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Morning Headlines 10/25/18

October 24, 2018 Headlines No Comments

Healthcare Leaders Merge to Accelerate the Promise of Value-Based Care

Medicare quality reporting company Mingle Analytics merges with care management software company SilverVue to form Mingle Health.

Health Startup Launches Telemedicine Platform Targeted At Migraine Sufferers

After raising a $15 million Series A, digital health company Thirty Madison will expand to offer telemedicine services for migraine sufferers.

Trump to sign opioid bill today

President Trump signs a bipartisan opioids bill into law that encourages telemedicine utilization for patients suffering from opioid use disorder and the adoption of EHRs among behavioral health providers.

HIStalk Interviews Rachel Marano, Managing Partner, Pivot Point Consulting

October 24, 2018 Interviews No Comments

Rachel Marano is managing partner and co-founder of Pivot Point Consulting of Brentwood, TN.

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Tell me about yourself and the company.

I’ve spent my entire career in healthcare IT, almost 18 years. I’m a computer science graduate. I started my first job at Cerner, where I learned the healthcare IT industry through the Cerner consulting concept. I eventually moved into the hospital side, going to work for Advocate Health Care to get off the road. I did a good bit of implementation and then worked my way into the Epic space and became a certified consultant for a variety of consulting companies. I did everything from build to project management at the project director level.

I launched Pivot Point Consulting in April 2011 with the intent of continuing in the healthcare IT industry, but as a consulting group and a vendor. I’ve seen multiple angles of the industry — software development, the hospital side, the consulting side, and now as an entrepreneur in the healthcare IT space.

What are the most important things you learned from working with Cerner and Epic and their products?

Their products are achieving the same goal, but have different ways of getting there. Both have strong implementation methodologies. Obviously their philosophies and corporate cultures are different. Cerner’s support model is different from Epic’s. Pivot Point Consulting serves both markets.

I’ve worked on both sides and have seen the advantages of both systems, the integration, and how they play in the industry. My roots are Cerner and I spent a good part of my career in Epic, so I think they are equally important in this industry. They create tremendous value for organizations. Many of our consultants have found themselves in both worlds over the years.

Cerner has made a lot of advances in their interoperability and in the international market, which has given them many additional clients. Epic continues to grow domestically and internationally. Epic has a unique way of managing the implementations — giving feedback, doing progress reporting, and ensuring success in install, implementation, and outcomes — which is different from how Cerner manages its clients. They are different animals, with both achieving the same end goal but with different paths to get there. We’ve seen tremendous success with our clients on both products.

How has hospital and health system consolidation affected the consulting business?

It’s certainly a different landscape when there is a lot of merger and acquisition activity. But by definition, that creates opportunity for migration, implementation, and optimization in consolidating older systems to one standard system. It has created a lot of strategy, advisory, and assessment-level work for us and in the entire industry. We’ve done quite a bit of M&A work in the last few years in helping with pre-planning, organizational IT strategic planning, and infrastructure planning for M&A.

We’re doing a large M&A strategy session right now with an organization in downstate Illinois. They didn’t know how to approach the amount of M&A they will be going after in the next 10 years and how that would affect them operationally, strategically, and financially. We put together roadmaps.

Consolidation has, from a consulting perspective, allowed us to look at the industry differently and to see the future state of where these systems will be. Many of them will be unified, integrated, and on similar platforms instead of best-of-breed. We’re going to see a lot more organizations on one platform where they can transfer data more easily.

Are large health systems in less of a hurry than before to rip and replace the systems of the hospitals they acquire in favor of the corporate standard?

One of our larger clients spent probably $200 million on Epic implementation over the years. They were bought by a much larger organization. Things are integrated between the two systems other than the Epic instances. The large organization is maintaining its existing Epic instance and the smaller organization will maintain its Epic instance. They’re both on Epic, but they are running independently by design.

The sheer cost of starting again, redefining workflow, and standardizing all these things between the two systems almost makes the juice not worth the squeeze after these organizations have spent so much money. Things are working, they’re getting the reporting that they need, they’re compliant, and their workflows and operations are efficient with those instances. It makes great sense for some organizations, less for others. Ultimately cost, resourcing, staffing, and other competing projects all come into play into that decision-making. But for some organizations, once they sign on the M&A dotted line, they’re moving forward and starting with the migration.

What projects are floating to the top of health system lists?

We’re seeing a lot of patient engagement, population health, privacy and security, optimization. A lot of managed services, outsourcing the support of these systems. More organizations are shifting energy away from EHR to ERP. The concentration is now that we have the data, what do we do with it? How are we using those measurements to improve performance, clinical outcomes, return on investment, and cash flow? It’s a much more advanced space.

Almost all of our clients are focused heavily on patient engagement initiatives in one way or another. How patients are interacting with their patient portals and what their experience is like from a technology perspective. Systems are in place, we’re live, software is working. Operations, workflow, and clinical and revenue cycle are functional. Where do we go from here in these Phase 2, 3, and 4 post-live scenarios?

Do health systems know what they want to do with population health and patient engagement or are they looking for direction?

Both. More-tenured organizations that have been on these EHR platforms and have software, analytics platforms, or tools are much further ahead in deciding what their initiative looks like or what it should mean. We have small organizations that haven’t even said the word. They’re looking for our guidance and our advisory around the right moves. What tools should we be working with? What vendors are good in this space? Should we be bringing Healthy Planet live? Should we be doing some type of integration?

Most of our large organizations are already underway and have someone leading the charge with population health in some regard. Some of our smaller organizations that might be a little bit further behind are looking for direction and directive. Some don’t know how to approach it, it’s lower on their list, and they’re still trying  to get their technology in order.

What do CIOs tell you is the hardest part of their job?

I haven’t heard as much about CIO turnover. You’ll see it with M&A, but jobs are also evolving into other areas. Some of our CIOs are more focused on innovation and driving revenue into the IT department where before it was more about creating a specific technology infrastructure.

Their challenges continue to be resourcing. I hear this consistently. How do we continue with additional future-state projects with the existing staff? How do we leverage organizations and potentially managed services or outsourced solutions to maximize our organizational resourcing?

Definitely innovation. We have CIOs who are focused on developing programs internally in their IT departments to drive revenue, to create revenue-generating entities within their organization that can align potentially with their IT shop. Potentially consolidating efforts with other local hospitals, leveraging other IT departments and their resources. We’ve seen a lot of unusual approaches to the post-EHR implementation world in CIO roles and evolving how they play in their organizations.

What are the issues most commonly involved when a health system calls you wanting to replace an incumbent consulting firm?

Typically we find that organizations are unhappy with the relationship, the level of consultant talent, or potentially the level of experience and ability. A lot of times, we’re called on because they’re unhappy with the level of service.

But we also find that organizations are looking for a firm that can do more than just one thing and can cast a wider net of service offerings. The group understands their culture, nuances, and their uniqueness and are able to go in other directions, whether it be at an advisory level, a managerial level, legacy, potentially on revenue cycle or clinical, training, and managed services. We’ve seen a good bit of that and we’ve seen organizations that are looking for companies at a certain KLAS level, where they’ve had vendors that have fluctuated in that KLAS standing. Organizations consistently say they’re looking for vendors within the top 10 in their category and that’s who they stick with.

Our focus is relationships, trusted advisory, strategic connections with our clients, and offering value. Being able to identify a challenge and provide a solution. We can do that at more of a strategic level, but also with staffing. We’re trying to approach it differently. We definitely do staffing, but we’ve always been a firm that has been consultant led and consultant driven. We have a different vision on how we work with clients and how we engage with them.

What are the biggest opportunities and threats for health systems, CIOs, and companies in the next 3-5 years?

Merger and acquisitions. We’re going to see in the next 10 years more and more organizations being consolidated, with fewer and fewer independent organizations. The challenges come with combining facilities, the cost of doing that, and technology integration. That will drive the future of the healthcare market. The continued advancement in the technology itself will also change how we are leveraging data.

Do you have any final thoughts?

Our organization is evolving and certainly has changed over the years. When Pivot Point started, we were focused pretty heavily on Epic and Cerner implementation. At that time, that was where the industry was, and that was the main focus of most organizations. We have changed with the times and evolved with the industry and continue to meet the needs of our clients.

We have cast a wider net into some of these divisions, departments, and areas where we see challenge and opportunity. A lot of that is around that managed services space and assisting clients with post-live initiatives. We’re going to continue to see more organizations putting energies in and around that as well as the strategic and more challenged areas around privacy and security, population health, mobility, and even compliance and infrastructure and technology.

A Machine Learning Primer for Clinicians–Part 2

Alexander Scarlat, MD is a physician and data scientist, board-certified in anesthesiology with a degree in computer sciences. He has a keen interest in machine learning applications in healthcare. He welcomes feedback on this series at drscarlat@gmail.com.

