This year, I’ll be on the lookout for higher-level dashboards and will have a specific interest in learning more about health IT software that helps bridge the business and clinical sides of medicine. After all, with accountable care organizations (ACOs) on the horizon, providers will need to establish links between their clinical performance and their financial performance. It sounds strange, but it’s not really a stretch to say that providers have to worry about how clinical outcomes affect their bottom lines for the first time in a long time.  

Staying focused at HIMSS is difficult. There’s an excess of topics being discussed in education sessions, too many exhibitors for any one person to visit (approximately 1,100) and so many people to meet up with that it’s impossible to see it all and it’s very easy to get distracted. But, with a core set of meetings and sessions that cover clinical decision support, analytics, and collaboration between stakeholders, I’ll try to stick to my plan.

Please contact me if your company has something new and interesting in clinical decision support or business intelligence solutions that align clinical and financial performance. My schedule is already packed, but I’m prepared for a week of sensory overload!

The MIT Enterprise Forum hosted a fascinating panel discussion on the future of the personal genome last evening (Feb. 8).  Hosted by Kevin Davies, editor-in-chief of Bio_IT  World and author of The $1,000 Genome, the panel included Dr. George Church of Harvard, Colin Hill, CEO of GNS Healthcare, Jamie Heywood, Co-founder of PatientsLikeMe, and Dr. Michael Pellini, CEO of Foundation Medicine.

The advancements in treatments made possible through understanding an individual patient’s genome are awe-inspiring. With specific examples from cancer therapies, Dr. Pellini described progress we’ve made in moving from a trial-and-error approach to treating cancer toward models that take the genomic and molecular structure of an individual’s tumor into account before narrowing down the treatment options to home in on the optimal therapeutic “cocktail”.

But the discussion underscored that the continued lack of real-world outcomes data to help build models of what works remains a big hurdle in advancing personalized medicine. Recording and aggregating outcomes data are the domain of EHR and EMR systems, in other words, the world that will be convening at HIMSS in 10 days. (See accompanying post)

The rates of adoption of EHR systems that have sufficient functionality to facilitate personalized medicine are too low and some doctors still complain that they don’t see enough benefit in transitioning from paper to electronic records.  Clearly, it’s not the patients’ benefits that these naysayers are considering! Granted, current EHR platforms have a ways to go in improving their usability, functionality, and interoperability. And there’s still too little emphasis on the importance of providing data directly to patients and accepting patient-reported sources of data. But, the failure to recognize benefits of digitized records over paper-based records for advancing the knowledge base in medicine needs to be overcome.

Panelists at last night’s forum also spoke of regulatory hurdles, overcoming privacy concerns, and the need to develop new models for evaluating longitudinal outcomes data to build into predictive models. Changes in reimbursement policies were noted, too.

The good news, however, is that computing power and complex analysis of huge amounts of data are not the factors that are holding back progress in personalized medicine.  The field of genomics has experience with big data and Moore’s law is on track to allow for the soon-to-be $1,000 genome to become the $10 or even $1 genome in another generation. All in all, the availability of longitudinal real world outcomes data stands out as the biggest problem.

How long will it take to overcome the remaining barriers to personalized medicine? That’s difficult to predict. But, the panelists presented an optimistic view of the future where a personalized approach to medicine can lead to a data-driven focus on maintaining wellness, not just treating illness.

For more on the Personalized Genome panel, see:

http://www.masshightech.com/stories/2012/02/06/daily37-Personal-genomes-hold-eventual-promise-for-treatments.html

http://www.chenpr.com/blog/uncategorized/stimulating-discussion-and-sold-out-evening-at-mitef/

There are so many good events to choose from, especially in the healthcare and health IT spaces, that it’s difficult to decide where to devote time-constrained resources. The Events page that we added to the Health Content Advisors site earlier this year lists all major events that I or my colleagues will be attending. At this point, only past 2011 events are listed, but we’ll update the list over the holiday period.

