1. Ordering to Administration: Parameters for a Successful Titratable Infusion Rebuild
Casey Olsen, Pharm.D.
PGY2 Pharmacy Informatics Resident
Milwaukee, Wisconsin

2. Disclosure
I have had no actual or relevant financial relationships to create a potential conflict of interest in relation to this program.
This project has been reviewed by the Aurora Health Care Institutional Review Board (IRB), and does not constitute human subject research. As such, it does not require Aurora IRB oversight.

3. Learning Objective
Identify available functionality within electronic health records that may be utilized to improve compliance to Joint Commission standards

4. Aurora Health Care Private, not-for-profit, integrated health system
16 hospitals
Integrated health care system with a shared electronic medical record
~20,000 orders for titratable infusions placed monthly

5. What are titratable infusions?
Intravenous medications with increasing or decreasing doses dependent on patient characteristics
Examples:
Nicardipine
Propofol
Heparin
Standards FAQ Details. Standards FAQ Details | Joint Commission. Published March 31, Accessed July 7, 2017.

6. Risk of Intravenous Infusions
Study Design
Prospective observational study
Population
107 nurses
568 intravenous medications
Six wards of teaching hospitals
Intervention
Researcher observation of nursing practice with documentation of procedural failures and errors
Primary Outcome
Frequency, type, and severity of errors
Results
69.7% had ≥ 1 clinical error
└25.5% categorized as serious
└91% were related to rate of infusion
Westbrook JI, Rob MI, Woods A, Parry D. Errors in the administration of intravenous medications in hospital and the role of correct procedures and nurse experience. BMJ Qual Saf. 2011;20(12):

7. Requirements when Ordering
Joint Commission Requirement
Process of Inputting Data
Medication name
Required at order entry
Medication route
Starting rate of infusion
Free text input with required responses or spaces for text entry that may be erased leaving an empty field
Incremental rate change parameters
Frequency of rate changes
Maximum rate/dose
Objective clinical goal
Standards FAQ Details. Standards FAQ Details | Joint Commission. Published March 31, Accessed July 7, 2017.

8. Dose, titration parameters, and goal are free text within order entry.
© 2018 Epic Systems Corporation. Used with permission.

9. Project Objective and Scope
Improve Joint Commission compliance and usability of titratable infusion orders
System-wide change with impact on:
66 medications
medication records
More than 100 order sets

10. Planning Develop understanding of current processes
August 2017
Literature search
Assemble interdisciplinary steering group
Set timeline via milestones
Build and test proof of concept with clinician approval
Finalize plan for build direction
October 2017

11. Steering Committee Clinical Educators IT
Pharmacist clinical coordinators
Nursing
Pharmacy
Medication safety officer (pharmacist) IT
Orders team (order sets)
Willow (medication build)
Clinical documentation

12. Initial Timeline Milestones
Deadline
Dependencies
9/1/2017
Approvals
9/29/2017
Build and validation
12/22/2017
Training materials
Communication
1/5/2018
Go-live
1/25/2018

13. Approvals Physician practice committee Pharmacist practice committee
Nursing practice committee
Patient safety committee (IT and clinical collaborative)
Neonatal and critical care specialty groups
Other miscellaneous information technology groups

14. Identified Opportunities for Change
Modified Titratable Infusions
Context-based ordering
Order parameter input
Bolus from bag

15. Identified Opportunities for Change
Modified Titratable Infusions
Context-based ordering
Order parameter input
Bolus from bag

16. Context-based ordering
Definition:
Enabling selection of a ‘single medication’ during the ordering process which will provide access to specific defaults, warnings, and dispensed product based on patient characteristics (ex. age and weight)
Pre-implementation practice:
Separate orders for neonatal and adult populations
Limited pediatric-specific orders
Problem: incorrect order may be selected

17. Context-Based Ordering Change
Pre-implementation
Post-implementation
Single order
Adult
Pediatric
Neonatal
Adult order
Adult
Neonatal order
Neonatal
Provider
chooses
Supported
population

18. Context-based Ordering Benefits
Selection
Less options available within the navigator
Data entry
Ensures relevant clinical decision support is used
Pertinent additional references can be provided
Improved ability to modify dispensed product in cases of shortage (using Epic orderable functionality)

19. Barrier Encountered
Neonatal and adult infusion orders historically used different bases (saline versus dextrose)
Decision made to maintain separate orders to ensure correct bases were selected for each population
Final outcome:
Two orders still exist for physicians (neonatal and adult/pediatric)
Each order has safety catches to ensure clinical decision support is appropriate

20. Identified Opportunities for Change
Modified Titratable Infusions
Context-based ordering
Order parameter input
Bolus from bag

21. Order Input Pre-Implementation
Dose, titration parameters, and goal are free text within order entry.
© 2018 Epic Systems Corporation. Used with permission.

