Why Is It So Difficult To Reduce The Cost Of Care?

This post was originally featured on EMRandHIPAA.

By refusing to pay for readmissions within 30 days of discharge from a hospital, Medicare has sent a strong message across the healthcare industry: < 30 day readmissions should be avoided at all costs. As a result, providers and vendors are doing everything in their power to avoid < 30 day readmissions.

This seems like a simple way to reduce costs, right? Well, not quite…

The vast majority of costs of care delivery are fixed: capital expenditures, facilities and diagnostics, 24/7 staffing, administrative overhead, etc. In other words, it’s extremely expensive just to “keep the lights on.” There are some variable costs in healthcare delivery – such as medications and unnecessary tests – but the marginal costs of diagnostics and treatments are small relative to the enormous fixed costs of delivering care.

Thus, Medicare’s < 30 day readmission policy doesn’t really address the fundamental cost problem in healthcare. If costs were linearly bound by resource utilization, than reducing readmissions (and thus utilization) should lead to meaningful cost reduction. But given the reality of enormous fixed costs, it’s extremely difficult to move down the cost curve. To visualize:

Medicare’s < 30 day readmission policy is a bandaid – not a cure – to the underlying cost problem. The policy, however, reduces Medicare’s outlays to providers. Rather than reduce (or expand, depending on your point of view) the size of the pie, Medicare has simply dictated that it will keep a larger share of the metaphorical pie for itself. Medicare is simply squeezing providers. One could argue that providers are bloated and that Medicare needs to squeeze providers to drive down costs. But this is intrinsically a superficial strategy, not a strategy that addresses the underlying cost problems in healthcare delivery.

So how can we actually address the fixed-cost problem of healthcare? Please leave a comment. Input is welcome.

Why Telehealth?

Telehealth, aka telemedicine, is one of the most important trends shaping the future of healthcare. It is one of the most effective and direct ways to achieve the triple aim of cost, quality, and access.

This blog post will attempt to explain the underlying problems in the healthcare delivery system that telehealth addresses. As a result of solving these problems, telehealth creates value along all dimensions of the triple aim.

Healthcare delivery is fragmented across medical discipline, location, and time. In a given location, it can be difficult to get the right specialist to a patient in need. Specialists are busy and have full schedules in their clinics everyday. Specialists don't want to leave their clinics and patients don't want to go to the specialists' clinics. The cost of travel - time, cost, and distance - is significant for all parties. Neither party wants to travel to see the other.

Within a given location, there is almost always significant supply and demand imbalances for healthcare services. Telemedicine addresses the supply and demand problem by making location irrelevant. In a world in which telehealth is the norm instead of the exception, a patient in need should be able to access a qualified specialist from a much larger pool than in the analog era of healthcare delivery. Solving the access problem by increasing supply in every location also addresses cost and quality problems. Telemedicine addresses cost problems by forcing providers to compete to provide the best care at the lowest price. Telemedicine addresses quality problems by reducing the time to care, which can meaningfully impact outcomes.

At Pristine, we're proud to pioneer a new avenue of telehealth. Our telehealth solutions are by far the lightest-weight and easiest to use in both physical and virtual terms. Are clients don't need any physical infrastructure or local servers at their local sites. In fact, our clients don't even need to install software on their Macs and PCs. Everything runs natively in the web browser in beautiful HD.

Our clients - UC Irvine, Brown, and soon to be several more - are using our solutions every day to address the supply-demand challenge of healthcare delivery, and as a result, are working towards the triple aim.

Onwards and upwards!

Big Brother or Best Friend?

This post was originally featured on EMRandHIPAA.

The premise of clinical decision support (CDS) is simple and powerful: humans can’t remember everything, so enter data into a computer and let the computer render judgement. So long as the data is accurate and the rules in the computer are valid, the computer will be correct the vast majority of the time.

CDS is commonly implemented in computerized provider order entry (CPOE) systems across most order types – labs, drugs, radiology, and more. A simple example: most pediatric drugs require weight-based dosing. When physicians order drugs for pediatric patients using CPOE, the computer should validate the dose of the drug against the patient’s weight to ensure the dose is in the acceptable range. Given that the computer has all of the information necessary to calculate acceptable dose ranges, and the fact that it’s easy to accidently enter the wrong dose into the computer, CDS at the point of ordering delivers clear benefits.

The general notion of CDS – checking to make sure things are being done correctly – is the same fundamental principle behind checklists. In The Checklist Manifesto, Dr. Atul Gawande successfully argues that the challenge in medicine today is not in ignorance, but in execution. Checklists (whether paper or digital) and CDS are realizations of that reality.

