EBMeDS Evidence Based Medicine electronic Decision Support Kortteisto Tiina Jousimaa Jukkapekka, Kom

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EBMeDS - Evidence Based Medicine electronic
Decision SupportKortteisto TiinaJousimaa
Jukkapekka, Komulainen Jorma, Kunnamo Ilkka,
Mäkelä Marjukka, Mäntyranta Taina, Rissanen
Pekka, Varonen Helena

• Minna Kaila, MD, PhD, Pediatric Allergist
• Adjunct Professor /University of Tampere
• Director /Institute for Health Welfare
• minna.kaila(at)kolumbus.fi or (at)thl.fi
• mobile [phone]
• No commercial conflicts of interest

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EBMeDS aim

• to develop,
• implement and
• evaluate
• a generic clinical decision support
• system.

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Electronic EBM guidelines
Clinical Decision Support
Structured Electronic Patient Record
Decision support combines medical evidence with
individual patient data. It produces tailored
alerts, prompts and guidance to physicians and
other professionals.
Varonen H, Kaila M, Kunnamo I, Komulainen J,
Mäntyranta T. Tietokoneavusteisen päätöksentuen
avulla kohti neuvovaa potilaskertomusta.
Duodecim 20061221174-81.
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Decision support Features critical to success

• Objective To identify features of clinical
  decision support systems critical for improving
  clinical practice.
• Method Systematic review, MEDLINE, CINAHL,
  Cochrane controlled trials register, up to 2003.
• Study selection Studies had to evaluate the
  ability of decision support systems to improve
  clinical practice.
• N 70.
• Decision support systems significantly improved
  clinical practice in 68 of trials.

Kawamoto et al, BMJ, 2005
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Predictors of improved clinical practice

• Automatic provision of decision support as part
  of clinical workflow (OR112.1 plt0.00001)
• Provision of recommendations rather than just
  assessments (OR15.4 p0.019)
• Provision of decision support at the time and
  location of decision making (OR7.1 p0.026)
• Computer based decision support (OR6.3 p0.029)
  
• Of 32 systems possessing all four features, 30
  (94) significantly improved clinical practice.

Kawamoto et al, BMJ, 2005
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Care testament
Kristiina Häyrinen ja Jari Porrasmaa, 2006
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EBMeDS - organization
project plan 2005-06
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EBMeDS timetable
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EBMeDS study project

• Baseline study at pilot sites 2006-2007
• Survey
• Health Care professionals
• Interviews
• Health Care Managers
• IT-experts

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Focus group study

• 39 physicians in 7 groups
• Both urban and rural physicians of different ages
  around Finland
• Between October 2005 and January 2006 by two
  moderators
• Audiotaped, transcribed, coded and interpreted

Varonen H, Kortteisto T, Kaila M for the EBMeDS
study group. What may help or hinder the
implementation of computerized decision support
systems (CDSSs) a focus group study with
physicians. Fam Pract 200825162-7.
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Subjects

• Age, median (range) 46 (27-56)
• Gender, per cent female 44
• Work experience as physician, median
  (range) 17 (0.5-30)
• Estimated daily computer use, hours, median
  (range) 5.5 (0.5-10)

Varonen H, Kortteisto T, Kaila M for the EBMeDS
study group. What may help or hinder the
implementation of computerized decision support
systems (CDSSs) a focus group study with
physicians. Fam Pract 200825162-7.
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Results Barriers of CDS

• Previous problems with health care IT
• Potential harm to doctor-patient relationship
• Threats to clinicians autonomy
• Potential extra workload due to excessive
  reminders

Varonen H, Kortteisto T, Kaila M for the EBMeDS
study group. What may help or hinder the
implementation of computerized decision support
systems (CDSSs) a focus group study with
physicians. Fam Pract 200825162-7.
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Facilitators of CDS

• Flexibility of the system tailored topics and
  possibility to switch off
• Reliability reliable knowledge-base and that
  trusted peers are developing the system
• Simplicity and ease of use
• Concise reminders that facilitate and help work
  processes

Varonen H, Kortteisto T, Kaila M for the EBMeDS
study group. What may help or hinder the
implementation of computerized decision support
systems (CDSSs) a focus group study with
physicians. Fam Pract 200825162-7.
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The main RCT study questions

