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Do Vendor Business Models Discourage EMR Innovation?

Posted on April 4, 2017 I Written By

Anne Zieger is veteran healthcare consultant and analyst with 20 years of industry experience. Zieger formerly served as editor-in-chief of FierceHealthcare.com and her commentaries have appeared in dozens of international business publications, including Forbes, Business Week and Information Week. She has also contributed content to hundreds of healthcare and health IT organizations, including several Fortune 500 companies. Contact her at @ziegerhealth on Twitter or visit her site at Zieger Healthcare.

Despite the ever-mounting levels of physician frustration, in some ways EMRs have changed little from their mass-market rollout. EMR interfaces are still counterintuitive, data sharing possibilities are limited, important information still lives in isolated silos and endless data entry is the rule rather than exception.

In theory, we could do better if we had a reasonable vision of what should come next. For example, I was intrigued by ideas proposed by Dr. Robert Rowley of Flow Health. He describes a model in which EMRs draw on a single, external data source which isn’t confined to any one organization. Providers would access, download and add data through a modern API.  Given such fluid access to data, providers would be able to create custom front-ends based on a collection of apps, rather than rely on a single vendor-created interface.

Unfortunately, EMR vendors are unlikely to take on a completely different approach like Rowley’s, for reasons inherent to their business model. After all, they have little reason to develop new, innovative EMRs which rely on a different data architecture. Not only that, the costs associated with developing and rolling out a completely new EMR model would probably be very high. And what company would take that chance when their existing “big iron” approach still sells?

Not only that, EMR vendors would risk alienating their customers if they stray too far off the ranch. While an innovative new platform might be attractive to some buyers, it might also be incompatible with their existing technology. And it would probably require both providers and vendors to reinvent workflows and transform their technical architecture.

Meanwhile, in addition to finding a way to pay for the technology, providers would have to figure out how to integrate their existing data into the new system, integrate the platform with its existing infrastructure, retrain the staff and clinicians and cope with reduced productivity for at least a year or two. And what would become of their big data analytics code? Their decision support modules? Even data entry could be a completely new game.

Smaller medical practices could be pushed into bankruptcy if they have to invest in yet another system. Large practices, hospitals and health systems might be able to afford the initial investment and systems integration, but the project would be long and painful. Unless they were extremely confident that it would pay off, they probably wouldn’t risk giving a revolutionary solution a try.

All that being said, there are forces in play which might push vendors to innovate more, and give providers a very strong incentive to try a new approach to patient data management. In particular, the need to improve care coordination and increase patient engagement – driven by the emergence of value-based care – is putting providers under intense pressure. If a new platform could measurably improve their odds of surviving this transition, they might be forced to adopt it.

Right now, providers who can afford to do so are buying freestanding care coordination and patient engagement tools, then integrating them into their existing EMRs. I can certainly see the benefit of doing so, as it brings important functions on board without throwing out the baby with the bathwater. And these organizations aren’t forced to rethink their fundamental technical strategy.

But the truth is, this model is unlikely to serve their needs over the long term. Because it relies on existing technology, welding new functions onto old, clinicians are still forced to grappled with kludgy technology. What’s more, these solutions add another layer to a very shaky pile of cards. And it’s hard to imagine that they’re going to support data interoperability, either.

Ultimately, the healthcare industry is going to be bogged down with short-term concerns until providers and vendors come together and develop a completely new approach to health data. To succeed at changing their health IT platform, they’ll have to rethink the very definition of key issues like ease of use and free data access, care coordination, patient engagement and improved documentation.

I believe that’s going to happen, at some point, perhaps when doctors storm the executive offices of their organization with torches and pitchforks. But I truly hope providers and vendors introduce more effective data management tools than today’s EMRs without getting to that point.

Will Health Analytics Be Regulated?

Posted on March 29, 2017 I Written By

John Lynn is the Founder of the HealthcareScene.com blog network which currently consists of 10 blogs containing over 8000 articles with John having written over 4000 of the articles himself. These EMR and Healthcare IT related articles have been viewed over 16 million times. John also manages Healthcare IT Central and Healthcare IT Today, the leading career Health IT job board and blog. John is co-founder of InfluentialNetworks.com and Physia.com. John is highly involved in social media, and in addition to his blogs can also be found on Twitter: @techguy and @ehrandhit and LinkedIn.

We’re seeing healthcare analytics pop up everywhere. There are a lot of definitions of healthcare analytics out there, but I like the idea that healthcare analytics makes healthcare data useful. Does that sound like something we desperately need in healthcare?

