Why Is It So Much More Difficult For Healthcare Providers To Adopt Health IT Than Medtech?

Medicine has made great strides over the last 50 years. Modern medicine looks a lot different than the medicine of 1966. Today providers are 3d printing bones, replacing organs, and conducting minimally invasive surgery.

Yet healthcare operations of 2016 are remarkably similar to healthcare operations of 1966. Why haven’t the delivery systems of healthcare changed? Why haven’t core provider operations changed much in the last 50 years, and why do providers struggle to adopt health IT even though they’ve adopted so many medical innovations?

The Innovation Has Been In The Tech, Not The Process

In short, because it’s much easier to adopt new medical treatments than to adjust the operations of a healthcare delivery system. The former is an incremental improvement. The latter requires business model changes, changing job roles, and more.

The R&D burden for the vast majority of medical innovation is extremely high. Achieving FDA clearance is incredibly difficult and expensive: tens of millions of dollars, and in the case of pharmaceuticals, hundreds of millions.

But once a new pill, cream, device, test, or treatment has been invented, the healthcare system can “adopt” it pretty easily. All of the existing infrastructure is in place — pharmacies, labs, ORs, physicians, surgeons, etc. A few examples:

The only cost to healthcare providers to prescribe a new pill is educating the physicians. Physicians are mandated to earn Continuing Medical Education (CME) credits, and many are active in their respective specialty-specific communities. Pharmaceutical companies know this and market to physicians through these channels. Once a physician has learned about a new treatment and is convinced of its clinical benefit, her organization — a solo practice or hospital — doesn’t need to do anything else in order to prescribe the new medication to the patient. The physician prescribes the treatment, and the patient will receive a prescription and a nearby pharmacy will dispense the pills. This process happens identically if the treatment is brand new or if it’s penicillin. The medication prescription process is remarkably unchanged in the last 50 years. Even moving from paper to e-prescribing hasn’t really changed the workflow around prescriptions. The pharma company will work with medical distributors like McKesson to ensure the treatment makes its way to pharmacies in every geography. Providers don’t need to worry about where the pill came from or how it got there.

Similarly, the only cost to a healthcare organization to adopt a new piece of lab equipment is the cost of the equipment itself. A hospital already has an ASCP-certified lab (the ASCP certifies labs for quality and safety standards), lab technicians, etc. The only additional costs to the hospital of adopting a new medical diagnostic tool is the device itself, and few hours of training per lab technician. The hospital doesn’t need to build any new physical or virtual infrastructure, or define new processes. Once the new device has arrived, physicians are educated, and can then place orders that will utilize that machine. The process change required is minimal.

A significant majority of medical innovations are “black boxes.” Physicians don’t need to understand every chemical reaction that will occur in the body after a pill is taken. The pill is effectively magic — the patient consumes it and gets better. The same is true of the latest diagnostic tools. Put the blood in, get an answer out.

There are certain medical innovations that require some operational changes. For example, let’s examine robotic surgery. Surgical robots are a “black box” like other physical devices — surgeons don’t need to understand the control systems in the robot that guarantee millimeter precision. But surgeons and surgical staff need to be trained and certified to conduct robotic surgery. The training program for daVinci, the leading surgical robot, typically takes a few months to complete. However, surgical robots don’t change the operational processes around surgery. Patients are still referred to surgeons by more general physicians, surgeons still consult patients before surgery, patients still come to the hospital, staff still prep and sterilize the OR, the patient is still anesthetized, and the patient is still prescribed bedrest, antibiotics, and perhaps other medications afterwards. Although the technical implementation of surgery is vastly different, the broader process around surgery hasn’t really changed.

Health IT Requires Material Process Change

Health IT innovations couldn’t be more different than medical innovations. Health IT solutions by definition are not medicine. Health IT solutions do not directly impact the health of the patient at all, even if the patient logs in and uses an app. No It solution will magically make a patient better, and no IT solution will diagnose. Medical diagnostics and treatments require chemistry. IT is not chemistry.

It’s important to note that all health IT solutions require some level of organizational workflow change. The change may be relatively trivial, but a workflow change is required. Many of the greatest opportunities to improve outcomes and reduce cost to be gained from adopting health IT require massive organizational changes. Omada Health is a great example of a radically different diabetes management service. In fact, Omada’s technology and service is so unique that the company chose not to sell the software to existing providers, but to act as providers themselves and contract directly with self-insured employers, payors, and in some cases, at-risk providers. Omada determined that their clinical service would be more effective if they built it themselves, rather than helping hundreds of organizations modify their existing operations. Their success indicates that this was probably the right decision.

