Selling the Hospital-based Incubator Idea to Your CFO

A case study of applying Lean Startup Methodology to validate the need for a Hospital Based Startup Incubator

Lean Startup Methodology (LSM) proposes that the creation of a product or service should be done through an iterative, end-user driven process. [1] A hospital-based startup incubator (HBSI) is no different from any other product or service in that regard. The application of LSM allows for rapid, low cost, and scientific validation of the value of a HBSI. The following case study applies LSM to the HBSI as a product of innovation.

Vision

The first step of LSM is identifying a vision followed by a series of iterative tests to validate or invalidate the strategies to achieve that vision. My vision for improving health for vulnerable populations is value optimization of hospital expertise and narrowing the gap between perceived problems for patients, providers, and payers and the proposed solutions to those problems.

Product, Strategy, Vision Pyramid [2]

Strategy

The proposed strategy for value optimization of hospital expertise and narrowing the gap between perceived problems and their solutions is the creation of a HBSI. As with any strategy, it is useful to explore analogs and antilogs to this strategy.[2]

Analogs are similar approaches that were successful at solving a similar problem. One analog is health-IT startup incubators. The California Health Foundation recently conducted a thorough review of these incubators. [3] A major weakness of the entire health incubator movement is its nascence and consequently lack of proof of worth. Success of these incubators was largely measured by the number of investment dollars. But investment dollars do not necessarily correlate with profit generation for investors nor improved outcomes for patients.

Other analogs include the investment vehicles that have been created at major academic medical centers and health systems including Mayo Clinic, Cleveland clinic, Kaiser Permanente, and Geissenger Health System. Each of these health centers have their own mechanism for funding clinical innovation, but they lack a robust incubation function. Learning from these analogs, the combination of best practices from health-IT incubators with the movement by hospitals to invest in innovation can inform the design of future HBSIs.

Antilogs are similar approaches that were unsuccessful at solving a similar problem. Traditional research and QI are hospital-based approaches to improve health directly or indirectly. Research is a very expensive and time-consuming approach to generating knowledge, which does not directly solve problems for patients or providers. QI is a fast and inexpensive process of improving care, but it does not create commercial value directly. And its validation is through clinical outcomes rather than willingness to purchase from an end user. Both research and QI fail to optimize the potential value of hospitals because the talents of hospital employees remain largely untapped.

Business Model

Informed by the analogs and antilogs, the next step in testing a HBSI for value is to lay out the business model. The basic elements of a business model are organized in a useful tool called the lean canvas.[2] We will use the canvas to organize our experiments to identify and eliminate risk in the business model.

 Lean Canvas [2] 

Customer Segments
It can be helpful to start with thinking about a business model by identifying who has problems that may benefit from your solution. The Chief Financial Officers (CFOs) of hospitals are a good starting point when addressing issues pertaining to revenue generation for hospitals. Department chairman have similar pain points because individual departments with the hospital are microsystems with own budgets and own needs for improving bottom and top lines. Similarly, population health managers can be a customer segment, particularly if the hospital is part of an Accountable Care Organization (ACO) or some other readmission risk-baring organization.

Lean Canvas: Customer Segments [2] 

A particular subset of customer that will test an innovation at its earliest stages just to be at the cutting edge of new technology is called the Early Adopter.[4]

From Cooper and Vlaskovitz in Guide to Entrepreneurship. Adapted from Geoffrey Moore. Crossing the Chasm. 1992. [4]

Early adopters of innovations that address budgetary issues may include departments that are well capitalized and looking to invest in ways to further grow their revenue. Although their time of affluence may be running out, for now at least, departments that perform a high volume of procedures are more likely to have discretionary budgets for innovation. Some specific examples include departments of orthopedics, dermatology, urology, and cardiology.

Problem
One of the major problems faced by CFOs include steadily shrinking reimbursement from payers because financial incentives are moving toward pay-for-performance rather than fee-for-service models. This shift will result in more care transitioning into the community and away from hospitals.

Lean Canvas: Problem [2] 

Another problem faced by the hospital CFO is the overreliance on revenue generated from charging overhead on research grants. Federal research dollars are fluctuant and currently very scarce. Although premier institutions with strong academic reputations have an advantage in attracting research funding, their competitive advantage is unlikely to overcome the revenue contracture due to cost-cutting programs from the Affordable Care Act (ACA). Additionally, non-elite academic medical centers and non-academic medical centers can not rely on those research revenue streams and require new infusions of cash to remain commercially viable.

The current solutions for these problems include cutting non-essential clinical services and revenue generation from royalties from licensing pharmaceutical and device technology. These alternatives are suboptimal. Cutting clinical services can impact quality of care. And royalties from pharmaceuticals and devices are static or diminishing streams of revenue with limited growth potential. Revenue from device innovation will be particularly limited because of stifling effect on innovation of a recent 10% tax on device manufacturers as part of the ACA.

