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. Author manuscript; available in PMC: 2014 Aug 19.
Published in final edited form as: S Afr J Inf Manag. 2013;15(1):10.4102/sajim.v15i1.528. doi: 10.4102/sajim.v15i1.528

Value of a mobile information system to improve quality of care by community health workers

Mark Tomlinson 1, Mary Jane Rotheram-Borus 2, Tanya Doherty 3, Dallas Swendeman 2, Alexander C Tsai 4,5, Petrida Ijumba 3, Ingrid le Roux 6, Debra Jackson 3,7, Jackie Stewart 1, Andi Friedman 8, Mark Colvin 9, Mickey Chopra 10
PMCID: PMC4137890  NIHMSID: NIHMS540596  PMID: 25147730

Abstract

Introduction

We will be unable to achieve sustained impact on health outcomes with community health worker (CHW)-based interventions unless we bridge the gap between small scale efficacy studies and large scale interventions. Effective strategies to support the management of CHWs are central to bridging the gap. Mobile phones are broadly available, particularly in low and middle income countries (LAMIC's), where the penetration rate approaches 100%. In this article we describe how mobile phones may be combined with mobile web-based technology to assist in the management of CHWs in two projects in South Africa.

Methods

This paper is a descriptive one, drawing lessons from two Randomised Controlled Trials (RCT's), outlining how a mobile phone information system can be utilized to enhance the quality of health interventions. We organized our comprehensive management and supervision system around a previously published management framework. The system is composed of mobile phones utilized by CHWs and a web-based interface utilized by CHW supervisors. Computerized algorithms were designed with intervention and assessment protocols to aid in the real-time supervision and management of CHWs.

Results

CHWs used mobile phones to initiate intervention visits and trigger content to be delivered during the course of intervention visits. Supervisors used the web-based interface for real-time monitoring of the location, timing, and content of intervention visits. Additional real-time support was provided through direct support calls in the event of crises in the field.

Conclusion

Mobile phone-based information system platforms offer significant opportunities to improve CHW-delivered interventions. The extent to which these efficiency gains can be translated into realized health gains for communities is yet to be tested.

Keywords: Mobile phones, community health workers, supervision, management, scale up

Introduction

With the health budgets of more than 40 African nations less than $30 USD per person annually, it is critical that more cost effective delivery strategies are identified for health care (Lewin et al., 2008). The personnel necessary for serving the health care needs for HIV-related disease cannot feasibly be met until the year 2050 (Anyangwe & Mtonga, 2007). Therefore, community health workers (CHWs) are increasingly identified as a potential vehicle for strengthening community based care, especially for maternal, newborn and child survival. While successful CHW programs alone will not solve the maternal and child health crises facing many poor countries, they have the potential to make a large impact by significantly expanding access to care (Haines et al., 2007a).

Health systems in many African and South Asian countries currently utilize CHWs in their programs as part of their plan to achieve the Millennium Development Goals and other human development priorities (Haines et al., 2007a). It is estimated that there are more than 40 million CHWs around the world at present (Lewin et al., 2010). Several trials have shown the efficacy of CHWs in reducing morbidity and mortality amongst neonates and infants (Bang et al., 2005, Sazawal and Black, 2003, Manandhar et al., 2004). However, most research evaluating the impact of CHW programs has been limited to small and short term interventions in heavily resourced research settings (Haines et al., 2007a). Larger scale CHW programs such as the national implementation of the Integrated Management of Childhood Illness (IMCI) strategy in Peru (Huicho et al., 2005), and the national CHW program in Sri Lanka (Walt et al., 1989) have been hindered by barriers to effective scale up. Among other things, large scale programs are frequently undermined by high attrition and low activity levels of CHWs, which are less likely in smaller scale initiatives where supervision is often more intense and consistent (Walt et al., 1989). While the use of CHWs has achieved many successes (Baqui et al., 2008, Rahman et al., 2008), the system has also been characterized by a lack of consistent supervision and linkages to the health system (Walley et al., 2008, Haines et al., 2007b).

