Welcome to the Tech Blog
My name is Miguel Alvarado; I’m Lumiata’s new CTO. I joined Lumiata on October 1st, only a little over 35 days ago. It has only been just over a month and Lumiata already feels like a second home.
This is my first blog post, and with this post, I would like to introduce the tech side of the Lumiata blog. From this post on, our data science (machine learning) and engineering teams will be posting every 2 to 3 weeks on various topics related to the work we’re doing. Things are very action packed here at Lumiata. Some of the things we’re doing are very challenging and interesting. We’re hoping to engage with the community at many different levels by regularly presenting what goes on here. The goal is to inform and inspire, as well as being informed and getting inspired ourselves.
Without further ado, welcome to Lumiata’s Tech Blog! Hopefully, we will be seeing you here periodically, checking out what is going on in our worlds of applied machine learning and engineering.
Moving on to my blog post topic - “CTO’s First 30 Days.” This is the first part of a three-part series. I will be posting again after 60 and 90 days. The series will attempt to depict my experience in a way that is hopefully informative to others going through similar experiences.
Before diving deep into my first 30 days, I would like to describe why I joined Lumiata, to set better context around my entire experience. In short, I wanted to be part of a technology organization that had big ambitions and was trying to change the world for the better. Prior to Lumiata, I had been in entertainment for some time, and while every single experience I had was interesting, challenging and exciting, I wanted to be part of something that was attempting to change fundamental aspects of our society. I also wanted to be part of something that was building software where Big Data and Machine Learning were at the heart of the product.
I've long thought that two of the most important pillars of society are education and healthcare. Recently, I realized that I wanted to be at a company helping to advance the needs within those pillars. Lumiata's mission to “make healthcare smarter” resonated incredibly well with my ambitions. Often, companies with big missions like that have short term goals that are scattered and unrealistic—that was not the case with Lumiata. I found that Dilawar Syed (CEO) had done a great job in the last 6 months to focus the company. Further, the mid-term goals were attainable while still pursuing the long-term ambitions that could change health care in fundamental ways.
Lumiata's initial goal is to provide risk and cost predictions for patients and populations to help optimize major health plans. Between reading academic papers on the topics that Lumiata shared with me and learning about the projects that Lumiata has been working on over the last few months, the team gave me very strong evidence that there is something special here. They showed me that there is a true problem area to pursue, but also that Lumiata has already been laying the groundwork in terms of providing robust Predictive Analytics solutions, built with Machine Learning.
What I saw was an organization that had found a very specific set of problems to solve, and had a proven way to do so with Machine Learning. I saw an organization ready to build software that would “industrialize” the proven predictive analytics capabilities it already had found a market-fit for. The Machine Learning knowhow was there; it was now a matter of applying industry software development “know-how” to scale it and industrialize it. This latter part is something that I felt I could personally help with.
This seemed like an ideal opportunity for me. An opportunity where I could combine software, big data and machine learning “know-how,” for a space that represents one of the core pillars in any society, that space being healthcare. Not to mention that the company already has a particular edge that customers are willing to pay for. That is ultimately why I joined Lumiata, and now my mission is that of the company - "make healthcare smarter."
The First 30 Days
There are two groups that report up to me, and I have only two direct reports that lead these groups. On one side, I have Nicolas Tilmans running software, data engineering and infrastructure. On the other side, I have Rohun Kshirsagar running all-things data science and machine learning. I like the fact that the reporting structure is clean and simple, and under these two gentlemen, the organization is completely flat.
Even before my first day at Lumiata, I started thinking about “what are the most important things for a CTO?” I wanted to form a mental model that would guide my initial endeavors. I knew that the first 90 days would be all about learning my context, especially in the area of healthcare, but I also knew that some important decisions would have to be made, so I was looking for a framework to guide my thinking.
I concluded that the 3 most important things for a CTO are:
Even though these are only 3 areas of focus, they’re rather comprehensive and really include everything that a CTO should be thinking about. Culture is all about people, and potential issues around people. This area includes aspects like hiring, morale, relationships and interactions between teams, coaching needs, conversations about low performers (if any), etc. In a way, it can be summarized as all-things people and their behaviors. I am a firm believer that people are the most important asset that any company has, so this fact can make culture potentially be the highest priority item to think about within the first 30-90 days.
