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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

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Minna Kaila, MD, PhD, Pediatric Allergist Adjunct Professor /University of Tampere

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  1. 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 +358 50 523 2021 No commercial conflicts of interest

  2. EBMeDS: aim to develop, implement and evaluate a generic clinical decision support system.

  3. 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 2006:122:1174-81.

  4. 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

  5. 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

  6. Ydintiedot Core data The care process Hoitoprosessin tiedot ID data Tunnistetiedot Ongelmat ja Problems and Function Toimintakyky Patient PIC Potilas diagnoses diagnoosit Investigations Tutkimukset Hoidon antaja Caregiver Fysiologiset Physiological Toimenpiteet Procedures measurements mittaukset treatment episode Hoitojakso, Hoitotyö Nursing care Lääkitys Medication - - individual or treatment chain tapahtuma tai palveluketju Apuvälineet Aids Yhteenveto Summary Terveyteen vaikuttavat tekijät Health factors Other info Muut tiedot Elinluovutus Organ donor status - - Lausunnot Certificates Jatkohoitoa Future treatment testamentti ja todistukset koskevat tiedot plans Hoitotahto Suostumus Agreements Care testament Kristiina Häyrinen ja Jari Porrasmaa, 2006

  7. Medical Society Duodecim Medical Publisher Duodecim KymSHP P-SSHP Product project Lead group Project manager Project secretary Project group Pilot projects Pilot lead group Project manager Project secretary Project groups (2) Study project Study lead group Senior Junior Study group Pro- Wellness Tekes Rohto Advisory committee Stakeholders FinOHTA University of Tampere School of Public Health EBMeDS - organization project plan 2005-06

  8. In practice Implementations Testing EBMeDS Study Pilot projects and technical development EBM scripts and guidelines Databases for drug treatment Project planning 2005 2006 2007 2008 2009 Virtual health check Guidelines and DB National EPR archive National EPR legislations National EPR definitions Project funding EBMeDS Demo Diabetes EBMeDS timetable

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

  10. 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.

  11. 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.

  12. 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.

  13. 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 2008;25:162-7.

  14. 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.

  15. EBMeDS RCT study exclude: *occupational health VHC VHC VHC VHC Ri/Ni Ri/Ni 0 time 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.

  16. 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.

  17. 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),

  18. 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).

  19. 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.

  20. 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.

  21. In practice Implementations Testing EBMeDS Study Pilot projects and technical development EBM scripts and guidelines Databases for drug treatment Project planning 2005 2006 2007 2008 2009 Virtual health check Guidelines and DB National EPR archive National EPR legislations National EPR definitions Project funding EBMeDS Demo Diabetes EBMeDS timetable

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

  23. 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-tutkimuksen (EBMeDS) haastattelut lähtötilanteessa. Stakes: Tutkimuspaperit 14/2007 • Varonen H, Kaila M, Kunnamo I, Komulainen J, Mäntyranta T. Tietokoneavusteisen päätöksentuen avulla kohti neuvovaa potilaskertomusta. Duodecim 2006:122:1174-81. • Kortteisto T, Mäntyranta T, Komulainen J, Kaila M. Lääkäreillä vielä paljon sanottavaa sähköisistä potilaskertomusjärjestelmstä. Suom Lääkäril 2008;63:1297-301 • 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 2008;25:162-7. • 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 POTILASTIETOJÄRJESTELMISSÄ 10. Korhonen H. Gradu 2009/Tay. TYÖN PIIRTEIDEN YHTEYS TERVEYDENHUOLLON AMMATTILAISTEN HOITOSUOSITUSASENTEISIIN

  24. 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”

  25. 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

  26. All guidelines are available in one search engine to 98 % of Finnish physicians as a part of Physician’s Database with 43000 documents

  27. 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 >20 million/year

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