To prepare students to take an active part in developing IT systems that reflect the needs and priorities from their working perspective. To apply some elementary programming and information handling concepts in the context of technology management.

Spreadsheet basics: MS Excel, cell attributes (number, character formats), relative/absolute, formulas functions inc arithmetic, trig, conditional), row/column calculations, configuring charts (category data line/bar, scatter plots, primary/secondary axes, formating), row/column calculations, functions (sum, sumproduct, statistical, financial), linking between worksheets, add-ins, pivot tables, macros. Spreadsheet automation: macros, visual basic for applications MS VBA, conditional looping and branching, vector (list) and matrix (array) lookup. Applications to observation and data analysis for building an evidence base: experimental observations (1) continuous variables (time), work hard versus work smart experiment, t-test to compare outcomes (manual and excel function). (2) binary attribute variable (present/absent), occurrence sampling, confidence intervals, chart on number line. (3) associative relationship: linear regression curve-fitting, trendline fit to observed data, extension to non-linear regression-based models. Process visualisation: MS Visio, 5S lean process improvement, flow charts, critical questioning matrix, performance improvement (time). Standard Time, rating observations: correction to standard time using linear regression trendline fit for correction and comparison of observers and methods (trendline function). Optimisation: MS Solver add-in, most profitable mix of products subject to constraints of capacity, market, and material availability. Decision philosophy: continuous improvement PDSA, evidence-informed decisions, scale of scientific evidence used in healthcare delivery.

On successful completion of this module, students should be able to: Explain and apply basic functionality of spreadsheets Explain and apply basic functionality of process mapping tools. To apply basic experimental procedure underpinning design of work systems (work smart, occurrence sampling, time observation rating) that incorporates basic statistical analysis (balanced experiment, comparison of continuous variable time, confidence interval on a proportion occurrence, linear regression on observation rating correction. To carry out a simple product mix optimisation using a spreadsheet-based solver. To relate the value of scientific and evidence-based approach in making technology decisions, and discuss some basic ethical questions relating to evidence base used in operational decisions.

On successful completion of this module, students should be able to: To feel comfortable with the spreadsheets as a core tool in managing data for technology management To respond to technology decisions from the perspective of data-driven evidence. To value statistical approach as supporting an evidence informed approach to decision-making. To respond to decision analysis from the perspective of optimal performance.

Use of computers.

The module will be delivered using mix of lectures and lab work. PC equipped lab (SR3015b)