Neil Pettinger

@kurtstat.bsky.social

426 Followers 783 Following

Healthcare data analyst. Aspiring Munroist.

This is a cross between a graph and a diagram. The horizontal axis is labelled "domain knowledge". The vertical axis is labelled "data knowledge". There's a blue square (labelled "Data analysts") positioned in the graph's top left quadrant and a red square (labelled "Managers") positioned in the graph's bottom right quadrant. Between the two squares is a double-pointed arrow labelled "Data conversations".
Stuart Hall's encoding/decoding model (adapted from his 1973 essay) made to look a bit smarter with blue and pink text boxes.
A scatterplot showing the relationship between Acute Medical Unit (AMU) 'heaviness' (measured along the horizontal axis) and Emergency Department (ED) length of stay (measured up the vertical axis). 366 overlapping grey dots in the chart represent the 366 days of the year. A "green zone of flow" has been (arbitrarily) drawn in the bottom left of the chart. NOTE: AMU 'heaviness' is calculated as each day's average number of occupied beds multiplied by each day's average length of stay so far.
A scatterplot showing the relationship between Acute Medical Unit (AMU) 'heaviness' (measured along the horizontal axis) and Emergency Department (ED) length of stay (measured up the vertical axis). Two red dots in the chart represent the 'worst' day of the year (23 Jan 2024, when four-hour compliance was 40%) and the 'best' day of the year (23 Jun 2023, when four-hour compliance was 79%). A "grey zone of flow" has been (arbitrarily) drawn in the bottom right of the chart.
A scatterplot showing the relationship between occupied beds (along the horizontal axis) and no. of "wrong" wards used (up the vertical axis). There are 366 blue dots on the graph, each dot representing a midday snapshot in 2023-24. As occupied beds increase, so do the number of wrong wards.
ggplot(data = df_resp_midday) + aes(x = total_fullness, y = no_of_wrong_wards) + gg_point()
A diagram that is basically a square. The horizontal axis is labelled Reason > judgement. The vertical axis is labelled Emotion > action. Within the square, a path is suggested first up the vertical axis (by using the visceral layer of design), then across the horizontal axis (using the behavioural layer of design), and finally up to the top of the vertical axis (using the reflective layer of design). The diagram is titled "Reason leads to judgement; emotion leads to action."
A scatterplot showing the relationship between the percentage of an emergency department's patients seen within 12 hours (measured along the horizontal axis) and the median ambulance turnaround time (measured in minutes up the vertical axis). The scatterplot has 43 blue dots on it, each dot representing a single week in the 43 -week period from 18 November 2024 to 8 September 2025. A clear pattern is visible. Weeks with high 12-hour compliance percentages are associated with shorter ambulance turnaround times. And weeks with low 12-hour compliance percentages are associated with longer ambulance turnaround times.
Two time series charts side by side. The chart on the left shows monthly attendances at NHS Scotland's Type 1 A&E departments. The chart on the right shows monthly four-hour compliance at NHS Scotland's Type 1 A&E departments. The time period is the same in both charts: July 2007 to June 2025. Attendances have been relatively stable (apart from the COVID-19 blip in 2020) at between 100,000 and 125,000 per month. By contrast, four-hour compliance has declined - first gradually (2011 to 2021), then sharply (2021 to 2023) - before stabilizing at between 60-70% in the last two years.
A scatterplot of the 282 Munros (mountains in Scotland more than 3,000 feet (914m) in height. The mountains are positioned on the 'map' according to their grid reference coordinates. Blue dots indicate Munros already climbed by Neil; red dots are the Munros still to be climbed.
A line graph showing monthly measures of compliance with the four-hour target for Type 1 A&E departments in NHS Scotland. The timeline goes from the start of 2008 to June 2025. Grey rectangles have been superimposed over the line to indicate successive 'stages' in the compliance trajectory.
A photo of the view from the top of Mount Keen. Trig point in the foreground.
