Effect of mobile application user interface improvements on minimum expected home visit coverage by community health workers in Mali: a randomised controlled trial

Jane E Yang, Diego Lassala, Jenny X Liu, Caroline Whidden, Isaac Holeman, Youssouf Keita, Yasamba Djiguiba, Sory Ibrahima N'Diaye, Fatou Fall, Kassoum Kayentao, Ari D Johnson, Jane E Yang, Diego Lassala, Jenny X Liu, Caroline Whidden, Isaac Holeman, Youssouf Keita, Yasamba Djiguiba, Sory Ibrahima N'Diaye, Fatou Fall, Kassoum Kayentao, Ari D Johnson

Abstract

Background: Proactive community case management (ProCCM) has shown promise to advance goals of universal health coverage (UHC). ProCCM community health workers (CHWs) face operational challenges when pursuing their goal of visiting every household in their service area at least twice monthly to proactively find sick patients. We developed a software extension (UHC Mode) to an existing CHW mobile application featuring user interface design improvements to support CHWs in planning daily home visits. We evaluated the effect of UHC Mode on minimum expected home visit coverage.

Methods: We conducted a parallel-group, two-arm randomised controlled trial of ProCCM CHWs in two separate regions in Mali. CHWs were randomly assigned to UHC Mode or the standard mobile application (control) with a 1:1 allocation. Randomisation was stratified by health catchment area. CHWs and other programme personnel were not masked to arm allocation. CHWs used their assigned intervention for 4 months. Using a difference-in-differences analysis, we estimated the mean change in minimum expected home visit coverage from preintervention to postintervention between arms.

Results: Enrolment occurred in January 2019. Of 199 eligible CHWs randomised to the intervention or control arm, 196 were enrolled and 195 were included in the analysis. Households whose CHW used UHC Mode had 2.41 times higher odds of minimum expected home visit coverage compared with households whose CHW used the control (95% CI 1.68 to 3.47; p<0.0005). Minimum expected home visit coverage in the UHC Mode arm increased 13.6 percentage points (95% CI 8.1 to 19.0) compared with the control arm.

Conclusion: Our findings suggest UHC Mode is an effective tool that can improve home visit coverage and promote progress towards UHC when implemented in the ProCCM context. User interface design of health information systems that supports health workers' daily practices and meets their requirements can have a positive impact on health worker performance and home visit coverage.

Trial registration number: NCT04106921.

Keywords: health services research; other diagnostic or tool; prevention strategies; public health; randomised control trial.

Conflict of interest statement

Competing interests: AJ, CW, DL, JEY, KK, SIN’D, YD and YK are employed by the non-profit organisation, Muso. The Muso team designed the Proactive Community Case Management approach. FF and IH are employed by the non-profit organisation, Medic. The Medic team serves as lead developer of the Community Health Toolkit open source project. Both Medic and Muso participated in the development of UHC Mode, as described in the manuscript. Both the CHT and UHC Mode are public goods to be made available open-source. All authors have completed the Unified Competing Interest form at http://www.icmje.org/downloads/coi_disclosure.docx (available on request from the corresponding author) and declare no other conflicts of interest.

© Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY. Published by BMJ.

Figures

Figure 1
Figure 1
Trial profile. CHWs, community health workers; UHC, universal health coverage.
Figure 2
Figure 2
Display of information on the household list in the standard CHW application (A1–A2) versus UHC Mode (B1–B2) and on individual household profiles in the standard application (A3) versus UHC Mode (B3). A1 shows the household list in the standard CHW application. A2 shows the household list in the standard CHW application, with default alphabetical ordering. B1 shows the household list in UHC Mode, which displays the time elapsed in days since the last home visit (colour coded red if the date of last visit was 30 or more days ago, and black if the date of last visit was fewer than 30 days ago). Red exclamation point icons emphasise households with fewer than two visits in the month, and a modified traffic light colour scheme (red, orange, light blue) show households receiving zero, one, or two or more visits in the month. B2 shows the household list in UHC Mode, with default chronological ordering by date of last home visit. A3 shows an individual household profile in the standard CHW application. B3 shows an individual household profile in UHC Mode, with information about the date of last visit and the monthly home visit count. CHW, community health worker; UHC, universal health coverage.
Figure 3
Figure 3
Monthly and preintervention versus postintervention CHW service area coverage of minimum expected home visits (percentage of households visited at least twice in a month), by arm, with health catchment areas combined (A) and stratified (B–C). Vertical blue dotted lines show the formal start of the postintervention period. Gray-shaded areas show the roll-out of UHC Mode in March 2019. CHWs, community health workers; UHC, universal health coverage.