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To recap from Part 1, the difference between traditional statistical models and ML models is their approach to a problem:

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We feed Statistics as an Input and some Rules. It provides an Output.

With a ML model, we have two steps:

  • We feed ML an Input and the Output and the ML model learns the Rules. This learning phase is also called training or model fit.
  • Then ML uses these rule to predict the Output.

In an increasing number of fields, we realize that these rules learned by the machine are much better than the rules we humans can come up with.

Supervised vs. Unsupervised Learning

With the above in mind, the difference between supervised and unsupervised is simple.

  • Supervised learning. We know the labels of each input instance. We have the Output (discharged home, $85,300 cost to patient, 12 days in ICU, 18 percent chance of being readmitted within 30 days). Regression to a continuous variable (number) and Classification to two or more classes are the main subcategories of supervised learning.
  • Unsupervised learning. We do not have the Output. Actually, we may have no idea what the Output even looks like. Note that in this case there’s no teacher (in the form of Output) and no rules around to tell the ML model what’s correct and what’s not correct during learning. Clustering, Anomaly Detection, and Primary Component Analysis are the main subcategories of unsupervised learning.
  • Other. Models working in parallel, one on top of another in ensembles, some models supervising other models which in turn are unsupervised, some of these models getting into a  “generative adversarial relationship” with other models (not my terminology). A veritable zoo, an exciting ecosystem of ML model architectures that is growing fast as people experiment with new ideas and existing tools.

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Supervised Learning – Regression

With regression problems, the Output is continuous — a number such the LOS (number of hours or days the patient was in hospital) or the number of days in ICU, cost to patient, days until next readmission, etc. It is also called regression to continuous arbitrary values.

Let’s take a quick look at the Input (a thorough discussion about how to properly feed data to a baby ML model will follow in one of the next articles).

At this stage, think about the Input as a table. Rows are samples and columns are features.

If we have a single column or feature called Age and the output label is LOS, then it may be either a linear regression or a  polynomial (non-linear) regression.

Linear Regression

Reusing Tom Mitchell definition of a machine learning algorithm:

” A computer program is said to learn from Experience (E) with respect to some class of tasks (T) and performance (P) – IF its performance at tasks in T, as measure by P, improves with experience (E)”

  • The Task is to find the best straight line between the scatter points below – LOS vs. age – so that, in turn, can be used later for prediction of new instances.
  • The Experience is all the X and Y data points we have.
  • The Performance will be measured as the distance between the model prediction and the real value.

The X axis is age, the Y axis is LOS (disregard the scale for the sake of discussion):

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Monitoring the model learning process, we can see how it approaches the best line with the given data (X and Y) while going through an iterative process. This process will be detailed later in this series:

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From “Linear Regression the Easier Way,” by Sagar Sharma.  

Polynomial (Non-Linear) Regression

What happens when the relationship between our (single) variable age and output LOS is not linear?

  • The Task is to find the best line — obviously not a straight line — to describe the (non-linear) polynomial relationship between X (Age) and Y (LOS).
  • Experience and Performance stay the same as in the previous example.

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As above, monitoring the model learning process as it approximates the data (Experience) during the fit process:

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From “R-english Freakonometrics” by Arthur Carpentier.

Life is a bit more complicated than one feature (age) when predicting LOS, so we’d like see what happens with two features — age and BMI. How do they contribute to LOS when taken together?

  • The Task is to predict the Z axis (vertical) with a set of X and Y (horizontal plane).
  • The Experience. The model now has two features: age and BMI. The output stays the same: LOS.
  • Performance is measured as above.

Find the following peaks chart — a function — similar to the linear line or the polynomial line we found above.

This time the function defines the input as a plane (age on X and BMI on Y) and the output LOS on Z axis.

Knowing a specific age as X and BMI as Y and using such a function / peaks 3D chart, one can predict LOS as Z:

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A peaks chart usually has a contour chart accompanying, visualizing the relationships between X, Y, and Z (note that besides X and Y, color is considered a dimension, too, Z on a contour chart). The contour chart of the 3D chart above:

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From the MathWorks manual

How does a 4D problem look?

If we add the time as another dimension to a 3D like the one above, it becomes a 4D chart (disregard the axes and title):

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From “Doing Magic and Analyzing Time Series with R,” by Peter Laurinec.

Supervised Learning – Classification

When the output is discrete rather than continuous, the problem is one of classification.

Note: classification problems are also called logistic regression. That’s a misnomer and just causes confusion.

  • Binary classification, such as dead or alive.
  • Multi-class classification, such as discharged home, transferred to another facility, discharged to nursing facility, died, etc.

One can take a regression problem such as LOS and make it a classification problem using several buckets or classes: LOS between zero and four days, LOS between five and eight days, LOS greater than nine days, etc.

Binary Classification with Two Variables

  • The Task is to find the best straight line that separates the blue and red dots, the decision boundary between the two classes.
  • The Experience. Given the input of the X and Y coordinate of each dot,  predict the output as the color of the dot — blue or red.
  • Performance is the accuracy of the prediction. Note that just by chance, with no ML involved, the accuracy of guessing is expected to be around 50 percent in this of type of binary classification, as the blue and the red classes are well balanced.

In visualizing the data, it seems there may be a relatively straight line to separate the dots:

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The model learning iteratively the best separating straight line. This model is linearly constrained when searching for a decision boundary:

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From “Classification” by Davi Frossard.

Binary Classification with a Non-Linear Separation

A bit more complex dots separation exercise, when the separation line is obviously non-linear.

The Task, Experience, and Performance remain the same:

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The ML model learning the best decision boundary, while not being constrained to a linear solution:

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From gfycat.

Multi-Class Classification

Multi-class classification is actually an extension of the simple binary classification. It’s called the “One vs. All” technique. 

Consider three groups: A, B, and C. If we know how to do a Binary classification (see above), then we can calculate probabilities for:

  • A vs. all the others (B and C)
  • B vs. all the others (A and C)
  • C vs. all the others (A and B)

More about Classification models in the next articles.

Binary Classification with Three Variables

While the last example had two variables (X and Y) with one output (color of the dot), the next one has three input variables (X, Y, and Z) and the same output: color of the dot.

  • The Task is to find the best hyperplane shape (also known as rules / function) to separate the blue and red dots.
  • Experience has now three input variables (X, Y, and Z) and one output label (the dot color).
  • Performance remains the same.

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From Ammon Washburn, data scientist. Click here to see the animated 3D picture.

How can we visualize a problem with 5,000 dimensions?

Unfortunately, we cannot visualize more than 4-5 dimensions. The above 4D chart (3D plus time) on a map with multiple locations — charts running in parallel, over time — I guess that would be considered a 5D visualization, having the geo location as the fifth dimension. One can imagine how difficult it would be to actually visualize, absorb, and digest the information and just monitor such a (limited) 5D problem for a couple of days.

Alas, if it’s difficult for us to visualize and monitor a 5D problem,  how can we expect to learn from each and every experience of such a complex system and improve our performance, in real time, on a prediction task? 

How about a problem with 10,000 features, the task of predicting one out of 467 DRG classifications for a specific patient with an error less than 8 percent? 

In this series, we’ll tackle problems with many features and many dimensions while visualizing additional monitors — the ML learning curves — which in my opinion are as beautiful and informative as the charts above, even as they are only 2D.

Other ML Architectures

On an artsy note, as it is related to the third group of ML models (Other) in my chart above, are models that are not purely supervised or unsupervised.

In 2015, Gatys et al. published a paper on a model that separates style from content in a painting. The generative model learns an artist’s style and then predicts the style effects learned on any arbitrary image. The left upper corner is an image in Europe and then each cell is the same image rendered in one of several famous painters’ styles:

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Scoff you may, but this week, the first piece of art generated by AI goes for sale at Christie’s.

Generative ML models have been trained to generate text in a Shakespearean style or the Bible style by feeding ML models with all Shakespeare or the whole Bible text. There are initiatives I’ll report about where a ML model learns the style of a physician or group of physicians and then creates admission / discharge notes accordingly. This is the ultimate dream come true for any young and tired physician or resident.

In Closing

I’d like to end this article on a philosophical note.

Humans recently started feeding ML models the actual Linux and Python programming languages (the relevant documentation, manuals, Q&A forums, etc.) 

As expected, the machines started writing computer code on their own. 

The computer software written by machines cannot be compiled nor executed and it will not actually run.

Yet …

I’ll leave you with this intriguing, philosophical, recursive thought in mind — computers writing their own software — until the next article in the series: Unsupervised Learning.

Morning Headlines 10/24/18

October 23, 2018 Headlines No Comments

Clearlake Capital to Acquire symplr

Clearlake Capital Group will acquire provider management, credentialing, and payer enrollment technology vendor Symplr.

