Search the KHIT Blog

Saturday, September 27, 2014

Back down in the Weeds': A Complex Systems Science Approach to Healthcare Costs and Quality

There is a mounting crisis in delivering affordable healthcare in the US. For decades, key decision makers in the public and private sectors have considered cost-effectiveness in healthcare a top priority. Their actions have focused on putting a limit on fees, services, or care options. However, they have met with limited success as costs have increased rapidly while the quality isn’t commensurate with the high costs. A new approach is needed. Here we provide eight scientifically-based steps for improving the healthcare system. The core of the approach is promoting the best use of resources by matching the people and organization to the tasks they are good at, and providing the right incentive structure. Harnessing costs need not mean sacrificing quality. Quality service and low costs can be achieved by making sure the right people and the right organizations deliver services. As an example, the frequent use of emergency rooms for non-emergency care demonstrates the waste of resources of highly capable individuals and facilities resulting in high costs and ineffective care. Neither free markets nor managed care guarantees the best use of resources. A different oversight system is needed to promote the right incentives. Unlike managed care, effective oversight must not interfere with the performance of care. Otherwise, cost control only makes care more cumbersome. The eight steps we propose are designed to dramatically improve the effectiveness of the healthcare system, both for those who receive services and those who provide them.

The US healthcare system suffers from high costs and low quality compared to healthcare systems internationally, as measured by reported life expectancy [4] and infant mortality. High rates of nosocomial infection (infections acquired in healthcare settings) as well as adverse drug effects (errors in the administration of medication) manifest the need for improvement in the system of care. At a cost of $2.5 trillion annually [6] the system is not delivering affordable, effective care. The paradox of higher costs and lower quality makes clear the existence of a systemic problem. How can we fix it? Complex systems science provides tools to address this question directly. In this paper we provide eight scientifically based steps toward reducing costs and improving quality. Our suggestions arise from an analysis of the US healthcare system in particular, but they are broadly applicable when adapted appropriately.

The eight steps are:

1. Separate simple care from complex care.
2. Empower workgroup competition as an incentive, and avoid regulating costs or quality.
3. Create superdoctor teams to rapidly diagnose and treat highly complex conditions.
4. Accelerate intake routing to rapidly identify the right provider.
5. Add redundancy to improve communication to prevent prescription errors.
6. Create disinfection gateways at spatial boundaries to reduce hospital-based infections.
7. Use e-records for research to supplement clinical studies.
8. Promote “First Day” celebrations to encourage healthy behavior...



Scientific principle—“Big data” research: Our increasingly complex world yields massive quantities of data, and we now have the scientific knowledge to perform pattern recognition on the data. Scientists are utilizing such “big data” methods in areas as diverse as genomics, finance, and crime prevention. If made available, the vast corpus of medical records should result in the discovery of opportunities for advancement in medicine. This approach complements the more traditional and more controlled framework of specially designed clinical trials.
Electronic records, which have become increasingly prevalent in recent years, represent a valuable repository of medical data. There are over 300 million people in the United States, most of whom are receiving some sort of medical care. If anonymized medical records were made available to researchers, these e-records could be leveraged to improve care at low cost.
In today’s quest to answer questions about medicine and human health, the large-scale, controlled clinical trial is central. New drugs, surgical techniques, non-surgical interventions and medical devices are typically tested in such studies, which require the creation of control and test groups, controlling for confounding factors such as age and lifestyle, and the tracking of patients...
[S]ince each person’s medical records may cover many years, we can learn about long term effects much more easily and cost-effectively by analyzing these available data than by conducting longitudinal studies on a particular therapy. Thus, we can use these data to discover long-term effects that may otherwise not be detected at all. Leveraging the availability of care data to increase our knowledge can’t and shouldn’t replace controlled studies or physician experience. But it can be a powerful and cost-effective tool, allowing us to utilize huge amounts of information and new methods of analysis to increase our medical knowledge, improving our ability to treat patients and take care of ourselves...
Interesting paper (pdf, 47 pages). Arguing CER in the foregoing, essentially.

