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

Maureen Perrin Digitisation of Public Health to Improve Population Health Informatics Conference 2019 Health and Clinical Outcomes 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/

Improving Health: Connecting Interventions DATA Source: Adapted from Frieden, NEJM. http://www.nejm.org/doi/pdf/10.1056/NEJMsa1511248

The availability of data alone does not improve health outcomes... Source: PHI Sahay et al, Twitter Epinet

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

Figure 1: Number of people infected with E. coli O157 (Canada, 2018) Source: https://www.canada.ca/en/public-health/services/public-health-notices/2018/outbreak-ecoli-infections-linked-romaine-lettuce.html

2014 WHO STEPS Survey – Kingdom of Tonga Source: https://www.who.int/ncds/surveillance/steps/2012_Tonga_STEPSReport.pdf

1 - Provide programs and So how do we set services 2 - Capture up digitization 7 - Take Action patient-level data projects to support people 6 - Interpret 3 - Submit data data using data? 5 - Analyze 4 - Validate Case study – Kingdom of Tonga data data

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 Reproductive� Health Environmental� Community� Health Health Tonga� Population Non- Communicable� Communicable� Disease� &� Disease Health� Promotion 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

Focusing on Data for Decision Making Strategic Revise Revise Annual Monthly Revise Daily No changes

Fanafana Ola: Everyone has a Role in Improving Health Ministry / And different data needs to Donor Partners support their work… Public Health Division • service delivery • Reproductive Health workload management Section • program planning • community/population District | Health Facility health assessment Nurses - Patient Care

Technology Environment Core Systems Analysis Supporting Systems Android tablet

Drilling Down into the Data Changes - Reproductive Health Contraceptive use 1 - Provide programs and services 2 - Capture 7 - Take Family Maternal Child patient-level Action Immunizations data Planning Health Service 6 - Interpret 3 - Submit data data Delivery Vital Stats Population 5 - Analyze 4 - Validate data data

Contraceptive Use – Paper Based World Daily record Monthly District Annual District District Annual Submission, Validate, Submission, Dashboard Analysis, Intrepret, Validation, Analysis, Action Report

Contraceptive Use – Digital World (Monthly)

Existing Monthly Process New Tablet entry Simplified paper form Validation and Analysis

Support – Submit, Validate User guide with data definitions

Validate Correctness Currency and Completion Correctness Consistency

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

1 - Provide programs and services 2 - Capture We’re only part 7 - Take Action patient-level data way round the circle – on one indicator in one 6 - Interpret 3 - Submit data data program … 5 - Analyze 4 - Validate data data “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 organization • Data at many more finger tips across organization • Context rests at the point of collection and must be passed upwards MoH IT Support

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

Put data in the ‘Good Enough’ Community Data context of evidence- informed decision Public Community making health Resources and Political Preferences expertise Evidence-informed decision making is using the best sources of information to achieve the best possible outcomes Guidelines/ Research Addresses uncertainty and data quality issue – fit for purpose Source: Adapted from https://www.nccmt.ca/about/eiph

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