We will be unable to achieve sustained impact utilizing CHWs unless we can bridge the gap between small scale efficacy studies and large scale interventions. An important part of this process lies in building effective strategies to support the management and supervision of CHWs (Rowe et al., 2005). The performance of CHWs in achieving their health objectives is influenced by multiple factors, but it is widely accepted that effective management is of fundamental importance (Rowe et al., 2005, Gray and Ciroma, 1988, Haines et al., 2007a, Kelly et al., 2001, Walt et al., 1989). The difficulties in establishing an effective information system that provides managers with high quality and timely information about the activities and performance of a dispersed group of workers may be an important cause of the loss of quality in information and supervisory systems as programs scale up.

South Africa has over 60 000 CHW's most of whom are paid by national government but employed, monitored, and evaluated by non-governmental organisations (NGOs). The National Department of Health in South Africa is currently implementing a Primary Health Care (PHC) revitalization strategy, where community based outreach functions are being prioritized and funded based to a large extent on Brazil's Family Health Program (Paim et al., 2011). Primary health care teams are currently being implemented in South Africa, linked to a local health facility and staffed by community health workers and nurses (NDOH, 2011). Central to this re-vitalization process is improving the management of CHW's and a renewed commitment to the use of Information and Communication Technology (ICT) and particularly mobile phones in this process (Leon and Schneider, 2011).

The rapid technological innovation in the mHealth field has led to a proliferation of untested methods and small-scale projects. This has recently begun to change with studies showing the potential impacts of scale up on mHealth innovations (Zurovac et al., 2011). The implementation of mobile phone information systems has the potential to provide practical and simple solutions to management difficulties at scale. With more than 5.3 billion mobile phones in use we have an organically created information platform that can rapidly diffuse innovations in health care (Tomlinson et al., 2009). In this article, we draw on our experience over the last six years to describe a system we developed and to suggest ways in which this information platform can be used to improve the supervision, management and quality of interventions provided by CHWs.

Methods

Management of community health care workers

CHWs are effective when there is an effective health system or NGO that provides supervision, supplies, monitoring and on-going training. However organizing these critical support activities at scale has proven very difficult. Our mobile information system was based on a previously published framework (Bosch-Capblanch and Garner, 2008). Their model (see Figure 1) links health care worker performance including activities of the CHW (counseling, education), outputs (coverage) and outcomes (morbidity); with aspects of management such as comparing service delivered against norms, problem identification and examining data against expected outputs. This model is in line with the work of Rowe and colleagues who have outlined a number of typical reasons for the inadequacy of supervision of CHWs at scale (Rowe et al., 2010). These include poor co-ordination, inadequate management skills, problems related to decentralization, increasing supervision workload, time required for supervision activities, lack of transportation, insufficient knowledge of how to plan visits and a lack of tools to assess supervision (Rowe et al., 2010).

Figure 1. Framework for supervision (Bosch-Capblanch and Garner, 2008).

Figure 1

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Our mobile information system

In partnership with a private, South Africa-based digital company (Clyral), we have developed a comprehensive information and supervision system that combines mobile phones with a web-based information interface. Our primary motivation was to develop a system of daily monitoring of CHW visits to aid in the supervision of a large cadre of CHWs. Specific focus areas (all of which are central to supervision (Rowe et al., 2010)) for the system were:

  1. Recruitment of study mothers, CHW assessment, and adaptive scheduling and visit planning;

  2. Intervention delivery support, fidelity monitoring and individual case monitoring;

  3. Caseload reporting and information management.

The system was designed to integrate all CHW activities into a web-based information narrative that is initiated the moment a participant is recruited; all intervention and data milestones are triggered automatically. Computerized algorithms have been designed to monitor and trigger the intervention and assessment protocols to aid in the real-time supervision and management of CHWs. These protocols include assessment of intervention fidelity, quality control, visit planning, and monitoring of CHW productivity. The web based information system was developed in partnership with a private digital company in South Africa, Clyral. Our system imposes few requirements with regards to the specific handset models to be used: the single requirement is that they need to be enabled for Java programming (Tomlinson et al., 2009). All of the CHWs were women with no previous administrative or data collection experience, but all had previous experience using basic mobile phone functions such as calls and SMS. Training CHWs in the use of the mobile phone application consisted of a one day training workshop, followed by several one hour refresher courses throughout the duration of the project. We also implemented weekly in-person group supervision, with individual supervision when necessary. Each CHW was supplied with her own project mobile phone.

