Iterative Health is pioneering the use of artificial intelligence-based precision medicine in gastroenterology (GI), with the aim of helping to optimize clinical trials investigating treatment of inflammatory bowel disease (IBD)

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Summary Public assistance healthcare programs like Medicaid and SNAP have been associated with increasing costs for years. Medicaid and SNAP costs have increased year over year from 2021 to 2023, and average annual spending is expected to peak in FY2024 by going well beyond . These are symptoms of broader problems, such as recurring costs, inefficient benefit and case-based processing, legacy information systems, program bottlenecks, time-consuming healthcare services delivery, lack of digitization, and low-quality patient and provider experiences. 9.9% $800Bn Introduction In this article, I discuss the most-critical business and technology challenges faced by public-assistance healthcare programs like Medicaid and SNAP, why their is an increasingly important decision, and how product management can aid in successful digital transformations of legacy healthcare information systems and processes. I also highlight product management frameworks, strategies, principles, AI models, and use cases to digitize healthcare information systems across the US. digitization Challenges Several critical problems associated with public assistance healthcare programs like Medicaid and SNAP emphasize the need for digitization. This section discusses these challenges and their impact on Medicaid, SNAP, and the recipients. Public assistance programs often rely on manual and paper-based processes, which are time-consuming, prone to errors, and result in significant administrative burdens. The manual processing of applications, eligibility verification, and benefits distribution leads to delays and inefficiencies in providing timely assistance to those in need and heightened risks of fraud and abuse. Manual and Paper-Based Processes: Limit program administrators from making informed decisions and providing high-quality services to beneficiaries. Without the ability to identify trends in healthcare service utilization, administrators may be unable to understand the needs of patients, leading to wasted resources, suboptimal healthcare outcomes, and even denied access to necessary care. Insufficient D ata Insights : Determining can involve extensive documentation, income verification, and compliance with specific criteria. Manual processing and lack of automation contribute to delays and errors in eligibility determination, potentially leaving eligible individuals without access to necessary healthcare or food assistance. Eligibility Determination Complexity: eligibility for public assistance programs Hinder the efficiency and effectiveness of healthcare services. Outdated technological systems can pose several challenges for public-assistance healthcare programs, including security risks, issues, and compatibility problems. Legacy Platforms: data integration Additional delays in accessing care, higher costs, and the utilization of additional staff resources further create administrative burdens that increase program and operational expenses. Administrative Burdens: Public assistance programs must adapt to changing socioeconomic conditions, legislative changes, and emerging healthcare challenges. Manual or legacy processes make it difficult to respond quickly and efficiently to these evolving needs, resulting in delayed policy implementations and potentially leaving vulnerable populations without essential support. Difficulty in Adapting to Changing Needs: Far from being impossible, the solution lies in cutting-edge, product-management-driven methodologies that leverage digitization. This allows for next-gen solutions that deliver improved end-user experiences, streamline processes, improve accessibility, enhance data integration and analytics, reduce fraud, promote user engagement, and . reduce operational costs In the subsequent sections, I detail key product management strategies and frameworks that product managers can leverage with a combination of AI business processes to effectively drive the digitization of public assistance healthcare programs, improve patient and provider experiences, and ensure better outcomes for all end-users and stakeholders. Product Management Strategies This involves understanding the needs and challenges of the beneficiaries and stakeholders of public assistance healthcare programs. Product managers can conduct user research, interviews, and usability testing to gather insights and ensure that the digitization efforts are aligned with the users' requirements. AI can be integrated by leveraging machine learning algorithms to analyze user data, identify patterns, and personalize the user experience, thus enhancing the effectiveness of the digital product. User-Centric Approach: Agile methodologies enable iterative and adaptive approaches to software development, which are well-suited for the dynamic nature of public assistance programs. Implementing agile practices like Scrum or Kanban allows for flexibility and responsiveness to changing requirements. Frequent feedback loops and short development cycles facilitate the early detection of issues and provide opportunities for quick iterations to enhance digital solutions for meeting evolving healthcare landscape needs. Agile Development Methodologies: Continuous improvement is essential for the long-term success of digitized public assistance programs. Regular evaluation and feedback mechanisms enable programs to identify areas for enhancement and optimization. Performance metrics, user analytics, and user feedback surveys can provide valuable insights into the effectiveness of digitized systems. Continuous Improvement Cycles: Leverage AI models and algorithms to analyze large volumes of healthcare data, identify patterns, and generate actionable product feature insights. AI can aid in optimizing healthcare services (think chatbots, intelligent eligibility predictions, automated case-based processing), personalized patient care, and predicting health outcomes. AI-Driven Decision Making: Prioritize robust data privacy and security measures to safeguard patient information. Comply with regulations like HIPAA and implement encryption, access controls, and secure transmission protocols. Data Privacy and Security: Product Management Frameworks Used to pinpoint the specific needs and preferences of target customers, develop a product that addresses those needs, and identify the particular features and benefits that underserved populations are looking for in a digital solution. This framework is essential because it helps deliver mission-critical features to optimize healthcare information system processes. It is beneficial for integrations with providers and data interfaces. For example, it can be used to modify eligibility determination calculation parameters to predict which applicant is eligible intelligently and needs premium payments VS which applicant does not require premium payments due to Type of Assistance (TOA). The Value-Proposition Canvas: Used to determine the most important features to customers, prioritize development efforts accordingly, and identify the features that had maximum user value within shortened time constraints and that could require the least product development efforts. This framework is essential because it provides an approach to building digital Self-Service and Worker Portals that are in sync for healthcare applications and application processing with real-time insights on case flows, requests for more information, and case document verifications. For example, it can be used to prioritize different case flows for role-based caseworker and supervisor evaluations to reduce incorrect processing and inconsistencies in case information and expedite responses to RFIs. The Kano Model: The emphasizes a build-measure-learn approach, where product managers build minimum viable products (MVPs), measure their impact, and learn from user feedback. Product managers can use the Lean Startup framework to iteratively develop and refine requirements for healthcare information systems based on real-time data and insights. Such an approach can also allow for the building of optimal flows that simulate case processing, managed care interactions, benefits determination, and eligibility predictions for targeted interventions to improve patient and provider experiences. Lean Startup: Lean Startup framework Finally, I want to highlight use cases that integrate the above strategies and frameworks for allowing product leaders to digitize public assistance programs through healthcare information systems successfully. The use cases allude to mission-critical end-to-end product management initiatives I led at Deloitte for helping multiple US States digitally transform their healthcare operations. They also provide insights on the results, the impact created, and why digital product management is critical in improving public assistance healthcare programs. Conclusion By combining product management principles with appropriate strategy frameworks, public assistance programs can evolve to meet the changing needs of beneficiaries, deliver more efficient services, and achieve positive social impact. As discussed in this article, digital product management’s role in next-gen service delivery is instrumental in advancing health equity and reducing healthcare disparities. Digital product managers should continue innovating and refining product offerings and systems implementation to prioritize enhanced healthcare services delivery, reduction in operational costs, and state-of-the-art user experiences.

