Dealing with EHR Dissatisfaction (Part 4)

This post is a continuation; Part 1 is at this link. In this post, I describe a vision of an ideal EHR system that can developed today.

Based on the complaints about EHRs, one can conclude that there would be increased satisfaction if EHRs are thought to be a component of an all-encompassing HIT system. This next-generation computerized system would manage clinical information, as well as administrative data, in a way that increases provider efficiency, enables providers to deliver high-value (cost-effective/safe/quality) preventive and acute care and rewards them for doing so, assists patients/consumers in taking better care of themselves (self-maintenance), and promotes population health.

So, imagine coming together to build a novel EHR-based HIT system that continuously improves clinical and economic outcomes by (in no particular order):

• Capturing clinical information accurately and automatically at point-of-care in real time in a way that requires little effort and workflow change

• Clearly presenting—to clinicians and patients—the biomedical, psychosocial (biopsychosocial) and economic information they need to: (a) bridge the knowledge gap (http://wellness.wikispaces.com/The+Knowledge+Gap) ; (b) make wise prophylactic, diagnostic, and treatment decisions; and (c) promote patient-centered cognitive support (http://curinghealthcare.blogspot.com/2009/06/meaningful-use-clinical-decision.html)

• Enabling networks of collaborators to: (a) perform clinical research in the field and lab through the streamlined collection, sharing, and analysis of large quantities of diverse clinical data; (b) build evolving health science knowledgebases with for clinical research, which transform this knowledge into evidence-based practice guidelines/protocols/pathways; (c) promote the continuity and coordination of care; (d) share observations, lessons learned, and best practices; and (e) effectively run PCMHs and ACOs

• Integrating sick care with well care (see http://wellness.wikispaces.com/Tactic+-+Well-Care+Sick-Care+Integration)

Also imagine that this next-generation EHR system:

• Facilitates ongoing biosurveillance and post-market drug & medical device surveillance

• Streamlines mandatory regulatory reporting.

• Connects providers/clinicians to one another in (a) loosely-coupled, occasionally connected, near-real-time, asynchronous, peer-to-peer mesh networks (e.g., DIRECT e-mail) and (b) tightly-coupled, continuously-connected, real-time networks (e.g., corporate VPNs)

• Secures PHI while it’s being exchanged (in transit) and while stored in a device (at rest), as well as protecting patient privacy

• Provides a hybrid approach to information access and exchange that includes Web-based tools, services and deidentified information stores in the cloud, along with standalone applications on users’ devices

• Provides useful business intelligence

I claim that, by working together, we can realize the vision described above by adding common off-the-shelf HIT tools and custom-built applications to EHRs. I know how this can be done!

Part 5 examines the question: When it comes to EHRs, whose satisfaction is important?

Dealing with EHR Dissatisfaction (Part 3)

This post is a continuation; Part 1 is at this link. In this post, I discuss what's needed to make EHRs more satisfactory and answer questions about my claims.

I wrote:

What’s needed is a paradigm-busting shift in which clinicians and software developers collaborate to create tools that are truly clinically useful and promote (rather than destroy) efficiency. We can start by redefining Meaningful Use – see my blog post at http://curinghealthcare.blogspot.com/2009/05/defining-meaningful-use-of-health-it_02.html

I then answered a question about my claim that over-reliance on XML and Web services is a big problem with HIT standards:

I'm not saying that XML can't work; just that XML is being over-used. When data sets are rather simple or XML is used for sending messaging operation instructions for web service, I have no problem with it. But when it comes to representing complex data, as in a CDA-based document or a complex form definition file, XML sucks!  

Here’s why: When you use XML in such cases: human readability is a joke, verbosity is huge, and parsing requirements and complexities are immense. It also forces conceptual incongruities just to keep data in hierarchies when such parent-child relationships are unnecessary. See http://curinghealthcare.blogspot.com/2009/12/dueling-data-formats.html and http://c2.com/cgi/wiki?XmlSucks.In fact, I’ve seen XML-related complexities bring the Query Health initiative to its knees (see http://curinghealthcare.blogspot.com/2012/08/disruptive-innovation-in-health-it-path_8.html).

