One or More Errors in 67% of the IV Infusions: Insights from a Study of IV Medication Administration Presented by: Marla Husch Northwestern Memorial Hospital
Northwestern Memorial Hospital Chicago, Illinois 750-bed tertiary care academic medical center providing a full range of services except pediatrics NMH Strategic Goals Best Patient Experience Best People Exceptional Financial Performance
Northwestern Memorial Hospital: Pharmacy Services Pharmacy services available 7 days/week, 24 hours/day Decentralized pharmacies: 6 satellites Pharmacy prepares all IV infusions except standard IV fluids which are floor-stocked items
What is needed Data to identify common causes of IV administration errors Studies investigating the frequency, cause, and severity of adverse events associated with IV infusion devices Ongoing research around evolving technology: positive and negative effects on IV administration
IV Pump FMEA : Failure Modes and Recommendations Failure Modes Recommendations Error in programming (RPN = 900) Error in initial set-up and handling of pump (RPN = 800) Inadequate patient assessment (RPN = 700) Develop policy and procedure for pump use Universal orientation through the academy Annual nursing competencies for utilizing pumps Surveillance to better understand trends Apply FMEA process to equipment prior to purchase
Goals of a Point of Prevalence Day to Validate the FMEA Team s Assessment of Risks Determine the actual frequency and potential severity of discrepancies associated with infusion pump orders, medications, labels, and programming, which may reflect errors, at Northwestern Memorial Hospital, on a first shift of a high-volume day among virtually all inpatients. Determine the potential impact of smart pumps on IV administration errors
Hypotheses The actual number of errors exceeds the number reported through incident reports IV pump errors are high-risk Programming errors occur frequently and have the potential to cause harm Smart pump technology will mitigate most IV administration errors associated with IV pumps
Methodology Observational approach Data collection criteria: First shift (0800-1700) on a high volume day All IV pumps in use on inpatient care units Exceptions include: Operating room s, Emergency Department, Postpartum, Labor and Delivery, and Recovery
Methodology Bedside: Documented the medication, rate displayed on the infusion pump, and rate documented on the fluid/medication label Medical Record: Compared the information documented at bedside to what was prescribed in the medical record Documented discrepancies and evaluated associated potential harm, using the NCC MERP scale Retrospective Potential impact of smart pump technology without an interface with other systems Three investigators (two nurses and a pharmacist) rated all rate deviation errors as to whether or not smart pump technology would have prevented the error (yes or no)
Error definition Any preventable event that may cause or lead to inappropriate IV medication use via an IV pump or patient harm while the medication is in the control of the healthcare professional, patient or consumer 3 Such events may be related to professional practice, healthcare products, procedures and systems including order communication, product labeling, compounding, dispensing, administration, education, monitoring and use 3 3. National Coordinating Council for Medication Error Reporting and Prevention (NCC MERP). NCC MERP Index for Categorizing Medication Errors. Available at http://www.nccmerp.org. Accessed January 20, 2003.
Error types Rate deviation Incorrect IV medication Delay of rate change or medication change No rate documented on label Incorrect rate documented on label Unauthorized medication Patient Identification error
Total census: 669 patients Results Total # patients eligible: 486 Total # patients with pump(s): 286 Total number of medications observed: 426
Results: Discrepancies Medications observed = 426 Observations with no discrepancy = 145 (34%) Observations with 1 or more discrepancies = 281 (66%) 426 Observations 390 Discrepancies 0.92 Discrepancies per observation 1.3 discrepancies per patient with an IV pump
Results: Types of Discrepancies n = 426 Patient identification error 13% Rate deviation 9% Incorrect IV medication 3% Unauthorized medication 16% Delay of therapy 1% Incorrect rate on label 4% No rate on label 46%
NCC MERP Index: Harm Category A B C D E F G H I Definition Capacity to Cause Error Error Did Not Reach Patient Error Reached Patient, No Harm Error Reached Patient, Required Monitoring Temporary Harm Temporary Harm, Prolonged Hospitalization Permanent Harm Intervention Necessary to Sustain Life Death National Coordinating Council for Medication Error Reporting and Prevention (NCC MERP). NCC MERP Index for Categorizing Medication Errors. Available at http://www.nccmerp.org. Accessed January 20, 2003
Results: Harm Number, frequency and potential severity of each type of error Type of error Total (n=389) Frequency per medication observations (n=426) * NCC MERP severity rating No rate on label 195 46% 195 Unauthorized medication Patient identification error C D E F 68 16% 65 3 55 13% 55 Rate deviation 37 9% 29 4 1 3 Incorrect rate on label 16 4% 16 Incorrect medication 14 3% 11 2 1 Delay of rate or medication change 4 1% 2 2 Total (%) n=389 373 (96) 8 (2) 5 (1) 3 (1) Note: * Percents in this column do not add to 100 because some medications had more than one error.
