Involving citizens in priority setting for public health research: Implementation in infection research

Background Public sources fund the majority of UK infection research, but citizens currently have no formal role in resource allocation. To explore the feasibility and willingness of citizens to engage in strategic decision making, we developed and tested a practical tool to capture public priorities for research. Method A scenario including six infection themes for funding was developed to assess citizen priorities for research funding. This was tested over two days at a university public festival. Votes were cast anonymously along with rationale for selection. The scenario was then implemented during a three-hour focus group exploring views on engagement in strategic decisions and in-depth evaluation of the tool. Results 188/491(38%) prioritized funding research into drug-resistant infections followed by emerging infections(18%). Results were similar between both days. Focus groups contained a total of 20 citizens with an equal gender split, range of ethnicities and ages ranging from 18 to >70 years. The tool was perceived as clear with participants able to make informed comparisons. Rationale for funding choices provided by voters and focus group participants are grouped into three major themes: (i) Information processing; (ii) Knowledge of the problem; (iii) Responsibility; and a unique theme within the focus groups (iv) The potential role of citizens in decision making. Divergent perceptions of relevance and confidence of “non-experts” as decision makers were expressed. Conclusion Voting scenarios can be used to collect, en-masse, citizens' choices and rationale for research priorities. Ensuring adequate levels of citizen information and confidence is important to allow deployment in other formats

Application of Natural Language Processing to Determine User Satisfaction in Public Services

Research on customer satisfaction has increased substantially in recent years. However, the relative importance and relationships between different determinants of satisfaction remains uncertain. Moreover, quantitative studies to date tend to test for significance of pre-determined factors thought to have an influence with no scalable means to identify other causes of user satisfaction. The gaps in knowledge make it difficult to use available knowledge on user preference for public service improvement. Meanwhile, digital technology development has enabled new methods to collect user feedback, for example through online forums where users can comment freely on their experience. New tools are needed to analyze large volumes of such feedback. Use of topic models is proposed as a feasible solution to aggregate open-ended user opinions that can be easily deployed in the public sector. Generated insights can contribute to a more inclusive decision-making process in public service provision. This novel methodological approach is applied to a case of service reviews of publicly-funded primary care practices in England. Findings from the analysis of 145,000 reviews covering almost 7,700 primary care centers indicate that the quality of interactions with staff and bureaucratic exigencies are the key issues driving user satisfaction across England

eHealth interventions for people with chronic kidney disease

This is a protocol for a Cochrane Review (Intervention). The objectives are as follows: This review aims to look at the benefits and harms of using eHealth interventions in the CKD population

PatientExploreR: an extensible application for dynamic visualization of patient clinical history from electronic health records in the OMOP common data model.

MotivationElectronic health records (EHRs) are quickly becoming omnipresent in healthcare, but interoperability issues and technical demands limit their use for biomedical and clinical research. Interactive and flexible software that interfaces directly with EHR data structured around a common data model (CDM) could accelerate more EHR-based research by making the data more accessible to researchers who lack computational expertise and/or domain knowledge.ResultsWe present PatientExploreR, an extensible application built on the R/Shiny framework that interfaces with a relational database of EHR data in the Observational Medical Outcomes Partnership CDM format. PatientExploreR produces patient-level interactive and dynamic reports and facilitates visualization of clinical data without any programming required. It allows researchers to easily construct and export patient cohorts from the EHR for analysis with other software. This application could enable easier exploration of patient-level data for physicians and researchers. PatientExploreR can incorporate EHR data from any institution that employs the CDM for users with approved access. The software code is free and open source under the MIT license, enabling institutions to install and users to expand and modify the application for their own purposes.Availability and implementationPatientExploreR can be freely obtained from GitHub: https://github.com/BenGlicksberg/PatientExploreR. We provide instructions for how researchers with approved access to their institutional EHR can use this package. We also release an open sandbox server of synthesized patient data for users without EHR access to explore: http://patientexplorer.ucsf.edu.Supplementary informationSupplementary data are available at Bioinformatics online

Virtual patients design and its effect on clinical reasoning and student experience : a protocol for a randomised factorial multi-centre study

Background Virtual Patients (VPs) are web-based representations of realistic clinical cases. They are proposed as being an optimal method for teaching clinical reasoning skills. International standards exist which define precisely what constitutes a VP. There are multiple design possibilities for VPs, however there is little formal evidence to support individual design features. The purpose of this trial is to explore the effect of two different potentially important design features on clinical reasoning skills and the student experience. These are the branching case pathways (present or absent) and structured clinical reasoning feedback (present or absent). Methods/Design This is a multi-centre randomised 2x2 factorial design study evaluating two independent variables of VP design, branching (present or absent), and structured clinical reasoning feedback (present or absent).The study will be carried out in medical student volunteers in one year group from three university medical schools in the United Kingdom, Warwick, Keele and Birmingham. There are four core musculoskeletal topics. Each case can be designed in four different ways, equating to 16 VPs required for the research. Students will be randomised to four groups, completing the four VP topics in the same order, but with each group exposed to a different VP design sequentially. All students will be exposed to the four designs. Primary outcomes are performance for each case design in a standardized fifteen item clinical reasoning assessment, integrated into each VP, which is identical for each topic. Additionally a 15-item self-reported evaluation is completed for each VP, based on a widely used EViP tool. Student patterns of use of the VPs will be recorded. In one centre, formative clinical and examination performance will be recorded, along with a self reported pre and post-intervention reasoning score, the DTI. Our power calculations indicate a sample size of 112 is required for both primary outcomes

Use of Discrete Choice Experiments in health economics: An update of the literature

The vast majority of stated preference research in health economics has been conducted in the random utility model paradigm using discrete choice experiments (DCEs). Ryan and Gerard (2003) have reviewed the applications of DCEs in the field of health economics. We have updated this initial work to include studies published between 2001 and 2007. Following the methods of Ryan and Gerard, we assess the later body of work, with respect to the key characteristics of DCEs such as selection of attributes and levels, experimental design, preference measurement, estimation procedure and validity. Comparisons between the periods are undertaken in order to identify any emerging trends.discrete choice experiments, health economics

Using technology to deliver cancer follow-up : a systematic review

Peer reviewedPublisher PD