I fought against this "let's use XML for everything" mentality since the mid-90's, but to no avail. Now the crap is hitting the fan!  

Bottom line is that there are much easier/simpler and more rational ways to represent complex data than XML, even hierarchical data. We've got to start busting maladaptive paradigms.

Regarding the web services issue, I have no problem with this architecture for the most part, but bringing SOAP and RESTful into the DIRECT Project, and relying on them exclusively in other Federal initiatives, adds similarly increased complexity, as well as vulnerability, as I discuss at http://curinghealthcare.blogspot.com/2012/07/oncs-direct-project-in-defense-of.html.

What we need is a hybrid approaches that balances web services and XML with simpler elegant methodologies.

These issues came to a head during the standards-making process at the S&I Framework Structured Data Capture (SDC) initiative where I fought for the inclusion of a much simpler approach to capturing data using electronic forms, but was voted down by the community (see http://wiki.siframework.org/Canddiate+Standards+List+Feedback). To their credit, however, they are allowing our team to pursue a pilot (on our own dime) even though it will demonstrate use of technology/methods that aren’t fully compliant with the implementation guide they are developing.

In any case, the kind of change we need isn't going to happen by EHR industry lobbyists or the Feds ... As I said before, it's got to be led by clinicians and researchers working closely with creative software developers (including nimble EHR/HIT vendors) who focus on designing and deploying clinical tools that are truly useful and useable.

In part 4 at this link, I discuss a practical vision of a next-generation EHR system that can be built today, which addresses the many criticisms of what is happening today.

Dealing with EHR Dissatisfaction (Part 2)

This post is a continuation; Part 1 is at this link.

The next issue turned to who should ultimately control a patient's data. Should it be the patient (patient access control), provider/clinician, care team/ACO/PCHM, HIE/gov't/CMS?

Here's what I wrote:

Some believe patients should have granular control of who gets to see their health data and, if de-identified and aggregated for population health research, that the patient should be compensated financially for authorizing such use of their data.

Concerns about PHI privacy and gov't control are certainly warranted and, I agree with Randall, that legacy systems have been incapable of doing what's needed to give patients peace of mind that storing their PHI in the cloud provides a high enough level of privacy protection. We cannot achieve the Triple Aim unless we have wide deployment of a secure, low-cost, always available, and simple way to exchange data from EHR to EHR, EHR to PHR, EHR to Population Health and After Market Surveillance repositories (with de-identified data), and from Person to Person (patient to clinician, clinician to patient, and clinician to clinician).

A pub/sub, loosely-coupled, mesh node network, with identity management and endpoint-to-endpoint encryption, is one way to achieve this.

The next issue raised is whether the actual goal of healthcare reform is to make it fail. I responded by saying:

I've heard from more than one person that current healthcare reform efforts were designed to fail. While I'm not sure about that, I AM confident that the cost, complexity, inefficiencies, and insecurities built into current implementation regulations are too big NOT to fail.

Nevertheless, the underlying goals of ARRA HIT--to improve care quality and contain costs/prices (i.e., increase value to the consumer)--are absolutely essential. We have a real big problem if all the spending and hassles are just a manipulation to funnel taxpayer's money into the coffers of certain big corporations under the guise of helping the common good.

As such, I believe physicians and other clinicians, researchers/informaticists, and HIT developers have a duty to participate in loosely coupled collaborative networks focused on ensuring that the HIT being developed is designed and used to improve patients' health and wellbeing, to minimize the burden and maximize the competency of providers, and to reward delivery of high value care.

I then went on to describe my experiences as a committed member of the government's HIT standards bodies:

Having been involved over the past few years in a half dozen HHS/ONC technical workgroups that determine EHR/HIT standards, my associates and I have been like David facing an arena of Goliaths. I've been dealing with the biggest EHR vendors, as well as Federal contractors and agencies. It's been a very frustrating and enlightening experience. We entered the arena with the faint hope that the powers-that-be would compare our simple, low-cost, highly capable, disruptive innovations to the complex, convoluted, expensive mainstream technologies currently being adopted.  

