Claus Duedals presentation on "Efficient use of data at Odense University Hospital" at the Healthcare DENMARK session, HIMSS 2015 in Chicago.

1. Efficent use of data at
Odense University Hospital
ww.cimt.dk
Claus Duedal Pedersen
Chief Innovation Officer

2. 2
Odense University Hospital (OUH)
- A main centre in Danish health care
• 1 of 3 major national health care centres
• Covers approximately 1.6 million citizens
• Highly specialised - Covers all surgical and
medical areas in 50 clinical departments
• Approx. 10,000 employees; 1,400 doctors and
4000 nursing and care personnel.
• The hospital's operating budget is approx. 830
million € a year (2013).
• Approximately 93,000 patients are hospitalised
at OUH every year.
• OUH has 1200 beds, and hospitalised patients
spend an average of 3.8 days in hospital.
Patient Satisfaction:
OUH performs surveys of patient satisfaction
following hospital treatment. In general, the
level of satisfaction is high. In the latest survey
more than 90 % of the patients stated that
they were "satisfied" or "very satisfied" with the
treatment they received in Odense and
Svendborg.

3. Clinical research (2013):
• OUH and University of Southern Denmark:
• 273 PhD students
• 82 professorships
• 1028 peer reviewed publications
• Almost 4,000 students participate in a
clinical study programme at OUH every
year.
• In 2013, external research funding
amounted to 20,6 million €.
Closely linked with the University of Southern Denmark
Faculty of Health
partly located in the
hospital grounds

4. • Electronic Medical Records (EMR) implementation started in 1993 (Svendborg).
• 95% digital in all processes and work flows (lab request, lab results, referrals, etc.)
• 2012: HIMSS Stage 6 recognition for the level of digitalization. 2014: Stage 5
• OUH has Denmark’s largest EMR installation and was the the first Danish university
hospital with EMR on all wards. October 2009 = approx. 9,300 employees
connected.
• By 2015 all hospitals in the Region of Southern Denmark will be using the EMR and
Patient Administration System of OUH, raising the number of users to approx.
20.000
• Focus on eHealth and telemedicine over the last decade –> All hospitals in the
region have implemented telemedicine services
An almost 100% digital hospital

5. Intelligent use of data

6. Input but not so much output
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7. Focus areas for use of data

8. Using clinical data beyond the day-to-day routines
(Patient@Home)
• Identification of high risk patients in the emergency ward
(PhD)
• Prediction of risk of osteoporosis (PhD)
• Text mining on EHR – can it be used to identify and avoid
adverse events in the clinic? Creation of data sets for automated
analysis of text in the EHR (Regional project)
• Creating a clinical data bank based on retired and existing
systems and databases (regional)

9. Why focus on patients with Alcohol problems
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• AUD causes and interferes with a series of diseases. Nevertheless, it has shown to be more than difficult to
implement systematic screening for AUD at the hospital departments, probably due to AUD being taboo.
• Evidence (signs) of AUD can be difficult to trace directly in EHRs. The signs are spread over various parts of
the EHR and can only be uncovered by looking at multiple types of data from multiple hospital admissions.
• Existing techniques and sub-models, stemming from data mining, machine learning, and natural language
processing need to be tailored and combined into a single coherent predictive model in order to make a
model that is strong across the various alcohol cultures in Europe.
• We want to enable a interventions to a targeted group of patients that show evidence of AUD, typically 15-
20% of the hospitalized patients in most cultures.
• The predictive models will avoid collection of redundant information from the patients on symptoms and
signs on AUD that are already present (directly or as proxy) in the EHR.
• The predictive models will provide the staffs with a higher confidence when approaching patients that show
evidence of AUD, since the decision support software will provide staff a clear reason to talk to the patients
about their drinking, contrary to doing so on the basis of more unspecific suspicions.
• Approaching patient with AUD during their hospitalization is considered a highly effective and evidence
based prevention strategy in the field, yet rarely implemented so far.

10. Intelligent use of data in the treatment of patients with
alcohol use disorder – the RELIP proposal
• Relip will adapt indicators and existing models into a coherent, overall
predictive model for detection of Alcohol Use Disorders (AUD)
• The coherent model will use existing (already available) digital medical data
from the electronic health records and other relevant systems
• A coherent predictive model for identifying AUD in patients, combined with
clinical decision support software will informs the staffs about the presence
of AUD.
• By the means of the coherent predictive model, Relip will provide improved
clinical decision support system (DSS) for clinical decision making in relation
to alcohol use disorder (AUD).
• To improve the health care systems capability to manage and prevent the
consequences of AUD by improving the decision and knowledge base.

11. Thank you!
Please feel free to read more on
www.cimt.dk
And please contact me or a colleague if you
have any questions