Technology is all about tech strategy and how it maps to product strategy. It includes coding standards, what ML frameworks and techniques are being used, how to go about technical design, best practices, metrics to measure the quality of technology being produced, buy vs build conversations, IP building, DevOps practices, architecture, security and privacy, compliance, technology vision, etc. These are the areas that first come to mind when one thinks about a CTO - all things technology. The problem is that sometimes technology is the only area that is considered and the people or operations parts suffer.
Operations are all about the day-to-day, the ways in which things are getting done, logistics, and processes. This includes thinking about how to capture requirements, how to document things, how to implement agile, processes around security, team rituals such as stand-ups, retrospectives, delivering feedback to customers, etc.
It’s clear how these things are very much related to one another, but it helps to think about them separately as three distinct areas of focus. Since these 3 constructs were going to be the core structure for my frame of reference, I asked my two direct reports to use these same 3 when we conduct our weekly 1:1’s. This has started to give us a similar frame of reference as a starting point for conversation.
Because culture and people are so high up there in my personal value system, the first week at Lumiata, I decided to have 1:1’s with everyone - data scientists, engineers, and also people outside of my technology organization. The goal of these meetings was to learn more about them and Lumiata. As an added bonus, I thought I would also get a good read for what the morale is like. I wanted to get enough material to educate myself, but also to find improvement opportunities.
What did I learn in that first week?
First of all, it took me over a week to talk to everyone. I scheduled meetings very tight next to each other so that I could get this done in the shortest amount of time possible, and it was very energy consuming.
One of the most important things I got from the meetings is that the morale is very high, everyone is incredibly smart, highly educated (many PhDs), and incredibly passionate about what we’re doing. Everyone really wants to make a dent in the world of healthcare. I did not hear unproductive complaints, nobody talked negatively about anybody that works currently at Lumiata, and everyone had a legitimate willingness to making things better. I was pleasantly surprised by how positive everyone was. I say that I was surprised, just because the status quo is that most organizations have issues here and there, especially when it comes to morale, and I did not find any red or even orange flags!
I did find that there is a lot of room for “better,” and these were my biggest takeaways:
1. Lumiata’s world (healthcare) is complex. So, better documentation is needed.
2. The ML side could inherit some aspects from the software world while keeping its highly experimental nature.
3. Too much time is spent with datacenter matters. Yes, Lumiata is not in a cloud provider (yet).
4. I got reminded that Security and Privacy are hard if you’re in healthcare, and we can’t miss a beat.
5. For every project, there is a heavy “integration” step at the end of it, bringing together the effort of many scientists and engineers.
6. We could use more test automation, and there are recent advances here that we can adopt.
7. There are times where there are data bugs, not just code bugs.
8. It is hard to understand the lineage from data sources to models to predictions.
9. Inconsistency with development process and people sometimes not knowing what is going on and when to expect things.
10. Lead time is currently very long.
1. Use Confluence as the tech wiki, the entry point for all documentation, and I gave that some initial documentation structure.
2. Literally everything should be documented.
3. One week sprints for all - data science and engineering.
4. Everything should be tracked in the same way for all teams in JIRA.
5. Hiring is a top priority for all.
Easy decisions, right? Now on to "not so easy things". This includes the following: what to do about the datacenter, how to deal with security in a scalable fashion without eating up tons of resources, how to better the QA automation world, and how to improve lead time. Again, the key is not to overthink things. It's also important to get out of the "thinking" phase and quickly into the "doing" phase. You learn much more by doing than just thinking about it.
I decided to start a major refactor of our environment codenamed Project Carabela, after the “conquistadors” that took the Carabelas (ships) to go find new lands. We are starting immediately with a focus on these areas:
* CI/CD, Continuous Delivery will be the fabric for everything we do, whether it is changing a model or some Spark code. Everything will be driven from a Jenkins pipeline.
* Security can be augmented with the help of a 3rd party. Lumiata puts a lot of effort and time in making sure that security is buttoned up year after year. With help we can scale these efforts as we grow.
* Adopt Great Expectations framework as part of the QA automation toolset. Add the practice of "data testing" in addition to code testing.