What we’ve found is that the standards-making process is rife with regulatory capture in which new standards are built on top of old standards without due consideration of modifying those standards in light of new and better technologies. Simplicity, efficiency, and usability are an afterthought. The result is an extraordinarily complex set of monolithic processes that few (if any) can implement and few are willing to use.  

Examples include, in no particular order: (a) an over-reliance on XML data representations and Web services, (b) long delays due to HL7 voting processes, (c) changes to DIRECT taking it from what was supposed to be one step above the fax to a convoluted amalgam of HISPs that make PHI vulnerable with exposure to man-in-the-middle attacks, (d) incestuous relationships between vendors and ONC that block innovation from “outsiders,” (e) reticence to deal with difficult clinical workflow issues, and (e) the natural tension between making huge leaps in EHR system capabilities and the “let’s just keep crawling until we can walk” mentality. The result is that money continues to be spent with dismal progress in enabling HIT to increase value to the consumer while enabling and rewarding providers for delivering such value.

It seems to me that most of these problems are due to business strategies and tactics supported by regulatory capture; it is not a technology issue, per se.

In part 3, at this link, I elaborate about the problems identified above.

Dealing with EHR Dissatisfaction (Part 1)

For the past few days, I’ve been involved in a very good conversation at LinkedIn HIMSS titled: Can we turn EHR dissatisfaction around?

Here’s a comment from person that struck a chord:

It took the doctor 2 minutes to dictate as opposed to 30 minutes to use speech, edit and/or type. In addition, if the text is not edited in most cases except for those of you have it down perfect, you get crappy documentation that is (oh my goodness) actually used down the road to treat a patient. I can also introduce you to several law suits filed due to the poor documentation of a physician who refused to edit his dragon speech and just let it go into the chart. I can also introduce you to a neurologist who spends 4 ADDITIONAL HOURS a day documenting in an EHR. He has been at it for over a year so apparently this brain surgeon is an idiot who can't be trained. All of your responses to this, even the "this is off topic" are very revealing as to one source of the problem. Like typical programmers, no one wants to hear they have a bad design or the product actually needs more work.

So, to stay on topic, "Can we turn it around?" yes but only when you listen to the experts in the field you are dealing with - medical record specialists and physicians - they seem to be the people you have left out of the equation.

I'm a clinician (clinical psychologist) and HIT software inventor/architect/developer with 33 years’ experience in both areas. What I've noticed is that programmers routinely rely on healthcare subject matter experts regarding product content, the same is often not true regarding workflow adaptation, usability and usefulness issues.

It's relatively easy to create databases and forms for data entry and presentation, but it's quit difficult to construct the inputs and outputs in ways that streamline workflows, integrate and organize complex interdisciplinary data in clinically meaningful manner, generate clinically useful information that supports decisions at point of care, and present that information in ways that promote knowledge and understanding that lead to greater value for the consumer/patient.

Herein lies the problem, imo, and it is where disruptive innovation is sorely needed!

Unfortunately, such creative destruction is often hindered by governmental regulatory capture in which big bucks direct the regulators who dictate the rules that constrain innovation, focus on appeasing big business, and drive up complexity and cost.

A short while later, Randall Oates, MD posted a comment that ended with:

It is time for more enlightened physicians to step up and assist/collaborate the transitions to processes generating EHR notes that are more clinically useful, while meeting basic billing/legal/reporting needs of others as well. Otherwise, physicians have to mainly blame themselves for systems that don't meet their needs.

To that I replied:

Randal, I'd to add to your excellent statement: "...and present, in a concise integrated view, the relevant interdisciplinary information needed to support their clinical decisions, at point of care, that is focused on continually improving outcomes and care value.

For past year and a half, I've been volunteering my time in a Federal workgroup (WG) called the 360X initiative and work alongside Cerner, Epic, and several other large EHR vendors and HIEs on implementing closed-loop referrals. In the WG, I'm a "little guy" offering novel ideas and methods to the "big guys."

