Modelling evolving clinical practice guidelines: a case of Malawi

Electronic medical record (EMR) systems are increasingly being adopted in low- and middle-income countries. This provides an opportunity to support task-shifted health workers with guideline-based clinical decision support to improve the quality of healthcare delivery. However, the formalization of clinical practice guidelines (CPGs) into computer-interpretable guidelines (CIGs) for clinical decision support in such a setting is a very challenging task due to the evolving nature of CPGs and limited healthcare budgets. This study proposed that a CIG modelling language that considers CPG change requirements in their representation models could enable semi-automated support of CPG change operations thereby reducing the burden of maintaining CIGs. Characteristics of CPG changes were investigated to elucidate CPG change requirements using CPG documents from Malawi where EMR systems are routinely used. Thereafter, a model-driven engineering approach was taken to design a CIG modelling framework that has a novel domain-specific modelling language called FCIG for the modelling of evolving CIGs. The CIG modelling framework was implemented using the Xtext framework. The national antiretroviral therapy EMR system for Malawi was extended into a prototype with FCIG support for experimentation. Further studies were conducted with CIG modellers. The evaluations were conducted to answer the following research questions: i) What are the CPG change requirements for modelling an evolving CIG? ii) Can a model-driven engineering approach adequately support the modelling of an evolving CIG? iii) What is the effect of modelling an evolving CIG using FCIG in comparison with the Health Level Seven (HL7) standard for modelling CIGs? Data was collected using questionnaires, logs and observations. The results indicated that finegrained components of a CPG are affected by CPG changes and that those components are not included explicitly in current executable CIG language models. The results also showed that by including explicit semantics for elements that are affected by CPG changes in a language model, smart-editing features for supporting CPG change operations can be enabled in a language-aware code editor. The results further showed that both experienced and CIG modellers perceived FCIG as highly usable. Furthermore, the results suggested that FCIG performs significantly better at CIG modelling tasks as compared to the HL7 standard, Arden Syntax. This study provides empirical evidence that a model-driven engineering approach to clinical guideline formalization supports the authoring and maintenance of evolving CIGs to provide up-to-date clinical decision support in low- and middle-income countries

Towards an architectural design of a guideline-driven EMR system: A contextual inquiry of Malawi

Computerised clinical practice guidelines are a key component of effective clinical decision support systems, especially in low-resource regions such as Malawi. To address shortages in staffing and budgets for training, the practice of task-shifting, the clinical practice guidelines (CPGs) enable health workers with limited training to provide a standardised level of care. However, CPGs are tradition-ally paper-based, with only a few CPGs having been computerised for Malawi's national electronic health record system. These CPGs have been hard-coded into the system, necessitating significant additional work to add support for future and revised CPGs. We further investigate CPG computerisation challenges in order to understand the motivations for the current computerised CPGs implementation. We use semi-structured interviews, code reviews, and observations in Malawi. Most significantly, we extend existing understanding of software engineering principles to the context of low-resource environments, noting that the tensions between conflicting stakeholder requirements, deadline and deliverable expectations, and good software engineering often result in systems that are harder to maintain, further exacerbating potential problems with longevity of ICTD deployments. We further suggest that a component-based approach in conjunction with communities of open source developers might help alleviate this problem by providing more scalable and robust CPG support

Characterisation of Clinical Practice Guideline Changes

Sub-Saharan Africa is facing a double crisis of high disease burden and shortage of healthcare resources. To cope with this challenge, many countries have adopted the practice of task-shifting with clinical practice guidelines (CPGs) as a key component. It is not unusual for CPGs to be revised or proved wrong, spurring frequent updates of state-mandated CPGs. This negatively affects maintainability of healthcare applications using those CPGs. Therefore, it is essential that the types of CPG changes are understood in order to develop clinical decision support systems that are maintainable through adequate support for CPGs. We take a bottom-up approach to analyse successive sets of CPGs so as to elucidate and characterise types of CPG changes overtime. The identified 10 type of changes in decisions, actions, and recommendations are exhaustive and affect fine-grained structural components of a CPG. We also determined their occurrences using Malawi’s HIV CPGs of 2008, 2011, and 2014 as case study. The results showed that the number of changes, as well as the type of changes that occur in successive versions, varies widely

Implementation of an Electronic Clinical Decision Support System for the Early Recognition and Management of Dysglycemia in an Inpatient Mental Health Setting Using CogStack:Protocol for a Pilot Hybrid Type 3 Effectiveness-Implementation Randomized Controlled Cluster Trial

Background:Severe mental illnesses (SMIs), including schizophrenia, bipolar affective disorder, and major depressive disorder, are associated with an increased risk of physical health comorbidities and premature mortality from conditions including cardiovascular disease and diabetes. Digital technologies such as electronic clinical decision support systems (eCDSSs) could play a crucial role in improving the clinician-led management of conditions such as dysglycemia (deranged blood sugar levels) and associated conditions such as diabetes in people with a diagnosis of SMI in mental health settings.Objective:We have developed a real-time eCDSS using CogStack, an information retrieval and extraction platform, to automatically alert clinicians with National Health Service Trust–approved, guideline-based recommendations for dysglycemia monitoring and management in secondary mental health care. This novel system aims to improve the management of dysglycemia and associated conditions, such as diabetes, in SMI. This protocol describes a pilot study to explore the acceptability, feasibility, and evaluation of its implementation in a mental health inpatient setting.Methods:This will be a pilot hybrid type 3 effectiveness-implementation randomized controlled cluster trial in inpatient mental health wards. A ward will be the unit of recruitment, where it will be randomly allocated to receive either access to the eCDSS plus usual care or usual care alone over a 4-month period. We will measure implementation outcomes, including the feasibility and acceptability of the eCDSS to clinicians, as primary outcomes, alongside secondary outcomes relating to the process of care measures such as dysglycemia screening rates. An evaluation of other implementation outcomes relating to the eCDSS will be conducted, identifying facilitators and barriers based on established implementation science frameworks.Results:Enrollment of wards began in April 2022, after which clinical staff were recruited to take part in surveys and interviews. The intervention period of the trial began in February 2023, and subsequent data collection was completed in August 2023. Data are currently being analyzed, and results are expected to be available in June 2024.Conclusions:An eCDSS can have the potential to improve clinician-led management of dysglycemia in inpatient mental health settings. If found to be feasible and acceptable, then, in combination with the results of the implementation evaluation, the system can be refined and improved to support future successful implementation. A larger and more definitive effectiveness trial should then be conducted to assess its impact on clinical outcomes and to inform scalability and application to other conditions in wider mental health care settings.</p