PokitDok Marketplace Launches New Features

By Nicole Fletcher,

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We’re excited to announce that new features have been added to the PokitDok Marketplace!

With an increasing focus on price transparency and consumer education, the PokitDok Marketplace is a excellent, working resource to see how our APIs can be implemented easily and quickly for any health application or system.

The San Francisco, Los Angeles, Seattle and Boston areas now feature select provider profiles by specialty when visitors go to shop, book and pay for healthcare services. Search results in each region showcase four providers/practices in that location’s top specialties.



Braintree, a payment processing system, has also been integrated into the marketplace, which allows physicians to receive real-time payments for appointments and services booked. This technology will continue to streamline the payment process for healthcare professionals, ensuring they receive payment for their services without the time-intensive insurance delays (learn more about our solution for that here).


Maybe another paragraph now that I’m re reading this.


Visit our website to learn more about our white label marketplace.


The opinions expressed in this blog are of the authors and not of PokitDok's. The posts on this blog are for information only, and are not intended to substitute for a doctor-patient or other healthcare professional-patient relationship nor do they constitute medical or healthcare advice.
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  Tags: Consumer, Dev, Enterprise, Provider

MedCity ENGAGE 2015 Recap

By John Riney,

Captain Picard says, "Engage."
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Our friends at MedCity News put on a great conference last week called ENGAGE - as you'd guess, engagement is the focus. And a good thing too, because patient engagement is rapidly becoming the biggest deal in healthcare, as more patients take charge of their healthcare decision-making. As a developer in the health world, it's all kinds of encouraging to see folks like Dr. Michael Willis, VP at Kaiser Permanente, and Susannah Fox, CTO of the US Department of Health and Human Services, talk about real, substantive efforts to enable interoperability and improve the patient experience.

So Bill, our new Senior Director of Business Development, and I set up our little booth and spread the gospel to Bethesda, MD -

PokitDok's little booth at MedCity Engage 2015

It was a great show, and we were glad to be a part of it. Thanks, MedCity News!

The opinions expressed in this blog are of the authors and not of PokitDok's. The posts on this blog are for information only, and are not intended to substitute for a doctor-patient or other healthcare professional-patient relationship nor do they constitute medical or healthcare advice.
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  Tags: Consumer, Dev, Enterprise

Identity Management in Healthcare: A PokitDok Solution

By Nicole Fletcher,

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PokitDok recently announced the release of our newest product: The PokitDok Identity Management System (IdM). This API allows hospital systems, telehealth and other innovative applications to search for a patient’s identity in an Enterprise Master Patient Index (EMPI) and/or a Master Patient Index (MPI), both typical components of EMR (Electronic Medical Record) or EHR (Electronic Health Record) Systems.

As discussed in a recent blog post, the Health Information Technology for Economic and Clinical Health (HITECH) Act of 2009 calls for complete implementation and use of EHRs by the end of 2015. Through the end of last year, the government offered physicians and hospital systems financial incentives to move forward with this change, allowing them to potentially make more money by implementing EHR systems. That means that by year’s end, all medical records will (or should) be electronic and yes, that means that until now, they have not been. Not only have American medical records not been digital, but to date, they are disconnected and duplicated within different medical systems.

Screen Shot 2015-07-16 at 1.39.45 PM

The PokitDok Identity Management System has the ability to change that. It maps a user identity into and across multiple EMR/EHR systems. For example, a patient account at a local urgent care clinic might be #1234, while at the hospital it might be #A0987. With the IdM API, it is possible to query and get/add a patient’s identity to any EMR hooked into our platform. This allows for an end-to-end communication path that has not been possible until now.

How It Works:


Aside from the grand statement that every consumer of healthcare ultimately benefits from what a product like this can accomplish, there are a few direct beneficiaries to note. Health systems lose millions if not billions annually as a direct result of inefficient or impossible data connectivity. The interoperability between multiple data repositories made possible by the PokitDok Identity Management System API will allow health systems streamlined connectivity, knowledge, and ultimately a reduction in time and money spent. Also, third party platforms from remote patient monitoring tools to telehealth startups, can easily tie into existing medical data that is currently trapped within EHRs/EMRs. All they have to do to get started, is point to this API.