Solution
The proposed solution to these problems is a HBSI to cultivate the raw insights of hospital employees to create commercial and social value.

Lean Canvas: Solution [2] 

Key Metrics
Key metrics are the measures that gauge the success of an innovation at creating value for the end user. In the case of a HBSI solving problems for CFOs, some measures of success may include 1) revenue generated from creation of a service or product, 2) patient outcomes achieved, or 3) new patient recruitment to hospital, among others.

Lean Canvas: Key Metrics [2]

Value Proposition
A value proposition is the hypothesized value that a solution will bring to a customer for solving their problem. The value proposition of a HBSI is the cultivation of innovations that can be commercialized leading to de novo revenue streams for the hospital and solutions for problems of patients and providers.

Lean Canvas: Value Proposition [2]

Competitive Advantage
The competitive advantage is the aspect of a product or service that makes it a uniquely better solution than current alternatives. In the case of a HBSI, the major alternative revenue streams include diminishing reimbursement for clinical care, stagnant research grants, and plateaued royalties from licensing drug or device intellectual property (IP). The competitive advantage of a HBSI relative to the other approaches is that it is a low cost, rapid return, scalable approach to revenue generation that can be employed by any hospital regardless of their prestigious reputation or resources.

Lean Canvas: Competitive Advantage [2]

Channels
Channels are the routes of distributing an innovation. The channels for distributing a HBSI include the office of the CFO, in which case the CFO would fund or facilitate the implementation of a HBSI. Similarly, the offices of department chairman can similarly serve as the conduit for hosting a HBSI.

Lean Canvas: Distribution Channels [2]

Distribution channels can also originate outside of the hospital. For example, a foundation or non-profit with the capacity for hosting and implementing healthcare delivery projects could sponsor and host a HBSI within a hospital. Similarly, industry sponsors could leverage their core pharmaceutical or device expertise to facilitate the deployment of a HBSI.

An interesting recent development that could make the latter a particularly mutually beneficial distribution channel is the increasing importance of "big data." Everyone from mobile data carriers to payers to “pharma plus” companies are realizing the potential competitive advantage gained from big data insights. Thus, in addition to conferring content expertise, industry sponsors may also be incentivized to make substantial cash investment in an HBSI. This precedent has been set in the health IT incubator community with formal sponsorship from GE of a class of entrepreneurs in the Startup Health Incubator.[5]

Cost Structure
The major costs of a HBSI include a small staff and small-scale technology development. Many costs can be defrayed through partnerships with business, technology, legal, and content experts in exchange for lead generation, brand strengthening, or other secondary gains. Furthermore, tech development is generally a minimal cost because a large amount of validation can be done with mock-ups, prototypes, and vaporware that are inexpensive but convey enough functionality to solicit feedback from customers. When incubated companies are prepared to begin scaling, they can do so outside of the hospital with external investment to build talent and technology. Consequently, hospitals would not need to build much capacity to run these companies internally.

Lean Canvas: Cost Structure [2]

Revenue Streams
Any revenue generated from the product or service incubated by a HBSI would inherently share royalties with the hospital and/or department where the innovation originated. However, hospital culture needs to change to be more embracing of smaller ownership over its innovations in order to allow these innovations to scale.

Lean Canvas: Revenue Streams [2]

An unfortunate legacy of the pharmaceutical and device era is that intellectual property (IP) policy of hospitals has traditionally been to take a very large stake of ownership in innovation. This was tolerable when IP could be licensed to a device or drug company, get absorbed into the R&D machinery of that company, and then the actual device or drug would be developed outside of the hospital.

In the HBSI model, much if not most of the product or service validation would occur within the hospital. Upon scaling beyond the hospital, the intervention becomes its own company with its own fundraising needs, and thus its own need for mitigating financing risk. If the antiquated IP policies persist, then startups will be incapable of raising external funding because investors will be reluctant to invest into an overly diluted equity pool. Hospitals should create IP policies that facilitate small hospital ownership to enable large value creation rather than large ownership of technologies that are value-less.

Mitigating Risk through Innovation Accounting

Financing risk is one of several types of uncertainty that threaten the viability of a commercial product. Now that the major components of a HBSI business model are identified, the next step to validating its value is identifying the largest risk to the business model and systematically eliminating that risk through a process called innovation accounting, which is described in my previous post comparing research, QI, and LSM. [2]

Lean Canvas: Hospital Based Startup Incubator [2]

The typical types of risk to a business model include product risk, customer risk, and market risk. The process of prioritizing risk is an opportunity to showcase user-led innovation applied to health care. User led innovation is a concept developed by Eric von Hippel at MIT and it involves the process of innovation by immediate consumers rather than by suppliers or producers of that innovation.[6] In healthcare, clinician-led innovation is very similar and simply means that clinician-scientists are the source of innovation because of their exposure to and struggle with the day-to-day problems of their work.

