Free EMR Newsletter Want to receive the latest news on EMR, Meaningful Use, ARRA and Healthcare IT sent straight to your email? Join thousands of healthcare pros who subscribe to EMR and EHR for FREE!

Embracing Quality: What’s Next in the Shift to Value-Based Care, and How to Prepare

Posted on June 13, 2017 I Written By

The following is a guest blog post by Brad Hill, Chief Revenue Officer at RemitDATA.

Whatever the future holds for the Affordable Care Act (ACA), the shift to value-based care is likely here to stay. The number of providers and payers implementing value-based reimbursement contracts has grown steadily over the past few years. A survey of 465 payers and hospitals conducted in 2016 by ORC International and McKesson revealed that 58 percent are moving forward with incorporating value-based reimbursement protocols. The study, “Journey to Value: The State of Value-Based Reimbursements in 2016” further revealed that as healthcare continues to adopt full value-based reimbursement, bundled payments are the fastest growing with projections that they will continue to grow the fastest over the next five years, and that network strategies are changing, becoming narrower and more selective, creating challenges among many payers and hospitals as they struggle to scale these complex strategies.

Given the growth of adoption of value-based care, there are certainly many hurdles to clear in the near future as policymakers decide on how they plan to repeal and replace the ACA. A January 2017 report by the Urban Institute funded by the Robert Wood Johnson Foundation revealed that some of the top concerns with some potential scenarios being floated by policymakers include concerns over an immediate repeal of the individual mandate with delayed repeal of financial subsidies; delayed repeal of the ACA without its concurrent replacement; and a cutoff of cost-sharing subsidies in 2017.

With the assumption that value-based healthcare is here to stay, what steps can you take to continue to prepare for value-based payments? The best advice would be to continue on with a “business as usual” mindset, stay focused and ensure all business processes are ready for this shift by continuing to:

  1. Help providers establish baselines and understand their true cost of conducting business as a baseline for assuming risk.
  2. Analyze your revenue cycle. Look at the big picture for your practice to analyze service costs and reimbursements for each – determine if margins are in-line with peers.  Identify internal staff processing time and turnaround times by payer. Evaluate whether there are any glaring issues or problems that need to be addressed to reduce A/R days and improve reimbursement rates.
  3. Determine whether there are reimbursement issues for specific payers or if the problem is broader in nature. Are your peers experiencing the same issues with the same payers?
  4. Capture data analysis for practice improvement. With emerging payment models, hospitals and practices will need expertise in evaluating data and knowledge in how to make business adjustments to keep the organization profitable.
  5. Determine how you can scale and grow specific payment models. Consider, for example, a provider group that maintains 4 different payment models and 10 different payers. The provider group will need to determine whether this system is sustainable once payment models shift.
  6. Break down department silos in determining cost allocation rules. Providers need a cost accounting system that can help determine exact costs needed to provide care and to identify highest cost areas. Cost accounting systems are typically managed by the finance team. There needs to be clinical and operational input from all departments to make a difference. Collaborate across all departments to determine costs, and design rules and methodologies that take each into account.
  7. Compare your financial health to that of your peers. Comparative analytics can help by giving you insights and data to determine your practice’s operational health. Determine whether you are taking longer to submit claims than your peers, have a higher percentage of denied claims for a specific service, percentage of billed to allowed amounts and more.

Though change is a part of the healthcare industry’s DNA, ensuring business processes are in line, and leveraging data to do so will help organizations adapt to anything that comes their way.

The EMR Vendor’s Dilemma

Posted on June 6, 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.

Yesterday, I had a great conversation with an executive at one of the leading EMR vendors. During our conversation, she stressed that her company was focused on the future – not on shoring up its existing infrastructure, but rather, rebuilding its code into something “transformational.”

In describing her company’s next steps, she touched on many familiar bases, including population health, patient registries and mobile- first deployment to support clinicians. She told me that after several years of development, she felt her company was truly ready to take on operational challenges like delivering value-based care and conducting disease surveillance.

All that being said – with all due respect to the gracious exec with whom I spoke – I wouldn’t want to be a vendor trying to be transformed at the moment. As I see it, vendors who want to keep up with current EMR trends are stuck between a rock and a hard place.

On the one hand, such vendors need to support providers’ evolving health IT needs, which are changing rapidly as new models of care delivery are emerging. Not only do they need to provide the powerhouse infrastructure necessary to handle and route massive floods of data, they also need to help their customers reach and engage consumers in new ways.

To do so, however, they need to shoot at moving targets, or they won’t meet provider demand. Providers may not be sure what shape certain processes will take, but they still expect EMR vendors to keep up with their needs nonetheless. And that can certainly be tricky these days.