I do think that there are lots of kinds of healthcare anayltics. One kind is an analytic that determines how you’re doing financially. I actually think that most organizations are doing pretty well with these kind of analytics. Is there more to do? Sure, but there are some pretty advanced healthcare financial analytics.

The next generation of healthcare analytics is going focus on improving care. We’re talking about real time analytics that analyze a patient’s health and wellness. We’re talking about analytics that help determine a course of treatment for a patient with a complex clinical history. We’re talking about analytics that influence the care a patient receives.

When I describe healthcare analytics in this way, does this sound any different than a medical device? It doesn’t to me when it comes to the risks associated with what the analytics could do for both good and bad. If that’s the case, should health analytics be regulated?

The answer is pretty clear to me that they should be regulated in some form or fashion. However, that doesn’t mean I think they will. There are a couple of major reasons why. First, the industry is unlikely to allow it to happen. Second, the government isn’t built to regulate healthcare analytics.

The first is pretty straight forward. There’s a reason why most software hasn’t been regulated yet. The second idea is a little more challenging. On face value, you’d think that the FDA could just start regulating healthcare analytics the way they do medical devices. The problem is that they are very different things to regulate. What’s required to regulate a medical device and for someone to oversee that regulation is very different than what’s needed to regulate a healthcare analytic. You can’t just flip a switch to turn on software and data regulation. It’s very different than medical device regulation.

While I certainly think there’s an argument to be made that healthcare analytics could and maybe even should be regulated, I don’t think that it will be regulated.

What do you think? Will we see healthcare analytics regulation?

Selecting the Right AI Partner in Healthcare Requires a Human Network

Posted on March 1, 2017 I Written By

Janae builds inbound social media sales and marketing plans for healthcare IT companies. Healthcare as a human right. Physician Suicide Loss Survivor. twitter: @coherencemed

Artificial Intelligence, or AI for short, does not always equate to high intelligence and this can have a high cost for healthcare systems. Navigating the intersection of AI and healthcare requires more than clinical operations expertise; it requires advanced knowledge in business motivation, partnerships, legal considerations, and ethics.

Learning to Dance at HIMSS17

This year I had the pleasure of attending a meetup for people interested in and working with AI for healthcare at the Healthcare Information and Management Systems Society (HIMSS) annual meeting in Orlando, Florida. At the beginning of the meetup Wen Dombrowski, MD, asked everyone to stand up and participate in a partner led movement activity. Not your average trust fall, this was designed to teach about AI and machine leaning while pushing most of us out of our comfort zones and to spark participants to realize AI-related lessons. One partner led and the other partner followed their actions.

Dedicated computer scientists, business professionals, and proud data geeks tested their dancing skills. My partner quit when it was my turn to lead the movement. About half of the participants avoided eye contact and reluctantly shuffled their feet while they half nursed their coffee. But however awkward, half the participants felt the activity was a creative way to get us thinking about what it takes for machines to ‘learn’. Notably Daniel Rothman of MyMee had some great dance moves.

I found both the varying feedback and equally varying willingness to participate interesting. One of the participants said the activity was a “waste of time.” They must have come from the half of the room that didn’t follow mirroring instructions. I wonder if I could gather data about what code languages were the specialty of those most resistant. Were the Python coders bad at dancing? I hope not. My professional training is actually as a licensed foreign language teacher so I immediately corroborated the instructional design effectiveness of starting with a movement activity.

There is evidence that participating in physical activity preceding learning makes learners more receptive and allows them to retain the experience longer. “Physical activity breaks throughout the day can improve both student behavior and learning (Trost 2007)” (Reilly, Buskist, and Gross, 2012). I assumed that knowledge of movement and learning capacity was common knowledge. Many of the instructional design comments Dr. Dombrowski received while helpful, revealed participants’ lack of knowledge about teaching and cognitive learning theory.

I could have used some help at the onset in choosing a dance partner that would have matched and anticipated my every move. The same goes for healthcare organizations and their AI solutions.  While they may be a highly respected institution employing some of the most brilliant medical minds, they need to also become or find a skilled matchmaker to bring the right AI partner (our mix of partners) to the dance floor.

AI’s Slow Rise from Publicity to Potential

Artificial Intelligence has experienced a difficult and flashy transition into the medical field. For example, AI computing has been used to establish consensus with imaging for radiologists. While these tools have helped reduce false positives for breast cancer patients, errors remain and not every company entering AI has equal computing abilities. The battle cry that suggested physicians be replaced with robots seems to have slowed robots. While AI is gaining steam, the potential is still catching up with the publicity.