CDS in CPOE works because physicians need to enter orders to do their job. But checklists aren’t as fundamentally necessary for any given procedure or action. The checklist can be skipped, and the provider can perform the procedure at hand. Thus, the fundamental problem with checklists are that they insert a layer of friction into workflows: running through the checklist. If checklists could be implemented seamlessly without introducing any additional workflow friction, they would be more widely adopted and adhered to. The basic problem is that people don’t want to go back to the same repetitive formula for tasks they feel comfortable performing. Given the tradeoff between patient safety and efficiency, checklists have only been seriously discussed in high acuity, high risk settings such as surgery and ICUs. It’s simply not practical to implement checklists for low risk procedures. But even in high acuity environments, many organizations continue to struggle implementing checklists.

So…. what if we could make checklists seamless? How could that even be done?

Looking at CPOE CDS as a foundation, there are two fundamental challenges: collecting data, and checking against rules.

Computers can already access EMRs to retrieve all sorts of information about the patient. But computers don’t yet have any ability to collect data about what providers are and aren’t physically doing at the point of are. Without knowing what’s physically happening, computers can’t present alerts based on skipped or incorrect steps of the checklist. The solution would likely be based on a Kinect-like system that can detect movements and actions. Once the computer knows what’s going on, it can cross reference what’s happening against what’s supposed to happen given the context of care delivery and issue alerts accordingly.

What’s described above is an extremely ambitious technical undertaking. It will take many years to get there. There are already a number of companies trying to addressing this in primitive forms: SwipeSense detects if providers clean their hands before seeing patients, and the CHARM system uses Kinect to detect hand movements and ensure surgeries are performed correctly.

These early examples are a harbinger of what’s to come. If preventable mistakes are the biggest killer within hospitals, hospitals need to implement systems to identify and prevent errors before they happen.

Let’s assume that the tech evolves for an omniscient benevolent computer that detects errors and issues warnings. Although this is clearly desirable for patients, what does this mean for providers? Will they become slaves to the computer? Providers already face challenges with CPOE alert fatigue. Just imagine do-anything alert fatigue.

There is an art to telling people that they’re wrong. In order to successfully prevent errors, computers will need to learn that art. Additionally, there must be a cultural shift to support the fact that when the computer speaks up, providers should listen. Many hospitals still struggle today with implementing checklists because of cultural issues. There will need to be a similar cultural shift to enable passive omniscient computers to identify errors and warn providers.

I’m not aware of any omniscient computers that watch people all day and warn them that they’re about to make a mistake. There could be such software for workers in nuclear power plants or other critical jobs in which the cost of being wrong is devastating. If you know of any such software, please leave a comment.

How Much Of The Healthcare Business Is Healthcare?

In The Great Re-Bundling of Healthcare, I argued that healthcare will be rebundled along new dimensions because technology will break assumptions that predicated bundling in the analog era of healthcare delivery.

In that post, I noted that a few industries have been completely dismantled and rebundled by technology:

The print publishing industry – newspaper and magazines – thought that their unique value was in their core product – news, editorials, and classifieds. But the unique value they delivered was in printing and distribution. When the Internet reduced the cost of printing and distribution to effectively $0 and free news became the standard, their businesses collapsed. Print publishers are left servicing the paper news market, which is a fraction the size of the overall digital news market.

Taxi companies thought that their local, retail, administrative, and regulatory overhead was necessary to solve the get-from-point-a-to-point-b problem. Using the Internet, UberLyft, and SideCar proved that none of those overhead functions matter, enabling a new era of get-from-point-a-to-point-b solutions. Taxi companies are left servicing the I-haven’t-heard-of-Uber and there-aren’t-enough-Uber-drivers markets, both of which are rapidly shrinking.

Hotels thought constructing buildings and staffing employees was the only way to solve the get-a-place-to-stay-for-the-night problem. Using the Internet, AirBnB proved that anyone can solve the get-a-place-to-sleep-for-the-night problem for anyone else. Hotels are left servicing the high-end, premium service market in the get-a-place-to-stay-for-the-night business.

These examples beg the question: when healthcare is completely rebundled around digital delivery, what businesses will healthcare providers really be in?

In the examples above, the Internet empowered laymen to circumvent legacy establishments. Using the Internet, laymen performed the same tasks more affordably than traditional retail businesses.