In sum, one of the biggest issues in healthcare to date, is interoperability. PokitDok’s latest product, coupled with our suite of other APIs (insurance connectivity, cross-EMR scheduling, e-commerce for healthcare, etc.), are a clear step in the right direction toward connectivity and interoperability among disparate systems. Stay tuned for future posts about what this might mean for the future of healthcare, specifically relating to personal ownership of your health and the consumer tendencies that come with that evolution.

Visit the PokitDok website for more information or read the full press release.

The opinions expressed in this blog are of the authors and not of PokitDok's. The posts on this blog are for information only, and are not intended to substitute for a doctor-patient or other healthcare professional-patient relationship nor do they constitute medical or healthcare advice.
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  Tags: Dev, Enterprise, Provider

Healthcare Transactions: Simple? . . .not yet

By Mary McKee,

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Screen Shot 2015-07-14 at 1.57.49 PM

The face of healthcare is changing and what’s becoming more and more apparent is that the supporting system, i.e. the technology, the inner workings, and the process of transferring information, desperately needs to be updated. Everything in this industry, from medical claims to enrollment changes that come with life events, goes through a process, which leads to multiple subsequent processes. Many of these have not been updated in decades.

With trillions of dollars spent on healthcare each year, why is the technology still so outdated? Information is easily accessible, and can be in front of anyone in milliseconds, rather than days or weeks. Yet, in healthcare, it can take that long or longer for information to get from employers or private marketplaces to healthcare carriers.

While you might think healthcare enrollment or claims submission is - or should be - a simple process, it’s not. It’s still standard for brokers or benefit administrators to submit hand-written enrollment forms for new employees via fax - or even mail. If these forms are, by some miracle, submitted electronically, they’re more than likely being funneled through clearinghouses that use large workforces to manually route, encrypt and send that information via antiquated processes such as FTP (file transfer protocol) or even email. Think about that next time you’re waiting months for a claim to be paid.

The volume of information is high, and there is so much manual labor involved that mistakes often happen. If you follow the news, it’s hard to for a year to pass without an open enrollment fiasco. Last year, for instance, thousands of people lost their healthcare coverage due to inadequate software solutions with Healthcare.gov or on private exchanges. Such mistakes can cause HIPAA violations, overwhelming slowness, and lost coverage - all of which reflect the ineffectiveness of obsolete technology.

A few major players, crippled by miles of red tape and bureaucratic systems, dominate healthcare, and are slow, resistant, and often unable to change. Saturday Night Fever may have been a great film back in the 1970s, but for our healthcare technology and processes to remain rooted in that era, is not sustainable.

Like so many other industries, healthcare is moving away from large, lumbering companies. Smaller more agile startups are using technology to push their way toward change. The healthcare industry is experiencing a drastic reformation and, like it or not, the future of their IT will be a big part of the change.

In our next post, we will be taking a deeper look into how things can and will be better, and what PokitDok is doing to lead the way. In the meantime, visit our website.

The opinions expressed in this blog are of the authors and not of PokitDok's. The posts on this blog are for information only, and are not intended to substitute for a doctor-patient or other healthcare professional-patient relationship nor do they constitute medical or healthcare advice.
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  Tags: Enterprise

Visualizing Medicare Physician Co-Occurrence and Payment Data Geographically - Part 2

By Alec Macrae,

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In Part 1 of this 2-part post, we introduced a dataset from the Centers for Medicare and Medicaid Services (CMS), defined co-occurrence relationships between physicians based on that data, and used data visualization to look closely into the CMS payment data geographically thereby uncovering some trends related to the highest grossing physicians. Now in Part 2, in an effort to further share the benefits of visualizing and analyzing data in graph form, we’ll delve into the co-occurrence relationships between these physicians and discuss what can be inferred.


Co-Occurrence Referral Relationships

In order to get a better idea of the co-occurrence relationships (defined in Part 1), let’s keep the vertex size the same as the image in Part 1 (based on total payments), but change the color of the vertices to represent the number of co-occurrence referral patterns (edges) between various physicians (vertices). To reiterate, an edge drawn between two physician vertices represents a patient that has seen both of those physicians within a 30-day time frame.