I sincerely believe that the clinical and technical folks in the workgroup do want to improve their products, despite the fact that I have had to repeatedly insist that instead of focusing on doing the minimum; we focus on dealing with the big, complex issues, especially regarding workflows.

I'm also involved in several other Fed workgroups that create HIT meaningful use standards. What I've noticed is a general tendency to keep things complicated, while at the same time, minimizing the scope of their efforts. Many times, when I’ve tried “push the capabilities bar” higher--and even offered innovative ways to do it)--I’ve heard the phrase: “We should first crawl and then walk before we try to run.” Unfortunately, after all these years, when it comes to enhancing EHR systems' clinical usefulness and security, there's too much crawling under the "low bar," and a general aversion to trying to "leap over the high bar."

I believe there are many reasons for this and they mostly relate to the “business layer” supported through regulatory capture by HHS/ONC.

After three decades of HIT involvement, I’ve come to the conclusion that what we need is to transform EHR products into “EHR systems” (a term I helped add to the Fed’s lexicon and workgroup charters). These EHR systems would be enhanced through integration with low cost, easy-to-use “companion applications” that fill in gaps in EHR usability, usefulness, interoperability, and protection of PHI.

The companion apps are likely to be disruptive innovations created by loosely-coupled collaborative networks of small nimble companies and individuals with diverse backgrounds and experiences, who share, discuss, evaluate, and continually evolve models (types of apps) focused on different use cases and types of end-users.

 [Continued in Part 2 at this link]

Data->Information->Knowledge: Formula for improving healthcare

As a clinician, health IT architect and computational model-builder, I’ve been focused for the past three decades on how to use health IT to transform data into information and information into knowledge, in a way that improve care value. I’ve come to realize that highly effective and efficient care delivery (including prevention, assessment of risk, and the diagnosis oand treatment of health problems) depends on useful, valid clinical knowledge providing evidence-based decision support.

This knowledge can help continually improve care outcomes though methods and tools such as patient-centeredcognitive support, computerized clinical decision systems, and evidence-based clinical practice guidelines/pathways. These things are necessary if we want bridge the knowledge gap.

 In any case, gaining this crucial knowledge depends on creating, continually evolving and disseminating useful, actionable, valid information and presenting it in a way that avoids overloading the clinician and patient.

 And generating such valuable information requires adequate amounts and diversities of valid and reliable data. Some of these requisite data can come from today’s "Big Data" stores, which are typically insurance claims (administrative) data. While such claims data have usefulness, they are grossly inadequate when it comes to creating the kinds of information and emerging the kinds of clinical knowledge necessary to improve care quality and cost in any truly meaningful way.

Personal Health Profiler™: Part 2

In my last post, I introduced our Personal Health Profiler™ (PHPro™) software application, a personal health knowledge system giving self-help (problem-solving) guidance and decision-support. I discussed how the PHPro offers a model of where personal health records (PHRs) ought to be heading.

Before I delve more into the particulars of the healthcare consumer and provider versions of our PHPro, I'd like to clarify a main premise I'm making: An abundance of relevant, personalized information about your physical, mental, and mind-body health problems and risks gives you greater understanding of how best to deal with them.

This may sound logical—the more you know and understand, the more effective your decisions and actions—but I've been debating this issue for many years with healthcare professionals who are more concerned about time constraints and information overload, than on comprehensive knowledge and understanding. As I wrote in a series of three posts about information overload and how to avoid it, accumulating massive amounts of health information over a person's lifetime need not cause overload if we deliver it in reports that save time and reduce confusion by:

• Filtering out irrelevant information, so a person stays focused on what's important
• "Serving up" the information as needed, rather than requiring a person to search for it
• Personalizing the presentation, so that the information is tailored to a person's preferences (i.e., it is presented in a personalized manner to minimize confusion, increase clarity, and maximize ease-of-use)
• Organizing and summarizing the information in a way that enables people to examine the data from different perspectives, as well as to "drill down" from the general to the underlying details
• Tailoring instruction (education materials) to a person's ability to learn and particular information needs.

The PHPro incorporates all these capabilities.

