Digital health and patient safety: Technology is not a magic wand

The use of novel health information technology provides avenues for potentially significant patient benefit. However, it is also timely to take a step back and to consider whether the use of these technologies is safe – or more precisely what the current evidence for their safety is, and what kinds of evidence we should be looking for in order to create a convincing argument for patient safety. This special issue on patient safety includes eight papers that demonstrate an increasing focus on qualitative approaches and a growing recognition that the sociotechnical lens of examining health information technology–associated change is important. We encourage a balanced approach to technology adoption that embraces innovation, but nonetheless insists upon suitable concerns for safety and evaluation of outcomes.

[1]  Enrico Coiera,et al.  The fate of medicine in the time of AI , 2018, The Lancet.

[2]  Athanasios Anastasiou,et al.  Automated classification of primary care patient safety incident report content and severity using supervised machine learning (ML) approaches , 2020, Health Informatics J..

[3]  C. Gidengil,et al.  Evaluation of symptom checkers for self diagnosis and triage: audit study , 2015, BMJ : British Medical Journal.

[4]  Dominic Furniss,et al.  Critical Barriers to Safety Assurance and Regulation of Autonomous Medical Systems , 2019, Proceedings of the 29th European Safety and Reliability Conference (ESREL).

[5]  P. Carayon,et al.  Work system design for patient safety: the SEIPS model , 2006, Quality and Safety in Health Care.

[6]  H. Mcdonald,et al.  Effects of computerized clinical decision support systems on practitioner performance and patient outcomes: a systematic review. , 2005, JAMA.

[7]  Adam Wright,et al.  Essential activities for electronic health record safety: A qualitative study , 2020, Health Informatics J..

[8]  Andrew Georgiou,et al.  A review of measurement practice in studies of clinical decision support systems 1998–2017 , 2019, J. Am. Medical Informatics Assoc..

[9]  Farah Magrabi,et al.  Does health informatics have a replication crisis? , 2018, J. Am. Medical Informatics Assoc..

[10]  Jonathan Cave,et al.  Timely Digital Patient-Clinician Communication in Specialist Clinical Services for Young People: A Mixed-Methods Study (The LYNC Study) , 2017, Journal of medical Internet research.

[11]  A. Localio,et al.  Role of computerized physician order entry systems in facilitating medication errors. , 2005 .

[12]  James D. Carpenter,et al.  Categorizing the unintended sociotechnical consequences of computerized provider order entry , 2007, Int. J. Medical Informatics.

[13]  Christopher Johnson,et al.  To Computerised Provider Order Entry system: A comparison of ECF, HFACS, STAMP and AcciMap approaches , 2020, Health Informatics J..

[14]  Farah Magrabi,et al.  Problems with health information technology and their effects on care delivery and patient outcomes: a systematic review , 2017, J. Am. Medical Informatics Assoc..

[15]  Ann Blandford,et al.  The devil is in the detail: How a closed-loop documentation system for IV infusion administration contributes to and compromises patient safety , 2019, Health Informatics J..

[16]  John Powell,et al.  What is an appropriate level of evidence for a digital health intervention? , 2018, The Lancet.

[17]  Suchi Saria,et al.  Better medicine through machine learning: What’s real, and what’s artificial? , 2018, PLoS medicine.

[18]  Lyvia Biagi,et al.  Prediction and prevention of hypoglycaemic events in type-1 diabetic patients using machine learning , 2020, Health Informatics J..

[19]  Sean White,et al.  Development and piloting of a software tool to facilitate proactive hazard and risk analysis of Health Information Technology , 2019, Health Informatics J..

[20]  Subhashini Venugopalan,et al.  Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs. , 2016, JAMA.

[21]  A. Sheikh,et al.  Evaluating eHealth Interventions: The Need for Continuous Systemic Evaluation , 2009, PLoS medicine.

[22]  Rainu Kaushal,et al.  Effects of health information technology on patient outcomes: a systematic review , 2016, J. Am. Medical Informatics Assoc..

[23]  B. Franklin,et al.  What is the impact of introducing inpatient electronic prescribing on prescribing errors? A naturalistic stepped wedge study in an English teaching hospital , 2020, Health Informatics J..

[24]  Sebastian Thrun,et al.  Dermatologist-level classification of skin cancer with deep neural networks , 2017, Nature.

[25]  Dean F Sittig,et al.  Current challenges in health information technology–related patient safety , 2018, Health Informatics J..