Healthcare

Hospitals are healing patients faster with 30-year-old Australian technology. Most healthcare facilities still operate in the dark. SolarTube skylights channel natural sunlight through reflective tubes directly into patient rooms and treatment areas. No electricity needed. Just free healing light all day. The healthcare transformation numbers: ↳ Faster patient recovery rates documented ↳ 15% staff productivity increase ↳ Reduced eye strain for medical professionals ↳ Lower patient anxiety during procedures Think about that. Tigoni Medical Center in Kenya installed SolarTubes in their COVID-19 facility. Healthcare workers reported less fatigue, increased alertness during long shifts. Patients showed dramatically improved morale and energy levels. At Rogaska Medical Center, natural daylight flooded clinics without unwanted heat. Staff comfort improved. Patient outcomes followed. Italian dental offices meeting occupational daylight standards found something unexpected: patients felt less anxious. Procedures became more comfortable. Natural light calmed nerves that fluorescent bulbs couldn't. Traditional Healthcare Lighting: ↳ Fluorescent tubes causing eye strain ↳ High electricity costs ↳ Artificial environments ↳ Staff fatigue increases SolarTube Healthcare Reality: ↳ Natural light reduces stress hormones ↳ Serotonin production increases ↳ Circadian rhythms regulate properly ↳ Recovery accelerates naturally But here's what stopped me cold: We're medicating depression while keeping people in artificial light. Jim Rillie invented this solution in the 1980s. Launched Solatube International in 1991. Now 2 million units worldwide bring natural light indoors. Healthcare facilities that adopt it see measurable improvements. Staff wellness increases. Patient satisfaction scores rise. Recovery times shorten. The Multiplication Effect: 1 hospital = hundreds healing faster 100 facilities = thousands of staff energised 1,000 installations = healthcare transformed At scale = medicine working with nature VCC in the UK experienced enhanced well-being building-wide. Staff and patients reported feeling calmer, healthier, happier. Simply from abundant daylight. We're not just installing skylights. We're installing wellness. One beam of natural light at a time. Follow me, Dr. Martha Boeckenfeld for innovations that heal environments and people. ♻️ Share if you believe healthcare should harness nature's healing power.

Polling vs Webhooks As systems grow more complex, choosing the right update strategy becomes crucial. Let me break down the two primary approaches that define real-time data synchronization: Polling: The Traditional Approach • Client periodically requests updates • Predictable but resource-intensive • Full control over request timing • Higher latency, higher costs at scale Webhooks: The Modern Push System • Server notifies client of changes • Event-driven and efficient • Near real-time updates • Better resource utilization Concrete Implementation Examples: Polling Works Best For: 1. Payment status checks 2. Order tracking systems 3. Basic monitoring tools 4. MVP implementations 5. Systems with predictable update patterns Webhooks Excel In: 1. Payment processing (PayPal) 2. Repository events (GitHub) 3. CRM integrations (Salesforce) 4. E-commerce inventory updates 5. Real-time messaging systems Key Decision Factors: - Update frequency requirements - Infrastructure complexity tolerance - Development team expertise - System scalability needs - Budget constraints Currently implementing these in production? Both approaches have their place. The key is matching the solution to your specific requirements rather than following trends.

Insurance Fraud For over last 15 years, I have been highlighting two aspects of Insurance Fraud 1. Fraudulent activities are getting more organized year on year 2. Next decade or so will see more fraud in Critical Illness and Personal Accident/ disability The case below is a live example of both: The appended ECG surfaced as evidence of heart attack in a critical illness claim of first heart attack - in 3 different claims. This ECG was, allegedly, taken in 3 different hospitals on 3 different patients in 3 different districts of two states. Why is it the same ECG? One may counter the allegation that 2 different persons can have the same ECG. Answer to this: - The flat line in V5 (highlighted with a box) is an artifact due to machine error and accepting that 3 different machines will have same artifact is ignoring the fraud (known as leakage in an organization) - Two independent, senior cardiologists have opined that these three ECGs belong to the same person. An ECG is akin to one's finger prints - no two persons can have EXACT same ECGs. Fortunately (unfortunately for the fraudsters) these attempts were made on the same insurer, hence were identified. In our 20 year+ journey in risk management, we have come across same ECG and same TMT being used for different proposals at policy inception stage but 3 cases, 3 districts, 3 hospitals - is first even for us. Insurers have to be more vigilant for critical illness and personal accident claims. Sanjiv Dwivedi Bhaskar Nerurkar Sweetie Salve Rajat Goyal Namrata Jain (Kumar) Manish Dodeja Priya Deshmukh-Gilbile Siddhartha Kansal Dr Sushma Jaiswal Dr Satish Kanojia Imtiaz Shaikh Preeti Desai Vishal Dubhashi #insurancefraud #organisedfraud #criticalillness #fraud management