Results

1. Recruitment, assessment, and adaptive scheduling and planning

We have been working with local CHWs in two projects in South Africa to explore how mobile information based supervision could be used to improve supervision and management of CHW's (see Panel 1 and Panel 2). In both projects, CHWs recruit expectant mothers in clusters through door-to-door home visits. The global positioning system (GPS) captures the geographical coordinates on the CHW's mobile phone for each household visited. As a CHW enters a household, the CHW begins monitoring the visit duration with a single click on the phone. Then, as she leaves the home at the end of the intervention visit, she repeats the click on the phone. GPS- and time- stamping functions are built into each mobile phone, allowing location and duration to be assessed and transferred in real-time to the web-based information, supervision and management system. The GPS function reduces the possibility of falsifying data substantially, as the time between visits and the location of the visit is automatically recorded and transmitted to the CHW supervisors. Compared to systems implemented in prior studies, the lag time between protocol deviations (by CHWs) and triggering of reactive monitoring (by supervisors) is thereby substantially reduced. Similar advantages exist for the assessment of interviewers and recruiters in research trials.

Panel 1: Goodstart 3

The Goodstart 3 study is a cluster randomized controlled trial being implemented in Umlazi, a peri-urban settlement close to Durban in South Africa with a population of approximately 1 million. The 2010 antenatal HIV prevalence in this district was 41% (SANDOH, 2010) and infant mortality is estimated to be 42 per 1000 live births (Day et al., 2011). Mobile phone connectivity in Umlazi is excellent. The goal of Goodstart 3 study is to develop, evaluate and cost an integrated and scalable home visit package delivered by CHW's targeting pregnant and postnatal women and their newborns to provide essential maternal/newborn care as well as interventions for Prevention of Mother to Child Transmission (PMTCT) of HIV. Umlazi has a mixture of formal and informal housing, and while it is a relatively well resourced peri-urban area it has a non-optimally functioning health system. The infant mortality rate is around 60/1000 live births [10], and while there is no reliable figure of the neonatal mortality rate, most estimates place the NMR at about 25/1000 live births. The HIV prevalence amongst antenatal clients is 44% [10]. The evaluation trial consists of 30 randomized clusters (15 in each arm) of the population. Results from this study are forthcoming.

Panel 2: The Philani Mentor Mothers Project (PMMP)

The Philani Mentor Mothers Project (PMMP) is a longitudinal, cluster-randomized controlled trial. The study was conducted in Khayelitsha, a peri-urban settlement of approximately one million people, on the outskirts on Cape Town, South Africa. Khayelitsha, like many South African township settings, has high levels of violence, with poor infrastructure, vast areas of informal houses (shacks), and high levels of unemployment (Nleya and Thompson, 2009). Mobile phone connectivity in Umlazi is excellent. The project aims to evaluate the effectiveness of a home-based care intervention delivered by CHWs for preventing and managing illnesses related to HIV, TB, alcohol use and malnutrition in pregnant mothers and their infants. The study has 1,200 mothers in 24 neighborhood clusters, 12 of which are intervention neighborhoods, and 12 of which are control neighborhoods. A cohort of women from each neighborhood is followed from pregnancy until their infants are 18 months old. The home visits are designed to be both supportive and educational in nature. They are intended to empower pregnant mothers to better protect the health of their families by accessing available clinic services, implementing preventive behaviors in daily life routines, and sustaining preventive behaviors over time. The home-based delivery strategy addresses the cluster of behaviors necessary to deal with chronic conditions simultaneously, as opposed to individually. Results from this study are forthcoming

Once a woman is enrolled in the program, a short intake questionnaire is completed using the mobile phone. This information includes her personal details, selected health information, gestation and risk profiling. Once received by the central web based programme, the system combines the woman's expected date of delivery with an algorithm for her health information to determine her relative level of risk. Based on the result, the system determines the quantity and scheduling of antenatal visits that she is required to receive from the CHW before her infant is born.