• 1) Do patient and problem specific EBMeDS
  reminders shown to professionals during clinical
  work have an effect on patient care measured by
  the number of all reminders triggered in repeated
  Virtual Health Checks (VHC, see below)?
• Reminders on drugs, e.g. interactions or
  contraindications, and other types of
  evidence-based reminders will be analysed
  separately.
• 2) In addition, we will explore the effect of the
  reminders on intermediate patient outcomes in
  specific groups of diagnoses. Also these outcomes
  are measured on the basis of reminders triggered
  in repeated VHCs. Mean values of laboratory
  parameters are also measured in the explanatory
  analyses.

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EBMeDS RCT study
exclude occupational health
VHC VHC
VHC VHC
Ri/Ni
Ri/Ni
time
0
Randomisation --- patient, whose reminders are
blocked (recorded only in log files) ------
patient, whose reminders are shown to his/her
physician or nurse VHC virtual health check R
number of reminders N total number of patients
The outcome variable is a number between 0 and
1. No patient data need to be analysed when the
values of the outcome variables are derived.
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Hypothesis

• in the intervention group the total number of
  EBMeDS reminders triggered in the repeated
  Virtual Health Checks (VHC) will decrease
  compared to the control group, indicating an
  improvement in the patient care.
• In a VHC all available reminders are triggered as
  a batch run in the group of patients to be able
  to compare their number in the intervention and
  control group.

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Intervention

• Visits or practitioner use of the patient record
  from group A /intervention patient specific
  reminders shown on screen to the practitioner
  during the visit,
• Visits or practitioner use of the patient record
  from group B /control reminder not shown on
  screen ( usual practice),

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Patient groups /exploratory

• - patients with diabetes (quality indicator level
  of HbA1c), dyslipidemias (quality indicator LDL
  cholesterol level, body mass index) or
  hypertension (quality indicator blood pressure
  level), and the UKPDS risk score xxx.
• - patients with cardiovascular risk factors
  (quality indicator cardiovascular risk according
  to SCORE xx or cardiovascular disease (quality
  indicator LDL cholesterol and total cholesterol)
• To assess the safety of drug therapy we will
  study patients with multiple medications (a
  minimum of 7 drugs with adult and one constant
  drug with child quality indicator proportion of
  patients with contraindication or interaction
  alerts in relation to the number of drugs in use)
• In addition, the result will be evaluated
  according to level of urgency of the reminders
  (three levels) and according to the treating
  professional (physician, nurse).

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• Practitioners Altogether 50 professionals
  (physicians, nurses, physiotherapists, speech
  therapists, and psychologist) in Sipoo Health
  Centre using the Mediatri patient record system
  during patient encounters, also at the inpatient
  wards (two wards where inpatients are treated by
  their primary care physicians).
• Population All patients of Sipoo Health Centre
  during the study (in the beginning of 1.3.2009)
  will be randomised into two groups. People moving
  into or out of the community during the study
  period will not be included in the study.

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The EBMeDS reminders

• based either on global EBM guidelines, national
  Current Care guidelines, or international and
  local drug databases.
• There are around 300 reminder script
  descriptions in the EBMeDS database. Many more
  reminders are generated using available drug
  databases, e.g. those on interactions,
  contraindications and indications. The total
  number of possible reminders is estimated to be
  about 16000.
• Categorized according to level of urgency level
  I (do this! Imperative), II (consider this and
  justify your decision of noncompliance) and III
  (this is relevant information for you).
• A set of reminders will be selected for this
  study before commencement depending e.g. on
  possible special interests due to ongoing
  development projects of Sipoo Health Centre and
  based on a pilot VHC. Disease entities relevant
  from the public health perspective will be
  targeted, such as type 2 diabetes and
  cardiovascular diseases. As new reminders are
  being generated the final decision on the study
  reminders will be made on February 2009.

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EBMeDS timetable
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• More information on EBMeDS
• www.kaypahoito.fi/decisionsupport/decisionsupport.
  htm
• Thank you for your attention!