* Re-design the main data pipelines so that there is a visible and deterministic approach to identifying lineage from raw customer data to predictions.
* Move to AWS!
(While there is more in the POC, these are the relevant areas for this post.)
The rest of my 30 days have gone into figuring out the details for this POC which should give us the basis for the new world that we’re moving to. CI/CD, easier to manage and less time-consuming security, data testing, lineage and being in AWS should help us scale things as we move forward in our mission of "making healthcare smarter."
How to measure success?
We are a data-driven company, so we're implementing metrics to measure the success of the POC. We're using the following:
* % of components that have CI/CD pipelines deploying to production in AWS
* Lead Time - from code completion to deploy to production
One can say that this POC includes some bold things, especially after only being here just over 30 days. I believe that important decisions can’t wait, and perhaps it’s the most important message in this blog post. My experience has shown to not postpone big decisions; the sooner you make them, the more time you have to switch gears if anything around the decision needs to be adjusted.
I would conduct these past 30 days in exactly the same way, if I had to do it over. To recap, the most important things in my 30 days have been as follows:
1. Establish a common frame of reference between me and my direct reports.
2. Talk to people to learn about the organization and my environment.
3. Don't over-think things, and start moving.
4. When there is a lot of uncertainty, and important decisions need to be made, kick off a POC to pave the ground. After all, you learn more by doing it than by just thinking about it.
5. People don’t like to document, but doing it pays dividends. Always push for it.
This was my first 30 days! Stay tuned for 60 and 90 days.
Lastly, to really go after our mission of “making healthcare smarter”, we need more engineers. We need very strong, seasoned engineers that have interest in helping us build a world class, machine learning platform for the healthcare industry. If you think you’ve got the chops, and what we’re doing resonates with you, I invite you to visit the careers section of our website.
Chief Technology Officer, Lumiata
As CTO, Miguel will be responsible for “all things technology” at Lumiata –– Engineering, Data Science and Infrastructure. With adoption of Lumiata Health Analytics accelerating, our technology stack and infrastructure must scale to ensure our customers continue to rely on Lumiata products for their critical operations.
As I got to know Miguel these past few weeks, his two leadership principles stood out for me. One, he is passionate about building high-performance and motivated teams. Two, he believes we must build technology that is inspired by what our customers want (not the other way around). These core principles will guide us as we build and scale enterprise-grade healthcare products and infrastructure to serve our customers.
Leaders have a foundational role to play in both creating products, as well as nurturing a company’s values and culture. I look forward to partnering with Miguel in building a company we’re proud of, and one that our customers love to work with.
A number of industries are effectively leveraging data science to realize greater efficiency and gain a number of benefits. When it comes to the healthcare industry, here are three advantages of using data science approaches:
1. Reliability, Verifiability, and Openness to Experiments
Much of the healthcare industry relies on the knowledge and experience of clinicians, administrators, and underwriters. Along with the insights and creativity of people, there is also the possibility of human error and inconsistency. Data-based approaches can be built to produce similar results under similar conditions, reliably, and importantly can be tested on millions of patients to verify performance—this also allows a fast cycle of experimenting with new methods.
2. Models Reveal Biases
Data science models are designed to identify complex patterns that can predict health outcomes or cost with minimal human intervention. In addition to improving predictions, such models can illuminate how traditional approaches might be biased. Are care programs selectively benefitting certain population groups better than others? Are certain patients not being reached? These questions can be as important as overall program effectiveness.
3. Predictions Often Improve with More Data
Perhaps the most exciting benefit of using machine learning-based models is that they can improve with more data, assuming the model has enough free parameters to discover new patterns and that the new data is sufficiently different from previous datasets. Much of the work in data science is in tuning models such that the number of features is high enough to capture important effects in the data, without introducing so many that the model overfits to the training set. In our experience, after model tuning, adding millions of new medical health records has significantly improved our predictive performance.
Stay tuned, as we take a closer look at each of these advantages and explore how healthcare can benefit from data science.
Senior Data Scientist, Lumiata
Lumiata's core is constructed with data science, as we build models for cost and disease prediction. This year, we've welcomed new members to our data science team with a shared dedication to use deep learning and machine learning to innovate healthcare. We're honored to introduce them.