After the birth of each participant's child, the system schedules the quantity and timing of her postnatal visits. A woman who is assessed to be at high risk (e.g., if she gives birth to an infant of low birth weight) is assigned to receive more home visits than a woman who is considered low risk. At present, this process occurs through a manual case consultation process rather than occurring automatically according to an algorithm loaded on the mobile phone (but the system can be designed so that this process is automated). Once this decision has been made, the information system automatically schedules and follows up on these ‘extra visits’.

For each visit that a participating mother is due to receive, a dated milestone is created on her participant page in the web-based Information Console (see Figure 2). The dated visit milestones for all mothers in the program filter through to a weekly timetable for each CHW. This timetable details which mothers she needs to visit that week (see Figure 3). At each visit the CHW records the duration and content of the visit on her mobile phone, which ticks off each milestone in the participant's lifespan as completed. Once completed, the visit reminder is removed from the weekly timetable. As can be seen from Figure 3, prior to the birth of the infant the console simply reflects antenatal visits completed thus far. The postnatal visits are portrayed as to be completed (TBC). As soon as the woman gives birth, the birth date and time is entered into the mobile phone and transferred to the web-based system, which automatically schedules and triggers all postnatal visits. Simultaneously, the CHW and the CHW supervisor receive a SMS notification of the birth and the date of all scheduled visits.

Figure 2. Icons.

Figure 2

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Figure 3. Intervention schedule.

Figure 3

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The GPS coordinates of each household could also be used in such a way that within each week, those mothers who live closest to each other are scheduled for appointments on the same day. This system could minimize the distance the CHW needs to travel each day and improve the efficiency of the time they spend in the field. Currently, the specific set ups used in our studies do not co-ordinate at this level of detail. However, the underlying computer code for the mobile information system has been structured so that significant changes to its function and content can be made, depending on the particular requirements of the specific project. The Good Start 3 study for instance is aimed at reducing neonatal mortality and improving levels of exclusive and appropriate feeding. As a result, visits are highly structured with a particular emphasis on visits occurring at particular times in the early neonatal period (e.g. 24-48 hours and 3-4 days after birth) (see Figure 3). In the Philani study on the other hand, visits are less structured - in terms of both the day on which visits must take place and the time taken for each visit. In order to both monitor the date of visit and time spent (per visit and across time), we developed an interface that included a cumulative time counter (almost like an egg timer) (see Figure 4). By tracking visit duration (and total time spent on the intervention), we are able to characterize the potentially dose-dependent response to the intervention, as well as calculate intervention delivery costs in a more precise way.

Figure 4. Participant dashboard.

Figure 4

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2. Intervention delivery support, fidelity monitoring and individual case monitoring

A significant difficulty when scaling up interventions is the issue of intervention fidelity and how to ensure quality control of intervention delivery. We developed a series of short questionnaires that were triggered on the mobile phones when the CHW entered the identification number of the household being visited. This questionnaire serves as a reminder about the core intervention messages to be covered at that visit, a check on intervention fidelity that the topics were covered, and as the data source for the adaptive scheduling of follow up visits and intervention content. Antenatal questions include whether the woman has made a booking at the antenatal clinic; what her feeding plan is and whether she has been tested for HIV. Postnatal questions include questions about maternal and neonatal illness; whether the baby received AZT; whether the mother went to the clinic at 6 days post-birth; breast health; whether the infant has been tested for HIV; and if the baby is HIV+ whether he or she is receiving cotrimoxazole.

In order to account for day to day participant availability, the system allows a CHW to suspend a visit based on the availability of the participant. If the CHW determines that she should return to complete a visit at some other time in the future, she can tick the appropriate box on the mobile phone. Her tick is immediately registered on the web based system. This is also the case for missed visits (participant not home when CHW arrives) or visits cut short for other reasons (e.g. an irritable infant). In this way, the system is able to monitor all aspects of the intervention such as CHW caseloads, clusters with higher levels of missed visits as compared to other clusters; reasons for missed or suspended visits; and CHW performance in real time on a daily basis. The web-based management system (and mobile phone connectivity) allows supervisors to (remotely) access the Information Console and monitor real time information about each participating mother and child in the intervention program, as well as information on each CHW's activities and caseloads.

