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Take one Palliative Care Data Standard, a
course of NZ Universal List of Medicines, the
New Zealand Formulary and the NZePS.
Avoid allergies: Integrate well!
Corinne Gower
Victoria University of Wellington and Houston Medical
Megan Peterson
Arohanui Hospice, Palmerston North
HINZ Presentation, Thursday 28th November 2013
Overview of presentation
• Introduction
• End Users Overview (Megan)
– Palliative Care Dataset
– Prescribing Solution

• Implementers Overview (Corinne)
– Palliative Care Dataset
– Prescribing Solution

• Lessons Learned
• Questions
Introduction
• Common themes
– Output requirements were specified
– Input and workflow had to be determined
collaboratively.

• Points of difference
– Palliative Care DS: Design and development phase
was the major phase. No third party product
integration.
– Prescribing: UAT phase was the major phase.
Extensive third party product integration.
Our Region
Palliative Care Data Definitions
Standard
HISO 10039.1 and HISO 10039.2
•Provides a basis of a common language for
recording information about service contacts
that can be shared and compared between
stakeholders, and for understanding palliative
care in NZ.
Data Issues
Pre Implementation
•
•
•
•
•
•

Multiple data entry screens
No definitions for fields
Non compulsory data entry
Lack of user understanding
Incomplete and inconsistent data output
Data could not be relied upon
Implementation
•
•
•
•
•

Resourcing
Change Management
Communication
Education of users
Challenges to work flow and
business processes
End User Recommendations
• Scope the project well.
• Strong working relationship with vendor and
external stakeholders.
• Determine impact on end to end business
processes (eg. referral management).
• Education sessions should including context
and constant reviewing of the data.
(Take with wine, repeat as required!)
Prescribing information workflow and
objectives
Workflow
• Prescription Creation
– Medication selection
– Interaction checking – allergies and drug-drug interaction
– Regular Medication recording

• Output – paper and electronic
– Correspondence – information sharing

• Incoming dispensing messages.
Objectives
• Reduce preventable medication errors
• Facilitate greater use of generic (non-trade/brand) drugs.
Solution Components
NZF
NZULM

Houston Medical
VIP.net

Patient Health Information
Management System
(Hospice)

NZePS

Dispensing System
(Pharmacy)
Clinician Requirements
• Doses: A list of doses associated with each medicine. Doses should
be easy to maintain.
• Medication readability:
– ‘paracetamol 500mg tablet’
– ‘paracet 500mg+pseudoeph HCl 30mg (&) chlorpheniramine mal
2mg+paracetamol 500mg+pseudoeph 30mg tabs’,

• Secondary use of prescribing information in correspondence
• Drug Allergy alerts: Recognition of existing drug allergy alerts
• Form/Strength: Option to prescribe either by form (1 tablets, 5 ml)
or by strength (10 mg).
– A pick list of administrative units is confined to units associated with
the medication in the NZULM.

• Supply: Need to request pharmacist to determine supply quantity.
– Administration Unit of ‘QS’
Prescribing Functionality Evaluation
•
•
•
•

NZF interaction checking is working well
Improved prescription generation
Prescribing data now makes greater clinical sense
Fewer scripts returned or queried by the pharmacy
because supply details have been overlooked
• Some optional fields, such as medication route, are being
omitted because they do not read well on the prescription.
• Increased generic prescribing
– System default and generic/trade cross matching

• No improvements in dose consistency
– Latin/non-Latin frequency and timing instructions.
Implementer Challenges
• Relationship Management: Huge overhead when piloting
• Challenge of justifying benefits: Having to sell government
strategy, Connected Health licenses. Documents and
implementation specifications have limited value in
communicating change drivers and benefits directly to
information system end users.
• Technical Complexity: Huge. With integration to third party
products you have to know where the boundaries are.
Medicines are included in the NZULM that are not yet
available.
• Collaboration: Need for regular site visits
• Expectations: Structured data entry is extremely difficult.
Community ePrescribing
Recommendations
• More work is needed to understand the
potential impact to clinician’s workflow when
a standard or specifications are developed.
• Need to ensure there is a clear path to convert
data (such as allergies) from old to new
system.
• Useful to have supporting documentation or
web based information, such as case studies,
to support implementation.
Lessons Learned
• Scope everything thoroughly
• Set realistic timeframes and adequately
resource the project
• Understand clinician workflow
• Engage clinicians, nurses and allied staff
• The perfect system does not exist
The importance of recognition –
What an achievement!
Thanks to:
Arohanui Clinicians
Houston Medical
Developers
Patients First Team
(Peter Jordan)
NZULM (Craig
Mabon)
NZF (Chris Hilder)
Questions

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Take one Palliative Care Data Standard, a course of NZ Universal List of Medicines, the New Zealand Formulary and the NZePS. Avoid allergies: Integrate well!

