Enhancing WHSE and Safety Reporting with NLG Narratives

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ENHANCING WHSE AND SAFETY REPORTING WITH NLG NARRATIVES How can the use of Natural Language Generation (NLG), make it easier to generate custom WHSE reports where the data has been accurately analysed and interpreted?

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We have an obligation to make the data meaningful to the audience

AND ensure quality data inputs, as in the systems or persons adding the observations that collectively represent the data the NLG narratives utilises need to be consistent.

DR. REBECCA MICHALAK

Managing Director, Keynote Speaker and Author @ PsychSafe

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WELCOME

If you have been working in the WHSE space for a while, you will understand how critical report writing is. The business and senior executives will always want to know how many injuries there are, what the trends are and are the WHSE team having an impact.

After all, there is no point in doing the work to improve outcomes if you can’t validate and share it in a meaningful way.

What’s more, reporting keeps the business, and its managers informed on progress against targets, highlights positive achievements as well as ‘opportunities for continuous improvement’.‍ Informed managers are then better able to make the appropriate decisions to manage WHSE outcomes.

Reporting is usually time-critical and competes with other priorities. Besides, not everyone is an expert at extracting, collating and interpreting data.

This article looks at two of the critical processes in report writing - data interpretation and information checking.

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01 DATA INTERPRETATION WHAT DOES ALL THAT DATA SAY ABOUT WHSE OUTCOMES? IS THERE A TREND, AND HOW SIGNIFICANT IS THE TREND? WAS IT THE SAME LAST YEAR?

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THE FIRST AND MOST IMPORTANT STEP IN REPORT WRITING

The Problem — Data interpretation is the process of obtaining meaning out of large amounts of information.

What does all that data say about WHSE outcomes? Is there a trend, and how significant is the trend? Was it the same last year?

Data interpretation can be a complicated and daunting task.

Getting this critical step wrong will lead to incorrect conclusions which do not stand up to closer scrutiny.

Often WHSE report writing can involve the analysis and interpretation of thousands of records from a reporting system.

The aim is to identify trends – that is the tendency for data to change over time.

By identifying trends, it is possible to enable the understanding of the impact of WHS risk reduction programs or the significance of various risk factors (e.g. manual handling across directorates or sites).

Getting the analysis and interpretation of the data correct is always the most critical part of the reporting process.

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‍ or example, suppose data is highly variable (variance), such F as small numbers of incidents at a site. In that case, it may be impossible to identify trends due to the randomness of the data.

However, variability in data can be managed by looking at longer periods, larger data sets or using ‘moving averages’.

An example of this would be lost time injuries (LTIs). There are usually low numbers of these month-by-month, with high variability.

Looking at monthly trends would see these numbers bouncing around and therefore difficult to interpret. By averaging over 6 or 12 months, the results are smoothed out, and trends become more evident.

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O ur brains leap to conclusions and are reluctant to consider alternatives; we are particularly bad at revisiting our initial assessment of a situation.

ANDREW CAMPBELL

Harvard Business Review

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A current example of this is looking at COVID-19 results. Day by day numbers are variable, so a more accurate picture is obtained using a 3 or 5-day moving average. In the below chart, averaging can then be overlaid with significant events to interpret their impact on COVID-19 cases.

Trends may also be cyclical – for example, there are fewer incidents over summer due to employees being on leave or less traffic on the roads. If we were to look only at the current summer results, it might give a false impression that the results are improving.

Instead, they use a larger data set (over 13 months) can reveal the longterm cyclical pattern and improve our interpretation of the data. These types of issues can make it difficult to interpret data correctly.

Besides, non-experts may perceive trends as significant where they are not, or incorrectly relate a trend to an unrelated cause.

In some cases, personal cognitive (or thinking) biases want to make causal links to a valued new program or training where the data cannot substantiate this.

The use of automated data analysis and narrative generation using NLG can assist in both the time required to interpret data and to reduce the potential for errors.

