Better use of CQC data could predict abuse in care homes

Better use of data could help predict and detect abuse in care homes and supported living services for people with a learning disability and autistic people, according to a new report from King’s College.

The review explored whether abuse could have been recognised earlier by Care Quality Commission’s (CQC) regulatory or inspection process and made recommendations about how CQC could improve its regulation of similar services and the use of data that it holds and collects.

It also revealed that the analytic potential of these data would be enhanced through its greater digitisation and that having this information in analysable form at service level could be helpful in developing risk indicators and identifying trends in abuse across services.

It builds on suggestions that there may be scope to use artificial intelligence (AI) to interrogate the CQC’s data efficiently and effectively.

Analysing CQC data for patterns of abuse

Researchers have consistently reported that people with a learning disability and autistic people are more at risk of abuse than other groups, especially when living in residential services (as compared to older people more generally, or those people with mental health needs).

The study revealed some of the complexities of using data, and particularly AI, to help predict and detect abuse in care homes and supported living services (which themselves are moving to more electronic care record systems and so may be considering possible AI usage). Whilst some of these complexities could be addressed within the existing regulatory and legislative context, many of the issues underlying this require an urgent wider multidepartmental central government policy response.

Natural Language Processing (NLP) gives the opportunity to harness the potential of some of CQC’s data. However, when using such methods, this research identified that it is important not to lose the ‘human perspective’ and that the contribution of public contributors and other stakeholders in research using NLP and in the CQC’s work more widely is essential.

The report identifies six recommendations for future research:

• Research how to improve prevention, detection and reporting of abuse. In particular
  how to make reporting routes more accessible to people with a learning disability,
  autistic people, their families more generally and how to support them in the process.
• Research to establish a clearly defined set of indicators that assess risk factors for
  poor care and abuse specific to care homes and supported living settings. These
  should be developed from a theoretical perspective, then explored how these can be
  measured using CQC data and other data.
• Research to understand and explore what relevant data might be held and collected
  by other agencies (e.g. local authorities, the NHS, other local and central government
  departments and providers) and how this might be accessed and analysed.
• Research about predicting and detecting abuse in other settings.
• Use of Natural Language Processing to identify themes in ‘Feedback on Care’ data to
  explore if this can provide a better understanding of the risk factors for abuse.
• Commissioning of (and contracting with) care homes and supported living services
  for people with a learning disability and autistic people and potential impact on, for
  example, service availability (e.g. local care market) and care quality and the potential
  implications for abuse of people within these services.

This research was  funded by the National Institute for Health and Care Research.

Alison Bloomer

Alison Bloomer is Editor of Learning Disability Today. She has over 25 years of experience writing for medical journals and trade publications. Subjects include healthcare, pharmaceuticals, disability, insurance, stock market and emerging technologies. She is also a mother to a gorgeous 13-year-old boy who has a learning disability.

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