operational excellence, . Failures and errors are inevitable and so reducing the time to discover and fix is imperative. As discussed, monitoring needs to be constant with preemptive checks continuously running for security, best practice, cost, and performance. However, we also need to be able to filter log events for errors and failures; . monitoring needs to be paired with a good serverless alerting strategy this is the first step in the alerting strategy Failure Detection from Logs Filtering log streams in real-time to detect failures is . Dashbird has for log data coming in, without the need for any code changes or agents. Filtering is also possible with other systems, however, it can be lengthy to set up and difficult to maintain as changes to serverless architecture takes place over time. critical to operations automated alerts Metric Alarms Understandably crucial is the use of metric alarms, however, a common challenge is the and . scale of metrics what to prioritize without too much noise We recommend starting with . For example, starting with an API Gateway and going downstream to the function and database used on that journey. . However, an alternative for those with time constraints is to focus on the API only. From here, errors or latency issues will inevitably show up for which you can investigate when needed. customer-affecting metrics All of these components should ideally be monitored is another great metric to prioritize for maximum impact. For resources with high memory usage, for example, there can be an increased delay in downstream services. This snowball effect making it important to identify early. Leading indicators can add a huge amount of strain Operational excellence also takes into account , and so and are good to include in your strategy too. While you might not have many risks of unexpected increased costs, we recommend and , ticking off one less thing to have to worry about. efficient spending cost monitoring alarms setting up limits alerts from the start When it comes to your alerting strategy, do it programmatically and centralize the alerts with other alerts. and so it makes perfect sense for the . A serverless application is already heavily intertwined alarms to have some level of integration too A Dashbird example of the alerts available: Serverless Security and Compliance Best Practice Serverless best practice involves and . Things like encryption, habitually setting up functions with the rule of least privilege and detecting redundant services to improve costs are all part of this. Dashbird continuously checks across the whole infrastructure to ensure this: continuous risk management assessment of optimization opportunities Also in best practice is to . Test failures and errors in a staging environment on a regular basis, often once a quarter is enough but the practice depends on the – an important consideration. plan and proactively practice incident management size and impact of the failure For unplanned incidences, it’s always best practice to , setting time aside for to . Importantly too, against stated goals and SLAs helps to give a and . document them review prevent it from happening again tracking actual performance full view of the application’s success areas that can be improved It goes without saying that and practice, especially in serverless. operational excellence takes a lot of time or continue doing: Some things you can do today to get started Read the . AWS Well Architected Serverless Whitepaper Work with AWS solutions architects and the serverless community. Use to implement best practices. third-party tools There are many on the market with free trials, like , reducing risk and commitment. Dashbird Previously published at https://dashbird.io/blog/operational-excellence-serverless-application/