Somehow, I chose a fantastic mix of live events to attend last year and I hope to make a repeat appearance at all of these events in 2012. I’m making plans for #HIMSS12, February 20-24 in Las Vegas now and hope to add the SIIA IIS conference, January 24-25 in New York to the list for 2012.

When we update the Events page, we’ll add links to blog posts, pictures and videos from the events. As a preview, here’s a short video interview I did with HCPlive.com at the Health2.0 conference in San Francisco:

Also, please check out my previous post on Using Game Dynamics that includes a link to a video of my session at Data Content11 that focused on using game dynamics in market research and provides examples from healthcare research, including PatientsLikeMe.   

That’s it for now. Happy holidays and best wishes for a 2012 that exceeds your expectations!

Janice

Anyone remember eWorld?  It was Apple’s attempt to create a search engine that was visual and fun. It didn’t work out, but Apple was ahead of its time in the mid-1990s. Now the time may just be right for eWorld-like next-gen visual and fun business information services.

I spoke of the dual advantages of applying techniques from the online game segment to enhance the engagement level of content and to collect more data about audience at InfoCommerce Group’s Data Content11 conference earlier this month.  The full presentation with video can be found here (minutes 4:30-12:32).

The presentation includes parallels from healthcare where companies like PatientsLikeMe are matching members of their patient community sites with relevant clinical trials. This is just one example of an online publisher that is serving as a matchmaker between its audience and researchers and creating value to all stakeholders as a result.

I saw eyes light up during my talk when I introduced the topic of applying game dynamics[1] to B2B and consumer health content to increase the engagement level and make the audience more valuable to researchers.  A “game layer” for business and consumer health information may seem inappropriate at first glance, but I suggest that borrowing some of the best features of popular games to make content more engaging, easier to navigate, and more personalized can pay dividends. The secondary benefits of collecting more information about your audience and their preferences as they interact with more responsive content are significant, too. Again to use examples from healthcare, if your audience includes the leading experts in a specialty area or a large group of patients with a specific disease or set of symptoms, serving as a matchmaker between your audience and market research firms could represent a new revenue stream, especially if you have compiled data that can be used to segment the audience better than alternatives that currently exist.

I thought of eWorld when I saw HumanaVille, on online resource for seniors who are Humana members. As I mention in my presentation, I’m not sure that a fun visual online interface is the answer to getting seniors engaged online, but it’s worth a try, especially if the alternative is a typical online directory. 

[1] For a primer on game dynamics, see this post: http://tomhumbarger.wordpress.com/tag/scvngr-game-dynamics-playbook/

At InfoCommerce Group, we understand that data by themselves don’t solve problems, but well-managed data can be programmed to serve any number of purposes. We call it “data that can do stuff”.

Applying best practices for data management requires planning and effort. Quantity without quality will get us nowhere. Join us for the Healthcare Roundtable discussion at Data Content11 next week and participate in a lively conversation about how we can corral the expanding sources of health data, “leverage the liberated data” as I wrote in September, and create a collaborative learning system that establishes a robust base for the next generation of evidence-based medicine and shared decisionmaking between patients, providers and payers. 

My first observation: nearly every speaker referred to the plethora of new apps and technology companies in healthcare. We’re beginning to get inundated by new apps that often compete with dozens of similar apps to do nearly the same thing.

Second, it is a safe statement to say that health remains a siloed ecosystem. Collaboration is improving as a result of internal and external forces, with the HITECH Act and ACA (Affordable Care Act) among the most powerful forces promoting change. But we’re at early stages of figuring out how to share data and collaborate for the good of patient outcomes and overall population health.

Yet in this technology-rich environment, the level of awareness of existing data sources is poor. We can liberate all the data in the world and make it available on the Web, but if entrepreneurs are focused on sexy new gadgets that add to the data explosion but do nothing to help organize and normalize the massive datasets that already exist, we’ll fail to make use of the data in meaningful ways (yes, I used the term “meaningful” on purpose).