Screen Shot 2015-07-14 at 9.05.30 AM

Here, the orange color now represents physicians with more co-occurrence referral patterns (edges), the purple color represents physicians with less co-occurrence, and the blue represents something in between the orange and purple. From a quick look at the graph, it becomes clear that while some higher grossing physicians appear to have a high rate of co-occurrence, not all high grossing physicians have the highest co-occurrence, as noted by some smaller orange vertices scattered throughout the graph. However, very few of the large vertices are on the purple side of the scale here, suggesting that very few of the top grossing physicians have a low co-occurrence. This indicates that, not surprisingly, having a high co-occurrence rate (perhaps even high referral rate) is an important and common feature among high grossing physicians.

Screen Shot 2015-07-14 at 9.06.00 AM

Looking at the colors of the edges, it appears that some of the strongest co-occurrence patterns include physicians from Florida. This can be seen by looking at the bright orange edges stretching from there to a region as far west as Texas and other regions as far north as New England. It is also interesting to note that among the largest vertices in Florida, the largest two were found to be physicians associated with fraud as noted in Part 1 seem to have a lower co-occurrence. This may suggest that high grossing physicians with low co-occurrence (large darker colored vertices) are more likely to be associated with fraud.

In layperson's terms, using color, the above graph showed us which physicians referred more patients to each other, and, noting vertex size, which physicians made more money. To gain further insight into just the co-occurrence referral patterns piece of the puzzle, let's resize the vertices by number of edges to emphasize and match the color scale. In addition, we will decrease the sensitivity of the color scale on the top end to yield a higher percentage of orange vertices.

Looking at this this graph, it appears there is a much higher co-occurrence rate, suggesting many more referrals between physicians on the eastern side of the country. Just looking at vertex density alone in the graphs posted so far, there is clearly a higher percentage of our 50,000 top-grossing physicians in the east as well. Perhaps higher physician density could be leading to higher co-occurrence rates organically, without one physician actually referring a patient to another physician. This is one caveat of the “referral” data provided by the CMS.

Larger orange vertices seem to be prominent in major cities, just like in the graph that sorted size and color by total payments; however, there are some larger orange vertices that are located in less major cities as well unlike the previous total payments graph. While looking at these graphs, feel free to draw your own conclusions and make additional observations  we did not directly discuss in this post. Also note the importance of visualizing data in different and unique ways, and the benefits that can be achieved from viewing data in a graph as opposed to merely looking at it in a table or spreadsheet.


Looking Deeper: Community Detection

In addition to visualizing the raw CMS referral (co-occurrence) data, performing data transformations or running the data through various algorithms and/or classifiers can also be useful. We will be using the Louvain community detection (aka clustering) algorithm, also called Louvain modularity method to, as the name implies, detect communities. These communities will represent groups of physicians which have seen a high number of similar patients within a 30-day time frame. (For those without a data science background: clustering and community detection are useful ways to help us find communities of physicians that tend to refer patients to one another.)

The Louvain modularity method optimizes Modularity, which in this case measures the density of links inside communities compared to links between communities. It does this by iteratively grouping vertices together into larger and larger groups of vertices with strong connections (highly weighted edges) until the final clusters (communities) have been found and the modularity can no longer be increased. The Louvain modularity method is designed for networks, aka graphs, which makes it a nice fit for our purposes, since we are using a graph to look for the relationship between doctors and their referral networks.

Screen Shot 2015-07-14 at 9.08.09 AM

The above graph was generated by performing the Louvain modularity method on the CMS co-occurrence data with a modularity of 0.948 (perfect would be 1.0), and then coloring the resulting communities for visualization. The size of the vertices here corresponds to the physician's payment information with larger vertices being higher grossing physicians.

With large scale graphs it can be difficult to tell just how many edges are between groups of vertices that are close together. The nice thing about using an algorithm to cluster groups of vertices, is that it takes guessing out of the equation and simply displays something that is easier to visually process. For example, looking at any of the graphs shown before clustering, we might not have been able to tell that there were strong connections between distinct communities along the east coast of the United States.