How to Optimize Large Scale Serverless Applications for Operational Excellence

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Georgia O'Keeffe, a pioneering American artist known for her iconic paintings of enlarged flowers, New York skyscrapers, and New Mexico landscapes, left an indelible mark on the art world. Beyond her artistic achievements, O'Keeffe's life also invites contemplation on the complex relationship between creativity and mental health. Delving into her story provides insight into the challenges she faced, the impact on her mental well-being, and the enduring connection between artistic genius and mental health struggles. Early Life and Artistic Beginnings: Born on November 15, 1887, in Sun Prairie, Wisconsin, O'Keeffe showed early promise as an artist. She attended the School of the Art Institute of Chicago and later taught art in Texas. However, it was her move to New York in the 1910s that marked a turning point in her career. There, she became associated with the American modernist movement, and her unique vision began to emerge. Mental Health Challenges: O'Keeffe's life was not without its share of challenges, and her mental health became a focal point of her personal struggles. While there is no definitive diagnosis, some historians and biographers have suggested that she may have experienced bouts of depression and anxiety throughout her life. These challenges manifested in her art and were particularly evident during periods of emotional turbulence. Art as Catharsis: O'Keeffe's artistic expression served as a powerful outlet for her emotions and inner turmoil. Her ability to translate complex emotions onto canvas allowed her to navigate and communicate her mental states in ways that words often could not. The vivid colors and bold forms in her paintings often mirrored the intensity of her internal struggles. New Mexico and Mental Renewal: In the 1930s, O'Keeffe made a significant shift in her life by relocating to New Mexico. The vast landscapes and the unique quality of light provided solace and inspiration. The move coincided with a period of renewed creativity and stability in her mental health. O'Keeffe's time in New Mexico marked a pivotal chapter in her life, where she found both artistic and personal renewal. Legacy and Reflection: Georgia O'Keeffe's legacy is one of artistic brilliance, but it also offers a lens through which we can examine the complexities of mental health. Her journey suggests that creative individuals, while endowed with extraordinary talents, may also grapple with internal struggles. O'Keeffe's ability to channel her emotions into art speaks to the therapeutic potential of creative expression and its role in mental well-being. The life and work of Georgia O'Keeffe stand as a testament to the intricate relationship between creativity and mental health. Her artistic journey, marked by triumphs and challenges, offers a nuanced perspective on the ways in which the mind of a creative genius can both fuel and be fueled by the ups and downs of mental health. O'Keeffe's story encourages us to appreciate the complexity of the human experience and underscores the importance of understanding and supporting the mental well-being of those who contribute to the world through their artistic endeavors. Also appears . here

Discovering the Intersection of Creativity and Mental Health: Georgia O'Keeffe's Journey