Even if an AI company has stellar computing ability, buyers should question if they also have the same design for outcome. Are they dedicated to protecting your patients and providing better outcomes, or simply making as much profit as possible? Human FTE budgets have been replaced by computing AI costs, and in some instances at the expense of patient and data security.  When I was asking CIOs and smaller companies about their experiences, many were reluctant to criticize a company they had a non-disclosure agreement with.

Learning From the IBM Watson and MD Anderson Breakup

During HIMSS week, the announcement that the MD Anderson and IBM Watson dance party was put on hold was called a setback for AI in medicine by Forbes columnist Matthew Herper. In addition, a scathing report detailing the procurement process written by the University of Texas System Administration Audit System reads more like a contest for the highest consulting fees. This suggests to me that perhaps one of the biggest threats to patient data security when it comes to AI is a corporation’s need to profit from the data.

Moving on, reports of the MD Anderson breakup also mention mismanagement including failing to integrate data from the hospital’s Epic migration. Epic is interoperable with Watson but in this case integration of new data was included in Price Waterhouse Cooper’s scope of work. If poor implementation stopped the project, should a technology partner be punished? Here is an excerpt from the IBM statement on the failed partnership:

 “The recent report regarding this relationship, published by the University of Texas System Administration (“Special Review of Procurement Procedures Related to the M.D. Anderson Cancer Center Oncology Expert Advisor Project”), assessed procurement practices. The report did not assess the value or functionality of the OEA system. As stated in the report’s executive summary, “results stated herein are based on documented procurement activities and recollections by staff, and should not be interpreted as an opinion on the scientific basis or functional capabilities of the system in its current state.”

With non-disclosure agreements and ongoing lawsuits in place, it’s unclear whether this recent example will and should impact future decisions about AI healthcare partners. With multiple companies and interests represented no one wants to be the fall guy when a project fails or has ethical breaches of trust. The consulting firm of Price Waterhouse Coopers owned many of the portions of the project that failed as well as many of the questionable procurement portions.

I spoke with Christine Douglas part of IBM Watson’s communications team and her comments about the early adoption of AI were interesting. She said “you have to train the system. There’s a very big difference between the Watson that’s available commercially today and what was available with MD Anderson in 2012.”  Of course that goes for any machine learning solution large or small as the longer the models have to ‘learn’ the better or more accurate the outcome should be.

Large project success and potential project failure have shown that not all AI is created equally, and not every business aspect of a partnership is dedicated to publicly shared goals. I’ve seen similar proposals from big data computing companies inviting research centers to pay for use of AI computing that also allowed the computing partner to lease the patient data used to other parties for things like clinical trials. How’s that for patient privacy! For the same cost, that research center could put an entire team of developers through graduate school at Stanford or MIT. By the way, I’m completely available for that team! I would love to study coding more than I do now.

Finding a Trusted Partner

So what can healthcare organizations and AI partners learn from this experience? They should ask themselves what their data is being used for. Look at the complaint in the MD Anderson report stating that procurement was questionable. While competitive bidding or outside consulting can help, in this case it appears that it crippled the project. The layers of business fees and how they were paid kept the project from moving forward.

Profiting from patient data is the part of AI no one seems willing to discuss. Maybe an AI system is being used to determine how high fees need to be to obtain board approval for hospital networks.

Healthcare organizations need to ask the tough questions before selecting any AI solution. Building a human network of trusted experts with no financial stake and speaking to competitors about AI proposals as well as personal learning is important for CMIOs, CIOs and healthcare security professionals. Competitive analysis of industry partners and coding classes has become a necessary part of healthcare professionals. Trust is imperative and will have a direct impact on patient outcomes and healthcare organization costs. Meetups like the networking event at HIMSS allow professionals to expand their community and add more data points, gathered through real human interaction, to their evaluation of and AI solutions for healthcare. Nardo Manaloto discussed the meetup and how the group could move forward on Linkedin you can join the conversation.

Not everyone in artificial intelligence and healthcare is able to evaluate the relative intelligence and effectiveness of machine learning. If your organization is struggling, find someone who can help, but be cognizant of the value of the consulting fees they’ll charge along the way.

Back to the dancing. Artificial does not equal high intelligence. Not everyone involved in our movement activity realized it was actually increasing our cognitive ability. Even those who quit, like my partner did, may have learned to dance just a little bit better.

