EBMeDS - Evidence Based Medicine electronic Decision Support Kortteisto Tiina Jousimaa 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 +358 50 523 2021 No commercial conflicts of interest
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Minna Kaila, MD, PhD, Pediatric Allergist Adjunct Professor /University of Tampere
EBMeDS - Evidence Based Medicine electronic Decision Support Kortteisto Tiina Jousimaa Jukkapekka, Komulainen Jorma, Kunnamo Ilkka, Mäkelä Marjukka, Mäntyranta Taina, Rissanen Pekka, Varonen Helena. Minna Kaila, MD, PhD, Pediatric Allergist Adjunct Professor /University of Tampere - PowerPoint PPT Presentation
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EBMeDS - Evidence Based Medicine electronic Decision Support
Kortteisto TiinaJousimaa Jukkapekka, Komulainen Jorma, Kunnamo Ilkka, Mäkelä Marjukka, Mäntyranta Taina, Rissanen Pekka, Varonen Helena
Minna Kaila, MD, PhD, Pediatric AllergistAdjunct Professor /University of TampereDirector /Institute for Health & Welfareminna.kaila(at)kolumbus.fi or (at)thl.fimobile +358 50 523 2021
No commercial conflicts of interest
EBMeDS: aim
to develop, implement and evaluate a generic clinical decision
supportsystem.
ElectronicEBM guidelines
Structured ElectronicPatient Record
Clinical Decision Support
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 2006:122:1174-81.
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
Predictors of improved clinical practice
• Automatic provision of decision support as part of clinical workflow (OR=112.1; p<0.00001)
• Provision of recommendations rather than just assessments (OR=15.4; p=0.019)
• Provision of decision support at the time and location of decision making (OR=7.1; p=0.026)
• Computer based decision support (OR=6.3; p=0.029) • Of 32 systems possessing all four features, 30 (94%)
significantly improved clinical practice. Kawamoto et al, BMJ, 2005
Ydintiedot
Potilas
Hoidon antaja
Hoitojakso,
- tapahtuma
tai palveluketju
Tunnistetiedot
Hoitotyö
Toimenpiteet
Toimintakyky
Tutkimukset
Ongelmat ja
diagnoosit
Fysiologiset
mittaukset
Terveyteen vaikuttavat tekijät
Suostumus Hoitotahto
Jatkohoitoa
koskevat tiedot
Elinluovutus -
testamentti
Apuvälineet
Lausunnot
ja todistukset
Hoitoprosessin tiedot
Yhteenveto
Lääkitys
Muut tiedot
Core data
Patient PIC
Caregiver
treatment episode
- individual or treatment chain
ID data
Nursing care
Procedures
Function
Investigations
Problems and
diagnoses
Physiological
measurements
Health factors
Agreements
Future treatment
plans
Organ donor status-
Aids
Certificates
The care process
Summary
Medication
Other info
Kristiina Häyrinen ja Jari Porrasmaa, 2006
Care testament
EBMeDS - organization
Product projectLead groupProject managerProject secretaryProject group
Pilot projectsPilot lead groupProject managerProject secretaryProject groups (2)
Study projectStudy lead groupSeniorJuniorStudy group
Medical SocietyDuodecim
Tekes
KymSHPP-SSHP
Medical PublisherDuodecim
Advisory committeeStakeholders
Rohto University of TampereSchool of Public Health
FinOHTA
Pro-Wellness
project plan 2005-06
EBMeDS timetable
2005 2006 2007 2008 2009
Project planning
Databases for drug treatment
EBM scripts and guidelines
Pilot projects and technical development
EBMeDS Study
Implementations
Testing
In practiceN
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Pro
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Vir
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EBMeDS study project
• Baseline study at pilot sites 2006-2007– Survey
• Health Care professionals
– Interviews • Health Care Managers• IT-experts
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 2008;25:162-7.
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 2008;25:162-7.
Results: Barriers of CDS
• Previous problems with health care IT• Potential harm to doctor-patient
relationship• Threats to clinician’s 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 2008;25:162-7.
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 processesVaronen 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 2008;25:162-7.
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.
EBMeDS RCT study
Ri/Ni
time0
VHC VHC VHC VHC
Ri/Ni
Randomisation─── patient, whose reminders are blocked (recorded only in log files)------ patient, whose reminders are shown to his/her physician or nurseVHC = virtual health checkR = 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.
exclude:*occupational health
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.
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),
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).
• 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.
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.
1. Kortteisto T, Kaila M & Komulainen J. Päätöksentuen tutkimus (EBMeDS). Stakes: Tutkimuspaperit 18/2006
2. Kortteisto T, Kaila M, Komulainen J. & Rissanen P. Esimiesten kokemuksia sähköisistä potilaskertomusjärjestelmistä: Päätöksentuki-tutkimuksen (EBMeDS) haastattelut lähtötilanteessa. Stakes: Tutkimuspaperit 14/2007
3. Varonen H, Kaila M, Kunnamo I, Komulainen J, Mäntyranta T. Tietokoneavusteisen päätöksentuen avulla kohti neuvovaa potilaskertomusta. Duodecim 2006:122:1174-81.
4. Kortteisto T, Mäntyranta T, Komulainen J, Kaila M. Lääkäreillä vielä paljon sanottavaa sähköisistä potilaskertomusjärjestelmstä. Suom Lääkäril 2008;63:1297-301
5. 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.
6. 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 2008;25:162-7.
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.
8. Kaila, Kortteisto, Kunnamo, Nyberg, Jousimaa, Komulainen, Mäkelä, Mäntyranta, Varonen, Rissanen. Virtual health check – a new automated quality measure for specified patient populations. Käsikirjoitus
9. Miettinen M. Gradu 2009 /JY. TIEDON LAATU TERVEYDENHUOLLON SÄHKÖISISSÄ POTILASTIETOJÄRJESTELMISSÄ
10. Korhonen H. Gradu 2009/Tay. TYÖN PIIRTEIDEN YHTEYS TERVEYDENHUOLLON AMMATTILAISTEN HOITOSUOSITUSASENTEISIIN
1. Homogeneity of health care (culture and value basis)2. Municipal ownership of all (public) health care
facilities3. Lack of any significant competition in health care4. 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”
Specific features that have promoted acceptance and wide use of guidelines in Finland
Lääkärin käsikirja (YKT) 1218 EBM Guidelines /concise & primary health care
Käypä hoito 278 Current Care / thorough & all of health care
Hoidon perusteet 247 National criteria for non-emergency care