Nevertheless, this still begs the question: Why do we need so much data? After all, there are health assessment instruments that ask as few as five or six questions (EQ-5D and SF-6D), behavioral health instruments that have as few as 24 or 32 questions (BASIS-24 and 32), and personal health records (including data storage web sites) that are limited to asking a few dozen questions and track a dozen or so types of information (i.e., a person's vital signs, diagnoses, medications, basic lab results, treatment procedures, allergies, inoculations, diet, sleep, activity levels, stress/mood and emergency contact information).

The PHPro, on the other hand, contains thousands of questions (using sophisticated branching logic, so only the questions pertinent to a person are asked), and manages hundreds of different data types (not to mention adding decision support and self-help tools). But why bother being so comprehensive? Isn't it true that "less is more?"

Having written extensively about this issue here and here, let me know say that when it comes to healthcare, the adage "more is less" not only doesn't apply, but less knowledge is dangerous! Healthcare providers and consumers often lack the knowledge needed for making consistently good, well-informed decisions. In fact, a knowledge gap is a key reason we have a healthcare crisis, and why consumers rarely get high-value care. That's because better health-related decisions come from having "deeper" (more complete) understanding of a person's body, mind, spirituality, and environment, which can only come from having an abundance of relevant knowledge. Why? Because the accuracy and dependability of your understandings depend directly on how much you know about a wide variety of important things.

So, when it comes to making decisions about preventing, diagnosing, self-managing, and treating health problems, more complete knowledge results in better decisions and outcomes.

For example, only after analyzing a considerable amount and diversity of information can we gain the knowledge we need to all the following:

• Determine relationships between the medications a person is taking and how their side-effects (or drug-drug interactions) may be causing or exacerbating the person's symptoms and abnormal lab test results
• Determine relationships between a person's medical conditions, stress levels, diet, metabolic functioning, and environmental influences, and the person's symptoms and abnormal lab test results
• Predict if one's health is improving or being maintained, or if risk factors are likely to become illnesses due to aging or deteriorating health
• Discover what types of treatments and preventive interventions work best for particular types of individuals with particular health conditions.

And when it comes to diagnosing complex or multifaceted medical conditions, and problems with a psychological component, a substantial amount of information is also often necessary. For example, it is important to assess the nature, severity, and etiology (causes) of the depressive symptoms in light of a person's current life-events, past experiences, and personal demographics. This means using a vast data pool that measures such things as:

• The intensity, frequency, duration, and cyclical time occurrences of the depression
• The etiology of the depression, including family history, current psychosocial and biomedical problems, medication side-effects, and psychoactive substance abuse
• The nature and degree of dysfunctional cognition associated with the depression such as thoughts of helplessness, hopelessness, suicidal ideation, self-deprecation, and existential/spiritual dilemmas, as well as cognitive slowing, rigidity, and focusing problems
• The nature and degree of concomitant (co-occurring) physiological symptoms such as lethargy versus agitation, changes in sleeping and eating patterns, and physical complaints
• The nature and degree of behavioral disruptions such as social alienation versus clinging dependence, and occupation or education dysfunction
• The nature and degree of coexisting emotional problems such as anger toward self, anxiety, guilt, and shame
• Demographics, such as age, sex, ethnicity, and socioeconomic status.

Even health status and risk appraisals used by wellness coaches often require extensive data for establishing, implementing, and evaluating well-care plans. This data pool includes such things as a person's:

• Background information (demographics)
• Health exams and interventions
• General health status
• Attitudes about health
• Symptoms
• Existing health problems/conditions
• Biometrics (weight, blood pressure, cholesterol levels, vital signs)
• Health risk factors
• Psychological/emotional quality of life
• Distressing life events
• Personal achievement & success, personal power & influence
• Self-esteem, self-competence & confidence
• Life purpose & meaning , goodness of life. life satisfaction
• Interest, involvement, and enjoyment of your daily activities
• Stress and trauma
• Work-related issues
• Caregiver responsibilities
• Social relationships
• Emotional state, mood
• Coping strategies
• Physical activity & exercise
• Nutrition
• Sleep
• Energy levels
• Wellness coaching preferences
• Areas to address with coach.

