5th International Conference on Well-Being in the Information Society, WIS 2014, Turku, Finland, August 18-20, 2014

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1 5th International Conference on Well-Being in the Information Society, WIS 2014, Turku, Finland, August 18-20, 2014 EVALUATION OF INTRAVENOUS MEDICATION ERRORS WITH INFUSION PUMPS Eija Kivekäs, MSc, RN, Doctoral student Kaisa Haatainen, PhD, Patient safety manager Hannu Kokki, PhD, MD, Kaija Saranto, PhD, RN, FACMI, FAAN Outline Greetings from the University of Eastern Finland, RECEPS and Kuopio University Hospital Background of the research project The study process with the first implications Discussion 1

2 Research Centre for Comparative Effectiveness and Patient Safety (RECEPS) Multidisciplinary Centre 1. Faculty of Social Sciences and Business Studies 2. Faculty of Health Science 2014 Research Centre for Comparative Effectiveness and Patient Safety (RECEPS) Established in August 2013 Jari Heiskanen, MSc Project coordinator (effectiveness) Eija Kivekäs, MSc Project coordinator (patient safety) Risto Roine Prof. in Patient safety from Jan 2014 Kaija Saranto Acting prof in Patient safety (Autumn 2013) Anna-Maija Tolppanen Development director, Adjunct prof. (on maternity leave) Irma Nykänen, PhD, Deputize for a development director on maternity leave

3 Research Centre for Comparative Effectiveness and Patient Safety (RECEPS) Mission We provide high-quality research on effectiveness and patient safety of different interventions and operational models. The research findings are communicated with healthcare professionals and the society. Increasing the awareness of the concepts of effectiveness and patient safety Rational decision-making Sustainable solutions Evaluation of Intravenous Medication Errors with Infusion Pumps The Research Project

4 Background (1/3) Medication safety in hospitals depends on the successful execution of a complex system of scores of individual tasks, which are prescribing, preparing, dispensing, transcribing and monitoring the patient s response. Patient safety is a matter of major concern that involves every health professional. Emerging technologies such as smart pumps can diminish medication errors as well as standardize and improve clinical practice with the subsequent benefits for patients. Background (2/3) A technology provides medication error-reduction capabilities via - Programmed limit alerts with audio/visual feedback to staff regarding erroneous orders - Improper dose calculations or programming errors - Smart intravenous pupms allowed the detection of many errors that would have been extremely difficult to find through any other mechanism However, smart pumps have not always achieved their potential, and important intravenous errors still persist. 4

5 Background (3/3) What is a Smart Infusion Pump? A smart infusion pump is computer-controlled and it can be programmed to deliver predefined amounts of medicines, nutrients and other fluids. Smart pump keeps the electronic records of infusion, which can be captured in the pumps software (data collection). Infusion pumps support the five rights of medication safety right medication, right dose, right time, right route and right patient. While smart infusion pump alone may prevent pump programming errors, they cannot prevent you giving the wrong drug or the wrong concentration, or giving the drug to the wrong patient. Previous Study (1/2) Adverse events Incidents reports in a Finnish study showed that 51% of over web-based incident reports concerned medication. The most common incidents were errors in documenting, dispensing and administering (Ruuhilehto K. et al. 2011). Observed intravenous medications illustrated several errors associated with orders, documentation, labeling and patient identification (Husch et al. 2005). Compliance Insufficient understanding about technology surfaced from a systematically reviewed the pre-existing literature on electronic health technologies and their impact on the quality and safety of health care delivery (Black et al. 2011). Identified lack of user compliance with soft alerts. Poor caregiver compliance with the drug library and dosage limits may have been explained the lack of advantage of smart pumps decision support (Hertzel & Sousa 2009, Rothschild et al. 2005). 5

6 Previous Study (2/2) Technology A new protocol using a combination of smart pumps, standard drug concentrations and human engineered medication labels decreased the infusion error rate decreased (3.1 to 0.8 per 1000 doses) from the pre-intervention to the post-intervention (Larsen et al. 2005). Workprocess Only a small reduction in overall dosing errors, a lager reduction in pump-related error. 9 / 10 pump programming errors occurred because users did not use the pump software. A high frequency of programming when a drug was not selected from the drug library (Adachi & Lodolce 2005, Eckel et al. 2006). Aims of the Project The research project Will identify the key issues of the use of infusion pumps and Develop strategies that will improve the prevention of intravenous medication errors (before-after comparison) des_1/6/1576/ cw/index.html 6

