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| Name | Class |
|---|---|
| Biomedical Research and Training Institute, Zimbabwe | OTHER |
| PACHI Malawi - Parent and Child Health Initiative Trust | OTHER |
| Ministry of Health and Child Welfare, Zimbabwe | OTHER |
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Neonatal mortality remains unacceptably high. Globally, the majority of mothers now deliver in health facilities in low resource settings where quality of newborn care is poor. Health systems strengthening through digitial quality improvement systems, such as the Neotree, are a potential solution. The overarching aim of this study is to complete the co-development of NeoTree-gamma with key functionalities configured, operationalised, tested and ready for large scale roll out across low resource settings.
Specific study objectives are as follows:
Every year 2.4 million newborn deaths occur worldwide. Up to 70% of newborn deaths are avoidable with implementation of standard-technology, evidence-based interventions. Health systems strengthening and education and training in newborn care are key to saving newborn lives. Implementation of evidence based interventions and guidelines can be supported through provision of reliable data systems, clinical decision support tools and education. Using open-source code and maintaining local data ownership the investigators have used iterative, human- and user-centered design methods and agile processes in software and data management development and design to develop the Neotree: a digital quality improvement system for postnatal facility-based care in low resource settings.
The Neotree aims to improve quality of care and newborn survival through combining data-capture, clinical decision-support, education in newborn care, and feedback of data to dashboards and national aggregate data systems. The investigators found the concept of device-enabled decision support to improve newborn care to be acceptable during workshops with healthcare professionals in Bangladesh (n~15; 2014) and developed and delivered a prototype of the app. Following this, the investigators co-developed and piloted an early version of the NeoTree with Malawian Healthcare Professionals (HCPs) (n=46; 2016-2017), who reported it was easy to use and helped them deliver quality care.
The research project described in this protocol will enable the investigators to complete the co-development of the Neotree in Zimbabwe and Malawi and generate evidence for how to test it at scale.
Methods and analysis: Mixed methods (i) intervention co-development and optimisation, (ii) pilot implementation evaluation and (iii) economic evaluation study. The Neotree will be implemented in two hospitals in Zimbabwe, and one in Malawi. Clinical and demographic newborn data will be collected via the Neotree, in addition to behavioural science informed qualitative and quantitative implementation evaluation data, cost data, measures of quality newborn care and usability data over the 2-year study period. Six-months of newborn outcome data and cost data will be collected from 2 hospitals receiving usual care for comparison. Case-fatality rate data will inform sample size calculations and study design for a large scale roll out. Training manuals will be refined. Neotree clinical decision support algorithms will be optimised according to best available evidence and clinical validation studies.
Our overall vision is to use best practice and information technology to improve clinical decisions for newborn care and increase rates of newborn survival in under-resourced health care settings. In this study, the care for an estimated 15,000 babies across the three test sites will be impacted by the Neotree. Through successful rollout across Zimbabwe and Malawi - the care for nearly 300,000 babies could be improved annually.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Behavioural science and implementation research | The investigators will recruit adult health care professionals, senior hospital managers and parents/carers of neonates. Approximate sample sizes in each country are as follows: Zimbabwe: ~80 Healthcare Professionals ; ~20 Carers/ parents of newborn babies and ~10 Hospital administrators/ managers. Malawi: ~40 Healthcare Professionals (~10 HCPs per focus group discussion); ~10 Carers/ parents of newborn babies and ~10 Hospital administrators/ managers. Total participants for new data collection: 180 (this number is likely to be much lower if there is limited staff turnover at neonatal units and if HCPs agree to participate in multiple research activities). | ||
| Cost data | A time-use survey will be conducted with a small sample of Healthcare professionals at all 3 hospitals where the Neotree is implemented (sample size ~30) to measure time spent for different activities/procedures carried out on or for a patient. | ||