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• Kortteisto T, Kaila M Komulainen J.
  Päätöksentuen tutkimus (EBMeDS). Stakes
  Tutkimuspaperit 18/2006
• Kortteisto T, Kaila M, Komulainen J. Rissanen
  P. Esimiesten kokemuksia sähköisistä
  potilaskertomusjärjestelmistä Päätöksentuki-tutki
  muksen (EBMeDS) haastattelut lähtötilanteessa.
  Stakes Tutkimuspaperit 14/2007
• Varonen H, Kaila M, Kunnamo I, Komulainen J,
  Mäntyranta T. Tietokoneavusteisen päätöksentuen
  avulla kohti neuvovaa potilaskertomusta. Duodecim
  20061221174-81.
• Kortteisto T, Mäntyranta T, Komulainen J, Kaila
  M. Lääkäreillä vielä paljon sanottavaa
  sähköisistä potilaskertomusjärjestelmstä. Suom
  Lääkäril 2008631297-301
• Komulainen J, Kunnamo I, Nyberg P, Kaila M,
  Mäntyranta T, Korhonen M. Developing an evidence
  based medicine decision support system integrated
  with EPRs utilizing standard data elements.
  Proceedings of the workshop AI Techniques in
  Healthcare Evidence-based Guidelines and
  Protocols. Ten Teije A, Miksch S, Lucas P (eds.)
  Riva del Garda, Italy, 28 August - 1 September
  2006.
• Varonen H, Kortteisto T, Kaila M for the EBMeDS
  study group. What may help or hinder the
  implementation of computerized decision support
  systems (CDSSs) a focus group study with
  physicians. Fam Pract 200825162-7.
• Kunnamo I, Kaila M, Komulainen J, Mustonen P,
  Nyberg P, Varonen H, Guyatt G. Electronic
  guidelines, decision support and standardized
  health records in Finland. Käsikirjoitus.
• Kaila, Kortteisto, Kunnamo, Nyberg, Jousimaa,
  Komulainen, Mäkelä, Mäntyranta, Varonen,
  Rissanen. Virtual health check a new automated
  quality measure for specified patient
  populations. Käsikirjoitus
• Miettinen M. Gradu 2009 /JY. TIEDON LAATU
  TERVEYDENHUOLLON SÄHKÖISISSÄ POTILASTIETOJÄRJESTEL
  MISSÄ
• 10. Korhonen H. Gradu 2009/Tay. TYÖN PIIRTEIDEN
  YHTEYS TERVEYDENHUOLLON AMMATTILAISTEN
  HOITOSUOSITUSASENTEISIIN

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Specific features that have promoted acceptance
and wide use of guidelines in Finland

• 1. Homogeneity of health care (culture and value
  basis)
• 2. Municipal ownership of all (public) health
  care facilities
• 3. Lack of any significant competition in health
  care
• 4. Practically identical university curricula in
  the 5 medical faculties
• 5. High national penetration of the internet
  technology and high computer proficiency and
• 6. One respected medical scientific society
  responsible of the service, physicians producing
  guidelines for physicians

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Calculators

•   Alkoholin käyttö Alcohol use
• Antikoagulanttiannostelu Anticoagulant dosing
• Ejektiofraktio Ejection fraction
• Energiankulutus Energy expentiture
• GFR-laskuri Glomerular filtration rate
• Haittaluokka ja prosentti Disability
  classification
• Kehon painoindeksi Body Mass Index
• Korjattu QT-aika QT time
• Kuivuman korjaus Rehydration
• LDL-laskuri LDL-cholesterol calculator
• PEF-laskuri PEF-calculator
• Reynolds Risk Score (naisille)
• SCORE-laskuri SCORE calculator
• Tavoitesyke Target rhythm
• UKPDS
• Veden vajaus hypernatremiassa Water deficit in
  hypernatremia

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All guidelines are available in one search
engine to 98 of Finnish physicians as a part
of Physicians Database with 43000 documents
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Use of EBMG, Current Care and related databases
in the Terveysportti Health Portal
1.6 guidelines opened per every
working-aged physician every day

• Number
• of guideline
• documents
• opened
• 10 million/year
• Total number
• of documents
• opened gt20
• million/year