If the thread is accurate and consistent then there is a greater chance of the messaging to be clear and any actions of remediation or improvement to be 100% relevant, rather than biased speculation.

ROB.B LOWE

Chief Operating Off icer and Principal WHS Practitioner @ Success Superpowers

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02 INFORMATION CHECKING HOW MANY TIMES HAVE YOU RECEIVED OR WRITTEN A REPORT THAT HAS NOT BEEN ADEQUATELY CHECKED?

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THE COMPLICATED PROCESS

The Problem — Reports are often very complex, and they also need to have high accuracy. Therefore, data and commentary checking are an essential part of the reporting process.

However, it is also a manual process that is, itself, prone to error and very time-consuming.

How many times have you received or written a report that has not been adequately checked?

The problem may be incorrect values or charts added from the source data or inaccurate interpretation of the data.

A simple cut and paste from a BI interface or MS Excel into an MS Word report can lead to errors when the data changes but the charts are not updated, or the report can reference the incorrect chart.

Rigorous checking is necessary to ensure reports are accurate and consistent.

The process is complicated by the fact that the brain tends to ‘skim-over’ and ‘summarise’ visual information.

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I n relatively simple terms, Natural Language Generation turns complex

data sets into insights that are easily understood by humans. In practice, using NLG on well-structured data results in faster and more impactful risk decision making.

CAMERON STEVENS

Solutions Engineer @ RealWear Inc., Digital Safety Concierge & Director of the Safety Innovation Academy

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This may create either ‘perceptual’ errors, where the mistake is not seen or ‘cognitive’ errors where the error is seen but misinterpreted.

The more complex the report, the more likely this is to occur, particularly where data and commentary are repeated – say in an ‘executive summary’ and multiple places in the body of the report.

Proper data checking is, therefore, critical to sending clear and consistent messaging on WHS outcomes.

Ensuring accurate, well-verified reports is a severe source of anxiety, both for those writing the reports and for those presenting them - whether this is to executives, the board, stakeholders or the public.

One way to reduce the human error and burden of error checking is to automate the process. A report generated by a combination of BI tools and commentary generated by NLG will always have the correct commentary associated with a chart.

However, there is still flexibility to modify the outcome by exporting the report into MS Word and changing or adding commentary.

Report production through using NLG enables managers to automate insight creation and decision making which is a prerequisite to success in a digitalised economy.

DR. ARASH KORDESTANI

Data Scientist and Assistant Professor @ Södertörn University, Stockholm

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03 NATURAL LANGUAGE GENERATION HERE ARE SOME EXAMPLE WHSE AND SAGETY PERFORMANCE REPORTS THAT BEEN GENERATED USING AUTO-GENERATED NARRATIVES.

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NLG AS A SOLUTION

The Problem — Reporting on WHS outcomes is a key requirement in all businesses; however, although safety management systems are usually good at collecting data, the reports they generate are not efficient.

It’s quite common that ordinary reports are incredibly colourful with too many elements on them that all compete for attention and in the end, leave the audience more perplexed rather than informed.

Additionally, significant resource in people’s time is spent each month to re-format data into a usable and polished report. Businesses then spend considerable time on adding analysis of data to enable interpretation for stakeholders.

This inefficient process inhibits the organisation’s capability in promoting data-supported decision-making at a larger scale.

Natural Language Generation (NLG) technology allows scaling human expertise in the analysis of data to the next level. It’s a perfect solution for companies that not only need custom reports but also want to promote more datasupported decisions across the organisation.

It provides a consistent overview and analysis of data based on built-in rules which makes the drawing of accurate inferences of data more practical.

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Like most reporting solutions, the reports are accessible through a Web portal. Once the user changes report parameters like the business stream or the date period or any other parameter they require, report data and the NLG narrative will be updated accordingly.

The highly formatted reports can be exported to common formats, and the output will be 100% identical to the report that appears on the screen.

It enables users to do further analysis in Excel or add more context to the NLG narratives in Word.

