EMR event log study shows 6 hrs of use per day, but implicitly belittles the clinician’s cognitive effort and the EMR’s support

In a recent paper in the Annals of Family Medicine by Brian Arndt and colleagues at the University of Wisconsin, the authors described the results of an analysis of the user event logs of their Epic EMR.  The authors determined that primary care clinicians used the system nearly 6 hours per day out of an 11.4 hour work day, and that 44% of that time was spent on tasks that the authors categorized as “clerical and administrative.”  It is an interesting paper, but I think it represents a lack of vision and insight on the part of the authors regarding the role that technology can and should play in supporting the cognitive effort of clinicians.
Most specifically, when a clinician was using EMR-based template charting and orders modules, the authors categorized that work as “clerical.”  Such a classification fails to acknowledge that the clinician is (or should be) using such modules to create a coherent, evidence-based plan of care:
  • using condition-specific templates that help remind her of the clinical observations and treatments to consider,
  • viewing reminders and order sets to help her to remember to include important evidence-based services in the plan of care,
  • receiving alerts to help her avoid ordering services that may cause an allergic response, conflict with other medications that the patient is taking, or that are dosed inappropriately considering the body mass of the patient,
  • viewing prompts for a needed referral for care management, or
  • receiving referral guidance to help direct specialty referrals to the specialists that have agreed to integrate care processes with the primary care practice within a clinically integrated network or accountable care organization.

The authors implicitly dismiss the cognitive work being done by the clinician when they are doing “documentation” and “ordering” and the support that the technology is providing to that cognitive work.  They reduce it all down to being wasteful paperwork, and suggest that it be eliminated through voice dictation or assignment of such paperwork to other members of the care team — both of which would preclude the decision support to those important cognitive processes.

I share the authors’ implied frustration with the failure of the current generation of health information technology to live up to the long-standing vision of supporting clinicians’ decision-making and coordination of care across a multi-disciplinary care team.  I acknowledge that today’s EMR-based care planning and coordination functionality feels like clerical work.  I’ve spent time on that problem.  I have two patents on proposed solutions to that problem.  I hope some day to help solve that problem, which I consider to be one of the most important problems in the broad fields of health care improvement, population health management and medical informatics.
I strongly agree with the idea of using user event logs to study how users actually spend their time and how they use application functionality, and I applaud the author’s efforts to validate the event log data with some direct observations.  But, I don’t agree with the authors’ suggestion that others should use their “EHR task categories” because they implicitly reject the vision of real technology-assisted care planning and coordination.  We shouldn’t give up on that vision.  We can and must do better to make it reality.
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In the dust-up about “rebooting” the EHR meaningful use incentive program, “the Emperor has no clothes” has been the most interesting response.

Cover of Reboot Meaningful Use ReportLast month, six Republican senators released a document entitled “Reboot: Reexamining the strategies needed to successfully adopt health IT.” The report asserts that the original goal of the $35 billion EHR meaningful use incentive program was to achieve “interoperability,” creating a “secure network in which hospitals and providers can share patient data nationwide.”  The report expresses concerns that the meaningful use incentives and federal HIT policy in general are not achieving this interoperability goal and are instead increasing costs, leading to waste and abuse, threatening patient privacy, and leading to unsustainable IT infrastructure.

Since then, a number of parties have offered written responses, including the American Hospital Association, the Texas Medical Association, a consortium of consumer groups,  a group of EMR vendors and the “Healthcare Innovation Council.”  Both the senators’ document and virtually all of the responses agree with the premise that the health care system needs to be improved, and that improvements to health information technology are a necessary component of any effort to improve the health care system.  Almost all parties also agree that the $18.5 billion spent so far on stimulating EMR roll-out has not yet led to any great improvement in the health care system.  But, different parties express very different views about whether the incentive program as currently designed will eventually lead to such improvements.  And, among those arguing for “rebooting,” there are diverse opinions about what a rebooted program should look like.

On one level, the senators’ report fits into the frustratingly conventional narrative of political polarization, with senators from one pole arguing that the administration from the other pole is doing a bad job.  On another level, it fits into the deeper philosophical differences of opinion regarding the role of government spending and regulation vs. private enterprise in the health care system.  On a third level, the report represents the special interest views of one set of constituents, health care providers, who like receiving government funded incentives, but would prefer the bar to be lowered to earn those rewards.

