Saturday, June 25, 2016

Convergence: The Future of Health

Among other reads, I'm working on finishing a couple more "omics" books,


UPDATE: I finished "The Age of Genomes." Very nice. Stay tuned. Goes to an ongoing prior riff, e.g., see here and here.

apropos, given that "omics" will be central going forward, saw this in a Brian Ahier tweet. So, FYI,

 
Dear Colleagues:

Humankind faces serious challenges in overcoming diseases, mitigating the rising costs of healthcare, and reducing health disparities. While Convergence cannot single-handedly solve these challenges, it will play a key role in accelerating progress in health and healthcare through research innovations.
Faculty members and participants from many universities, organizations, and firms came together to contribute to the development of this report. We now present it to the research and policy communities to illustrate the power and potential of Convergence research to improve health and healthcare through the integration of engineering, physics, computation, and life sciences.
Despite the incredible promise Convergence holds for advancing novel approaches to therapies, health analytics, drug delivery, diagnosis, and disease prevention, Convergence faces major barriers limiting its full potential to bring new and exciting health innovations to patients.
We hope this report, which builds upon findings from previous reports, will form the beginning of a multifaceted research strategy and highlight the many innovative opportunities made possible by Convergence. e report was drawn from a series of meetings with colleagues from across the country and from diverse stakeholders from academia, government, industry, and philanthropy.
We hope that its descriptions and recommendations will amplify the dialogue so that Convergence research strategies can advance at the campus and national levels. 

We look forward to your thoughts and questions. 
Link to the freely distributable full PDF report here.

Website link here

On page 49 of Convergence they get around to addressing issues pertaining to Health IT:
Big Data & Health Information Technology

Introduction
The idea of precision medicine—that we could know exactly what is wrong with a person and so precisely determine how to treat their condition—is very attractive. But the reality is that such precision is today really only available, even in part, for cancer, because most cancers have a strong genetic component and years of research on the human genome have begun to provide insights. Human health, however, depends not just on genetic factors, but even more critically on environmental and behavioral factors—what we are exposed to, what we eat, our lifestyle choices. And consistent data that allows comparison of these factors— what medical data scientists would call stratifying the phenotype—simply doesn’t exist for large numbers of people, not in electronic medical records, not anywhere. Partly this is due to the many different and incompatible electronic medical record systems, but it is more than that.


Diabetes, for example, is not a single disease but rather a collection of many different conditions that result in high blood sugar. People with diabetes, not surprisingly, often react very differently to the bewildering array of different medicines and treatment regimens now available, as well as to different diets and different environmental conditions.
 
The challenge is actually even more difficult, because the real goal is to understand what it means to be well, to function at the peak of our physical and mental capabilities, as well as to prevent or deal with illness. And while we know a lot about how to diagnose illness, we don’t know how to diagnose or measure wellness, which means that most preventive advice exists only as generalities: eat more vegetables, get more exercise, get enough sleep. So the challenge—and the opportunity—is to use Convergence research strategies to improve this lack of meaningful, comparable, scientifically-useful data and to develop advanced means to analyze such data.

New Opportunities
Consumer-focused Health IT. Addressing modern health challenges requires an improved understanding of wellness before onset of disease, as well as key signals of disease. To achieve that requires active consumer input of data on their health and lifestyle (such as blood sugar measurements and diet), but also passive data collection (with consumer consent). Passive data might include continuous measurements of environmental influences such as changes in the microbiomes or exposures to air- or food-borne toxins; physiological measurements like blood pressure and heart rhythms; and behavioral assessment tools like FitBit apps that can measure physical activity. In the near future, self-powered implanted sensors could monitor far more variables and report data wirelessly to smart phones, which also can track consumer locations and activities (again, with consumer permission). A number of these applications developed by MIT, Stanford, and other universities are now being implemented in smartphones, explicitly for research purposes. Consumers in large numbers are volunteering their data, potentially making smartphones the most impactful medical device in the history of the world. The integration of health apps with electronic health records, like the SMART app platform will be critical for data-driven insights into health.

The Convergence of smart mobile devices, advanced diagnostics, and deep learning algorithms to mine the data can play an important role in the development of passive methods for gathering physiological and other health information from patients. Additional passive data collection methods can be developed through the integration of advanced signal processing, bio-instrumentation, ultrasound sensors, flexible electronic patches, and other sensors to monitor biological systems. Smart software can potentially use facial images to differentiate between true and false pain and to manage pain in patients who can’t speak for themselves, such as babies and certain elderly populations. Similar software tools on smart phones can already measure eye movements in children to provide early screening for autism—and thus enable earlier intervention. Real-time monitoring of social interactions, physiology, and behavior can provide additional insights. Such data would greatly advance our understanding of obesity, drug addiction and Post-Traumatic Stress Disorder, for example, and provide new avenues for treatment and prevention...
OK, recall this?


Well, searching "electronic health record" in this report returns 56 hits. Here's just one:
Cognitive task and work analysis. The purpose of cognitive task and work analysis is to identify and describe the cognitive skills that are required to perform a particular task, such as making a diagnosis. The most common method used for such an analysis is an in-depth interview combined with observations of the specific task of interest (Schraagen et al., 2000). Because cognitive errors are an important contributing factor to diagnostic errors (Croskerry, 2003) these methods are likely to have considerable utility in efforts to reduce errors. Koopman and colleagues (2015) used cognitive task analysis to examine the relationship between the information needs that clinicians had in preparing for an office visit and the information presented in the electronic health record. They found a significant disconnect between clinician needs and the amount of information and the manner in which it was presented. This disconnect can lead to cognitive overload, a known contributor to error (Patel et al., 2008; Singh et al., 2013). The researchers recommended significant reengineering of the clinical progress note so that it matched the workflow and information needs of primary care clinicians.
We have much work to do. I remain concerned about the increasing volumes of heterogeneous data of varying data QA pedigrees pouring into EHRs at a time when the aggregate workflow "productivity treadmill" syndrome goes largely unaddressed. The Convergence report provides zero discussion of process/workflow factors, and makes but one vague reference to organizational "culture."

