Digitisation of Public Health to Improve Population Health - - PowerPoint PPT Presentation

Digitisation of Public Health to Improve Population Health and Clinical Outcomes

Maureen Perrin Health Informatics Conference 2019 Melbourne, Australia

Our Journey

• 1. Defining public health
• 2. Focus on people using data to

improve health, enabled by technology

• 3. Kingdom of Tonga: A Case

Study

Public Health – Part of the Health System

3

Source: http://www.health.gov.on.ca/en/pro/programs/publichealth/oph_standards/docs/protocols_guidelines/Ontario_Public_Health_Standards_2018_en.pdf

Blurring Lines

Source: modified from https://www.healthcatalyst.com/population-health/

Source: Adapted from Frieden, NEJM. http://www.nejm.org/doi/pdf/10.1056/NEJMsa1511248

DATA

Improving Health: Connecting Interventions

The availability of data alone does not improve health

• utcomes...

Source: PHI Sahay et al, Twitter Epinet

People taking action improves health

1 - Provide programs and services 2 - Capture patient-level data 3 - Submit data 4 - Validate data 5 - Analyze data 6 - Interpret data 7 - Take Action

Source: https://www.canada.ca/en/public-health/services/public-health-notices/2018/outbreak-ecoli-infections-linked-romaine-lettuce.html

Figure 1: Number of people infected with E. coli O157 (Canada, 2018)

2014 WHO STEPS Survey – Kingdom of Tonga

Source: https://www.who.int/ncds/surveillance/steps/2012_Tonga_STEPSReport.pdf

So how do we set up digitization projects to support people using data?

Case study – Kingdom of Tonga

1 - Provide programs and services 2 - Capture patient-level data 3 - Submit data 4 - Validate data 5 - Analyze data 6 - Interpret data 7 - Take Action

Kingdom of Tonga

• Population size – ~106,000 across

4 island groups

• Public health mandate includes:
• Prevention, promotion,

protection programs and services

• Publicly funded primary

care delivered in the community

• Multiple data challenges

Fanafana Ola - Project Vision

Tonga Population

Reproductive Health Environmental Health Communicable Disease Non- Communicable Disease & Health Promotion Community Health

Data – Information – Knowledge Public Health Action

To build a user-friendly, sustainable system that supports people in making evidence-informed decisions to improve the health of Tongans

Strategic Annual Monthly Daily

Focusing on Data for Decision Making

No changes Revise Revise Revise

Fanafana Ola: Everyone has a Role in Improving Health

Ministry / Donor Partners Public Health Division Reproductive Health Section District | Health Facility Nurses - Patient Care

And different data needs to support their work…

• service delivery
• workload management
• program planning
• community/population

health assessment

Technology Environment

Analysis Android tablet

Core Systems Supporting Systems

Drilling Down into the Data Changes - Reproductive Health

Immunizations Family Planning Maternal Child Health Service Delivery Population Vital Stats

Contraceptive use

1 - Provide programs and services 2 - Capture patient-level data 3 - Submit data 4 - Validate data 5 - Analyze data 6 - Interpret data 7 - Take Action

Contraceptive Use – Paper Based World

Annual District Submission, Validation, Analysis, Report District Annual Dashboard Monthly District Submission, Validate, Analysis, Intrepret, Action Daily record

Contraceptive Use – Digital World (Monthly)

Monthly Process

Simplified paper form Tablet entry Validation and Analysis Existing New

Support – Submit, Validate

User guide with data definitions

Validate

Correctness Currency and Completion Consistency Correctness

Analysis – Interpret (Kind of)

Key point: Examine place (district to island to national), trends over time, create indicators, build maps…

We’re only part way round the circle – on one indicator in one program …

1 - Provide programs and services 2 - Capture patient-level data 3 - Submit data 4 - Validate data 5 - Analyze data 6 - Interpret data 7 - Take Action

“Once we have the data, the energy to use it is dissipated!” – Sahay et al

Implications

• Addresses some of the data

challenges that were identified – but creates new ones

• Very high degree of change across
• rganization
• Data at many more finger tips across
• rganization
• Context rests at the point of

collection and must be passed upwards

MoH IT Support

Unlock data use through structured conversations

1 - Provide programs and services 2 - Capture patient-level data 3 - Submit data 4 - Validate data 5 - Analyze data 6 - Interpret data 7 - Take Action

Put data in the context of evidence- informed decision making

Evidence-informed decision making is using the best sources

• f information to achieve the best

possible outcomes

Source: Adapted from https://www.nccmt.ca/about/eiph

‘Good Enough’ Community Data

Community and Political Preferences Guidelines/ Research Resources

Public health expertise

Addresses uncertainty and data quality issue – fit for purpose

Think about roles, actions and data needs across organization

Facilitate conversations that build trust

Acknowledge that evidence –informed change takes time

Thank you

With acknowledgement and thanks:

• Dr Reynold Ofanoa and MOH leadership
• Sister Afu Tei and team
• Dr Ofa and team
• Dr Lousie and team
• Sione Tomiki and team
• Julie Bowen
• Walter Hurrell
• Nancy Tupou
• Siosaia Palavi
• Michael Nunan
• Edwin Monk-Fromont and BES team
• Latifa Mnyusiwalla, Dr Margie Kennedy and the Gevity team
• DFAT InnovationXchange
• University of Oslo and the DHIS2 community