| Neonatal admissions at Hospital sites where Neotree is implemented in Zimbabwe and Malawi | The investigators will record routine clinical admission, discharge and microbiological data for all newborns admitted to the newborn care units using the NeoTree as a replacement to paper-based forms. Individual-level patient data will be collected on all neonates admitted for care at Sally Mugabe Central (Oct 2019 to April 2022) and Chinhoyi Provincial Hospitals (Oct 2020 to April 2022) Zimbabwe, and Kamuzu Central Hospital (Oct 2019 to April 2022), Malawi. Given typical admission rates, this equates to a sample size ~12,000 babies in Zimbabwe and ~ 4000 babies in Malawi. Data will be collected from February 2019 to the end of the study, to explore trends over time and also include measures of quality newborn care. |
| |
| Comparative case-fatality rates in units using NeoTree and representative control sites |
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Neotree | Device | The Neotree is a digital app, data collection and quality improvement system. collecting and collating routine health data at the bedside for babies on admission and discharge and for laboratory results. it provides point of care education and clinical decision support to optimise the clinical care of sick and vulnerable newborns according to approved and best available clinical guidelines. |
| Measure | Description | Time Frame |
|---|---|---|
| Acceptability of the Neotree as a digital tool to improve neonatal care and survival using the Theoretical framework of acceptability (TFA) among newborn health care providers and parents/ families of sick/ vulnerable newborns. | Qualitative data collected via semi-structured interviews and focus groups will be collected. Topic guides will be informed by the TFA in order to assess acceptability of the Neotree to be embedded into usual clinical care to improve care and outcomes for sick and vulnerable babies in low resource settings. | 2.5 years |
| Feasibility of the Neotree as a digital tool to improve neonatal care and survival using the Theoretical domains framework (TDF) of feasibility among newborn health care providers and parents/ families of sick/ vulnerable newborns. | mplementation science evaluation of feasibility of the Neotree to be embedded into Qualitative data collected via semi-structured interviews and focus groups will be collected. Topic guides will be informed by the TDF in order to assess feasibility of the Neotree to be embedded into usual clinical care to improve care and outcomes for sick and vulnerable babies in low resource settings. | 2.5 years |
| Quantitative Usability (Systems usability score) and qualitative usability of the Neotree and usage (percentage of admitted babies with Neotree admissions data) of the Neotree | mplementation science evaluation of usability and usage of the Neotree to be for healthcare workers in low resource hospital settings in Malawi and Zimbabwe to optimise quality of care or newborns. | 2.5 years |
| Measure | Description | Time Frame |
|---|---|---|
| Cost of implementation | Costs of implementation of the Neotree to 3 newborn care units, 2 in Zimbabwe and 1 in Malawi | 2.5 years |
| Case fatality rates (deaths per 1000 babies admitted to newborn care unit) over time |
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Group 1: Healthcare professionals w
HCP will be recruited to the behaviour change and implementation science evaluation study at SMCH, KCH and CPH. Qualitative and quantitative methods will assess acceptability, feasibility and usability of the NeoTree. We estimate a sample size of 160 across sites addressing each aspect, and will carry out an assessment for data thematic saturation before conducting any further interviews (Sample size ~160). An additional 30 HCPs will be recruited to the health economic cost data.
Inclusion criteria:
Exclusion criteria:
● aged over 65 years (Zimbabwe only); No upper exclusion age in Malawi
Group 2: Parents/ carers
A qualitative study will be conducted with families and carers of newborns admitted to the intervention hospitals to assess acceptability of the NeoTree (Sample size ~30, followed by analysis for thematic saturation prior to carrying out further interviews).
Inclusion criteria:
Exclusion criteria:
● Parents aged under 18 years (Malawi) and under 16 years (Zimbabwe)
Group 3: Newborns admitted to newborn care units
Inclusion criteria:
Exclusion criteria:
● none
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Group 1: Healthcare professionals and managers working in newborn care units in the 3 hospitals.
Group 2: Parents/ carers of babies admitted to the 3 hospital sites. Group 3: All neonates admitted to the hospital sites
| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Michelle Heys, MD(Res) | Contact | 07541381106 | m.heys@ucl.ac.uk |
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| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Kamuzu Central Hospital | Recruiting | Lilongwe | Malawi |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 33084581 | Background | 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. | |
| 30713745 | Background | Crehan C, Kesler E, Nambiar B, Dube Q, Lufesi N, Giaccone M, Normand C, Azad K, Heys M. The NeoTree application: developing an integrated mHealth solution to improve quality of newborn care and survival in a district hospital in Malawi. BMJ Glob Health. 2019 Jan 16;4(1):e000860. doi: 10.1136/bmjgh-2018-000860. eCollection 2019. |
| Label | URL |
|---|---|
| Neotree charity website | View source |
Not provided
We are planning to create and host an open data set from the clinical Neotree data. All Neotree code is open source on github.
