HIS: HMIS, EHRs and the chronic care model

As an important component of the HIS, HMIS refer to health services data collected at a facility level, which can include characteristics such as systems data (personnel, training); activities (types of intervention delivered); service-user data (diagnoses, socio-demographic data, co-morbidities) (Lora et al., Reference Lora, Lesage, Pathare and Levav2017). A key purpose of this aggregated data is to support short-term management, service planning and resource prioritisation. In the long-term, HMIS may facilitate better governance, transparency, evidence-based decision-making; and quality and performance-based financing, ultimately supporting the health system to better meet the needs of the population (Upadhaya et al., Reference Upadhaya, Jordans, Abdulmalik, Ahuja, Alem, Hanlon, Kigozi, Kizza, Lund, Semrau, Shidhaye, Thornicroft, Komproe and Gureje2016). EHRs, differ from HMIS in that they are organised by individual patients rather than facilities, in order to facilitate tracking of service-users wherever they are present, supporting the sharing of information between points of care and the use of this data to guide clinical decision-making, thereby driving improvements in continuity and overall quality of care (Katurura and Cilliers, Reference Katurura and Cilliers2018).

Chronic care relies upon a measurement-based approach with the sharing of information between team members (Wagner et al., Reference Wagner, Austin and Von Korff1996). This model has been adapted for use in low resource settings where ‘task-shared’ models of mental healthcare have been widely researched and integrated into national policies and plans which are now beginning to be implemented at scale. Task-shared teams for mental healthcare typically include community health workers who visit households in their communities, medical officers or primary healthcare nurses and minimal input from specialists (van Ginneken et al., Reference van Ginneken, Tharyan, Lewin, Rao, Romeo and Patel2011). For mental health in LMIC, often community health workers will identify people who may require treatment before screening/diagnosis takes place at a primary healthcare centre. Depending on severity and treatment options available, formal diagnosis and initiation of treatment may take place at a secondary/tertiary facility. Increasingly, the staff at PHCs are trained to deliver psychosocial interventions for mental health, in line with WHO's Mental Health Global Action Plan (mhGAP) recommendations (WHO 2016). The development of structured care pathways, whereby care is organised according to explicit goals, roles and care processes and outcomes are documented and are likely to be essential to the success of these interventions (Schrijvers et al., Reference Schrijvers, van Hoorn and Huiskes2012). Currently, in many settings, little information is recorded about mental health or NCD service-users in primary care in LMIC, other than perhaps their attendance and diagnosis, often recorded using paper registers, making high quality, longitudinal, patient-centred care impossible.

HIS may be harnessed to meet the needs of chronic care models for mental health in PHCs of LMICs (Young et al., Reference Young, Chaney, Shoai, Bonner, Cohen, Doebbeling, Dorr, Goldstein, Kerr, Nichol and Perrin2007). Some examples can be seen from studies which have used HIS to deliver better management of diabetes (Young et al., Reference Young, Mintz and Cohen2004) and schizophrenia [19] in HIC settings. These studies have shown that HIS can effectively enable: (a) clinicians in clinical decision-making based on accessible clinical information about their individual patients, (b) clinical managers in the identification of pervasive problems in care, monitoring of performance, and improvement access to necessary treatments, and (c) public health administrators in understanding disease burden, monitoring quality and population health outcomes, and in creating and coordination of patient-centred community-based care.

