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.