UNASSIGNED: Many digital health interventions (DHIs), including mobile health (mHealth) apps, aim to improve both client outcomes and efficiency like electronic medical record systems (EMRS). Although interoperability is the gold standard, it is also complex and costly, requiring technical expertise, stakeholder permissions, and sustained funding. Manual data linkage processes are commonly used to \"integrate\" across systems and allow for assessment of DHI impact, a best practice, before further investment. For mHealth, the manual data linkage workload, including related monitoring and evaluation (M&E) activities, remains poorly understood.
UNASSIGNED: As a baseline study for an open-source app to mirror EMRS and reduce healthcare worker (HCW) workload while improving care in the Nurse-led Community-based Antiretroviral therapy Program (NCAP) in Lilongwe, Malawi, we conducted a time-motion study observing HCWs completing data management activities, including routine M&E and manual data linkage of individual-level app data to EMRS. Data management tasks should reduce or end with successful app implementation and EMRS integration. Data was analysed in Excel.
UNASSIGNED: We observed 69:53:00 of HCWs performing routine NCAP service delivery tasks: 39:52:00 (57%) was spent completing M&E data related tasks of which 15:57:00 (23%) was spent on manual data linkage workload, alone.
UNASSIGNED: Understanding the workload to ensure quality M&E data, including to complete manual data linkage of mHealth apps to EMRS, provides stakeholders with inputs to drive DHI innovations and integration decision making. Quantifying potential mHealth benefits on more efficient, high-quality M&E data may trigger new innovations to reduce workloads and strengthen evidence to spur continuous improvement.

Artificial intelligence (AI) has witnessed significant advancements, reshaping various industries, including healthcare. The introduction of ChatGPT by OpenAI in November 2022 marked a pivotal moment, showcasing the potential of generative AI in revolutionising patient care, diagnosis and treatment. Generative AI, unlike traditional AI systems, possesses the ability to generate new content by understanding patterns within datasets. This article explores the evolution of AI in healthcare, tracing its roots to the term coined by John McCarthy in 1955 and the contributions of pioneers like John Von Neumann and Alan Turing. Currently, generative AI, particularly Large Language Models, holds promise across three broad categories in healthcare: patient care, education and research. In patient care, it offers solutions in clinical document management, diagnostic support and operative planning. Notable advancements include Microsoft\'s collaboration with Epic for integrating AI into electronic medical records (EMRs), enhancing clinical data management and patient care. Furthermore, generative AI aids in surgical decision-making, as demonstrated in plastic, orthopaedic and hepatobiliary surgeries. However, challenges such as bias, hallucination and integration with EMR systems necessitate caution and ongoing evaluation. The article also presents insights from the implementation of NUHS Russell-GPT, a generative AI chatbot, in a hand surgery department, showcasing its utility in administrative tasks but highlighting challenges in surgical planning and EMR integration. The survey showed unanimous support for incorporating AI into clinical settings, with all respondents being open to its use. In conclusion, generative AI is poised to enhance patient care and ease physician workloads, starting with automating administrative tasks and evolving to inform diagnoses, tailored treatment plans, as well as aid in surgical planning. As healthcare systems navigate the complexities of integrating AI, the potential benefits for both physicians and patients remain significant, offering a glimpse into a future where AI transforms healthcare delivery. Level of Evidence: Level V (Diagnostic).

Cumulative data on patient fall risk have been compiled in electronic medical records systems, and it is possible to test the validity of fall-risk assessment tools using these data between the times of admission and occurrence of a fall. The Hendrich II Fall Risk Model scores assessed during three time points of hospital stays were extracted and used for testing the predictive validity: (a) upon admission, (b) when the maximum fall-risk score from admission to falling or discharge, and (c) immediately before falling or discharge. Predictive validity was examined using seven predictive indicators. In addition, logistic regression analysis was used to identify factors that significantly affect the occurrence of a fall. Among the different time points, the maximum fall-risk score assessed between admission and falling or discharge showed the best predictive performance. Confusion or disorientation and having a poor ability to rise from a sitting position were significant risk factors for a fall.

BACKGROUND: Electronic medical record system (EMRS) is a valuable system for safe access to the patient\'s data and increases health care quality. Manpower is one of the requirements for EMRS, among which manager is the most important person in any hospital. Taking into account manager\'s positive attitude and good commitments, EMRS will be implemented successfully. As such, we decided to assess manager\'s attitude and commitment toward EMRS in Isfahan hospitals in the year of 2014.
OBJECTIVE: This article aimed to determine the hospital managers\' attitude and commitment toward the implementation of EMRS.
METHODS: The present article is an applied analytic study. Research society consisted of the managers of all the hospitals in Isfahan that include hospitals affiliated to Isfahan University of Medical Sciences, private, and social security hospitals. This study was done in 2014. Data collection tools included a questionnaire for which reliability and validity were determined. Data were analyzed by means of SPSS 20.
RESULTS: Average score for the managers\' attitude toward EMRS in the city of Isfahan was 77.5 out of 100 and their average score for commitment was 74.7. Manager\'s attitude in social security hospitals was more positive than the private and governmental ones (83.3%). In addition, the amount of commitment by the managers in social security hospitals was higher than the same in private and governmental hospitals (86.6%).
CONCLUSIONS: At present, managers\' attitude and commitment in Isfahan hospitals toward EMRS are very high and social security hospitals show more readiness in this respect.