22. Order Parameter Input
Use of free text fields results in missed capture of required elements for orders
Benefits of discrete field use:
Limits potential for missing information1-2
Improved data tracking and reporting
Clinical decision support
Schneider E, Franz W, Spitznagel R, Bascom DA, Obuchowski NA. Effect of computerized physician order entry on radiologic examination order indication quality. Arch Intern Med. 2011;171(11):
Dumitru D. The Pharmacy Informatics Primer. American Society of Health-System Pharmacists. September 2008.

23. Order Input Post-Implementation
Dose field with age-appropriate ranges
Questions for other titration parameters
© 2018 Epic Systems Corporation. Used with permission.

24. Order Input Post-Implementation
Answering “Yes” opens next question
© 2018 Epic Systems Corporation. Used with permission.
Titration units question appears and automatically selects an answer based on patient characteristics

25. Order Input Post-Implementation
Titration unit question opens the titration parameter questions with proper order units
© 2018 Epic Systems Corporation. Used with permission.
Each Joint Commission requirement is a required question with buttons available to assist in ordering

26. Order Input Post-Implementation
Other fields are available for custom parameters
© 2018 Epic Systems Corporation. Used with permission.
Clinical goal allows multiple answers to be selected and allows free text input (rather than restricting to numbers)

27. Order Input Post-Implementation
Titration parameters appear readily on the MAR for nursing
© 2018 Epic Systems Corporation. Used with permission.

28. Barrier Encountered Weight based orders require dosing weight
Not documented in emergency department due to workflows in which weights are estimated
Concern over an estimated dosing weight continuing through admission
Final outcome:
Order-specific weight made available on infusion orders
Workflows put in place to transition order-specific weights to dosing weight when entered at admission

29. Weight Transition Workflow
Order-specific weight entered within order
Order is verified and administration begins in ED
Patient admitted and actual/dosing weight entered
Pharmacist alerted if significant weight difference
Pharmacist enters new order with new weight and back-calculated dose
Pharmacist communicates with nurse to the pump

30. Identified Opportunities for Change
Modified Titratable Infusions
Context-based ordering
Order parameter input
Bolus from bag

31. Bolus from Bag Definition: Pre-implementation practice:
Bolus of an intravenous infusion administered from the same bag as the continuous infusion
Pre-implementation practice:
Functionality exists within smart pumps, but has limited electronic medical record orders to facilitate its use

32. Documentation Options
Pre-implementation
On infusion orders with no prescribed order
On injectable orders with incorrect billing
Omitted entirely
Post-implementation
On newly created orders that function like other medication orders with no dispensing functionality

33. Barrier Encountered
One site used infusion pumps not supporting bolus from bag workflows
Bolus from bag orders would not be usable at this site leaving nurses unable to administer medication or force them to use unsafe workflows
Final outcome:
Bolus from bag orders automatically (behind the scenes) become a stand-in bolus order (most frequently injectable) when selected

34. Limitation Remaining
Smart infusion pumps make use of weight-based dose entry
Example: esmolol 500 mcg/kg bolus is entered into the pump as 500 mcg/kg rather than the calculated dose
Epic calculates weight-based doses on the MAR for nursing
Potential for error if nursing inputs calculated dose into the infusion pump rather than the pre-calculated weight-based dose
Smart infusion pumps have maximum limits built which will catch most potential errors

35. Build and Implementation
Build occurred from September 2017 to January 2018 with validation by clinical pharmacists
Go-live: January 25, 2018
Big bang go-live with all changes to all medications and order sets at one time

36. Results Required Parameter Pre-implementation (n=53)
Post-implementation
(n=52)
Starting rate of infusion
77%
96%
Incremental rate change
45%
Frequency of rate changes
Maximum rate/dose
73%
Objective clinical goal
94%
All above results are significantly different pre- versus post-implementation (p<.05; Chi-Squared Test)

37. Results

38. Results

39. Resource cost Activity Profession Hours Clinical content Pharmacist
100
Build
IT Pharmacist / Analysts
1700
Build validation
ICU Pharmacists
Ongoing clinical feedback
Pharmacists / Nurses / IT
Training material creation
Educators / IT Pharmacist
Project coordination
IT Pharmacist
200
Total
2300
Data estimated retrospectively

40. Conclusion
Use of discrete required fields for titration parameters improves adherence to Joint Commission criteria over free text fields with minimal impact to usability

41. Future Direction
Utilize discrete data elements for future clinical projects
Create and optimize clinical decision support from discrete fields

42. Ordering to Administration: Parameters for a Successful Titratable Infusion Rebuild
Casey Olsen, Pharm.D.
PGY2 Pharmacy Informatics Resident
Milwaukee, Wisconsin