Clinician-led innovation enables for optimization of risk identification because clinicians have the greatest insight into their own pain points. In the case of a HBSI solving a revenue problem for a hospital CFO, it would be optimal for this to be a CFO-led innovation. But clinician-entrepreneurs with insights into the financing of hospitals and healthcare are a good proxy for understanding hospital CFO pain points. In my opinion, I believe that product risk is the largest of the three risks, so I will briefly summarize customer and market risk as they pertain to the creation of an incubator and spend the majority of the risk discussion focused on product risk.

Customer risk is the uncertainty about whether there is a customer who perceives a problem big enough to make it worth solving and paying for the solution to that problem. Most hospital CFOs likely feel the pressures of Medicare cuts to readmission reimbursement and the shift away from fee-for-service. Consequently, they are probably interested in improving their top and bottom lines.

Market risk is the uncertainty about whether there are enough customers that can reliably purchase your product at a high enough cost to grow the company. With 5,724 hospitals in the US, each having at least one CFO and multiple department chairman, there is a large addressable market. [7]

Product risk is the uncertainty about whether something can be built that other people will want. This is the largest risk because a hospital based startup incubator has never been built and we do not have any empiric evidence that one can be built and made to achieve its goal.

In order to mitigate product risk, a series of experiments will need to be designed to mitigate that risk. The tests will run along 4 domains: understanding the problem, defining the solution, qualitatively validating, and quantitatively validating. [8] Within each of these domains, we will design one or more experiments that will follow the following 3 steps: build, measure, and learn (BML). Through a series of iterative BML cycles, we will validate or invalidate the customer need for an incubator.

Repeated BML Cycles to Discover Customer Validation [8]

Understand Problem
We have already made an educated guess about the problems faced by the CFO in the “problem” section of the lean canvas. We now need to validate these hypotheses. The test we will build is to actually ask the hospital CFO if in fact the problems we identified are real problems for her. The problems will need to be prioritized. And beyond our hypothesized problems, we will need to ask the CFO if there are other problems that we have not identified that she feels are particularly pressing.

Define Solution
If we validate our hypotheses about the CFO’s pain points, we will need to further define our solution by exploring if our proposed solutions would theoretically solve the CFOs problems. This test can and should be run simultaneously with the same interview in which we learn about the problem. A simple slide deck of the incubator idea is likely sufficient to convey the potential value of the HBSI. As with any product or service at this stage of testing, it is import to ask if the CFO is willing to pay for the HBSI. Payment may be in the form of dollars. Payment can also be willingness to dedicate another scarce resource: time and/or attention.

It is essential to elicit a concrete answer. If the CFO definitively says that she is willing to commit cash, than you have validated the need for an incubator and you have achieved problem-solution fit and done so very inexpensively.

If the CFO gives a resounding no, this is also very useful information, and your test has successfully invalidated your hypothesis. At this point, it is important to step back and form a different hypothesis, or pivot, with regard to the current incubator strategy. A pivot may include reframing the incubator value proposition or exploring different customers such as department heads.

 Customer Development [4]

If the CFO is equivocal, it is important to redesign the experiment to get more clarity from the end customer. It can be very enticing to try to proceed based on the assumption that the CFO will perceive your solution’s value the way that you do. But without “leaving the office” and actually asking her directly and getting confirmation, inaccurate assumptions may guide a long series of tests that were invalid before they were even conducted.

The value of LSM is the rigorous adherence to end-user validation. You do not have to achieve statistical significance with feedback from the CFO, but you do need to get direct confirmation that your assumptions about value are correct in order to actually create that value.

Qualitatively Validate
If the CFO validates the value of a HBSI, or confirms problem-solution fit, then it is time to move to the next step of eliminating product risk, which is building the minimum viable product (MVP). The MVP is the product with the fewest number of features needed to get users to pay in some form of a scarce resource.[4] With the creation of the MVP, we now move into the qualitative validation portion of eliminating product risk.

The MVP for an incubator includes the following bare minimum functions in order to transform a clinician-innovator's idea into a product or service that creates value.

Incubator Components
In the early stages of an incubator, the 3 minimal requisite components for the incubator include the clinician-innovator, and entrepreneur advisor, and a local sandbox for running tests. Hospitals are by default the sandbox. Entrepreneur advisors can be sourced internally or externally to a hospital. And the clinician innovator will likely come from a self-selecting group of scientists that have identified themselves as having an interest in innovation. As the incubator matures, it will require access to the following functions: technology development, legal guidance, fundraising, and networking with other innovators.

