1) Computer-based decision support systems can improve healthcare quality by ensuring patients receive the right care, preventing medical errors, and saving professionals' time. 2) A case example showed how a decision support reminder detected a patient's undiagnosed high blood pressure that would have otherwise gone untreated. 3) Future health data is expected to grow exponentially, necessitating computerized decision support to efficiently manage vast amounts of patient information.

1. Virtual health check and
computer based decision
support
Ilkka Kunnamo, MD, PhD, Chief Editor
Disclosure: I am a salaried employee of Duodecim Medical
Publications Ltd., the company that develops and and licenses the
EBMeDS decision support service
6.5.2014

2. How could computers
• Improve the quality of care – make sure
that everyone gets the right care at the
right time
• Prevent medical errors
• Save time of health care professionals
to be used on patient care

3. Case history
• A 40-year-old man visits the doctor because
of a hernia.
• The decision support system automatically
shows a reminder that the latest data on
blood pressure was from 3 years ago:
185/100.
• The doctor measures the blood pressure,
which is now 220/110.
• Without the reminder the high blood pressure
would have remained undetected, and the
patient would not have received treatment

4. How many health data items?
Future: millions (billions?) Now: about 100 000
Nigam Shah (Stanford)2013
Drugs Diseases Tests Devides Procedures

5. Diabetes (E11.9)
Coronary Disease (I20.8)
BP: 145/90 mmHg
Potassium: 6.1 mmol/l
EBMeDS
Decision
support
Clinical decision support
Electronic
health
record
Rule
library

6. Interactive algorithms are automatically populated by patient data
(diagnoses, medications, lab test results) from the EHR
The patient’s path and
the best choice is shown

7. Patient and System Data
Decision
Support
Database
Web Server
• Demographics
• Problems and Risks
• Measurements
• Interventions
Work Station
Data filter
EBMeDS architecture
Electronic Health Record
Trigger
Feedback
EBMeDS
Client
Component
Intra/Internet

8. Reminders are shown in the user
interface of the electronic health
record

9. EBMeDS decision support service:
statistics
• Developed by Duodecim (medical society and
publishing company) since 2003
• Multilingual: messages in 11 languages
• 800 rules in a rules library
• Integrated with ~ 20 EHR systems (including
Effica, Pegasos, Mediatri, Uranus, Dynamic
Health in Finland)
• > 40 % of Finnish primary care physicians and
> 50 % of hospital physicians use at least
some component of EBMeDS

10. EBMeDS messages
General reminders by complex rules 1324
Guideline links 6191
Drug Interactions 12583
Drug and Renal Malfunction 5028
Drug Contraindications 3387
Drug Indications 3124
Total Number of Reminders 31 637

13. If 200 000 comprehensive medication
reviews were performed in Finland
manually by clinical pharmacists reading
electronic health records and checking all
drug information from databases, the cost
would be 44 million euros at the lowest
market price of 218 €/review

14. EU-funded study on the effects of reducing
polypharmacy using the EBMeDS tool will
start in 2014

15. Fundus photography

16. Using a computer-generated
diagnosis-specific summary
• reduced the time needed to retrieve all
relevant data from the electronic health
record from 5.5 to 1.7 minutes
• saved 57 mouse clicks
Richelle J. Koopman et al. Annals of Family Medicine
2011;9 (no 5)

17. In a virtual health check all
decision support rules are
executed in a population of
patients, and resulting
reminders are listed.
Implementing evidence-based care on
populations

18. Examples of reminders triggered in a Virtual
Health Check for a population of 16 000 from a
set of 100 rules
• Blood-pressure lowering drug not used in moderately
high BP and high cardiovascular risk 396
• All beneficial drugs not in use in heart failure 143
• LDL cholesterol > 2.5 mmol/l in type 2 diabetes 69
• Metformin not in use in type 2 diabetes 61
• No visits for a patient with diabetes during last 13 months 58

19. The comprehensive medication review tool
can be applied to patient populations
Example: drug dosing suggestions based on
kidney function in a population of 16 000
• Check dosing (may be too high) 1164
• Drug not recommended/forbidden 28

20. Prioritization for health benefit
• The population is listed and sorted by care
gap and potential health benefit
• For each patient, the most important
interventions are put on top
• A general practitioner who knows the patient
is the best coordinator of care

21. Patients recording their own data
and checking their
• Symptom history and monitoring
• Family history
• Blood pressure, weight, height
• Peak expiratory flow
• Blood glucose
• ECG
• Pain intensity
• Functioning
• current medication list
• diagnosis list

22. Patients performing their own health checks

24. … and getting individualized advice

25. Decision support for patients and
professionals
Data in
professional
health record
Data in
personal health
record
Interactive
care plan
Decision
support
rules
Personalized
advice
Personalized
advice
Data
hub

26. Thank you!
ilkka.kunnamo@duodecim.fi
www.ebmeds.org