I was born in Arizona, and moved to Israel when I was still a baby. At 19 I graduated with a BSc from Tel Aviv University and moved back to the US to do a PhD in Mathematics at the University of Pennsylvania. I’ve spent the years before coming to Lumiata as a mathematician, having done research as faculty at three different universities: Penn State, Stanford University, and the University of Maryland. I’m excited about applying my knowledge and skills to improving the US healthcare system.
I have a background in Physics and Astronomy, with my PhD research on the evolution of distant galaxies. I decided to switch to data science in the tech industry, hoping to use machine learning and my quantitative skills to make direct impact to the society. I feel privileged to work in Lumiata, where I can not only work on advanced technology, but also solve important problems to improve healthcare and the quality of lives.
I recently graduated from the University of San Francisco with a Masters degree in Data Science. In addition to studying contemporary computation and analytical techniques, I worked with the LA County Registrar, analyzing county wide voting behaviour. It's been exciting to apply my experience to the health domain with Lumiata.
I joined Lumiata as a Data Scientist in May 2018. I have my Master’s degree in Biotechnology and four years of experience working in biopharmaceutical industry. I completed Graduate certificate program in Data science from Harvard University and I joined Galvanize Data Science immersive program to strengthen my skills to drive business decisions by leveraging data. I am passionate about using machine learning techniques to get actionable insights from big data to improve healthcare system. I strongly believe in Lumiata’s mission as data science has the potential to revolutionize healthcare.
I graduated from UC Berkeley in May, 2018 with a degree in Statistics and Computer Science and began working here at Lumiata a month later. During my time at Berkeley I participated in several Data Science internships and research projects that helped bolster my skills as a Data Scientist. I am excited to be working here with the mission to help people across the country by making healthcare more affordable.
We're proud to introduce Rohun Kshirsagar, Senior Director of Data Science. In this interview, hear what brought him to Lumiata and why he is passionate about his work here.
As the leader of the Lumiata Data Science Team, Rohun brings impressive experience in both data science and health science to his work. Prior to joining Lumiata in 2016, Rohun worked as a data scientist the Stanford University School of Medicine and, prior to that, at Transera/Broadsoft. He received both a Bachelor and Master degree from the University of California at San Diego, and did Doctoral studies and was a research assistant at Texas A&M University in pure mathematics.
Through data engineering, medical data enrichment and pattern discovery with resulting predictions, we do more with data as we strive to make healthcare smarter. Slide the < > below to see how we transform data into insights in four stages:
Lumiata Raises $11 Million to Accelerate AI Powered Health Analytics for Improving Healthcare, Introduces CEO Dilawar Syed
Funding from Khosla Ventures, BlueCross BlueShield Venture Partners, and Intel Capital to fuel transformation of healthcare across insurers, providers and employers through predictive analytics
San Mateo, CA, — July 11, 2018 — Lumiata, a provider of AI-powered health analytics for managing cost and risk, today announced it has closed $11 million in new funding co-led by Khosla Ventures and BlueCross BlueShield Venture Partners. The investment was secured under the leadership of Dilawar Syed, who was appointed CEO by the company’s board of directors in April. Funding will be used to accelerate Lumiata’s deployment of next-generation health analytics, enabling health plans, providers and self-insured employers to anticipate and ensure optimal care. Sandbox Industries, Intel Capital and others also participated in the current round. This latest investment brings Lumiata’s total funding to $31 million.
“With the appointment of Dilawar Syed as CEO, we have found not only an excellent and highly experienced leader, but also someone who knows how to build and grow enterprise products,” said Matt Downs, chairman of the Lumiata board of directors and Managing Director of Sandbox Industries. “Syed has already hit the ground running since joining Lumiata. In just a matter of weeks, he has led the company in securing this new round of funding and is firmly at the helm as Lumiata accelerates development of a new suite of predictive analytics products to support payers and risk-bearing entities.”