Kyruus Snags an Extra Investment of $4M from Salesforce Ventures

Kyruus lists its ProviderMatch for Salesforce on the Salesforce AppExchange and announces a $4 million Series D investment by Salesforce Ventures.

Industry Veterans Launch Laudio To Solve Health System Staff Burnout

Laudio releases its staff relationship management platform that allows hospital managers to manage employee recognition, accountability, burnout risk, onboarding and career development, and work issues.

News 10/24/18

October 23, 2018 News 1 Comment

Top News

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Clearlake Capital Group will acquire provider management, credentialing, and payer enrollment technology vendor Symplr. Terms were not disclosed.

Symplr acquired Cactus Software (provider management) in early 2016 and Vistar Technologies (provider management) in mid-2017.


Reader Comments

From Vaporware?: “Re: Cerner at DoD MHS. The system is not operable, much less interoperable, after 20 months of being live overall and Congress should get to the bottom of it. EHR stabilization is usually measured in low single-digit weeks. Any CIOs willing to compare?” Politico says that a preliminary DoD report concludes that MHS Genesis at Madigan Army Medical Center is “not effective and not suitable” – the same conclusion reached after evaluating the three previous test sites – and adds that the system is “not interoperable.”


Webinars

October 30 (Tuesday) 2:00 ET. “How One Pediatric CIN Aligned Culture, Technology and the Community to Transform Care.” Presenters: Lisa Henderson, executive director, Dayton Children’s Health Partners; Shehzad Saeed, MD, associate chief medical officer, Dayton Children’s Health Partners; Mason Beard, solutions strategy leader, Philips PHM; Gabe Orthous, value-based care consultant, Himformatics. Sponsor: Philips PHM. Dayton Children’s Health Partners, a pediatric clinically integrated network, will describe how it aligned its internal culture, technology partners, and the community around its goal of streamlining care delivery and improving outcomes. Presenters will describe how it recruited network members, negotiated value-based contracts, and implemented a data-driven care management process.

November 7 (Wednesday) 3:00 ET. “Opioid Crisis: What One Health Plan is Doing About It.” Presenter: Samuel DiCapua, DO, chief medical director, New Hampshire Health Families; and chief medical officer, Casenet. Sponsor: Casenet. This webinar will describe how managed care organization NH Health Families is using innovative programs to manage patients who are struggling with addiction and to help prevent opioid abuse.

Previous webinars are on our YouTube channel. Contact Lorre for information.


Acquisitions, Funding, Business, and Stock

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Kyruus lists its ProviderMatch for Salesforce on the Salesforce AppExchange and announces a $4 million Series D investment by Salesforce Ventures, increasing its total to $76 million.

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CNBC reports that Silicon Valley-based, VC-backed smart breast pump startup Naya Health —  whose CEO had blamed the company’s sluggish start on the male-dominated VC industry despite what she claimed is a $30 billion market opportunity – has apparently shut down and left customers without support for its $1,000 device amidst complaints from some of its users that it does not work.

The company that was hired to auction the assets of the defunct CareSync asks the court to expedite a $1 million sale to risk adjustment technology vendor Vatica Health, requesting via an emergency motion to finalize any and all bids by November 5. CareSync had raised $49 million before closing abruptly in June 2018.


Sales

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Island Hospital (WA) replaces its existing electronic signature product with that of Access, integrated with Meditech. 


People

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Divurgent hires Gary Germaine (Leidos Health) as VP of client services.

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Mordecai Kramer (Baim Institute for Clinical Research) joins Carevive Systems as VP of data generation and outcomes, life sciences.

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Medtronic co-founder Earl Bakken, who created the modern medical technology industry with the company’s introduction of the first implantable pacemaker in 1960, died Saturday at 94. The former electronics repairman founded the company with his brother-in-law, working from a Minneapolis garage to offer hospital equipment repair and TV installation.


Announcements and Implementations

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Healthcare Growth Partners looks at the vendor “haves and have-nots” from EHR consolidation brought on by high overall market penetration as well as hospitals employing physicians and acquiring practices (HGP estimates that over 50 percent of doctors now work for hospitals). Cerner and Epic are winning the replacement market; specialty EHR vendors are doing well in specialized practice areas not as well served by Epic and Cerner; but Healthland, the former Siemens, the former McKesson Enterprise, and Allscripts Practice Fusion are losing ground as the “have-nots.”

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Denver Health Epic Data Architect Mark Iannucci publishes Epic UserWeb Search on the Chrome Web Store, which allows search terms prefaced with “uw” to search UserWeb or those starting with “uw sherlock” to find occurrences in Sherlock logs, all while skipping the Galaxy screen (assuming you’re already logged into Epic UserWeb and are using the Chrome browser, of course).

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Laudio releases its staff relationship management platform that allows hospital managers to manage employee recognition, accountability, burnout risk, onboarding and career development, and work issues. The founder and CEO is industry long-timer Russ Richmond, MD. 


Government and Politics

A White House-proposed rule would not only push “junk” health insurance plans (short-term and association health plans) that potentially contain extensive exclusions, it would also encourage employers to offer healthcare imbursement arrangements that give each employee a fixed dollar amount with which to purchase their own insurance, which the administration acknowledges would reduce the number of insured people. All of the new recommendations would support states offering plans that are not ACA compliant in such areas as pre-existing conditions and lifetime payment limits.


Other

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A New York Times article provides advice from chronically ill people on how to make best use of the US healthcare system (or more precisely, two ways to do that plus two suggestions for bypassing it in favor of getting informal personal advice).

Researched published in JAMA finds that use of medical crowd-funding campaigns is growing, people often ask for money to obtain treatments that are not evidenced-based or that may be harmful, meaning those who donate are helping line the pockets of practitioners who are delivering questionable care.


Sponsor Updates

  • AdvancedMD will exhibit at the American Academy of Ophthalmology event October 27-30 in Chicago.
  • IDC MarketScape names Arcadia a leader in its assessment of US population health analytics vendors.
  • TMC names Atlantic.Net a 2018 Cloud Computing Security Excellence Award winner.
  • Bluetree will exhibit at the CHIME CIO Leadership Academy 2.0 October 29-30 in San Diego.

Blog Posts


Contacts

Mr. H, Lorre, Jenn, Dr. Jayne.
Get HIStalk updates. Send news or rumors.
Contact us.

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Morning Headlines 10/23/18

October 22, 2018 News No Comments

DSI’s Joseph J. Luzinski Asks Court to Speed Asset Sale of Florida-Based CareSync

Vatica Health makes a $1 million bid to acquire the assets of chronic care management company CareSync, which abruptly closed its doors in June.

MHS Genesis gets a bad review

Politico reports that Pentagon investigators have found Madigan Army Medical Center’s new Cerner-based software lacking in effectiveness, suitability, and interoperability.

Tabula Rasa HealthCare Announces the Launch of CareVention HealthCare

Tabula Rasa HealthCare launches a new division called CareVention Healthcare that will offer consulting services and technology, including its medication risk management software.

Curbside Consult with Dr. Jayne 10/22/18

October 22, 2018 Dr. Jayne 1 Comment

Clinician burnout is at epidemic levels, so I always keep my eye out for scientific papers looking at the issue. A recent paper titled “Implementing Optimal Team-Based Care to Reduce Clinician Burnout” talks about team-based care as a model that “strives to meet patient needs and preferences by actively engaging patients as full participants in their care, while encouraging all health care professionals to function to the full extent of their education, certification, and experience.”

The idea of working at the top of one’s education and licensure is one that I continue to struggle with as I work with physicians who feel that EHRs have turned them into data entry clerks. Although I work with some high-functioning offices, there are far too many where people are doing work that could be done by individuals with less training or experience and at a lower cost. Getting the team composition just right is a challenge, and in the corporate practices I work with, there are barriers such as headcount caps to content with.

I recently worked with a practice that was dealing with a “brick in, brick out” philosophy from their health system HR department. When a highly-paid and long-tenured RN retired, the practice wanted to split her salary and hire three lower-level resources to handle some high-volume office tasks. The hospital-focused HR team would have no part of that strategy, even though it was budget neutral and would benefit the practice, citing various policies and a temporary hiring freeze as barriers. The practice could hire a less-expensive resource to fill her shoes, but then it would lose that salary difference out of their budget for the following fiscal year, hobbling them in a different way.

The practice’s leaders elected to replace the nurse with a similarly-priced resource, which didn’t solve their problem, but preserved their overall budget in hopes that they might be able to make a change in the future if they could get the HR team onboard. It was sad to watch a practice be forced to make bad business decision that reduces their ability to deliver the patient care that needs to be delivered because the corporate structure couldn’t get out of their way.

The paper addresses digital barriers to team-based care, noting that “although EHRs have important advantages in terms of improving continuous access to legible clinical information, they are not optimally designed to support clinical care.” The authors encourage organizations to look at ways to expand the utility of EHRs, including:

  • Examining excessive signature requirements or mandates that physicians must perform certain documentation elements.
  • Accelerating information exchange.
  • Including systems other than EHRs in the discussion of interoperability, including patient health records, registries, etc.
  • Facilitating a learning health system including the use of predictive analytics and artificial intelligence.

