Re-tracing the footsteps: Thoughts on implementing guidelines for COVID-19 Surveillance in Low and Middle Income Countries

Rangarirai Matavire (ITI Nordic) discusses how guidelines of Clinical Decision Support (CDS) and Clinical Quality Measurement (CQM) can be implemented towards the monitoring of COVID-19 in low- and middle-income nations.

There has been a proliferation of advanced information system technologies in the public healthcare sectors of Low and Middle Income Countries (LMICs). Asides to a number of Web-based systems, many of these solutions are also built on mobile devices owing to the widespread use of both feature and smart phones, particularly those running on the Android operating system. Mobile devices are particularly useful for the provision of quality healthcare services to communities in remote areas, the often called next billion people. In many LMICs, local and global technology vendors, supported by international funding agencies, are active in developing public health information infrastructures for case-based surveillance, aggregated data reports and analytics. Heterogeneity in adopted technologies persists within and across LMICs, with choices made on custom or generic solutions and licensing models which range from proprietary, through open source, to copyleft. The net result of these interventions is that there is often a number of technologies developed and implemented within countries for different health programs and regions, with scarce usage of communication interfaces and limited alignment of processes between them. Information is consequently fragmented across these landscapes, with each vendor solution implementing its own approach for supporting health workers in the provision of quality care. While these systems are often adopted to encourage improved adherence to healthcare guidelines, research suggests that these systems have not lived up to expectations. The main reason is not whether individual solutions, or implementations, are aligned to guidelines, but rather if the net result of the different technologies has led to the expected improvement. In addition, in times of emergencies, such as the COVID-19 pandemic, guidelines change rapidly as new information comes in thereby exacerbating the problem. The ability of systems to share and implement the fast changing rules becomes of prime importance.

Training of nurses on a Mobile Health Information System in Zimbabwe

To understand how IT is implicated in the agenda for the strengthening of health information systems in LMICs, it is important to look at two of its envisioned roles in the provision of care, that is Clinical Decision Support (CDS) and Clinical Quality Measurement (CQM). Clinical decision support (CDS) is concerned with “reminders and alerts driven by rules” [1]. This has to do with a systems’ ability to provide recommendations to a health worker or patient during the course of care provision. On the other hand, Clinical Quality Measurement (CQM) is concerned with indicators on the quality of care based on the data collected on populations. The need for CDS and CQM is driven by a realisation that health workers do not adequately adhere to narrative clinical guidelines, and technology has been found to increase the quality of care in these contexts. In a situation where COVID-19, a highly contagious virus is concerned, CDS has to do with the ability of a system to generate alerts or reminders that recommend certain courses of action when collected data fires preconfigured triggers. For instance a patients’ travel history, alongside other symptoms, could trigger an alert when an information system recognises that the client was exposed when they visited a place with known high transmission, as has been done for the Zika Virus [2]. The system could therefore recommend that certain measures be taken with the client in question, including requesting a test or sending an alert to a regional response team. CDS is, by design, inherently linked with CQM, where the indicators on the percentage of clients who followed through with the recommended courses of action are analysed for decision making. In this context, the Fast Healthcare Interoperability Resources (FHIR) standard is being increasingly adopted to address the challenge of implementing guidelines across vendor offerings. This standard could enable for rapidly updating guidelines across compliant system systems in the event of emergencies.

Computable Care Guidelines are the standards driven executable resources which can be shared across systems to provide the logic for triggering rules when certain client data points are encountered. While health information systems have traditionally applied their own internal mechanisms to implement narrative care guidelines, there has been a risk of eroding their principles across different computer systems and thereby, increasing fragmentation [1]. Their implementation in a standard such as FHIR offers an opportunity for aligning the quality of care when different IT solutions are used at the point of care. In this context, the developer of the guideline, be it an international agency such as the World Health Organisation (WHO), or a local Ministry of Health (MoH), can publish the computable file for implementation by compliant systems [3]. In the event of an outbreak such as COVID-19, these guidelines can be rapidly changing, and therefore consumed and implemented in real-time by compliant systems. Currently, the World Health Organisation (WHO) is actively developing CCGs for Antenatal care (ANC) for LMICs which are to be implemented by a number of active vendors in these regions. It is therefore possible to see some of the ways in which this approach can be adopted for pandemic response information systems, and the benefits thereof. However, a few systems are close to, or capable of, running these executable guidelines currently. In this COVID-19 context, already compliant systems have much to offer to the response, and to improve the quality of care for clients who are able to benefit from these innovations [4]. It still remains however, that in the short to medium term, LMICs are well advised to channel investments to such infrastructural innovations as they stand to benefit during this, and in future epidemics. The capabilities of this approach are open and therefore present a fruitful direction for health IT research and development.

[1] Aziz A Boxwala, et. al., A multi-layered framework for disseminating knowledge for computer-based decision support, Journal of the American Medical Informatics Association, Volume 18, Issue Supplement_1, December 2011, Pages i132–i139

[2] Sanjeev Tandon, et. al., Emerging Infectious Diseases, Clinical Decision Support, and Electronic Health Records Meaningful Use , Healthcare Information and Management Systems Society (HIMSS), April 2017,  https://www.himssconference.org/sites/himssconference/files/pdf/39_0.pdf

[3] IHE, The Computable Care Guidelines (CCG) profile, https://wiki.ihe.net/index.php/Computable_Care_Guidelines

[4] Grahame Grieve, HL7, #FHIR, and Covid-19, http://www.healthintersections.com.au/?p=3012

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