Evaluating a web-based clinical decision support system for language disorders screening in a nursery school

The effects of computerised decision support systems on nursing and allied health professional performance and patient outcomes: a systematic review and user contextualisation

Background

Computerised decision support systems (CDSS) are widely used by nurses and allied health professionals but their effect on clinical performance and patient outcomes is uncertain.

Objectives

Evaluate the effects of clinical decision support systems use on nurses', midwives' and allied health professionals' performance and patient outcomes and sense-check the results with developers and users.

Eligibility criteria

Comparative studies (randomised controlled trials (RCTs), non-randomised trials, controlled before-and-after (CBA) studies, interrupted time series (ITS) and repeated measures studies comparing) of CDSS versus usual care from nurses, midwives or other allied health professionals.

Information sources

Nineteen bibliographic databases searched October 2019 and February 2021.

Risk of bias

Assessed using structured risk of bias guidelines; almost all included studies were at high risk of bias.

Synthesis of results

Heterogeneity between interventions and outcomes necessitated narrative synthesis and grouping by: similarity in focus or CDSS-type, targeted health professionals, patient group, outcomes reported and study design.

Included studies

Of 36,106 initial records, 262 studies were assessed for eligibility, with 35 included: 28 RCTs (80%), 3 CBA studies (8.6%), 3 ITS (8.6%) and 1 non-randomised trial, a total of 1318 health professionals and 67,595 patient participants. Few studies were multi-site and most focused on decision-making by nurses (71%) or paramedics (5.7%). Standalone, computer-based CDSS featured in 88.7% of the studies; only 8.6% of the studies involved 'smart' mobile or handheld technology. Care processes - including adherence to guidance - were positively influenced in 47% of the measures adopted. For example, nurses' adherence to hand disinfection guidance, insulin dosing, on-time blood sampling, and documenting care were improved if they used CDSS. Patient care outcomes were statistically - if not always clinically - significantly improved in 40.7% of indicators. For example, lower numbers of falls and pressure ulcers, better glycaemic control, screening of malnutrition and obesity, and accurate triaging were features of professionals using CDSS compared to those who were not.

Evidence limitations

Allied health professionals (AHPs) were underrepresented compared to nurses; systems, studies and outcomes were heterogeneous, preventing statistical aggregation; very wide confidence intervals around effects meant clinical significance was questionable; decision and implementation theory that would have helped interpret effects - including null effects - was largely absent; economic data were scant and diverse, preventing estimation of overall cost-effectiveness.

Interpretation

CDSS can positively influence selected aspects of nurses', midwives' and AHPs' performance and care outcomes. Comparative research is generally of low quality and outcomes wide ranging and heterogeneous. After more than a decade of synthesised research into CDSS in healthcare professions other than medicine, the effect on processes and outcomes remains uncertain. Higher-quality, theoretically informed, evaluative research that addresses the economics of CDSS development and implementation is still required.

Future work

Developing nursing CDSS and primary research evaluation.

Funding

This project was funded by the National Institute for Health and Care Research (NIHR) Health and Social Care Delivery Research programme and will be published in Health and Social Care Delivery Research; 2023. See the NIHR Journals Library website for further project information.

Registration

PROSPERO [number: CRD42019147773].

Improvements for Therapeutic Intervention from the Use of Web Applications and Machine Learning Techniques in Different Affectations in Children Aged 0–6 Years

Technological advances together with machine learning techniques give health science disciplines tools that can improve the accuracy of evaluation and diagnosis. The objectives of this study were: (1) to design a web application based on cloud technology (eEarlyCare-T) for creating personalized therapeutic intervention programs for children aged 0–6 years old; (2) to carry out a pilot study to test the usability of the eEarlyCare-T application in therapeutic intervention programs. We performed a pilot study with 23 children aged between 3 and 6 years old who presented a variety of developmental problems. In the data analysis, we used machine learning techniques of supervised learning (prediction) and unsupervised learning (clustering). Three clusters were found in terms of functional development in the 11 areas of development. Based on these groupings, various personalized therapeutic intervention plans were designed. The variable with most predictive value for functional development was the users’ developmental age (predicted 75% of the development in the various areas). The use of web applications together with machine learning techniques facilitates the analysis of functional development in young children and the proposal of personalized intervention programs.

A computational model for speech disorders using problematic phonemes with ontological reasoning

This work presents a method for data gathering to construct a corpus related to speech disorders in children; such corpus will serve as the base to generate some semi-automatic ontologies, in order to become a computational model to support therapists for diagnosis and possible treatment. Speech disorders, phonemes and some additional information are classified using taxonomies obtained from speech disorders specialized literature. Based on the obtained taxonomies, the ontologies, which structure and formalize concepts defined by the main topic authors, are developed. The ontologies are constructed following some parts of classic methodologies and their subsequent validation is made through competency questions. The development of the model is based on Natural Language Processing (NLP) and Information Retrieval (IR) techniques. Integration of the ontologies is made to be able to make a classification based in problematic phonemes; this is suggested as a complement to the diagnostic tool in the model.

