HIMSS17 Social Media Ambassador Janice McCallum on moving patient engagement beyond paternalistic compliance

This interview was originally published on on Feb 1, 2017:

Healthcare IT News asked the HIMSS17 Social Media Ambassador about what she’s looking forward to at this year’s show, things that get under her skin and she reveals facts about her professional background that even devout followers might not yet know about.

Q: What are you most looking forward to at HIMSS17?
 I’m looking forward to Ginni Rometty’s (CEO, IBM) opening keynote this year. IBM Watson Health has the might to create and market breakthrough technologies and it is exciting to see a woman, Deborah DiSanzo, leading that unit, as well as a woman leading the $80 billion parent company.

Q: What issues do you think are top-of-mind for your social media followers?
My social media followers look to me for evidence of big trends in health IT and healthcare delivery models. At HIMSS17, I expect concerns about how the new administration will affect regulations and reimbursements will be top of mind.

Q: Who’s your favorite healthcare hero? Why?
 I’ve never been one for hero worship; instead I reserve my admiration for all the people involved in healthcare who exhibit empathy and understand that what may be right for one patient may not be right for another patient. I include clinicians, researchers, other industry insiders and patient advocates in this group and I can say without hesitation that all of my fellow SMAs, past and present, are stellar exemplars of the people I admire!

Q: What’s your pet peeve? (Either on- or off-line?)
 My pet peeve is the disconnect between what providers and vendors call patient engagement programs and what patients actually need to become more engaged with their healthcare providers. For starters, patients need to have a voice in their care and they should have full access to data related to their care, including their complete health record.  Without fully including patients in their own health care decisions, patient engagement programs are nothing more than paternalistic compliance programs.

Q: What is something your social media followers do not know about you?
 Most of my social media friends and followers don’t know about my early hands-on experience with data modeling, which includes work at the OECD in Paris, the Urban Institute in DC, and in graduate school in Chicago. There are lots of stories I can tell about transferring data from mag tape to mainframes, to licensing data from Alan Greenspan, to staying awake powered by coffee to run econometric models all night! While studying for my MBA, I worked for my econometrics professor, John Abowd, who is now chief scientist leading research and methodology at the US Census Bureau. 


HIMSS17 Schedule, Meetups & Themes

Navigating the HIMSS annual conference is a challenge. This year, I’ve booked almost all of my time in advance—from dawn to way beyond sundown. I’ll wear comfortable walking shoes, since I’ll likely be speed-walking from one meeting to another across the exhibit hall in near constant motion for 3 days. Here are a few places you can catch me during the conference:

Monday, Feb 20:

11 - 11:45 am Meet the Social Media Ambassadors, HIMSS Spot Lobby C

evening           HIStalkapalooza

Tuesday, Feb 21

11 - 11:45 am HealthITChicks meetup, HIMSS Spot Lobby C

 4 -  5 pm        New England HIMSS Social Event at Lenovo Booth 6170

 6 - 8 pm         New Media Meetup, Cuba Libre Restaurant (will be late arrival!)

Wednesday, Feb 22

11 - 11:30 am  Social Media Ambassador Debates, HIMSS Media booth 2123

                        (Thrilled to be paired with Dr. Rasu Shrestha, Chief Innovation Officer at UPMC, to debate/have discussion on physician engagement with technology)

  2:30 pm         Facebook Live intereview at Conduent Health booth 951     

  5:00 pm         The Walking Gallery meetup, sponsored by Conduent Health


See my industry perspective article on healthbots and advances in clinical decision support here:

Look forward to seeing a few tens of thousands of other healthIT enthusiasts next week! You can reach me at



Searching for Healthbots That Advance Research and Clinical Information Discovery at HIMSS17

Improving the flow of information is a consistent motivator in everything I do in my professional life. With early experience as a researcher and product manager at a pre-Internet era search engine, my consulting practice has focused on helping information-centric companies to disseminate their content more effectively within the context of their business objectives. With this long-view of the publishing and information dissemination segments in mind, I’d like to offer some observations and predictions for trends at HIMSS17.