A hundred years ago, physicians were generalists, treating most medical conditions. Humanity didn’t have nearly as much medical knowledge and knowhow back then so that for the most part a single doctor could master what was known. That has changed

Medical knowledge now far exceeds a single expert’s ability to master it. Medical students receive a general training and then they specialize, seeking to learn just one small piece of what we know about medicine.

Specialists have become essential because of the complexity of care. The more we learn, the more kinds of specialists are needed. Increasingly, however, it is necessary to have patients see multiple specialists for a single problem, which causes fragmentation and delays the care. Furthermore—and critically—the interplay between multiple causes of a single condition, or multiple aspects of its treatment, makes it difficult for the separated specialists to address such complex problems. 

What is the solution? 

A human being is a single working system and specialists must be able to work together as an integrated unit for diagnosis and treatment. Specially constituted teams of physicians and other care providers who work together on a regular basis should address the more complex problems. The cost of having such a team in place might seem high, but for complex cases such a team will prove to be more effective and less costly than the alternative—the difficulties, delays, and costs inherent in multiple appointments. The challenge is making sure the teams can work together smoothly and efficiently, and with better results than specialists working separately.
A well-integrated team of specialist physicians can be thought of as a “superdoctor.” In order for medical teams to be superdoctors, they must get to know each other’s strengths
and styles and act together seamlessly. Well- integrated teams have the combined specialized knowledge of each member and more: they have the ability to relate these different domains of knowledge and combine them in new ways. Moreover, they can act rapidly with this combined knowledge. They can be an important part of the solution to the problems of fragmentation. 

Such teams have become standard practice in cancer care, where specialists in imaging, surgery, radiation therapy, and chemotherapy often meet and work together to treat patients. The wide diversity of cancers and of individual responses to treatment make the team approach necessary for effective care. These teams generally also include non-physician practitioners. While the team approach is most widely used for cancer, some medical centers, recognizing the problem of fragmentation in care, are using the team approach for other conditions. 

To be most effective, superdoctor teams need to work together on a regular basis. If you were to throw together several sports players—even professional athletes—to play as a team without training together, they would not play as well as they would with team members who they were used to. Similarly, medical teams must “practice” together to fully leverage their collective ability...
For one thing, all of this beckons me Down in the Weeds' again (here as well). In addition, I come back to my rant of late about the enervating friction posed by "psychosocial toxicity" in the healthcare workforce.
I recently challenged another physician's blog post definition of healthcare's "toxic workplace" as perhaps too narrow (given that it was simply a petulant litany of all the ways physicians are burdened by organizational and regulatory things they dislike). His response?

"Go to hell."
Finally, I can just hear the blog trolls (usually using untraceable screen handles, but all claiming to be physicians) dismissively noting the lack of the letters "MD" in the masthead of this monograph. Yaneer Bar-Yam is a physicist. His collaborators? No clue. I don't see "MD" anywhere.

BTW, see their list of healthcare papers here.

I repeat, from my "Talking Stick" post,
It's not just about me, or you. It's about us. i.e., it's equally about interpersonal relations and mutual perceptions -- organizational dynamics. It's about "culture."

It's about "Humble Inquiry," about being "Mindwise," about the nurturing of the mutual-accountability "Just Culture" necessary for a collegial, high-engagement, high-performance interdisciplinary team-based workforce.

All of which goes necessarily to "Leadership," as leaders are the only ones with the requisite authority -- the ones who ultimately set and enforce the tone of organizational culture for better or worse. "Critical thinkers" in a psychosocially toxic organization may well simply be seen as insubordinate troublemakers.

It's about authenticity at every level within an organization, and the nurturing of a healthy culture that supports it.

