Anmol Sandhu - St Vincent’s Hospital, Sydney - Clinical Decision Support- A 10 Year Review of Development, Maintenance & Evaluation

5th Annual EMM Conference

Clinical Decision Support

Development, Maintenance & Evaluation:

A 10 year review

Anmol Sandhu

Pharmacist – eMedicines Management

St Vincent’s Hospital, Sydney

Agenda

1. Clinical Decision Support (CDS) at SVHS

I. On or off?

II. Development & maintenance

2. CDS for Drug-drug interactions – a tough nut to crack

I. Context matters – who, what, where, when and how

II. Research & evaluation to inform implementation

16 March 2016 5th Annual EMM Conference - Clinical Decision Support Page 2


On or Off? CDS GO-LIVE 2005 CURRENT March 2016

INTEGRATED (ACTIVE):

Drug allergy and intolerance YES - prescriber alerted YES - added nurse alert 2012

Therapeutic duplication (by substance and class) YES YES

Pregnancy warnings YES NO – turned off 2012

Drug–drug interactions NO – manually added Azathioprine /

Mercaptopurine / Febuxostat + Allopurinol DDI

Research project underway – ability to

choose severity level now available

LOCALLY DEVELOPED (ACTIVE / PASSIVE):

Dose ranges YES - limited YES - limited

Rules YES - minimal YES – over 600

Pregnancy category X drugs N/A 2012 – rule developed. 2016 – review needed

Order sentences and sets – quick lists and

protocols

YES – wrote them for one ward at a time, not

a deal-breaker for go-live

YES - 1631 QL and 104 protocols

LOOK-UP TOOLS (PASSIVE – CONTEXTUAL ON SCREEN):

Reference texts: MIMS, AMH, Therapeutic

Guidelines, SVH Pharmacy Intranet

YES YES

MIMs interaction checker (manual) YES YES

SVH medicine profiles

- Drug & Treatment guidelines

- High risk information

YES – as rule hyperlinks - limited YES – changed format and improved access

– 2011-3

Changes made – research & user feedback


27.2% of orders triggered 1 or more

alerts

1. Disabled pregnancy alerts –

replaced with targeted local rules

2. Drs not using efficient system

functions – emphasised in

MedChart training & Pharmacy

JMO sessions

3. Drs did not value interruptive local

rules: many removed after huge

uptake of quick lists (pre-written

order sentences) Baysari, M. T., et al. (2014). "Optimising computerised alerts within electronic medication management

systems: A synthesis of four years of research." Stud Health Technol Inform 204: 1-6.

Local rules - Home Grown Safety

16 March 2016 5th Annual EMM Conference - Clinical Decision Support

Locally developed “Rules” July 2010 May 2012 March

2013

Feb 2015 Jun 2015 March

2016

Targeted to Drs / prescribers 93 125 129 149 153 156

Administration instructions 181 235 264 347 379 393

“Blocks” – prevent further action 14 28 30 39 41 32

Pharmacist specific clinical review

messages

39 53 61 57 55 55

Total 287 375 428 592 628 636

Unique combination of predicates and applicability options

• by substance, with route, with form, in formulary, has frequency

• being prescribed, about to be administered, transferring from MOA, to discharge

• patient’s gender, age, weight, location

• User’s name, role is….

Ability to set response type

Automate Pharmacy or Clinical review prompts

Ability to add hyperlinks

Page 5


Local rules – Home Grown Safety (2)

Administration instructions

Page 6


Local rules – Home Grown Safety (3)

Page 7


Bringing it all together

Clinical Decision Support

Quick List: Dose, Route,

Frequency, Admin time

Protocol (Order Set)

Local rules facilitate

AMS

Integrated references at point of

care

Admin guidance:

automated, referenced

On screen view


CDS for drug-drug interactions:

A tough nut to crack

Page 10

Current LTX

patient

13 Alerts for Drug

Interactions

17 instances where

Dr must complete

an action – override

+/- comment or

remove


Page 11

Prescriber view



Drug-Drug Interactions

Not turned on …………. Yet

1/3 orders triggers an alert (!!)

Over 8500 at go-live – all ON or all OFF (could not enable severity levels)

In 2016 there are over 10,000 DDI’s that could be activated

Severe = 2709 pairs

Working towards evidence-based solutions

Oct 2014 – DTC commissioned a multidisciplinary DDI Working

Group

Research projects underway with CHSSR (Dr Melissa Baysari)


CONTEXT MATTERS:

WHO, WHAT, WHERE, WHEN & HOW

ALERT FATIGUEWhat contextual considerations are

required?

What are the current challenges and

are there any proposed strategies?


Context matters – WHO

Considerations Challenges / Strategies

1. Feldstein, A., et al. (2004). "How to design computerized alerts to safe prescribing practices." Jt Comm J Qual Saf 30(11): 602-613.

2. Payne, T. H., et al. (2015). "Recommendations to Improve the Usability of Drug-Drug Interaction Clinical Decision Support Alerts.

WH

O

Who should see the DDI alert?

(Dr, Pcist, Nurse, Patient?)

• Who is responsible for acting on the alert?

• Divert or “forward” alerts to the relevant clinician:

e.g. diversion of time-dependent DDI alerts to nurse or alerts about INR monitoring to anticoagulation service

Are the decision makers targeted?

