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ISDS Annual Conference Proceedings 2012. This is an Open Access article distributed under the terms of the Creative Commons Attribution-
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ISDS 2012 Conference Abstracts

Evaluation of Clinical and Administrative Data to
Augment Public Health Surveillance
Joseph R. Egger*1, Brooks Lowe1, Erica Johnson1, Randy Durbin2, Erica Griffin2, Sanjaya
Dhakal3, Achintya Dey3 and Sam Groseclose3

1SciMetrika, LLC, Research Triangle Park, NC, USA; 2The Altarum Institute, Atlanta, GA, USA; 3Centers for Disease Control and
Prevention, Atlanta, GA, USA

Objective
To assess the utility of inpatient and ambulatory clinical data com-

piled by public and commercial sources to enhance the Centers for
Disease Control and Prevention’s surveillance activities.

Introduction
Medical claims and EHR data sources offer the potential to ascer-

tain disease and health risk behavior prevalence and incidence, eval-
uate the use of clinical services, and monitor changes related to public
health interventions. Passage of the HITECH Act of 2009 supports
the availability of standardized EHR data for use by public health of-
ficials to obtain actionable information. While full adoption of EHRs
is still years away, there are presently publicly- and commercially-
available EHR and medical claims data sets that could enhance pub-
lic health surveillance at a national, regional and state level. The
purposes of this evaluation were to i.) demonstrate the feasibility of
gaining access to such data, ii.) evaluate their ability to augment cur-
rent surveillance activities by developing measures for twenty sepa-
rate healthcare indicators (e.g., HIV screening), iii.) evaluate each
data source across a set of criteria needed for an effective surveil-
lance system, and iv.) assess the ability of the data sources to evalu-
ate changes in healthcare utilization and preventive services that may
be a result of the 2009 Health Reform legislation.

Methods
Ten separate data sources were selected for inclusion in the study

based on a number of criteria, including availability, representative-
ness, population, data structure and content, cost, and longitudinality.
In collaboration with staff from seven Divisions across the CDC, de-
tailed specifications were developed for twenty separate indicators
of healthcare utilization or preventive services using best practices
in healthcare quality measurement. Specifications were developed
separately for EHR and medical claims data due to their differing
structure, content and use of medical code sets and terminologies.
Specifications for EHR data sources relied on the National Quality
Forum (NQF) Meaningful Use (MUse) clinical quality measure spec-
ifications. The use of NQF MUse specification guidelines allowed us
to gauge the current ability of each data source to measure healthcare
utilization and preventive services as recommended by NQF, the na-
tional leader in healthcare measurement. Each of the data sources was
also evaluated across established public health surveillance criteria,
including data quality, representativeness, and flexibility, among oth-
ers. Data analysis was performed using SAS 9.3 (SAS Institute, Cary,
NC).

Results
All twenty of the healthcare indicators were developed for at least

one data source; however, many of the indicator specifications had to
be modified due to the low frequency of certain code sets (e.g., CPT-
4 II, LOINC). The observed strengths of medical claims data were
the relatively low cost, ability to track patients longitudinally, and the
standardized representation of procedures and diagnoses through use
of medical codes, such as ICD-9-CM, CPT-4 and HCPCS. The ob-
served strengths of EHR data sources were the availability of infor-
mation related to health behavior (e.g., current smoker), health
assessment (e.g., BMI), prognostic indicators (e.g., vital signs, labo-
ratory result), diagnostic testing, and functional status. While EHR
data also capture diagnoses using ICD-9-CM, procedures such as
medical and laboratory procedures remain documented through use
of free text or semi-structured text fields, making it difficult to
process.

Conclusions
Currently available healthcare data can improve the timeliness of

health outcome monitoring and add complementary information on
healthcare utilization to improve our interpretation of traditional pub-
lic health surveillance data. Medical claims data support measure-
ment of health outcomes and healthcare services provided to patient
populations; however, without clinical encounter information, they
cannot develop measures estimating the impact of services received
on quality of care. EHR data have richer clinical information; how-
ever, the continued use of non-standards-based medical codes and
free and semi-structured text fields make it difficult to analyze data
at scale. Meaningful Use and other HITECH initiatives are changing
this by incentivizing the standardization and aggregation of electronic
healthcare data. In time, these data may yield timely, accurate and ac-
tionable information for public health surveillance.

Keywords
Surveillance; Evaluation; Healthcare; Electronic Health Record

*Joseph R. Egger
E-mail: joseph.egger@gmail.com

Online Journal of Public Health Informatics * ISSN 1947-2579 * http://ojphi.org * 5(1):e30, 2013