1)      Growth in usage of digital devices and sensors will be a catalyst for progress in interoperability. This is a safe prediction, but I include it because it sets the stage for my other predictions. With the proliferation of devices and sensors, all of which produce data, we need some rationalization in the way the data are recorded and integrated for analysis. It won’t be sufficient to suggest that IT systems manage each device and its data separately. The devices will have to interact with other devices and with EHR & other systems. Another way to state this prediction: as the Internet of Things (IoT) develops into the Industrial IoT, transmitting data in multiple directions in real time for clinical and research purposes will be commonplace. Through consolidation among startups and the entry of big players, more resources will be devoted to resolving health data interoperability issues.

To make sense of the all of the data being produced by sensors and other devices, information systems that can interpret the data in context (i.e., AI/cognitive computing systems that incorporate machine learning techniques) will be needed. That brings me to my second prediction:

2)      Healthbots increasingly become the new interface to health information & health data for patient information. Chatbots have evolved from simple voice recognition technologies to cognitive computing interfaces that can execute complex commands and improve their utility over time with machine learning technologies. I expect success in the consumer space via Apple’s Siri, Google Assistant, Amazon’s Alexa and other examples to carry over to the patient engagement and patient education space quite rapidly, although a secure channel will be required for healthbots, whether the bot uses a voice, haptic, or typing interface. Telemedicine services represent an obvious segment where chatbot interfaces are already in place.

Applications in clinical decision support for professionals will emerge in areas where the knowledgebase is complex, but mostly contained to similar datasets (e.g., EHRs and medication reconciliation use cases). Chatbots make sense, too, in areas where hands-free use is important. But, overall, adoption within clinical enterprises will be hampered by data access issues and will take longer to reach wide acceptance.

See for an excellent round-up of opinions from industry leaders on the future of chatbots/healthbots.

My final prediction is a cross-industry trend that will improve information flows in general, but I address it here in context of clinical decision support (CDS).

3)      Information discovery will no longer require an active search. Search will still exist, but it will exist primarily in the background. In fact, Susannah Fox, former CTO at HHS, once called search “wallpaper technology, something we don’t even see anymore, yet it’s clearly an activity worth discussion[1]. [This prescient quote comes from a post written in early 2010; Susannah is one of the best prognosticators in health care, after all!]

This shift from blank search boxes and look-up tables toward surfacing relevant information based on context, prior behavior, collaborative filtering algorithms & other patterns has been occurring for some time. One example that bridges the search and discovery paradigm is Google’s inclusion of knowledge graph items that are displayed at the top of search results when one enters a disease such as ‘diabetes’ in the search box.  

Another example is the TrendMD model[2], which appends personalized contextual links to the article someone is reading. The links can be sourced from any of the 3,000+ sources within the TrendMD network of scholarly publishers and professional news sites, which allows related information from other fields or specialties to be surfaced, but offers the assurance that links won’t be sourced from unwanted advertising sites. Like machine learning-enhanced healthbots, the quality of the related links improve over time with increased usage as the algorithms learn about an individual’s preferences and gain knowledge from the broader community of users.

Closer to home for the HIMSS audience, CDS Hooks, an open standard within the SMART on FHIR framework[3], will advance the clinical decision support goal of delivering the right information to the right person at the right time in the right format within the right channel. However, as described above, cognitive computing and machine learning technologies can take this type of information alert to the next level and act on the data that are surfaced. It will take time for executable CDS to become widespread; mistakes are too costly and clear rules for executing clinical orders aren’t sufficiently established yet to create workflows that are generally acceptable.   

At this point, I’d like to insert a cautionary note about the importance of privacy and transparency in CDS and healthbot systems. Bots are becoming popular and can be rather addictive when they learn from large numbers of information sources and deliver personalized results. For example, it’s great when Google Maps redirects us around traffic accidents in near real time, but the downside of a mistake, say a 10 minute detour, doesn’t compare to the downside of an incorrect dosage or incorrect rehab instructions. We don’t want to become too dependent on bots without understanding how they calculate outputs and maintain privacy of the individuals using the bots.

