Digital Healthcare Organizations, Healthcare Analytics, Learning Healthcare System, Quadruple Aim

Collect, Connect, Analyze and Apply – Four Data and Analytics Competencies that All Digital Healthcare Organizations Must Master

There is a common axiom that today “every business is a digital business”.  There are several alternatives to this as well – “every business is a technology business” and “every business will become a software business” (Microsoft’s CEO Satya Nadella’s comment at the 2015 US Convergence conference).  Healthcare organizations are no exception – they are all digital, technology, and software businesses now.  And at the heart of all digital businesses is data and analytics.  This concept is well illustrated by the Gartner graphic describing their concept of a “digital business platform”.   They are not referring specifically to technology platforms (they do also have great graphics on this), but instead are referring to the components that digital organizations must master and connect to survive and thrive today.   Their graphic shows a circle in the middle, connected to 4 circles that surround it.   On the outside are the components of IT Systems, Things (devices, the internet of things), Customers, and Ecosystems.  In the center circle, connected to all other circles, is Intelligence.   Hence, my statement earlier that data and analytics are at the center of all digital businesses.  Today all businesses, and especially all healthcare organizations, must realize this, and must make improving their data and analytics capabilities a top strategic priority

What is a “digital business” though?  I have not found a definition that resonates with me.  At the simplest level, I see a digital business as a business that uses digital technology (as opposed to a business that only uses analog technology – I am sure some of these still exist, but are getting rarer).  As I have come to see things from a data and analytics perspective, to be successful, all digital healthcare organizations (meaning ALL healthcare organizations) must master the following data and analytics competencies – “collect, connect, analyze and apply” – to be successful.  I argue that the ease of obtaining and operationalizing these competencies gets harder as you go down the list.  The first two competencies – collecting and connecting – are primarily technical in nature.  Analyzing is a combination of people and technology skills.  Applying the insights obtained from the analysis is vastly more difficult.  This goes to the core of being a data driven organization, and involves people who are data literate, and who have processes in place (from individuals, teams, units, departments, to the whole organization) to apply the insights to make data-driven or insight-informed decisions.


Since almost all digital technology creates data that could be captured, important questions emerge.  What data should we collect?  Should we collect all data that is produced, even if we don’t have a current business case for using the data, but one might arise in the future?  If we collect only a subset of the data, what data should we collect?  Who makes those decisions?  Where should we store the data – in the source system, in an enterprise data warehouse (EDW), in a data lake (pick your favorite term for data lake if you don’t like that term), in a logical data warehouse, etc.?  It is clear that that some data needs to be collected, but probably not every piece of data from every digital asset.


It is extremely important that these data sources and data storage systems all be connected.   The business will need to obtain insights from this data, and the data needs to be able to accessed and brought together to be analyzed to get this insight.  It is no longer acceptable or practical to only have a “single source of truth” as EDW’s are sometimes called.  There are too many source systems now in an organization, and it is too time consuming, difficult and expensive to validate each source system’s data and put that into an EDW.   Organizations should focus on connecting the sources systems and storage systems, so that appropriate data from these multiple disparate sources and systems can be then analyzed.


You can’t analyze everything – there is not enough time, money, or people to do this.  You should focus on analyzing those things that are strategically important for your business.  Governance is key to helping the data and analytics organization prioritize it’s efforts.  In healthcare, these things often involve components of the “Quadruple Aim”.  The Quadruple Aim describes those factors that healthcare organizations should focus on to optimize their healthcare system, and its’ performance.  These include:

  • Improving the patient experience (quality of care delivered and satisfaction with their experience)
  • Improving the health of populations
  • Reducing the cost of health care
  • Improving the experience of health care providers and workers

In addition, understanding and optimizing the financial performance of the healthcare organization itself is of strategic importance.

The people aspects of analytics competency involve recruiting, training and retaining the skilled workforce to perform the analytics.  There is a spectrum of skill sets needed – from end-users and analysts who perform basic descriptive and diagnostic analytics, all the way up to data scientists.   Most organizations struggle with finding these talented people, so they must also develop internal training programs to acquire these skills.