Error examples Medication and dose infusing via IV pump Nicardipine 25mg/250mL @ 15mg/hour Medical record order Start Nicardipine now Hydromorphone 0.2mg/mL @ 1 mg every 30 minutes Hydromorphone 2mg every 30 minutes (verbal order never documented in medical record)
Error examples Medication and dose infusing via IV pump Medical record order Hydromorphone 1mg/mL @ 2mg every 15 minutes prn No new order upon transfer to ICU in medical record Amiodarone 0.5mg/minute Hydromorphone 1mg/mL @ 0.5mg every 15 minutes prn Order written 5 days prior: Amiodarone 1mg/min x 6 hours then 0.5mg/min x 18 hours Hydromorphone 0.2mg/mL @ 1 mg every 15minutes
Error examples Medication and dose infusing via IV pump Medical record order Heparin 200units/hour (2mL/hour) Heparin 1300units/hour (13mL/hour) Dopamine 800mg/250mL @ 2 mcg/kg/hour Dopamine 200mg/250mL @ 2mcg/kg/hour 0.9 normal saline @ 20mL/hour 0.9 normal saline @ 250mL/hour
Risk Data and Prevalence Day Data Programming and wrong medication discrepancies Risk Data 48 discrepancies reported over 2 years on 1st shift Prevalence Day Data 55 discrepancies observed in one day (January 29, 2003) on 1st shift
Study Limitations It is possible that the number of IV administration pump errors that occurred on the day that data was collected was different than other days Because most of the errors were intercepted by research team members the potential harm of the error if it had continued indefinitely was estimated based on our best clinical judgment The likelihood that smart pump technology would have prevented an observed error was based on our current knowledge of how this technology functions. Our knowledge about the benefits and limitations of this technology will continue to evolve, as more information becomes available
Conclusions The actual number of discrepancies associated with IV infusion devices exceeds the number reported through incident reports 7 more errors were observed in one day than were reported through incident reports in two years IV medication errors associated with infusion pumps occur frequently, have the potential to cause harm and are epidemiologically diverse 16 (4%) of the discrepancies identified had the potential to cause harm The extent and nature of these errors remains mostly unknown due to the lack of research Husch M, Sullivan C, Rooney D, et al. Insights from the sharp end of medication errors:implications for infusion pump technology. Qual Saf Health Care 2005;14:80-6.
Conclusions Rate deviation errors occur and have the potential to cause harm. Pumps equipped with software that checks programmed doses against preset limits specific to a drug and clinical location can decrease the likelihood of a small number of these medication errors. 37 (9%) of the 426 observed medications were programming discrepancies and 8 (21%) had the potential to cause harm Smart pumps with dose error reduction systems will have limited impact on patient safety unless they are fully integrated with information systems such as electronic medical record (EMR), computerized prescriber order entry(cpoe), bar-coded medication administration (BCMA), and the pharmacy information system (PIS) 97.3% of rate deviation errors were rated unlikely to be prevented by smart pump technology and only 0.003% of errors overall would have been prevented by smart pump technology without an interface to other systems Husch M, Sullivan C, Rooney D, et al. Insights from the sharp end of medication errors:implications for infusion pump technology. Qual Saf Health Care 2005;14:80-6.
Were the conclusions correct?