Likewise, the responses defending the meaningful use program can be viewed as defending the administration’s record, promoting public involvement in health care, and representing the views of another set of constituents, the health care IT vendors, who really like the tsunami of revenue they are receiving as a result of HITECH and who fear that “rebooting” might end up more like “unplugging.”

The most interesting response

Logo for Healthcare Innovation CouncilA group called the Healthcare Innovation Council released what I consider to be the most interesting of the responses to the senators’ “reboot” document.  This Council was assembled by Anthelio Health, a health care IT outsourcing  company and consultancy that is not among the EMR vendor insiders that are reaping the greatest rewards from HITECH.  The Council includes a few leaders of health care provider organizations and leaders from other health IT and analytics companies not including any major EMR vendors.  Their report is entitled “Let’s Admit the Emperor has No Clothes: It’s Time to Redesign EHRs to Improve Patient Care.” They assert that EHRs have not led to the envisioned improvement in the health care system, and offer their diagnoses:

EHR design issues

    • “EHRs, to date, have been fundamentally designed to create electronic versions of paper medical records.”
    • “EHRs focus on data collection mostly for regulatory compliance and financial reporting, not to assist physicians, nurses and other clinicians in providing higher quality more efficient patient care. “

EHR implementation issues

    •  “EHR implementations are often led as IT projects by teams that do not obtain robust, meaningful, future-focused input/involvement from nurses, physicians, pharmacy and other clinicians who provide patient care. The end result typically is that EHR implementations don’t make life better for EITHER the clinician or the patient.”
    • “CMS’ and healthcare providers’ focus has been to ‘just get EHRs up and running‘ in a way that meets CMS’ meaningful use requirements so that they can get meaningful use dollars, without regard to how that affects patient care.”

I see the problem the same way.  But, the tricky part is the remedy.

The Healthcare Innovation Council’s paper first advocates for increased involvement by clinicians (with an emphasis on nurses) in the redesign of EHR technology.  On the surface, this is not really a controversial point.  The Council’s paper reverently referenced Steve Jobs twice in the paper as an innovator and simplifier.  But, it is interesting to note that Jobs was famously against too much end user involvement in the design process, arguing that users don’t have an easy time re-conceptualizing things.  People wouldn’t have asked for an iPod, an iPhone, or an iPad because they had never experienced them before and had established mental models of how to buy music, navigate, take movies, read books, etc.  The problem is on display within the Council’s document.  They write:

  • “EHRs are not designed to reflect or facilitate the way in which providers deliver patient care, and thus disrupt, rather than enhance, patient care”
  • “Improved focus on EHR design and implementation that starts by mirroring the way care is actually delivered by nurses, doctors and other clinicians.”

They seem to be asking for EMRs that “repave the cow paths,” a problem I’ve discussed in a prior post.  But, the Council at least seems aware of the difference between status quo and real disruptive improvement:

  • “This basic design then would move to new, information enhanced processes that not only help clinicians do their jobs easier, but measurably improve patient care safety and quality.”
  • “Rethinking, redesigning and re-engineering nurse, physician and clinician workflows to take full advantage of the capabilities of the new (and evolving) EHR tools to result in improved healthcare processes and care experiences.”

In addition to advocating for clinician-led EHR redesign, the Council’s remedy also includes having the federal government require providers to demonstrate “actual patient care improvement and better patient care process” to earn the incentives.  That’s a lovely thought, but imagine how many pages of regulation it would take for the federal government to define specific care processes that it considered to be “better” and methods to document that such care process changes were connected to the EHR technology.  Beware of what you ask for.

The last line of the Council’s paper is, perhaps, the most interesting.  The authors agree with the senators that it is time to “reboot” the meaningful use incentive program before all the money is spent.  Then, almost as a throw-away line, they add:

  • “Unless that is done, then we urge Congress to halt CMS’ “meaningful use” EHR program and spend the remainder of the “meaningful use” funds on providing financial incentives for hospitals and other providers that demonstrate “meaningful improvements in patient care” through whatever means they choose, and leave it to the healthcare providers, not our federal government, to choose the most effective means to improve patient care.”

