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Healthcare Analytics Biggest Competitor – Excel

Posted on March 16, 2016 I Written By

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

This tweet highlighted an interesting observation I had after experiencing so many healthcare analytics pitches going into and at HIMSS. I’ll set aside the email comment for now (email is still very powerful if done right) and instead focus on Excel. Here’s what I discovered about healthcare analytics:

Excel is a healthcare analytics company’s biggest competitor.

It’s crazy to think about, but it’s true. When a healthcare organization is evaluating healthcare analytics platform the “legacy system” that they’re usually trying to replace is Excel. I can’t tell you how many times I heard analytics vendors say that “Hospital A was doing all of this previously on a bunch of Excel spreadsheets.” If you work at a hospital, you know that you have your own garden of Excel spreadsheets that are used to run your healthcare organization as well.

When you think about the features of Excel, it’s no wonder why it’s so popular in healthcare and why it’s a challenging competitor for most healthcare organizations. First, it’s free. Ok, it’s not technically free, but every healthcare organization has to buy it for a lot of reasons so that cost is already in their standard budget. Second, every computer in the organization has a copy of Excel on it. Third, the majority of people in healthcare are familiar with how to use Excel. Since we love to talk about healthcare IT usability, Excel is extremely usable. Fourth, Excel is surprisingly powerful. I know many healthcare analytics organizations could argue its limitations, but Excel is more powerful than most people realize.

That’s not to say that Excel doesn’t have its weaknesses. I’m sure that most organizations have experienced time wasted trying to figure out which Excel file has the accurate data or is the most up to date. No doubt you’ve experienced the multiple copy problem where 2 people are editing the same file and now you have 2 versions of the same file that need to be merged. Document management software has helped with this situation in many regards as it locks the file when someone starts to edit it and things like that. However, it’s still often a problem.

Another problem with Excel as compared with a true analytics platform is when you want to go in and slice and dice the data. What’s possible with a true analytics platform is so much more powerful when you want to really dive in and chop up the data in unique ways.

While possible in Excel, most uses of Excel are backwards facing data analysis and tracking. You can do some near real-time data analysis in Excel, but newer analytics platforms do a much better job of real time analytics using the latest data.

Of course, the biggest problem long term with Excel is that it can’t scale. Once you reach a certain amount of data points or a certain amount of complexity in the data, Excel falls on its face. However, most healthcare organizations are still working on small data, so Excel’s worked fine.

I’m sure there are many more issues. Hopefully some analytics vendors will chime in with more examples in the comments or on their own blogs. However, it’s worth acknowledging that for many organizations it’s really hard for them to find a healthcare analytics solutions that’s so much better than Excel. Plus, many of these expensive analytics solutions fail when it comes to some of the things that makes Excel great (ie. Free, Usable, Ubiquitous).

A Look At Precision Medicine Solutions Available Today

Posted on December 22, 2015 I Written By

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

Personalized and Precision Medicine are all the buzz since President Obama announced the Precision Medicine Initiative. However, after the government tragedy known as meaningful use, many are reasonably skeptical of government initiatives to improve healthcare. Plus, the rhetoric around what’s possible with precision medicine and the realities that most hospitals and doctors face every day feels like a massive disconnect.

The reality is that there’s good reason to be skeptical of precision medicine. Think about the scope of the problem. The world of health data that we live in today is 10-20 times bigger that it was even a decade ago. That’s a massive increase in the amount of data available. Plus, much of that data is unstructured data. Combine the volume of data with the accessibility (or lack therof) of that data and it’s easy to see why some are skeptical of really implementing precision medicine in their hospital today.

When you look at current EHR systems, none of them are built to enable precision medicine. First, they were built as massive billing engines and not as engines designed to improve care. Second, meaningful use has hijacked their development roadmap for years and will likely continue to hijack their development teams for years to come. Finally, there’s been so much money doing what they’re doing, what motivation do the entrenched EHR companies have to go out and do more?

The unfortunate reality of EHR systems is that they’re not built for real time availability of data analytics that provides improved care and precision, personalized medicine. Some may get there eventually, but we’re unlikely to see them get there anytime soon. I’ve heard precision medicine defined as a puzzle with 3 billion pieces. We have to start looking outside of traditional EHR companies to start solving such a complex puzzle.