Two scatterplots side by side. The axes are identical for both charts. The horizontal axis measures Acute Medical Unit (AMU) bed occupancy from 75% to 100%. The vertical axis measures compliance with the four-hour target from 30% to 100%. The graph on the left shows the points for each of the 52 weeks in 2019-20, when overall four-hour compliance was 82% and overall AMU bed occupancy was 87%. There is a clear top-left to bottom-right slope to the 52 dots. The graph on the right shows the points for the 52 weeks of 2023-24, when four-hour compliance was 48% and AMU bed occupancy was 93%. There is far less evidence of a straight line relationship in this right-hand chart. The overall impression one gets by looking at the two charts side by side is how utterly different they were. There is no overlap at all between the areas covered by the dots.
A chart that shows the monthly trend in the number of beds in NHS Scotland hospitals occupied by patients experiencing delays and - below - the trend n the number of patients experiencing >12-hour stays in Scotland's emergency departments. The time period is from July 2016 to June 2025. Grey shaded areas indicate some of the periods when the indicators were moving in the same direction.
A chart that shows how the ‘heaviness’ of an acute medical unit (AMU) affects the length of stay of patients in the emergency department (ED). The chart shows data for one hospital during the 366 days of 2023-24 and is divided into six horizontal barcode strips. The top strip shows the days of the year when the average length of ED stay was between eight and nine hours. The next strip shows the days when the average length of ED stay was between seven and eight hours. And so on, until we get to the bottom - sixth - strip, which shows the days when the average length of ED stay was between three and four hours. The horizontal axis measures the average heaviness of the AMU for each day (calculated as that day’s average fullness multiplied by the average length of stay of the patients captured in that day’s fullness snapshots), and the general pattern shown by the chart is of a bottom-left to top-right sweep.
A chart that shows how the fullness of an emergency department affects its performance against the four-hour target. The chart shows data for one hospital during the 366 days of 2023-24. is divided into five horizontal barcode strips. The top strip shows the (six) days of the year when four-hour compliance was between 70% and 80%, the next strip shows the (38) days when compliance was between 60% and 70%. And so on, until we get to the bottom - fifth - strip, which shows the (61) days when compliance was between 30% and 40%. The horizontal axis measures the average fullness of the ED for each day, and the general pattern shown by the chart is of a top left to bottom right sweep.
A chart showing all of the 522 weeks from mid-July 2015 to mid-July 2025. Each week is a blue dot. The vertical axis measures compliance with the four-hour target in Scotland's Type 1 A&E departments. The horizontal axis measures compliance with the 12-hour 'target'. Only seven of the 522 dots/weeks achieved compliance with both.
Two scatterplots on the same chart. The x-axis measures Emergency Department (ED) crowding - the average number of patients physically in the ED each day. The vertical axis measures four-hour compliance - the percentage of each day's attendances that were treated in less than four hours. There are red dots to show the experience for admitted patients and blue dots for non-admitted patients. Both the red and the blue cohorts show negative correlations, but the slope of the red dots is steep whereas the slope of the blue dots is shallow.
A chart showing the change in the weekly number of >12-hour stays in the Emergency Department at the Royal Infirmary of Edinburgh. The horizontal axis shows all of the weeks from late February 2015 to early July 2025. The vertical axis measures the number of >12-hour stays per week. A blue shaded area at the tight-hand end of the chart indicates a sharp reduction in the number of >12-hour stays during the 18 weeks from 9 March 2025 to 6 July 2025.
A chart showing two almost-identical bell-shaped curves. One is orange; the other is blue. The horizontal axis measures compliance with the NHS Emergency Department four-hour standard and is scaled from 0% to 100%. The vertical axis is unscaled and simply measures the number of days in 2023-24. The orange curve is centred on 48.1% compliance and represents the 183 days in the year when the ED had the most attendances per day (the average no. of ED attendances on these days was 355). The blue curve is centred on 48.9% compliance and represents the 183 days in the year when the ED had the fewest attendances per day (the average no. of ED attendances on these days was 305). The chart is titled: "The number of attendances doesn't really matter."
