Development and Evaluation of a New Technological Way of Engaging Patients and Enhancing Understanding of Drug Tolerability in Early Clinical Development: PROACT

PROACT 2.0: A new open-source tool to improve patient-doctor communication in clinical trials

The use of Digital Healthcare Products is leading to significant improvements in clinical practice. Herein, we discuss the development of PROACT 2.0 (Patient Reported Opinions About Clinical Tolerability v2.0), a novel open-source mobile and web application developed at Fondazione IRCCS Istituto Nazionale Tumori in Milan. It was developed in collaboration with The Christie, Manchester, in the context of work package 2 of the UpSMART Accelerator project, involving a consortium of referral cancer centers from the UK, Spain and Italy. PROACT 2.0 enhances communication between patients and healthcare providers in cancer clinical trials, allowing patients to report adverse events and side effects, and healthcare teams to collect valuable patient-reported outcome measures for treatment management. PROACT 2.0 supports text, audio, and video messaging, offering a secure, non-urgent communication channel that integrates with, or replaces, traditional methods. Its user-friendly and multilingual interface provides a new route for patient engagement and streamlines the handling of logistical information. Positive feedback from initial testing warrants future enhancements for broader applicability in cancer research and treatment.

Home-based Digital Assessments with Applied Sentiment & Emotion AI Capture Improved Quality-of-life in Asthma Patients

With the rise of digital transformation in the pharmaceutical industry, digital therapeutics are being integrated in drug development clinical trials. In the TWINKLE study, information about asthmatic patients' disease control and quality-of-life (QoL) was measured by daily video recording, in conjunction with daily electronic questionnaires and home-based spirometry. From the video messages, sentiment and emotion AI was applied to detect subtle QoL changes in asthmatic patients after receiving treatments. Sentiment scores, derived from patients' daily messages via natural language processing, correlated strongly with metrics of lung functions and outcomes of electronic questionnaires. However, video-derived emotional analysis exhibited strong interpersonal variations and systematic biases, yet still showed utility in detecting QoL changes after personalized calibration and signal aggregation. Compared to traditional patient-reported outcomes, all three categories of digital measurements were able to detect significantly improved asthma control from patients who responded to treatments. The result provides insights into developing novel digital outcomes through the application of connected digital devices and advanced AI tailored to clinical settings.Clinical relevance- Digital outcomes involving connected digital devices and AI for sentiment/emotion analysis could capture subtle QoL changes reliably and earlier than hospital visits, reducing burden and improving disease management. Integrating digital therapeutics in asthma drug development trials may prove to be feasible and valuable.

Values, challenges and future directions of big data analytics in healthcare: A systematic review

The emergence of powerful software has created conditions and approaches for large datasets to be collected and analyzed which has led to informed decision-making towards tackling health issues. The objective of this study is to systematically review 804 scholarly publications related to big data analytics in health in order to identify the organizational and social values along with associated challenges. Key principles of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology were followed for conducting systematic reviews. Following a research path, we present the values, challenges and future directions of the scientific area using indicative examples from relevant published articles. The study reveals that one of the main values created is the development of analytical techniques which provides personalized health services to users and supports human decision-making using automated algorithms, challenging the power issues in the doctor-patient relationship and creating new working conditions. A main challenge to data analytics is data management and security when processing large volumes of sensitive, personal health data. Future research is directed towards the development of systems that will standardize and secure the process of extracting private healthcare datasets from relevant organizations. Our systematic literature review aims to provide to governments and health policy-makers a better understanding of how the development of a data driven strategy can improve public health and the functioning of healthcare organizations but also how can create challenges that need to be addressed in the near future to avoid societal malfunctions.

Detecting Acute Otitis Media Symptom Episodes Using a Mobile App: Cohort Study

BACKGROUND

Population cohort studies are useful to study infectious diseases episodes not attended by health care services, but conventional paper diaries and questionnaires to capture cases are prone to noncompliance and recall bias. Use of smart technology in this setting may improve case finding.

OBJECTIVE

The objective of our study was to validate an interactive mobile app for monitoring occurrence of acute infectious diseases episodes in individuals, independent of health care seeking, using acute otitis media (AOM) symptom episodes in infants as a case study. We were interested in determining participant compliance and app performance in detecting and ascertaining (parent-reported) AOM symptom episodes with this novel tool compared with traditional methods used for monitoring study participants.

METHODS

We tested the InfectieApp research app to detect AOM symptom episodes. In 2013, we followed 155 children aged 0 to 3 years for 4 months. Parents recorded the presence of AOM symptoms in a paper diary for 4 consecutive months and completed additional disease questionnaires when AOM symptoms were present. In 2015 in a similar cohort of 69 children, parents used an AOM diary and questionnaire app instead.

RESULTS

During conventional and app-based recording, 93.13% (17,244/18,516) and 94.56% (7438/7866) of symptom diaries were returned, respectively, and at least one symptom was recorded for 32.50% (n=5606) and 43.99% (n=3272) of diary days (P<.01). The incidence of AOM symptom episodes was 605 and 835 per 1000 child-years, respectively. Disease questionnaires were completed for 59% (17/29) of episodes when participants were using conventional recording, compared with 100% (18/18) for app-based recording.

CONCLUSIONS

The use of the study's smart diary app improved AOM case finding and disease questionnaire completeness. For common infectious diseases that often remain undetected by health care services, use of this technology can substantially improve the accurateness of disease burden estimates.