Syed most recently served as president at Freshworks and helped the cloud-based software company grow to more than 100,000 customers worldwide. Prior to Freshworks, he served as CEO at Yonja Media Group and also served as head of Platform Operations at Yahoo. In 2010, President Barack Obama appointed Syed to the President’s Advisory Commission on Asian Americans and Pacific Islanders (AAPI), where he led White House engagements with AAPI entrepreneurs across the U.S. on a breadth of issues including healthcare and economic growth. Syed holds an MBA from The Wharton School of the University of Pennsylvania.
“The stakes couldn’t be higher as the demand for smarter healthcare at lower cost resonates with millions of Americans. Healthcare today is undergoing extreme volatility, and requires high-performance, state-of-the-art analytics that will arm payers, providers and employers with the insights they need to manage costs while delivering the best care possible," said Syed. “This new funding from industry leaders is a vote of confidence in Lumiata’s mission to tackle spiraling healthcare costs. I am very excited to be leading a talented team and partnering with our customers to deliver products that will change the trajectory of healthcare in our country.”
Since its launch, Lumiata has developed models to predict disease risk and onset of certain chronic conditions for over 20 million patient lives. In the first half of 2018, the company has developed cost and spend prediction models with significant demonstrated improvement in prediction.
"In five years, AI and other technologies will transform healthcare to lower costs, provide more access, and deliver better outcomes," said Vinod Khosla, Founder and Managing Partner at Khosla Ventures. "We believe that Lumiata has the leadership and the technological advantage to be a key player in this healthcare transformation."
“Lumiata has jumped out of the gate with building strong predictive models that have the power to radically disrupt and improve how insurers operate,” said John Banta, executive director, BlueCross BlueShield Venture Partners. “We are not only impressed with the data science and engineering team Lumiata has assembled, but also with the company’s leadership and vision of what they can deliver to millions across this country.”
Lumiata provides predictive analytics for managing health costs and risk impacting millions of lives. At the intersection of clinical knowledge, data science and deep learning, Lumiata works with major U.S. health plans and providers to manage costs and prioritize care. Based in Silicon Valley, Lumiata's team is comprised of data scientists experienced in advanced health informatics. For more information, visit https://www.lumiata.com/or follow @lumiata.
About Khosla Ventures
Khosla Ventures is a venture capital firm that provides venture assistance and strategic advice to entrepreneurs working on breakthrough technologies. The firm was founded in 2004 by Vinod Khosla, co-founder of Sun Microsystems, and focuses on transformative technology in consumer, enterprise, education, financial services, health, big data, agriculture/food, sustainable energy and robotics. Khosla Ventures is headquartered in Menlo Park, California. For more information about the firm's activities, please visit https://www.khoslaventures.com.
About BlueCross BlueShield Venture Partners
BlueCross BlueShield Venture Partners, L.P. is a corporate venture fund licensed by the Blue Cross and Blue Shield Association, an association of independent Blue Cross and Blue Shield companies. The fund invests in promising emerging companies of strategic relevance to Blue Cross and Blue Shield companies. Sandbox Industries is the exclusive provider of investment management services to BlueCross BlueShield Venture Partners. For more information, visit www.bcbsvp.com.
About Intel Capital
Intel Capital invests in innovative startups targeting artificial intelligence, autonomous vehicles, workload accelerators, 5G connectivity, virtual reality and a wide range of other disruptive technologies. Since 1991, Intel Capital has invested US $12.3 billion in 1,530 companies worldwide, and more than 660 portfolio companies have gone public or been acquired. Intel Capital curates thousands of business development introductions each year between its portfolio companies and the Global 2000. For more information on what makes Intel Capital one of the world’s most powerful venture capital firms, visit www.intelcapital.com or follow @Intelcapital.
Laurie Gibson for Lumiata
Our focus is to deliver greater precision in costs and risk to major health plans, care providers, and employers, and enable pathways for smarter, more tailored care. Our work builds on the promise of AI, clinical knowledge, and our team’s collective heart to change healthcare for the better.
Millions of Americans today remain uninsured or underinsured; often just one diagnosis away from bankruptcy. Employers, who in post-WWII America became the main source of healthcare coverage, find rising costs a top contributor to the cost of doing business –– affecting job creation and opportunity.
Clearly, the scale and complexity of the healthcare challenge is enormous. Yet, the promise of AI to bend the cost curve is awe-inspiring, especially when mission-guided entrepreneurs set out to tackle it — fearlessly. We can contemplate a more hopeful future, when employers of all sizes, health plans and care providers can become smarter agents in healthcare by embracing modern, predictive analytics.