...nurturing of the mutual-accountability "Just Culture" necessary for a collegial, high-engagement, high-performance interdisciplinary team-based workforce.
High morale and engagement and openness to the ongoing rigors of process improvement and effective high-cognitive burden teamwork simply requires it.
For electronic systems, auto-completion and simple check boxes should be avoided. These items are more prone to error precisely because they are quick and easy. Instead, it is important to have the prescriber provide all key information longhand and verify it. Writing something twice admittedly takes more time but the prevention of errors, as in writing checks, must be considered of primary importance.
Yeah, that'll go over really swell in the era of the put-upon provider. But, the authors are right. Consider, e.g.:
EHR Design: Default Values as a Cause of Errors

When designing software, a lot of care is given to squashing bugs. But what does one do when the design itself is the problem?  Spotlight on Electronic Health Record Errors: Errors Related to the Use of Default Values, an article published by the Pennsylvania Patient Safety Authority, sheds much needed light on this subject.  As the report notes, default values are usually considered a safety measure and not a potential source of errors.  Yet, their study found that default values introduced errors into EHR systems...

From a software design standpoint, these errors can be difficult to prevent because they rely on people to make alterations.  Using default values for medications or any type of order may seem helpful (e.g., assure some value is entered, save time by making common orders quick), but they make assumptions that, as these errors show, do not always hold.

Like many others, my encounter with this behavior happened with standard orders in hospitals.  Protocols for anticoagulants come to mind.   Systems are programmed to insist something be done, but are not necessarily smart enough to recognize a clear contraindication.  As such, the responsibility for preventing errors falls back onto busy, distracted clinicians – not a great error prevention strategy.

Attempts to prevent default values from accidentally going unchanged or assuring that user entries are accepted by the system can be maddening.  Using local validation rules (e.g., rules that apply only for that particular data entry value) makes error prevention difficult unless there is information available that provides “state” information as the process is occurring.

For example, if a user enters orders and does not change the default value, it could mean that he agrees with the default.  Of course, it could also mean that he simply forgot.  Resolving this issue requires more information about the ordering process itself. Here is one example of where workflow modeling can help in software design...
 This brought me back to my 3GL/4GL programmer days of the 80's. A "nul" value should not equate to "zero" or some other default value, but it too frequently does. Analytically, nuls must be regarded as "missing values" and static defaults must be coded with extreme care. Rigid RDBMS enforcement of stuff like "No Dupes, No Nuls" -- "relational integrity" at the data dictionary level -- is as necessary today as it was during my ancien time writing code.

BTW, Lovely comment over at THCB:
Jeff Goldsmith says:

Spoken like a spectator who’s never actually used the technology. In most EMR’s, including the market leaders, the data you actually need to “pinpoint” anything is buried six-ten clicks deep in completely unusable Windows 95 style user interfaces. If you’re lucky, you can “pinpoint” problems that happened four hours or two days ago. It’s almost impossible to find the real problems amid the bins full of templated excelsior. If you don’t believe me, ask your doctor to show you your electronic health record sometime. It’s virtually useless...
The people who’ve taken this technology furthest, like Kaiser and Geisinger, had to spend a small fortune on custom built electronic data repositories which abstract data from the patient records and organize it into useable population based files, and on custom built analytic routines and protocols to actually guide the care...

handy new timesavers???
Another zinger, same post:
platon20 says:
This article was written by an administrator who has zero experience treating patients, yet is a so-called “expert” on healthcare. Please notice the oxymoron in that.

Administrators are anxious to control doctors to “hold down costs” while at the same time paying themselves hundreds of thousands if not millions of dollars while supposedly “creating value” that doesnt exist...
Interesting concluding thoughts in the foregoing Bar-Yam et al paper:
Organizations of different types—companies, religious organizations, schools, towns, states—can set up programs that encourage people to take responsibility for their own health and lifestyle, and they can provide supportive communities toward that end. The organizations themselves can undertake new commitments to improve social health and community well-being.

Some people may want their goals and commitments to be private or to share them with friends; others may be pleased to share them publicly. The key is for familiar institutions and networks to support each person’s desire to improve his or her life and each person’s journey toward better health.

Social network follow-up interactions can be planned. Internet-based and mobile device apps with calendars, reminders, and checklists can be developed to support people in reaching their goals.