Financial incentives for improvements in patients care is what value-based reimbursement is all about.  The Healthcare Innovation Council is basically saying that if you want effective, real improvement, rather than just superficial “compliance,” you need to pay for value.  Of course, the nation is transitioning to value-based reimbursement.  But that process is going slowly and the percentages of reimbursement that is value-driven has tended to be small.  As I’ve argued before, incentives tied to compliance is the opposite of real improvement, no matter how hard you try to make compliance meaningful.  If so, maybe we really need to consider re-allocating the remaining meaningful use funds to speed up the transition to value-based reimbursement, rather than just rebooting meaningful use.

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New HIT ROI lit review is a methodologic tour de force, but misses the point

JAMIA logoRecently, Jesdeep Bassi and Francis Lau of the University of Victoria (British Columbia) published in the Journal of the American Medical Informatics Association (JAMIA) another in a series of review articles that have been written in recent years to summarize the literature regarding the economic outcomes of investments in health information technology (HIT).  Such articles answer the questions

  • “How much do various HIT technologies cost?”
  • “How much do they save?”
  • “Are they worth the investment?”

They reviewed 5,348 citations found through a mix of automated and manual search methods, and selected a set of 42 “high quality” studies to be summarized.  The studies were quite diverse, including a mix of types of systems evaluated, methods of evaluation, and measures included.  The studies included retrospective data analyses and some analyses based on simulation models.  The studies included 7 papers on primary care electronic health record (EHR) systems, 6 on computer-based physician order entry (CPOE) systems, 5 on medication management systems, 5 on immunization information systems, 4 on institutional information systems, 3 on disease management systems, 2 on clinical documentation systems, and 1 on health information exchange (HIE) networks.

Lau HIT ROI results

Key results:

  • Overall, 70% of the studies showed positive economic results, 24% were inconclusive, and 6% were negative.
  • Of 15 papers on primary care EHR, medication management, and disease management systems, 87% showed net savings.
  • CPOE, immunization, and documentation systems showed mixed results.
  • The single paper on HIE suggested net savings, but the authors expressed doubts about the optimistic assumptions made in that analysis about a national roll-out in only ten years.

My take:

Bassi and Lau have made an important contribution to the field by establishing and documenting a very good literature review methodology – including a useful list of economic measures, a nice taxonomy of types of HIT, and many other tools which they graciously shared online for free in a series of appendices that accompany the article.  They also made a contribution by doing some tedious work to sort through lots of papers and sorting and classifying the HIT economics literature.

But, I think they missed the point.

Like many others, Bassi and Lau have implicitly accepted the mental model that health information technology is, itself, a thing that produces outcomes.  They evaluate it the way one would evaluate a drug or a cancer treatment protocol or a disease management protocol.  Such a conceptualization of HIT as an “intervention” is, unfortunately, aligned with the way many healthcare leaders conceptualize their investment decision as “should I buy this software?”  I admit to contributing to this conceptualization over the years, having published the results of retrospective studies and prospective analytic models of the outcomes resulting from investments in various types of health information technologies.

Process PuckBut, I think it would be far better for health care leaders to first focus on improvement to care processes — little things like how they can consistently track orders to completion to assure none fall through the cracks, bigger things like care transitions protocols to coordinate inpatient and ambulatory care team members to reduce the likelihood that the patient will end up being re-hospitalized shortly after a hospital discharge, and really big things like an overall “care model” that covers processes, organizational structures, incentives and other design features of a clinically integrated network.  Once health care leaders have a particular care process innovation clearly in sight, then they can turn their attention to the health information technology capabilities required to enable and support the target state care process.  If the technology is conceptualized as an enabler of a care process, then the evaluation studies are more naturally conceptualized as evaluations of the outcomes of that process.  The technology investment is just a one of a number of types of investments needed to support the new care process.  The evaluation “camera” zooms out to include the bigger picture, not just the computers.

I know this is stating the obvious.  But, if it is so obvious, why does it seem so rare?

