Afroze Mobasher, Pharm. DAnalyst ConfigurerImperial College Healthcare NHS Trust

Andrew Heed, B. Pharm.CPIO, Integration Architect.The Newcastle upon Tyne Hospitals NHS Foundation Trust.

Local reflection of an optimisation project

Optimisation cycle

Collaborative optimisation

Future optimisation challenges.

ImplementedCerner Millennium® in 2009.◦ Electronic prescribing and administration◦ PAS◦ Theatre module◦ A&E◦ Electronic orders.

Not live with:◦ Problems and diagnoses◦ Documentation◦ Certain medication e.g. Insulin.

Small scale optimisation steps.

Anti-microbial stewardship:◦ Simple messages to reduce cephalosporin use

linked to improvements in prescribing in ED and over 65 yo – improvements in MRSA rates.

Specific order sets◦ Acetylcysteine for Paracetamol overdose◦ Hyperkalaemia rules and order sets.◦ Work best when designed with users.

AKI algorithm and alerts. Interaction rules. Parenteral nutrition ordering.

Optimisation cycle:◦ Measure◦ Analyse◦ Investigate◦ Design◦ Control◦ Test◦ Engage◦ Implement◦ Reflect◦ Exploit◦ Collaborate

You fill

in the

gaps

You fill

in the

gaps

You fill

in the

gaps

You fill

in the

gaps

You fill

in the

gaps

Metzger et al. Mixed Results In The Safety Performance of Computerized Physician Order Entry. Health Affairs 2010 29(4): 655-663

Hospit

al Score

s f

or

dete

cti

on o

f te

st

ord

ers

causin

g a

n A

DR a

ccord

ing t

o p

roduct

ePX system◦ Central drug catalogue

◦ Standardised nomenclature

◦ Central and configurable routes of administration, frequencies, units of measure

◦ Order sets.

◦ Order sentences.

◦ Legibility.

◦ Reporting.

◦ Tool for change.

Initial build “not good enough”:◦ No electronic blood glucose results.

◦ Still needed two charts.

◦ Unable to view drug doses in lab view.

◦ Ability to search by brand/ type.

◦ Couldn’t lock down unit of measure or route.

◦ Staff were new to the system.

◦ “Skill gaps” on my part.

Not going live was the correct decision.

Experience gained from smaller scale optimisation.◦ What works well and when

◦ Build re-thinks

◦ Rules, reporting, other system build

◦ Rapport.

◦ Multiple approaches

Experience from others.◦ IA course, discussion groups, UCERN, sign up to

safety.

Electronic blood glucose results.

MDT design group:◦ Baseline data from annual audit / datix reviews

◦ Specific Insulin build and views.

◦ Automated pharmacy referral.

◦ Automated referral to DSN.

◦ Specific targeted build Humulin R.

◦ Reporting tools.

Go-live◦ Used reporting to target change and review.

Electronic Glucose Monitoring View

A colour coded view of laboratory results, insulin doses, oral hypoglycaemics, glucocorticoids. to highlight out of range values.

Table 1. Inpatient Insulin prescription error types

occurring pre and post the introduction of

electronic insulin prescribing in Newcastle

Different types of error. . ◦ Dose not signed.

◦ Open-ended prescriptions.

◦ Doctors no longer required to review.

◦ The order requires a dose.

Review task for doctors.◦ Based on experience with pharmacy.

So if we can use the ePX system to do all that could we . . .

Based on data from Epx:◦ Unable to see all patients.

◦ Do we need to see all patients?

◦ Do we see high risk patients.

Can we optimise the system to prioritise clinical team?◦ Direct reviews

◦ Quantify service need and service delivery.

◦ Move away from location based service to a problem / patient-based service.

◦ “Better value” with targeted service?

Implemented a pharmacy task list◦ Based on requests from clinical team

◦ System rules generate a follow-up task attached to patient record.

◦ Some data collection forms.

◦ Initial good response

◦ Limited by volume of work and cluttered views

◦ Teams continued to be ward based

◦ “Feel like we have to clear the list”

Good system but some design flaws.

How to control:◦ Optimisation requests, template requests

Ownership and review

How to test and monitor

Workload and maintenance

Drive awareness of possibilities

Design principles◦ The simpler the better vs complex over

specification

Promotion and education

Safety case and closure report:◦ Is it needed?