 

Resources

California Department of Education. 2002. Physical fitness testing and SAT9 Retrieved May 20, 2003, from www.cde.ca.gov/statetests/pe/pe.html

Carter, A. 1998. Mapping the mind, Berkeley: University of California Press.

Czerner, T. B. 2001. What makes you tick: The brain in plain English, New York: John Wiley.

Dennison, P. E. and Dennison, G. E. 1998. Brain gym, Ventura, CA: Edu-Kinesthetics.

Dienstbier, R. 1989. Periodic adrenalin arousal boosts health, coping. New Sense Bulletin, : 14.9A

Dwyer, T., Sallis, J. F., Blizzard, L., Lazarus, R. and Dean, K. 2001. Relation of academic performance to physical activity and fitness in children. Pediatric Exercise Science, 13: 225–237. [CrossRef], [Web of Science ®]

Gavin, J. 1992. The exercise habit, Champaign, IL: Human Kinetics.

Hannaford, C. 1995. Smart moves: Why learning is not all in your head, Arlington, VA: Great Ocean.

Howard, P. J. 2000. The owner’s manual for the brain, Austin, TX: Bard.

Jarvik, E. 1998. Young and sleepless. Deseret News, July 27: C1

Jensen, E. 1998. Teaching with the brain in mind, Alexandria, VA: Association for Supervision and Curriculum Development.

Jensen, E. 2000a. Brain-based learning, San Diego: The Brain Store.

Reilly, E., Buskist, C., & Gross, M. K. (2012). Movement in the Classroom: Boosting Brain Power, Fighting Obesity. Kappa Delta Pi Record, 48(2), 62-66. doi:10.1080/00228958.2012.680365.

The Healthcare AI Future, From Google’s DeepMind

Posted on February 22, 2017 I Written By

Anne Zieger is veteran healthcare consultant and analyst with 20 years of industry experience. Zieger formerly served as editor-in-chief of FierceHealthcare.com and her commentaries have appeared in dozens of international business publications, including Forbes, Business Week and Information Week. She has also contributed content to hundreds of healthcare and health IT organizations, including several Fortune 500 companies. Contact her at @ziegerhealth on Twitter or visit her site at Zieger Healthcare.

While much of its promise is still emerging, it’s hard to argue that AI has arrived in the health IT world. As I’ve written in a previous article, AI can already be used to mine EMR data in a sophisticated way, at least if you understand its limitations. It also seems poised to help providers predict the incidence and progress of diseases like congestive heart failure. And of course, there are scores of companies working on other AI-based healthcare projects. It’s all heady stuff.

Given AI’s potential, I was excited – though not surprised – to see that world-spanning Google has a dog in this fight. Google, which acquired British AI firm DeepMind Technologies a few years ago, is working on its own AI-based healthcare solutions. And while there’s no assurance that DeepMind knows things that its competitors don’t, its status as part of the world’s biggest data collector certainly comes with some advantages.

According to the New Scientist, DeepMind has begun working with the Royal Free London NHS Foundation Trust, which oversees three hospitals. DeepMind has announced a five-year agreement with the trust, in which it will give it access to patient data. The Google-owned tech firm is using that data to develop and roll out its healthcare app, which is called Streams.

Streams is designed to help providers kick out alerts about a patient’s condition to the cellphone used by the doctor or nurse working with them, in the form of a news notification. At the outset, Streams will be used to find patients at risk of kidney problems, but over the term of the five-year agreement, the developers are likely to add other functions to the app, such as patient care coordination and detection of blood poisoning.

Streams will deliver its news to iPhones via push notifications, reminders or alerts. At present, given its focus on acute kidney injury, it will focus on processing information from key metrics like blood tests, patient observations and histories, then shoot a notice about any anomalies it finds to a clinician.

This is all part of an ongoing success story for DeepMind, which made quite a splash in 2016. For example, last year its AlphaGo program actually beat the world champion at Go, a 2,500-year-old strategy game invented in China which is still played today. DeepMind also achieved what it terms “the world’s most life-like speech synthesis” by creating raw waveforms. And that’s just a couple of examples of its prowess.

Oh, and did I mention – in an achievement that puts it in the “super-smart kid you love to hate” category – that DeepMind has seen three papers appear in prestigious journal Nature in less than two years? It’s nothing you wouldn’t expect from the brilliant minds at Google, which can afford the world’s biggest talents. But it’s still a bit intimidating.

In any event, if you haven’t heard of the company yet (and I admit I hadn’t) I’m confident you will soon. While the DeepMind team isn’t the only group of geniuses working on AI in healthcare, it can’t help but benefit immensely from being part of Google, which has not only unimaginable data sources but world-beating computing power at hand. If it can be done, they’re going to do it.