This inappropriate conceptualization of HIT as an intervention is not limited to our field’s approach to economic evaluation studies.  It is also baked into our approach to HIT funding and incentives, such as the “Meaningful Use” incentives for investments in EHR technology, and the incentives created by HIT-related “points” in accreditation evaluations and designations for patient-centered medical home (PCMH), accountable care organizations (ACOs), organized systems of care (OSC), etc.  The designers of such point systems seem conscious of this issue.  The term “meaningful use” was intended to emphasize the process being supported, rather than the technology itself.  But, that intention runs only about one millimeter deep.  As soon as the point system designers put any level of detail on the specifications, as demanded by folks being evaluated, the emphasis on technology becomes instantly clear to all involved.  As a result, the intended focus on enabling care process improvement with technology slides back down to a  requirement to buy and install software.  The people being evaluated and incentivized lament that they are being micromanaged and subject to big burdens.  But they nevertheless expend their energies to score the points by installing the software.

So, my plea to Bassi and Lau, and to future publishers of HIT evaluation studies, is to stop evaluating HIT.  Rather, evaluate care processes, and require that care process evaluations include details on the HIT capabilities (and associated one time and ongoing costs) that were required to support the care processes.

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Reports of the death of Cost-Effectiveness Analysis in the U.S. may have been exaggerated: The ongoing case of Mammography

Guidelines for the use of mammograms to screen for breast cancer have been the topic of one of the fiercest and longest-running debates in medicine.  Back in the early 1990s, I participated in that debate as the leader of a guideline development team at the Henry Ford Health System.  We developed one of the earliest cost-effectiveness analytic models for breast cancer screening to be used as an integral part of the guideline development process.  I described that process and model in an earlier blog post.  Over the intervening 20 years, however, our nation has fallen behind the rest of the world in the use of cost-effectiveness analysis to drive clinical policy-making.  As described in another recent blog post, other advanced nations use sophisticated analysis to determine which treatments to use, while Americans’ sense of entitlement and duty have turned us against such analysis — describing it as “rationing by death panels.”  Cost-effectiveness analysis and health economics is dead.

But, maybe reports of its death have been exaggerated.

recent paper published on July 5, 2011 in the Annals of Internal Medicine described the results of an analysis of the cost-effectiveness of mammography in various types of women.  The study was conducted by John T. Schousboe, MD, PhD, Karla Kerlikowske, MD, MS, Andrew Loh, BA, and Steven R. Cummings, MD.  It was described in a recent article in the Los Angeles Times.  The authors used a computer model to estimate the lifetime costs and health outcomes associated with mammography.  They used a modeling technique called Markov Microsimulation, basically tracking a hypothetical population of women through time as they transition among various health states such as being well and cancer free, having undetected or detected cancer of various stages and, ultimately, death.

They ran the models for women with different sets of characteristics, including 4 age categories, 4 categories based on the density of the breast tissue (based on the so-called BI-RADS score), whether or not the women had a family history of breast cancer, and whether or not the women had a previous breast biopsy.  So, that’s 4 x 4 x 2 x 2 = 64 different types of women.  They ran the model for no-screening, annual screening, and screening at 2, 3 or 4 year intervals.  For each screening interval, they estimated each of a number of health outcomes, and summarized all the health outcomes in to single summary measure called the Quality-Adjusted Life Year (QALY).  They also calculated the lifetime health care costs from the perspective of a health plan.  Then, they compared the QALYs and costs for each screening interval, to the QALYs and costs associated with no screening to calculate the cost per QALY.  Finally, they compare the cost per QALY to arbitrary thresholds of $50K and $100K to determine whether screening at a particular interval for a particular type of women would be considered by most policy-makers to be clearly costs effective, reasonably cost-effective, or cost ineffective.

The authors took all those cost effectiveness numbers and tried to convert it to a simple guideline:

“Biennial mammography cost less than $100 000 per QALY gained for women aged 40 to 79 years with BI-RADS category 3 or 4 breast density or aged 50 to 69 years with category 2 density; women aged 60 to 79 years with category 1 density and either a family history of breast cancer or a previous breast biopsy; and all women aged 40 to 79 years with both a family history of breast cancer and a previous breast biopsy, regardless of breast density. Biennial mammography cost less than $50 000 per QALY gained for women aged 40 to 49 years with category 3 or 4 breast density and either a previous breast biopsy or a family history of breast cancer. Annual mammography was not cost-effective for any group, regardless of age or breast density.”

Not exactly something that rolls off the tongue.  But, with electronic patient registries and medical records systems that have rule-based decision-support, it should be feasible to implement such logic.  Doing so would represent a step forward in terms of tailoring mammography recommendations to specific characteristics that drive a woman’s breast cancer risk.  And, it would be a great example of how clinical trials and computer-based models work together, and a great example of how to balance the health outcomes experienced by individuals with the economic outcomes borne by the insured population.  It’s not evil.  It’s progress.

