Our research

Our overall vision is to use data, best practice and evidence to improve newborn care in low resource settings (from tertiary hospitals through to rural health clinics) to improve survival and equitable access to universal care.

Our approach combines behavioural and implementation science, health economics, and quantitative analysis of clinical outcome data, to:

  • Explore barriers and enablers to improving care and implementing the Neotree platform
  • Refine and optimise the functionality of the Neotree platform
  • Optimise scale up and sustainability

Thus creating a digital learning health system for newborn care.

The Neotree system is a horizontal intervention which aims to comprehensively address the full range of newborn disorders, as opposed to being disease-specific. It is readily adaptable to local context – clinical, demographic and technology. For example, It has, and can be readily adapted to, incorporate new disease trends in outbreak situations, such as COVID-19.

Research Highlights

Paper describing Neotree Beta development and testing at Zomba Central Hospital in Malawi
Paper describing pilot implementation of Neotree at Sally Mugabe Central Hospital in Zimbabwe
The use of Neotree data during the COVID-19 pandemic to describe the impact on neonatal care in hospitals in Malawi and Zimbabwe
  • 2013-14

    2013-14

    UK

    UK based literature review

  • 2015-16

    2015-16

    UK & Bangladesh

    Bangladesh scoping visit Audit of Bangladeshi facilities

  • 2016

    2016

    UK

    Neotree-alpha & Neotree charity setup

  • 2016-17

    2016-17

    Malawi

    Pilot Feasibility, acceptability, usability study

    Development of clinical decision support

  • 2018-19

    2018-19

    UK & Zimbabwe

    Pilot Quality improvement Neotree impact on Sepsis

    Delphi study

  • 2019-22

    2019-22

    Malawi & Zimbabwe

    Pilot Implementation evaluation and optimization of Neotree functionality & decision support

  • 2022

    2022 onwards

    UK, Malawi, Zimbabwe

    Large scale evaluation / Optimisation of algorithms / Secondary data analysis / Innovation labs / Implementation in other country sites

Overview of research

2013-2015

Prototype development:

The Neotree platform was created by Michelle Heys and Erin Kesler who developed a basic version on a tablet with the computer science team at UCL (Tom Page, supervised by Patty Kostkova), funded by UCL Small Grants award (Principal Investigator Heys). Concepts were refined following a site visit to Bangladesh, a workshop with Bangladeshi newborn healthcare workers, n=15 and an audit of 6 newborn health facilities,unpublished).

In partnership with UCL Computer Science collaborators, the team developed a working prototype for the identification and management of hypothermia in newborns, with excellent preliminary feedback from Bangladeshi health workers on usability and clinical relevance (unpublished data, 2014).

2016

Alpha version development

Alpha version of the app was developed in the UK (Caroline Crehan, Charlie Norman and Matteo Giacconi, Queen Dube, Erin Kesler and Michelle Heys). Key functions developed in this stage were admission data capture and resuscitation clinical management support.

2016-2017

Pilot implementation evaluation and Beta version development in Malawi

Pilot implementation evaluation with 46 healthcare professionals demonstrated high acceptability and usability of the data capture of admission information and resuscitation support functions of the Neotree (Crehan 2019 and Crehan 2021). Healthcare professionals reported high perceived improvements in ability to deliver quality newborn care after using the Neotree on the ward. They described improved confidence in clinical decision making, clinical skills, critical thinking, and standardisation of care.

Clinical decision support function of the Neotree-beta was developed in two parts, all based on best available evidence including international (WHO/ HBB) and national guidelines (e.g. Malawian national guidelines (COIN). First, national and international standardised neonatal resuscitation guidelines were digitialised and incorporated into the Neotree. Second, a larger set (n=42) of clinical decision support algorithms were developed according to national and international guidelines and evidence in newborn care.

2017-2018

 Delphi study

A Delphi study was conducted to determine whether a panel of 22 neonatal experts with global expertise could address evidence gaps in neonatal guidelines included in the Neotree: sepsis, neonatal encephalopathy, respiratory distress and thermoregulation (Evans 2021). A qualitative study of neonatal experts was simultaneously conducted to explore barriers and enablers to improving quality of newborn care (Kaur et al, manuscript in progress).