Switching Out EMRs For Broad-Based HIT Platforms

Posted on February 8, 2017 I Written By

Anne Zieger is veteran healthcare consultant and analyst with 20 years of industry experience. Zieger formerly served as editor-in-chief of FierceHealthcare.com and her commentaries have appeared in dozens of international business publications, including Forbes, Business Week and Information Week. She has also contributed content to hundreds of healthcare and health IT organizations, including several Fortune 500 companies. Contact her at @ziegerhealth on Twitter or visit her site at Zieger Healthcare.

I’ve always enjoyed reading HISTalk, and today was no exception. This time, I came across a piece by a vendor-affiliated physician arguing that it’s time for providers to shift from isolated EMRs to broader, componentized health IT platforms. The piece, by Excelicare chief medical officer Toby Samo, MD, clearly serves his employer’s interests, but I still found the points he made to be worth discussing.

In his column, he notes that broad technical platforms, like those managed by Uber and Airbnb, have played a unique role in the industries they serve. And he contends that healthcare players would benefit from this approach. He envisions a kind of exchange allowing the use of multiple components by varied healthcare organizations, which could bring new relationships and possibilities.

“A platform is not just a technology,” he writes, “but also ‘a new business model that uses technology to connect people, organizations and resources in an interactive ecosystem.’”

He offers a long list of characteristics such a platform might have, including that it:

* Relies on apps and modules which can be reused to support varied projects and workflows
* Allows users to access workflows on smartphones and tablets as well as traditional PCs
* Presents the results of big data analytics processes in an accessible manner
* Includes an engine which allows clients to change workflows easily
* Lets users with proper security authorization to change templates and workflows on the fly
* Helps users identify, prioritize and address tasks
* Offers access to high-end clinical decision support tools, including artificial intelligence
* Provides a clean, easy-to-use interface validated by user experience experts

Now, the idea of shared, component-friendly platforms is not new. One example comes from the Healthcare Services Platform Consortium, which as of last August was working on a services-oriented architecture platform which will support a marketplace for interoperable healthcare applications. The HSPC offering will allow multiple providers to deliver different parts of a solution set rather than each having to develop their own complete solution. This is just one of what seem like scores of similar initiatives.

Excelicare, for its part, offers a cloud-based platform housing a clinical data repository. The company says its platform lets providers construct a patient-specific longitudinal health record on the fly by mining existing EHRs claims repositories and other data. This certainly seems like an interesting idea.

In all candor, my instinct is that these platforms need to be created by a neutral third party – such as travel information network SABRE – rather than connecting providers via a proprietary platform created by companies like Excelicare. Admittedly, I don’t have a deep understanding of Excelicare’s technology works, or how open its platform is, but I doubt it would be viable financially if it didn’t attempt to lock providers into its proprietary technology.

On the other hand, with no one interoperability approach having gained an unbeatable lead, one never knows what’s possible. Kudos to Samo and his colleagues for making an effort to advance the conversation around data sharing and collaboration.

A Look At The Role Of EMRs In Personalized Medicine

Posted on January 19, 2017 I Written By

Anne Zieger is veteran healthcare consultant and analyst with 20 years of industry experience. Zieger formerly served as editor-in-chief of FierceHealthcare.com and her commentaries have appeared in dozens of international business publications, including Forbes, Business Week and Information Week. She has also contributed content to hundreds of healthcare and health IT organizations, including several Fortune 500 companies. Contact her at @ziegerhealth on Twitter or visit her site at Zieger Healthcare.

NPR recently published an interesting piece on how some researchers are developing ways to leverage day-to-day medical information as a means of personalizing medical care. This is obviously an important approach – whether or not you take the full-on big data approach drug researchers are – and I found the case studies it cited to be quite interesting.

In one instance cited by the article, researchers at Kaiser Permanente have begun pulling together a dashboard, driven by condition types, which both pulls together past data and provides real-life context.

“Patients are always saying, don’t just give me the averages, tell me what happened to others who look like me and made the same treatment decisions I did,” said Dr. Tracy Lieu, head of Kaiser’s research division, who spoke to NPR. “And tell me not only did they live or die, but tell me what their quality of life was about.”

Dr. Lieu and her fellow researchers can search a database on a term like “pancreatic cancer” and pull up data not only from an individual patient, but also broad information on other patients who were diagnosed with the condition. According to NPR, the search function also lets them sort data by cancer type, stage, patient age and treatment options, which helps researchers like Lieu spot trends and compare outcomes.