Park spent some time describing Healthcare.gov and HealthData.gov and how they can act as a resource for entrepreneurs. I loved his analogy between HealthData.gov and NOAA data. He told an anecdote of how someone once told him that NOAA is unnecessary because one can find the same data in a more user-friendly application on Weather.com.  What the commenter didn’t realize is that NOAA data form the backbone of Weather.com. The federal government provides the data gathering, normalizing, and updating functions and then makes the data available to others who can overlay, combine, segment, analyze, integrate and distribute the data in any variety of mashed-up and improved formats.

The tradition of building data businesses on the foundation of federal, state, and local government data is strong. Savvy data publishing entrepreneurs have been digging deeply into government sources of data and providing new applications based on the data for centuries and new data products and services continue to emerge. The opportunities for leveraging data aren’t restricted to using government data by any means. Just look at companies like IMS Health that compiles data on prescribing behavior from pharmacies.

Some healthcare IT companies understand the power of leveraging data. In fact, athenahealth, Todd Parks’ former company, is one of them. Thomson Reuters Healthcare (which is in process of being acquired by Infosys) is another company that has built a big part of its portfolio around leveraging CMS data.

Bob Kocher, a partner at Venrock, also spoke at the Rock Health event. He stated that healthcare is the only industry where investments in IT haven’t led to labor-saving productivity improvements. I’m not surprised by this fact. We’ve had lots of new technologies in healthcare that help us do things we weren’t able to do before.  However, we haven’t been very good at building on our innovations to create a better healthcare system.  In today’s world, combining data with software to build tools that improve efficiency and productivity leads to much richer sets of products and services. Readers of this blog have heard this sentiment from me before and I’m known for defining “meaningful use” as the intelligent combination of IT and content.  It’s a theme worth repeating and I was pleased to hear it articulated so well by Todd Park, Bob Kocher and others yesterday.

Health Content Advisors is a media sponsor of Health 2.0 this year, so watch for daily updates to this blog, along with my Twitter feed @janicemccallum that will post more frequent updates from the meetings.  Follow the conference hashtag #health2con for updates from the entire group of attendees.

On the topic of conferences, the InfoCommerce annual event, Data Content11, is coming up soon (November 2-4) in Philadelphia. As always, some healthcare companies will be represented on the program, but the focus is on the broader issue of how to build successful data publishing businesses. This year’s conference program theme is: Cloud, Crowd, and Curation.  Join us for B2B data publishing’s best networking event and to learn from our Models of Excellence companies how to create and sustain high value data businesses.

For those who want to know more about Data Content11, please contact me at jmccallum@infocommercegroup.com.  Or, drop me a line if you want to meet up in San Francisco.

So, it is easy to predict consolidation between competing players that serve the same functions.  Just last week there were two acquisitions within the medical transcription sector that illustrate this trend: Nuance Communications acquired WebMedx and MedQuist acquired M*Modal.

IT won’t deliver true workflow efficiencies—and accompanying productivity gains—unless vendors  take a systems view of processes and focus on improving the workflow instead of simply digitizing existing paper-based processes.  I’m not saying anything new here. Anyone who has read Clayton Christensen’s Innovator’s Prescription or has lived through a disappointing EMR implementation project understands the problem.  We need a rationalization of vendors and solutions across the entire system. Vertical consolidation within each subsector reduces the confounding number of options for buyers, but doesn’t necessarily solve the larger problem of improving productivity (and by productivity I mean better outcomes and improved efficiency).

In a tangentially related event last week, the Society for Participatory Medicine (#S4PM) held a tweetchat that included some discussion of doctor-patient communication when the doctor is facing a computer screen. Unfortunately, existing EHR/EMR systems haven’t been optimized for the doctor-patient encounter. Voice input, touch screens, direct-from-device input, and even Kinect-style input all represent technologies that could vastly improve the data collection process during, before and after patient visits. Although it’s a small step, I am hopeful that the recent acquisitions in the medical transcription segment allow the consolidated companies more leeway to formulate a wider range of productivity-enhancing voice-data solutions and are representative of future merger activity to come.