BACK TO "IMPROVING DIAGNOSIS IN HEALTH CARE"

Keyword search topical phrases like "electronic health record," "Health IT," and "workflow." A small sample of what falls out:
Goal 1: Facilitate more effective teamwork in the diagnostic process among health care professionals, patients, and their families

Recommendation 1a: In recognition that the diagnostic process is a dynamic team-based activity, health care organizations should ensure that health care professionals have the appropriate knowledge, skills, resources, and support to engage in teamwork in the diagnostic process. To accomplish this, they should facilitate and support:

• Interprofessional and intra-professional teamwork in the diagnostic process.
• Collaboration among pathologists, radiologists, other diagnosticians, and treating health care professionals to improve diagnostic testing processes.

Recommendation 1b: Health care professionals and organizations should partner with patients and their families as diagnostic team members and facilitate patient and family engagement in the diagnostic process, aligned with their needs, values, and preferences. To accomplish this, they should:
• Provide patients with opportunities to learn about the diagnostic process.
• Create environments in which patients and their families are comfortable engaging in the diagnostic process and sharing feedback and concerns about diagnostic errors and near misses.
• Ensure patient access to electronic health records (EHRs), including clinical notes and diagnostic testing results, to facilitate patient engagement in the diagnostic process and patient review of health records for accuracy.
• Identify opportunities to include patients and their families in efforts to improve the diagnostic process by learning from diagnostic errors and near misses.

Tasks and workflow
The diagnostic process involves a series of tasks and an implicit or explicit workflow that contains and connects those tasks. A variety of challenges can occur with the tasks and workflow that are required to make a diagnosis, including: problems with the information (amount, accuracy, completeness, appropriateness), communication issues, the complexity of the task, a lack of situational awareness, poor workflow design, interruptions, and inefficiencies. These issues contribute to diagnostic error at each step in the information gathering, integration,and interpretation process; they can contribute to problems with the timeliness of information availability, and they can lead to problems in cognitive processing.

There are a variety of measurement approaches that can be used to evaluate tasks and workflow. It should be noted that these are best applied in the real world environment in which the diagnosis is being made. The methods include cognitive task and work analysis (Bisantz and Roth, 2007; Rogers et al., 2012; Roth, 2008), observation of care processes (Carayon et al., 2014), situation awareness in team performance (Carayon et al., 2014; Salas et al., 1995), workflow modeling (Kirwan and Ainsworth, 1992), and proactive risk assessment methods, including failure mode and effect analysis (Carayon et al., 2014). These methods are briefly described below.

Cognitive task and work analysis
The purpose of cognitive task and work analysis is to identify and describe the cognitive skills that are required to perform a particular task, such as making a diagnosis. The most common method used for such an analysis is an in-depth interview combined with observations of the specific task of interest (Schraagen et al., 2000). Because cognitive errors are an important contributing factor to diagnostic errors (Croskerry, 2003) these methods are likely to have considerable utility in efforts to reduce errors. Koopman and colleagues (2015) used cognitive task analysis to examine the relationship between the information needs that clinicians had in preparing for an office visit and the information presented in the electronic health record. They found a significant disconnect between clinician needs and the amount of information and the manner in which it was presented. This disconnect can lead to cognitive overload, a known contributor to error (Patel et al., 2008; Singh et al., 2013). The researchers recommended significant reengineering of the clinical progress note so that it matched the workflow and information needs of primary care clinicians.
Observations of care processes
Process observation is a means of verifying what exactly occurs during a particular process (CAHPS, 2012). Frequently, these observations are documented in the form of process maps, which are graphical representations of the various steps required to accomplish a task. The approach is able to capture the complex demands imposed on members of the diagnostic team, and allows for the “documentation of the coordination and communication required between clinicians to complete a task, use their expertise, tools, information and cues to problem solve” (Rogers et al., 2012). For example, Fairbanks and colleagues (2010) used this method to examine workflow and information flow in an emergency department’s use of digital imaging by applying both hierarchical task analysis and information process diagrams. The analysis identified gaps in how the information system for imaging supported communication between radiologists and emergency department physicians. In analyzing diagnostic error, this technique can identify the role that contextual or social factors play in assisting or impeding problem resolution (Rogers et al., 2012). Observations of care processes can also provide input for other work system analysis methods, such as cognitive task and work analysis as well as failure mode and effect analysis.

Methods for improving the selection, design, implementation, and use of technology involve some of the methods described above, such as workflow modeling, FMEA, and other proactive risk assessment methods. In particular, many health care organizations have been concerned about whether enough attention is being paid to the usability of health IT. For example, Friedberg and colleagues (2013) in a study of physician job satisfaction found that a number of factors related to electronic health records (EHRs) had a substantial impact on satisfaction, including: poor usability, the time required for data entry, interference in patient interactions, greater inefficiencies in workflow, less fulfilling work content, problems in exchanging information, and a degradation of clinical documentation. This study used a mixed method design which included semi-structured and structured interviews with physicians. Its findings were consistent with research using other methods to assess the extent to which EHRs are enhancing care delivery (Armijo et al., 2009; Unertl et al., 2009). The American Medical Informatics Association Board of Directors issued recommendations about improving the usability of EHRs which were based in large part on usability studies that had been conducted by Middleton and colleagues (2013). The use of various usability evaluation methods can help in ensuring that usability concerns are addressed as early as possible in the design process. For example, Smith and colleagues incorporated usability testing into the design of a decisionsupport software tool to catch missed follow-up of abnormal cancer test results in the VA (Smith et al., 2013). These various possible usability evaluation methods include heuristic evaluation methods, scenario-based usability evaluation, user testing, and the observation of technology in use (Gosbee and Gosbee, 2012).

