Machine learning functional impairment classification with electronic health record data

The power of artificial intelligence for managing pandemics: A primer for public health professionals

Artificial intelligence (AI) applications are complex and rapidly evolving, and thus often poorly understood, but have potentially profound implications for public health. We offer a primer for public health professionals that explains some of the key concepts involved and examines how these applications might be used in the response to a future pandemic. They include early outbreak detection, predictive modelling, healthcare management, risk communication, and health surveillance. Artificial intelligence applications, especially predictive algorithms, have the ability to anticipate outbreaks by integrating diverse datasets such as social media, meteorological data, and mobile phone movement data. Artificial intelligence-powered tools can also optimise healthcare delivery by managing the allocation of resources and reducing healthcare workers' exposure to risks. In resource distribution, they can anticipate demand and optimise logistics, while AI-driven robots can minimise physical contact in healthcare settings. Artificial intelligence also shows promise in supporting public health decision-making by simulating the social and economic impacts of different policy interventions. These simulations help policymakers evaluate complex scenarios such as lockdowns and resource allocation. Additionally, it can enhance public health messaging, with AI-generated health communications shown to be more effective than human-generated messages in some cases. However, there are risks, such as privacy concerns, biases in models, and the potential for 'false confirmations', where AI reinforces incorrect decisions. Despite these challenges, we argue that AI will become increasingly important in public health crises, but only if integrated thoughtfully into existing systems and processes.

Challenges and solutions of conducting dementia clinical trials: A palliative care at home pilot for persons with dementia

BACKGROUND

Recruitment and retention are common challenges in clinical trials, particularly with older adults and their caregivers who often benefit from palliative care but have significant strain from caregiving. In recent years, there has been an expansion in home-based palliative care programs, especially for patients with dementia. Because these programs often rely on physicians or advanced practice nurses, they are quite costly and may be difficult to staff due to workforce shortages.

METHODS

We created a novel program of home-based palliative care for patients with advanced dementia and their families, which centers around a community health worker, a social worker, and a nurse. We report on challenges our trial encountered and corresponding solutions.

RESULTS

We enrolled 30 patients and their 30 caregivers in our pilot trial of home-based palliative care. We found two significant barriers to enrollment: (1) the electronic health record was insufficient to determine the severity of patients' dementia; and (2) rates of follow-up survey completion were low, with completion rates at 6 months between 14 and 44%. We created an iterative training process to determine dementia severity from electronic health records and applied person-centered approaches to improve survey completion.

CONCLUSIONS

Electronic health records are not set up to include discrete fields for dementia severity, which makes enrollment of older adults with dementia in a clinical trial challenging. The strain of caring for a loved one with advanced dementia may also make participation in health-services research difficult for patients and their families. Novel approaches have the potential to counteract these challenges, improve recruitment and retention, and ultimately improve care for people with dementia and their caregivers.