These communities of providers can be thought of as large scale referral networks, assuming if a patient was seen by two physicians within a 30-day time frame, there was a referral of one physician by the other. Although a referral network spanning nearly the entire state of Texas seems farfetched to a certain extent, there is likely some truth behind the numbers. For example, the state boundary lines between Texas, Louisiana, Mississippi, Alabama, and Florida are quite clearly defined spanning across the Gulf of Mexico region, suggesting physicians in the south tend to refer patients within state.

For the sake of completeness, let’s decrease the size of the clusters to something that more closely resembles actual referral regions. This can be achieved by decreasing the resolution parameter of the Louvain modularity method.

Screen Shot 2015-07-14 at 9.09.16 AM

This time, we were only able to achieve a modularity of 0.903, which is slightly lower than before, but still good enough for our intents and purposes. By decreasing the size of the communities, it is not clear just how diverse different regions of the country are. For example, you might expect to see many smaller clusters of referral communities in the New England area due to its dense population across many states, which is somewhat evident here. However, Florida seems to have even more communities than New England, which again could be because Florida is such a popular retirement destination.


Conclusions and Tooling

There are many more observations and speculations that can be drawn from the visualizations in both this blog post (Parts 1 and 2) and the CMS dataset as a whole, but hopefully we have provided you with an informative and interesting peek into both.

The two datasets were loaded, joined, aggregated and filtered using GraphLab Create. The graph visualizations here were created using Gephi.

The opinions expressed in this blog are of the authors and not of PokitDok's. The posts on this blog are for information only, and are not intended to substitute for a doctor-patient or other healthcare professional-patient relationship nor do they constitute medical or healthcare advice.
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  Tags: Dev

Visualizing Medicare Physician Co-Occurrence & Payment Data Geographically - Part 1

By Alec Macrae,

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As a result of the Freedom of Information Act requests, the Centers for Medicare and Medicaid Services (CMS) have released an unprecedented amount of data on healthcare providers participating in Medicare. An earlier blog post went into more detail about what this data contains and explored the co-occurrence neighborhoods of the 5 top grossing physicians. This post will also look into the top grossing physicians and co-occurrence data, but will explore them in the context of geographical relationships across the United States. Part 1 of this post will specifically be introducing the data used, explaining why we use data visualization to analyze it, and exploring trends in the payment data by mapping the highest paid physicians across the United States.

What is Co-occurrence Data

Co-occurrence is the phrase we use for "referral patterns" among Medicare providers. Given that referral relationships among providers are inferred based on individual patients merely seeing multiple providers within a given timeframe, the word “referral” is slightly misleading for the weak nature of this relationship.

Co-occurrence relationships among physicians are inferred based on individual patients seeing multiple physicians within a given timeframe. For our purposes here we are using the 30-day interval data, meaning that if a patient sees provider A for some service and then provider B within 30 days, a referral from provider A to B is inferred.

Note that these are merely inferred referral relationships–there is no direct evidence presented in the data to indicate any actual referrals took place. These inferences are particularly weak in the case where a patient receives services by multiple providers, interleaved over time. For instance, consider a primary care physician performing an initial consultation, who orders diagnostic lab work for a patient, with a subsequent followup by the primary care physician. While an actual referral of sorts is present from the physician to the lab, the data will also include an inferred referral from the lab to the physician. Alternately, a patient may see two providers for completely unrelated issues during a 30-day timeframe, which results in an inferred referral.

Why use Data Visualization

A huge amount of data comes into our brains through our eyes every second, making our visual system extremely well built for visual analysis. One of the major aspects of data science is being able to conceptualize and understand the data you are working with, and data visualization is an incredibly useful tool that can help accomplish that goal. One common example that shows just how useful it can be is Anscombe’s Quartet, devised by statistician Francis Anscombe. Assume you have four data sets that share many characteristics: mean, variance, correlation, and regression. If you were looking at the data in a table, it may not be obvious if there were any major differences in the datasets. However, when the data is graphed, it becomes quite clear that the differences are vast. This post combines data visualization with the CMS co-occurrence data by mapping it across the United States.