Hospital Based Startup Incubator © Andrey Ostrovsky, MD 2013

Selection of Innovators for Incubation
Once an entrepreneur advisor has been identified, the process of incubation can begin. The first step is vetting ideas for incubation. Idea selection goes hand in hand with selecting clinician-innovators. The innovator selection process should prioritize passion and willingness to learn over most other criteria. For the sake of expediting value discovery, targeting innovations that will face the fewest regulatory barriers is a good place to start. So, software applications will likely be validated faster than drugs or devices. Also, business-to-consumer (B2C) products tend to be easier to get feedback on than business-to-business (B2B). So an app being sold directly to patients or providers would be easier to test for value than an app designed for insurance companies.

Innovation Bootcamp
Clinician-innovators typically do not have experience starting and operating a company. LSM facilitates efficient discovery of value of a proposed innovation. Although it is less familiar to scientists than research or QI, LSM is grounded in the scientific method and is not difficult to learn. [8] In order to get the clinician-innovators speaking the same language as the entrepreneurs, they need to go through a LSM bootcamp. The bootcamp would be partly didactic and mostly case-based exploration of LSM principles. The bootcamp would also set some basic expectations for the LSM experience, such as not expecting to get rich, since most startup companies fail.

Visioning
Once the clinician-innovator is familiar with the basic principles of innovation, the visioning stage can begin. This stage includes the same steps as we went through with the HBSI as a product above: 1) identify a clear vision, 2) propose several strategies to achieve that vision, and 3) design experiments that will lead to the creation of a product to execute the strategy. It is possible that the original product idea may not end up being the chosen strategy to test.

Business Model Creation and Validation
With the vision identified and documented, the entrepreneur advisor can walk the clinician-innovator through a 20-minute business model exercise to quickly identify its major components and prioritize their risk. Innovation accounting, or a series of iterative experiments with BML cycles, will be used to test and validate the proposed business model. [2]

Series of Iterative Experiments with BML Cycles [2,8]

HBSI Product-Market Fit
As the first innovation is passing through the bootstrapped incubator, it will be important to confirm if the incubator is creating the value to the CFO that you intended to create. In other words, the incubator as a product itself will need to validate its own product-market fit.

Validation of product-market fit will occur when the CFO is actually paying for your product in the form of money, time, attention, or all of the above. As product-market fit is being achieved, it is appropriate to formalize the entity of the incubator if this has not been done already. The “payment” from the CFO would likely be used to reinforce or create a dedicated incubator function within the hospital. And the structure of the incubator should reflect an augmented version of the elements outlined in the “Incubator Components” section above. Some of the functions may remain within the hospital. Other functions may need to be outsourced. Further recommendations about best practices for scaling an incubator will be explored in a later post.

Quantitative Validation
It will be important to assess the outcomes of an incubator based on the metrics outlined above. It will also be important to explore how the incubator can be scaled to other users inside and outside the hospital.

Like medical specialties, a HBSI can be subspecialized to achieve innovation efficiency within a specific domain. Tertiary academic medical centers may be very well suited for this type of incubator specialization because they are already structured along sub-specialty areas with their own channels of cultivating academic progress. That same machinery can be used to cultivate commercially viable clinical innovation.

Conclusion

LSM can be used to rapidly, inexpensively, and scientifically validate the value of a HBSI to a hospital CFO in solving crucial budgetary problems. If the value of this strategy is validated, then the HBSI will be faced with a unique set of risks to hospital-based incubation. [8] In addition to the previously mentioned legal and financing risk, other risks that will be important to mitigate include culture risk, regulatory risk, patient protection risk, and education risk. Further exploration of approaches to mitigate these forms of risk will be important to facilitate rapid value optimization through a HBSI.

References

(1) Reis E. The Lean Startup: How Today's Entrepreneurs Use Continuous Innovation to Create Radically Successful Businesses. 2012

(2) Maurya A. Running Lean: Iterate from Plan A to a Plan that Works. 2012.

(3) Apodaca A. Greenhouse Effect: How Accelerators Are Seeding Digital Health Innovation. California Healthcare Foundation. http://www.chcf.org/publications/2013/02/seeding-digital-health

(4) Cooper B & Vlaskovits B. The Entrepreneurs Guide to Customer Development. 2011.