Study protocol is underreview at BMJ Open (we will have a decision in next 3 months); statistical analysis plans have and will be published with each analytical manuscript. Final clinical study report will be published. Software code is already available on github.
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| ID | Term |
|---|---|
| D047928 | Premature Birth |
| D000071074 | Neonatal Sepsis |
| D066087 | Perinatal Death |
| D007567 | Jaundice, Neonatal |
| D007232 | Infant, Newborn, Diseases |
| ID | Term |
|---|---|
| D007752 | Obstetric Labor, Premature |
| D007744 | Obstetric Labor Complications |
| D011248 | Pregnancy Complications |
| D005261 | Female Urogenital Diseases and Pregnancy Complications |
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The investigators will collect outcome data from neonatal clinical records at two additional representative hospital sites in Zimbabwe over a 6 month period (sample size ~1200), to inform our sample size calculation for a full evaluation at scale in the future. Individual level data will be collected retrospectively from Bindura Provincial Hospital and Parirenyatwa Hospital (1/4/2021 to 01/10/2021 or beyond depending on sample size). |
| Clinical validation sub-study | The sample size for our diagnostic sub-study has been calculated using sepsis as the index diagnosis. Assuming sensitivity and specificity of 92% (lower 95% CI: 84%) >222 babies would need to be diagnosed with sepsis over five months, during which ~>2000 babies will be admitted with sepsis across sites (Sally Mugabe Central Hospital, Zimbabwe and Kamuzu Central Hospital, Malawi). If necessary, the investigators will continue to collect data throughout the duration of the study until our sample size is achieved. These data will be collected as part of the routine Neotree data collection. |
|
|
Case fatality rates of admitted babies to the 3 hospital units using the Neotree over time
| 2.5 years |
| Facility based neonatal mortality and stillbirth birth rates overtime | Overall deaths per 1000 live births and still birth rates in the 3 hospital units using the Neotree over time. | 1.5 years |
| Measures of quality of newborn care (aligned with WHO standards of quality newborn care) | Quantiative measures of standards of quality newborn care measured using the Neotree data. in the 3 hospital facilities where it is implemented. | 2.5 years |
| 5. number of babies with key diagnoses over time (e.g. prematurity, neonatal sepsis, neonatal encephalopathy) | Number and outcome (death/discharge) for key diagnostic groups | 2.5 years |
| Bindura Provincial Hospital | Recruiting | Bindura | Zimbabwe |
|
| Chinhoyi Provincial Hospital | Recruiting | Chinhoyi | Zimbabwe |
|
| Parirenyatwa Hospital | Recruiting | Harare | Zimbabwe |
| Sally Mugabe Central Hospital | Recruiting | Harare | Zimbabwe |
|
| 34006538 | Background | 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. |
| 33472853 | Result | Gannon H, Chimhuya S, Chimhini G, Neal SR, Shaw LP, Crehan C, Hull-Bailey T, Ferrand RA, Klein N, Sharland M, Cortina Borja M, Robertson V, Heys M, Fitzgerald FC. 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 Qual. 2021 Jan;10(1):e001043. doi: 10.1136/bmjoq-2020-001043. |
| open source code | View source |
| D000091642 | Urogenital Diseases |
| D018805 | Sepsis |
| D007239 | Infections |
| D009358 | Congenital, Hereditary, and Neonatal Diseases and Abnormalities |
| D018746 | Systemic Inflammatory Response Syndrome |
| D007249 | Inflammation |
| D010335 | Pathologic Processes |
| D013568 | Pathological Conditions, Signs and Symptoms |
| D003643 | Death |
| D051556 | Hyperbilirubinemia, Neonatal |
| D006932 | Hyperbilirubinemia |