This transaction set is generally used as a ‘next step’ following an appointment with a physician. The doctor will submit a 278 (via a practice management system) to the member’s insurance company to notify them of the following:
Scheduled inpatient or specialty care
Patient arrival or discharge from a facility
Health services information sent to service providers
Changes to previously sent information
For instance, if a patient is experiencing eye pain, he or she would first consult a PCP- who will then determine if that patient needed specialty care outside their scope. They would then request a referral to an ophthalmologist. This referral doesn’t just happen on a piece of paper; it is an electronic request… and response! The doctor, or the doctor’s office as the case may be, is asking a health insurance company to authorize the patient to receive specialty care. Since specialty care is generally more costly than preventative care, the health insurance company reviews the patients medical history and benefit summary to determine if the specialty care is indeed required or necessary.
In addition, if that patient then goes on to require eye surgery, the ophthalmologist will submit another 278 (this time, a request) to not only authorize the procedure/specialty care, but also to notify them of date of surgery, facility and any other pertinent information. In response to the physician request, the health insurance company will return a 278 with an authorization or referral number. This number is then included in the claim submitted by the physician to the health insurance company for payment.
These transactions link consumers, providers, and health insurance companies; allowing PokitDok to create the most comprehensive and transparent view of these interactions. The 278 transaction also helps reduce administrative costs through automation, thereby enabling provider offices to be more productive and increase data accuracy. As we move towards a more efficient electronic data interchange (EDI) system with the help of PokitDok’s X12 APIs, we’ll continue to leave phone calls, faxes and paper referrals in the past in favor of a more efficient, streamlined healthcare process.
In this day of managing our lives on smart phones, tablets and online cloud storage, the idea of electronic health records (EHRs) just makes sense.
The government has been nudging physicians and medical facilities to get on board with the HITECH (Health Information Technology for Economic and Clinical Health) Act of 2009, which calls for full scale use of EHRs by the end of this year. By providing financial incentives via its meaningful use program through 2014, doctors could make money (of course countered by what they spent on the software) by implementing EHR systems. Starting this year, physicians treating Medicare patients will actually be financially penalized if they do not integrate with an EHR system.
How then do electronic medical records affect patients? For the most part, it’s positive.
Influencing the cost and utilization of care
While the cost charged to patients for care probably won’t go down as a direct result of electronic medical records, patients may pay less because they’re undergoing fewer tests. If lab and diagnostic radiology results are available through a shared system, providers will be able to find the results they need without ordering duplicate exams. That’s also more convenient for patients, who don’t need to take time for additional testing or added exposure to radiation or needle sticks.
Patients may feel the pinch with duplicate exams if they are using multiple care providers who aren’t part of the same physician network, since it’s harder to coordinate care without shared records. While this was standard operating procedure in the past, it’s less so now. Patients in today’s world want a seamless healthcare experience.
Increasing patient safety and quality of care
The goal of healthcare systems is to increase patient safety and quality of care. Electronic medical records can help accomplish this goal in a number of ways.
Decreasing Error:Illegible writing is a major cause of medical errors. Typed documentation that comes with EMR implementation means that others reading the records can quickly and easily understand the patient’s status and current treatment. Automating Reminders: EHR systems have built-in reminders to inform physicians if they haven’t reviewed lab or pathology reports, or if a patient has not shown up for a follow-up visit. This allows for increased administrative efficiency and a decreased chance that health findings will be delayed due to a lack of patient follow-up. Patients fail to show up for appointments 5-50% of the time, according to a New York Times article, due to a wide variety of factors.
Double Checking Dosage: When medications are prescribed, the EHR software checks on behalf of the physician for potentially harmful drug interactions and confirms that the dosage prescribed is in range.
Decision Support:A feature of electronic health records, decision support guides doctors through a number of processes for certain medical conditions to ensure they're considering all treatment options available. These might include clinical guidelines, reference information related to the patient's condition, or any number of other factors.
Patient Accessibility:By signing up for a patient portal account, patients can personally access their medical records and results more quickly, giving them the opportunity to potentially catch an issue that might otherwise have fallen through the cracks. For example, if a pathology report suggests further follow-up due to an incidental finding, the patient can take action and ask the doctor about it, even if the doctor hasn’t brought it up to the patient.
Electronic Prescriptions:E-prescribing is not only convenient for patients (the filled prescription is waiting when you arrive), but since it’s typed, there’s less chance for medical error. In fact, a study published in the Journal of General Internal Medicine found that by e-prescribing medications in community-based practices, error rates decreased sevenfold over paper prescriptions. Using e-prescriptions can also decrease fraudulent use of paper prescription pads.
The downside to EHRs
On the doctors’ side, not everyone is happy about the use of EHR systems. First, they aren’t cheap. According to a 2013 Medical Economics survey, 77% of the largest practices said they spent upwards of $200,000 on their software systems. These offices said that electronic records haven’t made their practices more efficient, but rather, the expense of acquiring, implementing and maintaining the system, plus training staff, has increased the provider burden. In addition, doctors are lowering their personal efficiency as they take extra time to input patient information into the online record, instead of their former method of jotting down notes on a paper chart.