They go further to call for CMS to modernize “outdated” documentation guidelines that were created to support billing in the era of paper records. They also suggest that ONC and CMS “could make prescribed medication selection, alternatives, and pricing transparency available to clinical teams at the point of care as a regulatory EHR requirement.”

I’m sure vendors wouldn’t be too thrilled about additional requirements, but as a clinician, I would be thrilled to have that kind of functionality in my EHR. Right now, the only price transparency I have for medications is for the prescriptions we dispense in-house, which are either $10, $20, or $30 at the time of checkout. We don’t make a lot of money on them and we don’t run them through insurance but offer them as a convenience to patients who don’t want to have to stop by the pharmacy on the way home.

The article also looks at workforce barriers, including issues “from the training and mind-set of health care team members to team organization and leadership.” Employee turnover is a challenge for many of the ambulatory organizations I counsel, and usually it’s driven by several factors: inadequate interview and hiring processes, inadequate training, lack of on-the-job mentorship and support, and work/life balance challenges.

Poor interview and hiring processes can lead to mismatched expectations and poor fit with workplace culture. Poor training can lead not only to patient care issues, but to fear and trepidation for employees who feel they’re being asked to perform beyond their comfort zone. When I worked for Big Hospital System, new medical assistants received zero standardized training beyond HIPAA and other compliance trainings. Any clinical training was at the purview of the office manager, who didn’t report to the physicians in the office but rather to a regional administrator. The result was a staff that didn’t always know what they should know to be successful, which led to physician distrust and reluctance to allow them to handle even basic clinical tasks such as taking a blood pressure.

At my current practice, clinical support staff are put through a rigorous training program including clinical terminology, procedures, organizational culture, patient communication, and more. They are then scheduled a certain number of “training shifts” with a clinical leader, where they must complete their procedure logs and document their clinical tasks. These training shifts are added on to a practice’s regular staffing. Although they are training on the job, they’re not expected to immediately fill a standard scheduled position – they are there to learn.

We lose some folks along the way with this rigorous training. Mostly people who realize that our staff really do work at the top of their licenses and who aren’t on board with working as independently as we allow our staff or doing the procedures we expect our staff to perform on a daily basis. I’d rather lose them in training, though, rather than a month or two in.

Once training is complete, each employee is assigned to a “core team” of employees for the purposes of communication, mentoring, and ongoing training. This core team may or may not include people they work with regularly, which gives them the opportunity to have a sounding board about situations which may have happened in the clinic or with other employees. It also provides accountability for ongoing training and mentorship opportunities.

Lack of work/life balance certainly contributes to burnout, not only among physicians, but among all clinicians. I’ve worked with practices where employees can only request a certain number of days off each month regardless of how much vacation they have in their bank. I spoke to one nurse recently who was working during a family wedding because his son also had religious confirmation that month and he was only allowed to “protect” one weekend.

Although I realize the need to balance schedule coverage, this doesn’t build loyalty or allow team members to meet their personal needs. This employee made no secret of the fact that he’s interviewing for a position in telemedicine, where he can work more flexible schedules. Employers need to be in tune with the needs of the current workforce, especially in fields where there are shortages and competition among employers to be the workplace of choice.

The paper closes by noting that our “current payment system is not designed to offset the costs associated with forming, training, and sustaining clinical teams.” Because these tasks are often considered soft skills, organizations often give them less attention than hard-data items like patient volume, patient satisfaction scores, and clinical quality metrics. The money spent on building high-functioning teams is well worth it, but comes at a cost that might derive from a chicken-or-egg finance equation. Programs like the Comprehensive Primary Care Plus initiative are designed to provide this money up front, but only time will tell if that approach is as successful as we hope.

What is your organization doing to foster team-based care? What are they doing to unwittingly sabotage it? Leave a comment or email me.

Email Dr. Jayne.

Morning Headlines 10/22/18

October 21, 2018 Headlines No Comments

Head of IBM Watson Health leaving post after company stumbles, growing criticism

Deborah DiSanzo, general manager of IBM Watson Health for the past three years, will will take a demotion to the strategy team of IBM Cognitive Solutions.

Perficient Announces Formation of a Dedicated Digital Health Service Line

Consulting firm Perficient launches a digital health line of services that will focus on delivery of care, data and insights, and engagement.

CMS Responding to Suspicious Activity in Agent and Broker Exchanges Portal

CMS Administrator Seema Verma assures consumers open enrollment at Healthcare.gov will not be negatively impacted by the breach of 75,000 consumer files on the Federally Facilitated Exchanges for agents and brokers.

Monday Morning Update 10/22/18

October 21, 2018 News 2 Comments

Top News

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Stat reports that Deborah DiSanzo, general manager of IBM Watson Health for the past three years, will leave her role.

DiSanzo will be replaced by SVP John Kelly III, PhD, who wrote a defense of Watson Health in an August 2018 blog post in which he refuted an unflattering article by The Wall Street Journal.

DiSanzo will take a demotion to the strategy team of IBM Cognitive Solutions.

IBM announced last week that earnings from its cognitive offerings were down 6 percent year over year, although it said Watson Health is growing.


Reader Comments

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From Vaporware?: “Re: VA’s Cerner contract. Kudos to them for transparency in listing what they bought, but it looks like they and the VA will be running different systems. Also, DoD didn’t purchase CommonWell even though 60 percent of care happens outside MHS.” The VA’s list of which Cerner systems it and the DoD bought in their respective contracts reveals quite a few differences, some of them understandable due to the types of services offered. DoD skipped quite a few modules that while not useful in battlefield hospitals, would seem to have a place in the dependent care that makes up much of its volume. The DoD passed on modules for cardiology, gastroenterology, CommonWell, most of population health management, integrated radiology dictation, and all transaction services except for automated messaging. I didn’t realize that CommonWell is something you have to buy as an upfront cost, although its documentation says that health IT vendors may charge “commodity-like” fees. Cerner previously pledged not to charge users until at least through the end of 2019.


HIStalk Announcements and Requests

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Most of us might make our living advocating medical standardization, use of technology, and applying patient care experience to our own situation, but poll respondents don’t find those to be positives when choosing our own doctor, instead valuing participative decision-making. Debtor concludes, “Here is the problem with the concept of socialized medicine in the United States. Even among an informed group, we put personal patient ‘concerns’ and ‘decisions’ ahead of evidence-based guidelines and vetted treatment protocols. I fully support your right to have concerns and make decisions about your own health,  but I’d prefer not to pay for them if they’re not supported by science.” Matt says, “We get a ton of policy push in healthcare, which we’ve seen create its own echo-chamber to the detriment (in some very real cases) of beneficial practice. It runs its course until the downstream consequences create push back and the the policy is pulled back, which creates a difficult environment for real and helpful innovation.”

New poll to your right or here: where do you keep locally stored copies of your medical information?

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Only 20 percent of providers are using biometric patient identification, with most of the remaining 80 percent saying either there’s no business case for it or because they haven’t really thought about it. They aren’t really worried about patient perception or hacker concerns. Industry Analyst Supporter of Biometrics approves “perception deception” in using phones as the biometric reader, adding, “Most folks don’t blink about their biometrics being the vehicle to access their iPads and phones, but feel that their privacy and security regulated healthcare provider asking is too invasive.” Ed A warns of potential lawsuits for providers that fail to follow laws like the Illinois Biometric Privacy Act that require obtaining patient consent and following requirements for biometric use and retention. XCIO’s health system employer biometrically verifies identity in registration areas to reduce duplicate records, insurance fraud, and inaccurate patient billing. 


Webinars

October 30 (Tuesday) 2:00 ET. “How One Pediatric CIN Aligned Culture, Technology and the Community to Transform Care.” Presenters: Lisa Henderson, executive director, Dayton Children’s Health Partners; Shehzad Saeed, MD, associate chief medical officer, Dayton Children’s Health Partners; Mason Beard, solutions strategy leader, Philips PHM; Gabe Orthous, value-based care consultant, Himformatics. Sponsor: Philips PHM. Dayton Children’s Health Partners, a pediatric clinically integrated network, will describe how it aligned its internal culture, technology partners, and the community around its goal of streamlining care delivery and improving outcomes. Presenters will describe how it recruited network members, negotiated value-based contracts, and implemented a data-driven care management process.

November 7 (Wednesday) 3:00 ET. “Opioid Crisis: What One Health Plan is Doing About It.” Presenter: Samuel DiCapua, DO, chief medical director, New Hampshire Health Families; and chief medical officer, Casenet. Sponsor: Casenet. This webinar will describe how managed care organization NH Health Families is using innovative programs to manage patients who are struggling with addiction and to help prevent opioid abuse.