It will be interesting to see if breast cancer patient advocacy groups, mammographers and breast surgeons respond as negatively to the author’s proposal as they did to the last set of guidelines approved by the U.S. Preventive Services Task Force which called for a reduction in recommended breast cancer screening in some categories of women.


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Google engineering too slow? Facebook too invested in the wrong data model to adapt? Are you kidding me?

Few things in my work life are better than finding mind-blowing information from other industries and figuring out the implications for healthcare.

I recently read a set of slides by Paul Adams, a user experience designer that worked at Google and Facebook.  Although Adams’ presentation had 224 slides, the main thesis was relatively simple and obvious. The best insights usually are. Adams pointed out that online social media applications create a single category of relationships, called “friends.” They put every social relationship in that one bucket.  Wife, college sweetheart, boss, party friends, kids …  all just “friends.”  In contrast, real-world social networks — the kind that humans have cultivated for millions of years — are characterized by various groupings of people representing different roles, life-stages and social contexts, with different levels of strength and trust.

Diagram from Paul Adams' presentation on Real World Social Networks

He described the research that shows that people typically have 4-6 different groupings of friends.  People typically have fewer than 10 strong ties that consume most of their communication attention.  They typically don’t have more than 150 weak ties.  They have many “temporary ties” that may influence their behaviors for relatively short periods of time.  He points out that existing social media applications create problems for their users because the users publish information intended for one group of people that ends up being received by others.  Like wild party pictures being viewed by your prospective employer.

I came across Adams’ presentation through a link from a CNN article by Dhanji Prasanna that tells the story of how Adams developed these ideas when he was part of a team at Google that was developing Google’s response to Facebook.  The CNN article explains that Google had an engineering culture and a technology infrastructure that made them too slow to develop an application that took Adams’ insights to heart.  Adams then left Google to join Facebook.  But, Facebook was deeply invested in the simplified one-big-bucket social graph at the heart of the system that now has 750 million users.  So, despite Facebook’s “hacker” engineering culture that allows it to develop applications rapidly, they were unable to solve their fundamental problem.  They eventually launched Facebook Groups, which is a superficial answer to the insight that people have multiple groups of relationships.  But, Facebook’s central “one-big-bucket” friends model was apparently deemed too risky to touch.

My eyes rolled.  Google’s culture makes them too slow?  Facebook can’t innovate?  Are you kidding me?  If only we could experience a tenth of the agility shown by those two companies in health care, which has long suffered from a powerful aversion to risk and change in both care delivery and information technology.

But, there are deeper connections between Adams’ insights about social networks and our challenges in transforming our health care system.

First, the health behaviors of patients are strongly influenced by their social networks. For years, health care providers, health plans and vendors of wellness and care management services have attempted to promote smoking cessation, exercise, healthy diet, compliance with medication orders, and other health and lifestyle behaviors by designing “interventions” that target individual patients.  A whole industry of “health risk assessment” and “predictive modeling” was built up to try to identify which individual patients to target.  But, such an approach has produced unimpressive results.  That should not have been surprising.  Decades old research about the diffusion of innovations has shown that lifestyle behaviors in a population change through social networks.  People follow the lead of the people around them.  Therefore, to be effective, wellness and care management programs need to be designed to work through those existing social networks.  We need to be targeting groups of people that are already connected, rather than just reaching out to individuals.  We need to be designing our communications and incentive approaches so as to augment and leverage our patients’ social networks.  To support such social-network-oriented clinical programs, we need information systems that capture information about those social networks and that are designed to interact with them.   But, when we examine the fundamental data model and features of the market-leading electronic health record (EHR) systems, such capabilities are nowhere to be found.  Those vendors, blessed with a large installed base, may be unable to make such fundamental changes to their systems.  Like Google and Facebook, the leading  EHR vendors may not be agile enough to address our emerging understanding of the importance of social networks that exist among our patients.