Similarly, some doctors in the Medical Economics survey didn’t feel that patient care and physician/hospital coordination had seen the higher quality results they expected by this point. As with any major system change, this shift in business model is a work-in-progress that requires ongoing investment and assessment to yield long term return for everyone. It will be interesting to watch the EHR space - and the players in it (GE recently announced at HiMSS that they will no longer be pursuing the business) as they continue to grow and evolve with time. Undeniably, EHRs are an inevitable staple in the future of health; it’s really just a matter of building the foundation(which is where we come in) to make massive cost saving and increased efficiency possible.
In Part One of the Recommendation System Blog series, we looked at the underpinnings of an online retailer’s recommendation system using a graph database in addition to an online recommendation system of movie titles. Our visualizations however, only introduced three actors into the system; how might the problem change if we had thousands or millions of other data points to consider? And how does this system have the potential to connect you with better healthcare? In this post, we will explore how to use graph traversals and ranking algorithms to provide more complex product, or in our case healthcare provider recommendations.
Let us revisit a similar example as before, but this time, let’s look at recommendations from a healthcare system standpoint.
Rank by Most Views: GroupCount()
In this example, we want to rank providers by how many people who are similar to you, viewed the same provider you are viewing. One of the most basic ways to quickly calculate a recommendation of providers within this type of system is to do a quick group count on the set of providers who were viewed by people like you (we’ll get to how we can do that later).
In the image above, we take a set of people (shown on the left as vertices of a graph) and look at the entire set of doctors they have viewed. In this system, GroupCount() sorts the providers according to who was viewed the most. “Dr. E” is the highest recommended provider as he was viewed by 5 separate people in the system, while “Dr. B” is the least recommended provider as he was viewed by only one.
At PokitDok, we use Gremthon, a Python implementation of gremlin, to perform graph traversals. Gremthon was created by PokitDok’s Engineer #1, Brian Corbin; you can check out Gremthon on GitHub: https://github.com/pokitdok/gremlin-python
The Gremthon traversal to apply a group count on the providers viewed by consumers who also viewed a certain doctor would be:
What about this notion of finding people “like you”?
While there is a whole field of mathematics which examines different ways to rank the above list, let us look at applying filters for improved personal recommendations. First, the input set into this ranking system, shown in the animated gif at the top of this page, was selected according to the one doctor that you viewed and all other consumers who also viewed that doctor. If you’re willing and opt in, we can easily provide a much more personalized recommendation with a few more pieces of information, like your gender and age. Then, once you view a doctor, we can provide a more personal recommendation which ranks the doctors according to those viewed by people with similar age and gender:
With Gremthon, the graph traversal which provides recommendations from people who are either 5 years older or younger than you would be:
We can also apply filters in other areas of this traversal. For example we can apply a filter to only recommend providers that match in specialty with the provider you first viewed. When we, as patients, look for personalized health recommendations, we prefer to visit doctors who have been seen by people we know. Right now, the only way to do this is to ask around or post to social media for recommendations from friends and family. With this technology however, PokitDok, like the movie recommendation system from our last post, has the ability to recommend doctors to you based on the doctors your social media friends have previously visited (if they’re willing of course). The recommendation now looks like this:
Now, because we are HIPAA compliant, we’ll never tell you which friends went to which doctor (and we won’t know either). What we can do is give you provider recommendations based on the doctors visited by your online friend group at large. Now, you can hold off on that Facebook post for a general practitioner for that rash of yours…and instead find a recommendation based on your trusted digital network of ‘people like you’. We can give you the personalized recommendation you need... and save you some embarrassment.
Today at the 2015 HiMSS Conference in Chicago, we announced the release of our newest product - PokitDok's Health Credit Outcome (HCO). We envision this revolutionary tool as playing an integral role in the journey toward more affordable healthcare. Designed by our expert team of data scientists, the HCO product is a mathematically driven, probabilistic risk model that uses patient health data, claims and financial history to determine the financial 'lendability' of a patient. This new product addition has the potential to improve financial outcomes for lending institutions, payment solutions, health systems and medical practitioners, freeing up resources to support payment options for care.
How does it work, you ask? With the patient’s consent and the product's output, organizations can make informed lending options available to help finance expensive, non-acute procedures that often fall below the deductible threshold. Examples of such procedures may include: orthopedic surgery, imagery, or cosmetic treatments.
PokitBlog is starting a new trend called Technical Tuesdays #techtuesdays (Throwback Thursdays are so yesterday). In honor of our amazing technical and data science teams along with the insightful content they have to share, we're kicking this one off from our own Denise Gosnell, PhD, engineer and data scientist extraordinaire, as recently seen quoted in USA Today. Here's Part 1 of What it Really Means to be 'Somebody Like You'. And, don't forget to join us again next Tuesday for Part 2 of this post on #techtuesdays.