  • 1. Take one Palliative Care Data Standard, a course of NZ Universal List of Medicines, the New Zealand Formulary and the NZePS. Avoid allergies: Integrate well! Corinne Gower Victoria University of Wellington and Houston Medical Megan Peterson Arohanui Hospice, Palmerston North HINZ Presentation, Thursday 28th November 2013
  • 2. Overview of presentation • Introduction • End Users Overview (Megan) – Palliative Care Dataset – Prescribing Solution • Implementers Overview (Corinne) – Palliative Care Dataset – Prescribing Solution • Lessons Learned • Questions
  • 3. Introduction • Common themes – Output requirements were specified – Input and workflow had to be determined collaboratively. • Points of difference – Palliative Care DS: Design and development phase was the major phase. No third party product integration. – Prescribing: UAT phase was the major phase. Extensive third party product integration.
  • 4.
  • 6. Palliative Care Data Definitions Standard HISO 10039.1 and HISO 10039.2 •Provides a basis of a common language for recording information about service contacts that can be shared and compared between stakeholders, and for understanding palliative care in NZ.
  • 7. Data Issues Pre Implementation • • • • • • Multiple data entry screens No definitions for fields Non compulsory data entry Lack of user understanding Incomplete and inconsistent data output Data could not be relied upon
  • 9. End User Recommendations • Scope the project well. • Strong working relationship with vendor and external stakeholders. • Determine impact on end to end business processes (eg. referral management). • Education sessions should including context and constant reviewing of the data. (Take with wine, repeat as required!)
  • 10. Prescribing information workflow and objectives Workflow • Prescription Creation – Medication selection – Interaction checking – allergies and drug-drug interaction – Regular Medication recording • Output – paper and electronic – Correspondence – information sharing • Incoming dispensing messages. Objectives • Reduce preventable medication errors • Facilitate greater use of generic (non-trade/brand) drugs.
  • 11. Solution Components NZF NZULM Houston Medical VIP.net Patient Health Information Management System (Hospice) NZePS Dispensing System (Pharmacy)
  • 12. Clinician Requirements • Doses: A list of doses associated with each medicine. Doses should be easy to maintain. • Medication readability: – ‘paracetamol 500mg tablet’ – ‘paracet 500mg+pseudoeph HCl 30mg (&) chlorpheniramine mal 2mg+paracetamol 500mg+pseudoeph 30mg tabs’, • Secondary use of prescribing information in correspondence • Drug Allergy alerts: Recognition of existing drug allergy alerts • Form/Strength: Option to prescribe either by form (1 tablets, 5 ml) or by strength (10 mg). – A pick list of administrative units is confined to units associated with the medication in the NZULM. • Supply: Need to request pharmacist to determine supply quantity. – Administration Unit of ‘QS’
  • 13. Prescribing Functionality Evaluation • • • • NZF interaction checking is working well Improved prescription generation Prescribing data now makes greater clinical sense Fewer scripts returned or queried by the pharmacy because supply details have been overlooked • Some optional fields, such as medication route, are being omitted because they do not read well on the prescription. • Increased generic prescribing – System default and generic/trade cross matching • No improvements in dose consistency – Latin/non-Latin frequency and timing instructions.
  • 14. Implementer Challenges • Relationship Management: Huge overhead when piloting • Challenge of justifying benefits: Having to sell government strategy, Connected Health licenses. Documents and implementation specifications have limited value in communicating change drivers and benefits directly to information system end users. • Technical Complexity: Huge. With integration to third party products you have to know where the boundaries are. Medicines are included in the NZULM that are not yet available. • Collaboration: Need for regular site visits • Expectations: Structured data entry is extremely difficult.
  • 15. Community ePrescribing Recommendations • More work is needed to understand the potential impact to clinician’s workflow when a standard or specifications are developed. • Need to ensure there is a clear path to convert data (such as allergies) from old to new system. • Useful to have supporting documentation or web based information, such as case studies, to support implementation.
  • 16. Lessons Learned • Scope everything thoroughly • Set realistic timeframes and adequately resource the project • Understand clinician workflow • Engage clinicians, nurses and allied staff • The perfect system does not exist
  • 17. The importance of recognition – What an achievement! Thanks to: Arohanui Clinicians Houston Medical Developers Patients First Team (Peter Jordan) NZULM (Craig Mabon) NZF (Chris Hilder)