Strategic Digital Product Management for Improving Public-Assistance Healthcare

On December 27th, 2022—just a couple of days after Christmas and less than a month post-ChatGPT’s grand reveal to the world—a team of Google and DeepMind researchers published on a new large language model they named and its in a medical and clinical setting. their findings MedPaLM “encouraging results” And while the globe is still firmly grasped in a ChatGPT frenzy, its virtually anonymous “cousin” MedPaLM could potentially herald a not-too-distant revolution in how we administer care. So What Exactly Is MedPaLM? To understand what MedPaLM is, let’s first break down what each letter in its abbreviated name means (a bit of an acrostic poem, if you will): Med: Medical MedPaLM is based on Google’s 540 billion parameters strong and was specially adapted and optimized to be tested on , a newly formulated benchmark for testing large language models (LLMs) for medical and clinical applications combining six existing open question-answering datasets spanning professional medical exams, research, and consumer queries (NedQA, MedMCQA, PubMedQA, LiveQA, MedicationQA, and MMLU) as well as a new dataset named HealthSearchQA focused on general medical knowledge searched for by consumers. PaLM model MultiMedQA Pa: Pathways Pa stands for , a new AI architecture launched by in October 2021, designed to handle a multitude of tasks simultaneously and learn new tasks at hyper-speed. Breaking away from the conventional method of training machine learning models to perform a single task well, Pathways ushered in a new era of AI models capable of performing multiple tasks at once while also drawing upon and combining their existing skills to learn new tasks faster and more effectively. Pathways Google - Jeff Dean, Google Senior Fellow and SVP, Google Research. “We want [this] model to have different capabilities that can be called upon as needed and stitched together to perform new, more complex tasks – a bit closer to the way the mammalian brain generalizes across tasks.” ‍ Google's Pathways AI architecture ‍ LM: Language Model LM is short for (in this instance, a Large Language Model), a computational model designed to understand and generate human language. Or, in simpler terms, a statistical model that predicts the probability of the next word in a given sequence of words. Language Model Given a sentence such as "The cat sat on the ____," a language model can predict that the most likely word to follow is "mat," "rug," or "chair," based on the probabilities of these words appearing in the context of the sentence. Large language models can be trained on vast amounts of text corpora using machine learning algorithms to learn the patterns and structure of language. Once trained, these models can be utilized for a range of natural language processing tasks, including text generation, translation, sentiment analysis, and more. How Was MedPaLM Developed? MedPaLM is the product of an extensive iterative experiment conducted by a team of Google and researchers to assess the efficiency and accuracy of large language models for medical and clinical applications. DeepMind ‍ What does that mean? Imagine you’re trying to perfectly tune a violin. To hit the perfect note, you need to continuously increase and decrease string tension and turn the knobs ever so gently to reach the desired outcome. In many ways, this is precisely what MedPaLM’s creators did to achieve their objective. So rather than thinking about it as an entirely new language model, think about it as Google’s PaLM model deliberately and meticulously calibrated for maximum precision in a medical setting. Ok, so how was it “tuned”? To tune or, more precisely, train MedPaLM, its researchers used prompts. Prompts for large language models are pieces of text used to guide or direct the generation of a new text by the model. They are intended to help the model produce output that is relevant to a particular task or topic and can take various forms, such as fill-in-the-blank statements, questions, or context-setting phrases. So, for example: "Complete the following sentence: The capital of France is ____." "What is the best way to prepare a grilled cheese sandwich?" "Write a paragraph describing the causes of the American Civil War." "In this passage from a novel, a character is introduced: 'She was tall and thin, with piercing blue eyes and a serious expression on her face.' What is the character's name and what is she doing in this scene?" Why do I need a healthcare-purposed language model to tell me the best way to prepare a grilled cheese sandwich? You don’t. This is exactly why MedPaLM’s developers employed a technique called Instruction Prompt Tuning (IPT).  Instruction Prompt Tuning is a method of fine-tuning a large language model for a specific task by providing examples of desired input and output pairs in the form of prompts. So rather than construct an LLM with general answers to general topics, MedPaLM’s team used guidelines and exemplars from a panel of qualified clinicians for each consumer medical question-answering datasets to prompt tune Flan-PaLM (MedPaLM’s previous iteration) and form a hyper-healthcare-focused LLM: MedPaLM. ‍ ‍ So How’s MedPaLM Performing? Well, that really depends on what you want it to do. In terms of providing answers to consumer medical questions, 94.4% of MedPaLM’s answers were judged as directly addressing the user’s question intent—only 1.5% less than a human clinician. 80.3% of its answers were found to be helpful, 11% less than a human clinician but 30% better than its predecessor Flan-PaLM. Impressive? You bet. ‍ ‍ But in other fields tested on, MedPaLM leaves much to be desired. In 16.9% of its answers, MedPaLM retrieved incorrect information as opposed to 3.6% for human clinicians. Incorrect reasoning was seen in 10.1% of MedPaLM’s answers and in just 2.1% of clinician answers. Incorrect comprehension occurred in 18.7% of cases for MedPaLM and in 2.2% for clinicians. What Does This Mean for Healthcare? Trying to definitively predict how MedPaLM and other LLMs will be harnessed for clinical and medical purposes is a bit of a fool's game at this stage. As impressive as recent advancements in this field are, a single error could have disastrous results within the life-or-death stakes of medical care. One incorrect answer to a patient's query (i.e., what drug should be taken to address a specific condition) could lead to hospitalization and even death. This issue is further exacerbated by the fact that large language models are essentially enormous black boxes and lack the explainability or the ability to pinpoint exactly why they gave a certain input. Solving an error starts with knowing how to find the problem, and as spectacularly capable as MedPaLM may be, that's just one (out of many) things it can't do. So, What Can LLMs be Used for in Healthcare? Approximately 7,287 articles ( ) are published monthly in medical journals. To theoretically keep up with the literature, a physician would need to dedicate 627.5 hours per month, or about 29 hours per weekday, or 3.6 full-time equivalents of physician effort to the task. And though this isn't realistic in any scenario, LLMs are incredibly effective in summarizing, analyzing, and presenting key points from long-form text, drastically cutting down the time needed to stay up to date with the latest research. Summarizing and analyzing research papers: with an average of 5.16 minutes reading time Large language models can be used to improve the quality of clinical documentation by identifying missing or incomplete information in medical records. Improving clinical documentation: By generating appropriate codes for procedures and treatments, LLMs could help reduce errors and ensure clinicians are adequately compensated for their work. Coding and billing: Using LLMs, clinicians can identify trends and patterns in patient health and develop targeted interventions to improve population health outcomes. Population health management: By analyzing large amounts of data and identifying patterns and trends that may not be easily traceable with traditional statistical methods, LLMs can be employed to facilitate clinical research at increased velocity and scale. Clinical research: LLMs can be leveraged to sift through and analyze patient feedback and sentiment through thousands of surveys and social media interactions to identify critical areas for improvement in patient satisfaction, access, and experience. Patient satisfaction analysis: What About Appointment Scheduling? LLMs are only as good as the data they’ve been trained on. ChatGPT, for example—albeit its objectively impressive knowledge retrieval capabilities—was only trained on parameters dating to 2021. Any event that took place at any time past its cut-off date of September 2021 is entirely out of its scope. So even though Boris Johnson resigned as Prime Minister of the United Kingdom in September 2022 and has since been succeeded by two prime ministers (Liz Truss and currently Rishi Sunak), ChatGPT is (self-admittedly) completely unaware. ‍ ‍ But what if I train an LLM on a list of all of my health system’s physicians? Wouldn't that work? It would. But only until the moment even the most minute change takes place. So, for instance, if just one of your physicians moves to a different clinic, adds another specialty to their care offerings, updates their working hours, or even resigns, your appointment-scheduling LLM will provide your patients with wrong and misleading information. If even a single piece of data is no longer relevant, the entire model becomes corrupted and, therefore, obsolete. The same can be said for any use case requiring real-time data to perform accurately. So, for example, site search, prescription refills, and up-to-date FAQs. ‍ ‍ Oh, so LLMs can really only help me with administrative tasks? On their own, yes, which is nothing to sneeze at and will save healthcare practitioners innumerable hours otherwise spent tackling bureaucratic office work. But for any use cases that demand real-time, up-to-date data, LLMs can only serve as an ingredient of a much more adaptive and flexible conversational platform, not the entire dish. , Hyro’s CEO lays out the ingredients needed to mix LLMs with enterprise-level conversational AI platforms to create truly elastic patient interactions that are driven by continuously updated organizational knowledge (proprietary data) and dynamic business logic (ever-changing internal and external processes). In this in-depth analysis Israel Krush Big Hero 6 When asked in a recent what he thought of ChatGPT and other LLMs, Andy Chu, SVP of Product and Technology at Providence Digital Innovation Group, said the industry is still a a 2014 Disney film depicting a futuristic healthcare robot—But added he envisions this technology used to answer patient-related administrative, "decision-tree", or general health-education questions. Becker's Hospital Review interview "long way off from the days of Big Hero 6,” https://youtu.be/jbKSvOfqxF0?embedable=true Chu's thoughts echo a broader sentiment shared by most digital health leaders that LLMs such as MedPaLM should be greeted with equal parts of excitement and caution. While there's no question these extraordinary "machines" can automate and accelerate a vast spectrum of primarily administrative applications, there are still glaring red flags when it comes to medical and clinical use. Furthermore, LLMs' absence of data adaptability renders them ineffective for any goal which calls for current and evolving internal organizational knowledge. Chu put it best by saying: "It's important to remember that healthcare is very personal, and generative AI technologies are as good as the data accessed. Data sources within healthcare are rightfully secure and protected, so we must be careful not to overhype the promise — especially when it comes to areas such as symptom detections, triage, and clinical treatment suggestions." Also published here.

