See Part I of Justin Barnes' ACO blog


Statetate Medicaid officials are moving quickly to  understand and establish accountable care models around community ACOs, provider-led programs or hybrid models merging health plans and care providers.

This public-private initiative is being aided by organizations such as the non-profit Center for Health Care Strategies (CHCS) and the CMS Innovation Center, as all stakeholders realize the need for coordinated care for a patient population most in need of preventive and cost-efficient medicine that can build upon the Medicaid coverage expansion within ACA. Right now state Medicaid ACO pilot programs are being formed in at least seven states.

Meanwhile, many of the nation’s uninsured and elderly are increasingly taking advantage of the growth and accessibility of retail health clinics.
The number of Americans visiting these clinics for vaccinations, treatments for respiratory infections and preventive measures, for example, quadrupled – from nearly 1.5 to six million people - between 2007 and 2009, according to an August 15th Rand Corp. study published in Health Affairs. It is notable the study found that nearly 33 percent of these patients lack health insurance.

These rates will be impacted by the coverage mandate and the future of health insurance exchanges also within the Affordable Care Act, likely combining to fuel an increase in patient volumes at traditional practices as well, adding stress to our already strained delivery system in terms of the documented decline of the number of primary care physicians.

That dynamic will also continue to fuel expanding scope of practice debates on the roles of nurse practitioners (NPs) and physician assistants (PAs) moving within primary care. These issues are tied together provided that quality care can be achieved in retail settings, which I believe has been initially demonstrated and can continue to accelerate into more advanced primary care as an ambulatory option for more patients.

Steps to Accountable Care Success
Accountable care and care coordination in all of its forms is an essential building block for improved healthcare, along with EHR adoption, meaningful use and interoperability. In broad terms, this transformative journey seeks to improve patient safety and quality of care. The vehicle for that journey: further integration of care and a focus on disease management through new bundled payment models, value-based purchasing initiatives and benchmarking analysis. That’s where health information enters the picture. The robust use of data aggregation, analytics, and shared information directly support patient care coordination and population health management, which are the most critical clinical components of managing risk-based reimbursements.
For care providers and practices seeking to form or join an accountable care community, there are prerequisites to address:

1. Begin by assessing your EHR, interoperability and overall technology infrastructure, as well as your beneficiary patient volume. Then engage your peers, associations, payers, employers, and health systems in your community to identify government, private payer, or combined opportunities.

2. If your practice or organization is approached to participate in an ACO, evaluate it carefully. Consider your financial and strategic incentives for joining, data requirements, and access to bi-directional data and whether your commitment is binding or non-binding.

3. ACOs positioned for success should have three- to five-year plans that incorporate growth strategies and best practices. These include utilizing health information technology, engaging and educating patients, developing care management resources, and monitoring care delivery and follow-up.

4. It is also important to assess your own understanding of the different risk models being offered. Determine how much risk you can assume initially and over time.

The Supreme Court ruling on the ACA was a big step in this journey, and the next focal point is of course whether Shared Savings structures and the financial risk tracks succeed, causing more providers, health systems, private payers, and employers to embrace coordinated care and payment models.

We are seeing solid evidence of this already, which represents an encouraging sign of what the next several years will bring.


What questions do you have?


Justin Barnes is co-chair of the national Accountable Care Community of Practice (ACCoP), chairman emeritus of the EHR Association and a vice president at Greenway Medical Technologies. He has appeared before White House and Congressional panels on matters of health information technology on more than a dozen occasions since 2005, and has advised current and former presidential administrations on industry policy.

Now that the healthcare industry can work with clarity on care coordination strategies and programs, a new expansion of ACO models, trends in patient behavior and the companion issue of provider scope of practice have quickly emerged as critically-relevant spotlights.

And with a presidential political season upon us, mutual clarity on what the election returns could bring is also at hand based on conjecture that a GOP White House and/or Congress would attempt to counteract the Affordable Care Act. And here some historical perspective helps.

Simply put, even with the political leadership makeup potentially in flux this fall, there is strong bipartisan support for aligning payment and care delivery models with improving quality to create a smarter and sustainable healthcare system, backed by historical precedent.

For me and my colleagues in the trenches of pursuing fiscally sound care delivery nearly a decade ago, it is well remembered that the origins of accountable care reside within a 2004 HHS document entitled “The Decade of Health Information Technology: Delivering Consumer-centric and Information-rich Health Care.” This “Framework for Strategic Action” (as it is also known) was delivered to then-HHS Secretary and GOP-appointee Tommy Thompson. And it was delivered by the nation’s first National Coordinator for Health Information Technology, Dr. David Brailer.

