Combating Nurse Burnout: A Personal Journey and Technological Solution As a former nurse who experienced the intense pressures and burnout of the job firsthand, Sergey understands the challenges nurses face. During his time on the floor, he saw the toll burnout took not just on individual nurses, but on patient care and hospital costs. Today, as Chief Nursing Officer at In-House Health, he is dedicated to addressing these issues. Our platform uses advanced analytics and AI to optimize staff scheduling, balance nurse workloads, and anticipate patient needs. https://lnkd.in/d3T3UdAq
In-House Health
Hospitals and Health Care
Predictive scheduling and management platform for modern nursing teams
About us
Hospital nurse employment is challenged. Today's nurses want control and flexibility, and most hospitals are struggling to shift from an old model of a full-time workforce that works consistent shifts, limited in large part by legacy tech. Agencies have stepped in to fill the gap, and the result is unprecedented margin pressure from nursing. We founded In-House so hospitals can again become the employer of choice for nurses. We started with a predictive, clinically precise scheduling system. Within weeks of implementation, In-House saves nurse managers 5+ hours per week, and saves the hospital >10% of labor costs, $800K for an average single facility, by building better shifts. Send us a note at hello@inhouse.health to learn more.
- Website
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https://www.inhouse.health
External link for In-House Health
- Industry
- Hospitals and Health Care
- Company size
- 2-10 employees
- Headquarters
- New York
- Type
- Privately Held
Locations
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Primary
New York, US
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Tel Aviv, IL
Employees at In-House Health
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James Kerridge
Chief Nurse - Inpatient Services
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Allie Reilly
making tech useable, useful, and ethical | healthcare + policy + web3
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Ari Brenner
Founder, In-House Health: AI-driven scheduling & management tech platform for modern nursing teams | Founder, Stellar Health
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Nir Feinstein
Full Stack Developer
Updates
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Round-up of insights and predictions for the future for new, inter-facility float pool programs from our CEO Ari Brenner
Founder, In-House Health: AI-driven scheduling & management tech platform for modern nursing teams | Founder, Stellar Health
I have continued to view inter-facility float as a game-changer in responding to mounting hospital nurse shortages + nurse dissatisfaction with rigid employment models. At AHA in San Diego this week, I heard nursing leadership from Jefferson Health speak about their version of the program, which has impressively grown from 25 nurses moving through the full system to now 150+ in 2 years. At In-House Health, we're betting on programs like this to turn the tide in hospital nursing, with our supply/demand prediction engine powering increasingly scaled and flexible staffing models. https://lnkd.in/e6jFVZaD Kudos to the team at Jefferson for the presentation & the rapid scale up of their IFF program: Danielle Giovanniello Lauren Ciocco Meseret Meheretie, RN, BSN, MSN, CCRN
PR & Blog | Combating shortage and burnout with new float pool models
inhouse.health
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We are excited to be exhibiting at the AHA Leadership Summit next week in San Diego! Visit us at booth 608 to see a live demo of our automated workforce optimization platform. Chat with Ari Brenner and Zenith Tillemann-Dick about how In-House Health reduces labor costs and team burnout, while improving patient outcomes! Join us as we showcase our contributions to the flexible and sustainable workforce + financial and operational excellence conference tracks. See you there! #AHASummit #workforce #nursestaffing
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This is so exciting. 🚀 Thank you to all our followers for being with us! #nurses