Organizational characteristics
Culture, leadership, and management are some of the organizational characteristics that can affect the diagnostic process. Some of the culture-related issues that can contribute to diagnostic error are a lack of organizational support for improvements, conflicting messages about regulations, confusion about task responsibilities, and the perception by people that they should not speak up even when they know a problem is occurring. These issues have been identified in the broader context of patient safety but are likely to affect diagnostic processes as well…

Physical environment
Various characteristics of the physical environment (e.g., noise, lighting, layout) may affect the diagnostic process (Alvarado, 2012; Parsons, 2000). The physical environment places additional stresses on a diagnostic team that can affect the performance of cognitive tasks and information gathering, interpretation, and integration. For example, the layout and lighting of the radiology reading room may hinder accurate viewing of screens. Emergency departments are another example of a place where it makes sense to examine the effects of the physical environment on diagnostic errors (Campbell et al., 2007). Human factors/ergonomics methods can be used to evaluate the physical environment. These methods include, for example, making a direct assessment of noise and lighting with specific equipment (e.g., a light meter) and observing care processes in situ in order to identify challenges related to layout. For instance, observing the physical movements of clinicians can help identify communication among team members and the barriers posed by the physical environment (e.g., lack of available equipment or poorly located equipment; see Potter et al., 2004; Wolf et al., 2006.) In addition, surveys can also be used to gather data from a larger population of staff and patients about environmental characteristics, such as the adequacy of lighting and the perception of noise and its impact. In an example of this approach, Mahmood and colleagues surveyed nurses about the aspects of their physical environment that affected the risk of medication errors (Mahmood et al., 2011). Many of these factors contribute to latent errors—for example, creating conditions under which cognitive functioning is impaired because of the work environment itself.

Summary
The committee reviewed a number of methods for assessing the effects of the work system on diagnostic error. This section of the chapter highlights a number of those methods and illustrates how they have been applied in various health care settings to develop insights into the risks of error and to identify potential areas for improvement. The methods have in common the fact that they combine observation of the actual processes (tasks, communication, interaction with technology) with documentation of those processes. These methods can be relatively labor intensive, and they tend to require application at the individual site level, which implies that this is work that all teams and settings in which diagnoses are made need to become more skilled at undertaking. While standardized tools exist (surveys, methods of observation, and analysis of teams) and might be applied to samples of different types of teams and settings to identify particular vulnerabilities for diagnostic error, the most useful application of these methods is typically for improvement at the local level. The human factors science in this area suggests that a number of likely problems can be readily identified—that is, that deep study may not be necessary—but the complexity of the interactions among these various factors suggests that high levels of vigilance and attention to measurement will likely be necessary throughout the health care system…
Lots of things I've been addressing for a long time.

Below: Relevant to all of the foregoing, Dr. Carter's latest,
Usability Research: The Need for Standards
by JEROME CARTER on JUNE 27, 2016


With the possible exception of those living under rocks, everyone knows that EHR usability is a hot topic.  The million-dollar question is what to do about it. Understanding the nature of usability problems is always a good place to start. As one would expect, the number of usability studies reported in the literature has increased significantly in recent years. So, what have we learned? Unfortunately, it is hard to tell.

Determining the usability of a system requires objective standards or measures by which one can judge. What is the ideal number the steps to order a test, enter a note, look up an old history, or initiate follow-up of an abnormal result? No one knows, and since there are no objective standards for these processes, all results tend to be local (either for a given EHR system or a specific type of user). Beyond processes, the same questions could be asked about user interface elements.  Is there an ideal font size, text color, window position, etc. that would be ideal for all EHR users? Users differ in terms of clinical experience, problem-solving skills, cognitive support needs, ability to perceive color, and in other ways–there is no such thing as an “average” EHR user who can be represented by a panel of peers... 
Don't get me started.
__

ERRATUM
"Congressional Republicans hope to exit the defined benefit Medicare system and make it a defined contribution system, presumably so that sooner or later they can drown the contribution in the bathtub. Republican state legislatures have found as many ways as possible to exit Medicaid, or its expansion." - Joe Flower, "Time to Brexit the Health Care System?"
__

ON THE IMPLICATIONS OF "MISDIAGNOSIS"


For context, see "Retraction and the Rise of the Truth Jihadis" on THCB.

UPDATE

Converge this:
"Doctors all over the country are expected to deliver world-class clinical care while trying to keep up with the economic, technological, regulatory, payer, and organizational shifts that make being a doctor harder and harder. The ever-increasing demand for our time and availability, the way we are currently paid, the changing technology, and the advent of patients acting more like true consumers all contribute to this phenomenon. Physician burnout is a silent epidemic that poses serious challenges to patient health and our health care system." - STATnews, "Fighting the silent crisis of physician burnout"
____________

More to come...

Wednesday, June 22, 2016

#HCSummit16 final thoughts


Well, only in Lake Wobegon are all things above average all of the time. My #HCSummit16 experience was a bit of a disappointment, to be candid (and it had nothing to do with the Summit content). My long inbound flight delay out of SFO put me at MIA so late that I was running on fumes on Day 1 of the Summit, after only about 4 hours' sleep. The in-your-face asphyxiating heat/humidity combination (90F + 100% humidity, episodic T-storms) didn't help; I'd get out of my rental car and my glasses would immediately fog over. The oppressive heat also meant that the AC at the venue would typically be walk-in freezer cold. I was wishing I'd brought a jacket or sweater.

I was so tired by Wednesday afternoon there was no way I'd stay up late enough to attend John Toussaint's jazz band performance, as I did last year. I got back to my hotel (I was staying offsite) and promptly crashed out. Hated to miss that.

Then there was the limiting liability of attending solo, which meant that I'd not get to see most of the concurrent AM and PM learning sessions. Back when I was with HealthInsight during the REC days we typically had at least four or five people tag-teaming events such as HIMSS so we could compare notes and max out our individual and collective experience.

I'd said at the HCSummit16 outset I'd be particularly interested in "Leadership" presentations, and anything detailing the effective integration of Health IT into Lean initiatives.


Notwithstanding repeated tweets, I got no takers, and there was nothing in the presentations I did attend expressly addressing the HIT-Lean topic. Neither did casual talk with other attendees during lunch and intermissions yield any engagement.

It remains a priority issue of importance for me. It should be obvious just why, given that health care delivery remains an irreducibly high-cognitive-burden, fast-paced endeavor, one where data acquisition and evaluation are critical. BTW, see my May post "Technology, particularly the technology of knowledge, shapes our thought."

apropos of workflow, I'd brought up one aspect of this last year:


Most recently, I again brought this up during my recent HIMSS16 coverage.

Whatever. Continuing missed opportunity here.

The "Leadership" stuff was again fine, both as expressed in the Keynotes and Mark Graban's excellent CEO panel.