Challenges and opportunities

Challenges of and opportunities for effectively using information systems for chronic care of mental health in PHCs of LMICs can be analysed in terms of behavioural, technical and organisational determinants of HIS performance. This approach is based on the PRISM framework for the evaluation for HIS (Aqil et al., Reference Aqil, Lippeveld and Hozumi2009). Consideration of behavioural determinants covers all aspects of human involvement in the performance of information systems. This relates to ‘how people react to and use information’ (Aqil et al., Reference Aqil, Lippeveld and Hozumi2009). In EMERALD, a recent multi-country study, healthcare workers in Uganda and Nepal requested incentives to support their new role for collecting mental health data; whilst in the three other study countries (Ethiopia, Nigeria, South Africa), there was no evidence to suggest that data was being used to support service improvement (Ahuja et al., Reference Ahuja, Hanlon, Chisholm, Semrau, Gurung, Abdulmalik, Mugisha, Mntambo, Kigozi, Petersen, Shidhaye, Upadhaya, Lund, Evans-Lacko, Thornicroft, Gureje and Jordans2019). Other authors have suggested that, in general, data from HMIS is not commonly used for the district or community-level planning or resource allocation (Wickremasinghe et al., Reference Wickremasinghe, Hashmi, Schellenberg and Avan2016) and that data derived from HMIS are perceived to be poor quality and unreliable (Nnaji et al., Reference Nnaji, Oguoma, Nnaji and Nwobodo2010). There is insufficient management and administrative capacity to use data effectively to support decision-making (Mubyazi et al., Reference Mubyazi, Kamugisha, Mushi and Blas2004), and a lack of healthcare personnel with health informatics training to develop and maintain systems (Katurura and Cilliers, Reference Katurura and Cilliers2018). However, there is research evidence to suggest that there is an appetite for using data to support care delivery and planning: a qualitative evaluation suggested that healthcare workers and managers recognised the usefulness of collecting and aggregating data on mental health, in the same study, in Nigeria and Uganda, managers have expressed enthusiasm about using data to support service planning (Ahuja et al., Reference Ahuja, Hanlon, Chisholm, Semrau, Gurung, Abdulmalik, Mugisha, Mntambo, Kigozi, Petersen, Shidhaye, Upadhaya, Lund, Evans-Lacko, Thornicroft, Gureje and Jordans2019).

From the technical determinants perspective, there is a need to consider factors that are related to the specialised know-how and technology which is necessary to develop, manage and improve HIS processes and performance (Aqil et al., Reference Aqil, Lippeveld and Hozumi2009). There has been a proliferation of potential technological solutions, though there remains a high failure with the extent of adoption (Yogeswaran and Wright, Reference Yogeswaran and Wright2010; Kiberu et al., Reference Kiberu, Mars and Scott2017). It has been observed that introducing EHR in the context of fragmented and under-resourced health systems can be a threat to feasibility as well as perceived value. There are deficits in IT infrastructure, including, lack of computers, networking equipment, internet connectivity (Akhlaq et al., Reference Akhlaq, McKinstry, Muhammad and Sheikh2016; Katurura and Cilliers, Reference Katurura and Cilliers2018). In addition, interoperability between different EHR systems is poor and data related to population mental health and NCDs are particularly weak, with indicators seldom included in national information systems, offering a description of population burden and needs that is incomplete and unreliable at best (Jordans et al., Reference Jordans, Chisholm, Semrau, Gurung, Abdulmalik, Ahuja, Mugisha, Mntambo, Kigozi, Petersen, Shidhaye, Upadhaya, Lund, Thornicroft and Gureje2019). When collection and aggregation of data on mental health and NCDs are introduced, there are often concerns about additional time taken and the challenge of integration of the new data collection with existing information systems (Jordans et al., Reference Jordans, Chisholm, Semrau, Gurung, Abdulmalik, Ahuja, Mugisha, Mntambo, Kigozi, Petersen, Shidhaye, Upadhaya, Lund, Thornicroft and Gureje2019). On the other hand, publications from EMERALD demonstrate the feasibility of the development and implementation of mental health indicators designed to target universal health coverage in India, Nepal, Ethiopia, Nigeria, South Africa and Uganda (Upadhaya et al., Reference Upadhaya, Jordans, Abdulmalik, Ahuja, Alem, Hanlon, Kigozi, Kizza, Lund, Semrau, Shidhaye, Thornicroft, Komproe and Gureje2016). Forms were locally developed and used to capture the following indicators: service utilisation by disorder, severity, functioning, administered treatment, referral, follow-up and payment for services (Jordans et al., Reference Jordans, Chisholm, Semrau, Gurung, Abdulmalik, Ahuja, Mugisha, Mntambo, Kigozi, Petersen, Shidhaye, Upadhaya, Lund, Thornicroft and Gureje2019). High levels of completeness and correctness of data were achieved at time points measured, indicating overall feasibility and acceptability (Jordans et al., Reference Jordans, Chisholm, Semrau, Gurung, Abdulmalik, Ahuja, Mugisha, Mntambo, Kigozi, Petersen, Shidhaye, Upadhaya, Lund, Thornicroft and Gureje2019).

