ChatGPT: the future of discharge summaries?

Data-driven tools and techniques, particularly machine learning methods that underpin artificial intelligence, offer promise in improving healthcare systems and services. One of the companies aspiring to pioneer these advances is DeepMind Technologies Limited, a wholly-owned subsidiary of the Google conglomerate, Alphabet Inc. In 2016, DeepMind announced its first major health project: a collaboration with the Royal Free London NHS Foundation Trust, to assist in the management of acute kidney injury. Initially received with great enthusiasm, the collaboration has suffered from a lack of clarity and openness, with issues of privacy and power emerging as potent challenges as the project has unfolded. Taking the DeepMind-Royal Free case study as its pivot, this article draws a number of lessons on the transfer of population-derived datasets to large private prospectors, identifying critical questions for policy-makers, industry and individuals as healthcare moves into an algorithmic age.

Background Provision of high quality transitional care is a challenge for health care providers in many western countries. This systematic review was conducted to (1) identify and synthesise research, using randomised control trial designs, on the quality of transitional care interventions compared with standard hospital discharge for older people with chronic illnesses, and (2) make recommendations for research and practice. Methods Eight databases were searched; CINAHL, Psychinfo, Medline, Proquest, Academic Search Complete, Masterfile Premier, SocIndex, Humanities and Social Sciences Collection, in addition to the Cochrane Collaboration, Joanna Briggs Institute and Google Scholar. Results were screened to identify peer reviewed journal articles reporting analysis of quality indicator outcomes in relation to a transitional care intervention involving discharge care in hospital and follow-up support in the home. Studies were limited to those published between January 1990 and May 2013. Study participants included people 60 years of age or older living in their own homes who were undergoing care transitions from hospital to home. Data relating to study characteristics and research findings were extracted from the included articles. Two reviewers independently assessed studies for risk of bias. Results Twelve articles met the inclusion criteria. Transitional care interventions reported in most studies reduced re-hospitalizations, with the exception of general practitioner and primary care nurse models. All 12 studies included outcome measures of re-hospitalization and length of stay indicating a quality focus on effectiveness, efficiency, and safety/risk. Patient satisfaction was assessed in six of the 12 studies and was mostly found to be high. Other outcomes reflecting person and family centred care were limited including those pertaining to the patient and carer experience, carer burden and support, and emotional support for older people and their carers. Limited outcome measures were reported reflecting timeliness, equity, efficiencies for community providers, and symptom management. Conclusions Gaps in the evidence base were apparent in the quality domains of timeliness, equity, efficiencies for community providers, effectiveness/symptom management, and domains of person and family centred care. Further research that involves the person and their family/caregiver in transitional care interventions is needed. Electronic supplementary material The online version of this article (doi:10.1186/1472-6963-14-346) contains supplementary material, which is available to authorized users.

AI virtual assistants have significant potential to alleviate the pressure on overly burdened healthcare systems by enabling patients to self-assess their symptoms and to seek further care when appropriate. For these systems to make a meaningful contribution to healthcare globally, they must be trusted by patients and healthcare professionals alike, and service the needs of patients in diverse regions and segments of the population. We developed an AI virtual assistant which provides patients with triage and diagnostic information. Crucially, the system is based on a generative model, which allows for relatively straightforward re-parameterization to reflect local disease and risk factor burden in diverse regions and population segments. This is an appealing property, particularly when considering the potential of AI systems to improve the provision of healthcare on a global scale in many regions and for both developing and developed countries. We performed a prospective validation study of the accuracy and safety of the AI system and human doctors. Importantly, we assessed the accuracy and safety of both the AI and human doctors independently against identical clinical cases and, unlike previous studies, also accounted for the information gathering process of both agents. Overall, we found that the AI system is able to provide patients with triage and diagnostic information with a level of clinical accuracy and safety comparable to that of human doctors. Through this approach and study, we hope to start building trust in AI-powered systems by directly comparing their performance to human doctors, who do not always agree with each other on the cause of patients’ symptoms or the most appropriate triage recommendation.