The document’s goals of introducing health IT solutions to clinical practices, electronically connecting clinicians, using “information tools” to personalize care and advance population health reporting followed an executive order calling for widespread adoption of interoperable EHRs within 10 years.

That core bipartisan support for these goals, also evidenced by the success of meaningful use, has weathered the political winds, and no doubt like many in health IT, I keep a copy of this foundational document at hand.

To continue to get us to where we are today, the report was followed the next year by the Physician Group Practice (PGP) demonstration, a five-year program of 10 sites pursuing early shared savings goals. This program was widely resurrected as a reference point when the current Medicare Shared Savings proposals were first issued.

A year later, Dartmouth Medical School’s Dr. Elliott Fisher began voicing the concept and vocabulary of accountable care during a Nov. 9, 2006, Medicare Payment Advisory Commission (MedPAC) meeting then put to paper by year’s end. MedPAC’s research over the past year only further supports this evolution.

Today, more than 27 state legislatures have proposed programs related to accountable and coordinated care, and there are more than 250 accountable care communities active in the vast majority of states. More than 70 of these are led by physicians, nearly double the number only eight months prior. And while closely associated with the CMS Medicare Shared Savings program (rightfully so now that an additional 10,000 Americans are becoming Medicare-eligible every day), health plan, private payer and even employer models are keeping pace.

Tomorrow in Part II: How Medicaid Models, Patient Trends and Scope of Practice Move Accountable Care Beyond Medicare Shared Savings


Justin Barnes is co-chair of the national Accountable Care Community of Practice (ACCoP), chairman emeritus of the EHR Association and a vice president at Greenway Medical Technologies. He has appeared before White House and Congressional panels on matters of health information technology on more than a dozen occasions since 2005, and has advised current and former presidential administrations on industry policy.

One of the more challenging aspects of electronic health record (EHR) ownership is switching from an old EHR and to a new one. This process of moving from old to new is called conversion, and it ain’t easy.


The problem is the data. After a few years of use, a practice will have accumulated a fairly prodigious level of patient data (notes, prescriptions, problem lists, correspondence) and will appropriately want to move as much of that over to a new system.


Unfortunately, there are no consistent industry standards that dictate how EHRs should store data, so practices are left with the less than optimal solution of complex, custom conversions that map data fields from the old EHR database to the new EHR. Custom and complex always equal expensive in the EHR world.


So what’s a practice to do? Here are a few guidelines.


Make sure that you are switching for the right reason. It is easy to scapegoat an EHR for an implementation that failed due to the practice and not the EHR. Since switching systems is significant undertaking, it is important to clearly understand the desired outcome of a switch. A five star EHR cannot transcend a dysfunctional practice.


Be a conservative converter. Because of the variability in database structures between EHRs, the more information that you try to move from one system to another the more expensive, time consuming and challenging it gets. Start with the easy stuff, like patient demographics (name, address, etc.). Patient demographics are easy because the data is generally structured the same way between different EHRs. Structured data is always easier to move. Clinical data that may be structured in your existing system include discreet items such as medications, allergies, problems, and lab data. Non-structured data include narrative free text that is commonly found in progress notes.


Start the discussion with your EHR vendors (existing and new). A conversion is collaborative activity between the old and new EHR vendors and the practice. Your existing EHR vendor will be responsible for creating an output file of your patient data (for which there will be a fee) and your new EHR will responsible for importing that output file (also a charged item). The practice’s responsibility is to decide what gets moved. In order to understand what is possible and at what cost, you really need to get all three parties together to play in the same sandbox.


After considering the various options, it is possible that the best choice—due to cost and complexity reasons--is not to convert. In this case you would be treating the old EHR primarily as archive of historical information that would be manually transferred to your new system. You would use the same conversion strategy that you use with paper charts—transcribing data from one system to the other you see patients.


While there is extra work during this transition time, it does give you the opportunity to start fresh with clean data. The primary wrinkle here is making sure that you retain access to your old EHR data. For client-server systems, this is no problem since the data is resident locally. However, for internet based EHRs, you will have to negotiate either limited access or a copy of your data since it is stored outside of the practice in the cloud.


Watch the video below for more information on EHR conversion. What questions do you have?


Bruce Kleaveland is President of Kleaveland Consulting and a sponsored health IT correspondent for Intel.

An effective healthcare IT organisation cannot focus exclusively on technology alone. How you manage your IT function, in terms of critical organisational capabilities and management activities are just as important to enabling better patient care and improved business value outcomes.