◦ Who is responsible?

Standard change vs complex redesign

How does this affect other processes

Does it change the system from vendor perspective.

Training

Shelford Group collaboration

Sites using Cerner EPR

Varying experiences and scope.

Experiences of optimisation by prioritisation.

Brief:

“Design and develop a multi-system, real-time, dynamic process to create a medication Acuity Score to enable prioritisation of clinical pharmacy teams.”

Cross-site consultation on acuity “Scenarios”

Reviewed by EPR team.

Classified by type.

Classification Count

Antimicrobial Stewardship 17Therapeutic drug

monitoring 12

drug-drug interactions 11

Insulin 11

Anticoagulants 10

Location Based Risk 9

High risk drug / class 19

Patient factors 20

Total 109

Scenarios reviewed and assigned a rule template

Template type NumberOrder based. 36

Result based. 21

No template applicable. 18

Interaction 15

Clinical event with order 13

order detail - route of administration 6

order detail - location 5

Order event with co-factor 4

Admit rule 1

Considerations:◦ Complexity of rule:

Can it be done,

time to build,

Local variations in scope and content.

◦ Workflow.

Several options for rule output. Task based

Dynamic vs Static.

Scoring system.

What does the score mean.

30+ Rules in draft status.

Approx.10 templates developed:◦ Admission rules

◦ High risk order (unchanging)

◦ High risk order (changing)

◦ TDM rules (changing)

◦ Others e.g. weight.

Scenarios: high risk drug A or B, complicated by age over 70 and or low

body weight.

Specific rule

model:

Requires the

following:

Drug A rule

Drug A + patient over age 70 rule

Drug A + patient over age 70 + low body wt rule

Drug A + low body wt rule

Drug B rule

Drug B + patient over age 70 rule

Drug B + patient over age 70 + low body wt rule

Drug B +low body weight rule

Separate rule

model

Requires the

following:

Drug A rule

Drug B rule

age over 70 rule,

low body weight rule

Developed rule base at Newcastle.

Exported to colleagues at Imperial for localisation and testing.

Review build and agree final model.

Collaborate with The Newcastle University School of Pharmacy.

Evaluate the Acuity model.◦ Silent Live period.

◦ Review vs current practice.

◦ Define visualisation.

Clinical Informatics literacy within healthcare.◦ Many sites not using

◦ Many sites have low engagement

◦ Major barrier to communication on both sides

Both guilty of jargon

◦ Is what we get truly optimal

◦ Do we understand data and structure

Can we get informatics on undergraduate and post-graduate teaching?

Better prepared for using systems in practice

Opportunity to think differently

◦ Secondary uses of the system

◦ Management and use of data

◦ System development

Awareness of limitations of the system

Afroze Mobasher

Lithium Anticoagulants Clozapine Methotrexate Parkinsons medication Variable dose insulin Insulin infusions Digoxin NOAC Immunosuppresants Inotrope Chemotherapy

Hook was broken, importing the rules adds @quot; before and after the “LOGIC” piece which needs to be corrected for each rule

Competing pressures◦ Shared domain:

Chelwest meds go live 2019

Resources for process harmonisation

◦ Lloyds pharmacy paperless workflow

◦ PGD workflow

Prior experience of: ◦ EKM rules and Discern Expert◦ Meds Order Catalogue architecture◦ DTA build and design◦ iView build and design◦ Frontend PowerChart workflows

Recommended Cerner courses: ◦ B&M Core Foundations◦ B&M Documentation Management◦ B&M iView◦ B&M Medication Processes◦ EKM rules

Aligning imported meds order catalogue to local Synonyms

Encounter type Encounter status Frequencies DTA build Hook “logic” iView Build Identify locations: ICU, chemotherapy etc Testing and Validation: EPMA Pharmacist

Standardised nomenclatures:◦ Enables core rules with less localisation.

◦ Multum “Flip” - dm+d terminology to enable use of VTM terms where applicable.

◦ Snomed CT for problem based rules.

◦ Standardised laboratory nomenclature.

Vendor assistance◦ May need aliasing option to drive nomenclature?

◦ Creation of a package to install rules

◦ A wizard to automatically map

Thank you for your time