Second, the relationships between patients and care providers are types of social network relationships.  I call these care relationships.  When we talk about “accountable care,” we mean that some provider organization is taking responsibility for the quality and cost of care for a population of patients. When we talk about a “patient-centered medical home,” we mean a team of primary care physicians, nurses and other care providers proactively taking care of a group of patients. But, who exactly are those patients? We have developed some very crude primary care “attribution” logic that tries to derive care relationships from claims data.  But, we do a very poor job of validating such derived care relationships or proactively declaring new care relationships.  And we don’t keep track of changes in care relationships.  We don’t have established processes to inform the participants in those relationships when one of the parties determines that they don’t intend for the relationship to exist.  We don’t distinguish between different types of care relationships.  If a patient has heart failure and sees both a primary care physician and a cardiologist, we don’t explicitly declare which physician has the care responsibility for that patient problem.

Furthermore, the referral relationships among providers are also types of social network relationships.  As with Adams’ real-world social networks, these relationships among patients, primary care doctors, specialists, hospitals, home health care nurses, pharmacists, and others are complex and dynamic.   Yet, when you examine the systems we use to keep track of these relationships, they are primitive or non-existent.  Just as over-simplification of social network relationships has reeked havoc for social media users, so has over-simplification of care relationships, care responsibilities and referral relationships harmed clinical communications and accountabilities.  This deficiency ultimately reduces the effectiveness of care. As a result, patients are harmed.

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Klar 4: Why is it important for CMS to Share Claims Data with ACOs?

Ron Klar, MD, MPH

Ron Klar, MD, MPH is a health care consultant with a long history of involvement in federal health care policy and health plan innovation. He published a recent series of three posts regarding the draft rules for the Medicare Shared Savings Program (MSSP) in the Health Affairs Blog, an influential forum for debating health policy issues. This is my last in a series of 4 posts describing areas of agreement and disagreement with Dr. Klar. (The others are available at post 1, post 2 and post 3)

In his third post to the Health Affairs Blog, Dr. Klar proposed to eliminate CMS sharing of claims data with providers. He argued that it is too delayed to serve any useful purpose to the ACO, either for clinical operations or analysis.  He also argued that supplying ACOs with claims data would be expensive for both CMS and the ACOs that would need to create interfaces, databases and applications to receive and use the data.  He argued that it would distract providers from investment in electronic health records (EHR) and health information exchange (HIE).  Finally, he argued that it would violate the confidentiality of non-ACO providers who would be identified in the data.  Klar implies that all of the data needs of the ACO can be met with EHR data, augmented with HIE data.

I strongly disagree with this thinking.  The success of ACOs will require a transformation of the health care organization from one that reactively cares for individual patients to one that also proactively takes responsibility for a population of patients.  The analytics to support that transformation requires a comprehensive view of all the health care services received by the population.  Since patients are free to seek care from any provider participating in Medicare, only CMS can provide this comprehensive view of the data. An EHR may be richer and more up-to-date, but it lacks this comprehensive view.  An HIE might increase the completeness somewhat, but without data from the payer, it is not possible to know how much missing data might be beyond the reach on any particular HIE at any point in its development.  For the foreseeable future, EHR and HIE data are too inconsistently structured and too incomplete to give a true population-oriented measure of utilization.

In earlier demonstration projects of accountable care and care management, the participants complained that the data shared by CMS was not delivered in a useful and timely way.  So, not only must we keep the CMS claims data sharing in the ACO rules, we must also make sure that CMS does a better job of delivering it this time around.


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Notes and Thoughts re new ONC leader Farzad Mostashari and Senators Daschle and Bennett at Bipartisan Policy Center

Today I attended a webcast sponsored by the Bipartisan Policy Center, an organization “committed to the art of principled compromise.”

The webcast featured Farzad Mostashari, MD, ScM, the newly appointed National Coordinator for Health Information Technology, U.S. Department of Health and Human Services.  He was joined in a panel discussion by Senators Tom Daschle (Democrat, South Dakota) and Bob Bennett (Republican, Utah), who is currently a senior fellow at the Bipartisan Policy Institute.Dr.Mostashari articulated the key principles that will continue to guide the Office of the National Coordinator (ONC), as well as the ONC’s “theory of change.”