The console has a live report of all visits which have taken place, which can be filtered by date and by CHW, in order to establish how many visits are happening on any given day or week by any given CHW. The console also generates a ‘visit-time’ report (see Figure 5), which details for each participant in the intervention, not only how many visits they have had, but also the cumulative amount of time for all visits combined. In this way, one can easily identify outliers among participants or among CHWs. Based on these reports, together with the system flags that are generated, supervisors are able, in real time, to schedule a ‘shadow’ or ‘spot’ visit' for the supervisor to conduct checks on the quality of home visits. In addition, visits are randomly generated and scheduled based on each weekly schedule for each CHW. These visits allow the supervisor to validate the observational reports of the CHW or identify households which should receive extra support.

Figure 5. Visit report.

Figure 5

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The system can be used to optimize supervision at every level of the intervention program including:

  • Recruitment and scheduling of visits

  • Monitoring activity and progress of CHWs

  • Managing health information and emergencies

  • Accessing data such as date of visit to household and duration of visit

  • Provide knowledge of visit schedule

  • Pinpoint discrepancies and anomalies automatically with alerts sent to supervisors

For instance, a visit of very short duration in the first 48 hours of birth will be flagged as problematic (given the detail required during the visit) and supervisors will be informed, as well as a spot or shadow visit automatically scheduled.

3. Caseload Reporting

The console has a live report that allows managers access at any time to the current caseload of each CHW. This assists in the planning of staffing needs, as the system is able to send alerts when a CHW's caseload approaches a maximum threshold. The caseload report is able to differentiate between active and inactive cases for each CHW, allowing the report to give an accurate picture of each CHW's workload. When a participating family has temporarily left the area or declines to receive visits for any given period of time, their status in the system can be changed from active to inactive. The caseload reports detail only those families who are actively being visited by the CHW. Detailed information about the nature of each caseload is also summarized in the report. It is also possible to view the caseload filtered by any type of risk factor of interest, which is helpful in determining workloads when families or mothers meeting certain criteria qualify for extra intervention visits. The system also provides the data to aid the ‘face to face’ supervision that takes place about particular ‘cases’ and ‘problem areas’.

Conclusion

This article summarizes our experience in two South African RCT's with a mobile information system for supervising a CHW-delivered, home visit-based community health intervention for pregnant and postnatal women. Mobile phones offer unprecedented opportunities to improve the quality of care provided by CHWs. These devices could be used to dramatically speed up the process and ability of all countries and healthcare systems to meet their healthcare delivery goals. Yet, their potential has yet to be realized. Mobile technology has been widely used by private enterprises, which have made dramatic, rapid, and significant improvements and penetrated entire populations with exceptional speed. Health care, especially health interventions for LAMIC's have not had the technological expertise, the financial commitment, or the political will to implement these strategies broadly. The examples reported in this paper represent the first iteration in a process of continuous quality improvement, which must begin to keep pace with the possibilities offered by mobile technologies. The web-based mobile information system that we describe in this paper has been developed on an open-source platform with no proprietary conditions attached. In addition, the Java's core code is available under open-source distribution terms. It is therefore available to researchers, government health planners or non-governmental and community based organisations to assist in improved management to aid scale up of health interventions. The field and general approach is novel and there is a general lack of published literature describing mHealth system experiences with CHWs, particularly in the context of scaling up programs. This paper provides evidence that such systems are feasible to train, sustain, and scale-up CHW programs with a high degree of fidelity and relatively limited in-person supervisory support (i.e., weekly meetings instead of supervisors accompanying CHW teams in the field).

Future possibilities

In developing the two mobile systems we have been conceptualizing the future possibilities and applications that might further aid information gathering and management. While we are not as yet utilizing these applications, the technology is available to implement the following possibilities:

  • We are presently developing a set of screening questions and pictures that can be used by CHWs to screen women for common mental and physical illnesses. Once again it will take advantage of the ability of mobile technology to send information to a central data-base and receive specific information back in ‘real time.’

  • We are in the process of refining the system to ensure that upon SMSing the unique household number the database sends back a few of the key questions/points to be made for that particular visit, rather than general questions applicable to sessions as is currently the case. This function will also allow the database to send back more specific individually tailored questions or advice. This will be achieved by developing algorithms that can be reliably applied in the field by persons with limited training. Currently we use the information gathered from the mobile system to fulfill this function during face to face supervision, rather than the tailored advice happening automatically via the mobile information system.