Speaking of Keynotes, I was delighted by Dr. Patrick Conway's CMS presentation.


The CMS budget comprises about a quarter of federal spending. Effectively Leaning up cannot but have significantly beneficial effects. We'll see. The federal government suffers from what I call "Policy ADHD" roughly correlated with election cycles, and Lean is not a Gartner Hype Cycle management fad.

THE LEAN DENTIST

I didn't attend Dr. Bahri's session.


They handed out comp copies of his book. Very good.
Introduction
Walking into a dental practice in Jacksonville, FL one would hardly expect to find a learning laboratory for lean practices or a relentless lean pioneer. But that’s exactly what I found when I first visited the offices of Dr. Sami Bahri in 2007. I had already heard about the “lean dentist” from lean thought leaders like Jim Womack and John Shook, and respected lean practitioners like Jerry Bussell from Medtronic. They all said the same thing, “You've got to see this guy … He actually gets it!”


So what does it mean to “get it” when you’re the leader of an organization, whether it’s a dental practice with a dozen employees or a Fortune 500 company? Let’s remember that the “it” we’re talking about is real lean that strives to consistently deliver only value-creating services to the customer. The Lean Enterprise Institute (LEI) has chosen to share Sami Bahri’s story because he has proven that he understands what a lean transformation requires of a leader:

  • A deep understanding of how your customers define “value”and a willingness to build your organization around that definition
  • Getting your hands dirty in the real work of your organization to understand where the value is flowing … and where it’s not 
  • Treating your organization as a system rather than a collection of disconnected operations
  • A commitment to changing your own behavior while understanding what it takes to help others change
  • A passion for learning—about your customer, your process and all of its problems, and about creative countermeasures based on constant experimentation
  • An honest belief in the power of Plan-Do-Check-Act (PDCA) and a commitment to completing the improvement cycle every time
  • Becoming the teacher who models lean thinking, participates in lean experiments, and learns/teaches by asking the right questions, rather than providing the right answers
  • Humility to admit that knowledge is everywhere in the organization and that every improvement is temporary
Perhaps the most amazing part of this story is that Dr. Bahri is completely self-taught. Since opening his practice in Jacksonville in 1990, he has read virtually everything in the field of organizational improvement, from Deming’s Out of the Crisis, to Womack and Jones’ Lean Thinking, to Csíkszentmihályi’s Flow: The Psychology of Optimal Experience. Even more importantly, he began experimenting with his staff to convert his new knowledge from theory to practice. So in the process of understanding what it means to be lean, he became the teacher for everyone on his staff (including fellow dentists), the scientific observer for his lean business experiments, and the leader of a lean enterprise.

Dr. Bahri has a profound understanding of the essence of lean. He got this understanding in much the same way as the original developers of the Toyota Production System. He pursued it because of an unshakable belief that there had to be a better way and through his own hands-on PDCA experiments.


It’s been my great pleasure to get to know Sami well while bringing Follow the Learner to the market. The major challenge in the project has been to capture the deep understanding of lean that he has developed over his almost 30-year lean learning journey, while also preserving his gift for “keeping it simple.” In the process, he’s defined an equally “simple”—and very challenging—model of leadership. Butthese are the fundamentals, not the basics of lean leadership. If we’ve done our job, the book will help you to look at the fundamentals of your own lean implementation and the vital role that you must play in it as a leader within your organization.


To provide you with additional information about Dr. Bahri’s lean journey we have created a dedicated web page for this book at the LEI web site: lean.org/ftl...
It's a quick, accessible read. I fired up my Dragon and read in the conclusion.
Conclusion
I am including here a handful of lessons I have learned along the way to keep our lean efforts focused. These lessons have worked well for us and I welcome you to use them, but please also improve upon them and create your own.

Improve the process flow before you eliminate waste from individual operations. Start improvements close to the customer and spread the improvements backward toward the beginning of the value stream. Some operations can be totally eliminated, meaning you may never have to work on them.
Run your value–adding operations in a series and the support functions in parallel. While your value–added operations (e.g., crown or filling in our case) are running, your support functions (e.g., writing notes in the chart) can happen in parallel, at the same time. In practice, we try to keep the patient in the office only for the duration needed to perform the value–added treatment.
Start as small as you can when making improvements within your organization. Mistakes will only have a minor impact.
Change is most effective when it grows naturally.  Grows like an embryo, change needs to start small, drawing upon as few resources as possible, before it can spread outward to encompass the entire organization. Because we tend to think only in batch–and–queue terms even when implementing change, which tends to make a small change somewhere, and as soon as we see good results, we try to spread it over the entire organization. I found that policy to generate early resistance that could halt any change efforts.
We need to fight the desire to spread change on a large scale until we have implemented it as deep as necessary in a small area of the organization. In other words, go 1 inch wide and 1 mile deep, not the opposite.
Use small–scale experiments to prove to your coworkers that the changes you are proposing are actually improvements. Providing proof changes minds, establishes trust, and reduces resistance to change.
Shorten the lead time to make your processes more flexible and more responsive, so you can respond faster to market changes. Look for flexibility in your people's attitude, the flexibility to learn and to help whenever and wherever they are needed. This is the best way to constantly increase capacity in your organization.
Put the decision-makers together rather than in separate locations. To facilitate one–piece flow, we like people who need to make decisions and who owned different pieces of the puzzle to be located in one space around the patient.
Communication and decision–making will improve dramatically. For example, our treatment plan coordinator, Candace had an office that she kept even after she became flow manager. One day she was sitting across the table, discussing the schedule with me. I was holding a color printed schedule that showed me more details than the black and white printed schedule that she held. Because of that difference, she could not understand everything I described about the schedule. Frustrated, she came to sit next to me. As soon as we looked at the same paper, we had no more communication problems.

"If it is so difficult to communicate while across the table from each other," she reflected, "it must be even more difficult for the assistance to communicate with me when I am in my office and they are in the treatment room."

She decided to abandon the office that she had used for over 10 years. The next morning, she moved to a desk right behind the hygienists and dentists. From her new location she could hear the hissing of the cleaning machine, and when it stopped; she did not need anybody to tell her that the cleaning was finished. If anyone needed help with an insurance or scheduling question, they did not need to call her anymore because she was right there and could hear the discussion.