(5) Startup Health and GE. http://app.startuphealth.com/GE#.UU-yOVtARZ8

(6) Demonaco, Harold J, Ayfer Ali, and Eric von Hippel. “The Major Role of Clinicians in the Discovery of Off-label Drug Therapies.” Pharmacotherapy 26, no. 3 (March 2006): 323–32. doi:10.1592/phco.26.3.323 

(7) American Hospital Association. http://www.aha.org/research/rc/stat-studies/fast-facts.shtml

(8) Ostrovsky A. Comparing Research, Quality Improvement, and Lean Startup: A Crosswalk between Approaches to Innovation. http://www.disrupthealthcare.org/2013/03/comparing-research-quality-improvement.html

Comparing Research, Quality Improvement, and Lean Startup: A Crosswalk between Approaches to Innovation

Innovation in healthcare is fundamentally changing.[1] In order to understand the impact of new approaches to innovation on health, we need to be able to measure these approaches. In order to measure them, we need to be able to define them. The three approaches that will be reviewed are research, quality improvement (QI), and lean startup methodology (LSM). Research is fundamentally different from both QI and LSM. But the differences between QI and LSM are more subtle. The discussion below primarily focuses on the similarities and differences between QI and LSM. By providing a crosswalk between the three approaches to innovation, we hope to create a framework for measuring their comparative effectiveness on optimizing value for patients, providers, and the health system as a whole.

Definitions

Research is the rigorous application of the scientific method that is used to determine a causal relationship between an intervention and an outcome. [2] This approach stringently controls for variability. The gold standard to this approach to scientific experimentation is the randomized controlled trial (RCT).

QI is a systematic, iterative method of creating better care for a specific, local patient population. [3] Care can be improved by making it more safe, timely, effective, equitable, efficient, and/or patient-centered.

LSM is an approach to launching a product or service that relies on iterative scientific experimentation to shorten product development cycles and minimize risk to the business model. [4] This set of techniques emphasizes achievement of validated learning from customers as efficiently as possible.

Methodology

Research
Research is often used to test the efficacy and/or effectiveness of various types of intervention within a patient population. RCTs in particular may provide an opportunity to gather useful information about adverse effects, such as drug reactions. This study design typically helps to answer very focused, prescribed questions which remain fixed throughout the duration of the study. Similarly, the hypothesis is an educated guess that is also rigorously defined and is not subject to much change throughout the trial.

 Wikipedia.org

RCTs typically have four phases: enrollment, intervention allocation, follow-up, and data analysis. The cornerstone of RCTs is the randomization process which eliminates substantial bias from the study. RCTs are the gold standard for clinical trials and carry the most weight in influencing the establishment of clinical guidelines and best practices.

Quality Improvement 
QI is a model of improvement that tries to achieve a measurable change in a local care delivery to a specific patient population.[3] The initial approach requires the identification of an aim that is measurable over a specific timeframe with a definitive target population. The measures for assessing achievement of that aim include outcome measures, process measures, and a combination of the two.

To determine the potential impact of an intervention through QI, it is important to understand the basic elements of the system in which the intervention is being deployed. According Deming’s work, understanding the system requires Appreciation of the System, Understanding variation, the Theory of knowledge, and elements of psychology on change.[3]

This system understanding is used to gain an understanding of the primary and secondary drivers of the underlying problem. Primary drivers are the system components that contribute directly to the chosen aim or goal. Secondary drivers are elements of the primary drivers, which can be used to create change projects.

Once the aim is identified, measures established, and impact of the intervention contemplated, the aim is then transformed into an improvement goal that is made actionable through the Plan, Do, Study, Act (PDSA) cycle.

After each PDSA cycle, the learning is compiled and assessed for whether the intervention will be taken to scale, dropped completely, or will be subjected to further PDSA cycles. The purpose of the PDSA is to learn what works or does not work and why it did or did not work.

Lean Startup Methodology

The Lean Startup Methodology is an approach that combines fast-release, iterative development methodologies, customer-centric testing for value, systematic elimination of risk from the business model, and achievement of scalability through repeatable revenue-generation. [5]

LSM has its roots in manufacturing but was more recently refined in the IT startup community to inexpensively and quickly create software that people are willing to pay for. Although ideally suited for technological innovation, LSM is a generalizable process of validated learning that can be applied in myriad settings including non-profit and non-technological development of products and services.

The initial step of LSM is identifying a global vision for a large-scale problem that the innovator is passionate about solving. Once a vision is clarified, there is a rigorous process of selecting strategies for exploring how that vision can be achieved. Within each strategy, multiple product ideas are tested for value creation and execution of the vision.

The Lean Startup Up, Eric Reis 

 

Each strategy to achieve the vision of the innovator is processed through the lens of a business model. Heuristic tools accelerate traditional entrepreneurial thinking about business models into concise thought-experiments to identify the greatest risks to scaling a business model to the largest audience possible.

   Ash Maurya. Running Lean.