Information technology can do four fundamental things: collect, process, store, and share information. IT will never do anything more. When a provider organization adopts a novel health IT solution, there is an implicit acknowledgement that the organization was organized sub-optimally. When an organization adopts a novel piece of health IT software, the organization needs to rethink existing workflows and processes. Let’s use Patient IO as an example.

First, a quick primer on Patient IO. Patient IO is a cloud based care management platform that’s sold to large healthcare provider organizations. When hospitals discharge a patient after surgery, the discharge nurse typically provides the patient a few one-pagers that inform the patient on dietary restrictions, medication requirements, how to gradually get back into sports and athletics, etc. The patient is left to manage the entire post-discharge process herself. Using Patient IO, providers prescribe patients the app. The app sends regular reminders to patients using push notifications. For example, if the patient is supposed to walk .5 mile per day for the first week, then 1 mile a day for the 2nd week, the app will track activity on the user’s smartphone, and send the patient reminders throughout the day to increase activity. That data is reported back to the provider, and providers follow up with patients and their families as necessary to encourage activity. The same concept can be applied broadly for any care plan for any disease or procedure.

Adopting Patient IO is a big change for provider organizations. Previously, the organization may have staffed a few people to call patients and follow up after surgery. If the patient answered the phone, the caller may have asked a few questions about physical activity. The patient may have lied about the truth out of embarrassment. With Patient IO, nurses engage with dynamic dashboards based on hard data. These dashboards show compliance of patients based on time (eg all patients seen last week), by disease state (eg all diabetes patients), procedure (eg all patients who had knee replacement), and other factors that the nurse determines to be useful. The nurse then engages non-compliant patients with much greater rigor than the organization otherwise would have since the organization can devote energy and effort to help the patients most in need.

Patient IO is just an information arbitrage tool. Previously, healthcare organizations had no ability to track or understand this data. Now they do. As a result, it’s logical for them to rethink how they care for patients using this new tool. The tool itself does not make the patient better. Instead, the tool helps patients take better care of themselves, and helps providers engage with patients who are struggling with compliance.

Building Patient IO’s tech required 1/100th the financial resources that it took to develop a drug, but requires 1000x the organizational change. The same is true for most IT solutions. They are orders of magnitude more capitally efficient than traditional medtech, but require huge organizational changes to reap the benefits.

Over the last 50 years, providers haven’t developed the organizational capability to change their fundamental processes. They simply didn’t have a reason to. Although medicine was advancing rapidly, the advancement was literally contained to just the medicine. No one other than the vendor and the FDA really needed to understand the inner workings of the black boxes that were being invented. Healthcare delivery broadly remained unchanged until recently. Information technology is breaking old assumptions in healthcare delivery processes. This, coupled with the rapid succession of government mandates (meaningful use, ICD 10, managing lives at-risk, etc), has strained healthcare delivery systems. They are still learning how to adopt technology at the pace at which technology moves.

The future is incredibly exciting. As processes and medicine evolve together, we will be able to achieve results that were never before possible.

Do Next Generation Reimbursement Plans Align with Healthcare’s Business Models?

This post was originally featured on HIT Consultant.

In The Innovator’s Prescription, Clayton Christensen identifies one of the core problems in healthcare delivery: a mix of intertwined business models that create massive operational overhead and inefficiency. He describes three distinct business models in hospitals.

Healthcare’s 3 Distinct Business Models

1) Diagnostics and non-linear treatments – the process of diagnosing and treating many complex patients is a non-linear process. There are often many unknowns that cannot be predicted or understood without sophisticated testing and experimentation. With enough time, money, and energy, physicians can usually diagnose and treat the problem.

Christensen compares the diagnostic and treatment business to the strategy consulting business. Strategy consultants are rarely paid based on outcome, but rather based on the time and energy they put into solving the problem at hand. Consultants can’t guarantee an outcome on a pre-determined schedule because they simply can’t understand the depth of the problem prior to committing to solving it. They rely on specialized training to determine the root cause of problems and devise elegant solutions that balance the needs of all stakeholders.

2) Repeatable, known procedures – unlike the diagnostics described above, there is a massive sector of medicine that is highly knowable and repeatable. Physicians can guarantee outcomes for many procedures because the diagnostics and treatments are extremely well understood and formulaic. Providers can diagnose quickly against explicit, easily measurable criteria. With a well understood diagnosis in hand, providers can prescribe a treatment plan, and patients verify everything independently online; patients don’t need to rely on their physicians prescription, although many do. This is particularly common in surgery, as well as many office based procedures and cosmetics. Using Dr. Google, patients already do this today en masse. Dr. Google helps patients keep providers in check.