An Intro to MedPaLM: ChatGPT's Healthcare-Focused "Cousin" 

How Much Does it Cost to Develop a Health App?

HackerNoon Reporter: Please tell us briefly about your  background. I am a doctor and clinical pharmacologist and have been coding since I was a child. Thanks to good medical care, I survived my childhood and have since been committed to excellent health care for others. What's your startup called? And in a sentence or two, what does it do? The service epha.health uses advanced AI and algorithms to predict the risk and exposure in complex therapies and calculates possible drug alternatives based on patient factors. What is the origin story? Starting in the University Hospital Zurich, we have been working for years to prevent medication errors and to protect patients from unnecessary harm. We always had the dream to create the perfect app to support doctors in such a way that errors no longer occur. With the new technical possibilities we can finally make it happen. What do you love about your team, and why are you the ones to solve this problem? Everyone has different skills and together we are more than the sum of our skills. Together we have medical and pharmaceutical expertise, coding skills in backend and frontend, knowledge in data analytics and data science. Our joint decision is usually an iterative process, but better than any individual decision. If you weren’t building your startup, what would you be doing? Building another startup. At the moment, how do you measure success? What are your core metrics? Customers paying for the subscription of epha.health. What’s most exciting about your traction to date? Over the past four months since our launch, we have grown more than 50% per month in worldwide user web traffic. Our greatest success is that these users (doctors) started to pay for the subscription without being reimbursed from health insurances. What technologies are you currently most excited about, and most worried about? And why? With data science, we can increasingly generate new knowledge and make better predictions. My fear is that the knowledge in the wrong hands will lead to people being deprived of their freedom. That dystopian future has already begun when the Tesla autopilot shuts down for a day when you don't have your hands on the steering wheel. What drew you to get published on HackerNoon? What do you like most about our platform? Coincidence. I have to see. What advice would you give to the 21-year-old version of yourself? Wait for it. Good ideas take time to be recognised in society. What is something surprising you've learned this year that your contemporaries would benefit from knowing? Software provider in healthcare are on a different level. Epha.health was nominated as one of the best startups in  Zürich, Switzerland, in Startups of the Year hosted by HackerNoon.