The business canvas is a great example of how a several hour business plan can be distilled into a 20-minute exercise. Once the basic elements of the business model canvas are identified, the next step is to determine the highest risk elements of the business model. It is helpful to think about the 3 major types of risk to a business which include 1) product risk (can something be built that other people want); 2) customer risk (is there a customer who perceives a problem big enough to make it worth solving and paying for the solution); and 3) market risk (are there enough customers that can reliably purchase your product at a high enough cost to grow the company at a fast enough pace to make it worth investors time to invest in you in the first place to get your idea off the ground.)

Once the 3 risks are identified, they can be mitigated through a four step process of innovation accounting that keeps track of progress of each experiment being conducted in the value discovery and risk mitigation process. The four steps include: 1) understanding the problem, 2) defining the solution, 3) validating qualitatively, and finally 4) validating quantitatively. 

Within each of these four steps, there are series of smaller experiments that are conducted through an approach including building a test, measuring the impact of that test, and learning from the test. The learning from the build-measure-learn (BML) test is then used to inform the next series of tests.

Another way to look at LSM is through the lens of customer development, which is the rapid process of discovering what customers are willing to pay for. There are 4 elements of customer development: Customer Discovery, Customer Validation, Company Creation, and Company Building. These four stages overlap with the 4 steps of innovation accounting and highlight important thresholds of value creation including problem-solution fit (does the proposed solution address the assumed problem) and product-market fit (will the customer actually pay for the solution being offered to them). Customer development is intimately intertwined with product development, which are techniques for building technology in rapid iterative sprints to complement learning achieved through customer development. 

                   

Similarities Between QI and LSM

One of the only similarities that all three approaches have is that they are all grounded in the scientific method: question, hypothesis, methods, metrics, conclusions, use of outcomes to inform next hypothesis. Otherwise, QI and LSM are fundamentally different from research.

Visioning
An important similarity between QI and LSM is their grounding in improvement theory, particularly the theory employed in lean manufacturing made famous by Toyota. [4] Their common origins make QI and LSM share many overlaps. To begin the comparison at the macroscopic level, both approaches start with a broad problem and focus on a precise, actionable intervention to fix that problem.

The process of identifying a vision and executing a strategy in LSM serves a similar function as creating an aim in QI. Strategy creation in LSM also shares common elements with measurement and identifying drivers in QI. Identifying the intervention that will be evaluated through QI is analogous to identifying a product that will be tested for problem-solution fit in LSM. Once the vision/aim, strategy/methods, and products/intervention have been identified, both LSM and QI begin the testing process.

Experiments

The experimentation phase of QI and LSM is based on PDSA and BML cycles, respectively. The Plan and Do Stages in QI correspond with the Build Stage in LSM. These stages represent the definition and setup phase of the experiment.

The Do phase falls between the Build and the Measure phase of LSM and is represented by the experiments that are executed, the prototypes that are demoed, and the mock ups that are put in front of a customer.

The Study phase in QI and the Measure phase in LSM are similar. In LSM, the moment the experiment is shown to at least one customer, the measure stage begins and data is analyzed. The Study phase of QI also bleeds into the Learn phase in LSM because it entails comparing data to predictions and summarizing what was learned.

The Act Phase ties together the original visioning into a final intervention.

The next steps after the Act phase of QI and the Learn phase of LSM are based on the following: if the hypothesis was clearly validated, then the intervention is deployed in QI or scaled in LSM. If the hypothesis is clearly invalidated, then the intervention is dropped in QI or a pivot occurs in LSM. If the learning is unclear, both approaches propose refining the question and conducting further testing.

Iterating
In addition to overlap between the component parts of QI and LSM cycles, these approaches are also similar in the way they sequentially aggregate small scale tests to build knowledge over time.

The iterative tests in LSM are performed along the 4 key activities of eliminating risk in the business model (understand the problem, define solution, validate qualitatively, and validate quantitatively.) There are direct overlaps between LSM and QI along all 4 domains. Although many examples exist to demonstrate this overlap, we will highlight just a few.

Within the first domain of understanding the problem, an important question that needs to be answered is how to decrease product risk by having customers rank their problems. The analogous exercise in QI is identifying the primary and secondary key drivers of care delivery.

In the second domain of defining the solution, LSM addresses customer risk by defining the target audience for an intervention. This process has parallels with QI’s approach of creating an aim for a specific target population.

The third stage of mitigating risk through LSM is validating an intervention qualitatively. During this stage, a key LSM concept is the Minimum Viable Product (MVP). This is the product with the fewest number of features needed to get users to “pay” in some form of a scarce resource. Identification of the MVP is similar to clearly identifying the change that can be made through the QI intervention that will result in improvement.

The last stage of eliminating risk through experimentation in LSM is quantitative validation. The product risk is mitigated in this stage by identifying rigorous metrics for determining whether an intervention not only provides replicable value but if that value can be grown to a large scale. In QI, the analog is the establishment of measures for assessing achievement of the aim. And similar to LSM, these measures may be outcome measures, process measures, or a combination of the two.

