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Pediatric Informatics: Computer Applications in Child Health (Health ...

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244 M.G. Leu et al.<br />

cost-beneficial; and cl<strong>in</strong>ical decision support systems were only beneficial for large<br />

provider organizations. The other <strong>in</strong>terventions were not cost-beneficial. Despite<br />

these f<strong>in</strong>d<strong>in</strong>gs, provider-focused disease management systems may prove to be the<br />

least likely to be adopted given exist<strong>in</strong>g <strong>in</strong>centive structures.<br />

17.4.2 Provid<strong>in</strong>g Performance Measures<br />

and Feedback to Cl<strong>in</strong>icians<br />

In many health care organizations, the process of cl<strong>in</strong>ician performance management<br />

and feedback exists on a small scale (periodic random selection and manual<br />

review of charts). Some practices (such as federally-funded community health<br />

centers) are required to submit structured quality measures to external agencies for<br />

review (e.g., the <strong>Health</strong> Disparities Collaboratives, public health agencies), and <strong>in</strong><br />

many cases, this data abstraction is performed manually.<br />

The use of electronic report<strong>in</strong>g tools that extract data electronically from records<br />

is generally restricted to large practices and organizations (that can afford them).<br />

As the health <strong>in</strong>dustry moves to pay for performance measures for remuneration<br />

of services, 48 the automated collection of data from EHRs will become <strong>in</strong>creas<strong>in</strong>gly<br />

desirable if not mandatory. Research also suggests that health IT-supported<br />

performance monitor<strong>in</strong>g may improve adherence to cl<strong>in</strong>ical practice guidel<strong>in</strong>es. 49<br />

Such performance monitor<strong>in</strong>g requires basic functionalities:<br />

Identification of patients to whom cl<strong>in</strong>ical practice guidel<strong>in</strong>es apply<br />

L<strong>in</strong>kage of identified patients to cl<strong>in</strong>icians responsible for their care<br />

Assurance that data <strong>in</strong>tegrity is sufficient for analysis<br />

Control of identification/de-identification of data<br />

Assimilation of relevant patient historical data<br />

Incorporation of additional measures<br />

Creation and dissem<strong>in</strong>ation of reports<br />

Once these functions are realized, practices can derive performance measures<br />

to exam<strong>in</strong>e the appropriateness and impact of practice guidel<strong>in</strong>es on their own<br />

practices. 18<br />

17.4.2.1 Identification of Patients to Whom Cl<strong>in</strong>ical Practice<br />

Guidel<strong>in</strong>es Apply<br />

With the <strong>in</strong>creas<strong>in</strong>g desire to use evidence-based cl<strong>in</strong>ical practice guidel<strong>in</strong>es, and to<br />

comprehensively manage patients with chronic medical conditions through health<br />

IT (e.g., with automated rem<strong>in</strong>der and recall systems, or with remote communication,<br />

monitor<strong>in</strong>g and feedback systems), it is important to be able to easily identify<br />

patients which may benefit from these <strong>in</strong>terventions.

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