The types of analytics that can be performed are:

  • descriptive analytics (describing what happened – a provider scored 98% on making sure the patients in his panel achieved compliance with being screed for colorectal cancer)
  • diagnostic analytics (describing why something happened – a provider only scored 13% on making sure the patients in his panel achieved compliance with keeping their hemoglobin A1C level below a certain value – because he only managed to see 43% of his diabetic patients in that time period, and he had no formal education around this topic, and had no follow-up by his staff to see how his patients were complying with his recommendations)
  • predictive analytics – (predicting what could happen – which patients are likely to progress from being low risk for requiring a lot of money and resources to manage their medical conditions, to high-risk patients)
  • prescriptive analytics – (generating prescriptive information that can be used to guide care most effectively – recommending what treatments should be applied to a 67 year-old female patient with a history of breast cancer, diabetes and rheumatoid arthritis; who is on insulin, steroids and other immunosuppressive medications; who presents with an overwhelming systemic response to pneumonia called sepsis)

All healthcare organizations need to be able to perform descriptive and diagnostic analytics extremely well.  In addition, the analysts need to move from being “report writers” to true analysts – analyzing the data to obtain actionable insights and then educating the end-user on those insights.  In order to do this, analysts must be trained to do these analyses, then must acquire some domain knowledge of the areas they are responsible for, and they must establish good working relationships with those end-users.

Although not all organizations will acquire the talent and capabilities to perform predictive analytics, they must be capable of incorporating predictive algorithms developed by someone else into it’s system.  The ability to perform prescriptive analytics is still in development for the most part.  This capability must also be able to incorporated into an organization as it matures.

The technology side of analytics involve finding the suite of analytical and visualization tools needed to get the insights to answer the questions that arise, and communicate those insights.   Say what you will about Microsoft Excel – it is still a go to tool for most organizations, and for some organizations it is their only tool.  You can perform some pretty sophisticated analyses with Excel now, including predictive modeling.  Gartner has a great concept around a “Modern BI and Analytics Platform”.  This consists of an Information Portal, an Analytics Workbench, and a Data Science Laboratory.  The Information Portal consists of reports and dashboards, that can be created by end-users and report writers.  This is essentially the self-service BI component.  The Analytics Workbench is for the analysts to use, and involves more comprehensive analytics tools.  The Data Science Laboratory is for the Data Scientists to use – and consists of not only the basic analytical tools, but also advanced analytical tools, including the use of machine learning, deep learning, reinforcement learning, etc.  There is a need for ALL healthcare organizations to acquire the information portal and analytics workbench capabilities.  The data science lab is becoming extremely important and relevant, and this could be acquired within the organization, or a partnership could be develop with an analytics vendor to perform this service.

Just as important to getting the insights from analysis is communicating and visualizing the data and insights in the most effective ways.  Only presenting insights in the form of spreadsheets or pie charts is simply not acceptable today.  We are visual learners, and you can convey a lot more information, in a shorter period of time with effective visualization.  So it is key to acquire the technology to do this well, and then train the staff to not only be able to do this,  but also understand the critical importance of this component.


This is the most difficult competency, and it usually lies outside of the data/analytics organization and  within the service lines.  However, I propose that data and analytics leaders OWN making sure that the organization understands this concept, and helps the organization excel at it.  This is the competency of applying the insights learned from the analyses of data, to make better informed decisions on these key strategic initiatives.   Becoming a data-driven organization is HARD, much harder than the other stages.   This involves creating a workforce that is more data literate, and a culture that is obsessive about using data and analytics to answer every important question and drive every important process.

Data and analytics are also foundational to the  “Learning Healthcare System” concept as proposed by the Institute of Medicine (IOM) (there is a great overview of this concept here).  The IOM states that a learning healthcare system is “designed to generate and apply the best evidence for the collaborative healthcare choices of each patient and provider; to drive the process of discovery as a natural outgrowth of patient care; and to ensure innovation, quality, safety, and value in health care” (from article linked above).  At the core of the learning healthcare system is the ability to capture data, analyze the data to obtain insights, and then be able to quickly apply those insights to improve patient care.  It is impossible for an organization to “learn” if they don’t have robust data and analytics capabilities, and robust change management and change leadership capabilities.

The culture must be created where it is an expectation that people use data and insights to make better decisions – from the Board of Directors, to the CEO and Senior Leadership, all the way down to front-line workers.  This must be driven into all levels of the organization and into all processes.   This is the heart of performance or quality improvement, and operational excellence.  But it must be broader than that, and involve most, if not all, employees.   (Some of you may not agree with the last sentence, but I propose that housekeepers need to be data-driven – which rooms/areas need to be cleaned next because there is a shortage of a certain unit’s beds and patients are waiting to be transferred into these beds, etc.)

We are all collecting enormous amounts of data about our patients and our organizations – it is now time for healthcare organizations to do something with that data to further the Quadruple AIM, and all other important strategic initiatives.   Hopefully, understanding the framework of data and analytics competencies that are needed – collect, connect, analyze and apply – will help healthcare organizations do this.

