Where IS Health Care Going? Technology Leader’s Presentation

Last week, JoAnn Becker  and I ran an interactive discussion with the monthly TLA Manager’s breakfast meeting here in Chicago. We had a lively and excellent debate among a group of technology executives, health care executives, and other smart people about the real challenges of successfully deploying information technology to improve productivity and quality in delivering health care in this country.

That, of course, is an immense issue and would could barely scratch the surface in the hour we had. For those who are interested, we’ve uploaded our slides to Slideshare.


We used two recent TV ads from GE and IBM to kick off the discussion. On the surface, each provides a sense for the promise of information technology to make health care more effective:

GE TV ad – Doctors
IBM TV Ad – “Data Baby”

In the tradition of all good technology vendor advertising, both also completely gloss over the complex organizational adaptation and evolution necessary to bring these hypothetical worlds into being. They also gloss over the existing institutional and industry complexity that needs to be understood and addressed through a combination of design, leadership, and management.

Fred  Brooks, professor of computer science at UNC and author of The Mythical Man-Month : Essays on Software Engineering, draws a critical distinction in the final chapter of the book, which is titled "No Silver Bullet," between accidental and essential complexity. His point is that software is so difficult to design and develop because it must successfully model the essential complexity of the domain it addresses. Technology and software efforts can stumble on a variety of barriers and roadblocks, but failing to understand and address essential complexity is the worst.

Health care provides its own mix of accidental and essential complexity. If the decision makers aren’t careful to draw distinctions between accidental and essential, then a great deal of time and effort will be expended without corresponding returns. On the one hand, we may simply succeed in "speeding up the mess" as my friend Benn Konsynski so liked to put it. Or, we may obliterate  essential complexities in a quest for uniformity and productivity that is blind to those complexities. Or, finally, we may invest the appropriate level of design time and talent in systems that account for essential complexity and eliminate accidental complexity.


We drew on a variety of excellent resources in preparing for this talk and wanted to make them more easily available here.

Here are several books that provide useful context and background

Here are pointers to a variety of health care related web resources worth paying attention to:

Checklists for more systematic knowledge work

The Checklist Manifesto: How to Get Things Right, Gawande, Atul

The idea of a simple checklist to raise the quality of a routine practice seems innocuous enough. It also seems to rankle those with lots of education and experience as an unnecessary intrusion on their autonomy.

The canonical example is the story of the effort at Johns Hopkins Hospital to reduce central line infections in critical care settings. A central line is a catheter inserted into someone’s jugular vein in order to deliver medications. It’s a routine step for many patients in a critical care unit. It’s also a primary source of infection for patients in hospitals. While inserting a central line is straightforward for someone with the proper training, medical professionals will skip steps in the hustle and bustle. Peter Pronovost, a critical care specialist at Hopkins, developed a five-point checklist of the steps necessary to avoid central-line infections.

There’s absolutely nothing on the list that practitioners aren’t already trained to do and absolutely nothing controversial about the steps called for. Many of those professionals considered it an insult to have the obvious pointed out to them in written form. Yet when this checklist was deployed at Hopkins, central line infections dropped from 11% of patients to zero. Comparable results have been routinely achieved elsewhere.

Gawande reported these results first in an article in The New Yorker. In this book he expands on that story to look at

  • the origins of the modern checklist in WWII aviation
  • multiple examples of checklists deployed in other health care settings
  • the challenges inherent in developing checklists that work well in complicated environments
  • the difficulties in gaining meaningful acceptance of checklists among highly autonomous professionals

We live in an increasingly complicated and faster-paced world. But our memories are limited and fallible. The right piece of paper in the right place can compensate for those limitations and increase our capacity to deal with that world. The first balancing act is to design a checklist that increases our capacity to handle a situation significantly more than it increases the load on our limited memories. Pronovost’s checklist only touched on the five items most critical to preventing infections. It made no attempt to spell out every possible step in the process.

A checklist shouldn’t be confused with a procedure manual. Avoiding that confusion is an essential element in making organizational acceptance of checklists possible. Checklists are intended to improve and systematize the performance of those who are already proficient. In themselves, they are poor tools for developing proficiency in those still learning their craft.

This confusion between checklist and procedure is at the root of most resistance to efforts to deploy checklists in suitable settings.  Unfortunately, Gawande contributes to this confusion himself when he conflates checklists with project plans. Both are useful documents  but they serve different purposes and are constructed differently. I’d suggest that you skip the chapter on "The End of the Master Builder" on first reading. It makes the core argument clearer.

Even when properly designed and targeted as relevant aids for the proficient, there is still a change management and leadership challenge to address in deploying a checklist to support more effective practice. While Gawande offers a number of excellent stories and examples of implementing checklists in various settings, he isn’t looking for or tuned into the relevant details of organizational change.  This book provides excellent insight into why checklists work and what to think about when constructing them. Expect to look elsewhere for comparable advice on managing the associated change. Expect to need to do so as well.

As compelling as the rational evidence for checklists may be, orchestrating their adoption into the work practices of professionals presents a large hurdle. The hurdle, of course, is emotional. A checklist can be viewed as diminishing one’s expertise rather than as reinforcing it. Reversing that perception for both the expert and the rest of the organization is the key.

Social media experience at Mayo Clinic

PNG version of this image

Image via Wikipedia

[cross posted at FASTforward blog]

At last week’s Blogwell 2 conference in Chicago, Lee Aase from the Mayo Clinic shared their efforts to use social media to continue to share the Clinic’s message with the existing extended community tightly and loosely surrounding them. The Mayo Clinic has built a worldwide reputation over the course of many decades. Fundamentally, that reputation is a function of word of mouth. That makes social media in all forms a natural fit for Mayo.