For example, while everybody is talking about population health management, as far as I know we still haven’t adopted a widely-accepted model for adopting it. Sure, people are arriving at many of the same conclusions about pop health, but their approach to rolling it out varies widely.  And that makes things very tough for vendors to create pop health technology.

And what about patient engagement solutions? At present, the tools providers use to engage patients with their care are all over the map, from portals to mobile apps to back-end systems using predictive analytics. Synchronizing and storing the data generated by these solutions is challenging enough. Figuring out what configuration of options actually produces results is even harder, and nobody, including the savviest EMR vendors, can be sure what the consensus model will be in the future.

Look, I’m aware that virtually all software vendors face this problem. It’s difficult as heck to decide when to lead the industry you serve and when to let the industry lead you. Straddling these two approaches successfully is what separates the men from the boys — or the girls from the women — and dictates who the winners and losers are in any technology market.

But arguably, health IT vendors face a particularly difficult challenge when it comes to keeping up with the times. There’s certainly few industries are in a greater state of flux, and that’s not likely to change anytime soon.

It will take some very fancy footwork to dance gracefully with providers. Within a few years, we’ll look back and know vendors adapted just enough.

Few Practices Rely Solely On EMR Analytics Tools To Wrangle Data

Posted on May 23, 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.

A new survey done by a trade group representing medical practices has concluded that only a minority of practices are getting full use of their EMR’s analytics tools.

The survey, which was reported on by Becker’s Hospital Review, was conducted by the Medical Group Management Association.  The MGMA’s survey called on about 900 of its members to ask how their practices used EMRs for analytics.

First, and most unexpectedly in today’s data-driven world, 11 percent of respondents said that they don’t analyze their EMR data at all.

Thirty-one percent of respondents told MGMA that they use all of their EMR’s analytical capabilities, and 22 percent of respondents said they used some of their EMR’s analytics capabilities.

Another 31 percent reported that they were using both their EMR’s analytics tools and tools from an external vendor. Meanwhile, 5 percent said they used only an external vendor for data analytics.

According to Derek Kosiorek, CPEHR, CPHIT, principal consultant with MGMA’s Health Care Consulting Group, the survey results aren’t as surprising as they may seem. In fact, few groups are likely to get  everything they need from EMR data, he notes.

“Many practices do not have the resources to mine the data and organize it in ways to create new insights from the clinical, administrative and financial information being captured daily,” said Kosiorek in a related blog post. “Even if your practice has the staff with the knowledge and time to create reports, the system often requires an add-on product sold by the vendor or an outside product or service to analyze the data.”

However, he predicts that this will change in the near future. Not only will EMR analytics help groups to tame their internal data, it will also aggregate data from varied community settings such as the emergency department, outpatient care and nursing homes, he suggests. He also expects to see analytics tools offer a perspective on care issues brought by regional data for similar patients.

At this point I’m going to jump in and pick up the mic. While I haven’t seen anyone from MGMA comment on this, I think this data – and Kosiorek’s comments in particular – underscore the tension between population health models and day-to-day medical practice. Specifically, they remind us that doctors and regional health systems naturally have different perspectives on why and how they use data.

On the one hand there’s medical practices which, from what I’ve seen, are of necessity practical. These providers want first and foremost to make individual patients feel good and if sick get better. If that can be done safely and effectively I doubt most care about how they do it. Sure, doctors are aware of pop health issues, but those aren’t and can’t be their priority in most cases.

Then, you have hospitals, health systems and ACOs, which are already at the forefront of population health management. For them, having a consistent and comprehensive set of tools for analyzing clinical data across their network is becoming job one. That’s far removed from focusing on day-to-day patient care.

It’s all well and good to measure whether physicians use EMR analytics tools or not. The real issue is whether large health organizations and practices can develop compatible analytics goals.

The Sexiest Data in Health IT: Datapalooza 2017

Posted on May 15, 2017 I Written By

Healthcare as a Human Right. Physician Suicide Loss Survivor. Janae writes about Artificial Intelligence, Virtual Reality, Data Analytics, Engagement and Investing in Healthcare. twitter: @coherencemed

The data at this conference was the Best Data. The Biggest Data. No one has better data than this conference.

The sexiest data in all of healthIT was highlighted in Washington DC at Datapalooza April 27-28, 2017.  One of the main themes was how to deal with social determinants of health and the value of that data.  Sachin H. Jain, MD of Caremore Health reminded us that “If a patient doesn’t have food at home waiting for them they won’t get better” social data needs to be in the equation. Some of the chatter on the subject of healthcare reform has been criticism that providing mandatory coverage hasn’t always been paired with knowledge of the area. If a patient qualifies for Medicaid and has a lower paying job how can they afford to miss work and get care for their health issues?
a
Rural areas also have access issues. Patient “Charles” works full time during the week and qualifies for Medicaid. He can’t afford to miss a lot of work but needs a half a day to get treatments which affect his ability to work. There is no public transportation in his town to the hospital in a city an hour and a half away. Charles can’t afford the gas or unpaid time off work for his treatment.