Data Analysis
Research takes a very different approach to data analysis relative to QI and LSM. In research, the goal is to gather as much data as possible to prove or disprove a hypothesis through rigorous statistical measures. Testing is one large ‘blind’ test. And, outcomes are explicit and are measured for statistical significance with very high threshold for type I errors, typically with p-values needing to be < 0.05.

Both QI and LSM are very different from research. For both QI and LSM, the goal is to gather just enough data to learn and complete another cycle. They both entail testing that has many sequential, observable tests. And the certainty of the data in QI and LSM is implicit.

With LSM, testing is also sequential and observable, but it is guided by where the largest risk for the business model lies. The level of evidence in LSM depends where in the process of innovation accounting the tests are. As the testing progresses from understanding a problem to quantitative validation, the level of rigor of data increases.

There is further nuance in healthcare because often times adoption of an intervention may require a higher level of validation than a startup outside the walls of a hospital. So the level of validation depends also on the end user or purchaser of the intervention.

In general, though, data needed for early validation is closer to the level of precision of smoke signals or gross trends rather than that of p-values.

Differences Between QI and LSM

Cycle Time

Research takes the longest of the three approaches to deploy, typically lasting years. At the end of a cycle, whether the study gets published or not, there may be a role for follow up studies, but rarely does a research trial result in a direct improvement in care.

QI, on the other hand, does not take years to complete. Rather, it can take weeks to months. This approach employs rapid, small tests of change. Invalidation is a welcome outcome. After the cycle is complete, the next step is either local deployment or another cycle.

LSM has a very rapid cycle and is driven by rapid customer-centric iteration synchronized with 2-3 week agile product development sprints. The pace of discovery is typically accelerated by the (unlikely) opportunity for substantial financial gain and by typically very scarce resources. The goal of each cycle is rapid validation or invalidation. If validation happens, the company attempts to reproduce and scale the intervention. If a hypothesis is invalidated, a pivot occurs where we change just one element of the business plan but still incorporate previous learning. If neither validation nor invalidation happen, then the questions being asked need to be refined in order to achieve definitive validation or invalidation of a hypothesis.

Cost
Research is very expensive to conduct. On average, excluding overhead expenses, it costs slightly more than $6,094 (range, $2,098 to $19,285) per enrolled subject for an industry-sponsored trial, including $1,999 devoted to nonclinical costs. [6] Overhead is typically 60-90% on top of that cost. So the average cost per patient is approximately $10,000 to conduct a clinical trial. RCTs often require enormous resource investments. The recently completed Clinical Outcomes Utilizing Revascularization and Aggressive Drug Evaluation (COURAGE) trial, which compared optimal medical therapy with and without percutaneous coronary intervention (PCI) in 2287 patients, resulted in nearly $60 million in total costs shared by both public and private sponsors. [7]

The cost for conducting QI is much lower than for an RCT. Unlike RCTs, which have a high floor and a high cost ceiling, QI generally has a low floor and a low ceiling. Some literature suggests that an average QI project yielding a cost savings of $100,000 can cost about $15,000 to implement. [8]

Similar to QI, the cost of LSM has a very low floor. In fact, with current prototyping software available for free, the majority of validation through LSM can be done for free, excluding opportunity costs.

Although typically low, the ceiling for LSM has no real limit. The point of LSM is not to financially bootstrap the learning process, but rather to learn efficiently. Some forms of learning may be expensive, particularly when it comes to health systems innovation and policy creation. But LSM ensures the path of least resistance to discovering value which minimizes waste and in turn minimizes costs. A common mantra along this theme in the lean startup community is “fail fast, fail cheap, fail often.”

In addition to the differences in cycle time and cost, QI and LSM have profound differences in intent and outcome.

Purpose
The purpose of research is to discover new knowledge that is generalizable and advances the practice of medicine. For QI, the purpose is to bring new knowledge to daily practice and achieve local improvement in process or outcome.

LSM, however, has a very different purpose. The intent of LSM is to discover and/or create enough value in a service or product that someone will be willing to pay for it or exchange some other scarce resource such as time or attention for its use.

Example Illustrating Difference in Purpose between QI and LSM

Traditionally employed in the for-profit sector, the purpose of LSM was to efficiently discover the highest ROI products or services. This was achieved through developing repeatable and scalable business models as quickly and inexpensively as possible. At its core, the purpose of LSM is validated learning that informs commercial value creation. And validation for LSM is payment from the patient or end-user for a product or service.

Validation for QI, on the other hand, is improving care for the specific local patient population through making it more safe, timely, effective, equitable, efficient, and/or patient-centered. The important point to highlight here is that better care is not necessarily something a customer would want to pay for because they may want to spend their limited resource (money, time, attention) on something different, like paying rent.