With Watson-like self-diagnostics; an army of cheap, connected, sensors; and a wealth of freely available information on the web, laymen will increasingly self-diagnose and self-medicate whenever and however possible. This process will start at the low end – the simple stuff such as common colds, simple bumps and bruises – and increasingly move up market.

Over time, tri-corders (such as Scanadu), smartphone EKGs (such as AliveCor), smartphone ultrasounds, CTs, MRIs, and blood tests will empower patients to gather all of the necessary diagnostic information without ever visiting a retail medical facility. Patients will send data to providers electronically and consult with providers via video conference. The web will obviate the need for most retail overhead, capital expenditure, and labor cost associated with most care delivery.

Medicine will be disrupted from the bottom up. Hospitals won’t completely go away, but they will be left servicing the high-end of the market – ICUs, surgery, labor and delivery, and other high-acuity conditions – just as hotels, print publications, and taxis service the most expensive segments of their respective markets. The vast majority of care will be delivered as virtually and cost effectively as possible.

By circumventing retail establishments, medicine will centralize as geography loses relevance. Just as the hotel and taxi industries consolidated around mega-platforms such as Uber and AirBnB, healthcare will consolidate around provider hubs that service enormous populations. The mega healthcare systems will have the tools to centrally manage populations and interact with them contextually. The major health systems of the analog era that were bounded by geography will battle to become national behemoths as geography becomes irrelevant. Mayo Clinic, Cleveland Clinic, and others are already doing this by establishing virtual clinics across the country.

Why did the publishing industry, taxi industry, hotel industry, and travel agency industries collapse? Why will all of the old practices of medicine collapse? Cost. The most costly aspects of delivering care are labor and retail overhead. As increasingly small, localized, connected computers gather an increasingly large amount of data, computers will help patients self-diagnose and self-medicate without the need for expensive retail or labor overhead. Computers will automate inherently repetitive processes.

The Fundamental Challenge of ACOs

This post was originally featured on EMRandHIPAA.

I’ve been openly bullish on ACOs and capitated payment models. The only way to achieve the triple aim – quality, cost and access – is to create a system that is structurally incentivized towards those ends. The fee-for-service model will never be structured in a way that incentivizes the triple aim. On the other hand, ACOs do.

Early ACO data is mixed. Although some organizations succeeded in lowering costs and improving outcomes, about 1/3 dropped out of the ACO program entirely, and another 1/3 reported no significant cost or quality changes. Only 1/3 were “successful.”

Why? Why did some organizations succeed where others failed? What did each organization do differently? It’s been proven that some organizations can succeed under this model. But not everyone.

ACOs are disruptive to fee-for-service payment models. ACOs invert incentives. They invert how every employee should think about their job in the context of the larger care delivery system. In ACOs, healthcare professionals are implicitly asked to think about preventative care, which tends to lead towards both cost and quality improvements. On the other hands, in a fee-for-service model, healthcare professionals are only incentivized to simply treat the patient in front of them with no regard for prevention or cost.

When the board of directors of a given organization recognizes the need to change the course of a business, the board usually replaces the CEO. After a new strategy is devised, the new CEO typically replaces most of the executives and lays off a significant number of the existing staff. This accomplishes a few things:

1) reduces the burn, making the organization leaner and more capable of pivoting
2) replaces lots of senior and middle management, who were trained and wired around the old business model, and who may conspire against the new model if they don’t believe in it
3) sends a signal to the remaining staff that management is serious about change

Although this plan doesn’t guarantee success, it’s fairly common in large organizations because it can create impetus to break from the inertia of the status quo. The only thing worse than going after the wrong business model is maintaining one that’s failing.

This of course begs the question, how are providers adopting ACOs? Management at provider organizations that have adopted the ACOs are early adopters. They are pioneers. They are leaders. They can see a new, better, ACO-based future. The last thing management at these organizations is going to do is fire themselves after deciding to transition to an ACO.

In light of the above, I am particularly impressed by the early success of the ACO program. Only 1/3 dropped out. Given the fundamental change at hand, I would consider the early data a harbinger of better changes to come. I suspect that almost all of the remaining ACOs will see more significant improvements in years 2 and 3 as they mature and refine processes around value.

Why ICD-10?

This post was originally featured on EMRandHIPAA.

At least half a dozen folks have asked me to explain why HHS is mandating the transition to ICD-10. So I thought I’d write a blog post about the subject.

First, I’ll examine some of the benefits that proponents of ICD-10 site. Then, I’ll examine the cost of transition from ICD-9 to ICD-10.