Urban patient “Haley” returns to her local ER department more than once a week with Asthma attacks.  Her treatments are failing because she lives in an apartment with mold in the walls. As Craig Kartchner from the Intermountain Healthcare team responded to the #datapalooza  hashtag online- These can be the most difficult things to change.

The 2016 report to Congress addresses the difficulty of the intersection between social factors and providing quality healthcare in terms of Social Determinants of Health:

“If beneficiaries with social risk factors have worse health outcomes because the providers they see provide low quality care, value based purchasing could be a powerful tool to drive improvements in care and reduce health disparities. However, if beneficiaries with social risk factors have worse health outcomes because of elements beyond the quality of care provided, such as the social risk factors themselves, value based payment models could do just the opposite. If providers have limited ability to influence health outcomes for beneficiaries with social risk factors, they may become reluctant to care for beneficiaries with social risk factors, out of fear of incurring penalties due to factors they have limited ability to influence.”

Innovaccer just launched a free tool to help care teams track and monitor Medicare advantage plans. I went to their website and looked at my county and found data about the strengths in Salt Lake where I’m located. They included:

  • Low prevalence of smoking
  • Low Unemployed Percentage
  • Low prevalence of physically inactive adults

Challenges for my area?

  • Low graduation rate
  • High average of daily Air pollution
  • High income inequality
  • High Violent crime rate per 100,000 population

Salt Lake actually has some really bad inversion problems during the winter months and some days the particulate matter in the air creates problems for respiratory problems. During the 2016-2017 winter there were 18 days of red air quality and 28 days of yellow air quality. A smart solution for addressing social determinants of health that negatively impact patients in this area could be addressing decreasing air pollution through increased public transportation. Healthcare systems will see an increase in cost of care during those times and long term population health challenges can emerge. You can look at your county after you enter your email address on their site. This kind of social data visualization can give high level insights into the social factors your population faces.

One of the themes of HealthDataPalooza was how to use system change to navigate the intersection between taking care of patients and not finding way to exclude groups. During his panel discussion of predictive analytics, Craig Monson the medical director for analytics and reporting discussed how “data analytics is the shiny new toy of healthcare.”    In addition to winning the unofficial datapalooza award for the most quotes and one liners – Craig presented the Clinical Risk Prediction Initiative (CRISPI).  This is a multi variable logistic regression model with data from the Atrius health data warehouse. His questions for systems to remember in their data analysis selection are “Who is the population you are serving? What is the outcome you need? What is the intervention you should implement?”

Warning- Craig reminds us that in a world of increasing sexy artificial intelligence coding a lot of the value analysis can be done with regression. Based on that statement alone I think he can be trusted. I still need to see his data.

CRISPI analyzed the relative utility of certain types of data, and didn’t have a large jump in utility when adding Social Determinant Data. This data was one of the most popular data sets during Datapalooza discussions but the reality of making actionable insights into system improvement? Craig’s analysis said it was lacking. Does this mean social determinant data isn’t significant or that it needs to be handled with a combination of traditional modeling and other methods?  Craig’s assertion seemed to fly in the face of the hot new trend of Social Determinants of Health data from the surface.

Do we have too much data or the wrong use of the data? Most of the companies investing into this space used data sources outside the traditional definition to help create solutions with social determinate of health and Patient outcomes. They differed in how they analyzed social determinant data. Traditional data sources for the social determinants of health are well defined within the public health research.  The conditions in which you work and live impact your health.

Datapalooza had some of the greatest minds in data analytics and speakers addressed gaps in data usefulness. Knowing that a certain large county wide population has a problem with air quality might not be enough to improve patient outcomes. There is need for analysis of traditional data sources in this realm and how they can get meaningful impact for patients and communities. Healthcare innovators need to look at different data sources.  Nick Dawson, Executive director of Johns-Hopkins Sibley Innovation Hub responded to the conversation about food at home with the data about Washington DC.  “DC like many cities has open public data on food scarcity. But it’s not part of a clinical record. The two datasets never touch.” Data about food scarcity can help hospital systems collaborate with SNAP and Government as well as local food programs. Dawson leads an innovation lab at Johns Hopkins Sibley where managers, directors, VPs and C Suite leaders are responsible for working with 4 innovation projects each year.

Audun Utengen, the Co Founder of Symplur said “There’s so much gold in the social media data if you choose to see it.” Social data available online helps providers meet patients where they are and collect valuable data.  Social media data is another source to collect data about patient preferences and interactions for reaching healthcare populations providers are trying to serve. With so much data available sorting through relevant and helpful data provides a new challenge for healthcare systems and providers.