Startup Interview with Marco Egbring, CEO of epha.health

Crystal Evans, a 40-year-old with a progressive neuromuscular disease, fears bacteria will grow inside the silicon tube that connects her windpipe to a ventilator that pumps air into her lungs. As she , she used to have a thorough routine before the pandemic. It involved changing the plastic circuits several times a month to keep them sterile. told the Washington Post Now that the pandemic has caused a shortage of silicon and plastic needed for the tubing and made these materials harder to supply, Ms. Evans has to go by just one new circuit a month. What it means is that she and many patients like her have, strictly speaking, fallen prey to the disrupted supply chain. And estimates of those affected are in the tens of millions. The scale of disruption is rampant. According to Kaufman Hall, the healthcare consulting firm, since the beginning of the pandemic, 86% of US hospitals have such medical supply chain issues as complicated sourcing of supplies and 85% complained of price surges. reported And while in 2022, the healthcare industry seemed to have ground out a steady victory over the virus, the major fallout COVID-19 had on the healthcare supply chain (which is now being aggravated by Russia’s invasion of Ukraine) is still to be tackled. The key healthcare supply chain issues to be addressed span: Limited availability of critical pharmaceuticals, especially those involved in the treatment of acute respiratory illnesses, and personal protective equipment Troubles in procuring materials used for manufacturing pharmaceuticals Limited availability of base metals and raw materials used for manufacturing medical equipment Difficulties in producing and supplying semiconductors (used for MRI machines, blood pressure monitors, and other medical equipment) caused by disrupted access to such rare materials as neon gas and C4F6 chemical A surge in procurement and operational costs fueled by rising crude oil, gas, plastics, and energy prices Limited visibility into the global supply chain and the healthcare supply chain as a part of it Failed offshore procurement and the need to redesign sourcing strategies to nearshore As experienced , we at ITRex believe that it is high time healthcare supply chain leaders rethought their management strategies and started responding to the medical supply chain issues highlighted by COVID-19. We also believe that technology can become a strong ally to those who are ready to embark on a transformation journey. healthcare solutions development vendors To help you navigate change, we are pointing out key healthcare supply chain issues and spotlighting technology solutions that help overcome these challenges. Critical healthcare supply chain issues to tackle in 2022 Issue 1. Unnecessary spending attributed mainly to poor inventory management One of the critical issues in the medical supply chain is ever-rising and unnecessary spending. In fact, a of 2,127 US hospitals found that unnecessary spending on supply chain operations has reached about $25,7 billion a year. And a large share of those spendings, , can be freed up by optimizing inventory management. Navigant study $11 million per facility annually Hospital inventory management is a complicated process that involves predicting optimal inventory volumes, tracking deliveries, designing appropriate storage strategies, managing recalls and expired products, and more. It is often difficult for the supply chain employees to simply get the needed products to patients and physicians, let alone optimize the process. However, with the application of recent technological advances, supply chain executives may overcome acute inventory management issues in healthcare supply chains, realize potential savings, and set back the spike in operational costs. Solution: Optimizing inventory management Technology can tackle inventory management issues in the healthcare supply chain in a variety of ways. The following two, we think, drive the most impact: increasing visibility for the inventory and bringing in fresh insights driven from historical inventory data. Improving inventory visibility Statistics and the experience of our customers prove that it is not always easy for hospital workers to keep an eye on inventory and make sure they have all the needed items on hand. What is more, 85% of medical workers claim they have trouble documenting supplies and 25% of them say that they don’t always check product expiration or recall info. What could help address the healthcare supply chain issues related to inventory is, switching to a more transparent, technology-enabled inventory management. Keeping track of inventories via a secure, cloud-based platform and bringing in the elements of the Internet of Things, such as beacons or sensors, for real-time tracking drastically increases inventory visibility. It also makes it easier for supply chain employees to calculate optimal inventory volumes, handle suppliers, track deliveries, and get the needed items to patients and medical staff on time. Having a real-time view of the inventories helps keep track of the quality of the delivered items, thus facilitating compliance and the management of expired or recalled products. By increasing visibility into the inventory, hospitals can recover a share of the lost revenue stemming from inventory data loss and flawed inventory management practices. Annually, this lost revenue amounts to . a whopping $7 billion Tapping into predictive insights Bringing in technology, artificial intelligence (AI) in particular, into their supply chain management mixes, healthcare organizations can use the insights generated from predictive analytics to overcome such healthcare supply chain issues as uninformed decision-making and limited knowledge about the inventory. AI algorithms can, for example, improve demand forecasting based on historical demand data and the information about external factors influencing the demand, optimize inventory distribution across multiple storage locations, streamline the management of expired and recalled products, and more. As a result, hospitals can save money by right-sizing their inventory and improving their purchasing and storage strategies. Issue 2. Supply chain talent crisis coupled with mundane manual operations Another acute challenge in the healthcare supply chain is the talent crisis — healthcare organizations are struggling to recruit, develop, and retain supply chain management workers. According to , the crisis traces back to more than 15.9 million people relocating within the US since the start of the pandemic and 1.7 million people taking early retirement. Moreover, a 1.3 percent drop in the net immigration rate has had an impact on labor supply, which is set to last. McKinsey The lack of supply chain management professionals is amplified by the fact that much of healthcare supply chain management is still carried out manually. According to a of 100 hospital supply chain leaders, nearly half of the respondents rely on somewhat manual tools, like Excel spreadsheets. recent survey While a good fit for managing supply chains of a smaller scale, when applied to more complex operations, manual tools are slow, expensive, and prone to error. What technological advances can do is digitize and even automate a larger share of supply chain management operations, thus compensating for the lacking talent and freeing workers from completing routine tasks. Solution: Automating manual operations Beyond basic digitization of supply chain management via cloud-based platforms powered with tracking and analytics capabilities, technologies can help automate a share of operations, allowing supply chains to go by with less human force. The technologies in question span blockchain, robotic process automation (RPA), and . intelligent process automation (IPA) Blockchain, for example, has the potential to fundamentally transform the way healthcare approaches contract, price, and transaction management. In turn, RPA that involves creating, deploying, and managing bots emulating certain human actions can take over the following tasks: Processing orders Handling invoices and payments Overseeing inventory volumes Reading and comparing vendor quotations Communicating shipment statuses, and more And while RPA is used to perform repetitive tasks with minimal variation, intelligent process automation — which combines RPA and artificial intelligence — tackles more complex end-to-end processes, like: Shortlisting suppliers Assessing inventory stock levels Addressing backorders and managing recalls Reviewing vendor contracts Issue 3. Complicated assurance of supply and difficulties in predicting supply shortages Another key healthcare supply chain challenge that is expected to carry over throughout 2022 is the complicated assurance of supply. The industry is dealing with primary and secondary shortages of medical commodities, equipment, and essential medicines. Primary shortages take place whenever demand is spiking for products that are directly related to disruptions, like ventilation-related products or testing supplies in times of COVID. Secondary shortages occur when products are tied to disruptions indirectly. A raw material plant shutting down and affecting the manufacturing of products used in operating rooms is an example of a secondary supply shortage. While supply chain management tools allow predicting primary shortages to a certain extent, they do not provide visibility into secondary shortages. What advanced technologies can do is help healthcare organizations better understand how pandemic, geopolitical, transportation, labor, financial, cybersecurity, and other factors dynamically affect the supply chain. Solution: Predicting supply disruptions at all tiers Technological advances can help supply chain workers better understand risks occurring at the secondary supply tier and beyond, as well as predict supply shortages. Promising tech innovations span AI-based medical solutions that pinpoint products that become critical in various disruption scenarios and then, cross-reference equivalent products across a broad network of suppliers without competitive bias. Other solutions that are winning in popularity are advanced analytics and event monitoring systems that help predict primary and secondary shortages of supply. Demand sensing tools and tools connecting trading partners also help address the problem of supply shortages. Applying the solutions above, supply chain experts can significantly improve visibility into all supply tiers, gain a better understanding of the potential risks, and weigh out the alternative sources of supply. Issue 4: An overall lack of supply chain resilience Healthcare experts state that the current healthcare supply chains are reactive in their nature. The pandemic has highlighted the need for healthcare organizations to reconsider the way they approach strategic planning and risk prevention. What technology can do is support supply chain executives in strategic planning and provide access to insights that were out of reach before. Solution: Proactive planning with risks in mind Technology can change the very approach to managing the flow of products in healthcare, facilitating the three key aspects of a resilient supply chain: Integrated into supply chain management solutions, AI algorithms can help shortlist suppliers, run automated supplier evaluations, screen supplier contracts, as well as optimize supplier management by weighing out costs and lead times. Reliable supplier relationships Technology helps enable the transparency of workflows with products being tracked in real-time along the supply chain, as well as the transparency of communication — with all the nodes within the supply chain having access to the same real-time information. Transparency of workflows Leveraging the opportunities of IoT, AI, and RPA, healthcare organizations can predict perfect inventory volumes to meet the demand without the risk of overstocking or stockouts, manage the quality of delivered items, and set automated replenishments for quicker and easier restocking. Just-in-time, lean inventory management As a result, healthcare supply chain managers may develop far more balanced management strategies and prevent risks, rather than react to disruptions. Things to keep in mind when giving your healthcare supply chain a digital overhaul Prompted by the pandemic, healthcare organizations are stepping up their tech investments. According to , nearly 81% of healthcare organizations indicated they increased their investment in technology in 2021. a recent study However, to make sure the hefty investments do pay off, it is important to remember that adopting advanced techs — including AI, IoT, and RPA — has its specifics. We recommend keeping the following aspects in mind: Scaling technology solutions company-wide The healthcare supply chain is a complicated mix of nodes connected by multiple workflows and people, and it is hardly feasible to roll out technology that covers it all. Therefore, when digitizing your supply chains, we advise taking the iterative development path. Say, you choose to to automate a share of day-to-day operations. Start with running a thorough analysis of your organization’s workflows, pinpoint what can and cannot be automated, and prioritize possibilities for value creation across supply chain segments and workflows. Start rolling RPA to those segments that have the highest value-creation potential and drive faster ROI. Once the initial solution is up and running, you may start building on it and evolve the technology further — both vertically and horizontally. roll out an RPA solution Ensuring compliance with relevant healthcare IT regulations and standards Rolling out technology in the field as regulated as healthcare is not easy. When doing so, ensure that your technology partner has a deep understanding of the complexities and specifics of the healthcare system. It is crucial that they thoroughly study the context in which the technology will be used, including organizational settings and relevant stakeholder groups, before actually designing the solution. During development, make sure that your technology vendor thoroughly documents the entire process — from ideating to designing to engineering and testing your software solution. That would make it easier to communicate with regulating authorities. Finally, before rolling out technology company-wide, consider carrying out an external audit to bring potential compliance risks down to a minimum. When adopting healthcare supply chain management technology, you have to be on the lookout for threats. Therefore, it is imperative to design a thought-out cybersecurity strategy at the beginning of the development process and reference it throughout development for built-in safety. Make sure to lay out an action plan for preventing, detecting, and mitigating security risks, as well as consider running external security audits for extra safety. Maintaining IT infrastructure security If you have your mind on addressing healthcare supply chain issues by giving your supply chain a digital overhaul, . We will answer your questions and plan out further steps. contact our experts Also published . here