Finally, the organisational determinants perspective considers the contextual factors which influence performance through organisational rules, values and practices (Aqil et al., Reference Aqil, Lippeveld and Hozumi2009). Few LMIC governments prioritise the collection of mental health indicators as part of routine HMIS. Where data is gathered, these are often designed to be collected for the purposes of monitoring and upward reporting, limiting its capacity to support local decision-making (Mubyazi et al., Reference Mubyazi, Kamugisha, Mushi and Blas2004). There is often insufficient decentralisation to enable local planners to make meaningful decisions and a lack of co-ordination between different government policies and departments (Bossert and Beauvais, Reference Bossert and Beauvais2002). Changes to government and political unrest commonly disrupt implementation. Nonetheless, there are signs of positive developments in policy development and implementation. The Lancet Commission found that in 2015, 34 LMICs had adopted national EHRs systems and 41 LMICs used District Health Information Software (DHIS) 2 at a national scale for aggregate reporting from electronic or paper registers in facilities (Kruk et al., Reference Kruk, Gage, Arsenault, Jordan, Leslie, Roder-DeWan, Adeyi, Barker, Daelmans, Doubova, English, Elorrio, Guanais, Gureje, Hirschhorn, Jiang, Kelley, Lemango, Liljestrand, Malata, Marchant, Matsoso, Meara, Mohanan, Ndiaye, Norheim, Reddy, Rowe, Salomon, Thapa, Twum-Danso and Pate2018). One of the four objectives of the World Health Organization's (WHO) Mental Health Action Plan for 2013–2020 focuses on ‘strengthening information systems, evidence and research for mental health’ with a related target which aims for 80% of member states to report core mental health indicators through their routine monitoring systems by 2020 (WHO, 2013). To guide the process of strengthening mental health information systems (MHIS), the WHO developed a module for MHIS in its Mental Health Policy and Service Guidance Package (World Health Organization, 2005). The MHIS module proposes evidence-based principles and steps for MHIS development. In LMICs, there are several examples of EHRs for particular groups of patients, often those living with HIV (Manders et al., Reference Manders, Jose, Solis, Burlison, Nhampossa and Moon2010; Waters et al., Reference Waters, Rafter, Douglas, Bwanali, Jazayeri and Fraser2010). A number of LMICs are in the process of developing or introducing national systems of EHRs (e.g. India, South Africa, China, Ethiopia, Pakistan and Thailand) (Akhlaq et al., Reference Akhlaq, McKinstry, Muhammad and Sheikh2016).

Work within the ASSET programme https://healthasset.org/ Global Health Research Unit on Health System Strengthening in Sub-Saharan Africa (2017–2021, NIHR grant number: GHRU 16/136/54) aims to address some of the current evidence gaps for HMIS to support primary mental healthcare by drawing upon principles of user-led design to develop an application to support primary healthcare for depression and co-morbid chronic physical illness. Working in rural Ethiopia, the ASSET programme will explore the practicability of phones, linked to cloud servers to collect, aggregate, analyse and use HMIS data which consists of five indicators: (1) detection of a condition, (2) engagement on a care pathway, (3) intervention adherence, (4) retention and (5) ‘treatment to target’. On detection, the application will generate a care pathway, with prompted assessments and associated actions at each follow-up appointment. Patient data will be accessed by healthcare workers providing care in order to support continuity of care. Escalating reminders of overdue tasks and follow-up from supervisors were found to be effective in reducing delayed actions among community health workers in Tanzania. Within the ASSET application, the patient registry allows healthcare workers to focus attention on patients most in need: a ‘dashboard’, listing all NCD patients within a facility will highlight those who have ‘red flags’ – missed appointments, severe or deteriorating symptoms. Aggregated data can be used to compare the care quality and outcomes for healthcare workers, facilities and districts to; target supervision and support; identify best performing healthcare workers and facilities to mentor others; inform quality improvement initiatives, with real-time data to track effectiveness. We are working with facility managers and quality improvement/information teams to ensure data from the application is useable and attuned to existing reporting and review processes and timelines. A prototype has been built and one round of user testing completed, with a second round planned for 2020.

Recommendations for future research and development of information systems to support chronic care