7 Project Plan Year 1 (2014) Year 2 ( ) Year 3 ( ) Phase 1 Phase 2 Phase 3 1. Obtain a statement of the Comittee on Research Ethics 2. Development of data collection form 3. Observer training 4. Initial measurement of intravenous medication errors 1. Observation data analysis 2. Face-to-face-meeti ng for developing recommendations 3. Interventions to reduce intravenous medication errors 1. Second measurement of intravenous mediacation errors 2. Data analysis 3. Face-to-face-meeting for developing recommendatons 4. Publi cation of a fi nal report Bates, D.W. (2012) A National Study of Intravenous MedicationErrors: Understanding How to Improve Intravenous Safety with SmartPumps Data Collection Observation of the intravenous medication administration processes at each of five wards were made four days, eight hours per day. Two to four observers (registered nurses and pharmacists) went to a ward and conducted observation on patients to capture medication errors. Observers recorded all data requested on the data collection form. logy/2012/07/01/urology-andurgent-care-week-9/ 7

8 Operational definitions of medication errors types Error Type Definition 1. Wrong Dose The same medication but the dose is different from the prescribed order. 2. Wrong Rate A different rate is displayed on the pump from that prescribed in the medical record. Also refers to weight based doses calculated incorrectly including using a wrong weight. 3. Wrong Concentration An amount of a medication in a unit of solution that is different from the prescribed order. 4.Wrong Medication A different fluid/medication as documented on the IV bag label is being infused compared with the order in the medical record. 5. Known Allergy Medication is prescribed/administered despite the patient had a known allergy to the drug. 6. Omitted Medication The medication ordered was not administered to a patient. 7. Delay of Rate or Medication/Fluid Change An order to change medication or rate not carried out within 4 hours of the written order per institution policy. 8. No Rate Documented on Label Applies both to items sent from the pharmacy and floor stocked items per institution policy. 9. Incorrect Rate on Label Rate documented on the medication label is different from that programmed into the pump. Applies both to items sent from the pharmacy and floor stocked items. 10. Patient Identification Error Patient either has no ID band on wrist or information on the ID band is incorrect. 11. No Documented Order Fluids/medications are being administered but no order is present in medical record. This includes failure to document a verbal order. Bates, D.W. (2012) A National Study of Intravenous Medication Errors: Understanding How to Improve Intravenous Safety with Smart Pumps National Coordinating Council for Medication Error Reporting and Prevention NCC MERP 8

9 Sample and Setting Maternity ward (a pilot) The general medical ward The medical ICU The general surgical ward The surgical ICU Summary (without pilot) Patient patients who refused Infusion Medication 100 % 58 % 41 % 80 % 82 % Fluid 61 % 59 % 20 % 18 % Infusion pump 11 % 34 % 83 % 16 % 83 % - No infusion pump 89 % 60 % 17 % 84 % 10 % - Empty medication 'bag' 6 % 20 % January (a pilot), February and March 2014 Results (1/4) There was variation with regard to adherence of using a patient identification. The documentation of allergy varied between wards. The highest score was 56 % of observed patient (130/203) whose allergy was recorded to EPR system. Observation target Identification band existence Wards % Information of allergy - was documented % m/health_wristband_printer s.html 9

10 Results (2/4) Medication documenting differed between units - Each prescription was found to an electronic patient record - Wards used several and overlapping documentation forms and paper-based form to manage medication information. Results (3/4) Violations of medication policies regarding labeling were the frequent error types. Medication infusion Patient's name Name of preparer (nurse or pharmacist) Drug Dose or content of drug Composition of infusion (volume) Date / time / ward Finding % / ward 2 89 % / ward 0 89 % / ward 100 % / ward % / ward 8 89 % / ward % / ward 10

11 Results (4/4) Data collection in the five study units further indicated that errors were rated as A to B in the NCC MERP harm index, - Indicating that - Capacity to cause an error (A) - Error occurred but did not reach the patient (B) /01/19/kaiser-gets-greeninfusion-safer-iv-equipment Conclusion In this study, the most remarkable result was the review of medication processes which varied significantly across the wards. Interventions will be rated according to proportion of errors that might be potentially preventable through that strategy. The interdisciplinary team will use the expert recommendation to develop an intervention, which may or may not be the same at all wards. The re-measurement of iv. medication errors will be performed at all wards end of The effects of smart IV pump strategic interventions will be evaluated by comparing the pre and post intervention error data. 11

12 Thank you 12