Detection,Reporting and Management of Adverse Drug Reactions Pharmacy College...
PharmAC05 PEARLS- Optimizing Medication Warnings
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Fall ACs 2014
3. • Rockford Memorial Hospital
• Licensed Beds - 396
• Annual Discharges - 12,532
• Outpatient Visits - 275,793
• Emergency Visits - 51,291
• Level 1 Trauma Center
• Pediatric Intensive Care Unit
• Level III Neonatal Intensive Care Unit
• Rockford Health Physicians
• 10 Clinic Locations
• Visiting Nurses Association
• Home Health
• Durable Medical Equipment
• Hospice
4. Epic Implementation Timeline
• Ambulatory Clinics Go-Live
– April - October 2011
• OpTime Go-Live
– June 2011
• Inpatient Go-Live
– April 2013
• Upgrade to v2014
– June 2014
5. Learning Objectives
• Define Continual Improvement
• Understand the Core Principle of Continual Improvement
• Describe the Purpose of Continual Improvement
• How to Use Continual Improvement to Optimize Medication
Alerts
• Random Pearls
6. House Keeping
• FDB is Our Data Vendor
• Only Three FIS Records
– Inpatient
– Outpatient
– Pharmacist
• Almost All Warning Modules Turned On for All Users
– Two Exceptions: Drug-Food & IV Compatibility
7. Continual Improvement - Defined
• Change that is focused on increasing the
effectiveness of a process in incremental steps.
• “Getting better all the time”
8. Core Principle
• Understanding the Process - Self Reflection
– Meet with End Users
– Watch the Workflow
– Use Standard Epic Reporting Tools
• Medication Warning Statistics Report
– Do We Have a Problem?
11. Purpose of CI
• Identification and Reduction of Suboptimal
Processes.
• Suboptimal?
– Noisy alerts
– Repetitive reminder of known information
– Superfluous
– Overly sensitive
– Alert fatigue
12. Identification
• Medication Warning Statistics Report
– Improved in 2014
– More Improvement Coming in 2015 (Ask your Epic TS)
• Direct Feedback From End Users
• Watch End User Work
• Prioritize Effort
– We found it helpful to look at the number of alerts by category
» Drug-Disease (38%)
» Duplicate Therapy (19%)
» Drug-Allergy (14%)
» Dose (8%)
» Duplicate Med (8%)
» Other (3%)
14. Reduction
• Use Standard Epic Tools
– Interaction Settings Editor (FIS)
• Drug-Disease – Absolute and Relative alerts on
• Drug-Allergy – Active and Inactive ingredients on
– Duplicate Therapy Allowance
• Duplicate Therapy – Zero allowances
– Order Panel and Order Set Alert Suppression
– Dosing Rule Editor
– Review Pregnancy/Lactation Rules
17. Continual Improvement
• Using CI to Optimize
– Look at the Data Every Month
• Medication Warnings Statistic Report
– PharmACO5 FallAC-2014 –
» Kiersten Miller, PharmD BCPS Allina
– PharmAC 06 SpringAC-2012
» Nicole Goodnough PharmD and Brad Gordon MD
HealthPartners
• “Pain Factor”
– UGM-259 – UGM-2013
– Jeff Chalmers PharmD and Joe Gagliardi Cleveland Clinic
18. Continual Improvement
• Using CI to Optimize
– Prioritize and Focus
• Top 20 Alerts are likely causing Most of your Pain!
– UGM-298 – UGM 2011
» Richard Vaughn, MD and Tom Lonergan, PharmD SSM
• Talk to End Users
– An alert might be small in raw numbers but dominate for a
specific specialty
19. Pearls
• Lactation
– Pregnancy History
• Epic Counts SAB, TAB, and Ectopic as live births and will trigger
lactation alerts!
– 50% have no help text or the text states “No Data” or
“Insufficient Data”
• Pregnancy
– Data vendors retire rules- Not always retire by Epic
• Acetaminophen + Narcotic = Category D?
– 40% have no help text or the help text states “No Data” or
“Insufficient Data”
20. Pearls
• OSQ and PRL –
– Powerful Tool
– End User Satisfier
• Drug-Drug – Live Vaccine<–>Live Vaccine
– Noise
– In our set up the only way to get the alert was if you gave the
live vaccine appropriately on the same day.
