Systems E-approach for women at risk (SEWA)—A digital health solution for detection of high-risk pregnancies

Abstract

Background: High-risk pregnancy (HRP) puts current pregnancy at an increased risk of complications. In the absence of pre-existing HRP implementation model of the country, in collaboration with the Government of Himachal Pradesh, a new digital HRP model called the 'SEWA—A System E-approach for Women at risk' was developed. The current article demonstrates a model for the early identification and line listing of high-risk pregnant women (PW) with appropriate referrals and increased engagement with the healthcare workers using a digital tool in the form of the Android App. Methods: SEWA was implemented as a pilot intervention in two community development blocks of the Chamba district. The key implementation steps included finalizing protocols for the identification of HRPs, defining processes and roles, mapping health facilities, setting up the communication loop, and developing of digital solutions. The digital app, used by the auxiliary nurse midwife (ANM) and program officers, tracked PW for a year from October 19 to October 20 and recorded the ANC visits, referrals, and birth outcomes. A qualitative assessment was conducted among the health workers to find out their level of acceptance. Results: A total of 1,340 high-risk PW were identified. The intervention year saw a rise in the identification of HRP to 27.9% from 3.5% in the previous year. A total of 2,559 conditions were tagged to the identified 1,340 women categorized into current pregnancy (81%), previous pregnancy (16%), and any existing chronic illness (3%). A majority of the women who required urgent referrals were provided referrals. The application recorded 53% of the delivered HRP with a digital birth preparedness plan, prepared and shared with the PW and Accredited Social Health Activists (ASHA), by text message for compliance. Conclusion: The SEWA application is a feasible and sustainable solution to complement the competency of the care providers for early identification of the high-risk conditions and reduce the burden of preventable unprecedented deaths around the time of birth.

Barriers and Facilitators for the Use of Telehealth by Healthcare Providers (HCP) in India - A Scoping Review

“…Of the total 53 studies, nearly half the studies (45%) were quantitative(21,26–28,31,33– 36,38,39,42,43,45,50,51,53,55,58,59,63,64,66,70), 14 (26%) were qualitative (13,20,22,29,32,37,44,46–48,60,65,67,69), 12 (23%) were mixed-methods (23–25,30,40,41,52,54,56,57,62,68) and 3 (6%) were review studies. (49,61,71) No study on the use of mHealth was published before 2013 and 64% (13,20–51,55) of the studies were published after 2018 with the maximum (n=12, 23%)(13,22,24–31,33,34) number of studies being published in 2021. The studies were conducted in tertiary care or teaching hospital settings (n=28)(22,23,26–29,31–38,46,47,49,55–59,61,63,65,68,69,71), community health centers (n=6)(20,21,42,44,60,66), primary health centers (n=16)(13,30,40,43,48,50–53),(39,54<...…”

“…The studies were conducted in tertiary care or teaching hospital settings (n=28)(22,23,26–29,31–38,46,47,49,55–59,61,63,65,68,69,71), community health centers (n=6)(20,21,42,44,60,66), primary health centers (n=16)(13,30,40,43,48,50–53),(39,54,62,64,66,67,70), and other settings (n=4). (24,25,41,45) The use of mHealth was most studied in Karnataka (n=7)(13,28,41,52,66,67,71), followed by Gujarat (21,38,44,58,65), Maharashtra (26,28,32,49,66), and Tamil Nadu (26,36,37,51,53) (5 studies each) ( Fig 2 ). Findings from all included studies have been summarized in the S1 Table.…”

“…mHealth interventions were most commonly used by doctors (n=38)(21–23,25–27,30,31,35– 38,40–45,47–50,52–54,56–62,64,65,68–71), followed by community healthcare workers (n=18)(22,28–30,32–34,36,39,41,44,46,49,51,58,63,66,67), nurses (n=8)(13,20,22,41,42,55,56,68), allied health professionals (n=4)(43,54,68,70), auxiliary midwife nurses (n=3)(22,36,41), and others (n=8). (24,29,36,46,54,62,64,68)…”

“…Low digital literacy (n=10)(13,22,26,32,38,41,44,46,65,69), malfunctioning of the software (n=13)(13,20–22,24,25,29,31,37,43,66,67,69), and poor network connectivity (n=14)(20–22,24,32,34,38,41,44,47,53,55,67,69) were the most common human resource-related, application-related, and technical barriers respectively. Stigma related to technology (n=4)(13,38,68,69), worsening of disease-related stigma due to the use of technology (n=3)(41,55,62), lack of formative research (n=1)(69), and lack of human touch due to the use of mhealth (n=1)(34) were other barriers that were identified.…”

“…(21,32,44,49,61,62) Based on the WHO action framework on HRH, 29 (55%) studies focused on Human Resource (HR) management and aimed at improving the efficiency of available human resources. (13,22–24,28–30,33,35–40,42,43,46,51,54,55,57,58,61,64–67,69,71) Twenty-three (43%) studies involved mHealth interventions that aimed at the education and training of HCPs. (20,21,25–27,31,32,34,41,44,45,47–50,52,53,59,60,62,63,68,70) Only one study looked at the financial aspect of the intervention’s use by the HCPs.…”