The Acceptability and Effectiveness of Web-Based Developmental Surveillance Programs: Rapid Review

Background

Web-based developmental surveillance programs may be an innovative solution to improving the early detection of childhood developmental difficulties, especially within disadvantaged populations.

Objective

This review aimed to identify the acceptability and effectiveness of web-based developmental surveillance programs for children aged 0 to 6 years.

Methods

A total of 6 databases and gray literature were searched using a Preferred Reporting Items for Systematic Reviews and Meta-Analyses–informed protocol. Data extraction included variables related to health equity.

Results

In total, 20 studies were identified. Most papers implemented web-based versions of the Modified Checklist for Autism in Toddlers, Revised with Follow-Up screener for autism spectrum disorder or Parent Evaluation of Developmental Status screeners for broad developmental delay. Caregivers and practitioners indicated a preference for web-based screeners, primarily for user-friendliness, improved follow-up accuracy, time, and training efficiencies.

Conclusions

Although evidence is limited as to the necessity of web- versus face-to-face–based developmental screening, there are clear efficiencies in its use.

Trial Registration

PROSPERO CRD42019127894; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=127894

Developing a System for Processing Health Data of Children Using Digitalized Toys: Ethical and Privacy Concerns for the Internet of Things Paradigm

EDUCERE (Ubiquitous Detection Ecosystem to Care and Early Stimulation for Children with Developmental Disorders) is a government funded research and development project. EDUCERE objectives are to investigate, develop, and evaluate innovative solutions for society to detect changes in psychomotor development through the natural interaction of children with toys and everyday objects, and perform stimulation and early attention activities in real environments such as home and school. In the EDUCERE project, an ethical impact assessment is carried out linked to a minors' data protection rights. Using a specific methodology, the project has achieved some promising results. These include use of a prototype of smart toys to detect development difficulties in children. In addition, privacy protection measures which take into account the security concerns of health data, have been proposed and applied. This latter security framework could be useful in other Internet of Things related projects. It consists of legal and technical measures. Special attention has been placed in the transformation of bulk data such as acceleration and jitter of toys into health data when patterns of atypical development are found. The article describes the different security profiles in which users are classified.

Evaluation of an Expert System for the Generation of Speech and Language Therapy Plans

BACKGROUND

Speech and language pathologists (SLPs) deal with a wide spectrum of disorders, arising from many different conditions, that affect voice, speech, language, and swallowing capabilities in different ways. Therefore, the outcomes of Speech and Language Therapy (SLT) are highly dependent on the accurate, consistent, and complete design of personalized therapy plans. However, SLPs often have very limited time to work with their patients and to browse the large (and growing) catalogue of activities and specific exercises that can be put into therapy plans. As a consequence, many plans are suboptimal and fail to address the specific needs of each patient.

OBJECTIVE

We aimed to evaluate an expert system that automatically generates plans for speech and language therapy, containing semiannual activities in the five areas of hearing, oral structure and function, linguistic formulation, expressive language and articulation, and receptive language. The goal was to assess whether the expert system speeds up the SLPs' work and leads to more accurate, consistent, and complete therapy plans for their patients.

METHODS

We examined the evaluation results of the SPELTA expert system in supporting the decision making of 4 SLPs treating children in three special education institutions in Ecuador. The expert system was first trained with data from 117 cases, including medical data; diagnosis for voice, speech, language and swallowing capabilities; and therapy plans created manually by the SLPs. It was then used to automatically generate new therapy plans for 13 new patients. The SLPs were finally asked to evaluate the accuracy, consistency, and completeness of those plans. A four-fold cross-validation experiment was also run on the original corpus of 117 cases in order to assess the significance of the results.

RESULTS

The evaluation showed that 87% of the outputs provided by the SPELTA expert system were considered valid therapy plans for the different areas. The SLPs rated the overall accuracy, consistency, and completeness of the proposed activities with 4.65, 4.6, and 4.6 points (to a maximum of 5), respectively. The ratings for the subplans generated for the areas of hearing, oral structure and function, and linguistic formulation were nearly perfect, whereas the subplans for expressive language and articulation and for receptive language failed to deal properly with some of the subject cases. Overall, the SLPs indicated that over 90% of the subplans generated automatically were "better than" or "as good as" what the SLPs would have created manually if given the average time they can devote to the task. The cross-validation experiment yielded very similar results.

CONCLUSIONS

The results show that the SPELTA expert system provides valuable input for SLPs to design proper therapy plans for their patients, in a shorter time and considering a larger set of activities than proceeding manually. The algorithms worked well even in the presence of a sparse corpus, and the evidence suggests that the system will become more reliable as it is trained with more subjects.