1. CDSS CLINICAL DECISION SUPPORT SYSTEMS By: Dr Alireza Kazemi

2. Abbreviations ME = Medication Errors Any preventable event that may cause or lead to inappropriate medication use or patient harm CPOE = Computerized Physician Order Entry POE: Physician Order Entry NOE: Nurse Order Entry DSS = Decision Support System CDSS = Clinical Decision Support System 2

3. Definition of CDSS Clinical decision support systems (CDSS) are computer systems designed to impact clinician decision making about individual patients at the point in time that these decisions are made Aim make data about a patient easier to assess foster optimal problem-solving, decision-making, and action by the human

4. History

5. Types of decision support systems Knowledge-based CDSS Knowledge base Clinical inference (inference model) (reasoning engine) Interface non-knowledgebase CDSS 1) Trained Artificial neural networks Neurodes (=neurons in human body) weighted connections (=nerve synapses in human body) 3 layers; input output and hidden input -->receive data output --> communicate results hidden --> process incoming data and determine results ANN process patterns in patient's data to derive associations between patient's signs, symptom or lab tests and a diagnosis learn from examples derived from large data Advantages and disadvantages? 2) untrained Genetic algorithms Recombination components of random sets of solution are evaluated best ones are kept (Fitness model)

6. Concept of Knowledge-based Decision Support System 6 Database Software UI Knowledgebase User Client Lisa Inference engine

7. Background about medication errors In USA 7000 deaths / year happen due to medication errors 1 > 56% of medication errors happen in the prescription phase 2 In newborns, dosing errors are the most frequent type of medication errors 3 10-fold and even greater dosing errors are frequently reported in neonates 4,5,6 CPOE has been effective in reducing dosing errors in neonates 7,8 No previous study has investigated the effect of CPOE on reducing dosing errors in middle-income countries 7

8. Aim To find an appropriate model for adopting computerized provider order entry with clinical decision support functionalities in Iran, and evaluate the effect of the implemented model on patient safety 8

9. Project overview (Activities) 9 Situational analysis Design Implementation Test Adaptation Evaluation POE vs. NOE (Study IV) POE Traditional system Traditional vs. POE (Study II) POE NOE Quantitative Qualitative (Study I) (Study III) Needs assessment

10. Project overview (structure) Traditional vs. POE Qualitative Study I Quantitative Study II POE vs. NOE Qualitative Study III Quantitative Study IV 10 No DSS POE DSS1 DSS2 Traditional Study II Ext. Study II Traditional POE NOE POE POE & NOE DSS2

11. Hamadan, North West Iran, 1,700,000 inhabitants. 11 General setting 300 Km

12. Study I Aim To analyze the traditional prescription system To assess prescribers’ needs prior to implementation of CPOE and DSS Method (qualitative) Setting: Ekbatan Hospital FGD, 8 experts  Interview guideline Semi-structured interviews, 19 prescribers (interns, residents and attending) On-looker observations  40 h Analysis method Inductive thematic analysis 12

13. Study 1 – Results Traditional prescription system Physician-centered, top-bottom hierarchy No pharmacist is involved Reduction of dosing errors have priority CPOE and DSS? Physicians are positive towards CPOE Feedback to physicians, not nurses (they preferred POE) System should improve patient safety and prescription accuracy to encourage physicians to continue performing order entry Prescribers should not become DSS dependent for appropriate calculation of dosages (not affordable everywhere in Iran) Pilot in one of the most relevant wards for dosing errors 13

14. Studies II, III, IV - Setting Besat, a 400-bed tertiary-care referral teaching hospital Besat's neonatal ward is a 17-bed clinical ward 14

15. Dosing DSS architecture 15

16. 16 Wt= 3.25 Kg 3 rd day of Life 35 q12h Amikacin 30 q12h 10 * 3.25 = 32.5 q12h