The good news is that even though EHR vendors are not providing precision medicine solutions, we’re starting to see other vendors providing precision medicine solutions today. You no longer need to wait for an EHR vendor to participate.

One example of precision medicine happening today is the recently announced SAP Foundation for Health (we’ll forgive them on the somewhat confusing name). At the core of the SAP Foundation for Health is the SAP Hana engine. Unlike many EHR systems, SAP Hana was designed for real time data analysis of massive amounts of data and that includes both granular and free form data. You can see this capability first hand in the work SAP is doing with ASCO (American Society of Clinical Oncology) and their CancerLinQ project.

Dr. Clifford Hudis from CancerLinQ (Created by ASCO) described how personalized medicine to his grandfather was going around and visiting each patient. Over time that practice stopped and we started seeing patients in clinics where we generally only had one data set available to us: the clinical data that we captured ourselves on a paper chart. Unfortunately, as we moved electronic, we just recreated our paper chart world in electronic form. It’s too bad we didn’t do more during our shift to going electronic. However, that still means we have the opportunity to aggregate and analyze health data for the benefit of our patients. In some ways, we’re starting to democratize access to health data in order to enable precision medicine.

As Dr. Hudis pointed out, healthcare currently really only learns from patients who take part in clinical research trials. In other words, that only amounts to about 3% of adult patients who contribute to our learning. This limits our view since most clinical research trials have a biased sample which aren’t representative of the general population. How can we create personalized medicine if we only have data on 3% of the patient population? This is the problem CancerLinQ and SAP Foundation for Health are working to solve. Can they create a platform that learns from every patient?

ASCO together with SAP’s Foundation for Health is working to aggregate and analyze data across cancer patients regardless of whether they’re part of a clinical research study or not. In the past, Dr. Hudis pointed out that cancer tracking use to track cancer populations with simple groups like “small cell cancer” versus “non-small cell cancer.” That was a start, but had limited precision when trying to treat a patient. With this relatively new world of genomics, ASCO can now identify, track, and compare a patient’s cancer by specific genomic alterations. This is a fantastic development since tumors generally contain changed DNA. We can now use these DNA abnormalities to classify and track cancer patients in a much more precise way than we’ve done in the past.

This platform enables oncologists the opportunity to see real time information about their patient that’s personalized to the patients own genetic abnormalities. Instead of calling around to their network of oncologist friends, Cancer LinQ provides real time access to other patient populations with similar genetic abnormalities and could give them insight into what treatments are working for similar patients. This can also provide benchmarking for oncologists to see how they compare against their colleagues. Plus, it can show real time data to an oncologist so they can know how thorough and consistent they are with their patient population. Instead of working in a bubble, the oncologist can leverage the network of data to provide true precision medicine for their patients.

Another great example of precision medicine happening today is seen in the work of Carlos Bustamante, Professor of Genetics and Stanford University School of Medicine. Carlos is using SAP Foundation for Health to quickly identify genetic abnormalities in high performing athletes. Rather than recount the stories of Carlos’ work here, I’ll just link to this video where Carlos talks about the amazing insights they’ve found from studying the genomic abnormalties of high performing athletes. I love that his precision medicine work with high performing athletes has significant potential benefits for every patient.

Carlos is spot on in the video linked above when he says that the drop in genomic sequencing costs would be like taking a $400,000 Ferrari and now selling it for 10 cents. What originally took $13 billion and years of effort to sequence the first genome now takes $1500 and a few days. Access to every patient’s genome is going to change the types of drugs we develop, the treatment options we provide patients, our choice of drugs to treat a patient, and much much more. You can see that first hand in the work that ASCO and Stanford University School of Medicine are doing. Is there any more personalized medicine than the human genome?

Of course, the genome is just one of the many factors we’re seeing in the precision medicine revolution. We can’t forget about other variables that impact a patient’s health like environmental, behavioral, patient preference, and much more. We really are looking at a multi-billion piece puzzle and we’re just getting started. Remember that healthcare is not linear, but we’ve been treating it like it is for years. Healthcare is a complex matrices of challenges and we need our technology solutions to reflect that fact.