For more detail and information on the follow-on poll to name the top 5 health IT social media influencers, see Keith Boone’s post.

A special thanks to Michael Planchart (@theEHRguy) who conceived of the poll. The results are very helpful in steering people to the highest quality disseminators of health IT information.  I’m truly honored to be in the company of everyone who is on the list.

I’ve followed Google from the very early days when they burst on the scene as a new search engine when nobody thought we needed a new search engine.  Google transformed search by using an algorithmic approach to identify the most relevant results.  Among the three key factors that differentiated Google from the pack were 1) algorithms that ranked pages based on popularity (Page Rank) and 2) scale: the larger the collection of sites that were crawled, the better the results (at the time circa 2000).  Since its introduction, Google’s algorithms have changed many times, but the fundamental fact that Google prefers to depend on programmable solutions that don’t require human intervention remains constant.  And, Google continues to chase large-scale opportunities where it can become an essential layer of the infrastructure.

Factor 3) is the business model. Remember the early 2000s when we all wondered how Google would make money?  After Yahoo acquired Overture in 2003, the revenue model was decided.  Keyword-driven advertising became the preferred method to monetize traffic on the popular search engine sites and scale matters in this model.    

With these three key factors in mind: algorithms not people, scale, and an advertising-driven revenue model, let’s consider why Google Health was destined for failure.

Google has done an excellent job of staying ahead in the “scale” category.  Google loves large repositories of data that it can monetize via advertising.  Google Books is an example. Once the legal hurdles have been worked out, Google Books will run without much human intervention –and has the bonus of providing an e-commerce revenue stream along with an advertising revenue stream.

Where Google got into some early trouble with Google Books was they wanted to side-step the hard work involved in working out agreements with publishers, so they did a deal with AAP and the Writers Guild that required authors and publishers to opt-out and take action if they wanted to set their own pricing terms.  When it came to orphan works, Google would be the presumptive copyright owner if the rightful owner couldn’t be located. So far, Google Books fits with the scale and business model elements. But, Google Books required a heavy initial investment in scanning books.  That certainly requires significant human effort, but Google was able to hire inexpensive labor and only older books needed to be scanned.  Newer books are available in electronic form, so once the initial investment is completed, the humans that place the books on the scanners will no longer be necessary. 

With Google Health I have to agree with Kent Bottles who wrote that Google found the degree of complexity in healthcare too great because it required too much specialized conversion programming and relationship-building with multiple stakeholders.  This variability that cannot be easily managed algorithmically is beyond Google’s chosen core competencies.

With respect to revenue models, Google said they would never put ads around PHR data in Google Health.  I think I still have a copy of the initial terms and conditions that stated that Google planned to monetize Google Health through data-mining aggregated patient data and then presumably selling the results to interested parties, with Pharma undoubtedly at the top of the list. In order for the mined data to be useful, scale and consistency of content types and formats would be necessary.  The revenue models could include keyword and display ads or content sales.

So, in effect, Google Health fell short on all three factors: scale, ability to use an algorithmic approach to content management and search, and revenue model.  Yes, health represents over 17% of our economy, so scale was still a possibility, but rate of adoption was slow—and was named as the primary reason for discontinuing the service.  Google will continue to reap benefits of health care searches via its advertising programs on its existing online and mobile search engines, but a Google Health product that was defined as a personal health data repository just doesn’t represent a big enough opportunity for Google. 

Like most of the healthcare publishing and health IT community, I was excited when I first heard that Google was establishing a health group.  Note, I first wrote about Google Health in 2006 when Google Health was envisioned as part of the Google Co-op program that had a crowdsourced model that encouraged publishers and subject matter experts to tag healthcare information.  Google Health took several turns during its short lifetime and who knows, it could come back in five or ten years once the healthcare industry grows up to resemble the financial services industry where the majority of customers log on to their computers to manage their accounts.  But, even then, its business model will have to measure up to Google’s broad business objectives for it to have any chance of succeeding.