Kaiser has also supplemented the traditional clinical data with the results of a nine-question survey, which patients routinely fill out, looking at their perception of their health and emotional status. As the article notes, the ideal situation would be if patients were comfortable filling out longer surveys on a routine basis, but the information Kaiser already collects offers at least some context on how patients reacted to specific treatments, which might help future patients know what to expect from their care.

Another approach cited in the article has been implemented by Geisinger Health System, which is adding genetic data to EMRs. Geisinger has already compiled 50,000 genetic scans, and has set a current goal of 125,000 scans.

According to Dr. David Ledbetter, Geisinger’s chief scientific officer, the project has implications for current patients. “Even though this is primarily a research project, we’re identifying genomic variants that are actually important to people’s health and healthcare today,” he told the broadcaster.

Geisinger is using a form of genetic testing known as exome sequencing, which currently costs a few thousand dollars per patient. But prices for such tests are falling so quickly that they could hit the $300 level this year, which would make it more likely that patients would be willing to pay for their own tests to research their genetic proclivities, which in turn would help enrich databases like Geisinger’s.

“We think as the cost comes down it will be possible to sequence all of the genes of individual patients, store that information in the electronic medical record, and it will guide and individualize and optimize patient care,” Ledbetter told NPR.

As the story points out, we might be getting ahead of ourselves if we all got analyses of our genetic information, as doctors don’t know how to interpret many of the results. But it’s good to see institutions like these getting prepared, and making use of what information they do have in the mean time.

E-Patient Update: The Smart Medication Management Portal

Posted on December 16, 2016 I Written By

Anne Zieger is veteran healthcare consultant and analyst with 20 years of industry experience. Zieger formerly served as editor-in-chief of FierceHealthcare.com and her commentaries have appeared in dozens of international business publications, including Forbes, Business Week and Information Week. She has also contributed content to hundreds of healthcare and health IT organizations, including several Fortune 500 companies. Contact her at @ziegerhealth on Twitter or visit her site at Zieger Healthcare.

As I work to stay on top of my mix of chronic conditions, one thing that stands out to me is that providers expect me to do most of my own medication tracking and management. What I mean by this is that their relationship to my med regimen is fairly static, with important pieces of the puzzle shared between multiple providers. Ultimately, there’s little coordination between prescribers unless I make it happen.

I’ve actually had to warn doctors about interactions between my medications, even when those interactions are fairly well-known and just a Google search away online. And in other cases, specialists have only asked about medications relevant to their treatment plan and gotten impatient when I tried to provide the entire list of prescriptions.

Sure, my primary care provider has collected the complete list of my meds, and even gets a updates when I’ve been prescribed a new drug elsewhere. But given the complexity of my medical needs, I would prefer to talk with her about how all of the various medications are working for me and why I need them, something that rarely if ever fits into our short meeting time.

Regardless of who’s responsible, this is a huge problem. Patients like me are being sent with some general drug information, a pat on the back, and if we experience side effects or are taking meds incorrectly we may not even know it.

So at this point you’re thinking, “Okay, genius, what would YOU do differently?” And that’s a fair question. So here’s what I’d like to see happen when doctors prescribe medications.

First, let’s skip over the issue of what it might take to integrate medication records across all providers’s HIT systems. Instead, let’s create a portal — aggregating all the medication records for all the pharmacies in a given ZIP Code — and allow anyone with a valid provider number and password to log in and review it.  The same site could run basic analytics examining interactions between drugs from all providers. (By the way, I’m familiar with Surescripts, which is addressing some of these gaps, but I’m envisioning a non-proprietary shared resource.)

Rather than serving as strictly a database, the site would include a rules engine which runs predictive analyses on what a patient’s next steps should be, given their entire regimen, then generate recommendations specific to that patient. If any of these were particularly important, the recommendations could be pushed to the provider (or if administrative, to staff members) by email or text.

These recommendations, which could range from reminding the patient to refill a critical drug to warning the clinician if an outside prescription interacts with their existing regimen. Smart analytics tools might even be able to predict whether a patient is doing well or poorly by what drugs have been added to their regimen, given the drug family and dosage.

Of course, these functions should ultimately be integrated into the physicians’ EMRs, but at first, hospitals and clinics could start by creating an interface to the portal and linking it to their EMR. Eventually, if this approach worked, one would hope that EMR vendors would start to integrate such capabilities into their platform.

