Mohan Giridharadas is Founder & CEO at LeanTaaS, a market leader in providing AI-powered and SaaS-based capacity management, staffing, and patient flow software for health systems. The company’s software is being used by over 175 health systems across the country. LeanTaaS recently became the first digital health company to surpass a $1 billion valuation with the acquisition of Hospital IQ on January 10, 2023, which also follows Bain Capital acquiring a majority stake in the company on June 6, 2022.
You knew from an early age that you wanted to be an engineer, what sparked this interest?
Growing up, my dad was an engineer, as well as all three of my older brothers. I was always on a higher education path and ultimately entered IIT (Indian Institute of Technology) in Bombay. I studied electrical engineering in undergrad before coming to the United States and landing at Georgia Tech University for my Masters in Computer Science, and then Stanford University for my MBA.
You worked at McKinsey & Company for 18 years, what were some of the projects that you worked on and what were some lessons that you learned from the experience?
At McKinsey, I ran Lean Manufacturing and Lean Service Operations in North America, plus Lean Operations and Lean Service Operations in APAC. I left at the end of 2009 with two main ideas for starting LeanTaaS. First, I noticed that operational improvement projects everywhere were taking place on the back of excel spreadsheets. Additionally, any sort of process improvement efforts require a project team — whether they’re internal or external. These kinds of teams have project jobs that are constantly evolving with whatever the top of mind project may be. My vision of starting LeanTaaS was being able to deliver lean transformation capabilities that replace excel-level math with sophisticated math, and replacing the need for an on-the-ground project team with a Software as a Service (“SaaS”) platform.
Could you share the story of how a casual conversation at a Silicon Valley cocktail party led to the founding of LeanTaaS?
In Silicon Valley, any cocktail or dinner party inevitably hosts people who have started companies, sold companies, or taken them public. I was at one of these parties and someone new asked me what I did. I told him I was at McKinsey, but I had already decided I was leaving to start a software company. I wasn’t exactly sure of what it was going to be at that point. He looked at me, and said “that’s a pretty bold leap.” He explained that in my current role at McKinsey, I had the ability to get on the calendar of any chief executive. If I left to start my own company, without a product, technology, funding, customers — who was going to see me then? I was in my own head about this for a while, but ultimately I used this conversation as a foundational building block. My new company had to be something that could leverage the skills that had taken me 20 years to build, while also being disruptive and distinctive in the software space. This pivotal conversation helped me narrow down possibilities and focus on building a thematic software company with a very clear mission and purpose.
The original vision of LeanTaaS was broad, what made you pivot to focus on healthcare?
The LeanTaaS journey began in 2010 with an industry-agnostic approach. We were working with roughly 20 companies, including Google, Home Depot, and Flextronics to improve operational performance through custom-built SaaS applications.
Then, in 2013, we partnered with Stanford Health Care to solve their infusion scheduling challenge. We created an algorithm designed to optimally match available supply with ongoing demand signals. I knew from my past experience that matching supply and demand in an analytically rigorous manner is key to optimizing operational performance. Our solution worked, and we spent the next 18 months refining our algorithms and creating our first product, iQueue for Infusion Centers. In 2015, we pivoted to focus entirely on healthcare.
What are some of the machine learning technologies that are used to help optimize operations for hospitals and healthcare institutions?
As more and more healthcare data gets digitized, the opportunity exists to leverage that data to help providers more efficiently match supply and demand. Machine learning technologies have mathematical capabilities far beyond the human mind, and have been increasingly utilized to optimize operations in hospitals and infusion centers. These technologies leverage data-driven insights to improve efficiency, patient outcomes, and resource allocation. Some of the prominent machine learning technologies in healthcare optimization include:
Predictive Analytics: Machine learning algorithms can analyze historical data to predict patient admission rates, disease outbreaks, and patient outcomes. This helps hospitals allocate resources more effectively and plan for potential surges in patient demand.
Patient Flow Management: Machine learning can optimize patient flow by predicting discharge times or barriers, bed availability, and patient movement within the hospital. This reduces waiting times, improves patient satisfaction, and enhances resource utilization.
Resource Allocation and Scheduling: Machine learning can assist in scheduling hospital staff, operating rooms, and equipment based on historical data and real-time demand, ensuring optimal resource allocation. This is critical during the current healthcare staffing crisis.
How does LeanTaaS help mitigate healthcare staffing shortages?
Our technology, powered by AI and predictive analytics and fed by the health system’s historic and real-time data, supports health system leaders in fully optimizing their available workforce across inpatient units, infusion centers, and operating rooms. We do this through several ways:
Staffing solutions, to ensure available staff are configured across inpatient areas to best meet the needs of current and future patients. For example, iQueue for Inpatient Flow offers a staffing module that provides visibility across an entire health system, giving unit nursing leaders and the staffing office the time and insight to proactively identify staffing gaps and allocate available resources to best address barriers and meet individual patient needs. Using iQueue for Inpatient Flow, Health First achieved a 44% reduction in core floating across the health system to different levels of care, a 45-minute reduction in communicating the daily staffing plan, and 500 calls eliminated monthly to deploy staff.
Optimized scheduling tools that ensure staff work predictable, consistent days and take needed breaks. For example, iQueue for Infusion Centers empowers infusion leaders to create optimized schedules that factor in appointment mix, nurse and chair resources, and linked appointments. These schedules “smooth” midday peaks in daily schedules by placing appointments in optimal times, and predict likely add-on and no-show patients in upcoming days to give nurses consistent workloads that allow for regular breaks. With iQueue for Infusion Centers, Oregon Health & Science University achieved a 39% decrease in percent of days over max capacity, 14% decrease in peak chair utilization, and 31% decrease in running past scheduled close.