Top-Grossing Physicians

For the purpose of this post we’ll be examining only the data covering physicians, i.e. individual providers with an M.D. or D.O. degree. Payment and co-occurrence data for all other providers are excluded from the analysis and visualization below. Top-Grossing here refers to just 2012 Medicare reimbursements.

The aforementioned post lists the five top-grossing physicians for 2012 (most recent data available) in terms of Medicare reimbursements (copied here for convenience).

Screen Shot 2015-07-09 at 9.06.19 AM

Of course the list goes far beyond just these five doctors, so let’s take a look at the 50,000 top-grossing providers to better understand how they look displayed geographically across the U.S.


Here, the higher grossing physicians are represented by large orange vertices, while the lowest grossing physicians are shown as small purple vertices. The blue, medium-sized vertices represent physicians that fall somewhere in the middle. As you can see, our good friend Salomon Melgen ($20,827,328) is located in West Palm Beach, Florida and his runner-up buddy Asad Qamar ($18,154,753) closeby in Ocala, Florida. Melgen was recently sent to prison in a medicare fraud case earlier this year after investigators started looking into this very dataset for physicians who were billing medicare more than anyone else. Qamar was also accused of fraud, and it was revealed in the investigation process that he had even been “using his children as political pawns” - making large donations in their names.

Michael McGinnis ($12,577,006) shown above by the big, orange vertex in New Jersey, runs three practices from there. He states here that he is high on the list because nearly 30 pathologists who work at the three practices he runs were using his NPI (National Provider Identification Number) to bill patients. This could help explain why Dr. Michael McGinnis had many distinct clusters of networks in his co-occurrence neighborhood graph. Different pathologists using his NPI may have referred patients to different networks of other physicians. With pathologists using his NPI across 3 practices it’s easy to see how they may have had different referral patterns.

Looking at this graph, it is clear that there are some regions where higher grossing physicians seem to be more or less likely to be found. Major cities seem to be a common theme for highest grossing physicians across the United States, with Florida booming perhaps due to their large percentage of retired citizens and the 2 fraud cases already mentioned.

Now that we have discussed the vertices of the above map in detail, you might be wondering about what the edge in the above graph represent. These edges connecting the physician vertices together represent the co-occurrence relationships between these physicians. We will begin to explore and analyze more of what the co-occurrence relationships mean in part 2 of this post, while still keeping the insights from the payment data we discussed a relevant topic as well.

The opinions expressed in this blog are of the authors and not of PokitDok's. The posts on this blog are for information only, and are not intended to substitute for a doctor-patient or other healthcare professional-patient relationship nor do they constitute medical or healthcare advice.
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  Tags: Dev

New And Improved Developer Documentation

By John Riney,

New PokitDok API docs
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Let's talk about developer documentation, shall we? If you're anything like me as a coder, when you're scoping out a new API or technology, you want to know the following things:

  1. Does it do what I need?
  2. Is the documentation any good?
  3. How much does it cost?
  4. Is anyone else using it?

If I can't find those four things out in a few minutes, and without having to hear a sales pitch, your API isn't getting any further consideration. That's why, as PokitDok's technical evangelist, I've continually fought to maintain and improve our developer experience. Everything we do is plainly laid out on our Platform site, along with documentation, pricing, and sample code. This doesn't sound like all that big of a deal - any API should work that way - but the world of health is the last bastion for some pretty gruesome business practices. For example, some companies charge for access to their documentation. That's just not how we work.

Anyway, back on the topic of documentation - we've got new and improved docs! We saw some sites making really good use of the TripIt Slate framework, and decided to move to that. The content has been reorganized to put what you need to get started first, and they're searchable! We've also greatly expanded the documentation in the areas of Benefits Enrollment and Claims, providing complete coverage of these more complicated request types for those of us who aren't X12 experts. We'll be progressively adding more details and code examples for all our endpoints. Also, the source for our documentation is now publically hosted on our public GitHub repo. If you find an error, or something you think could be improved, send us a pull request! Here's a quick screenshot:

New PokitDok API docs

I'm really proud of the new docs. Check them out, and let me know what you think!