I see a beautiful future for precision medicine that’s already begun and builds into the future. We’re developing and targeting new drugs, devices and services that work for populations and individuals. We’re seeing new open, secure platforms that provide real-time flexible R&D analysis, genomics and other “omics” disciplines, patient cohort building and analysis, patient trial matching, and extended care collaboration solutions.

Data by itself is not valuable. However, the right engine on top of the right data is changing how we look at healthcare. We’re getting a much more precise view of each individual patient. Where have you seen precision medicine starting to take hold? What precision medicine solutions are you using in your organization?

Also, check out this infographic which looks at SAP’s view of precision medicine:
Personalized Medicine You Can Do Today

SAP is uniquely positioned to help advance personalized medicine. The SAP Foundation for Health is built on the SAP Hana platform which provides scalable cloud analytics solutions across the spectrum of healthcare. SAP is a sponsor of Influential Networks of which Healthcare Scene is a member.

New Data Driven Perspectives in Healthcare w/ @MandiBPro @Ashish_P @techguy

Posted on December 10, 2015 I Written By

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

UPDATE: Here’s the recorded version of our interview (Ashish had video issues, so he joined audio only)

As part of our ongoing series of Healthcare Scene interviews (see all our past Healthcare Scene interviews on YouTube), we’re excited to announce our next interview with Mandi Bishop and Ashish Patel where we’ll be talking about New Data Driven Perspectives in Healthcare. If you’d like to watch the interview live and get your questions answered, you can join us on blab, Monday, December 13th at Noon ET (9 AM PT).

In this interviews I’m lucky to have two of the most knowledgeable people in healthcare when it comes to various healthcare data sources and how to extract value out of that data. Plus, they’ll offer ways in which data has changed their perspective on healthcare. I’m also excited to hear about the new data sources that are available for health care and how we are using and will use that data to improve healthcare as we know it.


Here are a few more details about our panelists:

You can watch our interview on Blab or in the embed below. We’ll be interviewing our panelists for the first 30-40 minutes of the blab and then we’ll open up to the audience for questions for the rest of the hour. We hope you can join us live. We’ll also share the recorded video after the event.

On Premise versus Cloud Analytics

Posted on December 7, 2015 I Written By

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

We’ve long been hearing those people talk about the cloud. Most famously, Jonathan Bush has been beating the drum of the power of the cloud and how the cloud EHR vendors are going to take down their dinosaur client server counterparts. In the hospital world that has yet to happen for EHR software, but the cloud has still become a major part of every healthcare IT organization.

When we look at our personal lives, we all have data stored in the cloud. The cloud is a major part of most of our lives. I know I try to store nothing locally and run pretty much my entire business and personal life in the cloud. I had to recently replace my cell phone. Turns out that the change didn’t matter at all since everything I did was in the cloud. I literally didn’t even know the number of my cell phone. I just got the new phone, logged into the cloud and everything started to sync up (I did have to log in to a bunch of apps). It was beautiful.

In the latest movements towards the cloud, I’ve seen a lot of people talking about healthcare analytics heading to the cloud. It begs the question, “Will the cloud win out in the healthcare analytics space?

I think the biggest naysayers to cloud analytics are those who say that they aren’t planning to move all their data to the cloud and they’re not comfortable with all their EHR data in the cloud so they don’t see how cloud analytics will work for them. (Side Note: I always love how we claim privacy and security when we don’t want to do something, but we don’t actually do something to ensure the privacy and security of our data.)

No doubt opinions like this will slow the adoption of cloud analytics. Most vendors I know are going to offer either option for the forseeable future. However, there are some new technologies which leave your data in place, but can leverage the cloud to access the data as needed. I first saw this with SAP, but there are probably others that are doing it too. I think technologies like this will change many people’s view of using the cloud to handle their analytics.

On a much larger scale, I don’t think health care will have a choice but to use the cloud. I don’t see every health care organization building their own private cloud in order to do all of the genomic medicine which is starting to come. I don’t see every health care organization getting the benchmarking and “grand rounds” style of data that analytics providers can provide across disparate organizations. I don’t see most organizations being able to afford to build their own analytics engine on site.

