EHRs Aren't Ready For Pay For Performance
Meeting the pay for performance challenge requires more sophisticated capabilities in EHRs and more physician cooperation.
In order to get the healthcare community to switch from a fee-for-service model to one in which clinicians are paid for the quality of the patient care they deliver, the Centers for Medicare and Medicaid Services, as well as some private insurers, have established a long list of quality metrics to track clinicians' progress toward better care.
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Currently available EHRs can handle many of these quality measures. They can, for example, track the number of diabetics who have seen ophthalmologists for annual eye exams, measure the number of children who have been properly immunized, and keep a record of a patient's smoking status. But while collecting this kind of data will improve patient care and help justify a clinician's fees, it's only a baby step toward genuine P4P. The health IT we're currently using just isn't there yet.
Jonathan Weiner and his colleagues at Johns Hopkins University point out that although about 60% of U.S. physicians have some sort of EHR system, less than 25% of ambulatory care in the U.S. is substantially documented by EHRs. Of those, fewer than 10% are both comprehensive and interoperable across providers, said Wiener and his colleagues in their report in the International Journal of Quality in Health Care. It's these more comprehensive, sophisticated systems, wedded to advanced quality measures, that will get us where we need to go.
[ Is it time to re-engineer your Clinical Decision Support system? See 10 Innovative Clinical Decision Support Programs. ]
Weiner's team referred to some of these next-generation criteria as health IT-enabled e-quality measures (e-QMs) and HIT-system management e-QMs. A HIT-enabled e-QM would be the percentage of patients for whom real-time clinical support had been appropriately applied or the percentage of patients using a home monitoring device who tell their doctor about a "reportable event" like an episode of elevated blood glucose or blood pressure.
Similarly, the Hopkins report lists several health IT system management quality measures, including statistics on real-time clinical decision support alerts bypassed by clinicians and the percentage of patient allergy lists reviewed by patients through a Web portal.
It's these kinds of data that can make a sizable dent in our healthcare debacle, if applied across the board. But generating this information requires more sophisticated EHRs and clinicians who are willing to think structurally so to speak. And that's something many clinicians still rebel against.
So many doctors still bring their paper chart mentality and work habits to the EHR world. They prefer dictating their notes or typing free-text clinical notes into an EHR rather than filling in check boxes and drop down menus needed to generate structured data. But it's this structured data that's needed to meet many of the next-generation quality measures.
Of course, there are ways to turn unstructured clinical notes into structured data. As I've mentioned in previous columns, several commercially available natural language processing (NLP) programs have the ability to understand terms used in everyday speech--including a variety of synonyms for the same term--and extract the most relevant references from a narrative clinical note to populate all those annoying check boxes and drop-down menus that physicians love to hate.
Nuance has partnered with several other companies to combine NLP with voice recognition, taking the technology to the next logical level by developing what's being called clinical language understanding or CLU.
MModal and Nuance, for instance, have created a CLU tool that keeps doctors informed of relevant patient data as they add their comments to the EHR. According to Chris Spring, MModal's VP of health IT, the platform "listens" to a clinician's dictation in real time and tells her if she's missing any vital information already in the patient's chart.
IT tools like this bring us a step closer to real quality care. But until more providers invest in such technology, we can't hope to generate the metrics needed to fully transform healthcare.
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