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demographic data. These tools are designed to help clients identify and fix data
quality issues. Interestingly, this function is increasingly becoming the responsibil-
ity of office managers because the tool simplifies what was once thought of as a
highly technical function. In most practices, office managers are the people who
have historically dealt with coding issues. Analytics Manager allows larger provider
networks to incorporate data from loosely affiliated practices even though those
practices aren't formally using Wellcentive.
Outcomes Manager is where the data, once collected and aggregated, is used to
provide proactive management of wellness and chronic disease in the patient popu-
lation being cared for by the provider network. The user might be a care coordinator
in a PCMH model clinic but many smaller clinics are now outsourcing this function
to a new class of independent professionals often referred to as a Care Manager.
This allows providers to have access to this needed service without having a full
time employee devoted to the task.
The company recently released a fifth capability called Risk Manager. It applies
predictive modeling and risk stratification to patient populations in order to find
those patients at a high risk for poor clinical and financial outcomes.
Figure 5.5 from Outcomes Manager shows a list of Alerts defined by a practice.
For each, the number of qualifying patients (the denominator in the percentage cal-
culation) is shown along with the percentage of patients who don't meet this quality
metric. Adjacent to that is a graphical representation of the practice's current quality
performance versus their goal for each metric. Red flags indicate metrics below the
corresponding goal set by the practice. Practices can define the provider cohort
against which these metrics are calculated so, for example, cardiologists could be
compared only to other cardiologists. The View Patients link brings up the individ-
ual patients who don't meet the goal. The practice then has a number of options
including an automated system the company provides to contact each patient to
provide reminders, patient education or to request the patients make an appoint-
ment. These calls can be customized based on known clinical data so that, for exam-
ple, a patient with an elevated LDL could be asked to come in to be evaluated for
statin therapy. The company also offers a personal health record (PHR). Any patient-
entered data is considered unverified. The provider is alerted when it is input and, if
they verify the data, it can then be used by the quality management system. Thus,
for example, if the patient reports an increased blood pressure that data, once
verified, might trigger a “blood pressure not under control” alert for the patient.
Before moving on it's worth reflecting on what we have seen in this chapter. The
long time problem of interoperability is being solved, at least within the admittedly
limited domains of collecting, aggregating and visualizing clinical quality for
specifically defined risk metrics. This is possible because of the new technologies
we discussed earlier. It is hard for me to look at these systems and not be optimistic
about the future.
The Novo grid is a creature of the Internet. It's possible only because diverse
systems across a community can now be bound together into what is effectively a
single virtual database. One care coordinator using the iNexx app we examined
could, in theory, oversee the management of all the hypertensive or diabetic patients
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