There are about a dozen frequently cited reasons to switch from ICD-9 to ICD-10. But they can be summarized into three major categories:

1) The US needs to catch up to the rest of the world.

2) The more granular nature of ICD-10 will lend itself to data analysis of all forms – claims processing, population health, improved interoperability, clinical trials, research, etc.

3) ICD-9 doesn’t support the latest diagnoses and procedures, and ICD-10 does.

Regarding #1, who cares? Coding standards are intrinsically arbitrary. Sequels are not necessarily better than their predecessors.

Although #2 sounds nice, there are a lot of problems with the supposed “value” of more granular data in practice. Following the classic 80-20 rule of life (80% of value comes from 20% of activity), the majority of codes are rarely used. By increasing the number of codes six-fold, the system is creating 6x the opportunities to inaccurately code. There is no reason to believe that providers will more accurately code, but the chances of incorrect diagnosis are now significantly higher than they were before. Garbage in, garbage out.

Below are some specific examples of how increasing the number of codes will affect processes in the healthcare system:

Payers – payers argue that making codes more granular will improve efficiency in the reimbursement process by removing ambiguity. There is nothing further from the truth. Payers will use the new granularity to further discriminate against providers and reject claims for what will appear to be no reason. With 6x the number of codes, there are at least 6x as many opportunities for payers to reject claims.

Clinical trials – ICD-10 proponents like to argue that with more granular diagnosis codes, companies likeePatientFinder can more effectively find patients and match them to clinical trials. This notion is predicated on the ability of providers to enter the correct diagnosis codes into EMRs, which is a poor assumption. Further, it doesn’t actually address the fundamental challenges of clinical trials recruitment, namely provider education, patient education, and the fact that most patients aren’t limited to trials by diagnosis codes, but rather by other data points (such as number of years with a given disease and comorbidities).

Public health – ICD-10 proponents also claim that the new coding system will help public health officials make better decisions. Again, this is predicated on accuracy of data, which is a poor assumption. But the greater challenge is that the most pressing public health issues of our time simply don’t need any more granularity in diagnosis codes. Public health officials already know what the top 20 public health problems are. Adding 6x the number of codes will not help address public health issues.

Regarding #3, why do we need to reinvent the entire coding system and make the entire system more granular to accommodate new diagnoses and procedures? Why can’t we continue to use the existing structure and simply create new branches of the ICD tree using alphanumeric characters? Why do we need to complicate every existing diagnosis and procedure to support new diagnoses and treatments? We don’t. There are plenty of letters left to be utilized in ICD-9 to accommodate new discoveries in medicine.

Next, I’ll provide a very brief summary of the enormity of the cost associated with transitioning from ICD-9 to ICD-10. The root of the challenge is that a string of interconnected entities, none of whom want to work with one another or even see one another, must execute in sync for the months and years leading up to the transition. Below is a synopsis of how the stars must align:

EMR vendors – EMR vendors must upgrade their entire client base to ICD-10 compliant versions of their systems in the next couple of months to begin testing ICD-10 based claims. Given the timescales at which providers move, the burden of MU2 on vendors, and the upgrade cycles for EMR vendors, this is a daunting challenge.

Providers – providers don’t want to learn a new coding system, and don’t want to see 6 times the number of codes when they search for basic clinical terms. Companies such as IMO can mitigate a lot of this, but only a small percentage of providers use EMRs that have integrated with IMO.

Coding vendors – like EMR vendors, auto-coding vendors must upgrade their clients systems now to one that supports dual coding for ICD-9 and ICD-10. They must also incur significant costs to add in a host of new ICD-10 based rules and mappings.

Coders – coders must achieve dual certification in ICD-9 and ICD-10, and must double-code all claims during the transition period to ensure no hiccups when the final cut over takes place.

Clearinghouses – clearinghouses must upgrade their systems to support both ICD-9 and ICD-10 and all of the new rules behind ICD-10, and must process an artificially inflated number of claims because of the volume of double-coded claims coming from providers.

Payers – payers must upgrade their systems to receive both ICD-9 and ICD-10 claims, process both, and provide results to clearinghouses and providers about accuracy to help providers ensure that everyone will be ready for the cut over to ICD-10.

The paragraphs above do not describe even 10% of the complexity involved in the transition. Reality is far more nuanced and complicated. It’s clear from the above that the likelihood that all of the parties can upgrade their systems, train their staff, and double code claims is dubious. The system is simply too convoluted with too many intertwined but unaligned puzzle pieces to make such a dramatic transition by a fixed drop-dead date.