Artificial Intelligence, Cerner, Electronic Health Record (EHR), Healthcare Analytics, Healthcare Technology, Machine Learning

Cerner’s Strategy to Deploy “Intelligence” into the Cerner Ecosystem – Insights from the 2018 Cerner Strategic Client Summit

I feel privileged to have been invited for the second year in a row to Cerner’s Strategic Client Summit.  The meeting location – downtown San Diego – and the Summit content were both fantastic.  I will attempt to summarize a few key concepts, and why I feel optimistic about where Cerner is heading – both from an overall perspective as well as from an improved product and end-user experience perspective.

I was very impressed with the collaboration between Cerner President Zane Burke and Cerner’s new Chairman and CEO Brent Shafer.   I had the opportunity to have several conversations with both Zane, whom I have known for a while now, and Brent whom I just had an opportunity to meet for the first time.  I found Brent to be very personable, and very thoughtful about his vision for Cerner.  I was also impressed with the collaborative relationship between Zane and Brent, and think they will lead Cerner in the correct direction.

I am not trying to downplay the roles that many people play inside of Cerner, because there are a lot of great things going on, but I think there are two strategic people that Cerner needs to pay attention to in order to move their EHR to the next level.

The first is Paul Weaver, Cerner’s Vice President of User Experience.  I first saw and met Paul at the 2017 Strategic Client Summit in Dallas Texas, and was very impressed by his vision and enthusiasm.  He hails from the gaming industry, and brings his expertise to the world of healthcare software, where it is much needed.  Here is a link to a 13 minute podcast where Paul talks about the importance of the user experience.  At the 2017 Summit, he used the words “user delight” as his goal of how the interaction with the EHR would make end users feel.  I don’t know about you, but I have used many words to describe my feelings of how the EHR made me feel, and none of them were delight!  His message – all interactions with a software program elicit some type of an emotional reaction.  He wants those reactions to be positive, decreasing stress, and making both patients and healthcare providers/workers “happier and healthier”.  This is a laudable goal, and will help in the fight to combat physician (and other healthcare worker) burnout/suicide – since negative experiences with the EHR are almost always identified as one of the top contributing factors to burnout.  This is an EXTREMELY important area for Cerner to get right, and they need to support Paul Weaver in his efforts to accomplish his goals.

The second strategic person is David Cohen, Vice President for Intellectual Property Development.  David’s presentation, “Activating Intelligence to Transform Care”, was visionary, and he articulated the concepts of machine learning and artificial intelligence, and how to utilize them in healthcare better than anyone else I have heard or read to date.  I will provide some high level overview of his vision below.  At this time it appears they have branded these efforts as “Cerner Intelligence – Leveraging the Power of Data”.

Cerner sees the new demands on health care as being proactive health management (vs reactive sick care); cross-continuum care system (vs fragmented niche care); rewards for quality, safety and efficiency (vs rewards for volume); and person and care team-centric (vs clinician-centric).

Value “drivers” were presented.    These were specific areas where Cerner intends to deploy their Intelligence to make meaningful improvements.   These included clinical and quality drivers, operational drivers, financial drivers, and drivers around improving the experience.  I feel these are appropriate areas to start deploying this Intelligence.  I can post more on this when this information becomes publicly available, because there are some important key areas that if realized, will bring great value to organizations.

Where David’s presentation got really interesting was when he started presenting how Cerner’s areas of focus were on using machine learning, artificial intelligence, and knowledge management.   I am going to provide his definitions of each, because I think they are defined very nicely.

  • Machine Learning:  Leveraging the power of data and statistical methods to create new insights and workflow optimizations
  • AI experiences:  Leverage Artificial Intelligence capabilities that mimic human behaviors such as voice, vision, language, and conversation to enhance human abilities
  • Knowledge management:  Ensure data is complete, contextual, and accurately represented using standards based medical vocabularies\

David then started talking about “AI Experiences” (see diagram from Cerner below – reprinted with permission).   I am convinced that Cerner gets where they should be going in regards to incorporating AI into healthcare, on a very practical basis.  This starts with the inputs into the AI systems, the transformations of those inputs by the system, the incorporation into the knowledge management systems, and most importantly, the AI applications that will make the EHR a true virtual partner in the healthcare process – for providers, patients, and healthcare workers.  What was shown was more than a concept, and the demo’s they put on showed that they are making progress on these. The concept of a mouse-less and keyboard-less interaction with the EHR may be a reality, sooner rather than later.  I encouraged Cerner executives to support these initiatives deeply and at the highest levels.

Cerner AI


Overall, I am very excited and optimistic about Cerner’s vision, and for the prospect of them delivering meaningful improvements and solutions – both near-term and long-term.  Their focus on improving the user experience and making it “delightful” is a very important initiative.   Their focus on using data to improve – almost everything – is foundational for moving all of us forward.  My plea to Cerner is to continue to very deeply support these initiatives, and the talented people they have focused on these.