The “Practical Analysis” blog series is dedicated to answering the questions: “So what is analysis anyway?” and “How can I apply it in my world?” The first installments focused on the fundamentals from which modern computer-aided analysis and decision support have evolved. They featured topics like visualization, exploratory data analysis, and statistical process control.
In the next posts, we’ll explore the emergence of what we now know as “data science” from those beginnings. Let’s examine. (more…)
In my first post on exploratory data analysis (EDA), I discussed why it’s important to get a “sense” for the data you’re working with before applying analytical techniques that may come with critical assumptions and prerequisites.
In this post, I’ll expand on the concepts I discussed in that post and also examine some other exploratory techniques. The goal is to make you familiar with various forms of data analysis so you can use them to make the right decisions for your organization.
We humans like pictures. We’re wired to process images quickly and effectively.
The evolution of those capabilities over thousands of years has allowed us to survive by detecting and evading predators, finding forage, and excelling as predators ourselves. To support these activities and others, the image processing capabilities of our brains have become especially adept at distinguishing basic shapes, such as lines, circles and various polygons, as a precursor to fully comprehending what the actual imagery represents.
We are also capable of understanding complex concepts, abstractions and relationships. But not all of us are created equal in that respect – at least judging from the distribution of scores on the math component of college prep exams! But even the savants among us reach a limit as the volume of data and complexity of problems become intractable to the human mind.
And though we may differ in our analytical abilities, we do seem to share one thing: our ability to reason visually plays an important role in our problem solving. Let’s examine this in the latest “Practical Analysis” blog post. (more…)
Too much data and not enough useful information is one of the great paradoxes of the era of large scale, pervasive computing. Improving that balance is a key challenge for the modern-day analyst. That requires becoming familiar with the most appropriate tools for generating meaningful insights. And there are some fairly basic yet extremely powerful ones that you need to know about.
This next chapter of the Practical Analysis blog series, beginning with this post, is dedicated to endowing you with that knowledge.
The original idea for the “Practical Analysis” blog series came from a seemingly simple question: “What is analysis?” Answering that question took me on a fascinating journey from Florence Nightingale’s work to improve public health in the mid-19th century to the most recent developments in the fields of machine learning and artificial intelligence.
Though I’d found some interesting anecdotes and instructive examples, it felt that something was still missing. Like the answer to the original question. Maybe I needed to ask some different questions that would yield more useful answers such as: What are the essential tools that 21st century analysts should have in their toolbox? Where did they come from? What was the thinking behind them? And ultimately, why do they matter — today?
In my recent series of posts, I’ve been exploring various approaches to value-based care (VBC). Here I’ll delve into Accountable Care Organizations (ACOs) and their importance in the value-based landscape.
Tweet: Accountable care: The next frontier of value-based care
The ACO model represents what’s arguably the most progressive approach to delivering both care and value. It stands out in terms of its potential to significantly shift not just how care is provided, but how resources are allocated to assess and improve the well-being of a population. In effect, the emphasis is on “health” as opposed to “healthcare” in the traditional sense. (more…)
Healthcare organizations are making significant investments in data analytics – an estimated $11 billion last year, forecast to surpass $30 billion by 2022. But spending does not necessarily bring success. Healthcare organizations need to put thoughtful, collaborative work into laying the foundation for analytics success.
There are three crucial areas to address before any healthcare analytics project starts rolling down the tracks:
- User community perspectives, opportunities, and imperatives
- Data governance
- Business rules
Let’s take a closer look at each one.
Tweet: 3 Steps Toward Success in Healthcare Analytics
Lately, I have been thinking about the transition to value-based care in terms of a journey and envisioning an automobile dashboard as a means for presenting the most relevant information from perspectives that change throughout the trip. For example, a vehicle dashboard equipped with GPS can give “turn-by-turn” navigation while also measuring overall progress toward the destination. It can also provide the driver with visibility into different collections of helpful measures at a glance, and assist the driver in optimizing the trip plan based on priorities such as taking a scenic route versus the fastest one.
Tweet: Using data to navigate the value-based care journey
Similarly, analytics – done well – can give healthcare organizations visibility into their incremental (“turn-by-turn”) steps toward value-based care, exposing opportunities for course corrections while also showing how each step fits into the broader context of the entire journey. Here’s how. (more…)
Trust is the foundation of all good relationships—and not just between people. Trust in data is essential for healthcare organizations. This is especially true as the move toward value-based care demands increased, high-level collaboration among different constituencies within a healthcare enterprise.
Tweet: How to build a data foundation that supports value-based care
As care migrates away from straight fee-for-service, healthcare organizations must weigh investments, risks, and tradeoffs objectively with quantitative, trustworthy data. This kind of data driven decision-making will be critical to shaping the initiatives and high stakes choices required by value-based care. In this blog, I will detail three steps you can take to build a trusted data foundation. (more…)
The ground is shifting beneath the feet of healthcare leaders. The United States is inexorably moving toward value-based care, requiring healthcare organizations to start laying the foundation for more holistic, proactive, and population-oriented care. But today’s margins depend on the “piece work” model of fee-for-service, treating people primarily when they are already sick.
Tweet: Navigating the path to value-based care
How can healthcare organizations address both simultaneously? Well, the good news is that they are not mutually exclusive. Here are three suggestions for success. (more…)