Testing the applicability of digital decision support on a nationwide EHR
1. DD.MM.YYYY
Testing the applicability of digital decision
support on a nationwide EHR
Janek Metsallik, PhD researcher, lecturer, superviser, e-health architect
Health Technology Department, School of IT
Tallinn University of Technology
2. Towards personalized medicine in Estonia
• Urgency: continuous growth of accumulation of health data
• How to transform healthcare from reactive to predictive, preventive, and
personalised?
• Idea: personalized medicine
• Combine genetic data, environment data, life-style data, and health data
• Implement digital decision support systems
• Platform: Estonian nationwide EHR since 2008
• A health data source covering a person’s health data from birth to death
• All healthcare providers are legally responsible for sending data to the
system
• Scope: The Estonian programme of Personalized Medicine 2015-..
3. Is my case reported in EHR?
Document type Millions
Outpatient summary 24.2
Referral response 13.5
Dental note 3.6
Referral 3.0
Discharge letter 2.0
Vaccination note 1.1
Ambulance note 0.8
Child Health note 0.7
Section type Millions
Procedure 226.0
Analysis 103.3
Diagnosis 50.7
Encounter 83.1
Observation 23.1
Anamnesis 23.1
Vaccination 2.6
Surgery 0.9
2017 Study of Waiting Times in EHR:
“only 14% of documents had the necessary timestamps”
2019 Prognosis of duration of illness in EHR and EHIF:
“EHR has 6 times higher chance of missing data and 1.5
times less patient cases”
2019 Dental Care reporting to EHR compared to EIHD:
“Only 61% of providers report to EHR, roughly 51–64%
encounters are covered in EHR”
2019 Inpatient Care reporting to EHR compared to
EIHD:
“Only 85% of hospitals report inpatient care (88%
coverage) and 62% of hospitals report day care (93%
coverage)”
2010 Study of EHR data on 10 years:
“48% of the pancreatic malignancies patients missed the
corresponding pathology report; 52% records missed
some variables”
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4. This study
Research question:
➔ Does the data in the nationwide EHR fit for automated decision
support?
(What needs to be changed in the current health data exchange?)
Methods:
➔ Test a set of clinical decision algorithms on the past records in the
EHR database
5. Algorithm 1
Algorithm n
Algorithm
(logical script)
Parameter
Parameter
Semantics
mapping
Source context
Source context
Source context
Syntax mapping
Mapping
(database
scripts)
Data
availability
Data technical
compliance
Data validity
(statistical)
Parameter
checks
Case coverage
(data fitness)
Alert coverage
(relevance)
Algorithm
checks
Computing the
applicability indicators
Requirements
mapping
The Approach
6. The algorithms (EBMEDS by Duodecim [1])
1. Alerting on genetically determined high risk for statin-induced myo-pathy (scr01718)
2. ACE inhibitors, angiotensin-receptor blockers, and beta-blockers in patients with congestive heart
failure (scr00272)
3. Avoiding the combination of aspirin and clopidogrel in patients with-out specific indications
(scr01576)
4. Glucose tests for patients with hypertension, dyslipidemia, or cardio-vascular disease
(EBMPracticeNet) (scr01371)
5. Smoking cessation for secondary prevention in atherosclerotic disease (EBMPracticeNet) (scr01464)
6. Statins for the secondary prevention of cardiovascular disease (scr01069)
7. Adding or increasing diuretics in patients with congestive heart failure and fluid retention
(scr00274)
8. Alerting on genetically determined high risk for familial hypercholes-terolemia (scr01719)
9. Diagnosing hemochromatosis by genetic testing (scr01715)
[1] https://www.ebmeds.org/en/rules/
7. The Results: querying data for potential alerts in past
9 CDSS algorithms
6 parameter
categories
24 sections of
documents
0 alerts 0 alerts
Algorithm 7: Adding or increasing
diuretics in patients with congestive
heart failure and fluid retention
Data Set of Jan 2012 - May 2017
National EHR
[patients]
Diagnosis: 471 339
Weight change: 0
Medication: 0
Pärnu Hospital
[patients]
Diagnosis: 16 811
Weight change: 1
Medication: 1 032
172 data fields
18 327 alerts
Other
algorithms
Pärnu
Hospital
8. Algorithm National EHR Hospital EMR
1 Not applicable Not applicable
2 Partially applicable Partially applicable
3 Not applicable Partially applicable
4 Not applicable Partially applicable
5 Not applicable Not applicable
6 Not applicable Applicable
7 Not applicable Not applicable
8 Not applicable Not applicable
9 Not applicable Applicable
9. • Integrability - is the data available in the required location and form?
• Data is not integrated for meaningful decision support -> users have no access to data
• Prescription drugs vs health records, documents vs decision support
• Interoperability - is the data semantics computable over its pipeline?
• Data is not in a compatible form and terminology -> need for manual preprocessing
• Declared smoking vs interviewed smoking, home weight vs hospital weight
• Composability - is data purpose in a context understood?
• Problem: data is not captured in a reusable way -> duplication in data capture
• Left ventricular ejection fraction, familial hypercholesterolemia, specific genetic
test findings
What are the reasons for the discontinuity?
10. Conclusion: Are we ready for Clinical Decision Support?
Yes, we need to start learning the value of good quality shared data!
Risk of misuse of users time, risk of alert fatigue, and risk of clinical errors
Need for clear “fit for use” testing criteria for the decision support
Personalised medicine demands refactoring of the national EHR data
exchange models