Previous webinars are on our YouTube channel. Contact Lorre for information.


Decisions

  • Lavaca Medical Center (TX) went live on Cerner in April 2018
  • Pershing Memorial Hospital (MO) will go live on Cerner in June 2019
  • Kennedy Health System (NJ) will replace Cerner with Epic in 2019
  • Hutchinson Health Hospital (MN) will replace Microsoft Dynamics GP with Infor for financial and supply chain management in October 2018

These provider-reported updates are supplied by Definitive Healthcare, which offers a free trial of its powerful intelligence on hospitals, physicians, and healthcare providers.


People

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GetWellNetwork hires Sameer Siraj (Optum) as chief product officer.


Announcements and Implementations

A new Black Book report on HIM-related technologies names these winners:

  • Nuance (end-to-end coding, clinical documentation improvement, and health information management solutions in both inpatient and ambulatory settings; CDI software)
  • Optum360 (coding and CAC outsourcing)
  • MModal (document capture and transcription)
  • 3M (coding consulting, document imaging)
  • Dolbey (medical speech recognition)
  • Revspring (patient communications and financial satisfaction)
  • Recondo (patient identification and tracking)

Other

Patients who are involved in “non-emergent” ED visits exhibit the same symptoms as ED-appropriate visits 88 percent of the time, an analysis concludes, so it’s probably not reasonable for insurers to demand that patients make an accurate ED-or-not decision. One in six ED visits could be avoided by warning patients that their insurance won’t pay for a non-emergent visits, but such a policy would also discourage the 40 percent of those patients who have ED-appropriate symptoms from going there.

A study finds that hospitals accredited by Joint Commission deliver no better patient outcomes than those certified by other private groups, while hospitals with only a state survey accreditation perform just as well as any of them.


Sponsor Updates

  • Lightbeam Health Solutions publishes a new white paper, “Data-Driven Solutions Providers and Payers Need for Value-Based Care Alignment.”
  • LiveProcess will exhibit at the Health Care Association of New Jersey event October 23-25 in Atlantic City.
  • Meditech releases a new video, “Palo Pinto Mobile Clinic Uses Meditech Ambulatory to Bridge Care Gaps.”
  • Clinical Computer Systems, developer of the Obix Perinatal Data System, will exhibit at the HMHB Annual Meeting & Conference October 22-23 in Atlanta.
  • OmniSys will exhibit at the McKesson Pharmacy Systems Chain & Health System User Conference October 23-24 in Pittsburgh.
  • The SSI Group will exhibit at the MAPAM Annual Fall Conference October 22-23 in South Yarmouth, MA.
  • Surescripts and ZeOmega will exhibit at the 2018 CAHP Annual Conference October 22-24 in San Diego.

Blog Posts


Contacts

Mr. H, Lorre, Jenn, Dr. Jayne.
Get HIStalk updates. Send news or rumors.
Contact us.

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Weekender 10/19/18

October 19, 2018 Weekender No Comments

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Weekly News Recap

  • FDA updates draft guidance on managing cybersecurity issues for the premarket submission of medical devices
  • Digital prescription savings company OptimizeRx acquires interactive patient messaging vendor CareSpeak Communications
  • MIT will spend $1 billion to create an artificial intelligence college
  • Varian Medical acquires Noona Healthcare, whose software captures oncology patient-reported outcomes and supports symptom management
  • Pathology image detection support system vendor Deep Lens announces $3.2 million in seed funding and availability of its free VIPER service for pathologists
  • A judge rejects a bid by former Theranos executives Elizabeth Holmes and Ramesh Balwani to block prosecutors from extending their investigation deeper into the company

Best Reader Comments

The only way to improve things is to get [users]to open up about what’s on their mind. What you get is like an archeological dig where you are sifting and sorting, trying to find the treasures scattered amidst the dirt and rocks. (Brian Too)

I really wish folks would stop referring to the US healthcare “system.” We have a healthcare industry, not a system (unless you’re talking about Medicare or the VA), with competing entities looking for market share. Competitors don’t share information. Also, with the emphasis on reimbursement, preventive care (and pharmaceutical cures vs. treatments) take a back seat. (Kermit)


Watercooler Talk Tidbits

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Readers funded the DonorsChoose teacher grant request of Mr. V in rural Maine, who requested programmable robots for his student-driven coding class for grades 6-8. He reports, “The robotics and coding materials that you have allowed us to acquire have opened many new avenues for my students. Students have been able to try their hands at coding, program design, and problem solving. This project has offered students who struggle in other content areas like reading by offering them a chance to view reading in an entirely new light. The simplicity of the coding commands in combination with the ability to see their work in action has proven to be very successful in engaging a broad array of students. These materials have allowed students who have struggled in other aspects of their learning to become leaders.”

Epic tells Wisconsin utility regulators that its expected peak demand usage of electricity will double in the next 10 years, placing the company among the state’s top electricity users that are otherwise mostly manufacturing plants. That’s in addition to its extensive use of solar, wind, and geothermal energy.

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A hospital in England installs wall-mounted buttons near its doors that can be pushed by people who notice someone smoking their despite clearly visible “no smoking” signs. The buttons trigger the playing of a recorded announcement over a loudspeaker, with a child’s voice asking them nicely to stop using terms such as, “Someone’s mummy or daddy could be having their treatment today.” A pro-smoking group (who knew?) calls the system “Orwellian” and says a better idea would be to move the smoking area further away, but not so far that less-mobile smokers can’t reach it easily. That sounds great on paper, but as many of us have observed first hand, is a lot harder than it sounds, especially evenings and nights when patients, visitors, and employees are illuminated only by the glow of their cigarettes as darkness encourages them to choose personal convenience over posted rules. I was interested that the BBC announcement referred to “tannoy,” which is apparently like Kleenex having turned a company name (in this case, a loudspeaker manufacturer) into a common noun.

An Atlanta radiologist who made a fortune from his medical device patents is sentenced to six months in prison for a $2 million tax fraud scheme in which he claimed to be a minister who had taken a vow of poverty. Michael Jon Kell, MD made up a church, named himself as pastor, and placed all his assets in church accounts from which he funded vacations, private school tuition for his kids, online dating services, and ownership of his lavish mansion.

In England, the BBC notes that Member of Parliament Dan Poulter is working 28 hours per week at a side job – in his case, as a doctor taking psychiatric training – than any other member. The article also notes that his voting record is among the lowest of Conservative members.

A shoeshine man who worked from the halls of UPMC Children’s Hospital of Pittsburgh died this week at 76, having donated all of his tips since 1982 – over $200,000 — to the hospital’s Free Care Fund.


In Case You Missed It


Get Involved


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Morning Headlines 10/19/18

October 18, 2018 Headlines No Comments

FDA proposes updated cybersecurity recommendations to help ensure device manufacturers are adequately addressing evolving cybersecurity threats

The FDA updates draft guidance on managing cybersecurity issues for the premarket submission of medical devices.

Gauss Raises $20 Million in Series C from Northwell Health and Softbank Ventures Korea for AI-Enabled Platform for the Operating Room

Gauss Surgical raises $20 million in a funding round led by Northwell Health (NY) and SoftBank Ventures Korea, with help from seven other health systems.

Duplication causes headaches for state patient exchange

Vermont Information Technology Leaders struggles to pare down the number of duplicate patient records in the state’s HIE.

FDA and DHS increase coordination of responses to medical device cybersecurity threats under new partnership; a part of the two agencies’ broader effort to protect patient safety

FDA and DHS will work together to share information on cybersecurity vulnerabilities in medical devices so that threats to patient safety can be addressed more quickly.

News 10/19/18

October 18, 2018 News No Comments

Top News

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The FDA updates draft guidance on managing cybersecurity issues for the premarket submission of medical devices. FDA Commissioner Scott Gottlieb, MD says the document, initially penned in 2014, offers “recommendations for manufacturers on how they can better protect their products against different cybersecurity risks, from ransomware to a catastrophic attack on a health system.”


HIStalk Announcements and Requests

I don’t pay much attention to the “Like” button at the bottom of each HIStalk post, but did happen to notice that Alexander Scarlat’s first Readers Write installment on machine learning had already garnered several dozen clicks. It hasn’t quite gained the notoriety of the most popular post in recent memory, which deals with remedying poor clinician engagement with health IT. Both tap into several pieces of advice I give those interested in submitting editorial:

1. Readers will give your content more credence if you write from a place of experience. Both authors of the aforementioned posts have MDs, and other in-the-trenches educational and professional experience to back up their right to editorialize. Vendor authors – unless they too have immense clinical chops – will never quite escape the subconscious bias of readers who see a company name in the byline and immediately worry their time is being wasted by someone trying to sell them something.