Healthcare Supply Chain Issues and How to Solve Them with Technology

Debugging is not just about identifying errors—it's about instituting a reliable process for ensuring software health and longevity. In this post, we discuss the role of software testing in debugging, including foundational concepts and how they converge to improve software quality. https://www.youtube.com/watch?v=yap509UZz6M&embedable=true As a side note, if you like the content of this and the other posts in this series, check out my Debugging book, which covers this subject. If you have friends who are learning to code, I'd appreciate a reference to my Java Basics book. If you want to get back to Java after a while, check out my Java 8 to 21 book. The Intersection of Debugging and Testing Debugging and testing play distinct roles in software development. Debugging is the targeted process of identifying and fixing known bugs. Testing, on the other hand, encompasses an adjacent scope, identifying unknown issues by validating expected software behavior across a variety of scenarios. Both are a part of the debug fix cycle, which is a core concept in debugging. Before we cover the cycle, we should first make sure we're aligned with the basic terminology. Unit Tests Unit tests are tightly linked to debugging efforts, focusing on isolated parts of the application—typically individual functions or methods. Their purpose is to validate that each unit operates correctly in isolation, making them a swift and efficient tool in the debugging arsenal. These tests are characterized by their speed and consistency, enabling developers to run them frequently, sometimes even automatically, as code is written within the IDE. Since software is so tightly bound, it is nearly impossible to compose unit tests without extensive mocking. Mocking involves substituting a genuine component with a stand-in that returns predefined results, thus a test method can simulate scenarios without relying on the actual object. This is a powerful yet controversial tool. By using mocking, we're, in effect, creating a synthetic environment that might misrepresent the real world. We're reducing the scope of the test, which might perpetuate some bugs. Integration Tests Opposite to unit tests, integration tests examine the interactions between multiple units, providing a more comprehensive picture of the system's health. While they cover broader scenarios, their setup can be more complex due to the interactions involved. However, they are crucial in catching bugs that arise from the interplay between different software components. In general, mocking can be used in integration tests, but it is discouraged. They take longer to run and are sometimes harder to set up. However, many developers (myself included) would argue that they are the only benchmark for quality. Most bugs express themselves in the seams between the modules and integration tests are better at detecting that. Since they are far more important, some developers would argue that unit tests are unnecessary. This isn't true; unit test failures are much easier to read and understand. Since they are faster, we can run them during development and even while typing. In that sense, the balance between the two approaches is the important part. Coverage Coverage is a metric that helps quantify the effectiveness of testing by indicating the proportion of code exercised by tests. It helps identify potential areas of the code that have not been tested, which could harbor undetected bugs. However, striving for 100% coverage can be a case of diminishing returns; the focus should remain on the quality and relevance of the tests rather than the metric itself. In my experience, chasing high coverage numbers often results in bad test practices that persist in problems. It is my opinion that unit tests should be excluded from coverage metrics due to the importance of integration tests to overall quality. To get a sense of quality coverage, we should focus on integration and end-to-end tests. The Debug-Fix Cycle The debug-fix cycle is a structured approach that integrates testing into the debugging process. The stages include identifying the bug, creating a test that reproduces the bug, fixing the bug, verifying the fix with the test, and finally, running the application to ensure the fix works in the live environment. This cycle emphasizes the importance of testing in not only identifying but also in preventing the recurrence of bugs. Notice that this is a simplified version of the cycle with a focus on the testing aspect only. The full cycle includes a discussion of the issue tracking and versioning as part of the whole process. I discuss this more in-depth in other posts in the series and my book. Composing Tests with Debuggers A powerful feature of using debuggers in test composition is their ability to "jump to line" or "set value." Developers can effectively reset the execution to a point before the test and rerun it with different conditions without recompiling or rerunning the entire suite. This iterative process is invaluable for achieving desired test constraints and improves the quality of unit tests by refining the input parameters and expected outcomes. Increasing test coverage is about more than hitting a percentage; it's about ensuring that tests are meaningful and that they contribute to software quality. A debugger can significantly assist in this by identifying untested paths. When a test coverage tool highlights lines or conditions not reached by current tests, the debugger can be used to force execution down those paths. This helps in crafting additional tests that cover missed scenarios, ensuring that the coverage metric is not just a number but a true reflection of the software's tested state. In this case, you will notice that the next line in the body is a rejectValue call, which will throw an exception. I don’t want an exception thrown as I still want to test all the permutations of the method. I can drag the execution pointer (arrow on the left) and place it back at the start of the method. Test-Driven Development How does all of this fit with disciplines like Test-Driven Development (TDD)? It doesn't fit well. Before we get into that, let's revisit the basics of TDD. Weak TDD typically means just writing tests before writing the code. Strong TDD involves a red-green-refactor cycle: Red: Write a test that fails because the feature it tests isn't implemented yet.


Green: Write the minimum amount of code necessary to make the test pass.