An example of the difference in purpose between QI and LSM is no-shows to the clinic. A patient may want good healthcare, but they may not be willing to pay for it. For example, they may not be able to afford the co-pay or they would rather spend their copay on a cheeseburger. In the latter scenario, QI may lead to higher quality care but it is not care the end user is willing to pay for with their time or money, even though it may be better for them.

Another scenario demonstrating the difference between QI and LSM is when the end user is not the customer. For example, insurance companies, Medicare, or Medicaid (payers) typically cover the cost of most medical expenditures in the US. QI may lead to better care for the patient. But if better care does not result in lower cost or higher revenue for the payer, then that service may not be reimbursed and ultimately may never be used. In fact, a recent analysis by the Commonwealth Fund found that insurers paid less than 1 percent of premiums on quality improvement activities in 2011. [9]

Conversely, with LSM, an intervention would be developed through validation from the ultimate customer so that the health intervention (perhaps a wellness app) shows cost savings and ultimately would lead to reimbursement because the payer would find that valuable. The end vision is the same: improve health. The strategy of executing that vision is very different.

Scale
Most research publications provider small contributions to a larger body of research that may eventually lead to development of an intervention that may benefit patients. So there is usually no immediate, direct benefit to patients on a large scale from any one successful research study cycle.

In QI, the objective is to improve local problems so the resultant solutions are very context dependent and are limited in their scalability beyond the immediate system in which they are deployed. Since QI aims to improve the patient experience emanating from drivers at the “C-level” or microsystem-level down to the D-level or the patient experience level, then by definition, QI solutions are local and would need to be adapted and evolved if they were attempted to be scaled.

Since the origins of LSM are in the for-profit realm, the goal is to create products and services that will have the largest possible return on investment (ROI). Consequently, the methodology is designed to generate as much generalizability and scalability as possible.

Choosing the Right Approach

For logistics purposes, there are certain types of questions more suitable for QI or LS rather than RCTs, such as care delivery models, clinical workflow interventions, or technology assessments. But more broadly, the type of method for evaluation can be looked through the lens of the degree of belief that it will lead to improvement, the cost of failure, and the commitment to the intervention within the organization. [3]

As pretest likelihood of success decreases, cost of failure increases, and/or level of commitment decreases, then aim to do smaller tests of validation. As pretest likelihood of success increases, cost of failure decreases, and/or level of commitment increases, then aim to do larger tests of validation. With RCTs, its very hard to do a small-scale test of validation.

LSM is particularly well suited for testing when there is a low degree of belief because this approach is best at minimizing down-side risk, or the cost associated with a failure to validate. LSM is also well suited when the goal of creating an innovation is to meet the needs of the end user, to generate revenue, to build scalable solutions, and to move quickly.

Conclusion

Although one of the primary drivers of LSM is to discover commercial value, LSM can simultaneously be used as a vehicle to discover social or clinical value. LSM enables value optimization through the fastest and least expensive route to discovering value. And while there may not be sufficient validation for commercialization of an intervention developed through LSM, it can still provide value to patients or providers. So using LSM can simultaneously achieve the QI goal of creating better care and explore whether better care can also lead to the generation of revenue. And the ability to generate revenue has an indirect effect on improving care because it facilitates sustainability. In a time when hospitals are increasingly being squeezed due to healthcare payment reform, hospitals may benefit from exploring LSM as a vehicle for achieving the Triple Aim as well as protecting their bottom and top lines.


Special thank you for Dr. James Moses for his insights into QI.

References  


[1] Zuckerman et al. Health Services Innovation. JAMA 2013.

[2] Wikipedia.org

[3] IHI. QI Curriculum. 2013. http://www.ihi.org/offerings/IHIOpenSchool/Courses/Pages/default.aspx

[4] Reis E. Lean Startup. 2012.

[5] Vlaskovitz & Cooper. Entrepreneurs Guide to Customer Development. 2012.

[6] Emanual et al. The Costs of Conducting Clinical Research. http://jco.ascopubs.org/content/21/22/4145.full.pdf

[7] Nallamathou et al. Key Issues in Outcomes Research. Circulation. http://circ.ahajournals.org/content/118/12/1294.full

[8] Juran. The Quality Improvement Process. http://hairball.bumba.net/~rwa2/school/ense627/TelecomQuality/4003X_05.pdf

[9] Hall & McCue. Insurers’ Medical Loss Ratios and Quality Improvement Spending in 2011. Commonwealth Fund. http://www.commonwealthfund.org/~/media/Files/Publications/Issue%20Brief/2013/Mar/1671_Hall_how_insurers_MLRs_spending_differ_ib.pdf