Modeling Data Journeys to Inform the Digital Transformation of Kidney Transplant Services: Observational Study.

BACKGROUND: Data journey modeling is a methodology used to establish a high-level overview of information technology (IT) infrastructure in health care systems. It allows a better understanding of sociotechnical barriers and thus informs meaningful digital transformation. Kidney transplantation is a complex clinical service involving multiple specialists and providers. The referral pathway for a transplant requires the centralization of patient data across multiple IT solutions and health care organizations. At present, there is a poor understanding of the role of IT in this process, specifically regarding the management of patient data, clinical communication, and workflow support. OBJECTIVE: To apply data journey modeling to better understand interoperability, data access, and workflow requirements of a regional multicenter kidney transplant service. METHODS: An incremental methodology was used to develop the data journey model. This included review of service documents, domain expert interviews, and iterative modeling sessions. Results were analyzed based on the LOAD (landscape, organizations, actors, and data) framework to provide a meaningful assessment of current data management challenges and inform ways for IT to overcome these challenges. RESULTS: Results were presented as a diagram of the organizations (n=4), IT systems (n>9), actors (n>4), and data journeys (n=0) involved in the transplant referral pathway. The diagram revealed that all movement of data was dependent on actor interaction with IT systems and manual transcription of data into Microsoft Word (Microsoft, Inc) documents. Each actor had between 2 and 5 interactions with IT systems to capture all relevant data, a process that was reported to be time consuming and error prone. There was no interoperability within or across organizations, which led to delays as clinical teams manually transferred data, such as medical history and test results, via post or email. CONCLUSIONS: Overall, data journey modeling demonstrated that human actors, rather than IT systems, formed the central focus of data movement. The IT landscape did not complement this workflow and exerted a significant administrative burden on clinical teams. Based on this study, future solutions must consider regional interoperability and specialty-specific views of data to support multi-organizational clinical services such as transplantation.

Using interactive digital notebooks for bioscience and informatics education.

Interactive digital notebooks provide an opportunity for researchers and educators to carry out data analysis and report the results in a single digital format. Further to just being digital, the format allows for rich content to be created in order to interact with the code and data contained in such a notebook to form an educational narrative. This primer introduces some of the fundamental aspects involved in using Jupyter notebooks in an educational setting for teaching in the bio/health informatics disciplines. We also provide 2 case studies that detail how we used Jupyter notebooks to teach non-coders programming skills on a blended Master's degree module for a Health Informatics programme and a fully online distance learning unit on Programming for a postgraduate certificate (PG Cert) in Clinical Bioinformatics with a more technical audience.

Data journeys: Identifying social and technical barriers to data movement in large, complex organisations.

Managers in complex organisations often have to make decisions on whether new software developments are worth undertaking or not. Such decisions are hard to make, especially at an enterprise level. Both costs and risks are regularly underestimated, despite the existence of a plethora of software and systems engineering methodologies aimed at predicting and controlling them. Our objective is to help managers and stakeholders of large, complex organisations (like the National Health Service in the UK) make better informed decisions on the costs and risks of planned new software systems that will reuse or extend their existing information infrastructure. We analysed case studies describing new software developments undertaken by providers of health care services in the UK, looking for common points of risk and high cost. The results highlighted the movement of data within and between organisations as a key factor. Data movement can be hindered by numerous technical barriers, but also by other challenges arising from social aspects of the organisation. These latter aspects are often harder to predict, and are ignored by many of the more common software engineering methodologies. In this paper, we propose data journey modelling, a new method aiming to predict places of high cost and risk when existing data needs to move to a new development. The method is lightweight and combines technical and social aspects, but relies only on information that is likely to be already known to key stakeholders, or will be cheap to acquire. To assess the effectiveness of our method, we conducted a retrospective evaluation in an NHS Foundation Trust hospital. Using the method, we were able to predict most of the points of high cost/risk that the hospital staff had identified, along with several other possible directions that the staff did not identify for themselves, but agreed could be promising.

Applying Co-Design Principles for the Development of Health Education and Workforce Development.

The digital transformation of the UK's healthcare system necessitates the development of digital capabilities across the workforce. This ranges from basic digital literacy through to advanced skills with data and analytic methods. We present two projects that apply co-design to work with end-users and other stakeholders to produce a digital healthcare technologies capability framework aimed at the wider NHS workforce and a post graduate Clinical Data Science course aimed at bridging the gap between clinicians and the data-centric professions (e.g. analysts, data scientists, informaticians) to aid in digital transformation projects.

Educating the Healthcare Workforce to Support Digital Transformation.

Digital transformation of the healthcare workforce is a priority if we are to leverage the potential of digital technologies, artificial intelligence in clinical decision support and the potential of data captured within electronic health records. Educational programmes need to be diverse and support the digital novices through to the champions whom will be responsible for procuring and implementing digital solutions. In order to professionalise the workforce in this area, digital competencies need to be built into training from early on and be underpinned by frameworks that help to guide regulators and professional bodies and support educational providers to deliver them. Here we describe Manchester's involvement in the development of digital competency frameworks and our digital transformation education programmes that we have created, including a Massive Online Open Course and a professional development course for England's Topol Digital Fellows.

Artificial intelligence projects in healthcare: 10 practical tips for success in a clinical environment.

There is much discussion concerning 'digital transformation' in healthcare and the potential of artificial intelligence (AI) in healthcare systems. Yet it remains rare to find AI solutions deployed in routine healthcare settings. This is in part due to the numerous challenges inherent in delivering an AI project in a clinical environment. In this article, several UK healthcare professionals and academics reflect on the challenges they have faced in building AI solutions using routinely collected healthcare data.These personal reflections are summarised as 10 practical tips. In our experience, these are essential considerations for an AI healthcare project to succeed. They are organised into four phases: conceptualisation, data management, AI application and clinical deployment. There is a focus on conceptualisation, reflecting our view that initial set-up is vital to success. We hope that our personal experiences will provide useful insights to others looking to improve patient care through optimal data use.