Refactor: Clean up the code while ensuring that tests continue to pass. This rigorous cycle guarantees that new code is continually tested and refactored, reducing the likelihood of complex bugs. It also means that when bugs do appear, they are often easier to isolate and fix due to the modular and well-tested nature of the codebase. At least, that's the theory. https://www.youtube.com/watch?v=yImkjlm08Cw&embedable=true TDD can be especially advantageous for scripting and loosely typed languages. In environments lacking the rigid structure of compilers and linters, TDD steps in to provide the necessary checks that would otherwise be performed during compilation in statically typed languages. It becomes a crucial substitute for compiler/linter checks, ensuring that type and logic errors are caught early. In real-world application development, TDD's utility is nuanced. While it encourages thorough testing and upfront design, it can sometimes hinder the natural flow of development, especially in complex systems that evolve through numerous iterations. The requirement for 100% test coverage can lead to an unnecessary focus on fulfilling metrics rather than writing meaningful tests. The biggest problem in TDD is its focus on unit testing. TDD is impractical with integration tests as the process would take too long. But as we determined at the start of this post, integration tests are the true benchmark for quality. In that test, TDD is a methodology that provides great quality for arbitrary tests, but not necessarily great quality for the final product. You might have the best cog in the world, but if it doesn't fit well into the machine, then it isn't great. Final Word Debugging is a tool that not only fixes bugs but also actively aids in crafting tests that bolster software quality. By utilizing debuggers in test composition and increasing coverage, developers can create a suite of tests that not only identifies existing issues but also guards against future ones, thus ensuring the delivery of reliable, high-quality software. Debugging lets us increase coverage and verify edge cases effectively. It's part of a standardized process for issue resolution that's critical for reliability and prevents regressions. Debugging is not just about identifying errors—it's about instituting a reliable process for ensuring software health and longevity. In this post, we discuss the role of software testing in debugging , including foundational concepts and how they converge to improve software quality. debugging https://www.youtube.com/watch?v=yap509UZz6M&embedable=true https://www.youtube.com/watch?v=yap509UZz6M&embedable=true As a side note, if you like the content of this and the other posts in this series, check out my Debugging book, which covers t his subject. If you have friends who are learning to code, I'd appreciate a reference to my Java Basics book. If you want to get back to Java after a while, check out my Java 8 to 21 book . Debugging book , Debugging book t Java Basics book. Java Basics book. Java 8 to 21 book Java 8 to 21 book . The Intersection of Debugging and Testing Debugging and testing play distinct roles in software development. Debugging is the targeted process of identifying and fixing known bugs. Testing, on the other hand, encompasses an adjacent scope, identifying unknown issues by validating expected software behavior across a variety of scenarios. Both are a part of the debug fix cycle, which is a core concept in debugging. Before we cover the cycle, we should first make sure we're aligned with the basic terminology. Unit Tests Unit tests are tightly linked to debugging efforts, focusing on isolated parts of the application—typically individual functions or methods. Their purpose is to validate that each unit operates correctly in isolation, making them a swift and efficient tool in the debugging arsenal. These tests are characterized by their speed and consistency, enabling developers to run them frequently, sometimes even automatically, as code is written within the IDE. Since software is so tightly bound, it is nearly impossible to compose unit tests without extensive mocking. Mocking involves substituting a genuine component with a stand-in that returns predefined results, thus a test method can simulate scenarios without relying on the actual object. This is a powerful yet controversial tool. By using mocking, we're, in effect, creating a synthetic environment that might misrepresent the real world. We're reducing the scope of the test, which might perpetuate some bugs. Integration Tests Opposite to unit tests, integration tests examine the interactions between multiple units, providing a more comprehensive picture of the system's health. While they cover broader scenarios, their setup can be more complex due to the interactions involved. However, they are crucial in catching bugs that arise from the interplay between different software components. In general, mocking can be used in integration tests, but it is discouraged. They take longer to run and are sometimes harder to set up. However, many developers (myself included) would argue that they are the only benchmark for quality. Most bugs express themselves in the seams between the modules and integration tests are better at detecting that. Since they are far more important, some developers would argue that unit tests are unnecessary. This isn't true; unit test failures are much easier to read and understand. Since they are faster, we can run them during development and even while typing. In that sense, the balance between the two approaches is the important part. Coverage Coverage is a metric that helps quantify the effectiveness of testing by indicating the proportion of code exercised by tests. It helps identify potential areas of the code that have not been tested, which could harbor undetected bugs. However, striving for 100% coverage can be a case of diminishing returns; the focus should remain on the quality and relevance of the tests rather than the metric itself. In my experience, chasing high coverage numbers often results in bad test practices that persist in problems. It is my opinion that unit tests should be excluded from coverage metrics due to the importance of integration tests to overall quality. To get a sense of quality coverage, we should focus on integration and end-to-end tests. The Debug-Fix Cycle The debug-fix cycle is a structured approach that integrates testing into the debugging process. The stages include identifying the bug, creating a test that reproduces the bug, fixing the bug, verifying the fix with the test, and finally, running the application to ensure the fix works in the live environment. This cycle emphasizes the importance of testing in not only identifying but also in preventing the recurrence of bugs. Notice that this is a simplified version of the cycle with a focus on the testing aspect only. The full cycle includes a discussion of the issue tracking and versioning as part of the whole process. I discuss this more in-depth in other posts in the series and my book. Composing Tests with Debuggers A powerful feature of using debuggers in test composition is their ability to " jump to line " or " set value ." Developers can effectively reset the execution to a point before the test and rerun it with different conditions without recompiling or rerunning the entire suite. This iterative process is invaluable for achieving desired test constraints and improves the quality of unit tests by refining the input parameters and expected outcomes. jump to line jump to line set value set value Increasing test coverage is about more than hitting a percentage; it's about ensuring that tests are meaningful and that they contribute to software quality. A debugger can significantly assist in this by identifying untested paths. When a test coverage tool highlights lines or conditions not reached by current tests, the debugger can be used to force execution down those paths. This helps in crafting additional tests that cover missed scenarios, ensuring that the coverage metric is not just a number but a true reflection of the software's tested state. In this case, you will notice that the next line in the body is a rejectValue call, which will throw an exception. I don’t want an exception thrown as I still want to test all the permutations of the method. I can drag the execution pointer (arrow on the left) and place it back at the start of the method. Test-Driven Development How does all of this fit with disciplines like Test-Driven Development (TDD)? It doesn't fit well. Before we get into that, let's revisit the basics of TDD. Weak TDD typically means just writing tests before writing the code. Strong TDD involves a red-green-refactor cycle: Red: Write a test that fails because the feature it tests isn't implemented yet. Green: Write the minimum amount of code necessary to make the test pass. Refactor: Clean up the code while ensuring that tests continue to pass. Red: Write a test that fails because the feature it tests isn't implemented yet. Red : Write a test that fails because the feature it tests isn't implemented yet. Red Green: Write the minimum amount of code necessary to make the test pass. Green : Write the minimum amount of code necessary to make the test pass. Green Refactor: Clean up the code while ensuring that tests continue to pass. Refactor : Clean up the code while ensuring that tests continue to pass. Refactor This rigorous cycle guarantees that new code is continually tested and refactored, reducing the likelihood of complex bugs. It also means that when bugs do appear, they are often easier to isolate and fix due to the modular and well-tested nature of the codebase. At least, that's the theory. https://www.youtube.com/watch?v=yImkjlm08Cw&embedable=true https://www.youtube.com/watch?v=yImkjlm08Cw&embedable=true TDD can be especially advantageous for scripting and loosely typed languages. In environments lacking the rigid structure of compilers and linters, TDD steps in to provide the necessary checks that would otherwise be performed during compilation in statically typed languages. It becomes a crucial substitute for compiler/linter checks, ensuring that type and logic errors are caught early. In real-world application development, TDD's utility is nuanced. While it encourages thorough testing and upfront design, it can sometimes hinder the natural flow of development, especially in complex systems that evolve through numerous iterations. The requirement for 100% test coverage can lead to an unnecessary focus on fulfilling metrics rather than writing meaningful tests. The biggest problem in TDD is its focus on unit testing. TDD is impractical with integration tests as the process would take too long. But as we determined at the start of this post, integration tests are the true benchmark for quality. In that test, TDD is a methodology that provides great quality for arbitrary tests, but not necessarily great quality for the final product. You might have the best cog in the world, but if it doesn't fit well into the machine, then it isn't great. Final Word Debugging is a tool that not only fixes bugs but also actively aids in crafting tests that bolster software quality. By utilizing debuggers in test composition and increasing coverage, developers can create a suite of tests that not only identifies existing issues but also guards against future ones, thus ensuring the delivery of reliable, high-quality software. Debugging lets us increase coverage and verify edge cases effectively. It's part of a standardized process for issue resolution that's critical for reliability and prevents regressions.