ONC Principles

  • Innovation will be driven by private industry & free market
  • But recognize that problems with health care market require at least some amount of government action.  “Do the minimum amount of government action necessary, but no less.”  Government action is required to deal with the following key issues with the market:
    • Assure Market Competitiveness, particularly due to high switching costs for HIT by health care providers and due to information asymmetry, where providers have difficulty understanding technology and complex service level agreements.
    • Deal with Externalities, since the benefits of HIT investments do not all accrue to the providers
    • Deal with Public Goods, such as health IT standards

ONC Theory of Change

  • Constant attention to the goal: “Keep your eye on the prize.”
  • Pragmatism and practicality: “Keep our feet on the ground.”
  • Flexibility, particularly long rangeplans
  • Patient centeredness – always ask

I noted that Dr. Mostashari seemed to be referencing the long-acknowledged problems with the market for health care services, and then applied that to the market for HIT, where doctoral level providers are apparently at a disadvantage in buying HIT. Although I agree that HIT purchases are complex and that health care providers have historically performed poorly in making informed decisions about HIT investments, I think that this can be better resolved through making the health care delivery market itself more competitive, forcing health care providers to become more sophisticated purchasers (and more meaningful users!) of HIT, rather than having government fix that particular problem.

Other noteworthy points made by Dr. Mostashari include:

  • 65% of vendors that have HIT products that earned ONC certification have fewer than 50 employees.
  • Use of EMR was stuck at around 20% for years, and recently jumped to 30%.
  • Feds set goal for 20% reduction in readmits.
  • Another goal is to have the patient’s information follow the patient wherever they go.
  • He distinguished between the issues of interoperability of systems within a health care provider vs. interoperability of like systems between health care providers.
  • He said clinician adoption is a large problem, but interoperability is a larger problem.
  • Discussed the Direct project, in which the feds stated the provider-to-provider and provider-to-patient secure communication problem and convened diverse stakeholders to participate, creating a “do-ocracy”.  Within 90 days, they agreed upon secure e-mail as the primary method.  In another 90 days, they had developed simple common protocols.  Then, in another 90 days, most of the big HIT vendors have agreed to support those protocols.
  • Discussed goal of creating a “learning health care system” and the associated need for secondary use of data to evaluate treatment effectiveness and to serve public health, quality measurement.  These uses are not disruptive to privacy and confidentiality concerns.
  • Explained that, as technology improves, the concept of “what is de-identified” gets more difficult.
  • He envisions the need for “distributed queries” where the question goes to the data.  (I am doubtful of this concept, based on experience proving the need for sustained effort to clean and transform data to make it useful for valid analysis.)
  • Explained the challenge of rural health care, including lack of broadband internet access.  The Regional Extension Centers have an explicit mandate to address rural health care issues.

Some key points made by Senator Bennett:

  • Bennett describes the “nirvana” of HIT in terms of accessibility of health information, but he describes the intended outcome as increased quality and lower cost, asserting that if quality is not increasing and costs are not being reduced, then HIT is a waste of time.  He does not acknowledge that quality and cost are process transformation issues (not data access) and that there are many necessary ingredients to process transformation besides HIT.
  • Bennett argued for the importance of security and privacy protections by describing the risk to patients to having diseases identified and electronically documented in terms of future loss of insurance coverage.
  • Bennett told the story of the use of clinical data for analysis at Intermountain to identify the need for administering prophylactic antibiotics within one hour of an operation, but explained the externality problem: that Intermountain lost millions of dollars in revenue that was previously associated with inpatient treatment of iatrogenic infections.

Some key points made by Senator Daschle:

  • Daschle said that lack of “interoperability” is the nemesis of HIT success.
  • Daschle said that patient engagement starts with individuals taking responsibility for themselves, and at least partial responsibility for the cost of their own care.
  • Daschle says new health care marketplace will create more choices for people, and they have a responsibility to make informed choices.
  • Daschle and Bennett both advocated for individual mandate, and to begin to break the link between health insurance and employment.

Funding for Registries and Analytics?

The American College of Cardiology CEO asked for $500M federal funding for cardiology registries.  Daschle said it is logical to do so, and that we are creating a new environment for that to happen.  But then he said it starts with HIT, avoiding directly addressing the issue of plans for federal funding for registries and associated analytics.  Bennett said he also supports such funding, and joked that he is no longer in the Senate because he is a Republican that believes that not all government spending is evil.