1. Multisectoral collaboration and strong partnerships are essential: Although more challenging, a systems-thinking approach, involving the full range of stakeholders, is likely to be necessary. Like all health systems strengthening efforts, building strong partnerships will be essential to achieving successful work (Breuer et al., Reference Breuer, Hanlon, Bhana, Chisholm, Silva, Fekadu, Honikman, Jordans, Kathree, Kigozi, Luitel, Marx, Medhin, Murhar, Ndyanabangi, Patel, Petersen, Prince, Raja, Rathod, Shidhaye, Ssebunnya, Thornicroft, Tomlinson, Wolde-Giorgis and Lund2019). It is likely that in addition to funding secured from donors and research funding, the government will need to allocate resources to secure the sustainability of information systems. Congruence of initiatives with broader policy environments and information systems for other diseases will, therefore, be crucial to success: for researchers, building trusting relationships with Ministries of Health will, therefore, be essential. Capacity-building work with policymakers is likely to be important in increasing understanding of health systems strengthening interventions and research methodologies (Semrau et al., Reference Semrau, Alem, Abdulmalik, Docrat, Evans-Lacko, Gureje, Kigozi, Lempp, Lund, Petersen, Shidhay, Thornicroft and Hanlon2018). Information systems themselves will need to be well-aligned to both people, service models and accompanying workflows. The organisation of chronic care, in the form of care pathways, identifying different activities, decision points, where these occur, who is involved and how the different activities and actors relate to one another is a vital first step to making a task-shared service work. Research and development work will require collaboration between actors from different disciplines (learning technologists, software designers, clinicians, epidemiologists, service-user researchers), preferably taking an interdisciplinary approach. Participatory research methods will need to be used, both to understand more about individual needs and how information gathering, recording and use might fit into workflows, as well as building understanding and ownership among stakeholders of the information system that emerges from formative work. Building capacity for development and maintenance of HIS, the dissemination and utilisation of data will be a function of strong partnerships as well as being crucial to the success of the system as a whole (Kiberu et al., Reference Kiberu, Mars and Scott2017). This is likely to include the development of in-service training modules, mentorship programmes for different cadres of the health system and the development of career pathways for informaticians.

2. Locally meaningful mental health indicators, aligned to service models must be introduced: Evidence from the USA and elsewhere suggests that chronic care models supported by appropriate information systems have the potential to help drive quality improvement and support patient-centred care. However, for this to be the case, data collected must be accessible and perceived to be of value to users; users should have sufficient autonomy to make data-informed decisions. The EMERALD programme has demonstrated that it is feasible to develop mental health indicators for primary mental healthcare in LMIC settings, using consensus-building methodologies (Jordans et al., Reference Jordans, Chisholm, Semrau, Gurung, Abdulmalik, Ahuja, Mugisha, Mntambo, Kigozi, Petersen, Shidhaye, Upadhaya, Lund, Thornicroft and Gureje2019). National policymakers and healthcare workers have been the main focus HMIS development work in LMIC. Increasing the involvement of service-users in the development of indicators and information systems will help to improve the accountability of services and the health system as a whole. Likewise, ensuring that systems are in place to facilitate timely access to local planners and managers to data that is of high quality will be essential to initiating quality improvement activities. Incentivisation may be counterproductive. Social determinants of health which are likely to mediate both the severity of mental illness and the effectiveness of interventions may be largely outside of the control of the provider will interact with measures of quality of care, potentially increasing the risk of ‘cherry picking’ of less severely affected service-users in contexts where quality indicators are incentivised (Kilbourne et al., Reference Kilbourne, Beck, Spaeth-Rublee, Ramanuj, O'Brien, Tomoyasu and Pincus2018). There is limited evidence from TB care that incentivisation of complex objectives that require multiple behaviours may have adverse effects upon outcomes.

3.Technology used for data collection and management should be appropriate: To avoid failure, creative approaches are needed to work within the constraints of available infrastructure and technology. This is likely to involve ‘leapfrogging’ of intermediate phases of development used elsewhere, in favour of newer forms of technology which have the potential to contribute to reducing inequities of access to knowledge, information and power more rapidly (Akhlaq et al., Reference Akhlaq, McKinstry, Muhammad and Sheikh2016) (e.g. Mittal et al., Reference Mittal, Gandhi and Tripathi2010). For example, researchers have shown that it is feasible and acceptable to introduce smartphones to collect maternal health data in rural Ethiopia (Little et al., Reference Little, Medhanyie, Yebyo, Spigt, Dinant and Blanco2013; Medhanyie et al., Reference Medhanyie, Moser, Spigt, Yebyo, Little, Dinant and Blanco2015). Technology choices must be compatible with the local context, for example, having offline functionality in environments where internet connectivity is unreliable and compliance with regulatory and legal frameworks (e.g. Uganda's National eHealth Policy) (Government of Uganda, 2016). Evolving initiatives should therefore be consistent with national government plans, or, at the very least, research teams and donors should ensure that systems have good interoperability, for example, by adhering to specifications for standardised content and structure of core information, such as those set out by Fast Healthcare Interoperability Resources (Braunstein, Reference Braunstein2018).