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

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

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

How many health data items?

Future: millions (billions?) Now: about 100 000

Nigam Shah (Stanford)2013

Drugs Diseases Tests Devides Procedures

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

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

Patient and System Data

Decision Support

Database

Web Server

• Demographics • Problems and Risks • Measurements • Interventions

Work Station

Data filter

EBMeDS architecture

Electronic Health Record

Trig

ger

Feed

ba

ck

EBMeDS

Client Component

Intra/Internet

Reminders are shown in the user

interface of the electronic health

record

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

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

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

EU-funded study on the effects of reducing

polypharmacy using the EBMeDS tool will

start in 2014

Fundus photography

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)

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

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

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

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

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

Patients performing their own health checks

… and getting individualized advice

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

Thank you!

ilkka.kunnamo@duodecim.fi

www.ebmeds.org