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Why many clinical leaders prefer Registries to EHRs: Actively Structured Information

I went to the huge HIMSS’11 convention in Las Vegas a few weeks ago.  When visiting the bewildering number of vendor booths, I focused on a few key questions relevant to the challenges facing physicians and hospitals working to establish successful Accountable Care Organizations (ACOs):

1) What is the current state of clinical data exchange between Electronic Health Record (EHR) systems primarily used for acute hospital care vs. ambulatory care?

2) Do leading EHR systems have useful “registry” capabilities?

Nuance Booth at HIMSS


I have been hearing from many clinical leaders that the most popular EHR systems still do not support real clinical integration across settings, and are still weak in terms of the “population management” and clinical process improvement features that are present in existing chronic disease registry applications.  I was hoping to learn that these concerns were really based on a lack on familiarity with how to configure and use EHR systems, rather than any inadequacies of the systems themselves.  After all, clinical message and clinical document standards have been around for years, and population management features were prominently identified in recent Meaningful Use incentives.

The verdict?

Some progress has been made in leading EHR systems, but clinical integration across settings and population management are still major weaknesses.

But why?

It turns out that both problems are related to the same underlying issue: failure to appreciate the importance of “actively structured” information.

Health Information Technology (HIT) leaders have for decades focused primarily on a vision of computerizing health information, going paperless, and improving the access to that information by many members of the clinical team across settings.  To reduce the burden of capturing information that can be displayed on a computer screen, HIT vendors have developed document imaging, optical character recognition, and voice recognition technology.  And, they have created clinical documentation systems that allow the user to enter simple codes to insert blocks of text into their notes quickly.  Over the last 15 years, most HIT leaders have recognized that there is some value in “structured data” — health information in the form of codes that computers can interpret rather than just display.  In response to this recognition, EHR vendors have added “template charting” features that allow users to do exception editing of some coded values in notes.  They have developed sophisticated systems to interpret the meaning of textual information and convert that text into structured data.  And, they have created messaging standards that allow orders, clinical documents (notes) and continuity of care information to be shared across systems and care providers.

But, the problem that remains is that the structured data that is being captured and exchanged is “passively structured.”  Although the current crop of EHR systems may allow some of the health information to be represented with codes, the EHR tools and the care process itself do not assure the capture and transmission of particular pieces of information required to support particular down-stream uses of the data, such as reminders, alerts, quality and performance metrics, and comparative effectiveness studies.  The clinical leaders that are focused on improving care processes, and the health services researchers that are trying to measure performance and comparative effectiveness are well aware that missing data is crippling to the usefulness of passively structured data.  The real care process improvement and performance and effectiveness measurement efforts of Accountable Care Organizations requires “actively structured” health information.   To assure the completeness of the information, they use “registry” applications, questionnaires, and data collection forms.   And, they transmit this information in formats that rigorously enforce the meaning and completeness of the structured information.  The objective is not to capture as much structured data as possible.  The objective is to assure the accuracy and completeness of particular data elements that you are relying on for specific, strategically important purposes.

I believe that HIT professionals may be reluctant to make actively structured information a requirement because (1) they lack expertise and familiarity with the downstream uses of the data for care process improvement and measurement, and (2) they are concerned that the added burden of actively structured data capture on end-user clinicians may further worsen their problems with “clinical adoption” of their technology.   They are attracted to solutions that extract passively structured information from clinical notes, such as voice-recognition and “clinical language understanding” (CLU).  And they don’t realize that the big pile of passively structured data output by such solutions will fail to meet the requirements of Accountable Care Organizations.

To solve the problem, two main things are necessary:

  1. Refocus the design objectives of EHR products on the strategic goals of Accountable Care, rather than on the tactical goals of reducing medical records cost, “going paperless” or reducing security risks.  The biggest implication of this will be the integration of analytic and care process management functionality into EHRs, rather than viewing those as separate solutions to different problems.
  2. Create stronger organizational linkages between HIT professionals, the clinical leaders involved in care process improvement and the epidemiologists and biostatisticians that have the expertise to measure provider performance and treatment effectiveness.  Many hospitals, physician organizations and nascent ACOs are attempting to do this by hiring “chief medical informatics officers” (CMIOs).  But that will only solve this problem if the new CMIOs can bridge between HIT, process improvement and measurement disciplines.  If the CMIO role is viewed more narrowly as a liason between HIT professionals and front line clinician users, the focus will continue to be on unstructured or passively structured data to reduce barriers to HIT adoption.


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