We can dramatically improve health by inspiring individual responsibility and action. When people embrace their health as a personal opportunity and are also given community support, they reveal tremendous power to make lasting improvements in their own lives and each other’s.
Upstream, baby. We will have to venture all the way "upstream."

Joe Flower has a good new post up:

Health care is fragile. It survives in a much narrower band of circumstances than most of us realize. Right now many hospitals and systems are having a second down year in a row. They’re consolidating, laying off people, working through major shifts in strategy — all because of what we must admit (if we are honest) are relatively minor economic shifts, such as small reductions in utilization and Medicare payments, a blunting of accustomed price rises, and stronger bargaining from health plans.

If minor revenue stream problems put your entire institution in jeopardy of chaotic deconstruction, it cannot be called robust.

At the same time, an increasing number of vectors outside the sealed world of health care could overwhelm and kill your institution, from climate chaos to pollution disasters to epidemics and the loss of antibiotics.

These two concatenations of threats, within and without health care, have similar and interlocking answers. The extent to which your institution is bloated, profligate of resources and highly dependent on its current streams of revenue, energy and human resources is exactly the extent to which it is a system with very little reserve capacity. In an increasingly high-variance world, your survival depends on getting green, lean, resilient and smaller...
Good stuff, Mr. Flower.


Wherein IHI moves to appropriate and brand the High Ground. I'm surprised they didn't put "TM" after the phrase "Quality Improvement" (like the ChutzpahMeister who staked out "Lean Startup®").

PDSA is PDSA, not "PDSA ®", Science is "Science," not "Science ®."

UPDATE: From the paper:
Executive Summary

In the past 25 years, improvement in health care has grown from demonstration projects into a worldwide movement. Dominant in this movement has been an improvement approach grounded in the work of Walter Shewhart, W. Edwards Deming, Joseph Juran, and Associates in Process Improvement, and shaped in practice by the staff and faculty of the Institute for Healthcare Improvement (IHI). Today, this “IHI approach” to quality improvement (referred to as “IHI-QI” throughout this paper) provides a framework for thousands of improvement practitioners around the globe. Meanwhile, many people in health care have heard about Lean and the Toyota Production System (TPS) as a powerful method for improvement and cost reduction in manufacturing, and about its notably successful application in health care by influential organizations such as Virginia Mason Medical Center and ThedaCare.

People often want to know about the relationship between IHI-QI and Lean, and how they can best utilize one or both approaches to improve their own care systems. This white paper aims to address these issues, and argues that because IHI-QI and Lean are complementary ways of approaching improvement, it is not necessary to choose one over the other as a guide to action...

IHI-QI is a vibrant discipline. It has not ossified into dogma, thanks in good measure to the diversity, energy, and idealism of its adherents, and to the “open source” approach that IHI has promoted with regard to methods and content. IHI faculty have been encouraged to candidly share their best ideas, in the belief that the field can most rapidly and effectively advance health care quality through collaboration. Together, the IHI community has grown in an atmosphere of transparency and a spirit of “all teach, all learn.”

IHI-QI is often confused with one of its core elements, the Model for Improvement (see Figure 1). The Model — three clarifying questions and the Plan-Do-Study-Act (PDSA) cycle — has formed the mainstay of IHI’s teaching and improvement methodology over the years. But despite its fame, and despite its manifest utility in almost any life situation, the Model for Improvement is not synonymous with IHI-QI.

The Model for Improvement, developed by Associates in Process Improvement, is a general purpose heuristic for learning from experience and guiding purposeful action. More simply, it is an “algorithm for achieving an aim” at any scale. As a tool for gaining practical knowledge, it represents a radical distillation of pragmatic epistemology into a habit of immediate, sequential testing of changes. One objective of this paper is to reconsider the Model for Improvement in its proper place, as a pervasive guide for action within the larger context of IHI-QI.