A BBC graphic that uses overlapping bell curves to show changes in average temperatures
A chart showing two overlapping bell-shaped curves. The horizontal axis measures compliance with the NHS Emergency Department four-hour standard and is scaled from 0% to 100%. The vertical axis is unscaled and simply measures the number of days in 2023-24. The orange left-hand curve is centred on 44% compliance and represents the 183 days in the year when the ED was most crowded (the average no. of patients in the ED on these days was 90). The blue right-hand curve is centred on 53% compliance and represents the 183 days in the year when the ED was least crowded (the average no. of patients in the ED on these days was 65). The chart is titled: "Ninety's a crowd; sixty-five's a slightly smaller crowd."
A chart showing two overlapping bell-shaped curves. The horizontal axis measures compliance with the NHS Emergency Department four-hour standard and is scaled from 0% to 100%. The vertical axis is unscaled and simply measures the number of days in 2023-24. The orange left-hand curve is centred on 44% compliance and represents the 183 days in the year when the ED was most crowded (the average no. of patients in the ED on these days was 90). The blue right-hand curve is centred on 53% compliance and represents the 183 days in the year when the ED was least crowded (the average no. of patients in the ED on these days was 65). The chart is titled: "Ninety's a crowd; sixty-five's a slightly smaller crowd."
An image with the R code: curve(dnorm(x, mean = 69, sd = 4), from = 53, to = 85) followed by an image of a Normal curve, with a mean of 69 and a standard deviation of 4.
A horizontal bar chart (titled 'Off the charts') showing the length of time patients spent in an Emergency Department (ED) on a day when the ED achieved 51% against the four-hour standard. The vertical axis is measured in 15-minute intervals, starting with 0-15 mins at the bottom of the chart, and ending with >240 mins at the top of the chart. The longest bar - by far - is the top bar: the patients who spent longer than four hours in the ED. This top bar is so long that it extends beyond the right hand border of the chart so that it is literally 'off the charts'.
The three classic Bauhaus shapes (blue circle, yellow triangle, red square) re-arranged so that everything is contained within the red square. I quite like this.
The three 'classic' Bauhaus shapes: a blue circle enclosing both a yellow triangle and a red square.
Two scatterplots side by side. The chart on the left shows the relationship - or - rather - the lack thereof - between the number of A&E attendances per day and each day's four-hour compliance. The chart on the right shows the (quite strong) relationship between the level of crowding in the A&E each day and each day's four-hour compliance.
Three simple diagrams arranged horizontally across the page. The diagram on the left is a grey rectangular block labelled "Data" with the title "Data analysis". The diagram in the middle is the same but with a violet rectangular block labelled "UltraData" positioned below and to the right, with the title now including the phrase "Data presentation". The diagram on the right is the same but with a red rectangular block labelled "InfraData" positioned above and to the left, the title now including the phrase "Data identification".
A doughnut diagram titled "Flow_ology: doing patient flow metrics properly". The doughnut is split into three equal portions. The first is labelled "Verification", the second is labelled "Deliberation", and the third is labelled "Accountability".
A signpost pointing the way to Carn Ghluasaid. Quite a few of the letters are missing.
A chart that shows the lengths of stay of all the patients who were in an Acute Medical Unit (AMU) at midday on 5th March 2024. The 65 patients captured by the snapshot are shown as horizontal lines arranged vertically, with the patient with the earliest admission date at the top. The element of the stay prior to the census point is shown in blue. The element of the stay subsequent to the census point is shown in red.
There is R code in an R Studio script window on the left: # draw the scatterplot for compliance and fullness -------- ggplot() + aes(x = df_06$mean_fullness, y = df_03$compliance) + geom_point() And the resulting scatterplot is shown on the right. The fuller the emergency department, the lower the compliance with the four-hour standard.