How Software Testing Fits Into The Debugging Process

Edmund Hillary, a New Zealand mountaineer, once said, “It is not the mountain we conquer, but ourselves.” In the scorching heat of the business landscape, your company is a thirsty caravan desperate for insights to guide its journey. But beware, for many, their data strategy resembles a cracked water bottle in the desert – leaking potential and leading nowhere. Are you ready to quench your company's thirst for success with those three simple steps? Many leaders, desperate for a solution, picture a grand data strategy – a sprawling oasis filled with complex architecture and expensive tools. They gather their "data people," spend heavily, and build this technical utopia.  But here's the surprising truth: most companies never reach that oasis. Why? Because data strategy isn't about the bells and whistles. It's about clear, actionable goals. Set Clear KPIs! Start with the Destination, Not the Journey Think of your data strategy as a roadmap.  Before diving into infrastructure or systems, define the 3-5 key performance indicators (KPIs) that truly measure your company's health.  These are your North Stars, guiding everyone toward what matters most, and are the top of your priority to secure. In my book “Data is Like a Plate of Hummus’ which is based on my experience as a data leader and consultant, I many times refer to the KPIs refinement process as a funnel structure, keep things simple, keep them small, just like hummus, a very simple dish but with such powers to fill you stomach for the entire day, in my book I describe my method of refining the KPIs by starting with a set of questions, then pick the most relevant questions, and define which KPIs can answer them, the next step is to identify what action they will drive which gives you the indication to how often you will need it, sometimes seeing a KPI related to time on site every second week with the release cycles can help you understand if the new releases improved or not the user experience, you don’t need to see it daily as it will have nothing to the action of it. Next, establish data governance rules.  This isn't about restrictions but about setting expectations. When will the data be ready? What is the one true source of the data?  Who owns it? How quickly will issues be resolved?  Clear agreements create a supportive system between data users and providers.  Everyone knows their roles and what to do "when the you-know-what hits the fan" (because, let's face it, it will!). In many companies, the idea of data is that everything is important and needs to be used, but in reality, some data is less important than others 🦖.  This led us to hire more engineers and analysts and increase our investment in infrastructure, but as I mentioned a few times in previous articles and my book, will more than 5 KPIs change the decision that needs to be taken? If you need more than that, restart the process of defining your KPIs; you did something wrong there! When you start with too many KPIs it means that you need a consultant each time you need to change something in the dashboard, is it worth it? If you arrive at a meeting and one KPI shows two numbers, can you decide based on it? During my career, I learned an important thing: it’s enough that the data consumer spotted twice an error in the dataset, you lost their trust forever, and from this point, they will check the numbers before they believe you that the numbers are right, generating a few extra hours of work for validation, building a positive trust from the beginning with a system, that can notify the moment there is an issue with the data will save you long term extra investment and hustle! Building Scalable and Iterative Infrastructure Building Your Data Oasis: One Step at a Time Now that you know what data you need, let's talk infrastructure.  Think fast, affordable deployment. Forget a one-size-fits-all approach.  Start with a basic system that can be easily upgraded.  Treat it like a product platform, evolving in cycles.  Each cycle focuses on either improving infrastructure or optimizing costs. The truth is, as a user, you don't care if your data lives in a fancy mansion (Snowflake) or a sturdy apartment building (S3). What matters is getting accurate, timely data. Your team needs to think about how they maximize the buy/build approach for the first iteration because they will learn all the time about what works well and what does not, as well as where the issues lay. The main purpose of picking infrastructure should be to empower, first and foremost, the health metrics you picked and give open for more analysis to be done by the analyst. It needs to be something that will be easy to switch or optimize. But for me, the more important part of the infrastructure is to launch something I can test with the users and learn how it functions for them, whether they see issues or not, and as so, don’t deploy and forget until it breaks, the data people needs to go around and collect feedback, learn the use cases of people with the data and map and prioritize it in the road map. Embrace the Journey, Share the Oasis Data strategy is about making informed decisions for a successful journey.  By focusing on clear goals, strong governance, and scalable infrastructure, you can create your data oasis – one that fuels your company's growth. I am working nowadays on my new book, I would appreciate it if you share it in the comments or find me on Linkedin with more details about your data journey what worked for you, what didn’t, and looking back what would you have done differently? Edmund Hillary, a New Zealand mountaineer, once said, “It is not the mountain we conquer, but ourselves.” In the scorching heat of the business landscape, your company is a thirsty caravan desperate for insights to guide its journey. But beware, for many, their data strategy resembles a cracked water bottle in the desert – leaking potential and leading nowhere. Are you ready to quench your company's thirst for success with those three simple steps? Many leaders, desperate for a solution, picture a grand data strategy – a sprawling oasis filled with complex architecture and expensive tools. They gather their "data people," spend heavily, and build this technical utopia.  But here's the surprising truth: most companies never reach that oasis. data strategy Why? Because data strategy isn't about the bells and whistles. It's about clear, actionable goals . clear, actionable goals Set Clear KPIs! Set Clear KPIs! Start with the Destination, Not the Journey Start with the Destination, Not the Journey Think of your data strategy as a roadmap.  Before diving into infrastructure or systems, define the 3-5 key performance indicators (KPIs) that truly measure your company's health.  These are your North Stars, guiding everyone toward what matters most, and are the top of your priority to secure. 3-5 key performance indicators (KPIs) In my book “Data is Like a Plate of Hummus’ which is based on my experience as a data leader and consultant, I many times refer to the KPIs refinement process as a funnel structure, keep things simple, keep them small, just like hummus, a very simple dish but with such powers to fill you stomach for the entire day, in my book I describe my method of refining the KPIs by starting with a set of questions, then pick the most relevant questions, and define which KPIs can answer them, the next step is to identify what action they will drive which gives you the indication to how often you will need it, sometimes seeing a KPI related to time on site every second week with the release cycles can help you understand if the new releases improved or not the user experience, you don’t need to see it daily as it will have nothing to the action of it. Next, establish data governance rules .  This isn't about restrictions but about setting expectations. When will the data be ready? What is the one true source of the data?  Who owns it? How quickly will issues be resolved?  Clear agreements create a supportive system between data users and providers.  Everyone knows their roles and what to do "when the you-know-what hits the fan" (because, let's face it, it will!). data governance rules In many companies, the idea of data is that everything is important and needs to be used, but in reality, some data is less important than others 🦖.  This led us to hire more engineers and analysts and increase our investment in infrastructure, but as I mentioned a few times in previous articles and my book, will more than 5 KPIs change the decision that needs to be taken? If you need more than that, restart the process of defining your KPIs; you did something wrong there! When you start with too many KPIs it means that you need a consultant each time you need to change something in the dashboard, is it worth it? If you arrive at a meeting and one KPI shows two numbers, can you decide based on it? During my career, I learned an important thing: it’s enough that the data consumer spotted twice an error in the dataset, you lost their trust forever, and from this point, they will check the numbers before they believe you that the numbers are right, generating a few extra hours of work for validation, building a positive trust from the beginning with a system, that can notify the moment there is an issue with the data will save you long term extra investment and hustle! Building Scalable and Iterative Infrastructure Building Your Data Oasis: One Step at a Time Building Your Data Oasis: One Step at a Time Now that you know what data you need, let's talk infrastructure.  Think fast, affordable deployment . Forget a one-size-fits-all approach.  Start with a basic system that can be easily upgraded.  Treat it like a product platform, evolving in cycles.  Each cycle focuses on either improving infrastructure or optimizing costs. fast, affordable deployment The truth is, as a user, you don't care if your data lives in a fancy mansion (Snowflake) or a sturdy apartment building (S3). What matters is getting accurate, timely data. Your team needs to think about how they maximize the buy/build approach for the first iteration because they will learn all the time about what works well and what does not, as well as where the issues lay. The main purpose of picking infrastructure should be to empower, first and foremost, the health metrics you picked and give open for more analysis to be done by the analyst. It needs to be something that will be easy to switch or optimize. But for me, the more important part of the infrastructure is to launch something I can test with the users and learn how it functions for them, whether they see issues or not, and as so, don’t deploy and forget until it breaks, the data people needs to go around and collect feedback, learn the use cases of people with the data and map and prioritize it in the road map. Embrace the Journey, Share the Oasis Embrace the Journey, Share the Oasis Data strategy is about making informed decisions for a successful journey.  By focusing on clear goals, strong governance, and scalable infrastructure, you can create your data oasis – one that fuels your company's growth. I am working nowadays on my new book, I would appreciate it if you share it in the comments or find me on Linkedin with more details about your data journey what worked for you, what didn’t, and looking back what would you have done differently? I am working nowadays on my new book, I would appreciate it if you share it in the comments or find me on Linkedin