Features that reduce or eliminate time-consuming or stressful tasks for staff in their day-to-day work. For example, iQueue for Operating Rooms provides modules that simplify and digitize the booking processes in the OR, show a single source of truth for OR scheduling information, and allow schedulers to release time or request a case booking not with multiple phone calls, but with one click. By using iQueue for Operating Rooms, Baptist Health Jacksonville achieved a 46% reduction in abandoned calls, 4 additional cases scheduled daily per hospital, a 50% decrease in average handle time of calls, and 40% reduced call volume at their largest hospital.
Can you discuss some of the core features that assist with automating patient flow?
Managing inpatient capacity is one of the most critical challenges for hospitals. It requires a complex balancing act of coordinating bed availability, patient throughput, and staffing needs to ensure that the right resources are available to accommodate patient demand. Without the ability to proactively manage capacity and staffing, bottlenecks occur that impact patient flow, prompting high wait times, long lengths of stay, diversions, or patients leaving without being treated. This sort of operational practice results in inadequate patient care, reduced staff satisfaction, and lost revenue for the hospital.
LeanTaaS’ iQueue for Inpatient Flow solution empowers hospital leaders and frontline teams to coordinate bed availability, patient throughput, and staffing needs in unison to ensure capacity is available, priorities are aligned across care teams, and staff are allocated to the areas where they are needed most. iQueue gathers and analyzes data from existing systems (e.g. EHR, patient flow, workforce management, etc.) to dynamically adjust their capacity. iQueue supports every team member by constantly monitoring the operational health of their hospital to provide real-time insights and pinpoint barriers, allowing them to prepare for what’s coming. Through technology-enabled automation and transparency, hospitals can improve the way they work by proactively managing capacity to drive care progression, orchestrating daily discharges and transfers, and streamlining daily staffing.
For many hospitals, LeanTaaS was instrumental in dealing with the COVID-19 pandemic, what type of results were seen?
The COVID-19 pandemic very publicly pushed hospitals to the edge of their capacity. On the demand side, more patients suddenly needed intensive medical care, while on the supply side, there were shortages in PPE, then ICU beds, then regular beds, and finally nursing staff. To this day, hospital margins are razor thin from increased patient loads and decreased elective procedures. Without the ability to expand their footprint or buy more assets, hospitals have had to shift their focus to getting more out of their existing assets.
LeanTaaS solutions helped hospitals schedule surgeries, reduce administrative tasks, and ultimately become more cost-efficient — a win-win-win situation. For example, during the pandemic Novant Health faced a shortage of available operating room time, 8,000 postponed surgical cases, and low block utilization. The system implemented iQueue for Operating Rooms to help improve OR capacity and provide surgeons and their schedulers with a tool that made OR time simple to view, access, and share. The tool proved vital in navigating the aftermath of the return of surgical cases. Novant Health increased case volume by 4% and cleared its entire backlog, which had accumulated over 75-90 days, in just a further 90 days. They ultimately realized a 6.15X ROI, along with a greater breadth of engagement from surgeons and their practice administrators.
What are some of the challenges behind bringing healthcare into the modern era?
Traditionally, the healthcare industry has been defined by its antiquated legacy infrastructure that admires problems rather than proactively solves them; its special obligation to safety and accuracy that discourages trusting newer technologies; and finally, as financial resources are precious, its leaders cannot risk making an investment without a guaranteed reward. The pandemic actually helped change this tune. Health systems needed to rapidly adopt digital solutions like telehealth and our capacity management solutions to maintain access to care, and in the process they proved provider and patient demand for cutting-edge solutions, ROI, and that they could be more fast and agile in adopting technology than they even knew themselves.
After decades of lagging behind other industries, this recent surge in digital transformation is positioning healthcare to skip a technology generation. It cannot catch up to consumerism, but it can leap ahead and lead the charge into AI. Healthcare leaders now know how to prioritize safety, privacy, immediate results, and proven return on investment in their technology, which must offer truly supportive and useful workflows that preserve and enhance human expertise. In digitizing so rapidly, they have built the infrastructure needed to sustain new AI-based technologies with security.
However, a critical element of bringing new technology to healthcare is change management. Technology alone does not yield sustainable transformation. It must be paired with change management experts who can guide organizations through disruption towards results. That’s why earlier this year, LeanTaaS announced the launch of Transformation as a Service (TaaS), a first-of-its-kind service that guarantees outcomes. The TaaS offering provides each customer with a dedicated team that delivers the required services for implementing our technology, ensuring normalized data hygiene, automating and digitizing existing workflows, driving change management, establishing systemwide governance, and guaranteeing success.
Is there anything else that you would like to share about LeanTaaS?
We are continually listening to our customers and innovating based on their specific needs and advancements in AI. As such, we recently launched iQueue Autopilot, a first-of-its-kind, generative AI solution for hospital operations that provides hospital leaders with human-like conversations and actionable insights to support decision-making for patient flow, scheduling, command center, block management, staffing, and other capacity management use cases across both inpatient and outpatient settings. Given such accessible, immediate, and powerful support, hospital leaders will be able drive higher financial results and increase access to care; nurses and providers can freely dedicate their time and highest attention to patients; and staff can operate productively day-to-day while avoiding burnout. With iQueue Autopilot, LeanTaaS is bringing our vision of “air traffic control” for healthcare — managing patient flow and capacity optimization across the continuum of care on one single platform — to life.