NLG-enabled custom reporting is an enabler of existing technologies.

Implementing NLG and custom reporting, in most cases, does not require any changes to the current platforms. Data can come from any databases or safety management system that the organisation already uses. In most organisations, the reporting platform is already available, which eliminates the licensing cost of a third-party product. The solution also ensures the privacy of data as the reports may run on their pre-existing platforms.

NLG implementation is simple, cost-effective and complements existing systems to provide all stakeholders with timely, consistent and impactful reports that bring added value to the reporting process.

With the daily demand for quick, streamlined and unbiased interpretation and assessment of data, a technology like NLG can not only help enterprises to reduce their operational costs, but also to always stay ahead of their game.

NIMA YAHYAPOUR

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Work Health & Safety Performance Report Care & Safety Board — April 2020 Chart 1: Lost Time Injury Frequency Rate (LTIFR) LTIFR in April 2020 decreased slightly by approximately 3.2% compared to the same period last year. LTIFR remained above the target for nine months throughout the past year. When we compare the same periods, the number of LTI incidents remained the same, although we witnessed a spike in October 2019 while the least volume of incidents were lodged in July 2019. There has been a gradual decline in LTIFR from Nov 19.

If this continues in May, we will meet our target (12.0)

LTIFR — Number of lost-time injuries per million hours worked (rolling average) – – – Target LTIFR: 12.0

# of LTI Incidents

Chart 2: Total Recordable Injury Frequency Rate (TRIFR) TRIFR has increased by approximately 8% between April 2019 and Feb 2020. The highest value for TRIFR was in Feb 2020, and for 11 months TRIFR remained above the target of 28. The TRIFR has declined by 6% since Feb 20 (30.2 – 28.5). If this trend continues in May we will hit our target (28.0)

TRIFR— The measure shows the frequency rate of fatalities, lost time injuries, substitute work, and other injuries requiring treatment by a medical professional per million hours worked.

– – – Target TRIFR: 28.0

Document generation: Automatic Reporting Period: April 2020 Business Stream: Entire Business

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Work Health & Safety Performance Report Care & Safety Board — April 2020 Chart 3: LTI and MTI Comparison The number of LTI’s peaked in Oct 19 and MTI’s peaked in Nov 19. MTI’s have been lower in the last 5 months.

This has contributed to the lower TRIFR (Chart 2).

LTI

MTI

Chart 4: Injury Mechanism Analysis (Compared to the Previous Month) Comparing the mechanism of the injuries against last month, the number of incidents in three categories (body stressing, hitting stationary objects and falls from height) decreased. At the same time, the number of incidents by slip/fall/trip and environmental factors increased while the psychological category remained the same.

Injury Mechanism – Number of incidents per category

Document generation: Automatic Reporting Period: April 2020 Business Stream: Entire Business

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LET’S SEE HOW NLG CAN TRANSFORM YOUR REPORTS Through a 30-minutes free phone conversation, we can learn more about your unique situation and see how we can help with your reporting requirements.

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BOOK A DEMO Demo sessions are perfect to see how the reports we design can support stakeholders and promote customers for further engagement.

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TH A N K YO U

Relius Reports delivers custom and innovative business intelligence reports. The reports are designed to take the hassle out of the regular WHS outcome reporting. It combines simplicity and clarity with auto-generated narratives in plain English. The outcome is high-impact reports which streamline decision-making for managers and executives.

Natural Language Generation (NLG) technology allows scaling human expertise in the analysis of data to the next level. It’s a perfect solution for companies that not only need custom reports but also want to promote more data-supported decisions across the organisation. It provides a consistent overview and analysis of data based on built-in rules which makes the drawing of accurate inferences of data more practical.

NLG implementation is simple, and complements existing systems stakeholders with timely, consistent reports that bring added value to process.

cost-effective to provide all and impactful the reporting

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All rights reserved. https://www.reliusreports.com.au https://www.linkedin.com/company/reliusreports