Here, too, the PHPro manages all this information, and much more.

Up to this point, I've been focused on the importance of comprehensive knowledge to an individual's health & quality of life. This issue, however, extends to the quality of our entire healthcare system. Here, for example, is a link to an explanation of Sir Muir Gray, Chief Knowledge Office of Britain's National Health Services, about how the lack of adequate healthcare knowledge is preventing us from solving the following "magnificent 8″ problems:

• Errors and mistakes
• Poor quality healthcare
• Waste
• Unknowing variations in policy & practice
• Poor patient experience
• Overenthusiastic adoption of interventions of low value
• Failure to get new evidence into practice
• Failure to manage uncertainty.

He continues:

[We are currently in a] third industrial (and therefore, healthcare) revolution [that] is driven by citizens, IT and knowledge. Professionals are by and large two decades off the zeitgeist and this is not restricted to healthcare, it's seen across all professions.

Knowledge is the enemy of disease, the application of what we know will have a bigger impact than any drug or technology likely to be introduced in the next decade. I'm talking about three types of knowledge here Statistics, Evidence and Mistakes [italics added] we need to be able to deliver these as simply and abundantly as we deliver clean water… What we're introducing in NHS bodies is a Chief Knowledge Officer--because you need energy to make knowledge appear everywhere….We should be thinking systems rather than structures, recognizing the network that runs alongside every bureaucracy is responsible for innovation.

While I would argue that his three types of knowledge don't cover all bases, I fully agree that we need to do a much better job in generating, sharing, and using a great diversity of knowledge focused on fixing our healthcare system and improving outcomes all each patients/consumers.

While current day personal health records do little, if anything, to address these global, systemic problems, the PHPro is designed to do support the kind of research that generates the knowledge needed to solve the magnificent 8 problems.

So, the vision I'm espousing—which focuses on improving people's health and wellbeing through the development, sharing and use of wide-ranging knowledge—spans from the individual patient/consumer, sick-care provider and wellness coach to the entire healthcare system. My sincere desire to help turn this vision into a reality has kept me motivated for over 25 years in evolving the PHPro.

Click here for the last post in this series.

Knowledge, Standards and the Healthcare Crisis: Part 11 (conclusion)

In my previous post [click here for first in series], I began answering the question: What has to happen for good data to become useful knowledge that leads to ever-better and more affordable care?

I discussed why we often require large pools of diverse, non-redundant data to generate reliable/valid information that supports good health and healthcare decisions. I made the case that diagnostic, treatment method, and clinical & financial outcomes data standards should be defined by determining the specific pools of data we need to guide clinical decisions. These standard data pools should include every possible piece of data that might affect the reliability (dependability) and validity (accuracy) of a person's decisions. And the data pools should evolve on an ongoing basis via a thorough evidence-based process of collaborative scientific scrutiny in which data may be prudently added, deleted or modified.

These standard data pools should be used to obtain information over people's entire lifetimes to improve diagnostic and treatment decisions by depicting important trends, associations and cause-effect relationships of health-related signs (e.g., lab test results and vital signs), symptoms (e.g., self-reported physical and psychological problems), and the factors that influence them (e.g., exposure to disease and psychosocial stressors). Furthermore, any information systems used to gather, analyze, disseminate and report these data should be extremely flexible, convenient, and useful.

The answer to the question above, however, doesn't end here.

My quest for an answer began in 1981 as I started my clinical psychology practice. I asked myself back then: How can I obtain and use every important piece of information-about a person's mind, body, actions and environment-for the continuous improvement of the care I deliver?

This quest led me on a 25 year journey across a myriad of knowledge domains, including evidence-based medicine, psychology, the mind-body connection, conventional and complementary and alternative care, wellness, practice guidelines and pathways, decision support, knowledge management, health information technology (HIT), RHIOs and HIEs, outcomes research, public health, performance metrics, transparency, health insurance, competition between providers, the business of healthcare, economic models, politics, and so on.