21. Future
• Epic 2014
– Phase of Care Suppression
• Currently Using
– Releasing User Suppression
• Currently Using
– User Filtered Alert
• Not Using, Difficult Debate
• Epic 2015
– Drug-Disease
– Age/Sex (Medi-Span)
– geriatric/pediatric (FDB) rules
37. RHS Top Five Overrides
Description Type Severity % Pain-Factor
CODEINE DRUG-ALLERGY
DRUG CLASS
MATCH
83.78 50500.55
MORPHINE DRUG-ALLERGY
DRUG CLASS
MATCH
84.30493 47547.98
HMG-COA
REDUCTASE
INHIBITORS /
SELECTED
FIBRATES
DRUG-DRUG
SEVERE
INTERACTION
94.10569 43570.93
TRAMADOL DRUG-ALLERGY
DRUG CLASS
MATCH
83.6039 43056.01
ONDANSETRON
HCL/PF, IV, PRN
ORDER
DUPLICATE
THERAPY
(N/A) 87.31618 41475.18
38. Reducing Minimally Important
Medication Alerts in a Large Health
Care System
Kiersten Miller Anderson, PharmD, BCPS
Elaine Hogan Miller, PhD, RN
Anthony Berliner, MD
September 15, 2014
39. Objectives
• Identify Epic Functionality available to manage
and control medication alerts
• Describe, modify and evaluate the data
provided by the Epic Medication Warnings
Statistics Report
• Report and discuss pharmacist satisfaction
with changes in medication alerts
39
40. • 12 Hospitals
• 6 Affiliates
• 1754 staffed beds
• 90,000 medication
orders/week
• Live on Epic in 2004
• Currently using Epic 2012
Allina Health System
40
42. Alert Burden
• Office of the National Coordinator for Health
Information Technology (ONC), Leapfrog, Institute
of Medicine (IOM), and the Agency for Healthcare
Research and Quality (AHRQ)1,2,3
• Alert Fatigue
• Pharmacists play a key role4
43. Research Question
• How much does decreasing minimally
important medication alerts decrease the
volume of total alerts and improve pharmacist
satisfaction with medication alerts?
• Study reviewed and approved by the Allina
Institutional Review Board (IRB)
43
44. Epic Medication Warning Statistics Report
• Three reports: Settings, Summary, Detail
• Data Provided: Type, Description, Source, Warning
status, context, medications, severity, provider,
override reason, interaction ID, Interaction setting,
order set, patient location, order ID
• Location: Management Options (System
Definitions, Profiles, Utilities, etc.) -> Other System
Modules -> Willow -> Medication Warnings ->
Medication Warning Statistics Report
46. Management of Data
• A LOT of Information!
–100,000 alerts per week fired at urban hospital
–41,000 alerts per week fired at suburban
hospital
• Transfer Data to Access
–Queries
• Organize by number, type, provider, order set
• Sort largest to smallest
• Accurate counts
51. Alert Changes - Round 1
• Suppressed all Saline Flush duplicate
medication and duplicate therapy alerts at the
ERX level
• Suppressed all pregnancy and lactation
medication alerts on all obstetrics order sets
• Turned off duplicate medication alerts
between inpatient and outpatient medications
(Nova 126194)
51
52. Alert Changes – Round 2
• Updated System Definition setting for IR-Narcotics
• Increased System Definition setting for Antiemetics
and Antidepressants
• Suppressed duplicate therapy and duplicate
medication alerts on normal saline infusion ERXs
and MAR placeholder ERXs (i.e. Pharmacy consult,
patch off, protocol)
• Turned off Major Drug-Food alerts for Pharmacists
52
53. Alert Changes – Round 3
• Suppressed duplicate medication and duplicate
therapy alerts on all Anesthesia order sets
• Suppressed all Undetermined
Severity/Alternative Therapy alerts
• Customized the order in which the alerts display
• Disabled drug-drug interactions for
narcotics/phenothiazines and
anticoagulants/acetaminophen
53
54. Medication Alerts Pharmacist Satisfaction
Survey
• Percentage Questions
1. What percentage of alerts that you verify during
an 8 hour shift would you estimate are associated
with a medication alert
0-20%, 20-40%, 40-60%, 60-80%, >80%
2. What percentage of the alerts is clinically
relevant?