I could keep going on, but the way health care analytics is going I’m not sure that hospitals will have any choice but to embrace cloud analytics. I know this leaves many hospital CIOs uncomfortable. However, burying your head in the sand and acting like it’s not going to happen won’t make you more comfortable. Denial isn’t a good strategy. The best way to be comfortable with it and ensure that healthcare analytics clouds are safe, private and secure is for hospital organizations to make a real investment of time into what’s going on.

What do you think? Is the future of analytics at hospitals going to be in the cloud?

Software Design – Dilbert Cartoon

Posted on August 31, 2015 I Written By

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

I’m afraid I’ve seen this approach in far too many healthcare organizations. This is particularly true in health data analytics. Let me know if you can relate to this cyclical discussion. I do think it’s getting better though as more people have experience in the process. It’s just been a very long road.

EMRs Must Support Hospital Outcomes Reporting

Posted on August 25, 2015 I Written By

Anne Zieger is veteran healthcare editor and analyst with 25 years of industry experience. Zieger formerly served as editor-in-chief of FierceHealthcare.com and her commentaries have appeared in dozens of international business publications, including Forbes, Business Week and Information Week. She has also contributed content to hundreds of healthcare and health IT organizations, including several Fortune 500 companies. She can be reached at @ziegerhealth or www.ziegerhealthcare.com.

Should a hospital be paid if it doesn’t make its outcomes statistics public? Pediatric heart surgeon Dr. Jeffrey Jacobs says “no.” Jacobs, who chairs the Society of Thoracic Surgeons National Database workforce, recently told CNN that he believes reimbursement should be tied to whether a hospital shares data transparently. “We believe in the right of patients and families to know these outcomes,” said Jacobs, who is with the Johns Hopkins All Children’s Heart Institute in St. Petersburg, FL.

Jacobs’ views might be on the extreme side of the industry spectrum, but they’re growing more common. In today’s healthcare industry, which pushes patients to be smart shoppers, hospitals are coming under increasing pressure to share some form of outcomes data with the public.

I’ve argued elsewhere that in most cases, most hospital report cards and ratings are unlikely to help your average consumer, as they don’t offer much context how the data was compiled and why those criteria mattered. But this problem should be righting itself. Given that most hospitals have spent millions on EMR technology, you’d think that they’d finally be ready to produce say, risk-adjusted mortality, error rates and readmissions data patients can actually use.

Today, EMRs are focused on collecting and managing clinical data, not providing context on that data, but this can be changed. Hospitals can leverage EMRs to create fair, risk-adjusted outcomes reports, at least if they have modules that filter for key data points and connect them with non-EMR-based criteria such as a physician’s experience and training.

While this kind of functionality isn’t at the top of hospitals’ must-buy list, they’re likely to end up demanding that EMRs offer such options in the future. I foresee a time when outcomes reporting will be a standard feature of EMRs, even if that means mashing up clinical data with outside sources. EMRs will need to interpret and process information sources ranging from credentialing databases and claims to physician CVs alongside acuity modifiers.

I know that what I’m suggesting isn’t trivial. Mixing non-clinical data with clinical records would require not only new EMR technology, but systems for classifying non-clinical data in a machine-readable and parseable format. Creating a classification scheme for this outside data is no joke, and at first there will probably be intermittent scandals when EMR-generated outcomes reports don’t tell the real story.

Still, in a world that increasingly demands quality data from providers, it’s hard to argue that you can share data with everyone but the patients you’re treating. Patients deserve decision support too.

It’s more than time for hospitals to stop hiding behind arguments that interpreting outcomes data is too hard for consumers and start providing accurate outcomes data. With a multi-million-dollar tool under their roof designed to record every time a doctor sneezes, analyzing their performance doesn’t take magic powers, though it may shake things up among the medical staff.  Bottom line, there’s less excuse than ever not to be transparent with outcomes. And if that takes adding new functionality to EMRs, well, it’s time to do that.