Since April I’ve been huddled with our smart and impassioned team in San Mateo, customers who’re partnering with us everyday, and Lumiata’s iconic investors. It’s been an intense start to a purposeful journey I had sought after an amazing, memorable run at Freshworks.
These past few weeks at Lumiata have also reaffirmed my belief in an essential leadership principle: Passionate, empowered, and aligned people can turn a corner on any challenge or adversity — no matter its enormity, intensity, or type. Team Lumiata is on course. As we move forward, our execution will be guided and inspired each day by the outcomes we can drive for millions of Americans.
We are Lumiata. We make healthcare smarter.
President & CEO, Lumiata
SAN MATEO, Calif., May 11, 2018 (GLOBE NEWSWIRE) -- Lumiata, which applies artificial intelligence to multipoint aggregated medical data to enhance predictive analytics in healthcare, announces completion of a third party audit attesting to its achievement of the demanding requirements of the SOC 2 Type 2 and HIPAA data security standards.
The meticulous third-party examinations and assessments that led to compliance with the rigorous series of data security standards was administered by the professional IT compliance and audit staff at 360 Advanced, a national HITRUST CSF, Qualified Security Assessor, and Certified Public Accountant firm based in St. Petersburg, FL.
The SOC 2 Type 2, developed by the AICPA, is the most widely recognized authoritative guidance that provides service organizations a uniform method for disclosing independently assessed information about the design and operation of internal controls. HIPAA is designed to provide privacy standards to protect patients' medical records and other health information provided to health plans, doctors, hospitals and other healthcare providers.
“Achieving SOC 2 and HIPAA compliance affords Lumiata the opportunity to audit, crosscheck and improve our data and security practices,” said Nicolas Tilmans, Sr. Director of Engineering at Lumiata. “This is a critical step to building and scaling partnerships with our customers across the healthcare industry.”
Source: Globe Newswire
Once again, Lumiata is a proud sponsor of the annual RISE Nashville 2018 conference on healthcare risk adjustment. This year we are especially happy to be co-sponsoring with Health Data Vision, a leading medical record services and data management platform company.
Lumiata has entered into an agreement to power predictive analytics for the HDVI technology platform, called MRCS to enable more accurate HCC and provider identification. Lumiata’s predictive analytics uses machine learning and artificial intelligence (AI) to understand the current and future health trajectories of individual members.
Our comprehensive solution, Risk Matrix, produces high-quality stratified chase lists for America’s leading health plans to close gaps in accuracy. Our chase lists are stratified by a variety of criteria including members with the highest need for risk adjustment (HCC suspects) and providers with the highest HCC prevalence. Finally, Lumiata’s analyses are generated more quickly and frequently, and are enhanced with greater information to the plan to ensure RADV mitigation.
In our experience, Lumiata delivers:
Between 75-80% PPV for HCC prediction
Continuous analysis with less than 3 day turnaround for updated chase lists
Over two dozen ranking and provider models to support risk adjustment targeting and prioritization
Lumiata specializes in stratified, risk adjustment chase lists for Commercial/ACA, Medicare Advantage, ACO, and Medicaid populations. Our analyses are offered through a simple, yet flexible, data-as-a-service (DaaS) offering.
Visit Lumiata at RISE Nashville 2018 in the exhibit hall. Our booth is next door to HDVI’s so you can learn more about the full range of services our two companies can offer to improve your risk adjustment performance in the coming months.
Earlier this year, our team drew up a set of values that we want to uphold as a company. These values reflect who we are (including our quirks!), our aspirations, our strengths, and how we want to grow as a team. The two values I see come alive the most at the office are Teaching and Learning. Every day, as everyone settles into their desks, the team chatter slowly builds: the Data Engineering team problem-solve on clusters; the Data Science team discuss how to apply methods from a paper they just read to their work; and the Business Development team shares new announcements in healthcare and ideas on progress. We are a small, nimble and growing team, and our culture of Teaching and Learning guides how we challenge each other, while fostering mutual growth, innovation and discovery.