2. Of-the-moment topics written for an audience with significant experience working in the health IT trenches are key to a good read, and will often sustain relevance for some time. Submissions that offer a 1,000-foot view rather than diving into the nitty gritty will attract critics who aren’t afraid to lambast authors. (Granted, I try to filter those out, but some slip through.)

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3. Pop culture and humor are always good bets, provided they are in good taste. (I’m still shaking my head at the submission sent over with a curse word in the headline.) I often point interested parties to the “All I Needed to Know to Disrupt Healthcare I Learned from ‘Seinfeld’” series penned in 2015 by Bruce Bandes as a great example of original, humorous content that speaks to a timely topic.


Webinars

October 30 (Tuesday) 2:00 ET. “How One Pediatric CIN Aligned Culture, Technology and the Community to Transform Care.” Presenters: Lisa Henderson, executive director, Dayton Children’s Health Partners; Shehzad Saeed, MD, associate chief medical officer, Dayton Children’s Health Partners; Mason Beard, solutions strategy leader, Philips PHM; Gabe Orthous, value-based care consultant, Himformatics. Sponsor: Philips PHM. Dayton Children’s Health Partners, a pediatric clinically integrated network, will describe how it aligned its internal culture, technology partners, and the community around its goal of streamlining care delivery and improving outcomes. Presenters will describe how it recruited network members, negotiated value-based contracts, and implemented a data-driven care management process.

Previous webinars are on our YouTube channel. Contact Lorre for information.


Acquisitions, Funding, Business, and Stock

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Gauss Surgical raises $20 million in a Series C funding round led by Northwell Health (NY) and SoftBank Ventures Korea, with help from seven other health systems. Funding thus far comes to $52 million. The company has developed tablet-based software that uses machine learning and digital imaging to monitor maternal surgical blood loss in real time.

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Digital prescription savings company OptimizeRx acquires interactive patient messaging vendor CareSpeak Communications for an undisclosed amount.

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Muscular dystrophy nonprofit CureDuchenne invests in ZappRx, and will help the vendor optimize its e-prescribing and electronic prior authorization software for Duchenne patients.

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23andMe CEO Anne Wojcicki tells Rock Health conference attendees that she hopes to soon roll out a test that will help consumers better understand how their bodies react to certain antidepressants. Price points for similar services offered by Color Genomics and Albertsons grocery store pharmacists range from $250 to $750. The FDA shut down 23andMe’s first attempt at such a test in 2013 based on the fear that consumers could misinterpret the results as medical advice.


People

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Patrick Flavin (Outcome Health) joins Arches Technology as president.

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HCTec names Salome Isbell (MedHOK) CFO, Victor Ayers (Infor) VP of professional services, and Heather Espino (Centura Health) VP of clinical solutions.


Announcements and Implementations

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Bassett Medical Center (NY) adopts Masimo’s Patient SafetyNet and Root with Vital Signs Check across its 54-bed med-surg unit.

Massachusetts EHealth Collaborative and Cognizant will provide technical and financial consulting services to the MassHealth Delivery System Reform Incentive Payment technical assistance program’s ACOs and community partners.

Partners Connected Health adds a mobile app to its PGHDConnect program, giving users the ability to securely share health data with their providers from 250 devices.


Government and Politics

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The FDA and Department of Homeland Security announce they will work together to share information on cybersecurity vulnerabilities in medical devices so that threats to patient safety can be addressed more quickly.


Privacy and Security

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Following a similar GDPR-induced move in Europe, Apple gives US users the ability to view, edit, and delete data it has collected on them using a new tool on its privacy website. The tool does not apply to data collected by and stored on Apple devices, including biometric data like fingerprints and heart rates.


Other

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The Sequoia Project creates the Interoperability Matters Advisory Group and solicits nominations for workgroup members who will provide feedback and recommendations on interoperability endeavors. I was not aware that Sequoia relinquished Carequality earlier this month to operate as a standalone entity.

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In Dublin, St. James’s Hospital goes live on expanded Cerner functionality, making it the largest EHR implementation in Ireland’s history. The three-year project was dubbed “Project Oak” as an homage to the paper the Millenium conversion will save.

Vermont Information Technology Leaders struggles to pare down the number of duplicate patient records in the state’s HIE. An audit last year found 1.7 million unique records for 624,000 residents and patients from out of town. VITL staff have deemed at least 35 percent of those to be duplicates, and hope to have that number down to 21 percent by the end of the year. The struggle for a unique patient identifier in the Green Mountain State is real.


Sponsor Updates

  • EClinicalWorks will exhibit at CHCANYS18 Annual Conference and Clinical Forum October 21-23 in Tarrytown, NY.
  • FormFast will host virtual user group meetings October 23 and 24.
  • Healthwise will exhibit at the 2018 PNEG Conference October 19-21 in Fort Wayne, IN.
  • Foundations Health Solutions wins an Excellence in Technology Award from McKnight’s for its use of Hyland OnBase.
  • Formativ Health adds Conversa Health’s AI-powered chatbot messaging tool to its line of patient engagement services.
  • Imprivata completes the Zebra Technologies Validated Program for its Mobile Device Access.
  • Casenet becomes a founding member of the private-sector Da Vinci project, which aims to leverage FHIR to improve data-sharing in value-based care arrangements.
  • ZeOmega adds MCG Health’s Cite AutoAuth prior authorization software to its Jiva population health management technology.
  • HCTec publishes a new case study featuring Montefiore Health System.
  • NHS approves Elsevier as a supplier for its NHS England Health Systems Support Framework.

Blog Posts


Contacts

Mr. H, Lorre, Jenn, Dr. Jayne.
Get HIStalk updates. Send news or rumors.
Contact us.

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EPtalk with Dr. Jayne 10/18/18

October 18, 2018 Dr. Jayne No Comments

A reader recently asked how/where I keep my own personal medical records. I may have written about it in the past, but my strategy is always evolving, so I’ll share my answer. From my college and medical school days, I have a few paper documents, mostly pathology reports printed from our hospital’s HIM system, and an original vaccination record from our student health clinic. The vaccinations I also keep as a PDF, which becomes useful when I have to turn in my annual health form to volunteer at a youth summer camp. I always chuckle when I have to transfer that data, because I received my last two non-influenza vaccinations (Hepatitis A and Tdap) only because my staff mistakenly drew up doses that were going to have to go to waste, so I had them “waste” the vaccine into my left deltoid.

Beyond that, I have a thumb drive with my entire OB/GYN medical record, provided to me by my physician when she closed her practice. I’m pretty sure it’s not encrypted, and I’ve summarized the important parts into a Word document. I used to have an account on a commercial patient health record courtesy of my employer, but it was clunky and cumbersome, and frankly just creating my own word document was more useful. My genetic counseling records are all on paper, given to me at the end of my visit by my counselor. Her office does not store records electronically or communicate via patient portal. It’s very old-school. When my local health system began their conversion to Epic last year, I did download all my records from their portal, storing them as PDFs on my OneDrive. That way, I can access them from anywhere should I need them. I also store copies of my living will and healthcare power of attorney on the OneDrive, because I’ve seen too many bad things happen and I trot those documents out as needed.

It’s not an elegant solution, but as a physician I have a pretty good handle on my health status and can quickly put my fingers on the data I need even, if it’s not very well organized or categorized. I’m relatively young and healthy, so I don’t have a lot of records to track. I love the idea of patients having their own curated records that they can share, but that concept still scares a lot of physicians silly. I’ve seen some really good solutions on the market, but there hasn’t really been a lot of traction with patients, even with Apple on the scene. I do have an iBlueButton account with Humetrix, although I haven’t used it in a while. Hopefully I’ll stay healthy with no additional data to add.

Speaking of staying healthy, many of us in clinical informatics pride ourselves on delivering evidence-based care using robust clinical decision support tools. Still, the last mile in making evidence-based care a reality is often the conversation between the clinician, his or her staff, and the patient. During this year’s influenza vaccination season, we’re seeing patients who are resistant to the vaccine because of the perception that it was ineffective last year. This is borne out in a recent survey by Stericycle, which notes that a third of US respondents don’t plan to get a flu shot this year. Last year, influenza killed more than 80,000 people, but the data doesn’t appear to sway these folks. My staff has practiced and role-played various talk-tracks for patients, so we’ll have to see if we can continue to convince our patients that it’s the right thing to do. For certain, we’ll be getting an EHR-delivered score card at the end, so every vaccination counts.

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I recently learned about the Neighborhood Navigator tool, released by the EveryONE Project in partnership with the American Academy of Family Physicians. The tool uses more than 100 languages and integrates with Google Maps to help patients find directions and connect with social services for needs such as food, housing, transportation, employment, legal services, and more. There is a set of training videos for physicians to help them understand the tool and how to best refer patients.

