By Sarah Lake, research assistant, Charlotte Giblin, assistant psychologist and Dr Penny Trayner, clinical neuropsychologist and founder and CEO of Goal Manager.
Through advancements in technology, quantitative data in rehabilitation is becoming increasingly available. Considering this, it is important to reflect on the literature around quantitative data in rehabilitation to outline the benefits of these advancements.
The need for quantitative data presentation in rehabilitation
The large volume of patient data collected over the course of rehabilitative treatment makes rehabilitation a data-intensive industry (Pike et al., 2009). This volume of data presents the opportunity for data presentation through visual displays such as dashboards (Trayner et al., 2023), allowing for understanding and comprehension of large datasets (Peters et al., 2015). This promotes the adoption of technology to better understand data that is routinely collected as part of clinical care, advancing the discipline in that regard (Anderson et al., 2019; Schreyögg et al., 2006). Being better informed about data can allow for targeted service development initiatives improving aspects of care (Chishtie et al., 2022; Few, 2006).
How quantitative data can improve practice
The integration and development of quantitative data systems in rehabilitation enables healthcare services to enhance their care provision without additional time or resources (Boumrah et al., 2022; Trayner et al., 2019). The collection of quantitative data provides valuable insights, allowing services to better allocate resources. Understanding this data creates an evidence base for designing informed rehabilitation programs (Chantsoulis et al., 2015), which improves overall practice by tailoring programs to the needs of the individual, leading to more precise interventions and better outcomes.
The use of quantitative data to level the playing field
Through this, data-informed rehabilitation programs serve to ‘level the playing field’ in rehabilitation, as with better understanding of rehabilitation, better programs can be created regardless of economic or geographical factors. Traditionally, knowledge has been siloed among professionals (Skumsnes et al., 2023), resulting in the lack of a shared knowledge base (NHS Improving Quality, 2014). Access to quantitative data creates this shared base of information and understanding, ensuring that all services have the same availability of information, meaning they can all develop in similar ways, at the same rate. Better information availability also satisfies the growing want for quantitative, data-driven analytics within rehabilitative care (Madhavan et al., 2018; Schulze et al., 2023). The development of data-informed rehabilitation programs will ‘level the playing field’ (Trayner, 2023) further, as services would be motivated to adopt programs with a better evidence base. Services would be compelled to implement and utilise similar, evidenced programs, ensuring consistent care nationwide.
Quantitative data in saving money
Improvements to the standardisation of care through the use of quantitative data analytics will only increase the economic benefit of rehabilitative care. This is because, as aforementioned, no further time or resources are needed to complete such analyses (Trayner et al., 2019), due to the innovation of technological platforms that present this data. Goal Manager’s Data Dashboard is one such innovation, completing analysis at a service level to enable immediate and automatic auditing, providing services with their data outputs to target development (Trayner et al., 2023). UKROC (Turner-Stokes et al., 2022) outlined that the treatment of patients with severe brain injuries demonstrated total cost savings exceeding £4 billion for a population requiring specialist rehabilitation, demonstrating the current economic benefit of rehabilitation. Through the implementation of quantitative data analysis and understanding, this will only improve.
Conclusion
Overall, literature demonstrates the vast benefits of increasing the use of quantitative data within the discipline of rehabilitation. Better understanding of interventions will serve to improve their delivery across the nation, transcending previous geographical barriers in information sharing. Improved intervention delivery will also contribute to the economic benefit of rehabilitative care.
However, in the adoption of quantitative data in the discipline, the individualised nature of rehabilitative care should not be lost. Patients are not numbers, and goals are best when they have most meaning to the patient to which they are assigned. Though quantitative data can provide lots of knowledge around interventions on a service-level, preserving the holistic consideration of patients within rehabilitation remains extremely important.
Going forward
Based on the literature presented, here are some tips for best-practice quantitative data management in rehabilitation.
- Standardise the data that is being collected. As rehabilitation is a data-intensive industry (Pike et al., 2009), standardisation of data coming in allows for better data management.
- Use technology solutions, such as dashboards, to explain data collected. Better comprehension of data collected can improve understanding and present opportunities for targeted service development initiatives (Chishtie et al., 2022; Few, 2006).
- Compare relevant datasets with comparable datasets to ensure accuracy and reliability of collected data.
- Encourage the collection of Patient Reported Outcome Measurements (PROMs) to preserve individualised nature of rehabilitative care.
This article was co-written by Sarah Lake, research assistant, Charlotte Giblin, assistant psychologist and Dr Penny Trayner, Clinical Neuropsychologist and Founder and CEO of Goal Manager.
References
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