At present, Lean tools and methods are rapidly gaining adherents among aspiring health care improvers. As health care leaders have embraced the results-oriented discipline of industrial quality improvement, interest in more effective management systems has increased. The Toyota Production System (TPS), in particular, has received much attention. TPS is rooted in the innovations of Taiichi Ohno and colleagues in Toyota factories starting soon after the end of World War II. Adaptations of TPS are widely known by reference to one of its key principles of practice, “Lean” — the drive to devise nimble tasks, processes, and enterprises that maximize value and minimize waste in all its forms. Leading health care organizations, notably Virginia Mason Medical Center in Seattle, ThedaCare in Wisconsin, and the Pittsburgh Regional Health Initiative in Pennsylvania, have adopted TPS as their model for management and improvement, with widely recognized success...

The IHI Approach to Quality Improvement

For the purposes of this paper, we refer to IHI-QI as the approach to improvement developed by Associates in Process Improvement and promulgated by IHI, grounded in the work of W. Edwards Deming, with roots reaching deep into pragmatic philosophy, systems theory, Walter Shewhart’s statistical treatment of quality, human psychology and logic, and the scientific experimental method.

IHI-QI draws a fundamental distinction between the system to be improved and the techniques and methods used to improve it. IHI-QI seeks to formulate and codify generalizable knowledge that, when applied in other systems, can yield predictable improvements.

All improvement requires that changes be made in the system (though to be sure, not all changes are improvements). Building on the knowledge of subject matter experts, improvers target changes that are predicted to lead to improvement in a specific system. These changes are then tested and amended through iterative Plan-Do-Study-Act (PDSA) cycles to produce sustainable improvement. Such changes comprise the “content” of improvement...

In working to improve a system, IHI-QI practitioners employ an array of conceptual frameworks and methods drawn from many disciplines in order to understand and influence complex adaptive systems such as health care organizations. Selection of methods will vary greatly depending on the scope, scale, and context of the work...

Summary and Implications

Lean and the principles of TPS are in no way antithetical to the IHI approach to quality improvement, and vice versa. Lean is, in a sense, a complex and deep “application” of Profound Knowledge, a particular deployment of improvement in the realm of production systems, though it was not purposely conceived as such. IHI-QI is a general approach that guides the development and application of execution theories across a range of specified contexts to realize clearly stated goals. We can consider Lean and TPS to be an example of such an execution theory. The TPS package of interdependent change concepts was originally developed to optimize manufacturing production systems. It represents a “template” for improving such systems, with a set of predefined aims, change concepts, implementation roadmap, and tools...
Link to the full paper here. It's a good paper overall, nice historical summary of the evolution of QC/QA/QI legacy methods (long familiar to my wife and I), and a decent side-by-side tabulation of the putative "differences" between Lean and "IHI QI" (some of which, though, seem to be mere semantic quibbles in the service of turf branding).

But "Associates for Process Improvement" did not "develop" PDSA (originally called"PDCA," Plan-Do-Check-Act), they simply re-branded it with the phrase "Model For Improvement." The fact that Lean "was originally developed to optimize manufacturing production systems" is a blinding glimpse of the historically obvious (rooted in the TPS, "Toyota Production System"), and is irrelevant to its successful adaptation and application within various service industries, including the most complex of all, healthcare.

Old wine, new bottle?
(scroll down in the link)
Moreover, on the repeated "Lean is about reducing cost" thing, Lean Sensei Mark Graban writes:
Lean is Not About Cutting Costs...

Two pet peeves of mine are hearing people say things like “Lean is all about reducing waste” and or “Lean is all about cost cutting” (and thankfully others are also trying to dispel that myth). Another pet peeve is people drawing conclusions off of two data points, but we’ll come back to that later in this post.

Lean is not “all about” waste — we also focus on providing the right “value” to the patient or customer (doing the right thing the right way at the right time and the right place). Reducing waste is a big part of Lean, but it’s not the only thing.

Of course, we know that reducing waste is not exactly the same as “cutting costs.” Reducing wasteful activity in a process or value stream will often lead to lower cost, but it also leads to better flow and better quality, among other things...
Again, there's a ton of overlap/wiggle room in all of these characterizations. See also Mark's post
Lean is a “Generic” Term for TPS (and The Toyota Way), Says Dan Jones

More to come...

No comments:

Post a Comment