The Power of Medical Device Data Infographic

Posted on August 6, 2015 I Written By

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

One of the advantages of devices is that they’re really good at collecting vast amounts of data. One of the problems we have in healthcare is that our medical devices collect a lot more healthcare data than we actually use. It’s too bad since no doubt there is a lot more benefit we could receive from all the medical device data we’re collecting.

This point was really driven home when I saw the infographic below from Capsule which looked at The Power of Medical Device Data. Take a look and see what I mean and then ask yourself, how could we better use medical device data?
THE POWER OF MEDICAL DEVICE DATA to Healthcare

How Much Time Do You Spend Cleaning Your Data?

Posted on June 29, 2015 I Written By

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

I recently came across this really great blog post talking about data scientists wasting their time. Here’s a quote from the article (which quotes the NYT):

“Data scientists, according to interviews and expert estimates, spend 50 percent to 80 percent of their time mired in [the] mundane labor of collecting and preparing unruly digital data, before it can be explored for useful nuggets.”
– Steve Lohr, NYT

Then, they have this extraordinary quote from Monica Rogati, VP for Data Science at Jawbone:

“Data scientists are forced to act more like data janitors than actual scientists.”

Every data scientist will tell you this is a problem. They spend far too much time cleaning up the data and they all wish they could spend more time actually looking at the data to find insights. I’ve seen this all over health care. In fact, I’d say we have more data janitors than data scientists in healthcare. Sadly, many healthcare data projects clean up the data and then don’t have any budget left to actually do something with the data.

The solution to this problem is easy to write and much harder to do. The solution is to create an expectation and a culture of clean data in your organization.

I predict that over the next 5-10 years, healthcare data is going to become the backbone of healthcare data decision making. Those organizations that houses are a mess are going to be torn down and sold off to the hospital that’s kept a clean house. Is your hospital data clean or dirty?

Medical Device Vendors Will Inevitably Build Wearables

Posted on May 21, 2015 I Written By

Anne Zieger is veteran healthcare editor and analyst with 25 years of industry experience. Zieger formerly served as editor-in-chief of FierceHealthcare.com and her commentaries have appeared in dozens of international business publications, including Forbes, Business Week and Information Week. She has also contributed content to hundreds of healthcare and health IT organizations, including several Fortune 500 companies. She can be reached at @ziegerhealth or www.ziegerhealthcare.com.

As we’ve reported in the past, hospitals are throwing their weight behind the use of wearables at a growing clip. Perhaps the most recent major deal connecting hospital EMRs with wearables data came late last month, when Cedars-Sinai Medical Center announced that it would be running Apple’s HealthKit platform. Cedars-Sinai, one of many leading hospitals piloting this technology, is building an architecture that will ultimately tie 80,000 patients to its Epic system via HealthKit.

But it’s not just software vendors that are jumping into the wearables data market with both feet. No, as important as the marriage of Epic and HealthKit will be to the future of wearables data, the increasing participation of medical device giants in this market is perhaps even more so.

Sure, when fitness bands and health tracking smartphone apps first came onto the market, they were created by smaller firms with a vision, such as the inventors who scored so impressively when they crowdfunded the Pebble smartwatch.  (As is now legendary, Pebble scooped up more than $20M in Kickstarter funding despite shooting for only $500,000.)

The time is coming rapidly, however, when hospitals and doctors will want medical-grade data from monitoring devices. Fairly or not, I’ve heard many a clinician dismiss the current generation of wearables — smartwatches, health apps and fitness monitoring bands alike — as little more than toys.  In other words, while many hospitals are willing to pilot-test HealthKit and other tools that gather wearables data, eventually that data will have to be gathered by sophisticated tools to meet the clinical demands over the long-term.

Thus, it’s no surprise that medical device manufacturing giants like Philips are positioning themselves to leapfrog over existing wearables makers. Why else would Jeroen Tas, CEO of Philips’ healthcare informatics solutions, make a big point of citing the healthcare benefits of wearables over time?

In a recent interview, Tas told the Times of India that the use of wearables combined with cloud-based monitoring approaches are cutting hospital admissions and care costs sharply. In one case, Tas noted, digital monitoring of heart failure patients by six Dutch hospitals over a four-year period led to a 57% cut in the number of nursing days, 52% decrease in hospital admissions and an average 26% savings in cost of care per patient.