Most importantly, learn. However you can, learn. Even when I was teaching dentistry, I have found that learning was more fun than teaching. When I began learning lean management, I explored a new knowledge first with an interested individual and then with everyone on the staff. Today, I'm always adding to my network of fellow leaned learners from around the world, so that I can learn more and more creative lean applications. My only plan is to keep learning. My wish is that every leader becomes a lifelong lean the learner and teacher.

But start implementing lean today. Do not wait until you have learned "all about lien," including the leadership principles I've written about here. In the end, practices the only thing that will enable you to find your way by learning your own way. These personal lessons will determine the kind of leader that you will become. (Pages 87 – 90)
I went to Amazon to see whether it was offered there. No, but I did find this.


Okeee dokeee now... LOL.

"The Lean Dentist" is available here.

There is no shortage of worthy QI books to study. Four of my cut-to-the-chase favs:


Count me Old School Deming, for one thing.

CODA

I have a couple of technical recommendations. First, the lighting. Of all the conferences I attend every year, the Lean Healthcare Summit is by far the weakest in terms of presentation venue stage lighting. It's like one continuous sleepy Happy Hour ambience. Two sparse light trees at the back of the ballroom, outfitted with about four or five poorly aimed white spots each (the left side podium was literally in the dark), no backlighting, no floods.

The Health 2.0 conferences are the best in this regard. Pro A/V. We should learn from them.

Second, I lost track of the overloaded, impossible-to-assimilate slides, e.g.,


This syndrome is by no means unique to the Summit. We've all been to myriad presentations wherein the speakers drone on ad nauseum reading through their text-heavy slides (and, to be fair, I have not always uniformly walked my own talk -- pdf). Some of the Summit16 presentations were manic, with speakers plowing intensely through too much visual material.

I already know how to read.

Again, Health 2.0 conference presentations are -- well -- leaner, and consequently more effective in terms of information delivery. Attend. See for yourself. I'll be there again this fall.

One of John Shook's closing Keynote slides.


OK, I also recommend, to the extent practicable, application of the principles embodied here:


Performance in three acts
The Presentation Secrets of Steve Jobs is structured like one of Jobs’s favorite presentation metaphors: a three-act play. In fact, a Steve Jobs presentation is very much like a dramatic play—a finely crafted and well-rehearsed performance that informs, entertains, and inspires. When Jobs introduced the video iPod on October 12, 2005, he chose the California Theatre in San Jose as his stage. It was an appropriate setting as Steve divided the product introductions into three acts, “like every classic story.” In act 1, he introduced the new iMac G5 with built-in video camera. Act 2 kicked off the release of the fifth-generation iPod, which played video content for the first time. In act 3, he talked about iTunes 6, with the news that ABC would make television shows available for iTunes and the new video iPod. Jobs even introduced jazz legend Wynton Marsalis as an encore.

In keeping with Jobs’s metaphor of a presentation as a classic story, The Presentation Secrets of Steve Jobs is divided into three acts:

  • Act 1: Create the Story. The seven chapters—or scenes—in this section will give you practical tools to craft an exciting story behind your brand. A strong story will give you the confidence and ability to win over your audience. 
  • Act 2: Deliver the Experience. In these six scenes, you will learn practical tips to turn your presentations into visually appealing and “must-have” experiences.
  • Act 3: Refine and Rehearse. The remaining five scenes will tackle topics such as body language, verbal delivery, and making “scripted” presentations sound natural and conversational. Even your choice of wardrobe will be addressed. You will learn why mock turtlenecks, jeans, and running shoes are suitable for Jobs but could mean the end of your career.
Short intermissions divide the acts. These intermissions contain nuggets of great information culled from the latest findings in cognitive research and presentation design. These findings will help you take your presentations to an entirely new level.

Gallo, Carmine (2009-09-11). The Presentation Secrets of Steve Jobs: How to Be Insanely Great in Front of Any Audience (Location 178). McGraw-Hill. Kindle Edition.

ACT I
Create the Story


Creating the story, the plot, is the first step to selling your ideas with power, persuasion, and charisma. Succeeding at this step separates mediocre communicators from extraordinary ones. Most people fail to think through their story. Effective communicators plan effectively, develop compelling messages and headlines, make it easy for their listeners to follow the narrative, and introduce a common enemy to build the drama. The seven chapters—or scenes—in Act 1 will help set the foundation for presentation success. Each scene will be followed by a short summary of specific and tangible lessons you can easily apply today. Let’s review the scenes here:

  • SCENE 1: “Plan in Analog.” In this chapter, you will learn how truly great presenters such as Steve Jobs visualize, plan, and create ideas well before they open the presentation software.
  • SCENE 2: “Answer the One Question That Matters Most.” Your listeners are asking themselves one question and one question only: “Why should I care?” Disregard this question, and your audience will dismiss you.
  • SCENE 3: “Develop a Messianic Sense of Purpose.” Steve Jobs was worth more than $100 million by the time he was twenty-five, and it didn’t matter to him. Understanding this one fact will help you unlock the secret behind Jobs’s extraordinary charisma.
  • SCENE 4: “Create Twitter-Like Headlines.” The social networking site has changed the way we communicate. Developing headlines that fit into 140-character sentences will help you sell your ideas more persuasively.
  • SCENE 5: “Draw a Road Map.” Steve Jobs makes his argument easy to follow by adopting one of the most powerful principles of persuasion: the rule of three.
  • SCENE 6: “Introduce the Antagonist.” Every great Steve Jobs presentation introduces a common villain that the audience can turn against. Once he introduces an enemy, the stage is set for the next scene.
  • SCENE 7: “Reveal the Conquering Hero.” Every great Steve Jobs presentation introduces a hero the audience can rally around. The hero offers a better way of doing something, breaks from the status quo, and inspires people to embrace innovation (ibid, pp. 1-2)
ACT 2
Deliver the Experience


Steve Jobs does not deliver a presentation. He offers an experience. Imagine visiting New York City to watch an award-winning play on Broadway. You would expect to see multiple characters, elaborate stage props, stunning visual backgrounds, and one glorious moment when you knew that the money you spent on the ticket was well worth it. In Act 2, you will discover that a Steve Jobs presentation contains each of these elements, helping Jobs create a strong emotional connection between himself and his audience.

