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S9Abstract
Conclusions: Essentially, we found it possible to use Archetypes
and Templates to integrate a test set of intensive care data from 2
systems. By applying the openEHR approach for data modeling and
integration, detailed clinical models can be used for tasks such as
automated constraint checking, error reporting, data persistence, and
querying. Although medical scores such as the Glasgow Coma Scale
were a good fit for openEHR, voluminous data such as vital signs
and ventilation data needed some workarounds to work properly.
Especially, the demand of archetypes to be explicit about the meaning
of each data element might be problematic in some data integration
scenarios. On the one hand, this might be considered an advantage,
as it forces EDW developers and system analysts to work thoroughly.
On the other hand, this constraint might prevent pragmatic solutions
when a fast integration cannot be achieved or interpretation of data
can be conducted by the end-users. Although this work illustrates
some of the strengths and restrictions of the openEHR approach for
data integration tasks, our methodology is limited by the number
of used clinical concepts. A possible next step is the investigation of
the implications of openEHR-based information retrieval and the
semantic interpretation of data.
Key words: clinical information systems, data warehousing, detailed
clinical models, health care analytics, openEHR, secondary use.
Disclosure of Interest: None declared.
References
1. Dentler K, ten Teije A, de Keizer N, Cornet R. Barriers to the reuse
of routinely recorded clinical data: a field report. Stud Health Technol
Inform. 2013;192:313–317.
2. Chute CG1, Beck SA, Fisk TB, Mohr DN. The Enterprise Data Trust
at Mayo Clinic: a semantically integrated warehouse of biomedical
data. J Am Med Inform Assoc. 2010 Mar-Apr;17(2):131-5. http://
dx.doi.org/10.1136/jamia.2009.002691.
3. Goossen W, Goossen-Baremans A, van der Zel M. Detailed clinical
models: a review. Healthc Inform Res. 2010 Dec;16(4):201-14.
4. Beale T. Archetypes, constraint-based domain models for future-
proof information systems. Seattle, Washington, USA: Northeastern
University, Boston; 2002. pp. 16–32. (Eleventh OOPSLA workshop
on behavioral semantics: serving the customer: 2002.)
5. Frankel H. HL7 Working Group Meeting - Using Archetypes with HL7
Messages and Clinical Documents [PowerPoint presentation. 2011.
http://www.mz.gov.si/fileadmin/mz.gov.si/pageuploads/eZdravje/
Novice/gradiva_predstavitve_dogodkov/Open_EHR/7_integration.
pdf.
DEVELOPMENT OF MEDICAL DEVICES
D. Limaye
Hochschule Hannover, Hannover, Germany
Background: The medical devices sector helps save lives by provid-
ing innovative health care solutions regarding diagnosis, prevention,
monitoring, treatment, and alleviation. Medical devices are classified
into 1 of 3 categories in the order of increasing risk: Class I, Class II,
and Class III.1 Medical devices are distinguished from drugs for
regulatory purposes based on mechanism of action. Unlike drugs,
medical devices operate via physical or mechanical means and are
not dependent on metabolism to accomplish their primary intended
effect.2,3
Objectives: This study focused on regulations and differences in med-
ical device and pharmaceutical drug development. It also highlighted
the unique challenges faced while doing medical device development.
Methods: A US Food and Drug Administration and European
Medicines Agency website search was conducted to determine cur-
rent medical device regulations. A comprehensive literature search
was done from Google Scholar to determine the differences in drug
and medical device development.
Results: Designing well-controlled prospective clinical trials of
medical devices presents unique challenges that differ from those
faced in studies of pharmaceuticals. Clinical outcomes observed in
medical device studies, unlike drug trials, are influenced not only by
the product under evaluation and the patient but also by the skill
and discretion of the health care professional. Medically appropri-
ate alternative treatment regimens may not be available to provide
randomized, concurrent controls in device trials. Because devices are
often developed by small companies, financial constraints often limit
the new product development and testing.
Conclusions: Medical device development is faced with unique chal-
lenges. Managing the design issues in clinical trials and complying
with increasingly stringent regulatory guidelines is necessary to bring
new devices faster to market with reduced cost.
Key words: clinical trials, EMA, medical devices, USFDA.
Disclosure of Interest: None declared.
References:
1. What does it mean for FDA to “classify” a medical device?
USFDA. 2015. http://www.fda.gov/AboutFDA/Transparency/Basics/
ucm194438.htm.
2. Becker K. Clinical evaluation of medical devices. 2nd edition. 2006.
Humana Press Inc. USA. pp.3-4.
3. Abdel-aleem S. The Design and Management of Medical Device
Clinical Trials: Strategies and Challenges. John Wiley & Sons. 2011.
Pp.2-3.
QUALITY MANAGEMENT—NEW PERSPECTIVES
FOR MEDICAL DATA MANAGER
A. Haendel; and G. Michelson
University of Erlangen–Nuremberg, Erlangen, Germany
Increasingly, hospitals and other players in the health care sector will
inevitably compete in terms of quality. Interinstitutional and cross-
sectoral quality assurance has been pushed forward during recent
years. Institution-related outcomes are published and accessible to
the public. Due to new health laws, in the near future, quality results
of hospitals will not only be decisive for reimbursement increases or
price reductions of the remuneration but will also be a crucial factor
for a hospital’s survival. Hospitals that are not able to get quality
deficiencies under control may lose their public supply mandate.
Thus, the outcome of hospitals should be measured on the basis
of predefined quality indicators to reach the objectives described
earlier. Key indicators are, on the one hand, measures of medical
performance. In particular, these include, for example, the type and
numbers of surgical procedures as well as surgical complications in
a certain time period. Also included are structural statistics about
continuous medical education such as number of passed training
courses for medical doctors and nurses. Moreover, information about
patient safety are key indicators for quality assurance. Patient safety
indicators, for example, are the number of patient falls and side
effects of medication. These parameters have to be registered in a
structured form and in a fixed frequency. The method to provide
these indicators is a continuous comprehensive quality management,
including capturing and monitoring of all relevant data. This requires
the establishment of a professional operating system gaining all neces-
sary figures in daily clinical routine. Health information management
