Development of a computer-interpretable clinical guideline model for decision support in the differential diagnosis of hyponatremia

OpenClinical.net: Artificial intelligence and knowledge engineering at the point of care

Objective

OpenClinical.net is a way of disseminating clinical guidelines to improve quality of care whose distinctive feature is to combine the benefits of clinical guidelines and other human-readable material with the power of artificial intelligence to give patient-specific recommendations. A key objective is to empower healthcare professionals to author, share, critique, trial and revise these ‘executable’ models of best practice.

Design

OpenClinical.net Alpha (www.openclinical.net) is an operational publishing platform that uses a class of artificial intelligence techniques called knowledge engineering to capture human expertise in decision-making, care planning and other cognitive skills in an intuitive but formal language called PROforma.3 PROforma models can be executed by a computer to yield patient-specific recommendations, explain the reasons and provide supporting evidence on demand.

Results

PROforma has been validated in a wide range of applications in diverse clinical settings and specialties, with trials published in high impact peer-reviewed journals. Trials have included patient workup and risk assessment; decision support (eg, diagnosis, test and treatment selection, prescribing); adaptive care pathways and care planning. The OpenClinical software platform presently supports authoring, testing, sharing and maintenance. OpenClinical’s open-access, open-source repository Repertoire currently carries approximately 50+ diverse examples (https://openclinical.net/index.php?id=69).

Conclusion

OpenClinical.net is a showcase for a PROforma-based approach to improving care quality, safety, efficiency and better patient experience in many kinds of routine clinical practice. This human-centred approach to artificial intelligence will help to ensure that it is developed and used responsibly and in ways that are consistent with professional priorities and public expectations.

Local, Early, and Precise: Designing a Clinical Decision Support System for Child and Adolescent Mental Health Services

Mental health disorders often develop during childhood and adolescence, causing long term and debilitating impacts at individual and societal levels. Local, early, and precise assessment and evidence-based treatment are key to achieve positive mental health outcomes and to avoid long-term care. Technological advancements, such as computerized Clinical Decision Support Systems (CDSSs), can support practitioners in providing evidence-based care. While previous studies have found CDSS implementation helps to improve aspects of medical care, evidence is limited on its use for child and adolescent mental health care. This paper presents challenges and opportunities for adapting CDSS design and implementation to child and adolescent mental health services (CAMHS). To highlight the complexity of incorporating CDSSs within local CAMHS, we have structured the paper around four components to consider before designing and implementing the CDSS: supporting collaboration among multiple stakeholders involved in care; optimally using health data; accounting for comorbidities; and addressing the temporality of patient care. The proposed perspective is presented within the context of the child and adolescent mental health services in Norway and an ongoing Norwegian innovative research project, the Individualized Digital DEcision Assist System (IDDEAS), for child and adolescent mental health disorders. Attention deficit hyperactivity disorder (ADHD) among children and adolescents serves as the case example. The integration of IDDEAS in Norway intends to yield significantly improved outcomes for children and adolescents with enduring mental health disorders, and ultimately serve as an educational opportunity for future international approaches to such CDSS design and implementation.

A layered computer-interpretable guideline model for easing the update of locally adapted clinical guidelines

Maintenance of computer-interpretable guidelines is complicated by evolving medical knowledge and by the requirement to customize content to local practice settings. We developed a framework to support knowledge engineers in customization and maintenance of computer-interpretable guidelines specified in the PROforma formalism. In our layered approach, the computer-interpretable guidelines containing the original clinical guideline serves as the primary layer and local customizations form secondary layers that adhere to its schema while augmenting it. Java code unifies the layers into a single enactable computer-interpretable guidelines. We performed a pilot experiment to verify the effectiveness of a layered framework. In this first attempt, we evaluated the hypothesis that the layered computer-interpretable guidelines framework supports knowledge engineers in maintenance of customized computer-interpretable guidelines. Participants who used the layered framework completed an update process of the primary knowledge in less time and made fewer errors as compared to those using the single-layer framework.

Impulsando las directrices de la Ley de Calidad del SNS: modelos computacionales de guías de práctica clínica

La Ley de cohesión y calidad del Sistema Nacional de Salud promueve la utilización de nuevas tecnologías para hacer posible la aplicación de la evidencia científica por los profesionales sanitarios. En este sentido, existen herramientas tecnológicas, conocidas como modelos computacionales de guías de práctica clínica (computer-interpretable guidelines), que pueden ayudar a la consecución de este objetivo desde un prisma innovador. Su adopción puede llevarse a cabo de forma iterativa, teniendo un gran potencial inicial como herramientas formativas, de calidad y seguridad del paciente, en la toma de decisiones compartidas y, opcionalmente, podrán ser integradas con la historia clínica electrónica una vez sean validadas de forma rigurosa. En este artículo se presentan los avances de dichas herramientas, se revisan proyectos internacionales y experiencias propias en los que han demostrado su valor, y se ponen de manifiesto las ventajas, riesgos y limitaciones que presentan desde un punto de vista clínico.