Background: NHS case note data are a potential source of practice-based evidence which could be used to investigate the effectiveness of different interventions for individuals with a range of speech, language and communication needs. Consistency in pre- and post-intervention data as well as the collection of relevant variables would need to be demonstrated as a precursor to adopting this approach in future investigations of speech and language therapy intervention. Aims: To explore whether routine clinical data collection for children with speech sound disorder (SSD) could be a potential source for examining the effectiveness of intervention(s). Methods & Procedures: We examined case notes from three UK NHS services, reviewing 174 sets of case notes and 234 blocks of therapy provided for school-age children with SSD. Main contribution: We found there was significant variation in pre- and post-intervention data and variables collected by the services. The assessment data available in the case notes across all sites were insufficient to be used to compare the effectiveness of different interventions. Specific issues included lack of consistent reporting of pre- and post-intervention data, and use of a variety of both formal and informal assessment tools. Conclusions & Implications: The case notes reviewed were from three sites and may not represent wider clinical practice, nevertheless the findings suggest the sample explored indicates the need for more consistent and contemporaneous collection of data for children with SSD to facilitate the investigation of different interventions in practice. Researchers should work with the clinical community to determine a minimal dataset that includes a core outcome set and potential variables. This should be feasible to collect in clinical practice and provide a dataset for future investigations of clinically relevant research questions. This would provide an invaluable resource to the clinical academic and research communities enabling research questions to be addressed that have the potential to lead to improved outcomes and more cost-effective services. What this paper adds: What is already known on the subject While there is some evidence for the efficacy of therapy for children with SSD, studies typically focus on very specific populations who meet strict selection criteria and take place in university clinics or laboratory-style settings which do not reflect typical clinical practice in the UK and elsewhere. An alternative approach to investigating the effectiveness of interventions would be to use NHS case note data. It is not clear from the existing literature whether case note data are sufficiently robust to facilitate such an analysis. What this paper adds to existing knowledge This study found that case note data, in particular assessment data, were highly variable across services and would be insufficient to compare different interventions for this population. Agreement on what should be included in a minimal dataset for children with SSD is required to maximize the potential for NHS clinical case notes to become a resource for future research. What are the actual or potential clinical implications of this work? This study indicates that current clinical practice in SLT for children with SSD is inconsistent with regards to the reporting of pre- and post-intervention assessment data and other important variables in case notes. We make the case for agreeing a minimal dataset with a need for clinicians to work with researchers to determine core outcomes and additional relevant data, which can be feasibly collected in clinical practice.
|Number of pages||11|
|Journal||International Journal of Language & Communication Disorders|
|Early online date||26 Jul 2021|
|Publication status||E-pub ahead of print - 26 Jul 2021|
Bibliographical noteFunding Information:
This research was funded by Avon Primary Care Research Collaborative Research Capability Funding (grant number 16/17‐3YW).
© 2021 Royal College of Speech and Language Therapists
- speech sound disorder
- service delivery
- minimal dataset
- core outcomes