The opinions expressed in this blog are of the authors and not of PokitDok's. The posts on this blog are for information only, and are not intended to substitute for a doctor-patient or other healthcare professional-patient relationship nor do they constitute medical or healthcare advice.
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  Tags: Dev

The History of American Health Insurance: A Look Back In Time To Understand How We Got To Where We Are

By Nicole Fletcher,

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In America today, there’s a drastic discrepancy between people’s perception of health insurance and its definition; and understandably so, as it has fundamentally changed since its inception just decades ago. It could be argued that many of the frustrations relating to this topic stem from a near-universal knowledge gap, specifically regarding what health insurance is - and also, what it isn’t.

By definition, health insurance is the term used to describe any insurance that provides people with protection against the costs of medical services. Albeit vague, this definition for modern day Americans tends to reflect the belief that health insurance policies should cover pretty much everything - which, in most minds, is everything outside of a copay. Think for a moment though, about car insurance. It’s illegal to drive without it, it costs all drivers a monthly fee, and people understand its in-case-of-emergency role. For instance, you expect your insurance to cover an accident - but would never expect it to cover a tire rotation. Why don’t people think of health insurance the same way?

By looking to the past, we hope to shed some light on the short and surprising history of American health insurance and how the system came to be what it is today.

While the earliest forms of health insurance came to be in 1850 and were similar to today’s workers’ comp, the first modern policies were not formed until 1930, less than 100 years ago. Before WWII, health insurance was not a fundamental right and the exchange of medical goods and services were handled much differently than they are now.



Vintage illustration of the all powerful Family Doctor by Norman Rockwell from Upjohn Advertisement 1943

In the 1940s, doctors and their God-like reputations were on the rise. The popularity of alternative medicine had slowed and American doctors were wildly influential, making more than 2.5 times the salary of an average worker. Most providers conducted business in solo practices, where patients paid out of pocket for the services they received - in other words, they paid cash for their healthcare. In hospitals, a two-tiered system emerged. Wealthier patients paid for certain luxuries, like private rooms, while other ‘charity’ patients were treated in large, open spaces.


Black and white photo of an open hospital treatment space, designed for ‘charity’ patients.

As a result of the high labor demands brought on by the war (which spanned from 1939-1945), along with the strict wage control enacted by the government, employers had to come up with other methods of compensation with which to attract workers. By this time, the War Board had declared benefits like paid time off and health insurance to be considered ‘fringe benefits.’ thereby acting as the perfect loophole to incentivize and attract talent. This marked the first time health insurance, for families and for individuals, was tied to employers. The modern definition of health insurance was born.


President Truman proposed an optional public health insurance system in 1945.


In 1945, President Truman proposed an optional public health insurance system where participants would pay monthly for coverage of all time-of-need medical expenses. The government would then cover the cost of services incurred by any physician who chose to join the system. The plan would also replace member wages lost during the time of illness or injury. While the policy was popular with Americans, it was considered socialism by a number of prominent institutions, including the AMA.

Aside: The ideal of an ‘optional’ health insurance system is an interesting one and demonstrates a lack of understanding of how insurance companies work. If the government decided to make healthcare ‘optional’, only the sick would enroll and would subsequently, over-utilize. If that were to happen, premiums in the years to come would increase to compensate. Healthy people, thinking it unfair that they are paying for services they’re not using, would un-enroll, leaving an ever sicker population to over utilize even more. This is called a rate spiral. The way health insurance companies work now is purely based on averages. They bank on people consistently paying into a large pool that is not used by everyone all at once. The word ‘optional’ is one of the differences between Truman’s plan - and Obama’s.


By 1960, health insurance enrollment had skyrocketed to more than 140 million.

By the late 1950s, labor unions were aware of the future never-ending political battle centered around healthcare and opted to pursue a less desirable but more realistic goal: employer sponsored coverage. The private sector successfully attracted the best, white-collar employees by incorporating such ‘fringe benefits,’ and the public soon followed. By 1958, the number of employees enrolled in employer-run insurance plans had skyrocketed from 20.6 million in 1940, to more than 142 million, or ~75% of American lives.

Over the next few years, issues relating to age, cost and availability of healthcare coverage surfaced. Medicare and Medicaid were signed into law in 1965 by President Lyndon B. Johnson, thereby creating publicly run insurance for the elderly and the poor.