New Data sources can be paired with a consultative model for improving the intersection of accountable care and lack of access due to social factors. We have more sophisticated analytic tools than ever for providing high value care in the intersection between provider responsibility and social collaboration. This proactive collaboration needs to occur on local and national levels.  “It’s the social determinants of health and the behavioral aspects that we need to fund and will change healthcare” we were reminded. Finding local community programs that have success and helping develop a strategy for approaching Social Determinants of Health is on the mind of healthIT professionals.

A number of companies examine data from sources such as social media and internet usage or behavioral data to design improvements for social determinants of health outcomes.   They seek to bridge the gaps mentioned by Dawson. Data sets exist that could help build programs for social determinants of health.  Mandi Bishop started Lifely Insights centered around building custom community plans with behavioral insights into social determinant data. Health in all Policies is a government initiative supporting increased structure and guidelines in these areas. They support local and State initiatives with a focus on prevention.

I’m looking forward to seeing how the data landscape evolves this year. Government Challenges such as the Healthy Behavior Data Challenge launched at Datapalooza will help fund great improvements. All the data people will get together and determine meaningful data sets for building programs addressing the social determinants of health. They will have visualization tools with Tableau. They will find ways to get food to patients at home so those patients will get better. Programs will find a way to get care to rural patients with financial difficulty and build safe housing.

From a healthcare delivery perspective the idea of collaborating about data models can help improve community health and decrease provider and payer cost. The social determinants of health can cost healthcare organizations more money than data modeling and proactive community collaboration.

Great regressions, saving money and improving outcomes?

That is Datapalooza.

Using AI To Streamline EMR Workflow For Clinicians

Posted on May 10, 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.

Understandably, most of the discussion around AI use in healthcare focuses on data analytics for population health management and predictive analytics. Given the massive scale of the data we’re collecting, that’s no surprise.

In fact, one could argue that using AI technologies has gone from an interesting idea to an increasingly established parto the health IT mix. After all, few human beings can truly understand what’s revealed by terabytes of data on their own, even using well-designed dashboards, filters, scripting and what have you. I believe it takes a self-educating AI “persona,” if you will, to glean advanced insights from the eternity of information we have today.

That being said, I believe there’s other compelling uses for AI-fueled technologies for healthcare organizations. If we use even a relatively simple form of interpretive intelligence, we can improve health IT workflows for clinicians.

As clinicians have pointed out over and over, most of what they do with EMRs is repetitive monkey work, varied only by the need to customize small but vital elements of the medical record. Tasks related to that work – such as sending copies of a CT scan to a referring doctor – usually have to be done in another application. (And that’s if they’re lucky. They might be forced to hunt down and mail a DVD disc loaded with the image.)

Then there’s documentation work which, though important enough, has to be done in a way to satisfy payers. I know some practice management systems that integrate with the office EMR auto-populate the patient record with coding and billing information, but my sense is that this type of automation wouldn’t scale within a health system given the data silos that still exist.

What if we used AI to make all of this easier for providers? I’m talking about using a predictive intelligence, integrated with the EMR, that personalizes the way data entry, documentation and follow-up needs are presented. The AI solution could automatically queue up or even execute some of the routine tasks on its own, leaving doctors to focus on the essence of their work. We all know Dr. Z doesn’t really want to chase down that imaging study and mail it to Albany. AI technology could also route patients to testing and scans in the most efficient manner, adjusted for acuity of course.

While AI development has been focused on enterprise issues for some time, it’s already moving beyond the back office into day-to-day care. In fact, always-ahead-of-the-curve Geisinger Health System is already doing a great deal to bring AI and predictive analytics to the bedside.

Geisinger, which has had a full-featured EMR in place since 1996, was struggling to aggregate and manage patient data, largely because its legacy analytics systems couldn’t handle the flood of new data types emerging today.

To address the problem, the system rolled out a unified data architecture which allowed it to integrate current data with its existing data analytics and management tools. This includes a program bringing together all sepsis-vulnerable patient information in one place as they travel through the hospital. The tool uses real-time data to track patients in septic shock, helping doctors to stick to protocols.

As for me, I’d like to see AI tools pushed further. Let’s use them to lessen the administrative burden on overworked physicians, eliminating needless chores and simplifying documentation workflow. And it’s more than time to use AI capabilities to create a personalized, efficient EMR workflow for every clinician.

Think I’m dreaming here? I hope not! Using AI to eliminate physician hassles could be a very big deal.

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

Healthcare as a Human Right. Physician Suicide Loss Survivor. Janae writes about Artificial Intelligence, Virtual Reality, Data Analytics, Engagement and Investing in Healthcare. 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.