Engineering requirements of a Herpes simplex virus patient registry: discovery phase of a real-world evidence platform to advance pharmacogenomics and personalized medicine

Comprehensive pharmacogenomic understanding requires both robust genomic and demographic data. Patient registries present an opportunity to collect large amounts of robust, patient-level data. Pharmacogenomic advancement in the treatment of infectious diseases is yet to be fully realised. Herpes simplex virus (HSV) is one disease for which pharmacogenomic understanding is wanting. This paper aims to understand the key factors that impact data collection quality for medical registries and suggest potential design features of an HSV medical registry to overcome current constraints and allow for this data to be used as a complement to genomic and clinical data to further the treatment of HSV. This paper outlines the discovery phase for the development of an HSV registry with the aim of learning about the users and their contexts, the technological constraints and the potential improvements that can be made. The design requirements and user stories for the HSV registry have been identified for further alpha phase development. The current landscape of HSV research and patient registry development were discussed. Through the analysis of the current state of the art and thematic user analysis, potential design features were elucidated to facilitate the collection of high-quality, robust patient-level data which could contribute to advances in pharmacogenomic understanding and personalised medicine in HSV. The user requirements specification for the development of an HSV registry has been summarised and implementation strategies for the alpha phase discussed

Authors’ Response to Peer Reviews of “Machine Learning for Risk Group Identification and User Data Collection in a Herpes Simplex Virus Patient Registry: Algorithm Development and Validation Study”

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Agile requirements engineering and software planning for a digital health platform to engage the effects of isolation caused by social distancing: A Case study and feasibility study protocol

Background Social distancing measures have been put in place to reduce social interaction to slow transmission of coronavirus (COVID-19). For older people, self-isolation presents particular challenges for mental health and social relationships. As time progresses, continued social distancing could have a compounding impact on these concerns. Objective This project aims to provide a tool for older people, their families, and peers to improve their wellbeing and health during and after regulated social distancing. Firstly, we will evaluate the tool’s feasibility, acceptability, and usability to encourage positive nutrition, enhance physical activity, and enable virtual interaction during social-distancing. Secondly, we will be implementing the app to provide an online community to assist families and peer groups in maintaining contact with older people using goal setting. Anonymised data from the app will be aggregated with other real-world data sources to develop a machine-learning algorithm to improve COVID-19 patient identification and track for real-time use by health systems. Methods Development of this project is occurring at the time of publication, and therefore a case study design was selected to provide a systematic means of capturing software engineering in progress. To mitigate potential issues of non-adoption of the proposed intervention, the system was designed using the non-adoption, abandonment, scale-up, spread and sustainability (NASSS) framework. The application development framework utilised is based on Agile methods. The evaluation of the solution’s acceptability and usability shall be conducted as a feasibility study to analyse factors impacting the solution’s use, adoption and uptake. Results Making use of a pre-existing software framework for health behaviour change, a proof of concept was developed, and multi-stage application development and deployment for the solution created. Grant submissions to fund the project and study execution have been sought at the time of publication, and pre-discovery iteration of the solution has begun. Ethical approval for a feasibility study design is being sought. Conclusions This case study lays the foundations for future app development to combat mental and societal issues arising from social distancing measures. The app will be tested and evaluated in future studies to allow continuous improvement of the app. This novel contribution will provide an evidence-based exemplar for future app development in the space of social isolation and loneliness

Machine Learning for Risk Group Identification and User Data Collection in a Herpes Simplex Virus Patient Registry: Algorithm Development and Validation Study

Background Researching people with herpes simplex virus (HSV) is challenging because of poor data quality, low user engagement, and concerns around stigma and anonymity. Objective This project aimed to improve data collection for a real-world HSV registry by identifying predictors of HSV infection and selecting a limited number of relevant questions to ask new registry users to determine their level of HSV infection risk. Methods The US National Health and Nutrition Examination Survey (NHANES, 2015-2016) database includes the confirmed HSV type 1 and type 2 (HSV-1 and HSV-2, respectively) status of American participants (14-49 years) and a wealth of demographic and health-related data. The questionnaires and data sets from this survey were used to form two data sets: one for HSV-1 and one for HSV-2. These data sets were used to train and test a model that used a random forest algorithm (devised using Python) to minimize the number of anonymous lifestyle-based questions needed to identify risk groups for HSV. Results The model selected a reduced number of questions from the NHANES questionnaire that predicted HSV infection risk with high accuracy scores of 0.91 and 0.96 and high recall scores of 0.88 and 0.98 for the HSV-1 and HSV-2 data sets, respectively. The number of questions was reduced from 150 to an average of 40, depending on age and gender. The model, therefore, provided high predictability of risk of infection with minimal required input. Conclusions This machine learning algorithm can be used in a real-world evidence registry to collect relevant lifestyle data and identify individuals’ levels of risk of HSV infection. A limitation is the absence of real user data and integration with electronic medical records, which would enable model learning and improvement. Future work will explore model adjustments, anonymization options, explicit permissions, and a standardized data schema that meet the General Data Protection Regulation, Health Insurance Portability and Accountability Act, and third-party interface connectivity requirements. </jats:sec