This is my face finishing the last pieces of my documentation after my #ER shift. It's a face of frustration after spending way too much time documenting in a less-than-intuitive, inefficient EMR. It's the face of frustration from endless clicks, digital pop-up blockades, and seek-and-find missions for clicking the correct checkbox in an electronic health record to simply discharge a patient. The ultimate price of this inefficiency: compromised patient care, delays, errors, skyrocketing stress for healthcare professionals, and an overall decline in the system's effectiveness. It's time to streamline our processes for the sake of our clinicians and, most importantly, our patients. The problem: EMRs were made as billing platforms with patient care and clinical workflows as secondary considerations. The solution: 1. Put frontline clinicians back in the boardroom to fix these inefficiencies. 2. Reduce and eliminate unnecessary administrative tasks. 3. Utilize trainers to perform frequent check-ins with clinicians to ensure clinicians use the best and most efficient documentation methods. 4. Leverage new technologies (like AI, dictation software, ambient listening software) to reduce screen and keyboard time for clinicians. 5. Create standardized workflows for documentation. The more ways to do the same thing, the more challenging it is to teach and build efficiencies across a team. 6. EMR companies should use practicing, specialty-specific clinicians to guide design decisions. #HealthcareSystem #ClinicianBurnout #TimeForChange Cerner Corporation Epic MEDITECH #EMR ABIG Health #frontlineclinicians #nurses #physicians #hospitals

Health apps lose 96% of their users within 30 days - and nobody in digital health wants to talk about it. We've raised billions to change health behavior. But we're failing at the most basic metric: keeping people engaged. Here's the data nobody publicizes 👇 ▶ Health app retention is catastrophic Day 30 retention for health and fitness apps sits at just 3-4%. That means 96% of users are gone within a month of downloading. ▶ Mental health apps see massive dropoff A meta-analysis of 79 mental health app RCTs found 92% initial uptake but 18.6% dropped out before completion. People download. They don't stay. ▶ Even research apps can't keep users Stanford's MyHeart Counts study saw average engagement of just 4.1 days. Not weeks. Not months. Only four days. ▶ Clinical decision support tools get ignored AI-powered clinical alerts in hospitals have a 90-96% override rate. Some doctors receive 100-200 alerts daily, so they stop paying attention entirely. But here's what's interesting: the companies that actually reached IPO scale didn't rely on apps alone. Hinge Health pairs AI with physical therapy kits and human coaches. They claim AI reduces human clinician costs by 95% - but still include devices and human support. Because AI alone doesn't keep people engaged. Oura locked users in with hardware. You can't abandon an app when you've bought a $300 ring. The sunk cost drives continued use. Omada secured employer distribution. When your company pays and your health outcomes affect premiums, you stick with it. Accountability comes from employment, not the app. The pattern is clear: retention comes from external accountability - devices, human coaches, or employer pressure. Not better UX or gamification. App-only solutions don't work. The data proves it. And the companies that made it to IPO knew that from the start. So do you think digital health companies should be required to disclose retention rates the way they disclose clinical outcomes? #entrepreneurship #healthtech #AI