Intel Corporation, Innovation Value Institute, HIMSS Analytics USA and HIMSS Analytics Europe have come together to create an industry leading programme for hospitals to enhance their IT organisational capabilities towards achieving better eHealth outcomes. The programme, called the Healthcare Maturity Model, enables hospitals to generate an understanding of how the maturity of their healthcare IT services is influenced by the maturity of their underlying IT organisational capabilities to deliver and run those IT services.


In the below video, I sat down with Jim Kenneally, Senior Researcher, Intel Labs, and Michael Porter, Senior Advisor, the Innovation Value Institute, for a brief discussion about the HIT Maturity Model Program and how you can strive for achieving and running a fully digitized, virtually paperless hospital environment.


What questions do you have? For more information on the Maturity Model program, email me at


I frequently talk about the importance of creating shared services across a region in order to support health information exchange. I also advocate the use of secure healthcare cloud as a cost-effective means to overcome scarcities in clinical and IT expertise. One of the key infrastructure services required are patient identity management services. What is at stake with improving the accuracy of patient identity matching?

Patient identity matching is first and foremost a patient safety issue
The Bipartisan Policy Center issued a report in June 2012 “Challenges and Strategies for Accurately Matching Patients to Their Health Data.”  Care coordination requires assembling a view of current patient health information from a variety of sources across a region. The inability to accurately match patients to their health records can result in either missing information (false negatives) or incorrect information (false positives). Inaccurate matches can result in suboptimal care or worse, the risk of medical errors and adverse events. Indeed, College of Healthcare Information Executives (CHIME) conducted a May 2012 survey of 128 hospital CIOs who reported an average of 8 percent error rates with a range as much as 20 percent error rates. Also, 19 percent of those responding indicated their institutions experienced adverse events during the prior year as a result of inaccurate patient matches. More research is required in this area to accurately assess the true impact to patient safety because understandably, this is not an area that most institutions are comfortable with disclosing.

Most institutions do not have the resources nor expertise for ongoing curation of patient identity matching
To illustrate the magnitude of the problem, consider a typical community like Harris County, Texas: out of 3.4 million patients in the hospital database, 249,213 patients have the same first and last name; 76,354 patients share both names with four others; 69,807 pairs share both names and birthdates [source:  Houston Chronicle, 4/5/2011].

Medium-sized healthcare institutions currently operating patient identity services cite annual costs ranging from $500,000 to $1 million in human resources alone, not factoring the ongoing software and services expense. Respondents to the CHIME survey indicated a range of 0.5 to 20 full-time equivalents with more than three on average devoted to patient identity reconciliation. Smaller practices, clinics, rural hospitals, and independent physician associations do not have the expertise or the resources to support such a complex endeavor. More advanced institutions are starting to offer cloud-based services across a region in order to recoup some of their infrastructure costs.

Identity matching is not a problem unique to healthcare
The United States already has several regional and national identifiers in widespread use today, including social security number and state driver’s license. Additional healthcare identifiers typically include health insurance plan and local healthcare institution numbers. Indeed, despite the reluctance to use social security number out of a misplaced fear of identity theft, most clinicians in the U.S. now routinely collect further identity information including electronic copy of social security card, state driver’s license and patient photograph, in the misguided theory of fraud prevention (worsening likely the risk of data breach given that even more sensitive data is now being collected without the commensurate level of data protection and security practices applied). The central point here is that the problem posed by patient identity matching is not unique to healthcare, but applies to all government and financial-issued identities.

The same probabilistic matching algorithms are successfully applied and sold to state and federal governments worldwide because the problem is not unique. Each state has the same matching issue with their state driver’s license and other state-issued identities. What is unique is, in the United States, the perception that the healthcare identity is somehow politicized and taboo. Most countries treat healthcare identity (patient, provider, institution, device) as an important element which is required – for patient safety, for data quality, for consumer privacy, for fraud prevention, for claims processing, for benefits entitlement (whether public or private). Some countries simplify the governance by issuing a single identity for all government services including public and private financial institutions (e.g., your ATM card is your identity for all transactions), whereas some issue an identity separate for health transactions. Whatever the approach, most countries have learned (sometimes the hard way) that a single identity should be assigned to each individual at birth.

Data transparency, in addition to data protection, security and privacy, should be paramount in the use and disclosure of all consumer health and financial information
Data protection laws and regulations governing consumer health and financial information need to be applied consistently to any institution, associate or service provider or service who works with protected health and financial information. Security and privacy best practices, including encryption of sensitive data at rest and in transit, need to be consistently applied and monitored in the healthcare industry. Data transparency – the tracking, monitoring, audit and enforcement of how consumer health and financial information is used and disclosed – needs to be provided at the regional level for governments, institutions and consumers alike. In order for consumers to develop trust in the secure exchange of health information, they need to be able to easily inspect and review disclosures of their information across a region. They need to be assured that regular audit and oversight is maintained by several different levels of government, institutional, and independent auditors.