My colleagues in the physician lounge often lament the changes in healthcare brought on by the growing presence of the Internet and the rise of social media in everyday life. Data from recent surveys reveals some interesting statistics: 54 percent of millennials (and 42 percent of all adults) have either “friended” their provider on social media or would like to do so; 65 percent of millennials (and 43 percent of all adults) find social media appropriate to use to contact their provider about a health issue; and 32 percent of those surveyed have taken a health-related action as a result of information they read on social media.

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I stumbled across the “Shots by AAFP/STFM” app in the Google Play store. It includes full CDC vaccine schedules and footnotes, as well as dosing information, contraindications, and catch-up schedule information for all available vaccines. Content is written by immunization experts at the Society of Teachers of Family Medicine. You can also enter a patient’s age and various parameters to get a recommendation on what vaccinations are needed. I use multiple resources in trying to figure out vaccine schedules for people, so I’m looking forward to giving this a try to see if it will be my new one-stop-shop. It’s also available on iTunes.

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My slow day in the clinic allowed for a lot of Web surfing in between studying for boards, and I also stumbled upon ePrognosis from University of California, San Francisco. The site’s goal is “to be a repository of published geriatric prognostic indices where clinicians can go to obtain evidence-based information on patients’ prognosis.” I ran the profiles of my favorite community-living nonagenarians, and it looks like the odds of them continuing to do well are very good indeed.

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Working at an urgent care that also provides occupational medicine services, we see a number of patients who come in for drug screens. Many employers require these to be observed drug screens, so that there is no question of an employee substituting someone else’s urine sample. I chuckled when I saw this feature on a Florida convenience store that has had to put up a sign telling users not to microwave urine samples. Even our drug screens that are not observed include taking the temperature of the sample to make sure it’s within a valid physiological range, so if someone were going to try to microwave it, they’d have to get it just right. Still, it makes one think twice about using a public microwave.

Email Dr. Jayne.

Morning Headlines 10/18/18

October 17, 2018 Headlines No Comments

OptimizeRx Acquires CareSpeak Communications

Digital prescription savings company OptimizeRx acquires interactive patient messaging vendor CareSpeak Communications for an undisclosed sum.

Apple gives U.S. users tool to see what data it has collected

Following a similar GDPR-induced move in Europe, Apple gives US users the ability to view, edit, and delete the data it has collected on them.

Sequoia Project Launches Interoperability Matters Forum

The Sequoia Project creates the Interoperability Matters Advisory Group and solicits nominations for workgroup members who will provide feedback and recommendations on interoperability endeavors.

A Machine Learning Primer for Clinicians–Part 1

Alexander Scarlat, MD is a physician and data scientist, board-certified in anesthesiology with a degree in computer sciences. He has a keen interest in machine learning applications in healthcare. He welcomes feedback on this series at drscarlat@gmail.com.

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AI State of the Art in 2018

Near human or super-human performance:

  • Image classification
  • Speech recognition
  • Handwriting transcription
  • Machine translation
  • Text-to-speech conversion
  • Autonomous driving
  • Digital assistants capable of conversation
  • Go and chess games
  • Music, picture, and text generation

Considering all the above — AI/ML (machine learning), predictive analytics, computer vision, text and speech analysis — you may wonder:

How can a machine possibly learn?!

As a physician with a degree in CS and curious about ML, I took the ML Stanford/Coursera course by Andrew Ng. It was a painful, but at the same time an immensely pleasurable educational experience. Painful because of the non-trivial math involved. Immensely pleasurable because I’ve finally understood how a machine actually learns.

If you are a clinician who is interested in AI / ML but short on math / programming skills or time, I will try to clarify in a series of short articles — under the gracious auspices of HIStalk — what I have learned from my short personal journey in ML. You can check some of my ML projects here.

I promise that no math or programming are required.

Rules-Based Systems

The ancient predecessors of ML are rules-based systems. They are easy to explain to humans:

  • IF the blood pressure is between normal and normal +/- 25 percent
  • AND the heart rate is between normal and normal + 27 percent
  • AND the urinary output is between normal and normal – 43 percent
  • AND / OR etc.
  • THEN consider septic shock as part of the differential diagnosis.

The problem with these systems is that they are time-consuming, error-prone, difficult and expensive to build and test, and do not perform well in real life.

Rules-based systems also do not adapt to new situations that the model has never seen.

Even when rules-based systems predict something, it is based on a human-derived rule, on a human’s (limited?) understanding of the problem and how well that human represented the restrictions in the rules-based system.

One can argue about the statistical validation that is behind each and every parameter in the above short example rule. You can imagine what will happen with a truly big, complex system with thousands or millions of rules.

Rules-based systems are founded on a delicate and very brittle process that doesn’t scale well to complex medical problems.

ML Definitions

Two definitions of machine learning are widely used:

  • “The field of study that gives computers the ability to learn without being explicitly programmed.” (Arthur Samuel).
  • “A computer program is said to learn from experience (E) with respect to some class of tasks (T) and performance measure (P) — IF its performance at tasks in T, as measured by P, improves with experience E.” (Tom Mitchell).

Rules-based systems, therefore are by definition NOT an ML model. They are explicitly programmed according to some fixed, hard-wired set of finite rules

With any model, ML or not ML, or any other common sense approach to a task, one MUST measure the model performance. How good are the model predictions when compared to real life? The distance between the model predictions and the real-life data is being measured with a metric, such as accuracy or mean-squared error.

A true ML model MUST learn with each and every new experience and improve its performance with each learning step while using an optimization and loss function to calibrate its own model weights. Monitoring and fine-tuning the learning process is an important part of training a ML algorithm.

What’s the Difference Between ML and Statistics?

While ML and statistics share a similar background and use similar terms and basic methods like accuracy, precision, recall, etc., there is a heated debate about the differences between the two. The best answer I found is the one in Francois Chollet’s excellent book “Deep Learning with Python.”

Imagine Data going into a black box, which uses Rules (classical statistical programming) and then predicts Answers:

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One provides Statistics, the Data, and the Rules. Statistics will predict the Answers.

ML takes a different approach:

clip_image004

One provides ML, the Data, and the Answers. ML will return the Rules learned.

The last figure depicts only the training / learning phase of ML known as FIT – the model fits to the experiences learned – while learning the Rules.

Then one can use these machine learned Rules with new Data, never seen by the model before, to PREDICT Answers.

Fit / Predict are the basics of a ML model life cycle: the model learns (or train / fit) and then it predicts on new Data.

Why is ML Better than Traditional Statistics for Some Tasks?

There are numerous examples where there are no statistical models available: on-line multi-lingual translation, voice recognition, or identifying melanoma in a series of photos better than a group of dermatologists. All are ML-based models, with some theoretical foundations in Statistics.

ML has a higher capacity to represent complex, multi-dimensional problems. A model, be it statistical or ML, has inherent, limited, problem-representation capabilities. Think about predicting inpatient mortality based on only one parameter, such as age. Such a model will quickly achieve a certain performance ceiling it cannot possibly overpass, as it is limited in its capabilities to represent the true complexity involved in this type of prediction. The mortality prediction problem is much more complicated than considering only age.

On the other hand, a model that takes into consideration 10,000 parameters when predicting mortality (diagnosis at admission, procedures, lab, imaging, pathology results, medications, consultations, etc.) has a theoretically much higher capacity to better represent the problem complexity, the numerous interrelations that may exist within the data, non-linear, complex relation and such. ML deals with multivariate, multi-dimensional complex issues better than statistics.

ML model predictions are not bound by the human understanding of a problem or the human decision to use a specific model in a specific situation. One can test 20 ML models with thousands of dimensions each on the same problem and pick the top five to create an ensemble of models. Using this architecture allows several mediocre-performing models to achieve a genius level just by combining their individual, non-stellar predictions. While it will be difficult for a human to understand the reasoning of such an ensemble of models, it may still outperform and beat humans by a large margin. Statistics was never meant to deal with this kind of challenge.

Statistical models do not scale well to the billions of rows of data currently available and used for analysis.

Statistical models can’t work when there are no Rules. ML models can – it’s called unsupervised learning. For example, segment a patient population into five groups. Which groups, you may ask? What are their specifications (a.k.a. Rules)? ML magic: even as we don’t know the specs of these five groups, an algorithm can still segment the patient population and then tell us about these five groups’ specs.

Why the Recent Increased Interest in AI/ML?