The more I learned, the more I realized that what was needed is a way to define, validate and manage an enormous variety of data-across all consumer demographics, health problems/diagnoses, treatment methods/procedures, and professional disciplines-for people's entire lifetimes.

We understood that managing these data is a daunting task, which requires:

1. Gathering extensive sets of diagnostic, intervention (both well-care and sick-care processes), and outcomes (clinical and financial) data from both controlled studies and everyday practice
2. Sharing these data with research scientists and clinicians to establish and evolve evidence-based guidelines
3. Disseminating the guidelines to practitioners and consumers, along with useful educational/instructional materials they understand
4. Tracking the use of the guidelines and reasons for variance (i.e., why certain recommendations were not followed)
5. Evaluating outcomes data relevant to diagnoses and interventions
6. Enabling anyone to participate in the process, even if they have low bandwidth and occasional connectivity
7. Using cost-effective HIT and filling in existing gaps
8. Providing reliable and valid decision support tools
9. Empowering consumers to act responsibly and make wise choices
10. Fostering collaboration between providers, researchers, public health agencies, etc.
11. Supporting first responders and emergency room staff in disaster situations.

So, my colleagues and I invented an economical health information architecture that facilitates collaboration between loosely connected persons, as well as innovative decision aids. We also wrote a blueprint for a "Patient Life-Cycle Wellness System" and an Evidence-based HealthCare Decision Support System flowchart that describe a patient-centered, whole-person, birth-to-death strategy for continually improving care and wellness throughout the healthcare continuum.

Our strategy focused on building health science knowledgebases and using them with evidence-based decision-support tools by:

1. Collecting data about a patient, the patient's problem, the treatments rendered and the outcomes using different HIT tools. These data include clinical and financial outcomes, variance data, as well as patient and provider data.
2. Sending the date to research databases, stripped of patient identifiers, where scientists and other knowledge workers access, study and discuss the data collaboratively by (a) using analytic tools to find patterns in the data; (b) challenging one another's interpretations of the data, and the assumptions and predictions they make; (c) building clinical models reflecting diagnostic and associated treatment processes; and (d) sharing and evolving these models.
3. Validating or the invalidating the intervention-recommendation models. The validated models are supported by the scientific evidence showing that particular interventions are safe, effective, and efficient when used to treat particular types of patients with particular health problems in particular situations. The invalidated models have scientific evidence that shows when particular interventions are not safe, effective, and efficient when used to treat particular types of patients with particular health problems in particular situations; so they are useful for determining when not to use a certain intervention.
4. The validated and invalidated intervention-recommendation models become evidence-based practice guidelines, which are stored in health science knowledgebases. Each evidence-based practice guideline is associated with reference and instructional materials, which are also stored in the knowledgebases.
5. The evidence-based practice guidelines and related materials in the science knowledgebases are disseminated to authorized stakeholders, where they are stored locally and accessed for use in the decision support tools.
6. The decision tools send data about the care process and outcomes to the research databases, which is used to create new and modify existing practice guidelines. This is an ongoing feedback loop leading to continually improving guidelines and outcomes.

My answer to original question-What has to happen for good data to become useful knowledge that leads to ever-better and more affordable care?-is, therefore: We ought to (a) transform the current healthcare system to align it with the Patient Life-Cycle Wellness System blueprint and (b) build health science knowledgebases that are used with evidence-based decision-support tools. This strategy would help overcome the knowledge gap and promote continual improvements in the quality and efficiency (value, cost-effectiveness) of care delivery.

Knowledge, Standards, and the Healthcare Crisis: Part 1

There is widespread acknowledgement that our healthcare system needs radical transformation since:

• All patients “…are at risk for receiving poor health care, no matter where they live; why, where and from whom they seek care; or what their race, gender, or financial status is”[1]
• Healthcare is increasingly more expensive and less accessible[2], with more than 46 million uninsured in the U.S. from every age group and at every income level, 8 out of 10 being in working families[3]
• There is a “knowledge gap”—the healthcare community is drowning in oceans of information, yet doesn't know the best ways to prevent health problems and treat them cost-effectively.[4]

In this next series of posts, I will offer an answer to this daunting question: What can be done to drive continuous improvements in care safety, quality and efficiency, which would enable people to remain healthy longer, manage chronic conditions more effectively, and receive the best possible healthcare delivered in the safest and most economical way?