  • Over time we will refine this system so that the database sends back more specific individually tailored questions/advice. This will be achieved by developing algorithms that will analyse previously entered basic medical history (previous pregnancies, previous test results etc.) to generate key messages. For instance a mother who had a previous history of anemia will be reminded of the importance taking iron or getting her Hb checked again.

  • In many countries most births occur at home and the rapid and timely recognition of early neonatal illness is vital. We are planning (in the Philani study) to use mobile phones with an integrated camera in order for CHWs to take a photograph of the neonate if they are concerned about an infection or illness. This image can then be transferred in real time and assessed by a trained professional followed by an sms sent back to the CHW in order to ensure that the appropriate action is taken or referral made.

  • We have not been showing videos on the mobile phones, but we realize this feature is easily available to us in the future for two purposes: first, demonstration videos to support CHW message delivery or implementation of more complex intervention activities; and videos that CHWs show to mothers and families to supplement their verbal communication and paper-based materials.

  • Mobile phones offer the promise of linking data from clinics or hospitals to CHWs. Current studies of loss to follow-up along the “PMTCT cascade” are not based on linked data, only serial cross sections (Reithinger et al., 2007). Yet, as investments are made in health infrastructure, the types of information that need to be linked in integrated systems for health data are similar cross-nationally and regionally, including: tracking individuals or households' health visits over time, and anticipated, developmentally-linked or condition-linked milestones or check-lists. The functions in such a system are also similar, including: probing, monitoring, training, supporting, and educating. These linking activities and functions require the same programmed applications across many domains of health applications.

Ethical challenges

The type of data that are available on mobile phones is increasing steadily. Phones also allow data to be collected with a potentially lower respondent burden compared to previously developed platforms. CHWs utilizing mobile phones to improve the quality of their work performance can be supported and monitored in ways not previously possible. The potential intrusiveness of this technology for both the CHW and the mother or family being supported and monitored is unlikely to be appreciated by either the CHW or the family. It is critical that we develop standards and strategies for protecting this information with multiple levels of access specified and with the potential opportunities and risks outlined to those from whom the data are being collected. An international set of standards for protection of privacy and confidentiality with these types of platforms is urgently needed. In addition, despite the high level of mobile penetration in Africa, many persons consider themselves technologically challenged. In our experience with this mobile information system, training some CHW's on the system was easy while for others it was more challenging. However, these difficulties were resolved within a two week time period. Central to this was ensuring particularly tight monitoring during the early phase of implementation to ensure that difficulties were picked up and attended to early.

Limitations

Technology will not completely solve the human resource crises confronting healthcare systems globally, nor resolve all of the complex issues involved in scaling up large scale interventions. It will also not replace the need for the ‘human face’ of support as well as supervision. There is a danger that this ‘human face’ might be forgotten given the ubiquity of mobile technology. In addition, many of the reasons for inadequate supervision at scale relate to problems such as poor motivation caused by low salaries, limited professional and career development for supervisors and lack of incentives to mention but a few (Rowe et al., 2010). There are problems that cannot be solved by technology or a mobile information and management system. These require another set of human resource skills and interventions as well as a shift in the professionalization of community health work and supervision. In addition, many countries have policies that support the use of health care institutions rather than CHW's and the system that we have developed would have limited value in these contexts. Qualitative work exploring the interface between the ‘human actors’ and the new technologies being developed is vital. The field is currently expanding at a very fast rate, and this fast iterative approach should not be lost, but a certain amount of caution is warranted. We need to ensure that time and money are not wasted on ineffective programs in the belief that ‘technology’ will solve all problems.

Acknowledgments

The studies mentioned in this paper were funded by National Institute of Alcoholism and Alcohol Abuse (NIAAA) and Saving Newborn Lives. We acknowledge the support of the National Research Foundation (South Africa). Alexander Tsai acknowledges salary support from the Robert Wood Johnson Health and Society Scholars Program.

Declaration: No funding bodies had any role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

Footnotes

The authors have no competing interests to declare

MT, MRB, TD, DS, AT, PI, ILR, DJ, AF, MColvin, MChopra were all involved in developing the system. MT wrote the first draft of the paper. All authors contributed to writing the paper. All authors have approved the final version of the paper.

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