Christensen compares the procedural medicine business to the manufacturing business. Factories guarantee 99.X% of the widgets they produce will come out to spec. They even typically warrant that the widget will work for at least Y days, and offer refunds in the case of failure. Manufacturing businesses take a set of inputs and guarantee a set of outputs at a known cost. Similarly, many treatments have knowable inputs – the patient, diagnosis, and tools for the treatment – and can guarantee results with precision.

3) Wellness and chronic disease management – most of the attention and innovation happening in healthcare today revolves around wellness and chronic disease management. The premise of this business model is predicated on tracking and understanding one’s health on an ongoing basis to make better lifestyle decisions to avoid interacting with business models #1 and #2 described above.

The fundamental problem with chronic condition management is assuring adherence to the prescribed therapies. Most patients unfortunately don’t adhere to the prescribed policies that are intended to – and are generally effective at – preventing costly hospitalizations. Thus, the challenge of chronic condition management is really one of behavior modification and change. The most effective therapies for behavior change have been social in nature. Patient networks such as PatientsLikeMe, Alcoholics Anonymous, and others have proven extremely successful in changing behaviors at scale.

Unfortunately, healthcare insurers today don’t financially support and providers rarely prescribe these programs. Thus patients who need a chronic disease management system are forced to interact with a system that’s designed to treat acute conditions.

Christensen compares the chronic management business to other online-enabled networks such as eBay. The goal of the marketplace provider is to ensure rich, dynamic, and meaningful interaction between the market participants to maximize mutual value for both sides of the marketplace. If the marketplace provider fails to facilitate interaction, the market fails.

Do changing reimbursement models align with the underlying business models?

Providers are increasingly assuming risk for patient outcomes. There are a number of reimbursement models that allow providers to assume risk – capitation, bundled payments, shared savings, and more. Do these reimbursement align with the underlying business operations? Remarkably, the answer is “yes.” Below I’ll provide a broad description of the reimbursement models that the Center for Medicare and Medicaid Services (CMS) has authorized, and how each of those models works with the operational business models outlined above.

Shared saving models are reimbursement models in which providers bill in a traditional fee-for-service model. However, at the end of a given time period, typically 3 months, providers compare their billings with a predetermined benchmark given the risk-pool and size of the population they’ve been treating. If the provider bills less than the benchmark, the provider shares in some percentage of the savings (the insurance carrier – in many cases Medicare or Medicaid – shares the remainder).

Bundled payments is a model in which providers received a fixed payment for all care associated with a given episode of care. If the patient has complications or requires extra care as a result of the procedure, the provider must incur all of the costs associated with that care without additional reimbursement.

Capitation models are models in which providers receive a fixed amount of capital per patient per month that providers must care for. The rate is adjusted to accommodate for the risk associated with the patient population and regional differences in costs. Integrated delivery networks (IDNs) such as Kaiser Permanente and Geisinger are among the few delivery models that have achieved global – or in other words, 100% – capitation because they are both insurers and providers. Let’s revisit our three healthcare business models:

1) Diagnostics and complex treatments – it’s always been difficult to account for risk in consulting businesses. After all, the premise of consulting is to solve a challenging, not-yet-fully understood problem. Shared savings models are aligned with the consulting model. Shared savings models* accommodate the intrinsic risk associated with consultancy by not forcing providers to take on risk they can’t control for, but at the same time create upside opportunity for innovative providers who excel in diagnostics and complex treatments.

2) Repeatable, known procedures – bundled payments align incentives for knowable episodes of care. Bundled payments are analogous to warranties that come with widgets that factories produce. If the widget is bad for some reason, the manufacturer warrants that they’ll provide a new widget at no cost to the consumer. Similarly, bundled payments create incentives for providers to find ways to lower costs, improve efficiencies, and ensure repeatable, scalable quality. This model encourages quality and scale, enabling profitable privatization without fear of rationing and unethical, short term profitability-centric thinking. If there are complications, the provider must address the complications without any additional reimbursement. This model incentivizes providers to deliver high quality results every time. Patients win in a big way in this model.