References

    1. Hogan DR, Stevens GA, Hosseinpoor AR, et al. . Monitoring universal health coverage within the sustainable development goals: development and baseline data for an index of essential health services. Lancet Glob Health 2018;6:e152–68. 10.1016/S2214-109X(17)30472-2
    1. Rasanathan K, Muñiz M, Bakshi S, et al. . Community case management of childhood illness in sub-Saharan Africa - findings from a cross-sectional survey on policy and implementation. J Glob Health 2014;4:020401. 10.7189/jogh.04.020401
    1. Lewin S, Munabi-Babigumira S, Glenton C, et al. . Lay health workers in primary and community health care for maternal and child health and the management of infectious diseases. Cochrane Database Syst Rev 2010:CD004015. 10.1002/14651858.CD004015.pub3
    1. Okwundu CI, Nagpal S, Musekiwa A, et al. . Home- or community-based programmes for treating malaria. Cochrane Database Syst Rev 2013:CD009527. 10.1002/14651858.CD009527.pub2
    1. Lassi ZS, Bhutta ZA. Community-Based intervention packages for reducing maternal and neonatal morbidity and mortality and improving neonatal outcomes. Cochrane Database Syst Rev 2015:CD007754. 10.1002/14651858.CD007754.pub3
    1. Guenther T, Nsona H, Makuluni R, et al. . Home visits by community health workers for pregnant mothers and newborns: coverage plateau in Malawi. J Glob Health 2019;9. 10.7189/jogh.09.010808
    1. Tesfau YB, Kahsay AB, Gebrehiwot TG, et al. . Postnatal home visits by health extension workers in rural areas of Ethiopia: a cross-sectional study design. BMC Pregnancy Childbirth 2020;20:305. 10.1186/s12884-020-03003-w
    1. Munos M, Guiella G, Roberton T, et al. . Independent evaluation of the rapid scale-up program to reduce Under-Five mortality in Burkina Faso. Am J Trop Med Hyg 2016;94:584–95. 10.4269/ajtmh.15-0585
    1. Amouzou A, Hazel E, Shaw B, et al. . Effects of the integrated community case management of childhood illness strategy on child mortality in Ethiopia: a cluster randomized trial. Am J Trop Med Hyg 2016;94:596–604. 10.4269/ajtmh.15-0586
    1. Sitrin D, Guenther T, Murray J, et al. . Reaching mothers and babies with early postnatal home visits: the implementation realities of achieving high coverage in large-scale programs. PLoS One 2013;8:e68930. 10.1371/journal.pone.0068930
    1. Johnson AD, Thiero O, Whidden C, et al. . Proactive community case management and child survival in periurban Mali. BMJ Glob Health 2018;3:e000634. 10.1136/bmjgh-2017-000634
    1. Whidden C, Treleaven E, Liu J, et al. . Proactive community case management and child survival: protocol for a cluster randomised controlled trial. BMJ Open 2019;9:e027487. 10.1136/bmjopen-2018-027487
    1. Whidden C, Thwing J, Gutman J, et al. . Proactive case detection of common childhood illnesses by community health workers: a systematic review. BMJ Glob Health 2019;4:e001799. 10.1136/bmjgh-2019-001799
    1. World Health Organization . WHO guideline on health policy and system support to optimize community health worker programmes, 2018.
    1. Whidden C, Kayentao K, Liu JX, et al. . Improving community health worker performance by using a personalised feedback dashboard for supervision: a randomised controlled trial. J Glob Health 2018;8:020418. 10.7189/jogh.08.020418
    1. Ballard M, Bonds M, Burey J. CHW AIM: updated program functionality matrix for optimizing community health programs. community health impact coalition, 2018. Available: [Accessed 19 Feb 2021].
    1. Keitel K, D'Acremont V. Electronic clinical decision algorithms for the integrated primary care management of febrile children in low-resource settings: review of existing tools. Clin Microbiol Infect 2018;24:845–55. 10.1016/j.cmi.2018.04.014
    1. Zurovac D, Sudoi RK, Akhwale WS, et al. . The effect of mobile phone text-message reminders on Kenyan health workers’ adherence to malaria treatment guidelines: a cluster randomised trial. Lancet 2011;378:795–803. 10.1016/S0140-6736(11)60783-6
    1. DeRenzi B, Findlater L, Payne J. Improving community health worker performance through automated SMS. In: Proceedings of the fifth International Conference on information and communication technologies and development. New York, NY, USA: Association for Computing Machinery, 2012: 25–34.
    1. Holeman I, Kane D. Human-centered design for global health equity. Inf Technol Dev 2020;26:477–505. 10.1080/02681102.2019.1667289
    1. Ash JS, Berg M, Coiera E. Some unintended consequences of information technology in health care: the nature of patient care information system-related errors. J Am Med Inform Assoc 2004;11:104–12. 10.1197/jamia.M1471
    1. Middleton B, Bloomrosen M, Dente MA, et al. . Enhancing patient safety and quality of care by improving the usability of electronic health record systems: recommendations from AMIA. J Am Med Inform Assoc 2013;20:e2–8. 10.1136/amiajnl-2012-001458
    1. Gardner RL, Cooper E, Haskell J, et al. . Physician stress and burnout: the impact of health information technology. J Am Med Inform Assoc 2019;26:106–14. 10.1093/jamia/ocy145
    1. Wasunna B, Holeman I, Geniets A. Digital health interventions for community health worker training, ongoing education, and supportive supervision: Insights from a human-centered design approach. In: Training for community health: bridging the global health care gap. Oxford, UK: Oxford University Press, 2021: 1–71.
    1. Norman DA. The design of everyday things. revised and expanded editon. Cambridge, MA, London: The MIT Press, 2013: 347.
    1. Community Health Toolkit . Community health toolkit. Available: [Accessed 15 Feb 2021].
    1. Urban Mixed Migration . Mixed migration centre. Available: [Accessed 16 Feb 2021].
    1. Human Rights Watch . 'How much more blood must be spilled?', 2020. Available: [Accessed 16 Feb 2021].
    1. Bhattacharyya K, Winch P, LeBan K. Community health worker incentives and Disincentives: how they affect motivation, retention and sustainability, 2001. Available: [Accessed 18 Feb 2021].
    1. Pallas SW, Minhas D, Pérez-Escamilla R, et al. . Community health workers in low- and middle-income countries: what do we know about scaling up and sustainability? Am J Public Health 2013;103:e74–82. 10.2105/AJPH.2012.301102

Source: PubMed

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