Just as in Act 1, each scene will be followed by a summary of specific and tangible lessons you can easily apply today. Following is a short description of each scene in this act:

  • SCENE 8: “Channel Their Inner Zen.” Simplification is a key feature in all of Apple’s designs. Jobs applies the same approach to the way he creates his slides. Every slide is simple, visual, and engaging.
  • SCENE 9: “Dress Up Your Numbers.” Data is meaningless without context. Jobs makes statistics come alive and, most important, discusses numbers in a context that is relevant to his audience.
  • SCENE 10: “Use ‘Amazingly Zippy’ Words.” The “mere mortals” who experience an “unbelievable” Steve Jobs presentation find it “cool,” “amazing,” and “awesome.” These are just some of the zippy words Jobs uses frequently. Find out why Jobs uses the words he does and why they work.
  • SCENE 11: “Share the Stage.” Apple is a rare company whose fortunes are closely tied to its cofounder. Despite the fact that Apple has a deep bench of brilliant leaders, many observers say Apple is a one-man show. Perhaps. But Jobs treats presentations as a symphony.
  • SCENE 12: “Stage Your Presentation with Props.” Demonstrations play a very important supporting role in every Jobs presentation. Learn how to deliver demos with pizzazz.
  • SCENE 13: “Reveal a ‘Holy Shit’ Moment.” From his earliest presentations, Jobs had a flair for the dramatic. Just when you think you have seen all there is to see or heard all there is to hear, Jobs springs a surprise. The moment is planned and scripted for maximum impact. (ibid, pp. 85-86)
ACT 3
Refine and Rehearse


 So far, we’ve learned how Steve Jobs plans his presentations. We’ve talked about how he supports the narrative through his words and slides. We’ve discussed how he assembles the cast, creates demos, and wows his audience with one dynamic moment that leaves everyone in awe. Finally, you’ll learn how Jobs refines and rehearses his presentation to make an emotional connection with the audience. This final step is essential for anyone who wants to talk, walk, and look like a leader. Let’s preview the scenes in this act:

  • SCENE 14: “Master Stage Presence.” How you say something is as important as what you say, if not more so. Body language and verbal delivery account for 63 to 90 percent of the impression you leave on your audience, depending upon which study you cite. Steve Jobs’s delivery matches the power of his words.
  • SCENE 15: “Make It Look Effortless.” Few speakers rehearse more than Steve Jobs. His preparation time is legendary among the people closest to him. Researchers have discovered exactly how many hours of practice it takes to achieve mastery in a given skill. In this chapter, you’ll learn how Jobs confirms these theories and how you can apply them to improve your own presentation skills.
  • SCENE 16: “Wear the Appropriate Costume.” Jobs has the easiest wardrobe selection in the world: it’s the same for all of his presentations. His attire is so well known that even “Saturday Night Live” and “30 Rock” poked some good-natured fun at him. Learn why it’s OK for Jobs to dress the way he does but it could mean career suicide if you follow his lead.
  • SCENE 17: “Toss the Script.” Jobs talks to the audience, not to his slides. He makes strong eye contact because he has practiced effectively. This chapter will teach you how to practice the right way so you, too, can toss the script.
  • SCENE 18: “Have Fun.” Despite the extensive preparation that goes into a Steve Jobs presentation, things don’t always go according to plan. Nothing rattles Jobs, because his first goal is to have fun! (ibid, pp. 165-166)
Again, "to the extent practicable." Technical content presentations differ materially from short marketing content delivery. But, are we jamming too much in?

I also study the delivery approach contained in TED Talks. e.g.,


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More to come...

Friday, June 17, 2016

#HCSummit16 Day Two: We're all Shook up.

Beginning with the end in mind, John Shook's closing Keynote was worth the entire trip.


A natural communicator, this man. I hope they make the video of this talk available publicly.
Lean means working on the work: the value-creating work that occurs on the frontlines of your enterprise. - John Shook, June 11, 2015
Lots more to reflect on and write up. I'm on my way home to California. Stay tuned.

A NEW READ

apropos of Lean process QI,


Fundamentally, among the "process errors" we must be ever-vigilant against are dx errors, no? From THCB:
The National Academy of Medicine’s report offers an immediate suggestion to improve health care diagnosis today: teamwork. The same principle that rules sporting arenas and playgrounds across the world can reduce the number of diagnostic errors. The practice of medicine has traditionally been a lonely and risky competition. Doctors are used to calling the odds and making diagnoses without input from other members of the team, and nurses and physician assistants are taught not to question them. If a physician makes a mistake, there is a culture of blame, shame and fear of litigiousness, which makes it less likely that individuals will speak up or report a diagnostic error.

But better teamwork achieves much more than merely changing professional norms or local work culture...
'eh? You can spend 50-some bucks for the hardcopy or Kindle version via Amazon, or avail yourself of the free (if a bit unwieldy) National Academies PDF, which I've posted for your convenience. A long read. 450 pages. I've just started my close study. Of particular interest will be the Health IT - clinical cognition nexus. See, e.g., my May post "Technology, particularly the technology of knowledge, shapes our thought."

And, will triangulate what I learn with these two books previously cited on KHIT:


DAY 2: A FEW MORE RANDOM PICS

Cheryl DeMar
Norm Gruber
Mark Graban
Tim Johnson
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More to come...

Wednesday, June 15, 2016

#HCSummit16 Day One

I'm not gonna post much tonight, I'm fried. I first left SFO at 11:50 a.m. PDT yesterday, headed for MIA on American Airlines flight 931 scheduled to arrive at 8:30 p.m. EDT. We taxied out, and the fully-loaded 767-300 lumbered left onto the takeoff runway. I wasn't paying attention, as I was totally absorbed in Siddartha Mukherjee's amazing book "The Gene: An Intimate History."

All of a sudden I look up and we're pulling back to the gate. "Ladies and gentlemen, as you can see we've had to return to the departure gate. There's a pressure seal problem with the front galley door. We've called for mechanics, and we'll advise you further shortly." After about 40 minutes we were advised they had a crew of 5 mechanics working the problem, and we'd have to de-plane so they could pressurize the cabin once repairs were complete to verify the integrity of the fix.