Your Data Strategy is a Cracked Water Bottle in the Desert (and How to Fix It)

Product backlog is a key element in any product development process. One of the things that many product managers can agree on - the backlog is often never-ending. You’ll always have incoming requests - either from customers or from the market and competition. In this article, I’d like to share my tips for working as a product manager in fast-growing startups. I’ll also explain how these tips could be used in B2B products. Building a Backlog The product backlog is an ordered list of what is needed to be improved in the product. Ideally, this list should be dynamic and change depending on company objectives and vision. Tips on how to fill the backlog meaningfully: Invest time in the discovery process — Understand Your Market and Customers: Conduct regular market research and customer interviews to deeply understand their needs and pain points. Use customer feedback, surveys, and analytics data to inform what goes into the backlog. Read more about how to do a good product discovery: https://hackernoon.com/product-discovery-in-b2b-and-b2b2c-environments-a-guide-for-product-managers Incorporate the framework to strategically explore different opportunities Opportunity Solution Trees A visual framework for product development that maps out opportunities (desired outcomes or problems to solve) and potential solutions. It begins with a desired outcome at the top, branches into opportunities, and further branches into solutions for those opportunities. What it is: the framework helps teams systematically explore multiple paths to a goal and prioritize opportunities. Decisions are made by evaluating which solution paths most effectively lead to the goal. Application: Read more: https://www.producttalk.org/2023/12/opportunity-solution-trees/ Embrace Change and Stay Lean: Recognize that in a fast-growing startup, change is the only constant. Your backlog should be a reflection of this dynamic nature, kept lean and highly adaptable. This flexibility and adaptability are strengths of smaller teams, and they help to adjust in response to market changes quickly. Align with Business Goals: Ensure that the items in your backlog support the overall business objectives. Regularly review and adjust the backlog to align with any changes in business strategy. Break Down Large Tasks: Decompose larger, more complex backlog items into smaller, manageable tasks. This not only makes the backlog more navigable but also facilitates easier tracking and completion of tasks, as well as iterative delivery. Bonus: If your startup is sales-led, collaborate with Sales and Client Services: It's crucial to maintain close communication with the sales and client services teams - they are your primary connection to prospects and customers. These teams offer invaluable insights into users’ needs and expectations. Integrating their feedback into your backlog ensures that you're addressing real market demands. Prioritizing a Backlog When you’re growing rapidly, prioritization is KEY. Use prioritization frameworks to assess and rank backlog items, and remember to always adjust it for your own case. RICE / ICE : RICE scores projects or features based on four factors: Reach (how many people it affects), Impact (how much it will benefit them), Confidence (how sure you are about these estimates), and Effort (how many person-months it will take). Methodology : Calculate the RICE or ICE (excludes Reach) score for each item and prioritize those with the highest scores. The higher the score, the more favorable the project is. It allows you to balance the expected impact against the required effort. Application and Decision Making : For B2B, 'Reach' may focus on key accounts rather than end-user numbers, or you can exclude it from the estimation and use the ICE score. 'Impact' could also weigh long-term client relationships, such as expansion opportunities. Adjusting framework for B2B : Recommended, especially for assessing potential impact and prioritizing features. Requires careful adjustment in estimating Reach and Impact. B2B Startup Recommendation Kano Model : The Kano Model categorizes features based on how they affect customer satisfaction. Features are classified as Must-be, One-dimensional, Attractive, Indifferent, or Reverse. Methodology : Understand which category each feature in the backlog falls into. Must-be features are essential, attractive features can significantly boost satisfaction, and so on. Decisions are made by balancing these categories to create a product that meets basic requirements while also delighting customers. Application and Decision Making : For example, Must-be features could include industry-specific compliance or security needs. Adjusting framework for B2B : Useful for understanding customer satisfaction factors. Recommended for startups focusing on differentiating their product through unique value propositions. B2B Startup Recommendation MoSCoW : MoSCoW categorizes tasks or features into four groups: Must have, Should have, Could have, and Won't have. Methodology : List all features or tasks and categorize them into four groups. Prioritise 'Must haves' as they are critical for the project's success. 'Should haves' and 'Could haves' follow in priority. 'Won't haves' are not considered in the current scope. Application and Decision Making : 'Must haves' might include features essential for client operations or compliance. Adjusting framework for B2B : Recommended. It's straightforward and helps startups focus on essential features while managing limited resources. B2B Startup Recommendation Value vs Effort : This matrix plots tasks or features based on the value they provide against the effort required to build them. It helps in identifying high-impact, low-effort opportunities. Methodology : Assign value and effort scores to each feature. Prioritize those in the high-value, low-effort quadrant, as they offer the most return for the least investment. Application and Decision Making : For B2B, value assessment should include factors like new deals or potential for customer expansions. Adjusting framework for B2B : Recommended for its simplicity and effectiveness in identifying high-impact, feasible initiatives. B2B Startup Recommendation Eisenhower matrix : This matrix helps categorize tasks based on urgency and importance, with four quadrants: urgent and important, important but not urgent, urgent but not important, and neither urgent nor important. Methodology : Tasks in the 'urgent and important' quadrant are prioritized. This method helps in focusing on what truly matters and avoiding tasks that may seem urgent but are not important in the long run. Application and Decision Making : In a B2B context, tasks that directly affect client satisfaction or are needed to unlock new deals would fall into the 'urgent and important' category. Adjusting framework for B2B : Recommended, particularly for mapping high-level opportunities. B2B Startup Recommendation Custom Prioritization: Consider developing a custom prioritization method that aligns with your unique business model and market. Tips: Allow Time for Technical Debt: Always allocate time for handling tech debt, bug fixes, and incoming client requests. Optimize processes through early tech discoveries and proofs of concept, but limit the effort required for them by time-boxing these activities to a specific duration, such as one day. Maintain a Balanced Focus: Acknowledge that sometimes you won’t iterate, as other opportunities demonstrate higher potential impact, and that's okay. While focusing on immediate priorities, don’t lose sight of long-term goals and strategic initiatives. Again, occasionally include technical debt and infrastructure improvements in the backlog to ensure the long-term health of the product. Acting on a Backlog Aside from the discovery process, the product team also needs to implement practices to deliver a successful product delivery to the market. Prioritize rapid shipping to quickly gather feedback and iterate. This approach accelerates learning and product refinement. Ship Fast, Learn Fast: Break tasks into small, manageable parts for flexibility and quick adaptation to feedback or market changes. Embrace Small Iterations: Not all teams thrive with standard methodologies like sprints. Experiment and find a workflow that suits your team's unique dynamics. Customize Your Process: Always include a buffer in deadlines and roadmaps for customers, accommodating unforeseen challenges while maintaining quality. Set Realistic Deadlines: : They can provide valuable insights. Encourage feedback and ideas from the team These can inform future backlog prioritization. Use data from released backlog items: Be a Product Manager, Not a Backlog Manager As a product manager, your role extends far beyond just managing the backlog. It's crucial to avoid falling into the trap of being a "backlog manager," where the focus is narrowly placed on ticking off tasks. True product management demands a strategic approach, encompassing market understanding, customer engagement, and aligning development with broader business goals. It's about setting a vision for the product and ensuring that every task on the backlog not only resonates with customer needs but also contributes to the vision and drives the product forward in a meaningful way. Editor: Tanya Kamenskaya Design: Stas Yudin

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