3) Wellness and chronic disease management – capitation aligns with this model reasonably well, although capitation is plagued with a number of intrinsic incentive problems: capitation models create incentives for providers to fight with one another over the distribution of payments; capitation models also create incentives to ration care to achieve desired financial return. On the other hand capitation models create incentives for providers to pro-actively monitor and care for patients to help patients lead healthier lives and use fewer healthcare resources. If providers can work with patients change behavior to adhere to clinical prescriptions, hospitalizations can be avoided. In turn, providers, as the patients’ guide through the support groups and functions, can reap material financial reward.

It appears that the leadership at CMS has read Christensen’s writing. They’ve created reimbursement models that align with the three major business models present in healthcare delivery.

If only it were that easy.

The Fundamental Challenge of Building a Healthcare-Provider Focused Startup

This post was originally featured on EMRandHIPAA.

Over the past few years, the government imposed copious regulations on healthcare providers, most of which are supposed to reduce costs, improve access to care, and consumerize the patient experience. Prior to 2009, the federal government was far less involved in driving the national healthcare agenda, and thus provider IT budgets, innovation, and research and development agendas among healthcare IT vendors.

This is, in theory (and according to the government), a good idea. Prior to the introduction of the HITECH act in 2009, IT adoption in healthcare was abysmal. The government has most certainly succeeded in driving IT adoption in the name of the triple aim. But this has two key side effects that directly impact the rate at which innovation can be introduced into the healthcare provider community.

The first side effect of government-driven innovation is that all of the vendors are building the exact same features and functions to adhere to the government requirements. This is the exact antithesis of capitalism, which is designed to allow companies to innovate on their own terms; right now, every healthcare IT vendor is innovating on the government’s terms. This is massively inefficient at a macroeconomic level, and stifles experimentation and innovation, which is ultimately bad for providers and patients.

But the second side effect is actually much more nuanced and profound. Because the federal government is driving an aggressive health IT adoption schedule, healthcare providers aren’t experimenting as much as they otherwise would. Today, the greatest bottleneck to providers embarking on a new project is not money, brain power, or infrastructure. Rather, providers are limited in their ability to adopt new technologies by their bandwidth to absorb change. It is simply not possible to undertake more than a handful of initiatives at one time; management can’t coordinate the projects, IT teams can’t prepare the infrastructure, and the staff can’t adjust workflows or attend training rapidly enough while caring for patients.

As the government drives change, they are literally eating up providers’ ability to innovate on any terms other than the government’s. Prominent CIOs like John Halamka from BIDMC have articulated the challenge of keeping up with government mandates, and the need to actually set aside resources to innovate outside of government mandates.

Thus is the problem with health IT entrepreneurship today. Solving painful economic or patient-safety problems is simply not top of mind for CIOs, even if these initiatives broadly align with accountable care models. CIOs are focused on what the government has told them to focus on, and not much else. Obviously, existing healthcare IT vendors are tackling the government mandates; it’s unlikely an under-capitalized startup without brand recognition can beat the legacy vendors when the basis of competition is so clear: do what the government tells you. Startups thrive when they can asymmetrically compete with legacy incumbents.

Google beat Microsoft by recognizing search was more important than the operating system; Apple beat Microsoft by recognizing mobile was more important than the desktop; SalesForce beat Oracle and SAP because they recognized the benefits of the cloud over on-premise deployments; Voalte is challenging Vocera because they recognized the power of the smartphone long before Vocera did. Athena is challenging Epic and Cerner by pushing the cloud over on premise software. There are countless examples of asymmetric competition in and out of healthcare. Startups win when they compete on new, asymmetric terms. Startups never win by going head to head with the incumbent on the incumbent's terms.

We are in an era of change in healthcare. Risk based models are slowly becoming the dominant care delivery model, and this is creating enormous opportunity for startups to enter the space. Unfortunately, the government is largely dictating the scope and themes of risk-based care delivery, which is many ways actually stifling innovation.

This is the problem for health IT entrepreneurship today. Despite all of the ongoing change in healthcare, it’s actually harder than ever before to change healthcare delivery things as a startup. There is simply not enough attention of bandwidth to go around. When CIOs have strict project schedules that stretch out 18 months, how can startups break in? Startups can’t survive 18 month sales cycles.

Thus the is paradox of innovation: the more you're told to innovate, the less you actually can.

Can Life Sciences Companies Deliver Accountable Care?

This post was originally featured on HIT Consultant.

Healthcare providers continue to assume increasing amounts of risk in care delivery. This has major implications, not just for providers and patients, but also vendors in IT, diagnostics, therapeutics and devices. If providers assume risk, why shouldn’t their vendors?

