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Healthcare providers are confronted with some very real challenges when it comes to talent management. Retaining their people is imperative given the high-level of time and investment that goes into building people with the right level of specialisation. Unfortunately, these are also high pressure roles that are not always remunerated accordingly. 

This puts enormous strain on HR teams, who are also under pressure to quantify their initiatives and quickly demonstrate business impact.

While people data has thankfully equipped HR professionals with the means to create compelling business cases, efficiently predict and diagnose problems, and calculate the return on HR$ investment. Without the right approach and sound judgement, HR data can end up leading HR teams down the garden path, wasting time, money and damaging the functions reputation.

 

During a recent training gap analysis for a healthcare provider, I was fortunate to work with a client who indulged in my desire to look at not only the certainties of talent data, but also open to investing sufficient time to assess all the stuff we can’t measure. That is, applying the all-important judgement factor.

Thinking back over the exercise, there were multiple points where the data (without the right judgement) could have led us down the garden path. Thankfully, we uncovered many meaningful insights which transformed the impact of our project.

This case study highlights 3 principals to ensure your people data leads you to an effective solution:

 

1. Make sure you have sufficient data points.

Ask any maths teacher. When you measure things, you arrive at a predictable, clear and objective answer. Unless you’re measuring people of course, then you’ll need all the information you can find, and even then, you’ll still be left making assumptions.

Given the complexity and potential bias involved in measuring people, it is critical to have a comprehensive data set to work with.

During our project, we collected every HR and customer metric available from the business. We then put the whole lot on the table (so to speak) and compared what the patients told us about the service, versus the likely service issues caused as a result of poor employee performance.

From this, we started to notice patterns in the data, which helped us shape a number of likely assumptions.

 

2. Data will usually lead you to the symptom, but very rarely the cause

It’s important to know that often, quantitative data will lead you to the symptom, but it is the qualitative data that leads you to the cause.

Our analysis with a healthcare provider identified some typical problem areas, such as communication. Frontline managers didn’t communicate effectively with their frontline care workers. The care workers in turn, didn’t communicate effectively with their patients.

This type of communication breakdown can happen in any business, so not exactly a miraculous discovery!

It would be easy to run a series of communication workshops and assume all our problems would be solved. Wrong! All we had done was identify the symptom of poor communication, not the cause. The temperature if you like, but not the illness driving the temperature.

To find the cause, we needed to test our assumptions back with the people we were “measuring”.

 

3. Apply your judgement – liberally!

To dig a little deeper, we interviewed the employees using workplace scenarios which also helped us assess their role competence. Now, here comes the surprise…  we discovered that both the managers and care workers could in fact communicate!

So was the data wrong? No, the data simply needed human judgement to look past the symptom (communication) and to find the cause.

Our scenario based interviews, revealed that the employees had become de-sensitised to their roles. The frontline care workers, whilst they had the right intent and genuine care for their patients, the burden of delivering an emotionally taxing role, day after day, meant that they started to lose sight of the emotions and true needs of their patients.

It was a similar finding with the frontline managers, who had well over 50 reports each. The problems, advice and range of other support required understandably started to blur and the impact of each request was lost.

 

Key takeout:

While people data is a highly effective way of diagnosing problems, data won’t necessarily take you to the cause.

Often, there is no absolute metric that will compute the answer. We have to collect as many of the certainties data will offer, but the last bit requires our ability to judge the data, rather than add it.

It’s almost refreshing to discover that as far as human beings are concerned, we still need other humans to make the judgement call.

For those working in the aged-care industry and are interested in hearing about others case studies – James will be presenting at the LASA National Congress 2017, Gold Coast on Tuesday 17 October 2017. More details here.