Patient-Centered Care - Health Informatics in the Cloud - page 53

Fig. 5.1 popHealth Collects Quality Metrics without Moving PHI Outside of the Providers'
Control
That same group of providers can proactively manage both their care quality and its cost
to earn the maximum share of the reduced costs that Medicare, or a private insurer, would
rebate to them under an ACO or similar outcome-based payment model.
We will now look at some examples of the health informatics tools for patient-
centered care. I cannot promise that they are the best examples or the best for any
particular provider practice. My purpose in discussing them is to give a sense of
what can be done and how it is done given contemporary health informatics tech-
nologies and the mixed landscape of electronic health records that exists in most
communities.
popHealth is an open source quality measurement system funded by ONC and
developed by the Mitre Corporation. It runs over Laika, an open source EHR testing
framework intended to analyze and report on the interoperability capabilities of
EHR systems as a part of the EMR certification process. The technology approach
is similar to hQuery, another ONC funded open source effort that automates a more
generalized set of distributed queries across diverse EHRs for purposes such as
clinical research. We'll look at hQuery in more detail in Chapter 7 . popHealth cap-
tures summary clinical data in one of the standard formats from healthcare provid-
ers' EHRs.
Figure 5.1 shows how this works. Certified EHRs produce standard clinical sum-
mary files that are imported into popHealth software that is running on the provid-
ers' systems. This is a really key concept in popHealth. PHI never leaves the
providers' control, greatly simplifying HIPAA issues and alleviating other concerns
about how the data might be used.
Once it has extracted abstract data at the provider site for reporting purposes and
sent it to the central system, popHealth streamlines the automated generation of
summary quality measure reports on the providers' aggregate patient population. In
Fig. 5.2 data from 500 patients has been aggregated from a hypothetical group of 10
providers who could be using different EHRs.