The Centers for Medicare & Medicaid Services has proposed that Medicare Advantage plan revenues will remain flat going into 2027 at a moment when underlying medical costs, labor expenses, and pharmaceuticals continue to rise materially. What does this mean in practice? For beneficiaries: Over time, beneficiaries should expect less generous benefits, tighter utilization management, and narrower provider networks. Access may become more constrained—not necessarily through explicit benefit cuts, but through fewer participating provider groups and more selective contracting. The tradeoff between affordability and choice will become more acute. For brokers and distribution partners: Distribution costs in Medicare Advantage are largely fixed, particularly commissions and marketing infrastructure. As margins compress, plans will continue to reassess how (and how much) they pay for growth. This may include lower upfront commissions, greater reliance on retention-based compensation, or shifts toward more direct-to-consumer enrollment strategies. For provider groups: Provider organizations seeking rate increases will face a much tougher negotiating environment. With plan revenues constrained, upward pressure on provider rates becomes difficult to absorb. As a result, some provider groups may choose to exit Medicare Advantage entirely, while others will narrow participation to fewer plans. The result may be increased network fragmentation and heightened tension between plans and providers over risk, quality expectations, and total cost of care. For managed care company employees: Cost discipline will extend inward. Plans will be slower to hire, more selective about new investments, and may pursue workforce reductions. Expectations will shift toward higher productivity, flatter organizational structures, and doing more with fewer resources. For Investor-backed Medicare Advantage plans: The economics of growth will change. Longer payback periods, lower internal rates of return, and greater regulatory uncertainty will make Medicare Advantage investments less immediately attractive. Capital will still flow to the sector, but it will be more discriminating, favoring scale, operational excellence, and differentiated capabilities rather than growth at any cost. For small and regional health plans: Scale matters more than ever. Smaller plans will struggle to compete. Many may exit the market or seek partnerships, mergers, or acquisitions. Consolidation pressures are likely to intensify as fixed administrative and compliance costs consume a greater share of revenue. Time will tell whether the rate decisions outlined in the Advance Notice hold through the Final Rule. Regardless of the ultimate number, one thing is clear: Medicare Advantage is entering a period of transition. The era of easy growth is ending, and the next phase will be defined by tradeoffs—between generosity and sustainability, growth and discipline, innovation and affordability.

Dr. S M Ziaur Rahman, a medical professional, has made a commendable transition from a high-paying position in Delhi to establish a vital healthcare center in rural Bihar. His initiative directly addresses a critical gap in rural medical infrastructure, exemplified by his nominal charge of just ₹250 per patient visit. This significantly contrasts with typical urban healthcare costs, ensuring that financial barriers do not prevent individuals from receiving necessary medical attention. Dr. Rahman’s decision was deeply influenced by a poignant experience: witnessing the plight of a patient from Bihar who had to travel to Delhi for treatment due to the severe lack of adequate facilities in their native region. This encounter underscored the urgent need for local, high-quality medical services. By establishing this center, Dr. Rahman is not only providing essential care but also significantly contributing to the improvement of public health outcomes and fostering hope among countless underprivileged individuals in rural Bihar. His exemplary action highlights the transformative potential of dedicated medical professionals addressing critical healthcare disparities in underserved regions. LinkedIn LinkedIn News LinkedIn for Learning

One of the major highlight was the policy statement on the inclusion of Technology and AI to reduce the workload burden. Artificial Intelligence (AI) is revolutionizing nursing by introducing smart tools that enhance decision-making, patient monitoring, and care delivery. One major innovation is the integration of AI-powered clinical decision support systems (CDSS) that assist nurses in identifying early signs of deterioration, predicting patient outcomes, and recommending evidence-based interventions. These systems analyze vast amounts of patient data in real time, enabling nurses to act swiftly and accurately, ultimately reducing errors and improving patient safety. Wearable health devices and remote monitoring tools powered by AI also allow nurses to track vital signs continuously, even from a distance, promoting proactive care for chronic disease patients. AI is streamlining administrative and documentation tasks, giving nurses more time for direct patient care. Voice recognition technology and natural language processing are being used to automate nursing documentation, reducing burnout and improving workflow efficiency.