We’re already seeing this to some extent in emerging health IT companies. Most health IT innovation discussions revolve around driving value through population health, big data analytics and patient engagement. But many of these startups fail to any generate revenue until they prove the value of their solution through improved outcomes or reduced costs.

Life sciences companies, on the other hand, still generate all of their revenue in a fee for service (FFS)-like model. The more implants implanted, the more arteries unblocked and the more pills prescribed, the more these companies are rewarded, even if it’s the same patient receiving their third implant. The life sciences industry is still in the “sick care” business as opposed to the “health care” business.

How can life sciences companies transform from their traditional FFS business model to a new model that assumes risk and drives accountable care? How can they demonstrate value on a per patient basis? How can they re-shape their businesses to be more consistent with new care delivery models? Data.

Risk cannot be assessed without the measurement of data. It’s impossible to understand the efficacy of a treatment for a given patient if the outcome isn’t assessed in a granular, measurable way.

Today, efficacy data for treatments is typically captured at discrete points in time. Usually this happens when the patient sees the physician and the physician records a data point in the patient’s siloed electronic health record. Moreover, life sciences companies are only formally held accountable to data captured during clinical trials. After a treatment receives FDA approval and is commercialized, life sciences companies hardly understand how their treatments are performing in the wild.

The Internet of Health Things... In People

The road to better measurement of product efficacy may lie with embedded sensors. These sensors would capture data 100 or even 1000 times per day, rather than weekly or monthly during physician office visits.

For pharmaceuticals, that likely means pairing sensors with pills and capsules to measure specific changes in chemistry and its effect. In some cases, these sensors could even be ingestible! Imagine after taking medication, the medication itself could measure and report against the key indicators it’s supposed to effect. Companies like Proteus Digital Health are developing some of the core IP in this area already, and have plans to license to their technology to other pharmaceutical manufacturers. In the future, cholesterol-lowering statins are paired with a sensor to measure both the target of the drug (the HMG-CoA reductase enzyme crucial to cholesterol production) and overall cholesterol level. Then this data is reported back to the care team in real time.

Google Contact Lenses monitor glucose levels in real-time. With an always-connected passive monitor, diabetic patients could learn about the peaks and troughs of their insulin throughout the day and better manage their diabetes.

In the medical device world, one could embed sensors right into the device. For example:

Many pacemakers already contain sensors to adjust electrical impulse to match heart rhythms and conditions accordingly. What if that data were tracked historically and tied to particular events (e.g. meals, activities, stress)? Patients would be able to understand how their heart is reacting to their lifestyle.

Or what if the recipient of a total knee replacement also received accelerometers within that implant to measure motion and gait? The patient’s physical therapist could use this data to adjust the rehab schedule and long-term data could be used to assess the success rate of the implant, surgeon and physical therapist. Then aggregating that data could then be fed back to the device manufacturer so they could better understand how their device affects patients.

Onboard Storage of Connected Healthcare Data

Once a medical device captures data, the information could be transmitted to the cloud seamlessly. Patients wouldn’t have to remember to prick their fingers and go to the doctor to see how they’re doing. Just as businesses can take a pulse on themselves through dashboards and data, soon patients will be able to track and manage their health through measurable data in real time.

As patients better understand the impact of their treatments, they’ll react, creating virtuous cycles for effective treatments and vicious cycles for ineffective treatments. Patients will rightfully demand a new implant at no cost if their implant is shown to be statistically inferior to what they otherwise should have received.

Concluding Thoughts

Accountability has profound implications for the life sciences industry at every layer of operations. The entire product development, regulatory and commercialization strategies need to be re-thought around accountability. The most lucrative therapies will be those in which patients can clearly see and feel the benefits of the treatments.

Software is eating the world. Life sciences companies will need to embed intelligent software into their products and connect the local devices to proprietary cloud-based services. This will require massive changes in the development processes.

Regulatory processes will change. Perhaps the most important question to answer as a result of the regulatory process will evolve from, “Is the treatment safe and effective?” to “For whom is the treatment safe and effective?”

But commercialization strategies will change the most. The most successful life sciences companies won’t rely on providers as much to manage on-going success with a given therapy. Life sciences companies will employ data scientists to identify trends and patterns proactively. The data models will need to account for dozens, if not hundreds of variables. There is simply no way providers will be able to make sense of this data on a per-treatment basis so these companies will need to get more involved and develop a (even perhaps automated) direct relationship with the patient.