At HIMSS17, I look forward to reporting on notable advancements in these three areas and to putting the whole HIMSS experience in context of improved information flows and decision support for clinicians, researchers, and patients. 

See also this recent Firetalk video chat on chatbots and healthbots with Chuck Webster, MD and me:  



[2] TrendMD is a current client.

[3] See my blog on this topic:


Health IT Infrastructure Enables Clinical Decision Support within Workflow

“Infrastructure enables innovation” –Mignon Clyburn, FCC Commissioner 

I like this quote by Mignon Clyburn that Rob Havasy used in his presentation at the New England HIMSS National Health IT Week event last evening in Boston. People often balk at the effort and expense required for large infrastructure projects (remember, I’m from Boston and lived through the Big Dig!). Nonetheless, a strong reliable infrastructure is essential to establishing the basis for a vibrant and innovative ecosystem. 

Since attending my first national HIMSS meeting in 2010, this has been my consistent refrain: we need to establish foundational health IT infrastructure so that we can move on to disseminating information more efficiently and enabling advanced analytics. Large scale outcomes analysis and population health management simply aren’t feasible without a basic layer of data organization and management provided by open standards and interoperable systems. 

Much has been achieved in establishing the core record-keeping infrastructure. Currently, we’re making good progress in establishing interoperability standards for basic data exchange. Still, we need to go further than simple data exchange; the data that are exchanged have to be executable if we want to build real-time clinical decision support applications. In other words, we need a higher level of data interoperability that includes sufficient metadata to enable real-time integration into analytics systems for population health management analysis, diagnostic support systems, and the like.

CDS hooks

One of the initiatives in the health IT standards domain that I find promising is the CDS (clinical decision support) Hooks effort spearheaded by Josh Mandel, MD, a health informatics researcher at Harvard Medical School & Boston Children’s Hospital[1]. CDS Hooks works within the SMART on FHIR ecosystem to send notifications of information sources that may be of value to the user in real time. Users don’t have to know in advance that resources are available; instead relevant resources are presented within the user’s workflow for them to consult at their option.

For the most part, CDS resources have been important reference sources for academic and medical researchers, but their usage by practicing clinicians has remained limited. To move from being “nice to have” reference sources to truly achieving the goal of “making the right decisions as easy as possible to come by, and as easy as possible to execute”[2], clinical decision support tools need to be embedded in the workflow of the clinician, patient, or other decision maker.  There are still a lot of interoperability issues to work out, but I plan to watch the development in CDS Hooks and encourage publishers of evidence-based databases and other resources to explore intently how they can connect their resources to the SMART on FHIR ecosystem.

Delivering the right information to the person at the right time in the right format via the right channel (the 5 rights of clinical decision support) enables better decisions and supports improved information flows to all stakeholders, including patients. Advancements in core health IT infrastructure and improved interoperability standards will help make these 5 rights an everyday practice. That’s why #IHeartHIT.


[1] This interview with Josh in Healthcare Informatics provides a useful introduction to CDS Hooks:

[2] Jonathan Teich, MD quoted in, June 14, 2006.


CMS Hospital Star Ratings Offer Incremental Step Forward in Transparency

Would you consult a Michelin Guide if you were looking for the closest pizza place? No. But for people who are seeking a “once in a lifetime” dining experience on their special vacation, finding the right 3-star Michelin restaurant may be just what they want.

Michelin has a storied history and promotes their strict system for evaluating restaurants via their publishing & promotional efforts. Still, most consumers who are not familiar with Michelin’s methodology would probably guess that a 3-star rating isn’t so wonderful, compared to 4 and 5-star ratings doled out by so many other restaurant reviewers.[1]

My point is: when it comes to ratings, it is critical to know what universe is being rated and the methodology used to calculate the ratings.

On that score, the CMS Quality Star Ratings for hospitals offer an incremental step forward in improving the value of the Hospital Compare data to consumers. If nothing else, the Quality Star Ratings generate attention, which can lead to further research that uncovers richer information on which to base decisions.

Value of Ratings

Ratings and rankings of products and services will always be imperfect. So, why are ratings so popular? In part, because they fill an information gap for data that either aren’t available or aren’t easy to summarize because of their complexity. In essence, ratings are a signal of comparative quality and often a proxy for missing data.