We eventually re-boarded and left for MIA around 3 p.m. SFO time. I got to my hotel a little after midnight. Ugh.

Finished the Mukherjee book during the flight. Wow.

When I completed the final draft of the six-hundred-page Emperor of All Maladies in May 2010, I never thought I would lift a pen to write another book. The physical exhaustion of writing Emperor was easy to fathom and overcome, but the exhaustion of imagination was unexpected. When the book won the Guardian First Book Prize that year, one reviewer complained that it should have been nominated for the Only Book Prize. The critique cut to the bone of my fears. Emperor had sapped all my stories, confiscated my passports, and placed a lien on my future as a writer; I had nothing more to tell. 

But there was another story: of normalcy before it tips into malignancy. If cancer, to twist the description of the monster from Beowulf, is the “distorted version of our normal selves,” then what generates the undistorted variants of our normal selves? Gene is that story— of the search for normalcy, identity, variation, and heredity. It is a prequel to Emperor’s sequel...

Mukherjee, Siddhartha (2016-05-17). The Gene: An Intimate History (Kindle Locations 8794-8802). Scribner. Kindle Edition.
A must-read for those contemplating the future of health care as it pertains to the dx and tx roles of the "omics."

Speaking of books. One of the morning breakout learning sessions was "How to lead by asking effective questions." It is based on the Henry Schein book "Humble Inquiry," which I've cited on this blog. I attended.

I recommend quadrangulating it with three others:


Good session, Far to much to really get at in any depth in 75 minutes, but well-presented. Elevator speech summary? Too much of interrogative discourse, particularly in the workplace, is comprised of implicitly accusatory or otherwise directive questioning. e.g., the "loaded questions," which are really assertions disingenuously voiced in the nominal forms of questions. The tactic is at once dishonest and counterproductive. Think "Talking Stick."

Read the Schein book.

I've shot some Day One pics (weak light notwithstanding), but I'm fixin' to crash. Will try to catch up tomorrow. Hate to miss the music tonight.
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A FEW RANDOM PICS


Were those "Trump Steaks" at lunch?
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More to come...

Monday, June 13, 2016

Next up, #HCSummit16, the 2016 Lean Summit


RE-POSTING...


I'm flying to Miami shortly to cover this year's event (at the Doral this year). Last year's Summit in Dallas was off-the-hook fine. The 2016 agenda:
Wednesday
Keynotes

  • John Toussaint, ThedaCare Center for Healthcare Value
  • Patrick Conway, MD
  • Kathryn Correia
Learning Sessions
  • Leader Standard Work
  • How to Lead by Asking Effective Questions
  • How Government, Healthcare, and Lean Come Together
  • Lean Transformation Across Cultures: The lean journey of a disability hospital & newborn healthcare programme in East Africa
  • Population Health: A journey to deploying real time decision support
  • Lean Dentist
  • Business Intellligence is no longer an Option!
  • Experiments Around the Network AM
  • Experiments Around the Network PM
Thursday
Keynotes

  • Elizabeth Mitchell
  • John Shook, Lean Enterprise Institute
Learning Sessions
  • Engaging Physicians: Lean as Preventive Medicine for Burnout
  • Results Focused, Process Driven Ambulatory Clinic Redesign
  • Payment Reform: The Employers' Perspective
  • Improving Patient Experience, Patient Safety and Patient Progression through a Lean Management System
  • Doing the Splits in the ED: Emerging Models in Academic Medicine
  • Preparing Senior Leadership and The Lean Office for Organization Transformation
  • Applying Lean to Federal Healthcare Policy, the Story of a Strategic Design Event
  • Experiments Around the Network AM
  • Experiments Around the Network PM
The Lean Summits are comprised of people and organizations who are doing it. No mere theorizing and other abstract talk or dwelling on the myriad vexing problems in the health care system, which we all know exist.

I will be all eyes and ears. In addition to "Leadership" presentations, I'll be particularly interested in seeing evidence of the effective integration of Health IT into lean workflows, given the intractable hand-wringing over the putative "impediments" of digital health InfoTech.

INTERESTING NEW BOOK

Notice about this new title arrived on my inbox via my ASQ member feed.

Preface
Chapter 1 traces the origins of probability as an academic subject and high- lights the pervasiveness of statistics and probability in today’s popular culture. Chapter 2 introduces the reader to counting techniques to determine how many ways particular outcomes can occur. Counting possible outcomes is a fundamental piece of probability calculations. In Chapter 3, we begin the hard and rewarding work of learning probability concepts and rules, including the concepts of mutual exclusivity, sampling with and without replacement, odds, conditional probability, and Bayes’ theorem. Seven detailed examples are included at the end of the chapter to help solidify your understanding. After studying the first three chapters and completing the practice problems included in the companion workbook, readers will be prepared to answer any number of probability questions, from picking socks out of a drawer, to selecting lottery numbers, to choosing colleagues for committees, to deciding whether a manufacturing lot should be shipped to the customer.

Chapter 4 introduces commonly used “named” discrete probability distributions: the discrete uniform, binomial, hypergeometric, geometric, negative binomial (also known as the Pascal), and Poisson. The formulas, parameters, and uses for each distribution are introduced, and worked examples are shown for each distribution type. Useful approximations among the distributions are also presented, and a summary of the distributions is tabulated at the end of the chapter.

Chapter 5 covers continuous probability distributions, among them the well-known normal (also known as the Gaussian), standard normal, Student’s t, F, chi-square, and Weibull distributions. Lesser known but useful and interesting distributions are also included in this chapter: the uniform, triangular, gamma, Erlang, exponential, Rayleigh, lognormal, beta, and Cauchy. In addition, key theorems such as the law of large numbers and Chebyshev’s inequality are presented and explained. At the end of the chapter, a summary of the distributions appears for quick reference. After studying the material in Chapters 4 and 5 and completing the practice problems in the companion workbook, the reader will be able to select the appropriate distribution for a wide range of scenarios, state the formulas for the mean and variance for various distributions, and correctly evaluate probability statements.