17. 17 A s d h f ö a s h j A s d f l a ’ s d f l ö a s k A s ’ d l ö f k A s ’ d l f k a ’ s l ö A ’ s d ö l f k ’ a s d ö l f k ’ a g h j f g h j f g h j f Asdhföashj Asdfla’sdflöask As’dlöfk As’dlfka’slö A’sdölfk’asdölfk’a ghjfghjfghjf

18. Study II Aim To evaluate the effect of two interventions on reducing non- intercepted dosing errors: I) Physician order entry II) Dose decision support system 18

19. 19 Traditional (Period 1) POE w/o DSS (Period 2) POE+DSS1 (Period 3) No intervention POE DSS1 Days 2007 (May - July) 2007 (July - Oct) 2007 (Oct - Dec) Time Inter- ventio n Order entry DSS Func. N/A Event Study II (design)

20. Results – Study II (non-intercepted dosing errors) 20 P<.001 95% CI

21. Extension of Study II Aim To evaluate the effect of the DSS design on reducing non- intercepted dosing errors 21

22. 22 POE+DSS1 (Period 3) POE + DSS2 (Period 4) DSS1 2007 (Oct – Dec) Dec 2007 – Feb 2008 Time Inter- ventio n Order entry DSS Func. Event Ext. Study II (design) DSS2 Freq. First-time order + change in dosing criteria All erroneous orders Explanations

23. Results – Ext. Study II (non-intercepted dosing errors) 23 Conclusion of Study II POE with dosing decision support functionalities is effective in reducing non- intercepted dosing errors, especially when explanations are available in the warning and alerts appear in every erroneous order. P<.001 95% CI

24. 24  Duplications/redundancy  Dosing errors  Patient Safety  User Acceptability Asdhföashj Asdfla’sdflöask As’dlöfk As’dlfka’slö A’sdölfk’asdölfk’a ghjfghjfghjf Asdhföashj Asdfla’sdflöask As’dlöfk As’dlfka’slö A’sdölfk’asdölfk’a ghjfghjfghjf

25. Study III Aim To investigate care providers’ perceptions about the advantages and disadvantages of the two implemented models I) Physician Order Entry (POE) II) Nurse Order Entry (NOE) Methods Semi-structured interviews  attendings, residents and nurses After establishment of the POE method (6 months after start) After establishment of the NOE method (6 months after start) On-looker observations during the two periods Analysis method Inductive thematic analysis 25

26. Study III - Results ThemePOENOE Patient safety ( dosing errors)GOOD Duplication / redundancyExists (less)Exists Spent time on order entryHigh for physicians and nurses Less for both, especially physicians Collaboration & communicationLessMore Feasibility & continuityLessMore User acceptabilityStrong resistanceBetter acceptability Transferability in IranVery lowHigh Overall (Viability in the context)Less viableMore viable 26

27. 27  User acceptability  Continuity  Dosing medication errors

28. Study IV Aim To investigate whether NOE is at least as effective as POE in reducing non-intercepted dosing errors 28

29. 29 POE+DSS2 (Period 4) NOE + DSS2 (Period 5) POE+DSS2 Dec 2007 – Feb 2008 2008 (July-Sep) Time Inter- ventio n Order entry DSS Func. Event Study IV (design) NOE+DSS2 Freq. All erroneous orders Explanation

30. Results – Study IV (non-intercepted dosing errors) 30 P<.001 95% CI

31. Study IV – Results (severity) 31 Max registered dose Number of two-fold or greater overdose errors 95% CI

32. Conclusion - Study IV NOE+DSS2 is as effective as or even more effective than POE+DSS2 in reducing the rate and severity of non-intercepted medication dosing errors among neonates. 32

33. Thesis conclusion Dosing decision support systems can improve patient safety in neonatal wards. However, in order to successfully adopt a CPOE system, selection of order entry method and design of the DSS should be performed in close collaboration with care providers and with consideration for limitations in the local context. 33