Measuring and comparing quality among healthcare provider organizations presents an especially thorny problem as described by Andy Oram in a 2-part series on measuring quality[2].  One pertinent extract from part 2 for which I provided some input:

We are still searching for measures that we can rely on to prove quality–and as I have already indicated, there may be too many different “qualities” to find ironclad measures. McCallum offers the optimistic view that the US is just beginning to collect the outcomes data that will hopefully give us robust quality measures.

CMS Hospital Quality Star Ratings

What is the overall objective of the CMS Star ratings? In essence, the star ratings serve to condense the information in the Hospital Compare database and improve the usefulness of that data by making it faster and easier for consumers to assess quality of hospitals in a comparable manner. The hospital star ratings also complement other Star Rating initiatives from CMS that cover nursing homes, dialysis facilities, home health care ratings and health plan finder ratings.

The composite star rating for hospitals is based on 7 quality categories:

1. Mortality


2. Safety of Care


3. Readmission


4. Patient Experience


5. Effectiveness of Care


6. Timeliness of Care


7. Efficient Use of Medical Imaging



Categories were chosen to align with CMS Hospital Value-Based Purchasing (HBVP) program. The right-hand column lists the weights that were assigned each category in calculating the composite rating. Note, the methodology document describes how the weightings were calculated in more detail and should be consulted by those who really want to dig into the details. (See the Fact Sheet from CMS for a description of the ratings and a link to the methodology report:

The composite ratings and the underlying measures remain limited to values that are currently measured by HEDIS, HCAHPS surveys and other quality initiatives. As I mentioned in Andy Oram’s article, measuring outcomes in a meaningful and comparable way is still in early stage. In the future, clinical outcomes measures will improve and the ability to measure “effectiveness of care”, for instance, will improve and that category will likely be weighted more heavily (it is currently weighted at only 4% of the composite rating).

Limitations of CMS Ratings = Opportunities for Data Publishers

Back in 2011, after a lively presentation by then US CTO Todd Park (all of his presentations are lively!) on the topic of Data Liberación, I wrote:

Park spent some time describing and and how they can act as a resource for entrepreneurs. I loved his analogy between 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  What the commenter didn’t realize is that NOAA data form the backbone of 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.[3]

The door remains open to enterprising data companies that are willing to do the hard work of aggregating and integrating data from multiple sources and presenting the aggregated data clearly for consumers. In most cases, a single source of information isn’t sufficient to guide consumer decisions. Proximity and referrals will remain key determinants of choice of hospitals for consumers.  Furthermore, choice is restricted by whether providers are included a patient’s health plan network.

So, from my perspective, the new CMS Hospital Quality Star Ratings represent a step forward in the supply of source data and ratings methodologies that healthcare data publishers can leverage to publish and promote value-added quality guides and other transparency tools for healthcare consumers.

To close, I’ll quote the final paragraph from Oram’s article:

When organizations claim to use quality measures for accountable care, ratings, or other purposes, they should have their eyes open about the validity of the validation measures, and how applicable they are. Better data collection and analysis over time should allow more refined and useful quality measures. We can celebrate each advance in the choices we have for measures and their meanings.




[1] Priceonomics recently posted a good article on the Michelin Guides:  Accessed 8/2/2016.

[2] Part II of Oram’s 2-part series focuses on assessing and measuring healthcare quality:, accessed 8/2/16.

[3] Leveraging the Liberated Data, Health Content Advisors blog, Accessed 8/2/16.

Errors occurred while processing template[pageRendered/]:
StringTemplate Error: Can't parse chunk: {settingHomePageKBArticle}" target="_blank">Learn how.</a></li>
<li>If you have already selected a front page, make sure it is enabled. Click on the Cubes icon (top right) and then click the "enable page" button.</li>

: expecting '"', found '<EOF>'
StringTemplate Error: problem parsing template 'pageRendered/noDefaultModule': null
StringTemplate Error: problem parsing template 'pageRendered/noDefaultModule': null