Side-by-side comparison of results from 3 point-prevalence studies No DERS, No CPOE (2004) No DERS, CPOE (2006) DERS, CPOE (2007) Total Infusions Observed 426 461 450 Total Patients 286 294 266 Rate deviation error 37 42 33 Unauthorized error 68 32 62 Patient Identification error 55 3 2 Incorrect medication error 14 8 6 Delay of rate or medication change 4 1 3 Harm D 8 na 13 Harm E 5 na 3 Harm F 1 na 0
Percent utilization Guardrails utilization 100.00% 80.00% 60.00% 40.00% 20.00% 0.00% Guardrails Utilization by Profile and by Month Anesthesia Critical Care/ED Hematology/Oncology Labor and Delivery Med/ Surg Neonate <2kg Profile Neonate > 2kg Prentice 14 Jul-07 Aug-07 Sep-07 Oct-07 Nov-07 Dec-07 Jan-08
Consequences of alert fatigue and invalid alerts Clinicians are inundated with alerts of low or no clinical significance, leading to alert fatigue, high overrides rates and the potential to override even important alerts Profile Drug Name and Therapy Programmed Dose Rate Log Time Action Taken Patient Id Critical Care/ED fentanyl CONTINUOUS, 9990 mcg/h 999 ml/h 7/11/2007 3:38:53 Override 93721892 " fentanyl CONTINUOUS, 9990 mcg/h 999 ml/h 7/11/2007 3:38:55 93721892 " fentanyl CONTINUOUS, 150 mcg/h 15 ml/h 7/11/2007 3:39:19 93721892 Critical Care/ED fentanyl CONTINUOUS, 9990 mcg/h 999 ml/h 7/11/2007 3:42:13 Override 93721892 " fentanyl CONTINUOUS, 9990 mcg/h 999 ml/h 7/11/2007 3:42:15 93721892 Critical Care/ED fentanyl CONTINUOUS, 2000 mcg/h 200 ml/h 7/11/2007 3:42:47 Override 93721892 " fentanyl CONTINUOUS, 2000 mcg/h 200 ml/h 7/11/2007 3:42:48 93721892
What s in the literature? Alert fatigue as it pertains to Computerized Physician Order Entry (CPOE) Heish et al found 80% of allergy alerts were overridden in 1150 patients 1 At VA Puget Sound, Payne et al reports 69% of critical drug interactions were overridden and 88% of allergy-drug interaction alerts are overridden 2 At Beth Israel Deaconess Medical Center, Weingart et al, reports that physicians overrode allergy-drug alerts at a rate of 91.2% and highseverity drug-drug interactions at a rate of 89.4% 3 1 Hsieh TC, K. G., Jaggi T, Hojnowski-Diaz P, Fiskio J, Williams DH, Bates DW,Gandhi TK. (2004). Characteristics and Consequences of Drug Allergy Alert Overrides in a Computerized Physician Order Entry System. J Am Med Inform Assoc, 11, 482-491. 2 Payne TH, N. W., Hoey P. (2002). Characteristics and override rates of order checks in a practitioner order entry system. Proc AMIA Symp, 602-606. 3 Weingart, S. N., Toth, M., Sands, D. Z., Aronson, M. D., Davis, R. B., & Phillips, R.S. (2003). Physicians' Decisions to Override Computerized Drug Alerts in Primary Care. Arch Intern Med, 163(21), 2625-2631.
What s in the literature about harm related to overrides? Heish et al also found that about 1 in 20 drug-allergy overrides resulted in a significant adverse drug event (ADE) Hsieh TC, K. G., Jaggi T, Hojnowski-Diaz P, Fiskio J, Williams DH, Bates DW,Gandhi TK. (2004). Characteristics and Consequences of Drug Allergy Alert Overrides in a Computerized Physician Order Entry System. J Am Med Inform Assoc, 11, 482-491.
percent Seconds to overriding an alert n=21,394 100% 80% 60% 40% 20% 0% 76% 14% 6% 4% percent 1-2 seconds 3-5 seconds 6-10 seconds >10 seconds
Conclusions More research is needed to understand ideal use of decision support Any stand alone system or device will fail to have maximum impact on patient safety and staff satisfaction without appropriate integration between systems