Over the past several years there has been substantial interest in reducing avoidable emergency department (ED) visits. A wide variety of strategies have been employed to achieve these reductions, including:
Benefit design changes such as increasing visit copays or putting limits on the reimbursement of number of unnecessary ED visits by a single patient
Provider incentives through programs such as patient-centered medical homes (PCMHs) to reduce the avoidable ED rate
Structural delivery system changes to emphasize urgent care facilities and after-hours primary care
Many of these interventions rely on analytics based on the avoidable ED algorithm from New York University (NYU), which uses a probabilistic algorithm based on primary diagnosis code to identify the likelihood of avoidable ED visits within populations. Several analyses have now been done that analyze the effectiveness and/or the safety of these interventions.
The first analysis was done by the Washington state Health Care Authority (HCA). It cites an over 10% decrease in ED utilization and ED per member per month (PMPM) costs in the first six months of a program instituting seven best practices for Medicaid enrollees in the state. The best practices included the electronic exchange of information between emergency departments, patient education of ED utilizers, sharing of lists of frequent ED utilizers, development of ED care plans, guidelines and monitoring of narcotic prescribing, and the periodic review of feedback reports. For more information on this program, read HCA’s report, Emergency Department Utilization: Assumed Savings from Best Practices Implementation.
The second is a peer-reviewed study by ED physicians, whose conclusion is that the NYU ED algorithm does a relatively poor job in identifying an individual patient’s need for an ED visit. In this study they compared presenting complaint data with ED discharge diagnosis run through the NYU ED algorithm. They found that the presenting complaint predicted poorly whether the visit should have been avoided and that doing so could have safety consequences. While arguably the NYU ED algorithm wasn’t designed to guide individual patient decisions, the article is thought-provoking and undoubtedly can be cited as an argument against ED visit interventions. Read this recent article in the Journal of the American Medical Association (JAMA), “Comparison of Presenting Compaint vs. Discharge Diagnosis for Identifying ‘Nonemergency’ Emergency Department Visits,” for more information.
I’d expect many more articles to be published about these interventions in the coming months and years. It will be important for informatics to be aware of these evaluations.
As reported in Modern Healthcare, the Centers for Medicare and Medicaid Services (CMS) has provided nearly $4.5 billion in electronic health records (EHR) incentive payments. About $2.4 billion of that is under Medicare, which started EHR incentives in May 2011. The rest is under Medicaid, which began EHR incentives in January 2011. Hospitals have received the bulk of the total in both programs, about $3.1 billion. The program has more than 225,000 active accounts. The EHR incentive program is part of the American Recovery and Reinvestment Act of 2009, which authorized $19 billion for the EHR incentive program.
Community-based data pooling initiatives in Minnesota, Massachusetts, Oregon, Washington, and Wisconsin (commonly known as Chartered Value Exchanges or CVEs) have already shown that, at least using administrative data, it is possible to bring stakeholders to the table, get appropriate infrastructure in place, and begin using community health data to improve quality and transparency. These organizations may serve as models or building blocks for more meaningful use of EHR data nationally.
What kind of technology are we talking about? The ideal platform can measure utilization and determine if care adheres to certain evidence-based measures. Many insurers already employ this kind of analysis, but it is likely to be new in most provider organizations.
Today’s Milwaukee Journal Sentinel reports that the community data pooling organization, Wisconsin Health Information Organization (WHIO), has launched a database that will now be available to large healthcare systems and, eventually, to consumers. The database will improve transparency and allow better understanding of healthcare quality and cost dynamics:
The database is drawn from the experiences of more than 1.6 million people and 72 million treatment services. In April, WHIO will add data from Dean HMO and Medicaid, which includes BadgerCare, the state health program for the working poor, adding the experiences of 1 million more insured people to the database.
“To us, the real opportunity is to look across all the claims aggregated here and get a picture of where we have cost-effective health care being delivered in Wisconsin, and where we have an opportunity to improve the cost-effectiveness of health care,” said Karen Timberlake, secretary of the state Department of Health Services and a WHIO board member.
“There isn’t a database like this that’s been available to providers to measure these sorts of things. And if you can’t measure it, you can’t improve it,” said Larry Rambo, chief executive of Humana’s Wisconsin, Michigan and Illinois markets.
Wisconsin is among a handful of states – including Minnesota, Massachusetts, Oregon and Washington – that have put infrastructures in place for pooling health data to improve quality and transparency, according to a briefing paper written this year by the consulting and actuarial company Milliman.
Click here for more information on these information exchanges.
Pogue: What about the interoperability problem? Every software company selling these systems puts the data in a different format.
Blumenthal: We’re doing a lot in this office to try to minimize that problem. We’re gonna be helping states to create interoperability capability, capabilities to link records, to link institutions.
And we’re developing standards at the national level to make it possible for records to talk to each other. And then we’re also gonna be certifying records, to give physicians and hospitals some guidance about the capabilities of those records, so that they’ll know in advance whether or not the records are gonna be able to talk to each other.
These [standards discussions] are open processes. This will all be a very open and transparent process.
The interoperability problem is, of course, a big one—as explained here.
But one aspect of EHR adoption has been largely overlooked. A new healthcare reform briefing paper by Rich Moyer and Paul Leonardo examines how population-level analysis is key to unlocking the potential of electronic health records. Community data pooling allows analysis that makes healthcare quality more apparent and can help measure and improve the system on a regional basis. These information exchanges are currently operational in at least five states and are already yielding results (while hopefully preventing the formation of new information silos in the move toward EHR).
Ron Sims, Rich Moyer, Gail Graham, and Scott Armstrong continue to respond to Wednesday’s question about adoption of electronic health records.
Transcript:
Ron Sims: The original question was, “Who is going to come with the standards?” And I believe you’re going to see the Federal Government move on them with a great deal of aggression over the next couple of years, and there’s several reasons why. If you look at the issues that the three car companies face, one of the things that they’ve been raising on a pretty consistent basis is their healthcare costs, not only their pension costs, but their healthcare costs.
Over the past several years there has been substantial interest in reducing avoidable emergency department (ED) visits. A wide variety of strategies have been employed to achieve these reductions, including:
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