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...