Today OpenMed released a dataset the likes of which I have never seen before. They just put over 1 billion rows of psychiatric genetics data on Hugging Face. ADHD. Depression. Schizophrenia. Bipolar disorder. PTSD. OCD. Autism. Anxiety. Tourette syndrome. Eating disorders. 12 conditions. 52 landmark studies. Every genome-wide association study (GWAS) ever published by the Psychiatric Genomics Consortium, but now standardized and accessible in one place. Previously, accessing this data meant tracking down dozens of files scattered across FTP servers, wrestling with inconsistent formats, and spending more time debugging download scripts than doing actual science. Now it's one line of Python! Each row represents a statistical test: how strongly is this specific point in the human genome associated with this psychiatric condition? In more depth, each row is a single variant-phenotype association test from a GWAS meta-analysis. For every SNP, you get: • Variant ID (e.g. rs6702460) and genomic location (CHR/POS) • Effect allele and reference allele (A1/A2) • Effect size — BETA or OR — and its standard error (SE) • P-value, imputation quality (INFO), allele frequency (FRQ/MAF) • Sample sizes — total, cases, and controls (N/Nca/Nco) A typical single GWAS tests 7–15 million variants. We have 52 of them, many with multiple ancestry groups and sub-analyses. That's how you get to 1.14 billion rows. Hopefully, this will mean: 🧠 Earlier identification of people at genetic risk for psychiatric conditions (even before symptoms emerge!) 💊 Better drug targets: pinpointing the genes and biological pathways causally involved across multiple disorders 🔗 Understanding why conditions co-occur (e.g., depression and anxiety, ADHD and autism) through shared genetic architecture 🌍 More equitable research: ancestry-stratified data means findings that apply beyond predominantly European study populations Mental health research has long been underpowered relative to the scale of the problem. Open, accessible data is one way to change that 🤗

In SAP, the process of managing Accounts Receivable (AR) involves several key transactions, often carried out through the SAP Financial Accounting (FI) module. Below is a list of commonly used T-codes for Accounts Receivable in SAP: 1. Posting Customer Invoices and Payments: • FB70 – Post Customer Invoice: Used to post an invoice for a customer, which updates accounts receivable and sales revenue. • F-28 – Post Incoming Payments: Used to post a payment received from a customer (could be cash or transfer). • F-32 – Clear Customer: Used to clear open customer items (like payments or credit memos). 2. Customer Account Maintenance: • FD01 – Create Customer Master Data: Allows you to create a new customer in the system. • FD02 – Change Customer Master Data: Modify existing customer data. • FD03 – Display Customer Master Data: Display customer details without making changes. 3. Credit Management: • FD32 – Change Customer Credit Management: Used to update credit limits for customers. • FD33 – Display Customer Credit Management: Display customer credit information. 4. Customer Payments and Clearing: • F-39 – Post Payment: Manual posting of customer payments. • F-44 – Clear Open Items: Clears open customer items manually, like applying a payment to a specific invoice. 5. Account Reconciliation and Aging Reports: • F.13 – Automatic Clearing: Clears open items for customers automatically. • F-30 – Post Customer Down Payment: Used to post an advance or down payment made by a customer. • FS10N – Display G/L Account Balances: Can be used to check the balance for accounts receivable (G/L 100000, for example). • S_ALR_87012168 – Customer Open Items List: Displays open customer invoices and payments. • F.22 – Customer Aging Report: Provides a list of receivables broken down by overdue periods. 6. Credit Memos and Adjustments: • FB75 – Post Customer Credit Memo: Used to post credit memos (reduce amounts due from customers). • F-27 – Post Credit Memo for Customer: Another way to post credit memo for a customer. 7. Dunning (Reminders for Outstanding Payments): • F150 – Dunning: To create and send dunning letters to customers for overdue payments. • F150B – Dunning History: Displays the dunning history for customers. 8. Reports and Analysis: • S_ALR_87012182 – Customer Receivables Aging Report: Used to analyze aging of accounts receivable. • F-23 – Post Customer Payment and Clear: This is an alternative method for handling incoming payments and clearing. These T-codes cover a broad range of Accounts Receivable functions in SAP, from basic posting and customer account management to credit control, dunning, and reporting. However, If there is anyother T-codes, feel free to share.