ArticleBMJ quality & safety2015
Making sense of the shadows: priorities for creating a learning healthcare system based on routinely collected data.
Article in BMJ quality & safety, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
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Who cites it
36 citing papers in PubMed.
- Does adherence to evidence-based practices during childbirth prevent perinatal mortality? A post-hoc analysis of 3,274 births in Uttar Pradesh, India.BMJ global health · 2020Trial
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- Real World Evidence Versus Randomised Controlled Trials: Is the Future of Nutritional Sciences Research in Electronic Health Records?Nutrition bulletin · 2026Review
- Reducing Bias in the Evaluation of Robotic Surgery for Lung Cancer Through Machine Learning.Cancers · 2025Article
- Moving from crisis response to a learning health system: Experiences from an Australian regional primary care network.Learning health systems · 2025Article
- A scoping review of the electronic collection and capture of patient reported outcome measures for children and young people in the hospital setting.PLOS digital health · 2025Article
- How to establish and sustain a disease registry: insights from a qualitative study of six disease registries in the UK.BMC medical informatics and decision making · 2024Article
- Secondary uses of electronic prescribing and pharmacy data in UK hospital care: a national survey.BMJ open quality · 2024Article
- Characteristics of non-randomised studies of drug treatments: cross sectional study.BMJ medicine · 2024Article
- Mobilizing registry data for quality improvement: A convergent mixed-methods analysis and application to spinal cord injury.Frontiers in rehabilitation sciences · 2023Article
- Navigating data governance approvals to use routine health and social care data to evidence the hidden population with severe obesity: a case study from a clinical academic's perspective.Journal of research in nursing : JRN · 2022Article
- Spinal pain and major depression in a military cohort: bias analysis of dependent misclassification in electronic medical records.Social psychiatry and psychiatric epidemiology · 2022Article
- Participatory codesign of patient involvement in a Learning Health System: How can data-driven care be patient-driven care?Health expectations : an international journal of public participation in health care and health policy · 2022Article
- Review
- Implementation of a "real-world" learning health system: Results from the evaluation of the Connected Health Cities programme.Learning health systems · 2021Article
- Employing learning health system principles to advance research on severe neonatal and paediatric illness in Kenya.BMJ global health · 2021Review
- Intersections of machine learning and epidemiological methods for health services research.International journal of epidemiology · 2021Article
- Weekend effect on mortality by medical specialty in six secondary hospitals in the Helsinki metropolitan area over a 14-year period.BMC health services research · 2020Article
- Characterizing communication patterns among members of the clinical care team to deliver breast cancer treatment.Journal of the American Medical Informatics Association : JAMIA · 2020Article
- Best practices for authors of healthcare-related artificial intelligence manuscripts.NPJ digital medicine · 2020Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Socrates described a group of people chained up inside a cave, who mistook shadows of objects on a wall for reality. This allegory comes to mind when considering 'routinely collected data'-the massive data sets, generated as part of the routine operation of the modern healthcare service. There is keen interest in routine data and the seemingly comprehensive view of healthcare they offer, and we outline a number of examples in which they were used successfully, including the Birmingham OwnHealth study, in which routine data were used with matched control groups to assess the effect of telephone health coaching on hospital utilisation.Routine data differ from data collected primarily for the purposes of research, and this means that analysts cannot assume that they provide the full or accurate clinical picture, let alone a full description of the health of the population. We show that major methodological challenges in using routine data arise from the difficulty of understanding the gap between patient and their 'data shadow'. Strategies to overcome this challenge include more extensive data linkage, developing analytical methods and collecting more data on a routine basis, including from the patient while away from the clinic. In addition, creating a learning health system will require greater alignment between the analysis and the decisions that will be taken; between analysts and people interested in quality improvement; and between the analysis undertaken and public attitudes regarding appropriate use of data.
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