They are working across multiple fronts included a fan page on Facebook, multiple blogs, a YouTube channel, and Twitter. At the conference, Lee announced their most recent effort, Sharing Mayo Clinic, which is intended as a place to share people stories about the Clinic and to serve as a hub around which other social media efforts and coalesce.

i was struck by a number of things in Lee’s presentation and Mayo’s overall efforts. First and foremost was the value of simply diving in and learning from their experiences. Coupled with that was the additional leverage found in thinking systemically. The heart of their strategy here is to find and share the human stories connected to the Clinic every day. The technologies serve as multiple ways to get the story out and Lee and his team (which is much smaller than I would have predicted) are smart enough to not get in the way of those stories.

For example, although they are making extensive use of video in their storytelling, they are using the Flip Video Camcorder instead of a more complex (and intimidating) video set up. What they are learning is that the Flip provides good enough production values and doesn’t get in the way of the storytelling. I suspect that there’s more craft involved than Lee let on, but not so much that it is out of reach for any organization that’s willing to make a few mistakes in the early stages.

Lee closed with an intriguing observation about the value of Mayo’s investments in social media. Here’s how he put it:

As I approaches 0, ROI approaches infinity

I suspect that the average CFO would be a bit suspicious, but there’s an important point here. The financial investments in social media can start at zero and don’t need to get terribly far away. The real investments are in organizational time and attention and what Lee and others are demonstrating is that those costs are also readily manageable. Answering questions about ROI does not necessarily entail using a spreadsheet.


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Business models for health care: Andy Kessler’s take on the future of medicine

The End of Medicine: How Silicon Valley (and Naked Mice) Will Reboot Your Doctor, Kessler, Andy


Andy Kessler is a former Wall Street investment analyst turned author. He learned his trade following Silicon Valley and its successful, long-term, obsession with Moore’s Law. In that world, as technology scales, costs fall predictably, and new markets emerge. In The End of Medicine, Kessler takes to the world of health care and medicine to discover how and where that underlying investment model might apply. It’s an interesting premise and, despite some annoying stylistic quirks, Kessler delivers some real value. It doesn’t get to anything remotely like an answer, but it collects and organizes a lot of useful information that might help us get closer to one.

Kessler opts for a highly anecdotal style; presumably to put a more human face on a large, complex, subject. For me, he overshoots the mark and loses the big picture. The color commentary overwhelms the underlying story line, which was my primary interest. But there is a good story line that is worth finding and holding on to.

Medicine’s roots are in making the sick and injured better. Triage is baked into the system at all levels. Observe symptoms, diagnose problem, apply treatment, repeat. Over time we’ve increased our capacity to observe symptoms and have gotten more sophisticated in the treatments we can apply, but the underlying logic is based on pathology. Also over time, a collection of industries have evolved around this core logic and these industries have grown in particularly organic and unsystematic ways.

Kessler runs into these roots and this logic throughout his journey. However, coming from the semiconductor and computer industries, as he does, he doesn’t fully pick up on their relevance. As industries, computers and semiconductors are infants compared to medicine and health care. Not only do Kessler’s industries operate according to Moore’s Law, but they are structurally designed around it. His analysis of health care identifies a number of crucial pieces, but he stops short of assembling a picture of the puzzle.

Kessler focuses much of his attention on developments in imaging and diagnostics. Both areas have seen tremendous advances and hold out promises of continued technological development similar to what we’ve seen in semiconductors.

Imaging is a computationally intensive area that benefits fully as an application of computing technologies. What is far less clear is whether the current structure of the health care industry will be able to absorb advances in imaging technologies at the pace that will let Moore’s Law play out in full force.

There is a second problem with imaging technologies that applies equally to other diagnostic improvement efforts. As we get better and better at capturing detail, we run into the problem of correctly distinguishing normal from pathological. While we may know what a tumor looks like on a mammogram what we really want to know is whether that fuzzy patch is an early warning sign of a future tumor or something we can safely ignore. The better we get at detecting and resolving the details of smaller and smaller fuzzy patches, the more we run into the problem of false positives; finding indicators of what might be a tumor that turn out on closer inspection to be false alarms. Our health care system is organized around pathologies; we fix things that are broken. Because of that, the data samples we work with are skewed; we have a much fuzzier picture of what normal looks like than what broken looks like.

This is the underlying conceptual problem that efforts to improve diagnostics and early detection have to tackle. Kessler devotes much of his later stories to this problem. He profiles the work of Don Listwin, successful Silicon Valley entrepreneur, and his Canary Fund efforts. Here’s the conundrum. If you detect cancers early, treatment is generally straightforward and highly successful. If you catch them later, treatment is difficult and success is problematic. Figuring out how to reliably detect cancer early has a huge potential payoff.

The kicker is the word “reliably” and the problem of false positives, especially as you begin screening larger and larger populations. If you have a test that is 99% accurate, then for every 100 people you screen you will get the answer wrong for one person. The test will either report a false positive – that you have cancer when, in fact, you don’t – or a false negative – that you are cancer-free when you aren’t. As you pursue early detection, the false positive problem becomes the bigger problem. Screen a million people and you will have 10,000 mistakes to deal with, the vast majority of which will be false positives. That represents a lot of worry and a lot of unnecessary expense to get to the right answer.

Kessler brings us to this point but doesn’t push through to a satisfactory analysis of the implications. Implicitly, he leaves it as an exercise for the reader. His suggestion is that this transition will present an opportunity for the scaling laws he is familiar with to operate. I think that shows an insufficient appreciation for the complexities of industry structure in health care. Nonetheless, Kessler’s book is worth your time in spite of its flaws.

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