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Last weekend, In-House Health was thrilled to present at the Nursing Consortium of Florida Inc.’s 2024 conference, focused on discovering the magic at the intersection of technology and nursing. The conference exemplified, NCOF’s goal of providing a space for collaboration to solve nursing workforce issues facing the state of Florida, uniting nurses in practice and academia. As a nurse-founded AI product, we are passionate about leveraging AI to allow nurses to focus more on patient care and less on administrative burdens, such as weekly scheduling adjustments. Our Director of Growth and Partnerships, Zenith Tillemann-Dick, presented a framework for nursing teams to evaluate AI solutions. Given the fast-moving nature of AI and high stakes in healthcare, it's crucial to ensure new technology is solving the right problems with the right solutions. One core component of the framework is the importance of clinical insights to developing and structuring new solutions. In-House Health proudly integrates nurses' voices directly into our product development. Our AI model is purpose-built from the ground up to solve nurse staffing challenges through a predictive model for patient census and nurse workload. Our model is trained on diverse and extensive real-world clinical datasets. That training is then validated by a team of bedside nurses who manually review cases ensuring our model remains effective and highly accurate. #HealthcareInnovation #nursestaffing #Nursing #PatientCare #AIinHealthcare #InHouseHealth #nursing #workforcemanagement
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𝐍𝐮𝐫𝐬𝐢𝐧𝐠 𝐢𝐧 𝐍𝐮𝐦𝐛𝐞𝐫𝐬: 𝐓𝐡𝐞 𝐒𝐮𝐫𝐩𝐫𝐢𝐬𝐢𝐧𝐠 𝐒𝐭𝐚𝐭𝐬 𝐁𝐞𝐡𝐢𝐧𝐝 𝐄𝐟𝐟𝐞𝐜𝐭𝐢𝐯𝐞 𝐒𝐭𝐚𝐟𝐟𝐢𝐧𝐠 In the world of healthcare, numbers tell a powerful story, especially when it comes to #nurse staffing. Let's take a look at the statistics in different areas that reveal the impact of effective nurse staffing on patient outcomes, hospital efficiency, and the well-being of #healthcare professionals: 1. Adding just one patient to a nurse’s workload increases patient mortality by 7%, a landmark study by Aiken et al. (2002) in the Journal of the American Medical Association revealed. This statistic alone highlights the direct link between staffing levels and patient outcomes, emphasizing the important implications of staffing decisions. 2. Hospitals with better nurse staffing levels have 30% lower #burnout rates, research published in the New England Journal of Medicine by McHugh et al. (2011) showed. This significant reduction in burnout not only benefits nurses but also contributes to improved patient care quality. 3. Replacing a single nurse can cost hospitals between $28,000 to $51,000. Each percentage point change in RN turnover can cost or save the average hospital $270,800 annually, according to a study by NSI Nursing Solutions (2021). These figures underscore the financial imperative of retaining experienced nursing staff through effective scheduling and workload management. 4. Nurses working shifts longer than 12 hours are up to 3 times more likely to make an error. Despite this, the American Nurses Association reports that 43% of nurses work overtime every week, highlighting a clear area for improvement in scheduling practices. 5. Preliminary studies suggest that predictive staffing software can greatly reduce unplanned overtime (Healthcare Financial Management Association, 2019). This reduction directly impacts nurse satisfaction and patient care quality. 6. Reducing burnout by 30% through better staffing could lead to over 6,000 fewer hospital-acquired infections annually, resulting in up to $68 million in savings, a study by Cimiotti et al. (2012) in the American Journal of Infection Control revealed. These statistics paint a rather clear picture: effective nurse staffing is not just about filling shifts—it's a critical factor in patient safety, nurse well-being, and hospital financial health. As healthcare continues to evolve, leveraging data-driven staffing solutions becomes increasingly important in meeting these interconnected challenges. This brings us to the cutting edge of staffing technology: the application of artificial intelligence and machine learning in healthcare #scheduling. These advanced tools promise to revolutionize how we approach nurse staffing, offering the potential to optimize schedules, predict staffing needs, and ultimately improve both patient outcomes and healthcare worker satisfaction. image by freepik
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Ari Brenner on why predictive insights from In-House's platform are key to improving nurse schedules. "One of the key things that prevents nursing teams from staffing themselves effectively and sustainably is predicting demand for their services. Predicting demand for services is a problem that every industry faces, but I would argue it's particularly acute in healthcare. To put it bluntly, it's a lot worse if I get demand prediction wrong in healthcare - if I predict wrong how many patients I will have, or how sick they are in a hospital setting, lives are at stake. A lot of what makes nursing a very challenging thing to staff and schedule, and makes it demanding on teams, is the stakes of getting it wrong are very serious".