My answer focuses on the creation, use and evolution of valid health knowledge. Why? Because, I contend, the quality of care would improve dramatically and costs would drop precipitously if everyone:

• Knew the best ways to prevent illness, to avoid complications of chronic diseases, and to treat health problems in the most effective and efficient manner
• Used this knowledge to promote wellness, self-management, and recovery
• Participated in evolving this knowledge to make it ever-more useful and effective.

So, what would it take to foster widespread knowledge creation, use and evolution in our healthcare system?

Well, since knowledge emerges from information,[5] it is essential that both consumers/patients and providers have access to useful health information, including patient health data, care outcomes, and evidence-based guidelines. Furthermore, the information must be presented in a way tailored to each person’s needs and be made available whenever it’s needed. Unfortunately, this is much easier said than done for many reasons.

One daunting core problem involves exchanging patient data between disparate electronic record systems. After all, knowledge can’t grow and care can’t improve unless patients share their health information with their providers, providers share patient information with each other, and researchers have access to this information to develop evidence-based guidelines. And this must be done in a convenient and secure manner that protects patient privacy.

With cost estimates for developing a national health record system enabling patient data exchange being between $100-276 billion,[6] the question is, why must it be so expensive? Aren’t there any easy, inexpensive ways to do it? Let’s examine these questions.

One way to reduce health information exchange costs is by developing and using standards that promote interoperability between disparate health record systems.

Standards are models, principles, policies, or rules that provide an agreed-upon framework for doing and understanding things. When it comes to health information exchange and knowledge growth, at least two types of standards come into play: data and technology standards. These standards describe how health data are categorized and defined, how health outcomes and healthcare performance are measured, how healthcare knowledge is used, and how different software systems communicate with each other when exchanging data.

In my next post, I examine this double-edged sword of standards, pointing out their benefits and the thorny problems they create.

References:

[1] The First National Report Card on Quality of Health Care in America by RAND Corp (2006)

[2] Health Care Coverage in America: Understanding the Issues and Proposed Solutions by The Alliance for Health Reform (March 2007)

[3] The Current Situation - WellnessWiki

[4] The Knowledge Gap - WellnessWiki

[5] DIKUW Blog

[6] Linking Providers Via Health Information Networks by The Alliance for Health Reform (2006) and Dying for Data by R.N. Charette (2006)

Defining the HIT Gap

In my prior post, I discussed what HIT is now and what it needs to be. I now define the HIT Gap.

The HIT gap is a result of not making six essential needs a top priority; that is, current HIT does not adequately focus on:

1. Bridging the knowledge gap — using comprehensive, detailed knowledge of each person and the scientific research to (a) make the best possible treatment decisions within a personalized care framework, (b) deliver that care efficiently and effectively, and (c) enable all consumers to be informed participants in the healthcare decision process and in promoting their own health.
2. Managing care execution — Helping providers execute their plans of care.
3. Coordinating care — Coordinating care across multiple providers in the healthcare continuum, so such tools are needed.
4. Protecting public health — Implementing processes for ongoing biosurveillance, post-market surveillance, and first-responder assistance in case of emergencies, so such tools are needed.
5. Enabling complete connectivity — Enabling all stakeholders — patients, providers (including RHIOs, facilities, and individuals across all healthcare specialties/disciplines), purchasers, and payors — to compile and share all the data they need for which they are authorized.
6. Managing extensive data sets — Fostering the fluid access, exchange, analysis and reporting of an enormous diversity of healthcare data sets, including a wide range of physiological (medical and non-medical) and psychosocial data, across patients’ entire lifetimes, about (a) people's disease/dysfunction-specific symptoms and functioning levels; (b) treatment-specific process, clinical outcomes, and practice guideline variance data; (c) genetic data; and (d) expense/financial/utilization data.

Next time I'll present an innovative solution for filling the HIT Gap by satisfying these six unmet needs.