President Lyndon B. Johnson signed Medicare and Medicaid into law in 1965.

Since 1965, there’s been a relatively large lull in healthcare historical events. Patients experienced higher costs in the ‘80s as a result of more expensive technology and the first signs of system wide cracks; which were quickly and reactively patched. Managed care plans and Health Maintenance Organizations (HMOs) were created in direct response to the high cost of healthcare, but they only served as a temporary solution to the problem.

A universal healthcare system was proposed once again in 1993 by President Bill Clinton - but it was quickly squashed by Congress. By 2010, with the recession in full swing and the unemployment rate close to 10%, nearly 50 million Americans were uninsured for at least a portion of the year. As a result, prices continued to rise. That brings history close to the present with the Affordable Care Act of 2010. This plan was enacted to increase both the affordability and quality of health insurance, decrease the number of uninsured Americans by expanding insurance coverage, and reduce healthcare costs for people and the government.

In sum, the history of employee sponsored health insurance in America stems from a temporary solution to a war-time problem. This recruitment loophole led to generations of employer-reliant healthcare when really, direct employer involvement in employee health makes little sense. Employees shouldn’t feel shackled to jobs purely because of health insurance access, just like employers shouldn’t be completely responsible for solving employee health problems. What might happen to the structure of both business and healthcare if these two entities were separated? For now, we can only speculate but time will tell as will the future success - or failure of Obamacare.


The opinions expressed in this blog are of the authors and not of PokitDok's. The posts on this blog are for information only, and are not intended to substitute for a doctor-patient or other healthcare professional-patient relationship nor do they constitute medical or healthcare advice.
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  Tags: Consumer, Enterprise

Digital Health Summer Summit: A PokitDok Recap

By Nicole Fletcher,

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Last week PokitDok was a proud sponsor of the Digital Health Summer Summit in San Francisco. Thursday kicked off with a tour of UCSF’s state-of-the-art medical facilities along with a networking happy hour and panel, while Friday featured top notch speakers, debates, panels and... massive pretzels. From the start, the event was a total -everyone-stayed-til-the-end - success and as such, we’re here to offer a brief summary.

Thursday evening took place at the Redwood Room of downtown SF’s Clift Hotel with a cocktail hour set against a digital health backdrop. PokitDok board member Lisa Suennen moderated the fittingly titled: A Doctor, a Patient and an Entrepreneur Walk Into a Bar, with cocktail in hand. And yes, a doctor, a patient AND an entrepreneur were indeed represented.

Screen Shot 2015-06-24 at 3.00.59 PM

Dr. Reed Tuckson started Friday morning off right with an inspiring keynote highlighting our need to understand the role of digital health in context and that big data is only valuable with appropriate analysis, strategy, and application. In other words, data for data’s sake is not inherently valuable.


Another topic of the morning took the form of a debate entitled: What Does it Take to Avoid the Funding Valley of Death? With countless health tech incubators and seed funders, speakers noted the gap that lies between initial funding and both the subsequent backing and entrepreneurial guidance those early companies need to ‘make it’ in the valley of death. The panel, Jack Young of dRx Capital AG, Mark Schwartz of Launchpad Digital Health, Lynne Chou of Kleiner Perkins Caufield & Byers, and moderator, David A. Shaywitz, MD, PhD, of DNAnexus, discussed what early digital health and health-tech companies need to know to get through the chasm - and not wind up dying of thirst.

The big, bad - but ultimately good - electronic health record was another topic of discussion at #DHSummer. Speakers noted the frustrations that lie with underlying EMR technology and how its hindering infrastructure impacts the efficiency and effectiveness of this, the first generation of digital health. To learn more about how PokitDok is working to fix these issues, take a look at our cross-EMR scheduling API and our newest product that launched at the event, the PokitDok Identity Management System.

Finally, Yves Béhar, Chief Creative Officer of Jawbone, was a great surprise ending to the day. His opening statement, “Great design is not a luxury”, rang through the auditorium, enforcing the idea that the healthcare industry has a responsibility to educate, inform and make simple the complexities patients face daily in any way they can.

Oh, and we were also featured on Digital Health Summit LIVE - stay tuned for the video.