Now I imagine there could be holes in these ideas and I realize how challenging it is to get disparate health systems and providers to work together. But what I do know is that patients like myself get far too little guidance on how to manage meds effectively, when to complain about problems and how to best advocate for ourselves when doctors whip out the prescription pad. And while I don’t think my overworked PCP can solve the problem on her own, I believe it may be possible to improve med management outcomes using smart automation.

Bottom line, I doubt anything will change here unless we create an HIT solution to the problem. After all, given how little time they have already, I don’t see clinicians spending a lot more time on meds. Until then, I’m stuck relying on obsessive research via Dr. Google, brief chats with my frantic retail pharmacist and instincts honed over time. So wish me luck!

Healthcare Needs Clinician Data Experts

Posted on November 2, 2016 I Written By

Anne Zieger is veteran healthcare consultant and analyst with 20 years of industry experience. Zieger formerly served as editor-in-chief of FierceHealthcare.com and her commentaries have appeared in dozens of international business publications, including Forbes, Business Week and Information Week. She has also contributed content to hundreds of healthcare and health IT organizations, including several Fortune 500 companies. Contact her at @ziegerhealth on Twitter or visit her site at Zieger Healthcare.

This week I read an interesting article by a physician about the huge challenges clinicians face coping with unthinkably large clinical data sets — and what we should do about it. The doctor who wrote the article argues for the creation of a next-gen clinician/health IT hybrid expert that will bridge the gaps between technology and medicine.

In the article, the doctor noted that while he could conceivably answer any question he had about his patients using big data, he would have to tame literally billions of data rows to do so.

Right now, logs of all EHR activity are dumped into large databases every day, notes Alvin Rajkomar, MD. In theory, clinicians can access the data, but in reality most of the analysis and taming of data is done by report writers. The problem is, the HIT staff compiling reports don’t have the clinical context they need to sort such data adequately, he says:

“Clinical data is complex and contextual,” he writes. “[For example,] a heart rate may be listed under the formal vital sign table or under nursing documentation, where it is listed as a pulse. A report writer without clinical background may not appreciate that a request for heart rate should actually include data from both tables.“

Frustrated with the limitations of this process, Rajkomar decided to take the EHR database problem on. He went through an intense training process including 24 hours of in–person classes, a four-hour project and four hours of supervised training to obtain the skills needed to work with large clinical databases. In other words, he jumped right in the middle of the game.

Even having a trained physician in the mix isn’t enough, he argues. Ultimately, understanding such data calls for developing a multidisciplinary team. Clinicians need each others’ perspectives on the masses of data coming in, which include not only EHR data but also sensor, app and patient record outcomes. Moreover, a clinician data analyst is likely to be more comfortable than traditional IT staffers when working with nurses, pharmacists or laboratory technicians, he suggests.

Still, having even a single clinician in the mix can have a major impact, Rajkomar argues. He contends that the healthcare industry needs to create more people like him, a role he calls “clinician-data translator.” The skills needed by this translator would include expertise in clinical systems, the ability to extract data from large warehouses and deep understanding of how to rigorously analyze large data sets.

Not only would such a specialist help with data analysis, and help to determine where to apply novel  algorithms, they could also help other clinicians decide which questions are worth investigating further in the first place. What’s more, clinician data scientists would be well-equipped to integrate data-gathering activities into workflows, he points out.

The thing is, there aren’t any well-marked pathways to becoming a clinician data scientist, with most data science degrees offering training that doesn’t focus on a particular domain. But if you believe Rajkomar – and I do – finding clinicians who want to be data scientists makes a lot of sense for health systems and clinics. While their will always be a role for health IT experts with purely technical training, we need clinicians who will work alongside them and guide their decisions.

Artificial Intelligence Can Improve Healthcare

Posted on July 20, 2016 I Written By

Anne Zieger is veteran healthcare consultant and analyst with 20 years of industry experience. Zieger formerly served as editor-in-chief of FierceHealthcare.com and her commentaries have appeared in dozens of international business publications, including Forbes, Business Week and Information Week. She has also contributed content to hundreds of healthcare and health IT organizations, including several Fortune 500 companies. Contact her at @ziegerhealth on Twitter or visit her site at Zieger Healthcare.

In recent times, there has been a lot of discussion of artificial intelligence in public forums, some generated by thought leaders like Bill Gates and Stephen Hawking. Late last year Hawking actually argued that artificial intelligence “could spell the end of the human race.”

But most scientists and researchers don’t seem to be as worried as Gates and Hawking. They contend that while machines and software may do an increasingly better job of imitating human intelligence, there’s no foreseeable way in which they could become a self-conscious threat to humanity.