The recent increased interest in AI/ML is attributed to several factors:

  • Improved algorithms derived from the last decade progress in the math foundations of ML
  • Better hardware, specifically designed for ML needs, based on the video gaming industry GPU (graphic processor unit)
  • Huge quantity of data available as a playground for nascent data scientists
  • Capability to transfer learning and reuse models trained by others for different purposes
  • Most importantly,  having all the above as free, open source while being supported by a great users’ community

Articles Structure

I plan this structure for upcoming articles in this series:

  • The task or problem to solve
  • Model(s) usually employed for this type of problem
  • How the model learns (fit) and predicts
  • A baseline, sanity check metric against which model is trying to improve
  • Model(s) performance on task
  • Applications in medicine, existing and tentative speculations on how the model can be applied in medicine

In Upcoming Articles

  • Supervised vs. unsupervised ML
  • How to prep the data before feeding the model
  • Anatomy of a ML algorithm
  • How a machine actually learns
  • Controlling the learning process
  • Measuring a ML model performance
  • Regression to arbitrary values with single and multiple variables (e.g. LOS, costs)
  • Classification to binary classes (yes/no) and to multiple classes (discharged home, discharged to rehab, died in hospital, transferred to ICU, etc.
  • Anomaly detection: multiple parameters, each one may be within normal range (temperature, saturation, heart rate, lactic acid, leucocytes, urinary output, etc.) , but taken together, a certain combination may be detected as abnormal – predict patients in risk of deterioration vs. those ready for discharge
  • Recommender system: next clinical step (lab, imaging, med, procedure) to consider in order to reach the best outcome (LOS, mortality, costs, readmission rate)
  • Computer vision: melanoma detection in photos, lung cancer / pneumonia detection in chest X-ray and CT scan images, and histopathology slides classification to diagnosis
  • Time sequence classification and prediction – predicting mortality or LOS hourly, with a model that considers the order of the sequence of events, not just the events themselves

HIStalk Interviews Rizwan Koita, CEO, CitiusTech

October 17, 2018 Interviews No Comments

Rizwan Koita is CEO of CitiusTech of Princeton, NJ.

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Tell me about yourself and the company.

I’m the founder and chief executive officer of CitiusTech. We founded the company in 2005. This is my second company — I started a tech support company. Before that, I spent about five years with McKinsey & Company.

When you and I spoke last in 2015, CitiusTech was about 1,600 or 1,700 people strong. We are now at 3,200 people. It’s been a fairly strong growth year this past year and over the last few years. We do a whole bunch of stuff in healthcare technology for our customers across what we call the Clinical Value Chain.

What is driving the company’s strong growth?

From the revenue perspective, we are now part of the Healthcare Informatics Top 100. Our revenue was $127 million last year and are on track for close to $150 million this year. We also made a strategic investment in a company called FluidEdge Consulting, which is at about $25 to $30 million. We are hoping that, on a consolidated basis, we will end this year with revenue of about $175 million. As you can see, that’s a very significant jump from where we were last year.

The growth of the company is essentially coming in a couple of areas. We do a lot of work with payer organizations in the US market. We do a lot of work with provider organizations. Both of those markets have accepted CitiusTech solutions and our services very nicely. We also work with some of the medical software and technology companies and support their growth. That business is actually doing quite well. It’s a fairly homogeneous growth across our offering with providers and tech companies as well as with payer organizations. To a smaller extent, we work with pharma organizations as well.

There is a tremendous shift toward data management, a tremendous shift toward analytics, and now a significant shift toward data science and machine learning. We at CitiusTech have a significant amount of expertise in these areas. We’ve been able to do value-added work for our customers.

How will artificial intelligence and machine learning affect healthcare in the next five to 10 years?

I’m going to talk about history a little bit. Ten years back, the emphasis was on deploying what I would call foundational applications, such EMRs, health information exchanges, and connectivity software. A lot of big problems in data integration still remain and are getting solved. Steadily the focus of the industry has moved towards, what do we do with all the patient data, clinical data, financial data, and operational data that is getting generated? What’s the best way to manage that data? That could be on-premise, cloud, or a more traditional enterprise data warehouse versus big data solutions.

After the data management problem starts to get solved, the next logical question is, how do we start to use more analytics? Increasingly there is a lot of focus on what I would call the standard analytics, like regulatory reporting and and Level 1 analytics. But as the industry is maturing, we see a tremendous focus towards a slightly more advanced analytics. How do you take this massive amount of data that is now getting captured — EMR, lab, pharmacy, or claims — and put it together to be able to solve more complex problems? These are often not possible to solve using traditional analytics, But some large healthcare entities are using machine learning and AI tools to use that information to drive their problem solving.

If you look at the market, there are a lot of smaller proofs of concept and very interesting pilots going on. But the number of real-life deployed applications at scale is still small. You have lots of tools and utilities, but a small number are actually being used for inpatient care at scale. We are trying to help our customers solve that problem.

There is a dichotomy between what’s happening in pilots, research, or academic settings but little of it in production. In the next five to 10 years, we are going to see a tremendous number of successful models getting deployed in the real world for improving patient care, improving efficiency, and reducing cost, all of which are critical for healthcare.

Will use of AI and machine learning create a competitive advantage for health systems that deploy them more quickly or skillfully?

There will be a clear stratification of the types of organizations that can use machine learning and AI. At a simple level, if you take the provider market and hospital systems, a very large entity — Mayo Clinic, Cleveland Clinic, New York Presbyterian, Baylor Scott & White, and other large health systems — will be able to gather that information, and for research purposes or otherwise, build and create their own models.

The bulk of the healthcare ecosystem will largely be dependent on the vendor community to facilitate the use of such advanced tools. If I had to fast-forward five to 10 years, I would say that a lot of the deployment of these tools will be driven by the vendor community — EMR vendors, medical imaging vendors, lab services companies, or some of the other guys who have the financial, intellectual, and technical horsepower. They can aggregate large data sets, build models, and then test those models and get them through the FDA approval process and other barriers that are required before deploying these models in the real world. I see a greater likelihood of that happening. Some of the very large health systems also have a strong R&D inclination and have the ability to drive innovation, but that would be much harder for mid-tier and small hospital systems.

Thousands of models are being created today in healthcare using machine learning and AI. These models can be created in hospital research centers, academic institutions, or by five guys in a garage who have deep clinical insight. If you look at thousands of these models and then look on the production side, you find that the number of real-life applications in production is low.

The reason for that is that customers are getting bombarded by a lot of models — created internally or externally — but they don’t necessarily have the skills required for model validation. Imagine that I’m a large medical imaging company. Tons of folks are coming to me and saying they have great algorithms for medical imaging. I as a medical imaging company must have the horsepower to be able to put together a team that can independently take clinical data, run it through the models, validate the efficacy of the models, and fine-tune the models before I can validate whether the model is effective or not. Model validation is a huge pain area for the industry.

The second area is model operationalization. If you have a validated model, the task of integrating it with the clinical workflow is reasonably complex. Say, for example, that I have a model in medical imaging. Knowing that it’s a validated model, I still must be able to incorporate that model into the workflow of a radiologist. If it’s a colon cancer detection algorithm, then the characteristics of the colon cancer patient’s image needs to then fire up this AI or machine learning algorithm. The algorithm should be able to give back a response that is clearly visible to that radiologist or specialist who is looking at the colon cancer image. The radiologist should be able to either accept or reject the proposition or the findings of the machine, the AI algorithm. Once they accept it, that information should get fed back into the algorithm to incrementally optimize and enhance the algorithm. The result should be presented back as part of the report or to the patient or what have you.

It requires a certain degree of engineering effort to incorporate the model into the clinical workflow in addition to meeting the data science capability. To operationalize the model, you need a bundle of different skill sets — data sciences, product development, QA and validations, and perhaps FDA certification.

We find that technology companies and hospital systems that are trying to operationalize their data science models often don’t have that blend of capabilities that is required for them to truly operationalize the model. We end up with a scenario in which there are a lot of pilot models, the number of models that are validated are fewer, and the number of models that are operationalized is really, really small. Obviously these things will change in the next five years, so we’re at a very exciting juncture, but it will require a serious level of thought on the part of the stakeholders to be able to actually achieve the validation operationalization, which is one of CitiusTech’s core value-add to our customers.

Do you have any final thoughts?

Our company is on an interesting trajectory where are helping our customers drive innovation in healthcare. We are also seeing tremendous growth from a business perspective. I’m really excited about the kind of work that we are doing for the segments that I described. We are setting up a very strong advisory board that we will announce in the next two or three weeks. We’re doing other things to drive the growth of the company both organically and inorganically, actively engaging with other companies that may have complementary skills and solutions to ours. I’m really excited about the growth part of the company and looking forward to the next five years.

Morning Headlines 10/17/18

October 16, 2018 Headlines No Comments

Influential Leapfrog Group Jumps In To Rate 5,600 Surgery Centers

Hospital grading organization Leapfrog Group will launch safety and quality surveys of the country’s 5,600 outpatient surgery centers after an investigation revealed poor oversight and substandard clinical practices.

M.I.T. Plans College for Artificial Intelligence, Backed by $1 Billion

MIT will spend $1 billion to create an artificial intelligence college spanning all five of its schools that will begin instruction in the fall of 2019.

This company, led by veteran athenahealth execs, just raised $300m

Medicare Advantage insurer Devoted Health raises $300 million in a Series B round, increasing its total to $362 million.

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