Lastly, switching to ICD-10 now seems a bit shortsighted in light of the changes going on in the US healthcare system today. ICD-10 is already a decade old, and in no way reflects what we’re learning as we transition from volume to value models of care. It will make sense to change coding schemes at some point, but only when it’s widely understood what the future of healthcare delivery in the US will look like. As of today, no one knows what healthcare delivery will look like in 10 years, let alone 20. Why should we incur the enormous costs of the ICD-10 transition when we know what we’re transitioning to was never designed to accommodate a future we’re heading towards?

At the end of the day, the biggest winners as a result of this transition are the consultants and vendors who’re supporting providers in making the transition. And the payers who can come up with more reasons not to pay claims. Some have claimed that HHS is doing this to reduce Medicare reimbursements to artificially lower costs. Although the incentives are aligned to encourage malicious behavior, I think it’s unlikely the feds are being malicious. There are far easier ways to save money than this painful transition.

The ICD-10 transition may be one of the largest and most complex IT coordination projects in the history of mankind. And it creates almost no value. If you can think of a larger transition in technology history that has destroyed more value than the ICD-9 to ICD-10 transition in the US, please leave a comment. I’m always curious to learn more.

One Small Wink For Man, One Giant Head Nod For Mankind

We would love to issue a huge congratulations to our partners at Rhode Island Hospital (RIH). They just went live with Pristine EyeSight on Google Glass in the ER for dermatology consults.

One of the fundamental challenges of delivering care in an ER is that patients can show up at any time of day with any problem. Often times, the ER staff need to call in a specialist. The problem is that those specialists aren't sitting around doing nothing; they're usually busy in their own clinics (which are rarely in the same building), or at home on call. The physicians and nurses at RIH are using EyeSight on Glass to beam in dermatologists on demand. This is a significant win for all parties:

For the patient - the patient will been treated more quickly than they otherwise would have, which reduces clinical risk and allows the patient to go home sooner. Approximately half of all wait times in ERs can be attributed to waiting for the right provider to arrive.

For the local provider - the local provider will spend less time hunting down the specialist in need, and will finish the exam with the patient more quickly, leading to increased throughput. The local provider may even go home sooner and see their children sooner :).

For the consulting provider - the consulting provider derives an enormous efficiency benefit. EyeSight saves them the pain of driving in or walking over. If the consulting provider is forced to leave their clinic, their clinic schedule will fall behind, leaving all of their patients unhappy and frustrated.

For the ER - the ER benefits by improving throughput, reducing risk, and ultimately increasing HCAHPS scores (which have a high correlation with wait times in outpatient settings).

Last week, the clinical teams at RIH went live with Pristine EyeSight. ER physicians are using EyeSight to securely beam in dermatologists to provide live consultations of skin wounds, rashes, and lesions in the ER. Because dermatologists work in fast-paced outpatient clinics, it's been difficult for dermatologists to come into ERs to see patients. Using Pristine EyeSight, we're improving access and outcomes in ways that were never before possible.

Looking ahead, there are even greater opportunities. We started with dermatology in the ER. We can't wait to leverage this technology with other specialties in the ER, and throughout the hospital.

We've been working with RIH's leadership team for months, and couldn't be happier with the results. We'd like to send a special shout out to Dr. Paul Porter, Dr. Peter Chai, and Dr. Roger Wu for their tenacity in helping us through the Institutional Review Board (IRB), IT, and administrative processes so quickly and diligently to make this a reality.

And lastly, as CEO, I'd like to thank our team for all of their hard work to make this a reality. We've been refining the system for a long time to ensure high reliability and performance. Our client success team has also done a spectacular job in training and deployment.

The Irony Of Healthcare Standards

This post was originally featured on EMRandHIPAA.

Healthcare delivery should be standardized. Medicine is, after all, primarily a science. Providers must diagnose and treat patients. Clinicians must form hypotheses, test hypotheses, and act. As providers obtain new information, they must adjust their thesis and repeat the cycle until patients are treated. Although there is an art to patient interaction, the medical process itself is scientific.

Science is based on repeatable, nullable hypotheses. Diagnostics and treatments are too.

And yet, it’s widely known that healthcare delivery is anything but standardized. Even basic pre-operative checklists vary dramatically across locations. Although some of this variation can be accounted for by physical constraints and capital limits, most of the aberrations can be attributed to management and culture. Checklists and protocols attempt to standardize care, but even the protocols themselves are widely debated within and between organizations.

