Electronic prescribing and training prescribers at induction

Transcription

1 Electronic prescribing and training prescribers at induction Presented by: Sarah P. Slight MPharm, PhD, PGDip Date: 14 th July 2015

2 Overview 1. Review the literature on training eprescribers 2. Describe the vulnerabilities of eprescribing systems U.S. FDA CPOEMS Study MedMarX Study 3. Summary

3 Literature review Background Health Education Kent, Surrey and Sussex Improve the prescribing practices - in their region, Implementation of electronic prescribing systems Aim To gain an understanding of the types of support offered to newly employed prescribers on the use of electronic prescribing systems.

4 Literature review Methods Databases Medline, Embase and CINAHL Inclusion criteria all study types, no restrictions on time period Exclusion criteria not published in English. MESH terms related to electronic prescribing, training and prescriber Reference lists were also examined for additional papers.

5 Literature review Search Strategy Total articles identified (N= 1155) (Embase 422, Medline 444, CINAHL 289) Excluded based on title (N=1095) Excluded based on abstract (N=45) Excluded based on full text (N=9) Included (non-embase or medline) based (N=1) Total articles included (N=7)

6 Literature review Key findings 1.Combination of different training methods and tools 2.Clinical based scenarios known problem areas 3.Use of assessments 4.Online training

7 Literature review Future training A choice of alternative training methods Training should cover both procedural tasks and cognitive tasks Training about the challenges and pitfalls of electronic systems is important A minimum level of training before clinicians are authorised to use the system.

8 2. U.S. Food and Drug administration (FDA) CPOE Medication Safety Study

9 2. FDA CPOEMS Study Aim To examine the characteristics of CPOE systems and explore the potential for errors in prescribing, transcribing, and dispensing, particularly as these relate to drug names and visual interfaces.

10 Methods Test sites Diverse organisations (6 sites) using 10 electronic prescribing systems Test case scenarios CPOEMS Assessment Tool (CAT) which incorporated specific medicationordering scenarios to capture screen display features and functions 18 areas of interest E.g., (1) how drug names are listed; (2) how combination product names are listed; (3) how drug names with modifiers are listed; (4) drug names listed as brand/generic; (5) use of drop-down menus; etc.

11 2. FDA CPOEMS Study - Findings A: Searching for a drug

12 2. FDA CPOEMS Study - Findings A: Searching for a drug

13 2. FDA CPOEMS Study - Findings B: Auto-complete or auto-fill functionality

14 2. FDA CPOEMS Study - Findings C: Drug name displayed descriptors or indications being included

15 2. FDA CPOEMS Study - Findings C: Drug name displayed descriptors or indications being included

16 2. FDA CPOEMS Study - Findings D: Ordering the desired dose

17 2. FDA CPOEMS Study - Findings D: Ordering the desired dose E: Additional fields Special instructions and Comments

18 Key messages Lots of variation in how drugs are displays between sites, within sites, and within a single system Trainees need to be aware of this variation, as they may need to use different systems when on rotation Important to include in their training

19 3. U.S. MEDMARX Study

20 Aim To test the vulnerabilities of a wide range of leading electronic prescribing systems to different types of medication errors

21 Methods Test sites Diverse organisations (16 sites) using electronic prescribing systems Test case scenarios Used 13 test scenarios of recurring problems Conducting the tests Users given detailed instructions of how to enter the erroneous orders on test patients.

22 Findings 1. The existence of alert warnings 2. The wording of alert warnings 3. The timing of alert warnings 4. The level of severity of alert warnings 5. CPOE workarounds

23 The existence of alert warnings Example: Test Case 1 - Allergy checking 1. Enter patient allergy to lisinopril in test patient s chart. 2. Enter order for captopril 12.5 mg PO (by mouth) TID The same electronic prescribing system responded differently at different sites May relate to how the information was entered onto the system No look-alike sound-alike (LASA) warnings were displayed at any of the sites

24 The wording of alert warnings Example: Test Case 4 - Duplicate drug checking 1. Enter order for Lovenox 40 mg SQ (subcutaneous) daily. 2. Enter order for Lovenox 100 mg SQ BID (twice daily). The wording was often found to be confusing Warnings for all active orders appeared on the same screen Relevant warning could easily get lost in the long list of other warnings that show up simultaneously.

25 The timing of alert warnings Example: Test Case 2 - Drug-drug interaction (DDI) checking 1. Enter order for Imdur (isosorbide Mononitrate) 30 mg PO daily. 2. Enter order for Revatio (sildenafil) 20 mg PO TID. The timing of alert warnings differed across electronic prescribing systems Displayed after both Imdur and Revatio had been selected and the order signed off Duplicate therapy warning appeared after the two drugs had been ordered and signed.

26 The level of severity of alert warnings Alert warnings varied in their level of severity in different electronic prescribing systems. Information only alert Alert Warning (Site 6) Test Case 2: DDI checking Hard Stop critical alert (Sites 4 & 5) Test Case 4: Duplicate drug checking Alert Warning Interruptive alert (Site 7 outpatient system) Hard Stop critical alert (Sites 7 inpatient system)

27 Electronic prescribing workarounds Users developed a number of workarounds such as 1. using the other option, 2. making free text entries in the special instructions field, 3. changing the default settings, and 4. selecting off formulary drugs. Test Case 9: Wrong units (mg vs. mcg), 1,000-fold overdose 1. Enter order for Synthroid (levothyroxine) 100 mg PO daily.

28 Summary Electronic prescribing systems often failed to prevent important medication errors. There is a clear role for post-implementation testing using a variety of clinical scenarios that could enable organisations to improve their systems. Prescribers need to be aware of particular systems features and pitfalls Next steps to develop a tool to assist with eprescriber training

29 Further information Slight SP, Eguale T, Amato M, Seger AS, Bates DW, Schiff GD. The Vulnerabilities of Computerized Physician Order Entry Systems: A Qualitative Study. J Am Med Inform Assoc. (In press) Brown C, Reygate K, Slee A, Coleman J, Thomas S.K., Bates D.W., Husband A.K., Watson N, Slight SP. The training of qualified professionals to use electronic prescribing systems: why is it so important? J Am Med Inform Assoc. (In development)

30 Any questions?