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Medical device data not only fills the void often found in the systems, but can also ensure their adoption in the months and years to come.
Five reasons why medical device data is vital to the success of EHRs.
1. EHRs are just a vehicle, not the end goal. Although 2011 was all about meaningful use, most don’t realize MU is all about data – not EHRs. For example, the government needs data for cost comparisons, healthcare professionals need it for treatment research and chart management, and patients need it for choosing the right provider and treatment. Right now, we know Medicare and Medicaid are paying more than 50 percent of the nation’s healthcare costs, but doing so as ‘fees for services’ without regard to what treatments, medications, or tests really work. The evidence-based research that goes into figuring out what works and what doesn’t is the foundation of what has been known as Comparative Effectiveness Research (CER), which is being rebranded as Patient Centered Research. The government needs tons of data for CER, which is designed to inform healthcare decisions by providing evidence on the effectiveness, benefits, and harms of different treatment options.
2. It’s doctors, not the technology, who help patients make the right decisions. When it comes to research for treatment options, evidence is generated from studies that compare drugs, medical devices, tests, surgeries, or ways to deliver care. In the best cases, researchers review evidence about the benefits and harms of each choice. Then, over time, researchers conduct studies that generate new evidence of effectiveness or comparative effectiveness of a test, treatment, procedure or other service. Although CER may sound like it’s all about the government and evidence-based medicine to contain healthcare costs, ultimately, its about providing treatment comparison choices to help make informed decisions. In the end, it’s still healthcare professionals, not technology, that must deliver tools to the patients that can help [them] and their families select the right treatment options.”
3. Medical device data is the least error-prone. It’s important to recognize where data comes from, since it’s “ultimately why the government is giving away billions in incentives for healthcare IT. The structured data is computable and quantifiable data, while unstructured data is essentially dictations, documents, and anything that isn’t computable or easily analyzable (but electronic nonetheless). In a typical provider environment, medical information comes from patients, which is often error prone; observations made by professionals, which is useful but expensive and slow; and labs and diagnostic data, which is also slow and expensive. Medical devices have been around for quite a few years, but only recently are they able to emit useful data for analysis. They are getting cheaper and more consumer-friendly, [and they] can compute health data in real-time, have lower error rates than patient or professional observations, and are faster to get data from than a lab.
4. The ‘empty EHR’ syndrome is prevalent. Professionals can and should use data from any source they can get their hands on. Medical devices and mobile apps … are starting to be prevalent enough to adopt new software because of the kinds of devices they support . The ‘empty EHR syndrome,’ what I call the biggest problem in the world of MU-focused implementations, is very real and will keep EHRs from reaching full value. The solution? Help software developers adopt a “new world” where medical devices are connected in greater numbers. Technology that allows medical data to be sent from devices – the same way e-books can be read on Kindle devices – is the next big thing and includes using 3G cellular from mobile phones and software APIs. It’s the last piece that’s most important. [APIs can] immediately enable EHRs to start consuming the data. Not everything will be as easy as I’m making it sound here, but the value of medical device data in filling EHRs with really meaningful data is substantial enough to get behind [technology like this] and give it a shot.
5. Focusing on the right kind of data will ensure success. Many of the existing meaningful use incentives in Stage 1, promote the wrong kinds of collection. Unreliable, slow, and error prone. Instead, accurate, real-time data is only available from connected medical devices and labs/diagnostic equipment, so we need to focus on that data. Given that meaningful use and CER advocates are promoting structured data, collection for reduction of medical errors, analysis of treatments and procedures, and research for new methods, it’s important to see we’re not going to get real gains until medical device vendors are collected to tools [that allow them to provide] data directly into EHRs or clinical data warehouses.
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