It’s also widely known that most innovations take the better part of two decades to roll out through the US healthcare system. For an industry that should be at the cutting edge, this is painful to acknowledge.

There’s a famous saying that vendors represent their clients. It should be no surprise that major health IT vendors are slow to innovate and respond. Providers are used to slow changes, and have come to expect that of their vendors. Since providers often cannot absorb change that quickly, vendors become complacent, the pace of innovation slows, and innovations slowly disperse.

In the same light, health IT vendors are equally unstandardized. In fact, health IT vendors are so unstandardized that there’s an entire industry dedicated to trying to standardize data after-the-fact. The lack of standards is pathetic. A few examples:

Claims – Because insurance companies want to reject claims, they have never agreed on a real standard for claims. As such, an entire industry has emerged – clearing houses – to help providers mold claims for each insurance company. In an ideal world, clearing houses would have no reason to exist; all claim submissions, eligibility checks, and EOBs should be driven through standards that everyone adheres to.

HL7 – It’s commonly cited that every HL7 integration is just that: a single HL7 integration. Although HL7 integrations share the same general format, they accommodate such a vast array of variety and choice that every integration must be supported by developers on both sides of the interaction.

As a technologist, the lack of interoperability is insulting. Every computer on this planet – Windows, Mac, iOs, Android, and other flavors of Linux – communicate via the TCP/IP and HTTP protocols. Even Microsoft, Apple, and Google play nicely within enterprises. But because of the horribly skewed incentives within healthcare, none of the vendors want their customers to interact with other vendors, even though cooperation is vital.

Perhaps the most ironic observation is that technology is widely considered to be hyper-competitive. Despite hyper-competition, the tech giants have coalesced around a common set of standards for communication and interoperability. Yet health IT vendors, who operate within a vertical that prides itself on its scientific foundations, fail to communicate at the most basic levels.

Keep It Simple, Stupid!

This post was originally featured on EMRandHIPAA.

There are an enormous number of startups trying to solve the medication adherence problem. Broadly speaking, these startups are trying to solve the problem through three avenues:

1) Hardware, i.e. smart pill bottles

2) Semi-intelligent software driven reminders

3) Patient education

The most effective solutions are likely to incorporate all three.

The hardware space has been the most interesting simply because of the variety of solutions cropping up.AdhereTech and CleverCap have developed unique pill bottles that control and monitor dispensing via proprietary smart pill bottles. They also incorporate software for notifications. Unfortunately, all smart pill bottle makers are bounded by FDA regulations because they physically control medications through a combination of hardware and software. FDA regulations will slow time rollout of these solutions to market and create enormous new expense.

I recently learned about PillPack, a startup that just raised $4M. They compete asymmetrically in the medication adherence by not making any hardware at all!

The problem with the pill bottle is that there are dozens of pills in a single container. Measuring and controlling output and consumption is intrinsically a difficult problem. PillPack solves these problems by simply averting the issue entirely. PillPack pre-packs pills by dose. This is particularly valuable because they pre-pack multiple kinds of medications that need to be taken at the same time.

PillPack doesn’t yet have any intelligent software that monitors when medications are taken. But with granular packaging, sensing and controlling the medications becomes dramatically easier than ever before. I suspect this will the marquee feature of PillPack 2.0. Once they add the ability to detect when a pack is opened, they can begin adding intelligent software alerts and reminders to patients and their families.

PillPack has a far more lucrative distribution strategy than companies who have to produce and distribute hardware. PillPack can scale their customer base incredibly quickly through B2C marketing. B2C marketing isn’t easy; Pillpack faces a significant challenge in terms of patient and provider education, but it’s one that’s definitely addressable. If PillPack’s service is as good as I think it is, they should develop incredibly happy customers, which will lead to recurring revenues and strong referrals.

The moment I saw Pillpack, I immediately recognized it as one of those “duh” business. We’re going to look back in 10 years and wonder why this wasn’t always around. Their solution solves so many of the pain points around taking medications on time and is coupled with a lucrative business model that feeds off of recurring revenues from long term customers.

The genius of their business is that they are tackling the medication adherence problem from a unique angle: packaging and distribution. They’ve bundled that solution into a simple and elegant package (pun intended) that helps patients avoid the pain of the modern US healthcare system: going to the pharmacy, fighting with the pharmacist, and manually tracking when to take how much of each medication.

Full disclosure: I have no relationship(s) with PillPack.