This past month, we welcomed five new hires to the team. Their expertise and diverse backgrounds will add to Teaching and Learning at Lumiata, and we are thrilled to introduce them here! Meet the new Lumiata Class, or 'Lumis' as we like to call ourselves.
Mohsen Shokrollahi, Systems Administrator: As an IT professional with a decade of experience, I enjoy sharing knowledge and helping others achieve a greater level of technical expertise. I have a range of accomplishments and experience in network administration and engineering, data center support, and server design and implementation. I’ve also worked extensively on scaling optimization with high availability, developing support procedures to reduce staff costs and outages, and improving problem resolution efforts. I’m looking forward to applying my troubleshooting and problem-solving abilities to Lumiata’s day-to-day operations, and designing, implementing and operating cost effective solutions for the company’s IT infrastructure.
Min Guo, Data Engineer: I am passionate about distributed computing and big data technology. I joined Lumiata’s Data Engineering team in October this year and am already excited about the possibilities of how Lumiata can change the future of healthcare. I earned my Master’s degree in Computer Science at New York University in 2016. I also hold Ph.D. in Physics, and am looking forward to learning more from my colleagues.
Erica Ferguson, Human Resources and Office Manager: I bring two decades of professional experience in human resources and customer service. I joined Lumiata because of its culture, and I’m excited to be part of its work in transforming the future of healthcare. My responsibilities at the company include management of human resources, company culture activities and making sure the office runs smoothly on a daily basis by supporting all company life functions. I have a Bachelors or Arts degree in Organizational Management from Ashford University.
Alexandra Pettet, Data Scientist: I come from a background in Pure Mathematics, with several years of experience working in academia. I entered the tech industry with a specific interest in working in healthcare, where machine learning and AI have the potential to profoundly improve the quality of lives. I feel very privileged to be working at Lumiata, where I have discovered a friendly and supportive team, and a mission I believe in.
Renzo Frigato, Data Engineer: I have a background in Mathematics and nine years of Software Engineering experience from Stanford, Amazon and Accenture. This summer, I participated in the Insight Data Engineering program to deepen my knowledge on big data systems. As part of the program, I created a personal project to compare multiple recommendation algorithms of news articles, using technologies like Kafka, Spark and Elasticsearch. I am very excited to apply my skills at Lumiata. Making healthcare better is an ambitious and important task, and leveraging data effectively is essential to accomplishing it.
In healthcare, the quality of chase lists and chart-pulls can be the difference between accurate risk scores and reimbursements, or missed care opportunities that significantly reduce HEDIS and Star ratings. And, these lists are only as good as the level of action taken on them.
Chase lists are a critical juncture in the data journey, but are often also the weakest link. Because of poor data quality, data latency, and data delivery, intended actions are rarely taken. We recently heard from one of our large payer customers they get about a 1 percent response rate on chase lists generated for the purposes of more accurate risk adjustment coding. One percent!
And this, by the way, is fairly typical, whether the list is for the purposes of population health management, risk adjustment, chart pull requests, or disease management. The rate at which such lists generate the hoped-for response is rarely better than about 8 percent in our experience.
Breakdowns in the Data Journey
We’ve seen instances in which an action is recommended on patient data that is 12 months old. If that patient is at risk for diabetes and the action recommended is to intervene, waiting 12 months to take an action is too long. It’s already too late.
Why do chase lists in healthcare perform so badly? In our experience, it boils down to four overarching factors that have a domino effect:
These are the breakdowns we want to address at Lumiata, so that ultimately, the lists we generate inspire action and empower healthcare’s workforce.
So, if we are to transform health plans’ data into lists that inspire a response around disease management or population health management or risk adjustment, it’s important the process:
Our previous blog goes through how we transform the data journey for health plans. We want our customers to become experts of their data, not just administrators of it. If we can do that, we believe that the humble chase list–that critical juncture in the data journey–can become an engine that empowers professionals to move the needles that matter the most for care and cost management.
“Healthcare runs on lists.”
We say that a lot at Lumiata. You’ll see it on our website, in our presentations, even in how we introduce ourselves. Here’s why.
