Britain's hard-pressed carers need all the help they can get. But that should not include using unregulated AI bots, according to researchers who say the AI revolution in social care needs a hard ethical edge. A pilot study by academics at the University of Oxford found some care providers had been using generative AI chatbots such as ChatGPT and Bard to create care plans for people receiving care. That presents a potential risk to patient confidentiality, according to Dr Caroline Green, an early career research fellow at the Institute for Ethics in AI at Oxford, who surveyed care organisations for the study. "If you put any type of personal data into [a generative AI chatbot], that data is used to train the language model," Green said.

Large language models (LLMs) have emerged as valuable tools for many natural language understanding tasks. In safety-critical applications such as healthcare, the utility of these models is governed by their ability to generate outputs that are factually accurate and complete. In this work, we present dialog-enabled resolving agents (DERA). DERA is a paradigm made possible by the increased conversational abilities of LLMs, namely GPT-4. It provides a simple, interpretable forum for models to communicate feedback and iteratively improve output. We frame our dialog as a discussion between two agent types - a Researcher, who processes information and identifies crucial problem components, and a Decider, who has the autonomy to integrate the Researcher's information and makes judgments on the final output. We test DERA against three clinically-focused tasks. For medical conversation summarization and care plan generation, DERA shows significant improvement over the base GPT-4 performance in both human expert preference evaluations and quantitative metrics. In a new finding, we also show that GPT-4's performance (70%) on an open-ended version of the MedQA question-answering (QA) dataset (Jin et al. 2021, USMLE) is well above the passing level (60%), with DERA showing similar performance. We release the open-ended MEDQA dataset at https://github.com/curai/curai-research/tree/main/DERA.

Healthcare leaders don't need reminding that they face serious nurse shortages. It's a challenge they have faced for years – and it's become a critical concern as record numbers leave their practices due to burnout and exhaustion. The November 2021 Hospital IQ Survey found that a full 90% of nurse respondents were considering resigning to seek new careers. Another disturbing result: 71% of nurses with 15 or more years of experience – an invaluable resource to health systems – said they were on the verge of leaving. The stakes to relieve the stress on nurses have never been higher.

Patients are set to get easier access to their medication lists and care plans through the NHS App under the government's new data strategy. New requirements for data sharing across the entire health and care system are also set to come into place, with new legislation to be introduced to require all adult social care providers to provide information about the services they fund. Published today (June 22), the NHSX draft strategy'Data Saves Lives: Reshaping health and social care with data', aims to capitalise on the work undertaken using data during the pandemic to improve health and care services. In a bid to establish openness, the government committed to publishing the first transparency statement setting out how health and care data has been used across the sector by 2022. Under the proposals, patients are set to gain more control over their health data, while data will also be used to improve care and treatment.

Some healthcare provider organizations are using machine learning and other forms of artificial intelligence to provide clinicians with the best evidence-based care pathways. A group's aim could be to improve a patient's care plan based on personalized analytics. Another goal could be the further merging of evidence-based care paths with historical utilization and outcomes in order to offer optimal patient care. Provider organizations might be using social determinants of health combined with machine learning to offer clinically meaningful services. Healthcare IT News talked over these ideas with Niall O'Connor, chief technology officer at Cohere Health, a vendor of artificial intelligence technology and services designed to improve the provider, patient and payer experiences.

Careline Health Group, a healthcare organization that provides Hospice and Physician Service care for families and patients who face serious or terminal illness, has implemented Muse Healthcare's machine learning and predictive modeling tools to meet the needs of their patients. The Muse technology evaluates and models every clinical assessment, medication, vital sign and other relevant data to perform a risk stratification of these patients. The tool then highlights the patients with the most critical needs and visually alerts the agency to perform additional care. It also makes accurate changes to the care plans based on the condition and location of the patient (LTC, SNF or in home). According to Careline Health Group's Chief Executive Officer, Joe Mead, data from Muse provides meaningful insights for their patients.

This article proposes a method for automated service selection to improve treatment efficacy and reduce re-hospitalization costs. A predictive model is developed using the National Home and Hospice Care Survey (NHHCS) dataset to quantify the effect of care services on the risk of re-hospitalization. By taking the patient's characteristics and other selected services into account, the model is able to indicate the overall effectiveness of a combination of services for a specific NHHCS patient. The developed model is incorporated in Monte-Carlo Tree Search (MCTS) to determine optimal combinations of services that minimize the risk of emergency re-hospitalization. MCTS serves as a risk minimization algorithm in this case, using the predictive model for guidance during the search. Using this method on the NHHCS dataset, a significant reduction in risk of re-hospitalization is observed compared to the original selections made by clinicians. An 11.89 percentage points risk reduction is achieved on average. Higher reductions of roughly 40 percentage points on average are observed for NHHCS patients in the highest risk categories. These results seem to indicate that there is enormous potential for improving service selection in the near future.

Two-thirds of attendees polled at a recent innovation summit by The Economist agree that healthcare is the sector that will benefit the most from artificial intelligence. However, questions loom on exactly how it will help the industry, and perhaps more importantly, if there is the possibility of it accomplishing what it promises. The latter concern was recently experienced by IBM when its Watson cognitive recognition system was used as part of the "moon shot" project launched by the MD Anderson Cancer Center to diagnose and recommend treatment plans for certain forms of cancer. The project costs spiraled past $62 million while the system had yet to be used on an actual patient. The extremely bold and ambitious initiative failed to deliver.

The health ministry is considering the use of artificial intelligence to draw up care plans for nursing care insurance recipients. The ministry will launch a national survey as early as August on how AI is being developed and on examples of AI use for nursing care, ministry officials said. It also plans to compile by the end of the current fiscal year, which runs to March 2019, a report that will assess the effectiveness of AI-based care plans in alleviating the burden of care-givers and preventing worsening conditions in those who need nursing care, the officials said. The report will also list related problems to be tackled. Before an older person certified as needing long-term care can receive nursing care insurance services, a care plan needs to be drawn up.

Artificial intelligence is a key component of the future of healthcare. Indeed, the era of the AI doctor seems to be unavoidable. Today, we see AI in hospitals helping clinicians identify medical risks; predict when to provide targeted, life-saving interventions; form treatment plans for patients with rare diseases; and deliver precision medicine. However, one of the benefits associated with AI may actually be a disadvantage in healthcare. While AI excels at making unbiased, purely logical decisions, it cannot yet appreciate the complex blend of emotional, social, cultural and physical needs of people.