In this module you will learn about many of the innovations in healthcare and the opportunities as well as challenges that these new technologies and ways of working afford the health sector.  You will become familiar with the change management literature so that you can learn and critique different approaches to embedding change within healthcare. 

The module is designed to provoke you to think differently about innovation in terms of understanding opportunities as well as challenges.  You will come to understand innovation pathways and how to adapt new innovations into the daily practices of healthcare.  This course is designed to expose you to thought leaders and innovators in healthcare who are defining the health sector of tomorrow.  You will have a number of guest speakers and site visits including UCL academics and other experts engaged in robots, virtual reality, telemedicine and AI solutions for healthcare.  You will not only learn about the work these groups are undertaking, but how to introduce innovation, appraise options, undertake assessments and design pathways of implementation.

To ensure that the implementation of innovation reaps benefits such as increased productivity and better outcomes for patients, you will need to understand change management. Additionally you will learn how to think holistically about innovation as a manager in terms of how new innovations impacts policies, regulations, carers and patients as well as health professionals.  Patients as collaborators in the innovation process will also be explored.

Consequently, you will come to appreciate that innovation requires change management at multiple levels within an organisation if it is to add sustainable benefits.  

You will also learn how to develop yourself as an agile and adaptive leader, but also how to lead organisational cultural change as a critical aspect of leadership in today’s world.

Data Wrangling|Data Science Pipeline|Data processing

The DAPS module will cover technology, principles and applications of data collection, preparation, and storage for data science systems. In particular, the...