2018

Beta version development and piloting in Zimbabwe:

In 2018 the Neotree was further developed and implemented in Sally Mugabe Central Hospital (annual delivery rate ~12,000; newborn care case fatality rate ~210 per 1000 admissions), Harare, Zimbabwe (funded by the Healthcare Infection Society PI Fitzgerald). The primary goal was to evaluate this as a digital Quality Improvement (QI) system to improve outcomes in neonatal sepsis. Baseline audit: Chimhini et al 2021. First year of data and QI experience described here: Gannon et al 2021, for example when the app with management guideline support was implemented in SMCH, there was a sustained fall in oral antibiotic prescribing on discharge (not an evidence-based treatment) from 97% to 2%.

2019-2020

Beta version development and piloting Malawi

In 2019 the Neotree was implemented also in Kamuzu Central Hospital (annual delivery rate ~4500, case fatality rate ~250 per 1000 admitted babies), Malawi (Caroline Crehan, Tim Hull-Bailey, Michelle Heys, Msandeni Chiume; April 2019 – Oct 2019, funded through remaining Naughton-Cliffe Matthews funds, private fundraising and pro-bono contributions). This phase of work focused on the development of the data dashboard prototype to visualise the Neotree data on the ward, and also a behavioural science embedded pilot implementation evaluation of the data capture functions of the Neotree. When the app was initially rolled out in KCH, over 80% of babies were admitted hypothermic. This dropped to less than 50% after the data dashboard was deployed (Mgusha 2021, manuscript in preparation). The underlying code for the app was rewritten and the data pipeline developed (Yali Sasson, Dan Silksmith with South African Baobab web services team).

2019-2022

Gamma version development and pilot implementation evaluation Malawi and Zimbabwe:

In Oct 2019 a Wellcome Trust-funded digital innovation award (PI Heys/ Chiume/ Chimhuya) expanded the co-development of Neotree functions and conducted behavioural sciences embedded pilot implementation evaluation in 3 hospitals (Kamuzu Central Hospital, Malawi and Sally Mugabe Central Hospital and Chinhoyi Provincial hospitals (~3000 annual births, case fatality rate ~190 per 1000 admissions), Zimbabwe). To date we have gathered data for more than 16,000 babies, and more than 350 healthcare professionals have interacted with the Neotree system. Data thus far demonstrate ongoing high acceptability, feasibility and usability and evidence of improvements in clinical care.

At the same time (2019-2020) Sam Neal (UCL MRes student supervised by Felicity Fitzgerald, Mario Cortina Borja and Michelle Heys), working together with Gwen Chimhini and David Musorowegomo, undertook a series of substudies to address gaps in evidence based guidelines in neonatal sepsis in low resource settings. First, he commenced a literature review of prediction models for sepsis, protocol published: Neal et al 2020, final results anticipated summer 2021. Second, he constructed a dataset from the routine admission and discharge Neotree data from the neonatal unit of Sally Mugabe Central Hospital, Zimbabwe and then developed a clinical prediction model to diagnose neonatal sepsis was then developed on this dataset using multiple logistic regression (thesis summary: Neal 2020).

Similarly (2020-2021) Edna Mugwagwa (UCL BMedSci student supervised by Mario Cortina Borja, Michelle Heys and Simba Chimhuya), working together with Hannah Gannon and Marcia Mangiza, undertook a substudy to address gaps in evidence based guidelines in neonatal encephalopathy in low resource settings

Neotree deployment has been robust despite external crises (e.g. industrial action, economic collapse and COVID-19) and data have been used to improve care and monitor healthcare outcomes during COVID-19 (Chimhuya 2021). COVID-19 clinical management and infection control guidance was incorporated into the Neotree app. Neotree data from Zimbabwe are also being used as part of a national study assessing the impact of COVID-19 pandemic on maternal infant transmission of HIV. (PI Gibb; funded via Viiv healthcare). Dr Hannah Gannon (UCL PhD student) is currently undertaking her MRC funded PhD exploring the impact of external crises on implementation of digital interventions in low resource settings.

2020: Wellcome Trust enrichment Award in open data and public engagement (PI Heys/Wilson, Open data lead: Yali Sasson, Nushrat Khan; Public Engagement Lead: Emma Wilson). We were awarded these enrichment funds to further strengthen our public engagement activities, working with ArtGlo Africa and the UCL co-production collective.