In an effort to foster similar results for other hospitals, Philips is building an open digital platform capable of linking to a wide range of wearables, feeds doctors information on their patients, connects patients, relatives and doctors and enables high-end analytics.  That puts it in competition, to one degree or another, with Microsoft, Qualcomm, Samsung, Google and Apple, just for starters.

But that’s not the fun part.  When things will get really interesting  is when Philips, and fellow giants GE Healthcare and Siemens, start creating devices that doctors and hospitals will see as delivering medical grade data, offering secure data transmission and integrating intelligently with data produced by other hospital medical devices.

While it’s hard to imagine Apple moving in that direction, Siemens must do so, and it will, without a doubt. I look forward to the transformation of the whole wearables “thing” from some high-end experimentation to a firmly-welded approach built by medical device leaders. When Siemens and its colleagues admit that they have to own this market, everything about digital health and remote monitoring will change.

Do Hospital #HIT Leaders Need Business Coaches?

Posted on May 4, 2015 I Written By

Anne Zieger is veteran healthcare editor and analyst with 25 years of industry experience. Zieger formerly served as editor-in-chief of FierceHealthcare.com and her commentaries have appeared in dozens of international business publications, including Forbes, Business Week and Information Week. She has also contributed content to hundreds of healthcare and health IT organizations, including several Fortune 500 companies. She can be reached at @ziegerhealth or www.ziegerhealthcare.com.

Though they don’t always cop to it, a goodly number of senior business leaders pay very good money — I’ve heard quotes as high as $10,000 a year — for the help of an executive coach. Part high-end consultant, part amateur therapist, executive coaches help VPs and C-suite execs make better decisions by giving them an unvarnished view of their current situation and the inspiration to carry out their most ambitious plans.

This may have something in common with bringing on a partner like, say, Deloitte, but it’s decidedly different. While executive coaches may have worked in a bigshot consulting firm like PwC, their relationship is decidedly with the individual, and a trusted one at that.  The process of executive coaching sounds like a very useful one. (I’ll probably try it someday — when I have $10,000 to spare!)

The thing is, while I could be missing something, I’ve never heard so much as a hint that senior HIT executives are retaining executive coaches. It makes me wonder whether CtOs and VPs of IT still define their job largely by technical skills rather than their capacity for making strategic decisions with hospital- or system-wide implications.

The inescapable reality is that HIT execs have long outgrown supergeek status and are increasingly a key part of their healthcare organization’s future. So if they’re open for growth, HIT leaders may very well want to test out the executive coaching model, particularly in working out the following:

  • ACO development:  While the ACO contracting and development process may be led by other departments, health IT leaders have the power to make or break these agreements by how they support then. A VP of business development may spearhead such efforts, but it’s the health IT exec who will make or break how effectively the ACO handles population health support, risk management, data analytics and more.
  • Managing digital health: I hardly need to remind HIT execs of this, but the most important directives as to how to work with digital health tools aren’t going to come from the CEO down, but from the CIO or VP up. With the healthcare industry just beginning to grasp the value app-laden smartphones and tablets, smart watches, sensor-laden clothing, telemedicine and other rapidly emerging  technologies can bring, it’s the health IT exec who must lead the charge. And that means knowing how to solve critical business problems that extend well beyond IT’s boundaries.
  • EMR transformation: As hard as you’ve worked on implementing and tuning your EMR, it’d be nice to think you could stick a fork in it and consider it done.  But EMRs are having new demands placed on them seemingly every day, including integration of massive volumes of wearables and other patient-generated data; number-crunching and making sense of population health data; connecting revenue cycle management functions with EMRs and much more.  Deciding how to handle this spectrum of issues is the job of a business/tech thinker, not solely an IT guru.

Look, I’m not suggesting that the executive coaching is for everyone, health IT executives included. But I do believe that the right kind of executive coaching relationship could help HIT leaders to make a smoother transition into the even more critical role they are inheriting today. And anything that supports that transition is probably worth a shot.