0-20%, 20-40%, 40-60%, 60-80%, >80%
54
55. Pharmacist Survey (cont.)
• Remaining Questions on a five point scale from
1(strongly disagree) to 5 (strongly agree)
3. Medication alerts provide information that helps
me in my clinical decision making
4. I am satisfied with the number of alerts that I
see.
5. I am likely to take action inside the medication
alert window (i.e. discontinue a medication, double
click on the interaction)
55
56. Survey (cont.)
6. Allergy alerts provide information that helps me in my
clinical decision making
7. Drug-Drug Interaction alerts provide information that
helps me in my clinical decision making
8. Duplicate Therapy/Duplicate Medication alerts provide
information that helps me in my clinical decision making
9. Pregnancy alerts provide information that helps me in
my clinical decision making
10. Lactation alerts provide information that helps me in
my clinical decision making
56
57. Outcomes
• Decrease in Medication alerts
–40% at urban hospital
• 100,000 alerts/week down to 60,000 alerts/week
–33% at suburban hospital
• 41,500 alerts/week down to 28,000 alerts/week
• Pharmacist Satisfaction Survey
–8 of 10 questions had a statistically significant
change in response
57
58. Pharmacist Satisfaction Before and After Alert
Changes In Order of Strength of Mean Change
Item Before and After
Alert Modification
Mean Median Statistical
Significance
Level***
Difference in means
Before and After
Alert Modification
Percent Alerts
Clinically Relevant
(Q2)
Before
After
1.6
3.9
1
4
.000 2.3
Satisfied Number
Alerts (Q4)
Before
After
1.9
3.7
2
4
.000 1.8
MA Help CDM**
(Q3)
Before
After
3.5
4.5
4
5
.000 1
Percent Orders
MA* (Q1)
Before
After
4.6
3.9
5
4
.000 .7
Duplicate Alerts
Help CDM (Q8)
Before
After
3.1
3.7
3
4
.000 .6
Drug Drug Help
CDM (Q7)
Before
After
3.6
4
4
4
.001 .4
Lactation Alerts
Help CDM (Q10)
Before
After
2.9
3.2
2
3.5
.000 .3
Pregnancy Alerts
Help CDM (Q9)
Before
After
2.9
3.3
3
4
.14 .4
Clinical Action in
Alert Window (Q5)
Before
After
3.1
2.8
3
3
.15 .3
Allergy Alerts Help
CDM (Q6)
Before
After
3.9
3.6
4
4
.02 .3
58
*Medication
Alerts
** Clinical
Decision Making
***t test set at
p=<.05
59. 7 Questions showed a Statistically Significance
Increase in Pharmacist Satisfaction
In order of the highest to lowest mean value change :
• Percent of alerts that are clinically relevant (Q2)
• Satisfied with number of alerts (Q4)
• Medication alerts help with clinical decision making
(Q3)
• Percentage orders per shift associated with a
medication alert (Q1)
• Duplicate Therapy/Duplicate medication alerts help
clinical decision making (Q8)
• Drug-Drug Interaction alerts help in clinical decision
making ( Q7)
• Lactation alerts help in clinical decision making (Q10)
59
60. Limitations of Study
• Time frame
– 6 months between pre and post survey
• Survey was voluntary and anonymous
• Response rate
–104 (50%) Pre Survey
–44 (25%) Post Survey
60
61. Lessons Learned
• Modification of alerts was successful
–Decrease in total alerts
–Increase in Pharmacist Satisfaction
• Data volume is large and takes time to
interpret and analyze
• Teamwork
• Continual Process
61
64. References
1. Phansalkar, S, et al. High priority drug-drug interactions for use in
electronic health records. J Am Med Assoc 2012; 19: 735-743.
2. Phansalkar, S, et al. Drug-drug interactions that should be non-
interruptive in order to reduce alert fatigue in electronic health
records. J Am Med Inform Assoc 2013; 20:489-493.
3. McKibbon, K Ann, et al. The effectiveness of integrated health
information technologies across the phases of medication
management: a systematic review of randomized controlled trials. J
Am Med Inform Assoc 2012; 19:22-30.
4. Saverno, Kim R, et al. Ability of pharmacy clinical decision
support software to alert users about clinically important drug-drug
interactions. J Am Med Inform Assoc 2011; 18:32-37.
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