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In the realm of workplace safety, Personal Protective Equipment (PPE) serves as an armor shielding employees from potential hazards, ensuring their well-being in high-risk environments. However, enforcing and monitoring PPE compliance has long been challenging for organizations, leaving room for lapses that can lead to severe consequences. Enter Vision AI, a revolutionary technology that promises to transform PPE monitoring like never before. Powered by advanced algorithms and machine learning, Vision AI equips cameras to recognize and analyze real-time data, accurately detecting the presence or absence of various PPE items. This blog sets its sights on the captivating world of Vision AI and its profound impact on PPE monitoring. As we embark on this journey, we will uncover organizations' challenges in ensuring proper PPE usage and explore how Vision AI steps in as a game-changer to address those concerns. The Current State of PPE Monitoring Personal Protective Equipment (PPE) encompasses an array of safety apparatus designed to shield employees from potential dangers they may encounter in their workplace. This indispensable gear, ranging from helmets and safety goggles to masks, gloves, and vests, is meticulously crafted to mitigate particular hazards across various industry sectors. Take, for example, the construction field, where workers don robust helmets to defend against the peril of falling debris. In contrast, healthcare professionals utilize specialized masks and gloves as essential tools to halt the transmission of infectious diseases, reflecting the nuanced application of PPE in diverse occupational environments. PPE is critical for ensuring the safety and well-being of workers in high-risk environments, making it an indispensable component of workplace safety protocols. However, effectively monitoring PPE usage presents a complex challenge for organizations today. Human monitoring, though essential, is prone to human error and may need help addressing a workplace’s scale and intensity. Ensuring consistent PPE compliance among employees becomes increasingly complex in large or dynamic work environments where constant monitoring is not feasible. Additionally, the diverse nature of PPE across industries and job roles further complicates the monitoring process. As organizations strive to meet regulatory requirements and uphold safety standards, they face the crucial task of consistently ensuring that each worker dons the appropriate PPE. Balancing efficiency, accuracy, and privacy concerns in the PPE monitoring process poses a significant hurdle that demands innovative solutions to enhance workplace safety. The Solution: Vision AI for PPE Monitoring To overcome the limitations of manual supervision, organizations are exploring modern technologies such as Computer Vision, also known as Vision AI. Today, Vision AI is emerging as a game-changer in Personal Protective Equipment (PPE) monitoring, allowing organizations to monitor compliance 24/7. This automated approach to PPE monitoring plays a significant role in reducing accidents and injuries due to PPE violations and unsafe work behavior. Fueled by advanced algorithms and machine learning, Vision AI introduces a new dimension of precision and efficiency in monitoring PPE usage. By empowering cameras to detect and analyze real-time data, Vision AI becomes a vigilant sentinel, safeguarding employees and workplaces from potential hazards. How Vision AI Monitors PPE Usage and Compliance? Vision AI operates on real-time detection, utilizing sophisticated algorithms to identify and track PPE compliance. Equipped with cameras strategically placed across the work environment, the system scans and monitors employee activities, recognizing various PPE items such as helmets, safety goggles, masks, gloves, and vests with remarkable precision. When a PPE violation is detected, the Vision AI system instantly generates real-time alerts and notifications. These timely interventions promptly address non-compliance, minimizing potential safety risks and creating a safer workplace for organizations. Core Capabilities of Vision AI for PPE Monitoring Having learned how Vision AI for PPE monitoring works, let's have a look at some of the core capabilities of Vision AI-powered solutions: Vision AI excels in real-time detection of Personal Protective Equipment (PPE). Equipped with advanced algorithms, it swiftly identifies and tracks various PPE items worn by employees, such as helmets, safety goggles, masks, gloves, and vests. The technology's ability to process data instantaneously ensures immediate alerts and notifications when non-compliance is detected, enabling swift corrective actions to enhance workplace safety. Real-Time Detection: : Vision AI solutions have exceptional accuracy and precision. Through continuous learning and improvement, the system improves its capabilities, reducing false positives and false negatives. This high level of accuracy instills confidence in organizations, ensuring that each instance of non-compliance is reliably detected and addressed. High Accuracy and Precision : A crucial aspect of scalability involves the seamless deployment and integration of Vision AI models with existing camera and surveillance infrastructure. This adaptability makes the technology well-suited for a wide array of industries and workplaces, such as manufacturing, construction, and healthcare. Regardless of the sector, Vision AI can be fine-tuned to align with specific PPE monitoring needs. Its capacity to effortlessly accommodate various PPE types and configurations empowers organizations to personalize the system according to their distinct safety protocols. Scalability and Integration : Beyond real-time detection, Vision AI offers valuable data analytics and insights. It collects and analyzes data on PPE usage and compliance, providing organizations with crucial information to identify patterns, trends, and areas for improvement. This data-driven approach enhances decision-making, enabling targeted training programs and safety measures to boost overall compliance and safety. Data Analytics and Insights Vision AI is designed to continuously learn and improve its capabilities. As it gathers more data and encounters diverse PPE scenarios, the system refines its algorithms, achieving even greater accuracy over time. This adaptability ensures that Vision AI remains at the forefront of PPE monitoring technology, delivering reliable results to safeguard employees effectively. Continuous Learning and Improvement: : By promoting PPE compliance and addressing non-compliance in real-time, Vision AI cultivates a robust workplace safety culture. Employees become more aware of the importance of wearing appropriate PPE, knowing that the system is vigilant in ensuring their well-being. This heightened safety consciousness fosters a secure and productive work environment. Enhanced Workplace Safety Culture : Recognizing the importance of privacy and data security, Vision AI solutions incorporate robust measures to protect sensitive information. Organizations can implement ethical and responsible practices to strike the right balance between safety and privacy concerns, assuring employees that their personal data is safeguarded. Privacy and Security Measures Incorporating these core capabilities, Vision AI revolutionizes PPE monitoring, setting new standards for workplace safety and compliance. As the technology continues to evolve, its potential to elevate safety practices and protect employees remains limitless, making Vision AI an indispensable tool for organizations striving to create secure and thriving work environments. Embracing Vision AI for PPE Monitoring opens doors to a safer, proactive, and more efficient business future. With its real-time detection, high accuracy, and adaptability, Vision AI provides a robust solution to overcome the challenges of manual PPE monitoring. Vision AI's data-driven insights further empower organizations to make informed decisions and implement targeted measures for continuous improvement. By harnessing the power of this transformative technology, businesses can enhance workplace safety, improve overall compliance, and foster a culture of proactive safety consciousness among employees. Vision AI for PPE monitoring

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