Papers, abstracts and other

2022

Chimhuya S., Neal S.R., Chimhini G., et al Indirect impacts of the COVID-19 pandemic at two tertiary neonatal units in Zimbabwe and Malawi: an interrupted time series analysis, BMJ Open, 2022 https://bmjopen.bmj.com/content/12/6/e048955

Crehan C, Chiume M, Mgusha Y, et al. Usability-Focused Development and Usage of NeoTree-Beta, an App for Newborn Care in a Low-Resource Neonatal Unit, Malawi. Front. Public Health 2022 10:793314. doi: 10.3389/fpubh.2022.793314

Heys, M, Kesler, E, Sassoon, Y, et al. Development and implementation experience of a learning healthcare system for facility based newborn care in low resource settings: The Neotree. Learn Health Sys. 2022;e10310. doi:10.1002/lrh2.10310

2021

Mgusha Y, Nkhoma DB, Chiume M, et al Admissions to a Low-Resource Neonatal Unit in Malawi Using a Mobile App and Dashboard: A 1-Year Digital Perinatal Outcome Audit. Front. Digit. Health 2021 3:761128. doi: 10.3389/fdgth.2021.761128

Gannon H, Chimhuya S, Chimhini G, et al Electronic application to improve management of infections in low-income neonatal units: pilot implementation of the NeoTree beta app in a public sector hospital in Zimbabwe BMJ Open Quality 2021;10:e001043. doi: 10.1136/bmjoq-2020-001043 https://bmjopenquality.bmj.com/content/bmjqir/10/1/e001043.full.pdf

Evans M, Corden MH, Crehan C, Fitzgerald F, Heys M. Refining clinical algorithms for a neonatal digital platform for low-income countries: a modified Delphi technique. BMJ Open. 2021 May 18;11(5):e042124. doi: 10.1136/bmjopen-2020-042124. PMID: 34006538; PMCID: PMC8130744.

Stevenson AG, Tooke L, Edwards EM, Mangiza M, Horn D, Heys M, Abayneh M, Chimhuya S, Ehret DEY. The use of data in resource limited settings to improve quality of care. Semin Fetal Neonatal Med. 2021 Feb;26(1):101204. doi: 10.1016/j.siny.2021.101204. Epub 2021 Feb 3. PMID: 33579628.

2020

Crehan C, Kesler E, Chikomoni IA, Sun K, Dube Q, Lakhanpaul M, Heys M. Admissions to a Low-Resource Neonatal Unit in Malawi Using a Mobile App: Digital Perinatal Outcome Audit. JMIR Mhealth Uhealth. 2020 Oct 21;8(10):e16485. doi: 10.2196/16485. PMID: 33084581; PMCID: PMC7641784. https://pubmed.ncbi.nlm.nih.gov/33084581/

G. Chimhini, S. Chimhuya, L. Madzudzo, M. Heys, C. Crehan, V. Robertson, et al. Auditing use of antibiotics in Zimbabwean neonates, Infect Prev Pract, 2 (2020), p. 100046,  doi: 10.1016/j.infpip.2020.100046

Neal SR, Musorowegomo D, Gannon H, et al Clinical prediction models to diagnose neonatal sepsis: a scoping review protocol BMJ Open 2020;10:e039712. doi: 10.1136/bmjopen-2020-039712

2019

Crehan C, Kesler E, Nambiar B, et al The NeoTree application: developing an integrated mHealth solution to improve quality of newborn care and survival in a district hospital in Malawi BMJ Global Health 2019;4:e000860. https://gh.bmj.com/content/4/1/e000860

2021

Crehan C., Chiume-Kayuni, M., Mgusha Y., Dinga P., Hull-Bailey, T., Normand C., Sassoon, Y., Greenwood, K., Lorencatto, F., Lakhanpaul, M., Heys, M. Usability-focused development of NeoTree Beta App for a low-resource neonatal unit. (accepted abstract, in press: Archives of Disease in Childhood) 2021

Crehan, C., Chiume, M., Mgusha, Y., Lakhanpaul, M., Lorencatto, F., Fitzgerald, F., Normand, C., Shair, F., Hull-Bailey, T., Heys, M. Usability-focused development of a feedback dashboard for a low resource neonatal unit. (accepted abstract, in press: Archives of Disease in Childhood) 2021

Kaur, E., Heys, M., Evans, M., Crehan, C., Costello, A., Chiume, M., Wilson, E. on behalf of the NeoTree Team Barriers to achieving quality neonatal care in low resource settings: perspectives from a unique panel of neonatal health experts (accepted abstract, in press: Archives of Disease in Childhood) 2021

Mgusha, Y., Nkhoma DB., Chiume-Kayuni M., Gundo B. Gundo R., Shair F., Dinga, P., Lakhanpaul M., Lorencatto, F., Heys M., Crehan, C. Admissions to a Low-Resource Neonatal Unit in Malawi using a Mobile App and Dashboard: A One Year Digital Perinatal Outcome Audit (accepted abstract in press: Archives of Disease in Childhood) 2021