The appendices contain the distribution road map, a graphic of all the probability distributions presented in the text and how they are related. Probability tables for the binomial and Poisson distributions as well as cumulative probability tables for the binomial, Poisson, standard normal, Student’s t, chi-square, and F distributions are also provided.

As extensive as the list of rules, theorems, and distributions covered in the text happens to be, this book is by no means comprehensive! The distributions presented in the text were carefully chosen for their applicability to the types of problems that arise in the quality field. Univariate distributions not covered include the Laplace and extreme value distributions, as well as the Pearson series of distributions. There also exists a multitude of multivariate distributions in which arrays of random variables are modeled. These distributions include the Dirichlet, multivariate normal, Hotelling’s T2, and the Wishart and require a working knowledge of matrix algebra. To learn more about these distributions, you can consult a thicker and more densely written text!

Even though my outline was carefully crafted, I did experience scope creep in the writing process. Just as soon as I would finish one section, I would invariably have an idea in the shower of yet another formula, relationship, example, or interesting fact to add. Finishing the book was becoming a Sisyphean task. In order to send a completed manuscript to the publisher, I had to either stop showering or decide that, as it stood, the book more than covered what was necessary. To my family’s great relief, I chose the latter option.

It is my hope that as you read this book you underline new terms, highlight formulas, write in its margins, and refer to it often. It would be gratifying to see dog-eared copies of The Probability Handbook on office shelves or opened up during certification exams.

Feel free to contact me with comments or questions about the book or to learn more about courses based on the book. Visit www.6sigma.university.
 
Nice that she includes Bayes and Chebyshev. Color me both Bayesian and Chebyshev-ist (pdf).

Expensive book, at $99 retail and $60 ASQ member price. I personally don't need it: been there, done that (and I already have a huge stash of advanced stats books in my stacks).


For someone prepping for one of the ASQ certification exams, however, it's probably well worth the money.
Introduction
The lottery has been characterized as a tax on the mathematically naive. Consider a player who uses a “system” to carefully curate his picks based on his anniversary date, his child’s age, and the current phase of the moon. Unfortunately, all the superstition in the world can’t overcome the tyranny of random chance: a player choosing the numbers 1 2 3 4 5 has the same probability of winning as our player using his “system.” As the popular financial advisors on television tell us, in the long run it would be better to invest the dollar than to spend it on a lottery ticket. But what would be the fun in that?

The lure of easy money is nothing new. For centuries, gamblers have tried to outsmart other players, as well as fate, in the hopes of scoring the big win. Not surprisingly, the study of probability traces its origins to games of chance. Unlike the lottery, which is based on pure luck, many games involve decision making and strategy that can be crafted by using probability concepts. Girolamo Cardano (1501–1576), by turns quite a successful professional gambler, mathematician, and physician, wrote the first treatise on winning betting strategies for cards and dice using the concepts of probability. The work was published posthumously almost a century later in 1663. At about this same time, the mathematicians Blaise Pascal and Pierre de Fermat were conducting a lengthy correspondence concerning the solution to the “Problem of Points,” in which the stakes in an unfinished game of chance involving coin flips must be fairly divided between two players.

Probability has since evolved beyond rolls of the dice and flips of a coin to influence almost every aspect of our lives. Medical researchers, meteorologists, and even online dating sites use probability to estimate disease risk, create weather forecasts, and match clients, respectively...
Nothing in the table of contents regarding "design of experiments." I'd be looking to bone up on areas of "Clinical Study Design." Beyond books, there's a ton of freely available stuff out on the 'net. e.g.,


Another great free stats resource: "StatSoft has freely provided the Electronic Statistics Textbook as a public service since 1995."

There's a good bit of relatively non-technical discussion of probability issues in Dr. Hatch's book, which I first referred to here, on June 8th.

I originally envisioned Snowball in a Blizzard as a book that would focus on methodological aspects of human-subjects research, mainly the difficulties of study design and the subtleties of statistical interpretation. When, for instance, does a relative risk value diverge from an odds ratio, and why are the two often confused? What is a Type I versus Type II error? How do we “power” studies? A few years ago, as I was struggling with these kinds of issues in my professional work, I thought that they would be ideal subjects to illuminate to a general audience. I can see now that these fairly technical matters were unlikely to help nonspecialists have a more thorough understanding of clinical research, and it is probably why I received fairly tepid responses from literary agents. 

Over time, I realized that more was to be gained by telling stories about the consequences of these issues, and that I could occasionally sprinkle the text with brief explanations of the more essential methodological points. For instance, I thought it absolutely critical to explain the concept of positive predictive value in order to show why the USPSTF does not universally recommend mammograms for women under age fifty. One can’t easily grasp the justification for the task force’s reasoning without being acquainted with the notion of positive predictive value; once one understands the concept and sees the truly lousy predictive value of a positive screening mammogram in this age group, it’s hard to understand why there was (and is) so much fuss in the first place. However, I shelved the idea of devoting entire chapters, say, to the difference between nested case-control and case-cohort studies or the beauty inherent in the Mann-Whitney U test. Such subjects, fascinating though they can be to epidemiologists, would probably be valuable to nonspecialists only as a soporific. 

Thus, I elected to prioritize narration over technical explanation to describe these points, and whether I have succeeded or failed at that task, I leave for you, the reader, to judge. However, I do believe that there is one statistical concept worth exploring in a little more detail than the structure of this book allowed for because so much of what I have discussed in the previous pages relies on it: significance. I can’t speak for the basic research scientists, but for clinicians statistical significance is in many ways the yardstick by which we measure relevance in medical knowledge...

Hatch, Steven (2016-02-23). Snowball in a Blizzard: A Physician's Notes on Uncertainty in Medicine (pp. 241-242). Basic Books. Kindle Edition.
"OFF-TOPIC" ERRATUM

In the wake of Orlando,

In December 2012, a gunman walked into Sandy Hook Elementary School in Newtown, Connecticut, and killed 20 children, six adults, and himself. Since then, there have been at least 1,000 mass shootings, with shooters killing at least 1,140 people and wounding 3,942 more.

The counts come from the Gun Violence Archive, a database that tracks events since 2013 in which four or more people (not counting the shooter) were shot at the same general time and location...
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More to come...