In this episode, we were delighted to be joined by Ari Brenner, Founder of In-House Health. This episode is brought to you by Lee Watton Tech Talks: The People and Planet Podcast explores how technology is tackling the world’s most pressing problems such as: 🏥 Healthcare, 🌍 Climate Change 🎓 Education. To listen to more, tune in via the links below👇 YouTube: https://lnkd.in/eWZbZXVf Spotify: https://lnkd.in/eApNeZ7m
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Throughout our work at In-House Health, we speak to numerous nursing managers and bedside nurses. Among the usual concerns such as staffing shortages, work safety, burnout, and so on, we also often hear about “mental health”. As a nurse-centric organization, we investigated how addressing controllable aspects of the staffing crisis can lead to better mental health management within the nursing profession. What is mental health anyway? Mental health is defined as “a state of well-being in which every individual realizes his or her own potential, can cope with the normal stresses of life, can work productively and fruitfully, and is able to make a contribution to his or her community” (WHO, 2004). Sounds reasonable. Nursing talk around mental health is often related to problems within the nurses’ working environments - items such as inadequate staffing, the ability to provide safe care, working long hours with high levels of fatigue, and a sense of not being valued. When facilities are understaffed, nurses and other healthcare workers are under pressure to work overtime and accept additional shifts without adequate rest periods between shifts. One study of hospital nurses working successive 12-hour shifts found that the majority slept less than six hours between shifts (Geiger-Brown et al. 2012). Other research has found that people working rotating shifts sleep up to four hours less when they work at night (Han, J. 2024). The relationship between understaffing and fatigue is cyclical. Poor staffing and mandatory overtime lead to fatigue, which can lead to burnout and more healthcare workers choosing to leave the bedside to protect their health and mental health. This leads to more understaffing and the vicious cycle goes on. There are multiple aspects to nursing mental health, some cannot be controlled (coping with illness, death, misery), this is why nursing is often perceived not as a profession, but rather as a calling or a vocation. This is exactly why we at In-House Health want to improve aspects that can be controlled. We want to allow nursing managers to create better-staffed shifts allowing bedside nurses to rest properly and maintain a work-life balance. Join us in our mission to bring sustainability back to nursing. #mentalhealth #nursing #nursewellbeing #burnout #staffingcrisis
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We're hiring for Sales, Ops & Product (US) and Engineering (Tel Aviv). Alongside some great rounds here in Ben Lang's list, including other Denver + TLV folks & a near-historic # of instances of "AI" in a single list
Here’s a list of 26 fast-growing early-stage startups who’ve all announced their seed funding in the past few weeks from top-tier funds like Sequoia, Index, Benchmark, and Bessemer. ALL are hiring. 1) Yellow - 3D AI tools for gaming (remote) 2) Teal - SMB accounting (NYC) 3) Liminal - gen AI security (Denver) 4) Superpower - next gen digital clinic (Los Angeles) 5) Leya - gen AI for legal services (Stockholm / Krakow) 6) Sagetap - b2b SaaS discovery (Bay Area) 7) GoodDay Software - retail OS with Shopify (Austin) 8) In-House Health - AI management for nursing (Denver / Tel Aviv) 9) Numeric - AI powered close automation (Bay Area / NYC) 10) Fizz - student debit card (NYC) 11) Roam - more affordable home ownership (NYC) 12) Remepy - hybrid drugs (Tel Aviv) 13) APEX - AI security platform (Tel Aviv) 14) Seam AI - AI interface for customer data (Bay Area / NYC / LA) 15) Log10.io - improve LLM accuracy (remote) 16) Anon - integration platform for AI (Bay Area) 17) Patlytics - AI patent platform (NYC) 18) Tabs - b2b billing (NYC) 19) Browserbase - headless browsers (Bay Area) 20) Libretto - tools for LLM devs (NYC) 21) Faircado - second-hand e-commerce (Berlin) 22) StackAI - enterprise gen AI (Bay Area / remote) 23) Inventive - AI analyst for your customers (remote US / Canada) 24) Greptile - AI staff engineer (Bay Area) 25) Cartwheel - text-to-animation platform (NYC) 26) Seven AI - new SOC (Boston) *follow me (Ben Lang) for more lists like these + follow next play*
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We are expanding our coverage while trying to make the workplace better for #nurses and patients. Thank you Geektime גיקטיים
Co-founder | Chief Nursing Officer | Product Evangelist @ In-House Health. Optimizing nurse scheduling to empower healthcare providers.
Great post about In-House Health on Geektime גיקטיים Thank you for telling our story! We are on a mission to help #nurses 👩⚕️
האם פיתוח ישראלי ישדרג את היחס והטיפול שאנחנו מקבלים בבתי החולים? | גיקטיים
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