A combination of a single national identifier, along with improved adherence to data quality standards, and consensus approach to probabilistic matching algorithms are required
Selecting a single national identifier certainly reduces the cost and complexity associated with patient identity matching but must be done in conjunction with improved adherence to data quality standards across a required minimum set of patient demographics (e.g., HL7 standard data types for name, birthdate, address, etc.). Even when a national identifier is required, there will always be legacy systems which are unable to incorporate the new identifier, so a combination approach is always required. Improvements must also be made to probabilistic matching algorithms, taking a consensus approach to define levels of quality matches using weighted confidence scores across a standard set of demographics – those matches which can be automatically inferred vs. those which need to be referred for human curation and disambiguation.

An honest and open debate at the national level is required to move forward on improving the accuracy of patient identity matching. A national identifier is preferred as more expedient and more cost-effective but if this proves too politically problematic, innovations are possible through a consumer-directed voluntary identifier.

What are your experiences and concerns with patient identity matching?

As a HIMSS Level 7 facility, Deaconess Health System knows all about Big Data. For the past couple years, the Evansville, Ind.-based health care organization has been implementing systems that would enable it to move beyond the collection and storage of information, so it can start applying data in a more meaningful way.


With money now budgeted for this effort, CIO Todd Richardson plans to add a full-blown data warehouse, which will enable him to merge all of the organization’s data and then begin the serious business of sifting and sorting for actionable value.


“We’re making great progress with the tools we’ve got, but we see this new addition as the Holy Grail,” Richardson said. “We’re excited about it.”


But selling Deaconess on the project’s value was one thing. Change management is another.


Although the usual turf wars and overall resistance to change can make for an uphill battle, Richardson is convinced the key to winning organizational buy-in comes from making the case that everything links back to data—and then articulating that message in real-world terms to the executive team, who may not necessarily understand IT.


For the data warehouse project, Richardson recalls a breakthrough moment when he attended HIMSS with Deaconess’ Chief Medical Officer. By spending time in a booth together with a vendor, the CMO could see for himself what Richardson had been talking about for months.


“When you start with the CMO, who is responsible for the quality of patient care, you can tie back what it is you’re trying to do with why you’re trying to do it,” Richardson said. “Combine that with healthcare reform, ACOs, patient-centered medical homes, and the need to track your patients and to analyze your data and show outcomes.”


Being able to demonstrate value through data this way is also becoming increasingly relevant in a payment environment shifting from a fee-for-service model to a quality-based system.


Since each incremental step forward ushers in a series of changes that staff are likely to resent at some level, Richardson feels it’s all the more important for HIT professionals to get down in the trenches early on in the process. Soliciting critical input from the people you will need on your team as the project moves forward will help ensure the project is architected in the way that best meets staff needs and hospital objectives.


What questions do you have?


As a B2B journalist, John Farrell has covered healthcare IT since 1997 and is Intel’s sponsored correspondent.

Mobile technologies help clinicians work more efficiently and enable the care continuum outside of traditional care settings. Increasingly, healthcare workers are expecting to use their own personal mobile device-of-choice within the enterprise setting. This creates issues for IT around device support and serious concerns about data security.


To take a closer look at how personal devices impact health IT management, Intel and Microsoft are teaming up for a presentation next Wednesday, Aug. 8, at 9 a.m. Central Time, during the HIMSS Virtual Briefing “Mobile Health IT: A Glimpse into the Technologies at Work Today in Healthcare.” At this session, you will learn techniques for dealing with these mobile issues. Your goal is to allow data to be available when and where needed while providing your end-users with maximum flexibility consistent with good security practices. We’ll help you get there.


Here’s what on the agenda:

•    How to select the Right Device for Right Workflow
•    Review which Mobile Devices are better for selected workflows
•    Explore how to maximize compute flexibility with good security measures


I’ll be joined by Gareth Hall, Industry Solution Manager, Microsoft, for the discussion and we’ll show you how to get the most out of going mobile. Here’s a sneak peak at what you can expect to hear:

One size does not fit all
Different tasks (e.g., consumption, creation, sharing, collaboration) require different mobile tools. Find the right device for the right task.

Collaborative workflows
Collaborative teams treating patients in the lowest-cost setting consistent with quality care can change the care delivery paradigm. For example, they can decrease readmissions, unneeded trips to the emergency department, and delay and rework within the hospital.

Secure, flexible compute models
How you deliver information to mobile devices can maximize the IT investment, data security, and flexibility. Secure computing is an imperative—no breaches allowed!

What questions do you have about mobile?

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