The opinions expressed in this blog are of the authors and not of PokitDok's. The posts on this blog are for information only, and are not intended to substitute for a doctor-patient or other healthcare professional-patient relationship nor do they constitute medical or healthcare advice.
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  Tags: Enterprise

The Democratization of Healthcare: Defined and Demonstrated

By Nicole Fletcher,

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‘The Democratization of Healthcare’ is an emerging topic referencing the knowledge-driven power in medicine and its inevitable shift from the doctor to the patient. This fundamental transition provides the masses access to their own health information like never before and puts them more and more in the driver’s seat. This post outlines a few real-life examples that ended poorly, to say the least, as a direct result of the fragmented healthcare system, along with the future possibilities that can come with industry evolutions.

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Author and medical professional, Eric Topol speaks extensively about what this transition might mean for the future of medicine in his book, The Patient Will See You Now. He notes that MDs will no longer be considered ‘medical deities’, but rather professionals with whom patients will consult to get the proper treatment on the path of least resistance. His point is that giving patients access to their health data and the information necessary to educate themselves is key because afterall, who has a higher interest in an individual’s health, than the individual himself?


Certainly, EHRs and the digitization of health records brought on by the Affordable Care Act help doctors know more about patients in real time, but large gaps still exist in health and insurance data connectivity. Late last year, one Wisconsin woman went into cardiac arrest and was taken to a hospital in Madison. That hospital she was brought to was out of her insurance network, while another in-network hospital was only three blocks away. Now, at the age of 30, instead of planning her wedding, this woman is frantically scouring the Internet for solutions to her $50,000+ of bankruptcy-inducing medical bills (and that’s after a near-90% reduction in fees). The most troubling part is that had she been taken to the other hospital, her out of pocket cost would have been just 3%, or about $1,500.

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Similarly, in his book, Topol mentions a recent incident with his 92-year-old mother-in-law. During a routine hospital visit, she was experiencing low, but not alarmingly low, blood pressure. Further lab results showed low, but again not scarily low, sodium levels. She was then checked into the hospital - perhaps, Topol thinks, just because she happened to be there - and prescribed a high sodium intravenous infusion. The following day - yes, they advised an overnight stay - they opted to give her a subcutaneous heparin injection because they were worried an elderly woman lying in bed might be at risk for a blood clot. It just so happened that Topol was on the phone with his wife while this conversation took place. He frantically told her to stop the injection because his mother-in-law was taking another blood thinner for stroke risk reduction for her underlying atrial fibrillation arrhythmia. The heparin injection could and likely would have killed her. The mistake was avoided - but just barely and only because of Topol’s timing, medical background and personal knowledge of the patient. To cap off the experience and price tag, the patient was then required to stay another night and because of hospital back ups, was not released until later the following afternoon.


These are just two of countless tales featuring the disconnected systems and lack of information access in healthcare. Imagine what might have happened if there was some way for the on-scene EMTs to access the Wisconsin woman’s records and insurance information. They could and would have re-routed the three blocks to accommodate her network or better yet, the GPS would have automatically registered her information and mapped their route accordingly. She also would have avoided bankruptcy at the age of 30. Similarly, if Topol’s mother-in-law, or perhaps daughter, had access to her health records, she or her doctor would have made sure her recent prescription addition was updated in the system. Pre-heparin injection, the docs would have been notified of the lethal mix and avoided the mistake.

The technology to turn these statements into realities exists. From a drastic drop in medical error and certainly avoidable death, to a plummet in costs for both the patient and the hospital, these mistakes can and should be reduced drastically.

As mentioned above, EHRs will be considered standard protocol by the end of this year and that is a huge feat in itself; but that doesn’t mean your health records are your own or that everything from childhood immunizations to current prescriptions exist in the cloud - yet. We have a ways to go before cloud-based, real-time medical data access is the norm but digital health technology is rapidly advancing and aggressively fighting its way into new and existing systems alike to make the healthcare experience better for everyone.

The opinions expressed in this blog are of the authors and not of PokitDok's. The posts on this blog are for information only, and are not intended to substitute for a doctor-patient or other healthcare professional-patient relationship nor do they constitute medical or healthcare advice.
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