Neal, SR., Gannon, H., Chimhini, G., Fitzgerald, F., Heys, M., Chimhuya, S., Cortina-Borja, M. on behalf of the NeoTree team. Diagnosing early-onset neonatal sepsis in low-income and middle-income countries: development of a multivariable prediction model from routine clinical data. (accepted abstract in press: Archives of Disease in Childhood) 2021

2018

073 A qualitative study of healthcare workers perceived barriers and facilitators to neonatal care in a central hospital in malawi. C Crehan, T Huq, E Kesler, Q Dube, M Lakhanpaul, M Heys, Archives of Disease in Childhood 103 (Suppl 2), A30-A30

2015

G259 (P) A systematic review of health worker-led interventions to reduce mortality in low birth weight neonates in low and middle-income institutional settings. E Kesler, A Costello, M Heys, K Azad Archives of Disease in Childhood 100 (Suppl 3), A112-A113

2020

Viiv (£217,517; 01/09/2016 – 01/09/2017) (Collaborators, received XX) Unintended consequences of the COVID-19 pandemic on prevention of mother-to-child transmission of HIV and syphilis in Zimbabwe.

Wellcome Trust Public engagement enrichment award (£100,000: UNS81821; 01/10/2020 – 06/4/2021)

Wellcome Trust Open Data enrichment award: (£50,000; UNS81821; 01/10/2020 – 06/4/2021)

2019

Wellcome Innovator Award (£776,276; UNS81821) Evaluating the NeoTree: An eHealth solution to reduce neonatal mortality in two low income countries: Malawi and Zimbabwe

2018

UCL Global Engagement Fund (£2000) Improving quality of newborn care in under resourced settings: Zimbabwe-UCL collaborative

Healthcare Infection Society (£10000). Reducing mortality from neonatal sepsis: a pilot mixed methods approach in Zimbabwe.

2016

Global Health Development Fund (£5,000; 01/09/2016- 01/09/2017) Testing the acceptability, feasibility and usability of the NeoTree in Malawi: An eHealth solution to reduce neonatal mortality in low-income countries

2015

Naughton/Clift-Matthews Global Health Fund (£15,000; 01/04/2015 – 01/02/2017) Developing the DECREASE trial. Decreasing Case fatality rates of newborns through E-health, Audit and Supportive Education: a cluster randomised stepped-wedge trial (cRCT) of the Newborn Care Network.”

2014

UCL, Small Grants £4,000; 01/08/2014- 30/06/2015) Developing the DECREASE trial. Decreasing Case fatality rates of newborns through E-health, Audit and Supportive Education: a cluster randomised stepped-wedge trial (cRCT) of the Newborn Care Network.”

2021

“Indirect impacts of the COVID-19 pandemic at two tertiary neonatal units in Zimbabwe and Malawi: an interrupted time series analysis”; Samuel R. Neal

Royal College of Paediatrics and Child Health Annual Conference 2021

2020

Dec 2020: Prediction model for neonatal sepsis using routine data in low- and middle-income settings (Samuel R. Neal)

BPAIIGWinter2020

Feb 2020: UCL ICH PPP – Data science symposium (Heys)

https://www.ucl.ac.uk/child-health/sites/child-health/files/ppp-data-science-michelle-heys.pdf

Feb 2020: UCL IHE TechSharing Seminar Series – Global Digital Health

2019

March 2019: Durban

March 2019, CASMI Medical Innovation Leadership meeting

2018

Health services research conference, Nottingham

2017

Digital health conference (Crehan)

Feb 2017: Young entrepreneurs in Malawi Pitch innovative ideas

2020

British Paediatric Allergy, Immunity and Infection Group Winter Meeting Second Prize for Trainee Presentations, Samuel R. Neal “Development of a clinical prediction model to diagnose neonatal sepsis in low-income and middle-income countries using routine clinical data”

International Child Health Group David Morley Prize, Samuel R. Neal, “Indirect impacts of the COVID-19 pandemic at two tertiary neonatal units in Zimbabwe and Malawi: an interrupted time series analysis”

2019/2020

The Neotree was voted as one of the top five innovations developed at UCL in the #madeatUCL competition

Research team:

Co-investigators

Professor Mario Cortina Borja
Dr Fabiana Lorencatto
Dr Kristina Curtis
Dr Hassan Haghparast-Bidgoli
Dr Gwen Chimhini
Professor Monica Lakhanpaul

Implementation and research team

Deliwe Nkhoma
Tarisai Chiyaka
Dr Emma Wilson
Dr Hannah Gannon
Dr Nushrat Khan

Collaborators

Dr Alex Stevenson

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