The other day, I hopped on a favorite online media streaming service and started looking for something to watch. As I clicked from movie to movie, I noticed something – the recommendations were getting smarter with each click. As a frequent online shopper, I've become accustomed to the product recommendation pane that typically appears at the bottom of a page, but this particular one intrigued me. As I browsed around the site, the recommendation section changed from “people who viewed this item also viewed...” to “people who viewed this product ultimately watched..." and the suggestions were right on.
My data-centric mind turning, I wondered, how are they doing this?
Being my nerdy self, I took to the drawing board to get a general idea of the logic behind this morphing algorithm. I wondered if they were using a graph database to make these suggestions. And if so, how would that work?
Let’s look at what is happening here from a graph perspective. [mental check: have you gone through our intro to graph theory? It’s a quick one - head over here and then come back.]
Using graph theory, this is what your interaction graph looks like when you start browsing movies on an online streaming service:
The power of an online streaming recommendation system comes from the thousands of people who have previously looked at the same movies you are currently looking at. Consider two customers who looked at the same movies as you did, and let’s visualize their interaction graph:
In this example, your online browsing experience is directly influenced by the sequence of movies previously selected by people like you [in this case, our Alice and Bob]. The data trails from previous customers give us the golden list: the movies watched by other people who also clicked on the movies you are interested in. In real time, the whole interaction comes together like this:
As you click through your favorite movies, the power of graph databases enables dynamic and online processing of your data to create real time recommendations specifically for you.
While this is just an initial glimpse into the wide world of recommendation systems, it is natural to begin to think of ways to improve the system. For example, if we throw a few thousand more people like you into the mix, the more data we have and thus, the more personalized the recommendations become. We can choose to include varying windows of historical information into the system; the possibilities and techniques are endless.
Why would we, PokitDok, be educating you about graph systems set against a movie-watching backdrop? To find out, consider this a scene setter and stay tuned for a health inspired part two where we will demonstrate some our latest breakthroughs in Graph Theory technology along with what it could mean for the future of healthcare.
HxRefactored, going on now in Boston, is an event designed to highlight the work being done to improve healthcare through technology and design. In addition to exhibiting and attending this year, PokitDok CEO, Lisa Maki will be participating in two panels. Here's an overview of what to expect.
Follow us on Twitter for live updates and and be sure to attend! We're also thrilled to have been named winner of the first ever Design For Health Awards for Best Consumer Facing Design! In celebration of this honor, we thought we'd give you a deeper look into our design approach.
If we've learned nothing else, it's that the customer journey is inherently better if it's simple - with as little friction as possible. Our creative team works diligently to ensure our digital storefront is user-friendly and clear -and our most recent website update reflects just that. From the patient experience shopping, booking and paying for healthcare in our consumer marketplace, to the way our API and enterprise clients interact with our website, our goal is simply to guide our users through their journeys with as few roadblocks as possible.
Below is a video we put together explaining the human-centered design approach we took for our marketplace site. Enjoy and stay tuned for a recap of HxRefactored!
The PokitDok team is very proud to announce the release of our brand new Medical Procedural Code API. With this, health systems, healthcare technology providers, and Electronic Medical Record (EMR) software providers now have dynamic access to information that streamlines billing and reimbursement.
Essentially, we’re able to unlock data sourced from the American Medical Association’s (AMA) proprietary CPT® code set (current procedural terminology) and make it available through an Application Programming Interface (API) in real time based on our Health Level-7 (HL7) compliant PokitDok Platform. It is designed to impro ve care coordination and general admin in a way that can significantly reduce health IT costs and streamline operations in a huge way.
To break this down even further, every medical, surgical, and diagnostic service has a numerical reference, or CPT code, to ensure uniformity on the back end. Established and published by the American Medical Association, CPT codes help providers track and pursue reimbursement for the aforementioned medical services with insurance companies.
One of our advisors, Paul T. Sheils, former CEO of Medscape.com, MayoClinic.com, Aetna Health Information Solutions and DocSite said, “This is a milestone for the healthcare industry as it transitions to cloud-based, real-time, value-based service models.”
PokitDok is now the first to host this kind of data in the secure, HIPAA compliant cloud.
Developers can take a stroll through our Medical Procedural Code Documentation HERE.
At PokitDok, we consistently work to make ourselves, our company, and the products we create - better. We live, breathe and code by the mantra: design, deploy, iterate, repeat. It’s to that end that we have recently re-imagined our website to better reflect the needs of health businesses and developers in the digital health space.
Our homepage, PokitDok.com, will now act as a gateway to access every part of our business, offering a deeper dive into our products and solutions - from seamless X12 insurance connectivity and scheduling, to e-commerce for healthcare and beyond.
Our marketplace website, where consumers can shop, book and pay for healthcare services, is easily accessible from the home page -
- and from the expanding menu located at the top right. Providers looking to claim their practice, to login, or for more information, can similarly click on the designated link in the expanding menu. These links are also conveniently located in the footer section of every page on the website.
We invite you to check out our updated look and stay tuned for our next blog post - featuring a deeper dive into our design process.