Healthcare is finally on a march towards accountability. And although it’s been a painful march, the march continues. The effects are slowly permeating throughout the healthcare ecosystem at every layer. It’s often said that un-innovative industries are that way because that’s what their customers demand. Hospitals have traditionally been FFS, but are transitioning to assume risk. Thus, it’s only natural that their vendors will be forced to do the same, although they may be kicking and screaming along the way. 

Aligning Incentives Across Disparate P&Ls

My company sells solutions that typically span multiple avenues of care. We’ve encountered a unique problem: incentives to improve care coordination rarely align when disparate P&Ls accrue to different players across the continuum of care. In other words, split P&Ls pose a destructive risk to care coordination and ultimately outcomes.

How does this play out in the real world?

Ambulances

Most health systems do not own or operate their own ambulances (Atlantic Health System and NS-LIJ being notable exceptions). Instead, ambulances are typically run by local governments or private companies. Why is this a problem? Many of the most critically ill patients arrive to hospitals via ambulance. Many of these patients are are in time-critical conditions. Ambulances should have the best tools to help save those patients and improve outcomes and suffering. All of the care that ambulances provide should be coordinated with the receiving hospital.

However, ambulances, especially publicly-operated ambulances, run on extremely tight margins; they can’t afford to invest in many new technologies. Hospitals won’t invest in tools for ambulances – even for at-risk patients – since hospitals won’t actually control the deployment of the technology to ensure they impact outcomes for at-risk patients.

But what if hospitals owned the ambulances that fed the hospitals? In this model, as hospitals move towards risk-based care-delivery models, incentives will be aligned to deploy mobile technologies into ambulances to improve time-to-care, diagnostics, and even triage patients to avoid hospitalizations entirely. Specifically, what if every ambulance was equipped with a mobile X ray, CT, EKG, ultrasound, and a suite of standard diagnostic tools (blood pressure, thermometer, stethoscope, etc? Upon arriving at a non-emergent patient’s home, the paramedics could locally diagnose and triage the patient with a virtual physician’s input and avoid non-essential ER admissions.

But that can only happen if incentives – specifically P&Ls – are aligned across the continuum of care.

Outsourced physician Management Services (e.g., EmCare)

Many hospitals contract with physician groups to staff service lines in the hospital. Although these groups provide real value – e.g., more flexible hours and operational processes – than employed physicians these groups also break up how P&Ls are accrued.

For example, many anesthesiologists align as a group to contract with hospitals. Within their practice, these MDs may find a new automated anesthesia monitor that enables more effective management of residents and CRNAs across multiple ORs. In turn, anesthesiologists should be able to extend the MD:mid-level ratio, drive improvement in patient safety, and make more money. But, concerns about damage, theft, and losing the hospital contract render these same anesthesiologists unlikely to ever buy the equipment themselves. Hospitals will also be reluctant to invest since they won’t accrue the financial benefits of improved labor productivity since the financial benefits accrue to the anesthesiologist group, not the hospital.

But Modularization Works In Other Industries

Indeed, most value-chain centric industries are highly modular. Each layer of the value chain can independently optimize itself and control how it interacts with the layers of the value chain above and below it. A few examples:

In the movie value chain, movie production studios don’t own and operate theaters; theaters are independent

In the retail value chain, retailers usually don’t act as distributors, and distributors don’t usually act as producers

With the exception of Apple (who by no means control the entire value chain), most of the computing value chain is modular; retailers like Best Buy have no hand in chipset design, chipset manufacturing, OEM design, OEM manufacturing, operating systems, Internet infrastructure, internet service providers, or cloud services.

Modularization In Healthcare Delivery: Can it work?

Healthcare delivery is not a linear value chain. Each player in the healthcare delivery system doesn’t build incremental, linear value on top of its suppliers. Rather, healthcare delivery involves the coordination of a breadth of disparate resources to A) diagnose, B) treat, and, C) manage chronic conditions / maintain wellness (these are the three different businesses that Clayton Christensen astutely observes in his excellent book, The Innovator’s Prescription).

Healthcare could perhaps be modularized if a certain set of providers acted to diagnose a patient, then handed off the patient to another set of providers for treatment, who in turn would transfer the patient to another set of providers whose job it was to manage ongoing chronic care. However, this arrangement is only tenable if: 1) the boundaries between these three different businesses are clear and distinct, and 2) the providers in each have a high degree of confidence in the “output” from their “suppliers” (e.g., an accurate diagnosis).

What are your thoughts? Have you seen other scenarios where disparate P&Ls lead to mis-aligned incentives? Have you seen risk-based payment models that successfully bridge disparate P&Ls?