Designing an integrated, nurse-driven and home-based digital intervention to improve insulin management in under-resourced settings

Piotie, Patrick Ngassa; Wood, Paola; Webb, Elizabeth M.; Hugo, Jannie; Rheeder, Paul

doi:10.1177/20420188211054688

Cited by 7 publications

(17 citation statements)

References 45 publications

(74 reference statements)

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“…Although some algorithms have been developed to assist physicians in insulin titration, only a few have been validated in clinical trials 31 , 32 . We conducted a proof-of-concept feasibility trial demonstrating the viability of the RL-DITR system in inpatients with T2D.…”

Section: Discussionmentioning

confidence: 99%

Optimized glycemic control of type 2 diabetes with reinforcement learning: a proof-of-concept trial

Wang,

Liu,

Ying

et al. 2023

Nat Med

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The personalized titration and optimization of insulin regimens for treatment of type 2 diabetes (T2D) are resource-demanding healthcare tasks. Here we propose a model-based reinforcement learning (RL) framework (called RL-DITR), which learns the optimal insulin regimen by analyzing glycemic state rewards through patient model interactions. When evaluated during the development phase for managing hospitalized patients with T2D, RL-DITR achieved superior insulin titration optimization (mean absolute error (MAE) of 1.10 ± 0.03 U) compared to other deep learning models and standard clinical methods. We performed a stepwise clinical validation of the artificial intelligence system from simulation to deployment, demonstrating better performance in glycemic control in inpatients compared to junior and intermediate-level physicians through quantitative (MAE of 1.18 ± 0.09 U) and qualitative metrics from a blinded review. Additionally, we conducted a single-arm, patient-blinded, proof-of-concept feasibility trial in 16 patients with T2D. The primary outcome was difference in mean daily capillary blood glucose during the trial, which decreased from 11.1 (±3.6) to 8.6 (±2.4) mmol L−1 (P < 0.01), meeting the pre-specified endpoint. No episodes of severe hypoglycemia or hyperglycemia with ketosis occurred. These preliminary results warrant further investigation in larger, more diverse clinical studies. ClinicalTrials.gov registration: NCT05409391.

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Section: Discussionmentioning

confidence: 99%

Optimized glycemic control of type 2 diabetes with reinforcement learning: a proof-of-concept trial

Wang,

Liu,

Ying

et al. 2023

Nat Med

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“…29 In Tshwane, a nurse-driven application has supported people as they start insulin with some success. 30 Guidelines should specify the timing and broad content of counselling for patients regarding the use of insulin. The likelihood of requiring insulin at a later stage should be discussed soon after diagnosis.…”

Section: Discussionmentioning

confidence: 99%

Factors influencing insulin initiation in primary care facilities in Cape Town, South Africa

Mathose

Mash²

2023

S Afr Fam Pract

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Background: Type 2 diabetes (T2DM) is a leading cause of mortality in South Africa and resistance to the use of insulin is common. This study aimed to explore factors that influence the initiation of insulin in patients with T2DM in primary care facilities in Cape Town, South Africa.Methods: An exploratory descriptive qualitative study was conducted. Seventeen semi-structured interviews were held with patients eligible for insulin, on insulin and primary care providers. Participants were selected by maximum variation purposive sampling. Data were analysed using the framework method in Atlas-ti.Results: Factors related to the health system, service delivery, clinical care and patients. Systemic issues related to the required inputs of workforce, educational materials, and supplies. Service delivery issues related to workload, poor continuity and parallel coordination of care. Clinical issues related to adequate counselling. Patient factors included a lack of trust, concerns about injections, impact on lifestyle and disposal of needles.Conclusion: Although resource constraints are likely to remain, district and facility managers can improve supplies, educational materials, continuity and coordination. Counselling must be improved and may require innovative alternative approaches to support clinicians who face high number of patients. Alternative approaches using group education, telehealth and digital solutions should be considered.Contribution: This study identified key factors influencing insulin initiation in patients with T2DM in primary care. These can be addressed by those responsible for clinical governance, service delivery and in further research.

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“…11 To address these challenges related to insulin management in primary care, a multi-disciplinary team from the University of Pretoria in South Africa developed a nursedriven and home-based telehealth intervention named the Tshwane Insulin project (TIP). 12 The TIP intervention was developed using the Integrated Chronic Disease Management (ICDM) framework 13 and comprises a facilitylevel intervention, where professional nurses evaluate PLWD and initiate insulin, an individual-level intervention where community healthcare workers (CHWs) monitor patients at their homes and provide educational information, whilst using telehealth to enable physician-directed insulin titration if needed, and a community-level intervention aimed at empowering CHWs to support PLWD and raise awareness of diabetes. 12 The TIP intervention was developed in four sequential phases following the guidance of the United Kingdom (UK) Medical Research Council (MRC) for designing and evaluating complex interventions.…”

Section: Introductionmentioning

confidence: 99%

“… 12 The TIP intervention was developed using the Integrated Chronic Disease Management (ICDM) framework 13 and comprises a facility-level intervention, where professional nurses evaluate PLWD and initiate insulin, an individual-level intervention where community healthcare workers (CHWs) monitor patients at their homes and provide educational information, whilst using telehealth to enable physician-directed insulin titration if needed, and a community-level intervention aimed at empowering CHWs to support PLWD and raise awareness of diabetes. 12 …”

Section: Introductionmentioning

confidence: 99%

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Factors affecting the implementation of a complex health intervention to improve insulin management in primary care: A SWOT analysis

Piotie

Filmalter

Mohlala

et al. 2022

Afr. j. prim. health care fam. med.

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Background: In South Africa, initiating and managing insulin in primary care for people living with type 2 diabetes (PLWD) is a major challenge. To address these challenges, a multidisciplinary team from the University of Pretoria (South Africa) developed the Tshwane Insulin project (TIP) intervention.Aim: To determine internal and external factors, either facilitators or barriers, that could influence the implementation of the TIP intervention and propose strategies to ensure sustainability.Setting: Tshwane District, Gauteng province, South Africa.Methods: We used the SWOT framework to qualitatively analyse the strengths, weaknesses, opportunities, and threats influencing the implementation of the TIP intervention. Four field researchers and three managers from the TIP team participated in an online group discussion. We also conducted semi-structured interviews with healthcare providers (HCPs) (seven nurses, five doctors) and patients with type 2 diabetes (n = 13).Results: Regardless of the identified weaknesses, the TIP intervention was accepted by PLWD and HCPs. Participants identified strengths including app-enabled insulin initiation and titration, pro-active patient follow-up, patient empowerment and provision of glucose monitoring devices. Participants viewed insulin resistance and the attitudes of HCPs as potential threats. Participants suggested that weaknesses and threats could be mitigated by translating education material into local languages and using the lived experiences of insulin-treated patients to address insulin resistance. The procurement of glucose monitoring devices by national authorities would promote the sustainability of the intervention.Conclusion: Our findings may help decision-makers and health researchers to improve insulin management for PLWD in resource-constrained settings by using telehealth interventions.

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Designing an integrated, nurse-driven and home-based digital intervention to improve insulin management in under-resourced settings

Cited by 7 publications

References 45 publications

Optimized glycemic control of type 2 diabetes with reinforcement learning: a proof-of-concept trial

Optimized glycemic control of type 2 diabetes with reinforcement learning: a proof-of-concept trial

Factors influencing insulin initiation in primary care facilities in Cape Town, South Africa

Factors affecting the implementation of a complex health intervention to improve insulin management in primary care: A SWOT analysis

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