• Retrospective DDI alert monitoring report sent to AMOs

• Ability to “snooze” alerts

Disable frequently overridden DDI alerts for specialties

Allow Drs to self-define alert content / severity thresholds?

• Unable to agree to turn off any alerts hospital-wide: - variable levels of clinician DDI knowledge- differing specialties.

• >50% disagreed with option to disable or self-define alert content.1

• Nil evidence currently. Error-management strategy & legal implication require consideration.2


Context matters - WHAT

WH

AT

What knowledgebase

(source) to use to inform alerts?

1. Commercial / vendor based KB: (overly inclusive = ↑ alerts)

2. Cleansed / edited commercial KB

3. Customised – created, maintained in-house

4. Core set of DDIs

What severity threshold to

enable?

Clinical significance and severity ratings differ significantly between KBs

- Major DDI in one KB, may only be a moderate DDI in another.

No consistent, standardised method to assess clinical significance

Creation of KBs could be optimised by using > 1 source e.g. combination of

commercially-derived severity rankings, literature review and expert group

opinions.



WH

AT What data elements

should

be included in the KB & alert algorithm?

To improve specificity; KB and DDI alert algorithms should include contextual modulators for patient & prescription:

Examples:- Age, gender, co-morbidities, genotype, renal / hepatic function- Dose, Route- Pathology / drug levels - Medicines on admission- Vital signs

Requires interoperability

Context matters – WHAT (cont.)

• Incorporation of dosing limits into

DDI alert logic for interactions that

are dose-dependent.

• 55% of DDI alerts inappropriate if

the alert logic considered the DDI-

specific upper-dose limits

• 30% of DDI alerts could be modulated

i.e. made more specific if just 5 types of

pathology results were included in the

algorithm

• 83/100 DDI alerts could have severity

decreased by considering: prescription

(n=13), co-medication (n=11), patient

data (n=36), or combination (n=23)

• Alerts for potassium-increasing

DDIs

• Specificity increased by 95.5% after

inclusion of current potassium

levels.

• DDI alert only fired if hyperkalaemia

likely, unlike most which fire at Px

Context matters - HOW


HO

W

How should DDI alerts be

presented?

Interruptive vs. Non-interruptive

- Tier alerts according to severity

- Clinical impact of designating alerts as non-interruptive?

- Dynamic diversion from interruptive to non-interruptive

Design and content of the DDI alert

interface

Proven to influence acceptance of DDI alerts

Human Factors principles should be considered in design (e.g. colour, text-based info, prioritisation, corrective actions)

Clinician Response

Mandatory acknowledgement ?

‘Hard stop’ ?

Override reason: predefined list or free text?

Cease/modify Px or order labs from alert



Context matters – WHEN / WHERE

When should the DDI alert fire?

At onset of DDI prescription ? Only trigger when parameters are met ?

(e.g. hyperkalaemia)

DDIs with delayed effect?

Fire again when an interacting drug is stopped or dose is changed?

First time only?

Off during business hours / ward round ? On after hours?

Where should DDIs alerts be enabled? How can we target high risk groups? Hospital-wide?

General wards vs. ICU ICU patients: impaired ability to metabolise drugs, larger number of drugs prescribed, particularly high risk, and

frequent dose changes)

One study identified just 20 DDI pairs that accounted for 90% of all interactions in ICU

Inpatients and outpatientsHospitalisation is a known risk factor for DDIs (complex medication regimens, care by multiple clinicians, transfer between

departments).

In a nutshell

More questions than answers….

Evaluations in clinical practice are lacking

No formal guidelines or standards

Vendor support required to improve utility of DDI alerts

Alert design, screening interval is same for all alerts, Duplications – turn on substance,

turn off class.

Interoperability between EMM, EMR, devices etc. required to

improve specificity



Evidence-based research to inform decisions:

Collaboration with CHSSR (Dr. Baysari)

1. Quantify impact of enabling

DDI alerts (severe) +/-

Therapeutic Duplications

2. Assessment of DDI alerts for compliance with HF compliance using the Instrument for

evaluating human factors principles in medication-related decision support alerts (I-

MeDeSA) Multi-site (SVHS, Concord, MUH); DDI alerts from EMMS (MedChart, MOSAIQ, PowerChart, TrakCare), GP systems

(Best Practice, Medical Director) and Pharmacy dispensing systems (i.Pharmacy, Fred)

3. Multivariate modelling - Identify patient groups that receive more DDI alerts with aim to increase specificity of

alerts.

4. Review of DDI alerts with Medicines on Admission – improve specificity

5. Effectiveness of DDI alerts in preventing DDIs – huge multi-site project (early stages),

Acknowledgments

SVHS DDI Working Group – Prof Ric Day, Prof Jo Brien, Dr P Savage, Dr M Baysari, K Richardson

Dr Melissa Baysari – CHSSR, Macquarie University

Dr Dennis Armstrong – CSC

Participating EMM study sites

Kate Richardson – EMM Pharmacist, SVHS

Maureen Heywood – EMM Pharmacist, SVHS

SVC Clinic Foundation


Thank You

Anmol Sandhu – eMedicines Management Pharmacist

[email protected]

Health & Medicine

Anmol Sandhu - St Vincent’s Hospital, Sydney - Clinical Decision Support- A 10 Year Review of Development, Maintenance & Evaluation