We are in a red zone
I had the incredible opportunity of attending the 2017 Health Evolution Summit earlier this year. It’s an annual gathering of industry leaders deeply involved in transforming healthcare. The discussion point that interested me the most was around accelerating business transformation. One of the key messages was, “We are in the red zone. We can’t get to value fast enough. Too many people can’t afford the care we need.”
This message resonated strongly with me. Every day, I hear about broken or stale business processes in healthcare that are ripe for reinvention. The downstream effects of failing to accelerate are more financial waste, suboptimal care and poor health for people across the country. We can’t afford to move slowly.
How to apply radical reinvention in day-to-day healthcare operations
To transform healthcare’s operations today, in a fast, meaningful and measurable way, we need to look at the common denominator that drives revenue, cost, utilization and care management activities. Therefore, one of the core questions for us at Lumiata, is:
It starts with lists.
Lists are that common denominator. They drive the prioritization of actions needed in an industry that faces huge supply and demand constraints. I once heard a program director at an ACO say their providers are extremely list-oriented. Once they have a list they trust, it’s their primary tool for more effective and targeted care coordination and prioritization.
But, these lists have very low ROI. The program director went on to say that their providers often do not trust the accuracy of their lists, because they offer no clinical explanation of why certain patients have been flagged for certain diseases. While these lists are expensive to produce, providers rarely act on them. The result of inaccurate lists is low visibility of the current and future health states of members and the cost implications, little improvement in capture of coding opportunities and correcting missed diagnoses, which translates to inefficiency, financial waste, and poor risk, care and cost management.
Therefore, although seemingly mundane, healthcare’s lists are a core nugget of productivity and efficiency where we can apply pointed innovation–from chart-pull lists and provider lists associated with risk adjustment, Star ratings and quality management, to chase lists and utilization lists that drive care management and revenue management.
What if health plans were able to automatically and continuously derive highly accurate lists from their disparate sources and types of data? Could that give them continuous visibility of highest-risk members, coding and care management opportunities, which they could use as a collaboration tool for providers?
What would our world look like?
More timely and effective use of data, more accurate risk adjustment, improved Star ratings, higher quality of care, less focus on labor-intensive, redundant tasks, and therefore better allocation of resources.
Artificial intelligence can create smarter,
more accurate, and more timely lists that drive business processes
We need to move beyond traditional analytics. Most solutions today are unable to handle the complexity of health data, don’t surface the most important next steps that bring the greatest bang-for-buck, and don’t provide any transparency into models. At Lumiata, we are using AI to build a medical “brain” that can be applied toward the day-to-day processes in list generation and application–from data preparation that is able to ingest and standardize more and disparate sources of data, to predictive analytics that enrich data with medical knowledge and apply deep learning to create more clinically-relevant models, to generation of lists that are more contextual and can be tailored based on business objectives.
Here’s how we do it:
We’re excited about the possibilities. Transforming healthcare’s lists is our first step toward a greater vision of ultimately embedding AI across the healthcare spectrum. By starting with healthcare’s lists, we transform business processes with tangible gains in the near-term, for better care for those who need it the most, and more efficient operations for health plans that are pivotal to healthcare access.
Founder and CEO, Lumiata
There’s an old joke – although maybe it’s not funny – about the quality of healthcare: Fifty percent of the doctors in the world finished in the bottom half of their class.
Clearly that’s harsh, but one thing is true: the nature of healthcare often requires doctors and medical professionals to make quick decisions based on limited sets of data. Which makes healthcare ripe for improvement through artificial intelligence.
One company working to do that is Lumiata, a startup striving to improve individual quality of life and lower the cost of healthcare delivery by predicting health risks with data...
(Click HERE to read the rest of the article by Jeff Haden.)
The hysteria surrounding both machine learning and artificial intelligence is substantiating mathematician Alan Turing’s assertion that eventually “no one will be able to speak of machines thinking without expecting to be contradicted”.
Indeed now, the concept of the ‘rise of robots’ is no longer merely a plot in a Hollywood sci-fi blockbuster, but an area being explored by academics, researchers and technologists across the world.
Silicon Valley start-up Lumiata, for example, uses AI to map out current and future health trajectories of individuals and provide detailed reasons behind every prediction.
(Click HERE to read the rest of the article by Sooraj Shah.)