In fact, it seems far more likely that AI will work to serve human needs, including healthcare improvement. Here’s five examples of how AI could help bring us smarter medicine (courtesy of Fast Company):

  1. Diagnosing disease:

Want to improve diagnostic accuracy? Companies like Enlitic may help. Enlitic is studying massive numbers of medical images to help radiologists pick up small details like tiny fractures and tumors.

  1. Medication management

Here’s a twist on traditional med management strategies. The AiCure app is leveraging a smartphone webcam, in tandem with AI technology, to learn whether patients are adhering to their prescription regimen.

  1. Virtual clinicians

Though it may sound daring, a few healthcare leaders are considering giving no-humans-involved health advice a try. Some are turning to startup Sense.ly, which offers a virtual nurse, Molly. The Sense.ly interface uses machine learning to help care for chronically-ill patients between doctor’s visits.

  1. Drug creation:

AI may soon speed up the development of pharmaceutical drugs. Vendors in this field include Atomwise, whose technology leverages supercomputers to dig up therapies for database of molecular structures, and Berg Health, which studies data on why some people survive diseases.

  1. Precision medicine:

Working as part of a broader effort seeking targeted diagnoses and treatments for individuals, startup Deep Genomics is wrangling huge data sets of genetic information in an effort to find mutations and linkages to disease.

In addition to all of these clinically-oriented efforts, which seem quite promising in and of themselves, it seems clear that there are endless ways in which computing firepower, big data and AI could come together to help healthcare business operations.

Just to name the first applications that popped into my head, consider the impact AI could have on patient scheduling, particularly in high-volume hostile environments. What about using such technology to do a better job of predicting what approaches work best for collecting patient balances, and even to execute those efforts is sophisticated way?

And of course, there are countless other ways in which AI could help providers leverage clinical data in real time. Sure, EMR vendors are already rolling out technology attempting to help hospitals target emergent conditions (such as sepsis), but what if AI logic could go beyond condition-specific modules to proactively predicting a much broader range of problems?

The truth is, I don’t claim to have a specific expertise in AI, so my guesses on what applications makes sense are no better than any other observer’s. On the other hand, though, if anyone reading this has cool stories to tell about what they’re doing with AI technology I’d love to hear them.

When Did A Doctor Last Worry About Social Determinants of Health (SDOH)?

Posted on June 16, 2016 I Written By

John Lynn is the Founder of the HealthcareScene.com blog network which currently consists of 10 blogs containing over 8000 articles with John having written over 4000 of the articles himself. These EMR and Healthcare IT related articles have been viewed over 16 million times. John also manages Healthcare IT Central and Healthcare IT Today, the leading career Health IT job board and blog. John is co-founder of InfluentialNetworks.com and Physia.com. John is highly involved in social media, and in addition to his blogs can also be found on Twitter: @techguy and @ehrandhit and LinkedIn.

I’ve heard over and over the importance of social determinants of health (SDOH) and their impact on healthcare costs. The concept is fascinating and challenging. There are thousands of examples. A simple one to illustrate the challenge is the patient who arrives at the emergency room with a fever. The doctor treats the fever and then sends them back to their home where they have no heat and are likely to get sick again.

I ask all the doctors that read this blog, when was the last time you worried about these various social determinants of health (SDOH) in the care you provided a patient?

I’ll be interested to hear people’s responses to this question. I’m sure it would create some incredible stories from doctors who really care about their patients and go above and beyond their job duties. In fact, it would be amazing to hear and share some of these stories. We could learn a lot from them. However, I’m also quite sure that almost all of those stories would end with the doctor saying “I wasn’t paid to help the patient this way but it was the right thing to do.”

Let me be clear. I’m not blaming doctors for not doing more for their patients. If I were a doctor, I’m sure I’d have made similar decisions to most of the doctors out there. They do what they’re paid to do.

As I’ve been sitting through the AHIP Institute conference, I’m pondering on if this will change. Will value based reimbursement force doctors to understand SDOH or will they just leave that to their health system or their various software systems to figure it out for them?

I’m torn on the answer to that question. A part of me thinks that most doctors won’t want to dive into that area of health. Their training wasn’t designed for that type of thinking and it would be a tough transition of mindset for many. On the other hand, I think there’s a really important human component that’s going to be required in SDOH. Doctors have an inherent level of trust that is extremely valuable with patients.

What do you think of SDOH? Will doctors need to learn about it? Will the systems just take care of it for them?